From 6c9e3734987647f8041debe25445f770ce8b0e0b Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 18:24:04 -0700 Subject: [PATCH 01/29] prepare data --- prepare_Xy.ipynb | 759 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 759 insertions(+) create mode 100644 prepare_Xy.ipynb diff --git a/prepare_Xy.ipynb b/prepare_Xy.ipynb new file mode 100644 index 0000000..0107dc8 --- /dev/null +++ b/prepare_Xy.ipynb @@ -0,0 +1,759 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "mediterranean-smoke", + "metadata": {}, + "source": [ + "# import and prepare data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "expressed-synthetic", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pickle\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "hazardous-treaty", + "metadata": {}, + "source": [ + "load data from Mercado et al., drop outlier" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "loving-minister", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dimensionsbond typenamevoid fraction [widom]supercell volume [A^3]density [kg/m^3]heat desorption high P [kJ/mol]heat desorption error high P [kJ/mol]absolute methane uptake high P [molec/unit cell]absolute methane uptake error high P [molec/unit cell]...num sulfurnum siliconverticesedgesgenuslargest included sphere diameter [A]largest free sphere diameter [A]largest included sphere along free sphere path diameter [A]absolute methane uptake high P [v STP/v]absolute methane uptake low P [v STP/v]
02amidelinker91_CO_linker87_NH_hcb_relaxed0.90012049204.128057260.21322810.951620.315667233.28922.789059...0011017.1901415.6496117.19004176.16049820.988007
12amidelinker91_CO_linker88_NH_hcb_relaxed0.87923449390.074419297.96338711.817560.478028250.61643.464625...0011017.3491615.7694317.34916188.53207322.643282
22amidelinker91_CO_linker7_NH_hcb_relaxed0.85826950036.985281289.39724911.863780.140491255.15100.921036...0011016.8403215.6190716.84024189.46176422.566022
32amidelinker91_CO_linker8_NH_hcb_relaxed0.85706549135.924517370.06363312.488420.823728257.33682.377728...0011013.9308512.3216713.93085194.58896227.373459
42amidelinker91_CO_linker10_NH_hcb_relaxed0.85801649540.680132367.04015112.259240.191371253.26203.177484...0011016.0692313.4879116.06921189.94309224.774006
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5 rows × 53 columns

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" + ], + "text/plain": [ + " dimensions bond type name \\\n", + "0 2 amide linker91_CO_linker87_NH_hcb_relaxed \n", + "1 2 amide linker91_CO_linker88_NH_hcb_relaxed \n", + "2 2 amide linker91_CO_linker7_NH_hcb_relaxed \n", + "3 2 amide linker91_CO_linker8_NH_hcb_relaxed \n", + "4 2 amide linker91_CO_linker10_NH_hcb_relaxed \n", + "\n", + " void fraction [widom] supercell volume [A^3] density [kg/m^3] \\\n", + "0 0.900120 49204.128057 260.213228 \n", + "1 0.879234 49390.074419 297.963387 \n", + "2 0.858269 50036.985281 289.397249 \n", + "3 0.857065 49135.924517 370.063633 \n", + "4 0.858016 49540.680132 367.040151 \n", + "\n", + " heat desorption high P [kJ/mol] heat desorption error high P [kJ/mol] \\\n", + "0 10.95162 0.315667 \n", + "1 11.81756 0.478028 \n", + "2 11.86378 0.140491 \n", + "3 12.48842 0.823728 \n", + "4 12.25924 0.191371 \n", + "\n", + " absolute methane uptake high P [molec/unit cell] \\\n", + "0 233.2892 \n", + "1 250.6164 \n", + "2 255.1510 \n", + "3 257.3368 \n", + "4 253.2620 \n", + "\n", + " absolute methane uptake error high P [molec/unit cell] ... num sulfur \\\n", + "0 2.789059 ... 0 \n", + "1 3.464625 ... 0 \n", + "2 0.921036 ... 0 \n", + "3 2.377728 ... 0 \n", + "4 3.177484 ... 0 \n", + "\n", + " num silicon vertices edges genus \\\n", + "0 0 1 1 0 \n", + "1 0 1 1 0 \n", + "2 0 1 1 0 \n", + "3 0 1 1 0 \n", + "4 0 1 1 0 \n", + "\n", + " largest included sphere diameter [A] largest free sphere diameter [A] \\\n", + "0 17.19014 15.64961 \n", + "1 17.34916 15.76943 \n", + "2 16.84032 15.61907 \n", + "3 13.93085 12.32167 \n", + "4 16.06923 13.48791 \n", + "\n", + " largest included sphere along free sphere path diameter [A] \\\n", + "0 17.19004 \n", + "1 17.34916 \n", + "2 16.84024 \n", + "3 13.93085 \n", + "4 16.06921 \n", + "\n", + " absolute methane uptake high P [v STP/v] \\\n", + "0 176.160498 \n", + "1 188.532073 \n", + "2 189.461764 \n", + "3 194.588962 \n", + "4 189.943092 \n", + "\n", + " absolute methane uptake low P [v STP/v] \n", + "0 20.988007 \n", + "1 22.643282 \n", + "2 22.566022 \n", + "3 27.373459 \n", + "4 24.774006 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('properties.csv')\n", + "# we remove row 48225 since it is an outlier for void fraction feature # can be seen by df[df[' void fraction [widom]'] > 1]\n", + "df = df.drop(48225)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "rubber-scott", + "metadata": {}, + "source": [ + "define new feature as density of elements" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "chinese-antenna", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dimensionsbond typenamevoid fraction [widom]supercell volume [A^3]density [kg/m^3]heat desorption high P [kJ/mol]heat desorption error high P [kJ/mol]absolute methane uptake high P [molec/unit cell]absolute methane uptake error high P [molec/unit cell]...largest included sphere along free sphere path diameter [A]absolute methane uptake high P [v STP/v]absolute methane uptake low P [v STP/v]density of carbondensity of fluorinedensity of hydrogendensity of nitrogendensity of oxygendensity of sulfurdensity of silicon
02amidelinker91_CO_linker87_NH_hcb_relaxed0.90012049204.128057260.21322810.951620.315667233.28922.789059...17.19004176.16049820.9880070.0073160.00.0043900.0029270.0014630.00.0
12amidelinker91_CO_linker88_NH_hcb_relaxed0.87923449390.074419297.96338711.817560.478028250.61643.464625...17.34916188.53207322.6432820.0072890.00.0043730.0029160.0029160.00.0
22amidelinker91_CO_linker7_NH_hcb_relaxed0.85826950036.985281289.39724911.863780.140491255.15100.921036...16.84024189.46176422.5660220.0086340.00.0071950.0028780.0014390.00.0
32amidelinker91_CO_linker8_NH_hcb_relaxed0.85706549135.924517370.06363312.488420.823728257.33682.377728...13.93085194.58896227.3734590.0073270.00.0029310.0043960.0043960.00.0
42amidelinker91_CO_linker10_NH_hcb_relaxed0.85801649540.680132367.04015112.259240.191371253.26203.177484...16.06921189.94309224.7740060.0072670.00.0029070.0043600.0043600.00.0
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5 rows × 60 columns

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" + ], + "text/plain": [ + " dimensions bond type name \\\n", + "0 2 amide linker91_CO_linker87_NH_hcb_relaxed \n", + "1 2 amide linker91_CO_linker88_NH_hcb_relaxed \n", + "2 2 amide linker91_CO_linker7_NH_hcb_relaxed \n", + "3 2 amide linker91_CO_linker8_NH_hcb_relaxed \n", + "4 2 amide linker91_CO_linker10_NH_hcb_relaxed \n", + "\n", + " void fraction [widom] supercell volume [A^3] density [kg/m^3] \\\n", + "0 0.900120 49204.128057 260.213228 \n", + "1 0.879234 49390.074419 297.963387 \n", + "2 0.858269 50036.985281 289.397249 \n", + "3 0.857065 49135.924517 370.063633 \n", + "4 0.858016 49540.680132 367.040151 \n", + "\n", + " heat desorption high P [kJ/mol] heat desorption error high P [kJ/mol] \\\n", + "0 10.95162 0.315667 \n", + "1 11.81756 0.478028 \n", + "2 11.86378 0.140491 \n", + "3 12.48842 0.823728 \n", + "4 12.25924 0.191371 \n", + "\n", + " absolute methane uptake high P [molec/unit cell] \\\n", + "0 233.2892 \n", + "1 250.6164 \n", + "2 255.1510 \n", + "3 257.3368 \n", + "4 253.2620 \n", + "\n", + " absolute methane uptake error high P [molec/unit cell] ... \\\n", + "0 2.789059 ... \n", + "1 3.464625 ... \n", + "2 0.921036 ... \n", + "3 2.377728 ... \n", + "4 3.177484 ... \n", + "\n", + " largest included sphere along free sphere path diameter [A] \\\n", + "0 17.19004 \n", + "1 17.34916 \n", + "2 16.84024 \n", + "3 13.93085 \n", + "4 16.06921 \n", + "\n", + " absolute methane uptake high P [v STP/v] \\\n", + "0 176.160498 \n", + "1 188.532073 \n", + "2 189.461764 \n", + "3 194.588962 \n", + "4 189.943092 \n", + "\n", + " absolute methane uptake low P [v STP/v] density of carbon \\\n", + "0 20.988007 0.007316 \n", + "1 22.643282 0.007289 \n", + "2 22.566022 0.008634 \n", + "3 27.373459 0.007327 \n", + "4 24.774006 0.007267 \n", + "\n", + " density of fluorine density of hydrogen density of nitrogen \\\n", + "0 0.0 0.004390 0.002927 \n", + "1 0.0 0.004373 0.002916 \n", + "2 0.0 0.007195 0.002878 \n", + "3 0.0 0.002931 0.004396 \n", + "4 0.0 0.002907 0.004360 \n", + "\n", + " density of oxygen density of sulfur density of silicon \n", + "0 0.001463 0.0 0.0 \n", + "1 0.002916 0.0 0.0 \n", + "2 0.001439 0.0 0.0 \n", + "3 0.004396 0.0 0.0 \n", + "4 0.004360 0.0 0.0 \n", + "\n", + "[5 rows x 60 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elements = ['carbon', 'fluorine', 'hydrogen', 'nitrogen', 'oxygen', 'sulfur', 'silicon']\n", + "for el in elements:\n", + " df[\"density of \" + el] = df[' num ' + el] / df[' supercell volume [A^3]']\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "given-procurement", + "metadata": {}, + "source": [ + "get feature matrix and target vector" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "german-terminology", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# features: 12\n" + ] + } + ], + "source": [ + "features = [' void fraction [widom]', ' density [kg/m^3]', ' largest included sphere diameter [A]', ' largest free sphere diameter [A]', ' surface area [m^2/g]',\n", + " 'density of carbon', 'density of fluorine', 'density of hydrogen', 'density of nitrogen', 'density of oxygen', 'density of sulfur', 'density of silicon']\n", + "print(\"# features: \", len(features))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "handled-parish", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of Y: (69839,)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([155.17249134, 165.88879162, 166.8957419 , ..., 161.97256899,\n", + " 155.38761768, 155.48070341])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = df[' deliverable capacity [v STP/v]'].values\n", + "print(\"shape of Y: \", np.shape(y))\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "distinct-origin", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (69839, 12)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[9.00120000e-01, 2.60213228e+02, 1.71901400e+01, ...,\n", + " 1.46329186e-03, 0.00000000e+00, 0.00000000e+00],\n", + " [8.79234000e-01, 2.97963387e+02, 1.73491600e+01, ...,\n", + " 2.91556556e-03, 0.00000000e+00, 0.00000000e+00],\n", + " [8.58269000e-01, 2.89397249e+02, 1.68403200e+01, ...,\n", + " 1.43893561e-03, 0.00000000e+00, 0.00000000e+00],\n", + " ...,\n", + " [8.85007000e-01, 2.59378413e+02, 1.60821800e+01, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [7.37251000e-01, 5.14847059e+02, 1.15594900e+01, ...,\n", + " 1.43384585e-03, 0.00000000e+00, 0.00000000e+00],\n", + " [7.77576000e-01, 5.01030978e+02, 1.15140800e+01, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = df[features].values\n", + "print(\"shape of X: \", np.shape(X))\n", + "X" + ] + }, + { + "cell_type": "markdown", + "id": "congressional-robert", + "metadata": {}, + "source": [ + "Min-Max normalize the features so that they lie in $[0, 1]$. this is ok to do over all data as opposed to just training because in BO we will compute the cheap features of every COF in the database." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "israeli-baseline", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature 0 in [ 0.0 , 1.0 ]\n", + "feature 1 in [ 0.0 , 1.0 ]\n", + "feature 2 in [ 0.0 , 1.0 ]\n", + "feature 3 in [ 0.0 , 1.0 ]\n", + "feature 4 in [ 0.0 , 1.0 ]\n", + "feature 5 in [ 0.0 , 1.0 ]\n", + "feature 6 in [ 0.0 , 1.0 ]\n", + "feature 7 in [ 0.0 , 1.0 ]\n", + "feature 8 in [ 0.0 , 1.0 ]\n", + "feature 9 in [ 0.0 , 1.0 ]\n", + "feature 10 in [ 0.0 , 1.0 ]\n", + "feature 11 in [ 0.0 , 1.0 ]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0.90081723, 0.20318731, 0.15909104, ..., 0.15284004, 0. ,\n", + " 0. ],\n", + " [0.8775969 , 0.23561078, 0.16100571, ..., 0.30452924, 0. ,\n", + " 0. ],\n", + " [0.85428875, 0.22825336, 0.15487906, ..., 0.15029605, 0. ,\n", + " 0. ],\n", + " ...,\n", + " [0.88401512, 0.20247029, 0.14575075, ..., 0. , 0. ,\n", + " 0. ],\n", + " [0.71974518, 0.42189136, 0.0912957 , ..., 0.14976442, 0. ,\n", + " 0. ],\n", + " [0.7645771 , 0.41002478, 0.09074894, ..., 0. , 0. ,\n", + " 0. ]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in range(np.shape(X)[1]):\n", + " X[:, i] = (X[:, i] - np.min(X[:, i])) / (np.max(X[:, i]) - np.min(X[:, i]))\n", + " print(\"feature\", i, \" in [\", np.min(X[:, i]), \",\", np.max(X[:, i]), \"]\")\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "strong-tenant", + "metadata": {}, + "outputs": [], + "source": [ + "with open('inputs_and_outputs.pkl', 'wb') as file:\n", + " pickle.dump({'X': X, 'y': y}, file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ca83efa6bf5d63222679436f2f0a7172ec63fb12 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 20:27:45 -0700 Subject: [PATCH 02/29] make y a matrix --- prepare_Xy.ipynb | 70 +++++++++++++++++++++++++++++------------------- 1 file changed, 42 insertions(+), 28 deletions(-) diff --git a/prepare_Xy.ipynb b/prepare_Xy.ipynb index 0107dc8..b9e478e 100644 --- a/prepare_Xy.ipynb +++ b/prepare_Xy.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "mediterranean-smoke", + "id": "endangered-bryan", "metadata": {}, "source": [ "# import and prepare data." @@ -10,8 +10,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "expressed-synthetic", + "execution_count": 2, + "id": "physical-physics", "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "hazardous-treaty", + "id": "completed-small", "metadata": {}, "source": [ "load data from Mercado et al., drop outlier" @@ -30,8 +30,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "loving-minister", + "execution_count": 3, + "id": "complimentary-somalia", "metadata": {}, "outputs": [ { @@ -278,7 +278,7 @@ "[5 rows x 53 columns]" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -292,7 +292,7 @@ }, { "cell_type": "markdown", - "id": "rubber-scott", + "id": "comparative-protein", "metadata": {}, "source": [ "define new feature as density of elements" @@ -300,8 +300,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "chinese-antenna", + "execution_count": 4, + "id": "adequate-italic", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ "[5 rows x 60 columns]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -562,7 +562,7 @@ }, { "cell_type": "markdown", - "id": "given-procurement", + "id": "above-plain", "metadata": {}, "source": [ "get feature matrix and target vector" @@ -570,8 +570,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "german-terminology", + "execution_count": 5, + "id": "sweet-cleaners", "metadata": {}, "outputs": [ { @@ -590,8 +590,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "handled-parish", + "execution_count": 8, + "id": "allied-berlin", "metadata": {}, "outputs": [ { @@ -604,11 +604,16 @@ { "data": { "text/plain": [ - "array([155.17249134, 165.88879162, 166.8957419 , ..., 161.97256899,\n", - " 155.38761768, 155.48070341])" + "array([[155.17249134],\n", + " [165.88879162],\n", + " [166.8957419 ],\n", + " ...,\n", + " [161.97256899],\n", + " [155.38761768],\n", + " [155.48070341]])" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -616,13 +621,14 @@ "source": [ "y = df[' deliverable capacity [v STP/v]'].values\n", "print(\"shape of Y: \", np.shape(y))\n", + "y = np.reshape(y, (np.size(y), 1))\n", "y" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "distinct-origin", + "execution_count": 9, + "id": "numerous-continuity", "metadata": {}, "outputs": [ { @@ -650,7 +656,7 @@ " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -663,7 +669,7 @@ }, { "cell_type": "markdown", - "id": "congressional-robert", + "id": "mechanical-argument", "metadata": {}, "source": [ "Min-Max normalize the features so that they lie in $[0, 1]$. this is ok to do over all data as opposed to just training because in BO we will compute the cheap features of every COF in the database." @@ -671,8 +677,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "israeli-baseline", + "execution_count": 10, + "id": "european-rebate", "metadata": {}, "outputs": [ { @@ -711,7 +717,7 @@ " 0. ]])" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -725,14 +731,22 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "strong-tenant", + "execution_count": 11, + "id": "automated-appliance", "metadata": {}, "outputs": [], "source": [ "with open('inputs_and_outputs.pkl', 'wb') as file:\n", " pickle.dump({'X': X, 'y': y}, file)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "rotary-asbestos", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From aa7ef64209a65f870d69a626ace9c7fc0d12e3bb Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 20:55:31 -0700 Subject: [PATCH 03/29] cory's BO run --- BO_run.ipynb | 254 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 254 insertions(+) create mode 100644 BO_run.ipynb diff --git a/BO_run.ipynb b/BO_run.ipynb new file mode 100644 index 0000000..88527d3 --- /dev/null +++ b/BO_run.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "excessive-leader", + "metadata": {}, + "source": [ + "# BO runs" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cooked-willow", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from botorch.models import FixedNoiseGP, SingleTaskGP\n", + "from gpytorch.kernels import ScaleKernel\n", + "from gpytorch.mlls import ExactMarginalLogLikelihood\n", + "from botorch import fit_gpytorch_model\n", + "from botorch.acquisition.analytic import ExpectedImprovement\n", + "import numpy as np\n", + "import pickle\n", + "import sys\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "native-compilation", + "metadata": {}, + "source": [ + "load data from `prepare_Xy.ipynb`" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "selective-wheat", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "markdown", + "id": "distinguished-craps", + "metadata": {}, + "source": [ + "convert to torch tensors" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "promotional-samoa", + "metadata": {}, + "outputs": [], + "source": [ + "X = torch.from_numpy(X)\n", + "y = torch.from_numpy(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "developing-population", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([69839, 12])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.size()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "selected-fever", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([69839, 1])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.size()" + ] + }, + { + "cell_type": "markdown", + "id": "primary-oxygen", + "metadata": {}, + "source": [ + "number of COFs for initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "changed-tsunami", + "metadata": {}, + "outputs": [], + "source": [ + "nb_COFs_initialization = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "differential-halloween", + "metadata": {}, + "outputs": [], + "source": [ + "def bo_run(nb_iterations):\n", + " # select initial COFs for training data randomly\n", + " ids_acquired = np.random.choice(np.arange((nb_data)), size=nb_COFs_initialization, replace=False)\n", + "\n", + " # initialize acquired X, y\n", + " X_acquired = X[ids_acquired, :]\n", + " y_acquired = y[ids_acquired]\n", + " # standardize outputs\n", + " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", + " print(y_acquired.size())\n", + " \n", + " for i in range(nb_COFs_initialization, nb_iterations):\n", + " print(\"iteration:\", i)\n", + " # construct and fit GP model\n", + " model = SingleTaskGP(X_acquired, y_acquired)\n", + " mll = ExactMarginalLogLikelihood(model.likelihood, model)\n", + " fit_gpytorch_model(mll)\n", + "\n", + " # compute aquisition function at each COF in the database\n", + " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", + " acquisition_values = acquisition_function.forward(X.unsqueeze(1))\n", + "\n", + " # select COF with maximal aquisition value, which is not in the acquired set already\n", + " # find COF with highest value of acquisition function, not in acquired set already\n", + " ids_sorted_by_aquisition = acquisition_values.argsort(descending=True)\n", + " for id_max_aquisition_all in ids_sorted_by_aquisition:\n", + " if not id_max_aquisition_all.item() in ids_acquired:\n", + " id_max_aquisition = id_max_aquisition_all.item()\n", + " break\n", + "\n", + " # acquire this COF\n", + " ids_acquired = np.concatenate((ids_acquired, np.array([id_max_aquisition])))\n", + "\n", + " # update X, y acquired\n", + " X_acquired = torch.cat([X_acquired, X[id_max_aquisition, :].unsqueeze(0)])\n", + " y_acquired = y[ids_acquired, :] # start over to normalize y properly\n", + " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", + " print(\"\")\n", + "\n", + " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition])\n", + " print(\"\\tbest y acquired:\", y[ids_acquired].max())\n", + " return ids_acquired" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "working-necklace", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([10, 1])\n", + "iteration: 10\n", + "\n", + "\tacquired COF 44897 with y = tensor([183.6189], dtype=torch.float64)\n", + "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", + "[34647 35678 69727 52355 23924 10911 42976 54307 59610 62690 44897]\n", + "torch.Size([10, 1])\n", + "iteration: 10\n", + "\n", + "\tacquired COF 5174 with y = tensor([190.2927], dtype=torch.float64)\n", + "\tbest y acquired: tensor(190.2927, dtype=torch.float64)\n", + "[ 5125 54841 57876 20364 16192 11446 30070 25210 15672 44596 5174]\n", + "torch.Size([10, 1])\n", + "iteration: 10\n", + "\n", + "\tacquired COF 44897 with y = tensor([183.6189], dtype=torch.float64)\n", + "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", + "[ 4354 63357 16040 50514 55005 43266 11741 11211 47269 46420 44897]\n" + ] + } + ], + "source": [ + "nb_runs = 3\n", + "nb_iterations = 11\n", + "for r in range(nb_runs):\n", + " ids_acquired = bo_run(nb_iterations)\n", + " print(ids_acquired)\n", + " torch.save({'ids_acquired': ids_acquired}, 'bo_run' + str(r) + '.pkl')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9131f570f4c58eb7fab2286d6f6d8ecaa4dea03f Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 21:01:25 -0700 Subject: [PATCH 04/29] set up for 50 runs --- BO_run.ipynb | 191 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 151 insertions(+), 40 deletions(-) diff --git a/BO_run.ipynb b/BO_run.ipynb index 88527d3..864fbbb 100644 --- a/BO_run.ipynb +++ b/BO_run.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "excessive-leader", + "id": "differential-cuisine", "metadata": {}, "source": [ "# BO runs" @@ -10,8 +10,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "cooked-willow", + "execution_count": 1, + "id": "imperial-drawing", "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "native-compilation", + "id": "smaller-surrey", "metadata": {}, "source": [ "load data from `prepare_Xy.ipynb`" @@ -37,8 +37,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "selective-wheat", + "execution_count": 2, + "id": "attached-fortune", "metadata": {}, "outputs": [ { @@ -47,7 +47,7 @@ "69839" ] }, - "execution_count": 24, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -61,7 +61,7 @@ }, { "cell_type": "markdown", - "id": "distinguished-craps", + "id": "sunrise-definition", "metadata": {}, "source": [ "convert to torch tensors" @@ -69,8 +69,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "promotional-samoa", + "execution_count": 3, + "id": "turned-equation", "metadata": {}, "outputs": [], "source": [ @@ -80,8 +80,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "developing-population", + "execution_count": 4, + "id": "jewish-happiness", "metadata": {}, "outputs": [ { @@ -90,7 +90,7 @@ "torch.Size([69839, 12])" ] }, - "execution_count": 26, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -101,8 +101,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "selected-fever", + "execution_count": 5, + "id": "occupational-danish", "metadata": {}, "outputs": [ { @@ -111,7 +111,7 @@ "torch.Size([69839, 1])" ] }, - "execution_count": 27, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -122,7 +122,7 @@ }, { "cell_type": "markdown", - "id": "primary-oxygen", + "id": "middle-fitness", "metadata": {}, "source": [ "number of COFs for initialization" @@ -130,8 +130,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "changed-tsunami", + "execution_count": 6, + "id": "bacterial-trademark", "metadata": {}, "outputs": [], "source": [ @@ -140,8 +140,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "differential-halloween", + "execution_count": 7, + "id": "worthy-technician", "metadata": {}, "outputs": [], "source": [ @@ -167,8 +167,7 @@ " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", " acquisition_values = acquisition_function.forward(X.unsqueeze(1))\n", "\n", - " # select COF with maximal aquisition value, which is not in the acquired set already\n", - " # find COF with highest value of acquisition function, not in acquired set already\n", + " # select COF to acquire with maximal aquisition value, which is not in the acquired set already\n", " ids_sorted_by_aquisition = acquisition_values.argsort(descending=True)\n", " for id_max_aquisition_all in ids_sorted_by_aquisition:\n", " if not id_max_aquisition_all.item() in ids_acquired:\n", @@ -191,8 +190,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "working-necklace", + "execution_count": null, + "id": "light-enzyme", "metadata": {}, "outputs": [ { @@ -202,27 +201,139 @@ "torch.Size([10, 1])\n", "iteration: 10\n", "\n", - "\tacquired COF 44897 with y = tensor([183.6189], dtype=torch.float64)\n", - "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", - "[34647 35678 69727 52355 23924 10911 42976 54307 59610 62690 44897]\n", - "torch.Size([10, 1])\n", - "iteration: 10\n", + "\tacquired COF 25973 with y = tensor([202.8485], dtype=torch.float64)\n", + "\tbest y acquired: tensor(202.8485, dtype=torch.float64)\n", + "iteration: 11\n", "\n", - "\tacquired COF 5174 with y = tensor([190.2927], dtype=torch.float64)\n", - "\tbest y acquired: tensor(190.2927, dtype=torch.float64)\n", - "[ 5125 54841 57876 20364 16192 11446 30070 25210 15672 44596 5174]\n", - "torch.Size([10, 1])\n", - "iteration: 10\n", + "\tacquired COF 19859 with y = tensor([167.1718], dtype=torch.float64)\n", + "\tbest y acquired: tensor(202.8485, dtype=torch.float64)\n", + "iteration: 12\n", + "\n", + "\tacquired COF 20704 with y = tensor([186.0405], dtype=torch.float64)\n", + "\tbest y acquired: tensor(202.8485, dtype=torch.float64)\n", + "iteration: 13\n", + "\n", + "\tacquired COF 26565 with y = tensor([207.3958], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 14\n", + "\n", + "\tacquired COF 26438 with y = tensor([196.1757], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 15\n", + "\n", + "\tacquired COF 20686 with y = tensor([184.8380], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 16\n", + "\n", + "\tacquired COF 66078 with y = tensor([190.6755], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 17\n", + "\n", + "\tacquired COF 18327 with y = tensor([180.1421], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 18\n", + "\n", + "\tacquired COF 26553 with y = tensor([193.3796], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 19\n", + "\n", + "\tacquired COF 16404 with y = tensor([171.2998], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 20\n", + "\n", + "\tacquired COF 29870 with y = tensor([196.7961], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 21\n", + "\n", + "\tacquired COF 29861 with y = tensor([199.7203], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 22\n", + "\n", + "\tacquired COF 16566 with y = tensor([198.7518], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 23\n", + "\n", + "\tacquired COF 30535 with y = tensor([179.8166], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 24\n", + "\n", + "\tacquired COF 16532 with y = tensor([182.4495], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 25\n", + "\n", + "\tacquired COF 30638 with y = tensor([150.3752], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 26\n", + "\n", + "\tacquired COF 25951 with y = tensor([196.5800], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 27\n", + "\n", + "\tacquired COF 25981 with y = tensor([205.4922], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 28\n", + "\n", + "\tacquired COF 20663 with y = tensor([192.2748], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 29\n", + "\n", + "\tacquired COF 33366 with y = tensor([204.8117], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 30\n", + "\n", + "\tacquired COF 25964 with y = tensor([182.0329], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 31\n", + "\n", + "\tacquired COF 33330 with y = tensor([195.5827], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 32\n", + "\n", + "\tacquired COF 33338 with y = tensor([129.6895], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 33\n", + "\n", + "\tacquired COF 26507 with y = tensor([200.4408], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 34\n", + "\n", + "\tacquired COF 33332 with y = tensor([205.9635], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 35\n", + "\n", + "\tacquired COF 6455 with y = tensor([188.9276], dtype=torch.float64)\n", + "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", + "iteration: 36\n", + "\n", + "\tacquired COF 33347 with y = tensor([208.4302], dtype=torch.float64)\n", + "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", + "iteration: 37\n", + "\n", + "\tacquired COF 33370 with y = tensor([196.7202], dtype=torch.float64)\n", + "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", + "iteration: 38\n", + "\n", + "\tacquired COF 33349 with y = tensor([206.7448], dtype=torch.float64)\n", + "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", + "iteration: 39\n", + "\n", + "\tacquired COF 33344 with y = tensor([199.9046], dtype=torch.float64)\n", + "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", + "iteration: 40\n", + "\n", + "\tacquired COF 33374 with y = tensor([185.7611], dtype=torch.float64)\n", + "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", + "iteration: 41\n", "\n", - "\tacquired COF 44897 with y = tensor([183.6189], dtype=torch.float64)\n", - "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", - "[ 4354 63357 16040 50514 55005 43266 11741 11211 47269 46420 44897]\n" + "\tacquired COF 33319 with y = tensor([188.8364], dtype=torch.float64)\n", + "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", + "iteration: 42\n" ] } ], "source": [ - "nb_runs = 3\n", - "nb_iterations = 11\n", + "nb_runs = 2\n", + "nb_iterations = 50\n", "for r in range(nb_runs):\n", " ids_acquired = bo_run(nb_iterations)\n", " print(ids_acquired)\n", From c60242127e7ebb7a5a2b92145399314dda01848d Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 22:08:26 -0700 Subject: [PATCH 05/29] rf run --- BO_run.ipynb | 188 +++++++++---------------------- prepare_Xy.ipynb | 70 +++++------- random_forest_run.ipynb | 239 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 319 insertions(+), 178 deletions(-) create mode 100644 random_forest_run.ipynb diff --git a/BO_run.ipynb b/BO_run.ipynb index 864fbbb..0d72bfe 100644 --- a/BO_run.ipynb +++ b/BO_run.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "differential-cuisine", + "id": "structured-glass", "metadata": {}, "source": [ "# BO runs" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "imperial-drawing", + "id": "banned-consequence", "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "smaller-surrey", + "id": "stone-adrian", "metadata": {}, "source": [ "load data from `prepare_Xy.ipynb`" @@ -38,7 +38,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "attached-fortune", + "id": "adjacent-alignment", "metadata": {}, "outputs": [ { @@ -55,13 +55,14 @@ "source": [ "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "y = np.reshape(y, (np.size(y), 1)) # for the GP\n", "nb_data = np.size(y)\n", "nb_data" ] }, { "cell_type": "markdown", - "id": "sunrise-definition", + "id": "divided-release", "metadata": {}, "source": [ "convert to torch tensors" @@ -70,7 +71,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "turned-equation", + "id": "textile-cabinet", "metadata": {}, "outputs": [], "source": [ @@ -81,7 +82,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "jewish-happiness", + "id": "christian-subdivision", "metadata": {}, "outputs": [ { @@ -102,7 +103,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "occupational-danish", + "id": "martial-devon", "metadata": {}, "outputs": [ { @@ -122,7 +123,7 @@ }, { "cell_type": "markdown", - "id": "middle-fitness", + "id": "southwest-tobacco", "metadata": {}, "source": [ "number of COFs for initialization" @@ -131,7 +132,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "bacterial-trademark", + "id": "coated-fault", "metadata": {}, "outputs": [], "source": [ @@ -141,7 +142,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "worthy-technician", + "id": "stunning-public", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +155,6 @@ " y_acquired = y[ids_acquired]\n", " # standardize outputs\n", " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", - " print(y_acquired.size())\n", " \n", " for i in range(nb_COFs_initialization, nb_iterations):\n", " print(\"iteration:\", i)\n", @@ -181,7 +181,6 @@ " X_acquired = torch.cat([X_acquired, X[id_max_aquisition, :].unsqueeze(0)])\n", " y_acquired = y[ids_acquired, :] # start over to normalize y properly\n", " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", - " print(\"\")\n", "\n", " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition])\n", " print(\"\\tbest y acquired:\", y[ids_acquired].max())\n", @@ -190,144 +189,60 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "light-enzyme", + "execution_count": 9, + "id": "innovative-peeing", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\n", + "RUN 0\n", "torch.Size([10, 1])\n", "iteration: 10\n", "\n", - "\tacquired COF 25973 with y = tensor([202.8485], dtype=torch.float64)\n", - "\tbest y acquired: tensor(202.8485, dtype=torch.float64)\n", + "\tacquired COF 66825 with y = tensor([182.3584], dtype=torch.float64)\n", + "\tbest y acquired: tensor(182.3584, dtype=torch.float64)\n", "iteration: 11\n", "\n", - "\tacquired COF 19859 with y = tensor([167.1718], dtype=torch.float64)\n", - "\tbest y acquired: tensor(202.8485, dtype=torch.float64)\n", + "\tacquired COF 44897 with y = tensor([183.6189], dtype=torch.float64)\n", + "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", "iteration: 12\n", "\n", - "\tacquired COF 20704 with y = tensor([186.0405], dtype=torch.float64)\n", - "\tbest y acquired: tensor(202.8485, dtype=torch.float64)\n", + "\tacquired COF 7312 with y = tensor([169.0128], dtype=torch.float64)\n", + "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", "iteration: 13\n", "\n", - "\tacquired COF 26565 with y = tensor([207.3958], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 14\n", - "\n", - "\tacquired COF 26438 with y = tensor([196.1757], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 15\n", - "\n", - "\tacquired COF 20686 with y = tensor([184.8380], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 16\n", - "\n", - "\tacquired COF 66078 with y = tensor([190.6755], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 17\n", - "\n", - "\tacquired COF 18327 with y = tensor([180.1421], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 18\n", - "\n", - "\tacquired COF 26553 with y = tensor([193.3796], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 19\n", - "\n", - "\tacquired COF 16404 with y = tensor([171.2998], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 20\n", - "\n", - "\tacquired COF 29870 with y = tensor([196.7961], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 21\n", - "\n", - "\tacquired COF 29861 with y = tensor([199.7203], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 22\n", - "\n", - "\tacquired COF 16566 with y = tensor([198.7518], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 23\n", - "\n", - "\tacquired COF 30535 with y = tensor([179.8166], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 24\n", - "\n", - "\tacquired COF 16532 with y = tensor([182.4495], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 25\n", - "\n", - "\tacquired COF 30638 with y = tensor([150.3752], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 26\n", - "\n", - "\tacquired COF 25951 with y = tensor([196.5800], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 27\n", - "\n", - "\tacquired COF 25981 with y = tensor([205.4922], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 28\n", - "\n", - "\tacquired COF 20663 with y = tensor([192.2748], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 29\n", - "\n", - "\tacquired COF 33366 with y = tensor([204.8117], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 30\n", - "\n", - "\tacquired COF 25964 with y = tensor([182.0329], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 31\n", - "\n", - "\tacquired COF 33330 with y = tensor([195.5827], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 32\n", - "\n", - "\tacquired COF 33338 with y = tensor([129.6895], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 33\n", - "\n", - "\tacquired COF 26507 with y = tensor([200.4408], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 34\n", - "\n", - "\tacquired COF 33332 with y = tensor([205.9635], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 35\n", - "\n", - "\tacquired COF 6455 with y = tensor([188.9276], dtype=torch.float64)\n", - "\tbest y acquired: tensor(207.3958, dtype=torch.float64)\n", - "iteration: 36\n", - "\n", - "\tacquired COF 33347 with y = tensor([208.4302], dtype=torch.float64)\n", - "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", - "iteration: 37\n", - "\n", - "\tacquired COF 33370 with y = tensor([196.7202], dtype=torch.float64)\n", - "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", - "iteration: 38\n", - "\n", - "\tacquired COF 33349 with y = tensor([206.7448], dtype=torch.float64)\n", - "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", - "iteration: 39\n", - "\n", - "\tacquired COF 33344 with y = tensor([199.9046], dtype=torch.float64)\n", - "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", - "iteration: 40\n", - "\n", - "\tacquired COF 33374 with y = tensor([185.7611], dtype=torch.float64)\n", - "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", - "iteration: 41\n", - "\n", - "\tacquired COF 33319 with y = tensor([188.8364], dtype=torch.float64)\n", - "\tbest y acquired: tensor(208.4302, dtype=torch.float64)\n", - "iteration: 42\n" + "\tacquired COF 64842 with y = tensor([191.1083], dtype=torch.float64)\n", + "\tbest y acquired: tensor(191.1083, dtype=torch.float64)\n", + "iteration: 14\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_runs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\\nRUN\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mids_acquired\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbo_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_iterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids_acquired\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'ids_acquired'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mids_acquired\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bo_run'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.pkl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mbo_run\u001b[0;34m(nb_iterations)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleTaskGP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_acquired\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_acquired\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mmll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExactMarginalLogLikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mfit_gpytorch_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;31m# compute aquisition function at each COF in the database\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/botorch/fit.py\u001b[0m in \u001b[0;36mfit_gpytorch_model\u001b[0;34m(mll, optimizer, **kwargs)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_state_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0msample_all_priors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m \u001b[0mmll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack_iterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOptimizationWarning\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m 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"\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m inputs=inputs)\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 145\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 146\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -335,6 +250,7 @@ "nb_runs = 2\n", "nb_iterations = 50\n", "for r in range(nb_runs):\n", + " print(\"\\n\\nRUN\", r)\n", " ids_acquired = bo_run(nb_iterations)\n", " print(ids_acquired)\n", " torch.save({'ids_acquired': ids_acquired}, 'bo_run' + str(r) + '.pkl')" diff --git a/prepare_Xy.ipynb b/prepare_Xy.ipynb index b9e478e..9728159 100644 --- a/prepare_Xy.ipynb +++ b/prepare_Xy.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "endangered-bryan", + "id": "stylish-avenue", "metadata": {}, "source": [ "# import and prepare data." @@ -10,8 +10,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "physical-physics", + "execution_count": 1, + "id": "federal-consequence", "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "completed-small", + "id": "about-understanding", "metadata": {}, "source": [ "load data from Mercado et al., drop outlier" @@ -30,8 +30,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "complimentary-somalia", + "execution_count": 2, + "id": "humanitarian-willow", "metadata": {}, "outputs": [ { @@ -278,7 +278,7 @@ "[5 rows x 53 columns]" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -292,7 +292,7 @@ }, { "cell_type": "markdown", - "id": "comparative-protein", + "id": "corresponding-substitute", "metadata": {}, "source": [ "define new feature as density of elements" @@ -300,8 +300,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "adequate-italic", + "execution_count": 3, + "id": "threatened-stability", "metadata": {}, "outputs": [ { @@ -548,7 +548,7 @@ "[5 rows x 60 columns]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -562,7 +562,7 @@ }, { "cell_type": "markdown", - "id": "above-plain", + "id": "monetary-brick", "metadata": {}, "source": [ "get feature matrix and target vector" @@ -570,8 +570,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "sweet-cleaners", + "execution_count": 4, + "id": "christian-tradition", "metadata": {}, "outputs": [ { @@ -590,8 +590,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "allied-berlin", + "execution_count": 5, + "id": "dedicated-activity", "metadata": {}, "outputs": [ { @@ -604,16 +604,11 @@ { "data": { "text/plain": [ - "array([[155.17249134],\n", - " [165.88879162],\n", - " [166.8957419 ],\n", - " ...,\n", - " [161.97256899],\n", - " [155.38761768],\n", - " [155.48070341]])" + "array([155.17249134, 165.88879162, 166.8957419 , ..., 161.97256899,\n", + " 155.38761768, 155.48070341])" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -621,14 +616,13 @@ "source": [ "y = df[' deliverable capacity [v STP/v]'].values\n", "print(\"shape of Y: \", np.shape(y))\n", - "y = np.reshape(y, (np.size(y), 1))\n", "y" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "numerous-continuity", + "execution_count": 6, + "id": "available-blowing", "metadata": {}, "outputs": [ { @@ -656,7 +650,7 @@ " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -669,7 +663,7 @@ }, { "cell_type": "markdown", - "id": "mechanical-argument", + "id": "immediate-savings", "metadata": {}, "source": [ "Min-Max normalize the features so that they lie in $[0, 1]$. this is ok to do over all data as opposed to just training because in BO we will compute the cheap features of every COF in the database." @@ -677,8 +671,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "european-rebate", + "execution_count": 7, + "id": "realistic-issue", "metadata": {}, "outputs": [ { @@ -717,7 +711,7 @@ " 0. ]])" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -731,22 +725,14 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "automated-appliance", + "execution_count": 8, + "id": "radical-bikini", "metadata": {}, "outputs": [], "source": [ "with open('inputs_and_outputs.pkl', 'wb') as file:\n", " pickle.dump({'X': X, 'y': y}, file)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "rotary-asbestos", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/random_forest_run.ipynb b/random_forest_run.ipynb new file mode 100644 index 0000000..bf9d052 --- /dev/null +++ b/random_forest_run.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 96, + "id": "separated-outline", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pickle\n", + "import torch\n", + "import time\n", + "\n", + "from sklearn.metrics import r2_score, mean_absolute_error, explained_variance_score, mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "import autosklearn.regression\n", + "from sklearn.ensemble import RandomForestRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "elect-mapping", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "generous-greece", + "metadata": {}, + "outputs": [], + "source": [ + "def rf_run(nb_training_data, nb_acquire):\n", + " print(\"RF run w \", nb_training_data, \"training data and\", nb_acquire, \"acquired.\")\n", + " # test/train split\n", + " X_train, X_test, y_train, y_test, ids_train, ids_test = train_test_split(X, y, np.arange(nb_data), train_size=nb_training_data)\n", + "\n", + " # train random forest on training data\n", + " rf = RandomForestRegressor()\n", + " rf.fit(X_train, y_train)\n", + "\n", + " # hv random forest make predictions on test data\n", + " y_pred = rf.predict(X_test)\n", + "\n", + " # rank the test predictions\n", + " ids_test_ranked = np.flip(np.argsort(y_pred))\n", + "\n", + " # acquire the COFs in the test set with highest predicted property\n", + " ids_acquire = ids_test[ids_ranked[:nb_acquire]]\n", + "\n", + " # return the acquired COFs but also the trained COFs which count.\n", + " ids_acquire_incld_training = np.concatenate((ids_acquire, ids_train))\n", + " \n", + " assert np.size(np.unique(ids_acquire_incld_training)) == nb_training_data + nb_acquire\n", + " \n", + " return ids_acquire_incld_training" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "found-japanese", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "RUN 0\n", + "RF run w 25 training data and 25 acquired.\n", + "[58527 62321 45853 13274 42290 51234 35298 63189 1273 28980 11906 60007\n", + " 55780 45555 1756 25299 37383 58011 56546 64500 69710 2201 43411 24978\n", + " 1311 2669 7365 69716 45263 2068 57313 16539 34581 33007 39590 55529\n", + " 26496 36215 31376 53126 38189 39054 58505 15733 42146 45071 39338 48444\n", + " 40797 7433]\n", + "RF run w 50 training data and 50 acquired.\n", + "[23681 16553 431 43235 44212 69826 23494 61211 45706 36239 25495 60465\n", + " 66325 38389 46947 23161 28664 39126 41091 34210 65566 40535 59766 49436\n", + " 31750 63486 18088 21971 6551 45159 10703 57890 51750 64584 18927 7910\n", + " 51565 46526 51216 51402 69408 27043 61787 12539 69609 11483 37919 48775\n", + " 21305 36931 29135 47822 43360 18751 35274 32053 22513 44924 63898 61109\n", + " 61769 29207 11269 57926 8550 41769 23917 7962 52619 12844 55227 52926\n", + " 6731 40969 8781 46004 29359 8866 22996 66153 1869 64551 69355 55074\n", + " 19210 62748 15950 57961 28854 67643 12500 69147 62542 55934 51438 24453\n", + " 59008 39278 67280 15871]\n", + "RF run w 75 training data and 75 acquired.\n", + "[13213 11341 49763 58136 67364 5610 49709 9207 9678 25103 10489 25874\n", + " 63684 58405 49690 5246 22049 15208 5783 60674 10789 69830 68742 6341\n", + " 57375 56018 48309 58564 44631 8717 67171 4683 68571 25884 21945 51963\n", + " 48804 63780 877 41137 14920 7034 17181 54555 67454 69451 263 32736\n", + " 11324 34112 49859 39077 35502 41732 36843 9270 52557 25531 28474 28141\n", + " 4433 57248 3667 30016 67625 64910 39616 49072 11714 29423 6885 40102\n", + " 56457 29428 28617 35604 25352 33660 61446 45320 9214 13367 54314 12436\n", + " 14745 49325 17652 23276 69249 55731 45300 8337 41074 46537 59311 64040\n", + " 49580 20906 69234 1321 67865 12254 25007 17042 58662 16793 40172 38969\n", + " 50937 19488 35883 36487 55096 65643 16718 6108 69547 1314 6062 34958\n", + " 68670 41567 59951 29308 64264 53153 51362 14408 2753 33736 33021 15014\n", + " 4308 57919 16530 26821 32909 860 60514 49632 23394 22171 67536 6709\n", + " 38141 1285 26561 48191 44149 927]\n", + "RF run w 100 training data and 100 acquired.\n", + "[19750 44944 20542 60914 51657 23644 15727 42393 65125 59229 8958 40986\n", + " 19610 31096 65900 11342 62902 67959 25891 27423 44777 34344 66766 58303\n", + " 50889 37549 51659 38563 35956 58348 64768 30781 34824 66948 38414 47669\n", + " 40172 539 16071 35985 65143 43701 43307 9436 9199 35636 69741 60856\n", + " 17816 19517 48768 23906 9488 63387 64792 1980 67223 25757 47263 31577\n", + " 2291 10192 35179 66485 11777 8203 30388 58735 43417 54592 5193 16243\n", + " 4266 35147 64651 63069 17395 22844 22243 26412 43117 8678 1699 30044\n", + " 65328 67769 15863 19745 56552 39981 12305 23026 47965 60795 21543 65467\n", + " 7376 54431 67500 56312 49046 33469 66501 64597 22423 29907 45359 35392\n", + " 20996 52004 46394 87 16389 14523 31537 68179 42556 61007 66820 51699\n", + " 275 2848 48431 29529 68534 35880 7406 24903 66587 23928 43790 58870\n", + " 34125 66194 52622 22731 53975 18476 30830 47551 11550 12174 6945 23702\n", + " 54122 36777 60414 62542 24669 62156 55424 48069 33442 34536 4412 5386\n", + " 52941 24972 49044 66693 47317 58713 54207 52501 41677 24738 56733 38721\n", + " 64702 43819 45848 45082 37359 54521 21692 12498 14718 2852 27605 26916\n", + " 31387 69322 62683 65654 37660 64974 54053 21592 45719 54974 62760 55776\n", + " 42568 16511 65664 65988 27234 52337 64812 40186]\n", + "\n", + "\n", + "RUN 1\n", + "RF run w 25 training data and 25 acquired.\n", + "[ 5341 53695 26875 37639 47133 25574 57741 1489 20926 39157 63964 11284\n", + " 33966 42865 43901 50774 19818 61630 32464 10183 57112 50998 15869 856\n", + " 57038 3596 34791 51713 23561 1547 46230 68907 2919 66556 7765 45594\n", + " 2337 4692 47421 56722 4801 29746 30281 41383 43209 14247 7226 26077\n", + " 4208 55262]\n", + "RF run w 50 training data and 50 acquired.\n", + "[29214 28808 60104 63744 54236 27049 47089 7221 61451 2246 14081 40548\n", + " 14096 1306 36509 67421 21807 44698 45318 59607 22996 3903 52999 21389\n", + " 372 49421 65281 29554 47647 10813 61872 28354 50207 52966 45802 33740\n", + " 47177 36421 38550 50853 43386 60910 50786 2981 45924 22758 6177 917\n", + " 40484 2818 33126 54569 66171 39851 58444 14655 22953 8819 9241 47913\n", + " 39830 6267 19351 68421 35448 66694 67758 39371 42828 32755 40803 49860\n", + " 2502 31829 62852 41836 31148 62208 35100 56585 22099 64869 819 69667\n", + " 51446 54254 24801 44165 52680 9892 23994 36894 13728 32788 53452 51286\n", + " 69178 16288 26243 63946]\n", + "RF run w 75 training data and 75 acquired.\n", + "[58839 52064 45618 42022 13751 17153 40388 44262 45530 52841 44141 35271\n", + " 39814 35606 54795 4528 33910 41737 4206 31408 64114 30495 24667 48099\n", + " 62589 67195 35875 59039 35481 8763 37216 68252 13110 45567 42642 37363\n", + " 46733 571 6054 5221 53183 28168 41987 31781 18387 41294 9784 68823\n", + " 16338 36570 18540 64854 42436 14964 18239 39055 49775 5416 30048 60700\n", + " 20266 55488 14709 69246 49979 28113 13635 40892 46878 48996 6029 68195\n", + " 3523 48778 28378 31929 13808 9626 9902 61251 21454 2856 60009 26296\n", + " 9892 16351 4298 15296 24762 27512 6701 43365 37439 14386 25672 49413\n", + " 24541 29420 4252 56222 42651 31463 32463 40479 59080 1299 38643 69061\n", + " 46827 13851 20584 13624 49763 43736 13302 65766 69153 21721 3815 11481\n", + " 40999 19846 62500 15946 61931 50892 18599 39588 16592 8736 6421 18965\n", + " 51077 21941 61699 42931 6355 16705 20672 53900 9801 39520 68408 50053\n", + " 33686 41812 16299 30087 29218 45967]\n", + "RF run w 100 training data and 100 acquired.\n", + "[24133 52295 49534 8073 51071 50507 13705 54621 24677 20882 8434 50224\n", + " 56360 5598 20792 58330 31092 7305 55415 1134 61894 20112 45518 28728\n", + " 37736 54777 5308 46653 45067 69723 63965 23417 37321 1853 39684 34606\n", + " 17654 27874 5917 8596 11316 52931 69020 41380 43785 41148 63755 42138\n", + " 63670 61023 58695 49027 26083 18875 56801 27069 33837 42077 19637 56108\n", + " 38644 54692 56929 34967 29081 56424 42243 69444 19940 4910 7057 60933\n", + " 66948 49541 48722 29398 26133 6244 31955 38436 62796 21756 10302 24801\n", + " 12495 62925 25838 63488 46625 44679 13423 39299 49103 2730 16258 27231\n", + " 1533 54900 43315 64046 13181 55591 13151 8872 42363 50097 61351 23153\n", + " 59265 33900 3 32801 60683 8551 57044 33237 27344 43471 54577 26605\n", + " 32222 59523 66945 22802 62300 18744 58692 13697 11285 3309 4686 58813\n", + " 28468 47530 6512 30007 31462 41066 56535 6383 14898 59860 21717 39823\n", + " 44607 53661 5009 19243 34836 10360 66385 5332 11496 59261 26212 64219\n", + " 60314 56884 58461 19536 48660 59783 6671 14009 9688 26123 31959 43374\n", + " 11348 8505 59093 28804 62857 24988 22282 16632 68302 40654 14983 66999\n", + " 64131 19433 60607 22470 30799 33913 11425 24733 59803 62182 32186 60693\n", + " 49310 36010 31620 40637 4061 22287 43891 56911]\n" + ] + } + ], + "source": [ + "nb_runs = 2\n", + "nb_evals_budgets = [50 * i for i in range(1, 5)]\n", + "for r in range(nb_runs):\n", + " print(\"\\n\\nRUN\", r)\n", + " for nb_evals_budget in nb_evals_budgets:\n", + " # decide how to spend the evals budget here. say 50/50\n", + " nb_training_data = nb_evals_budget // 2\n", + " nb_acquire = nb_evals_budget // 2\n", + " assert nb_training_data + nb_acquire == nb_evals_budget\n", + " \n", + " ids_acquired = rf_run(nb_training_data, nb_acquire)\n", + " print(ids_acquired)\n", + " torch.save({'ids_acquired': ids_acquired}, 'rf_run' + str(r) + '.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "accessible-functionality", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 986dd2acd37519762a747d4a1b5472cb967b9448 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 22:39:48 -0700 Subject: [PATCH 06/29] add RF --- random_forest_run.ipynb | 230 +++++++++++++++++++--------------------- 1 file changed, 108 insertions(+), 122 deletions(-) diff --git a/random_forest_run.ipynb b/random_forest_run.ipynb index bf9d052..cc1a365 100644 --- a/random_forest_run.ipynb +++ b/random_forest_run.ipynb @@ -2,8 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": 96, - "id": "separated-outline", + "execution_count": 1, + "id": "gothic-guest", "metadata": {}, "outputs": [], "source": [ @@ -20,23 +20,31 @@ }, { "cell_type": "code", - "execution_count": 97, - "id": "elect-mapping", + "execution_count": 2, + "id": "saving-visibility", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (69839, 12)\n" + ] + }, { "data": { "text/plain": [ "69839" ] }, - "execution_count": 97, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "print(\"shape of X: \", np.shape(X))\n", "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", "nb_data = np.size(y)\n", "nb_data" @@ -44,16 +52,96 @@ }, { "cell_type": "code", - "execution_count": 98, - "id": "generous-greece", + "execution_count": 3, + "id": "wireless-developer", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.17621688, 0.2123145 ])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ids_train = [2, 5]\n", + "np.linalg.norm(X[1, :] - X[ids_train, :], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "current-diamond", + "metadata": {}, + "outputs": [], + "source": [ + "def diverse_train_test_split(X, train_size):\n", + " ids_train = [np.random.randint(0, nb_data)] # initialize with one random point; pick others in a max diverse fashion\n", + " # select remaining training points\n", + " for j in range(train_size - 1):\n", + " # for each point, compute its min distance to training set\n", + " min_distances_to_train_set = np.zeros((nb_data, ))\n", + " for i in range(nb_data):\n", + " # compute its distance to all points in the training set\n", + " distances_to_train_set = np.linalg.norm(X[i, :] - X[ids_train, :], axis=1)\n", + " min_distances_to_train_set[i] = np.min(distances_to_train_set)\n", + " # select point with max min distance to train set (Furthest from train set)\n", + " ids_train.append(np.argmax(min_distances_to_train_set))\n", + " assert np.size(np.unique(ids_train)) == train_size\n", + " ids_test = [i for i in range(nb_data) if not i in ids_train]\n", + " assert np.size(np.unique(ids_test)) == nb_data - train_size\n", + " return np.array(ids_train), np.array(ids_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "twelve-fruit", + "metadata": {}, + "outputs": [], + "source": [ + "ids_train, ids_test = diverse_train_test_split(X, 25)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "entire-terminal", + "metadata": {}, + "outputs": [], + "source": [ + "diversify_training = True" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "naval-partnership", "metadata": {}, "outputs": [], "source": [ "def rf_run(nb_training_data, nb_acquire):\n", - " print(\"RF run w \", nb_training_data, \"training data and\", nb_acquire, \"acquired.\")\n", + " if diversify_training:\n", + " print(\"diverse RF run\")\n", + " else:\n", + " print(\"RF run\")\n", + " print(\"\\teval budget\", nb_training_data + nb_acquire, \"=\", nb_training_data, \"training data and\", nb_acquire, \"acquired.\")\n", " # test/train split\n", - " X_train, X_test, y_train, y_test, ids_train, ids_test = train_test_split(X, y, np.arange(nb_data), train_size=nb_training_data)\n", - "\n", + " if diversify_training:\n", + " ids_train, ids_test = diverse_train_test_split(X, nb_training_data)\n", + " else:\n", + " ids_train, ids_test = train_test_split(np.arange(nb_data), train_size=nb_training_data)\n", + " \n", + " X_train = X[ids_train, :]\n", + " X_test = X[ids_test, :]\n", + " \n", + " y_train = y[ids_train]\n", + " y_test = y[ids_test]\n", + " \n", " # train random forest on training data\n", " rf = RandomForestRegressor()\n", " rf.fit(X_train, y_train)\n", @@ -65,20 +153,21 @@ " ids_test_ranked = np.flip(np.argsort(y_pred))\n", "\n", " # acquire the COFs in the test set with highest predicted property\n", - " ids_acquire = ids_test[ids_ranked[:nb_acquire]]\n", + " ids_acquire = ids_test[ids_test_ranked[:nb_acquire]]\n", "\n", " # return the acquired COFs but also the trained COFs which count.\n", " ids_acquire_incld_training = np.concatenate((ids_acquire, ids_train))\n", " \n", " assert np.size(np.unique(ids_acquire_incld_training)) == nb_training_data + nb_acquire\n", " \n", + " print(\"\\tmax y acquired = \", np.max(y[ids_acquire_incld_training]))\n", " return ids_acquire_incld_training" ] }, { "cell_type": "code", - "execution_count": 99, - "id": "found-japanese", + "execution_count": null, + "id": "ahead-albania", "metadata": {}, "outputs": [ { @@ -88,105 +177,11 @@ "\n", "\n", "RUN 0\n", - "RF run w 25 training data and 25 acquired.\n", - "[58527 62321 45853 13274 42290 51234 35298 63189 1273 28980 11906 60007\n", - " 55780 45555 1756 25299 37383 58011 56546 64500 69710 2201 43411 24978\n", - " 1311 2669 7365 69716 45263 2068 57313 16539 34581 33007 39590 55529\n", - " 26496 36215 31376 53126 38189 39054 58505 15733 42146 45071 39338 48444\n", - " 40797 7433]\n", - "RF run w 50 training data and 50 acquired.\n", - "[23681 16553 431 43235 44212 69826 23494 61211 45706 36239 25495 60465\n", - " 66325 38389 46947 23161 28664 39126 41091 34210 65566 40535 59766 49436\n", - " 31750 63486 18088 21971 6551 45159 10703 57890 51750 64584 18927 7910\n", - " 51565 46526 51216 51402 69408 27043 61787 12539 69609 11483 37919 48775\n", - " 21305 36931 29135 47822 43360 18751 35274 32053 22513 44924 63898 61109\n", - " 61769 29207 11269 57926 8550 41769 23917 7962 52619 12844 55227 52926\n", - " 6731 40969 8781 46004 29359 8866 22996 66153 1869 64551 69355 55074\n", - " 19210 62748 15950 57961 28854 67643 12500 69147 62542 55934 51438 24453\n", - " 59008 39278 67280 15871]\n", - "RF run w 75 training data and 75 acquired.\n", - "[13213 11341 49763 58136 67364 5610 49709 9207 9678 25103 10489 25874\n", - " 63684 58405 49690 5246 22049 15208 5783 60674 10789 69830 68742 6341\n", - " 57375 56018 48309 58564 44631 8717 67171 4683 68571 25884 21945 51963\n", - " 48804 63780 877 41137 14920 7034 17181 54555 67454 69451 263 32736\n", - " 11324 34112 49859 39077 35502 41732 36843 9270 52557 25531 28474 28141\n", - " 4433 57248 3667 30016 67625 64910 39616 49072 11714 29423 6885 40102\n", - " 56457 29428 28617 35604 25352 33660 61446 45320 9214 13367 54314 12436\n", - " 14745 49325 17652 23276 69249 55731 45300 8337 41074 46537 59311 64040\n", - " 49580 20906 69234 1321 67865 12254 25007 17042 58662 16793 40172 38969\n", - " 50937 19488 35883 36487 55096 65643 16718 6108 69547 1314 6062 34958\n", - " 68670 41567 59951 29308 64264 53153 51362 14408 2753 33736 33021 15014\n", - " 4308 57919 16530 26821 32909 860 60514 49632 23394 22171 67536 6709\n", - " 38141 1285 26561 48191 44149 927]\n", - "RF run w 100 training data and 100 acquired.\n", - "[19750 44944 20542 60914 51657 23644 15727 42393 65125 59229 8958 40986\n", - " 19610 31096 65900 11342 62902 67959 25891 27423 44777 34344 66766 58303\n", - " 50889 37549 51659 38563 35956 58348 64768 30781 34824 66948 38414 47669\n", - " 40172 539 16071 35985 65143 43701 43307 9436 9199 35636 69741 60856\n", - " 17816 19517 48768 23906 9488 63387 64792 1980 67223 25757 47263 31577\n", - " 2291 10192 35179 66485 11777 8203 30388 58735 43417 54592 5193 16243\n", - " 4266 35147 64651 63069 17395 22844 22243 26412 43117 8678 1699 30044\n", - " 65328 67769 15863 19745 56552 39981 12305 23026 47965 60795 21543 65467\n", - " 7376 54431 67500 56312 49046 33469 66501 64597 22423 29907 45359 35392\n", - " 20996 52004 46394 87 16389 14523 31537 68179 42556 61007 66820 51699\n", - " 275 2848 48431 29529 68534 35880 7406 24903 66587 23928 43790 58870\n", - " 34125 66194 52622 22731 53975 18476 30830 47551 11550 12174 6945 23702\n", - " 54122 36777 60414 62542 24669 62156 55424 48069 33442 34536 4412 5386\n", - " 52941 24972 49044 66693 47317 58713 54207 52501 41677 24738 56733 38721\n", - " 64702 43819 45848 45082 37359 54521 21692 12498 14718 2852 27605 26916\n", - " 31387 69322 62683 65654 37660 64974 54053 21592 45719 54974 62760 55776\n", - " 42568 16511 65664 65988 27234 52337 64812 40186]\n", - "\n", - "\n", - "RUN 1\n", - "RF run w 25 training data and 25 acquired.\n", - "[ 5341 53695 26875 37639 47133 25574 57741 1489 20926 39157 63964 11284\n", - " 33966 42865 43901 50774 19818 61630 32464 10183 57112 50998 15869 856\n", - " 57038 3596 34791 51713 23561 1547 46230 68907 2919 66556 7765 45594\n", - " 2337 4692 47421 56722 4801 29746 30281 41383 43209 14247 7226 26077\n", - " 4208 55262]\n", - "RF run w 50 training data and 50 acquired.\n", - "[29214 28808 60104 63744 54236 27049 47089 7221 61451 2246 14081 40548\n", - " 14096 1306 36509 67421 21807 44698 45318 59607 22996 3903 52999 21389\n", - " 372 49421 65281 29554 47647 10813 61872 28354 50207 52966 45802 33740\n", - " 47177 36421 38550 50853 43386 60910 50786 2981 45924 22758 6177 917\n", - " 40484 2818 33126 54569 66171 39851 58444 14655 22953 8819 9241 47913\n", - " 39830 6267 19351 68421 35448 66694 67758 39371 42828 32755 40803 49860\n", - " 2502 31829 62852 41836 31148 62208 35100 56585 22099 64869 819 69667\n", - " 51446 54254 24801 44165 52680 9892 23994 36894 13728 32788 53452 51286\n", - " 69178 16288 26243 63946]\n", - "RF run w 75 training data and 75 acquired.\n", - "[58839 52064 45618 42022 13751 17153 40388 44262 45530 52841 44141 35271\n", - " 39814 35606 54795 4528 33910 41737 4206 31408 64114 30495 24667 48099\n", - " 62589 67195 35875 59039 35481 8763 37216 68252 13110 45567 42642 37363\n", - " 46733 571 6054 5221 53183 28168 41987 31781 18387 41294 9784 68823\n", - " 16338 36570 18540 64854 42436 14964 18239 39055 49775 5416 30048 60700\n", - " 20266 55488 14709 69246 49979 28113 13635 40892 46878 48996 6029 68195\n", - " 3523 48778 28378 31929 13808 9626 9902 61251 21454 2856 60009 26296\n", - " 9892 16351 4298 15296 24762 27512 6701 43365 37439 14386 25672 49413\n", - " 24541 29420 4252 56222 42651 31463 32463 40479 59080 1299 38643 69061\n", - " 46827 13851 20584 13624 49763 43736 13302 65766 69153 21721 3815 11481\n", - " 40999 19846 62500 15946 61931 50892 18599 39588 16592 8736 6421 18965\n", - " 51077 21941 61699 42931 6355 16705 20672 53900 9801 39520 68408 50053\n", - " 33686 41812 16299 30087 29218 45967]\n", - "RF run w 100 training data and 100 acquired.\n", - "[24133 52295 49534 8073 51071 50507 13705 54621 24677 20882 8434 50224\n", - " 56360 5598 20792 58330 31092 7305 55415 1134 61894 20112 45518 28728\n", - " 37736 54777 5308 46653 45067 69723 63965 23417 37321 1853 39684 34606\n", - " 17654 27874 5917 8596 11316 52931 69020 41380 43785 41148 63755 42138\n", - " 63670 61023 58695 49027 26083 18875 56801 27069 33837 42077 19637 56108\n", - " 38644 54692 56929 34967 29081 56424 42243 69444 19940 4910 7057 60933\n", - " 66948 49541 48722 29398 26133 6244 31955 38436 62796 21756 10302 24801\n", - " 12495 62925 25838 63488 46625 44679 13423 39299 49103 2730 16258 27231\n", - " 1533 54900 43315 64046 13181 55591 13151 8872 42363 50097 61351 23153\n", - " 59265 33900 3 32801 60683 8551 57044 33237 27344 43471 54577 26605\n", - " 32222 59523 66945 22802 62300 18744 58692 13697 11285 3309 4686 58813\n", - " 28468 47530 6512 30007 31462 41066 56535 6383 14898 59860 21717 39823\n", - " 44607 53661 5009 19243 34836 10360 66385 5332 11496 59261 26212 64219\n", - " 60314 56884 58461 19536 48660 59783 6671 14009 9688 26123 31959 43374\n", - " 11348 8505 59093 28804 62857 24988 22282 16632 68302 40654 14983 66999\n", - " 64131 19433 60607 22470 30799 33913 11425 24733 59803 62182 32186 60693\n", - " 49310 36010 31620 40637 4061 22287 43891 56911]\n" + "diverse RF run\n", + "\teval budget 50 = 25 training data and 25 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "diverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n" ] } ], @@ -202,17 +197,8 @@ " assert nb_training_data + nb_acquire == nb_evals_budget\n", " \n", " ids_acquired = rf_run(nb_training_data, nb_acquire)\n", - " print(ids_acquired)\n", - " torch.save({'ids_acquired': ids_acquired}, 'rf_run' + str(r) + '.pkl')" + "# torch.save({'ids_acquired': ids_acquired}, 'rf_run' + str(r) + '.pkl')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "accessible-functionality", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 7063c86ff1aa08dff57d583b8c91f581d10bd485 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 23:28:11 -0700 Subject: [PATCH 07/29] add viz --- viz.ipynb | 329 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 329 insertions(+) create mode 100644 viz.ipynb diff --git a/viz.ipynb b/viz.ipynb new file mode 100644 index 0000000..c5551be --- /dev/null +++ b/viz.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "optional-crowd", + "metadata": {}, + "source": [ + "# viz" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "chicken-academy", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" + ] + } + ], + "source": [ + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "import pickle\n", + "import pandas as pd\n", + "import torch\n", + "from sklearn.decomposition import PCA\n", + "\n", + "cool_colors = ['#00BEFF', '#D4CA3A', '#FF6DAE', '#67E1B5', '#EBACFA', '#9E9E9E', '#F1988E', '#5DB15A', '#E28544', '#52B8AA']\n", + "\n", + "search_to_color = {'BO': cool_colors[0], 'random': cool_colors[1], 'evolutionary': cool_colors[2], 'RF': cool_colors[5], 'RF (div)': cool_colors[3]}" + ] + }, + { + "cell_type": "markdown", + "id": "advisory-neighborhood", + "metadata": {}, + "source": [ + "load data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "backed-slide", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (69839, 12)\n" + ] + }, + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "print(\"shape of X:\", np.shape(X))\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "y = np.reshape(y, (np.size(y), 1)) # for the GP\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "markdown", + "id": "soviet-travel", + "metadata": {}, + "source": [ + "load search results" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "experienced-jerusalem", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ids_acquired': array([[16274, 36458, 68938, 28867, 1614, 31160, 32144, 32632, 22648,\n", + " 16501, 66097, 15142, 25981, 31975, 20704, 26565, 13582, 26507,\n", + " 26332, 29870],\n", + " [60245, 20040, 9607, 65519, 27430, 13330, 66251, 44068, 49512,\n", + " 8563, 23494, 13310, 26188, 14998, 20580, 66097, 25981, 26268,\n", + " 20723, 33044],\n", + " [52580, 38118, 40421, 50304, 57391, 55540, 28843, 8079, 25990,\n", + " 42676, 44551, 65585, 65338, 7267, 30056, 58680, 66103, 55829,\n", + " 37344, 65232]]),\n", + " 'nb_runs': 3,\n", + " 'nb_iterations': 20}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bo_res = pickle.load(open('bo_results.pkl', 'rb'))\n", + "bo_res" + ] + }, + { + "cell_type": "markdown", + "id": "bridal-antique", + "metadata": {}, + "source": [ + "# PCA and viz of acquisition of BO" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "equivalent-glory", + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=2)\n", + "pca.fit(X)\n", + "X_2D = pca.transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "standard-bangladesh", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#low dimensional (PCA) visualization of the entire dataset\n", + "plt.hexbin(X_2D[:, 0], X_2D[:, 1], C=y)\n", + "plt.xlabel('PCA dimension 1')\n", + "plt.ylabel('PCA dimension 2')\n", + "cb = plt.colorbar(fraction=0.02, pad=0.04)\n", + "cb.set_label(label=\"deliverable capacity\\n[L STP/L]\")\n", + "plt.xticks()\n", + "plt.yticks()\n", + "plt.gca().set_aspect('equal', 'box')\n", + "plt.tight_layout()\n", + "plt.savefig('feature_space_colored_by_DC.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "rational-frequency", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "which_BO_run = 0\n", + "\n", + "fig, ax = plt.subplots(1, 4, sharey=True, sharex=True, figsize=[3*6.4, 4.8])\n", + "nb_acquired = [10, 12, 15, 19]\n", + "# gray background\n", + "for a in ax:\n", + " a.set_aspect('equal', 'box')\n", + " a.hexbin(X_2D[:, 0], X_2D[:, 1], C=0.3 * np.ones(nb_data), cmap=\"binary\", vmin=0, vmax=1)\n", + " \n", + "for i in range(4):\n", + " ids_acquired = bo_res['ids_acquired'][which_BO_run][:nb_acquired[i]]\n", + " assert len(ids_acquired) == nb_acquired[i]\n", + " # use above colorbar to assign color!\n", + " ax[i].scatter(X_2D[ids_acquired, 0], X_2D[ids_acquired, 1], \n", + " c=y[ids_acquired], marker=\"+\", s=55, vmin=cb.vmin, vmax=cb.vmax)\n", + " ax[i].set_title('{} acquired COFs'.format(nb_acquired[i]))\n", + " ax[i].tick_params(axis='x', labelsize=10)\n", + "ax[0].set_ylabel('PCA dimension 2', fontsize=14)\n", + "\n", + "ax[2].tick_params(axis='y', labelsize=0)\n", + "\n", + "\n", + "fig.text(0.5, 0.2, 'PCA dimension 1', ha='center', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig(\"feature_space_acquired_COFs.pdf\")" + ] + }, + { + "cell_type": "markdown", + "id": "appropriate-breakfast", + "metadata": {}, + "source": [ + "# search efficiency\n", + "first, max $y$ among acquired set." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "signal-fortune", + "metadata": {}, + "outputs": [], + "source": [ + "y_max_mu = np.zeros(bo_res['nb_iterations'])\n", + "y_max_sigma = np.zeros(bo_res['nb_iterations'])\n", + "for i in range(1, bo_res['nb_iterations']):\n", + " y_maxes = np.max(y[bo_res['ids_acquired'][:, :i]], axis=1) # among runs\n", + " \n", + " y_max_mu[i] = np.mean(y_maxes)\n", + " y_max_sigma[i] = np.std(y_maxes)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "awful-emperor", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vtcCUepgxsPCxikh56jIJMbN17j4qetxCF4NQ3b1cP/LfCaxy95fStk03s6eATcD57v734oQmPbF0WxiDIcm0tIculmVNsKw5JBzLmqL75uzVTd89Ai6aDgOiNVs2tITCZLsPVhl1EUkuW0vI0WmPD6H/zYQ5Hrgx7fkKYKq7v2ZmbwHuMLM9ojVzOjCzM4AzAKZOnVqQYCW7lnZY3BS+CPuLNg/dS629/J+3rR2WpyUZy5rC89XNnWftJPHR8fDZKdtn82xpDcnfXpqKK2Uk/XN88FhV0ks56c4Te3Te3KNv6NF5XX5kp6+O6+4P9ujVS5SZ1QD/C7wltc3dm4Cm6PETZvYKMBN4PH6+u88B5gDMmjWrvyVnZWlNc/jSLpdprtm4w92vwU+Xwmt9VamsD1QB50yFD43bvi1V32T/YZqKK+Ul/XN89IzR+hwvkqRrx1wE/MndH0nb9g7gUHe/MF/B5dHBwAvuvjS1wczGAuvcvc3MdgJ2ATpViZXS4w4vb4Nh/aAV5JVGuGRx50XgiqHGYGIdTK4Pi8YdMwbekPYHY2t7WDlXU3FFpKeSfnR8HLgktu0Z4BagZJMQM7sROBAYY2ZLgQvd/RrgODp2xQAcAHw7Gv/SDpzp7hkWJJdSs6E1VB0dV1/sSHqusQ2uXgG/WRVadAplbC1Mqg+zXKbUR4/rQ+IxtrbrlqV2DzNl9h4S6qCIiPRE0iRkINAQ29YADOnbcPqWux/fxfZTMmy7Fbg13zFJ31u0bfsAyXL0tw3wg1dhRXPnffUG03o526SasIDc5PqOCceEehjQgy6U1FTcXQfBJE3FFZFeSJqEvAwcBvwpbdvBwCt9HpFINzS2wcqmsKJruVnZBJctCUlIJm8fBl/ZMSQMpWRtC+xQH2qDiIj0RtIk5PvAzWZ2JTCfMF7iU4RuGpGiWd4USqKX07TQ1na4YRVctSIM7IwbVwtfmgrvGVF6P9f6lrCK7u5DSi82ESk/SYuV3WZmjcBZhDLoi4Dj3f2ePMYmklWbw8Jt5TUt9+nN8P1XwwDUuGrgI+PDAnCDS7B7aUsr1FWFqbj9YRaSiBRf4o9vd/8THbtjRIpqTTO0eHnUptjQAj9ZCn98LfP+Nw6Gr+0IM0u0XMHWtjAV9+3DQiIiItIXEichZlZN6IYZS1iJHAil0fMQl0hOCxphaAm2GKRrd7hzbaj5sbGt8/5h1aHw1zFjSmul3eZ2aGgL92YwvBreNLQ0W2hEpHwlrROyD3AbMJVQOdWi+zYg65BAMxsONLt7o5lVAScBbe7+694ELpVtY2v4Ui/ldWJeaoDvL4Zntmbe/77R8LkppbHabKuHpGNbe/jPPbAqDIgdUxcSPbV+iEg+JG0J+TFwO3AB8CqwA3Ap8HCWc1LuBr4I/Av4JnA60GJmu7n7ed2MVwSAJds6fzG6w02r4e8bQjdNMbUDz24JWXrcTgPg3B1hn6GFjmq7doeG9jC7CKDWwjTecVHSMVAtHiJSAEmTkDcCh7h7k5mZu28xs68ATwO/zXHubsAT0eMTCevQbAL+ASgJkW5raoelTdtXb025bW1Y7bVU1VfBJybCieOhtsAtC+6hlWNrW2jCrALG1MIuA0Kl2cHVmu0iIoWXNAlpSXu80czGARuBCQnOrY5Koe8I1Ln7cwBmNrJ7oYoEK5tCl0H6GIrmdrhmedFCyumdw8O6K5O6WfNjXXPm1pSeGFkTCoyNqA2tHaU0BkVEKlPSJOQJQgvGXcCDwK8JFVOfSXDuf83sfMJ4kj8DmNlEYHN3gxVpd1iwDYbHfnPvfg1Wt2Q+p5jG18E5O8C7RnS/pWFdS5h+vNvg3sdRX1X41hcRkVySJiGnE1pwIYzvuBgYCpyS4NzPAj8DmtOOP4QoIRHpjnUtoVshfbG6VofrV3Y87r2jw4yTYhpUBbsM6llNjY2t4fw3D9WgUBHpv5IWK1uW9vg14BNJL+DuTwPviG2bC8xN+hoiKQsaO08TvX9dGCOSUmPwqckwoYRnzmSztS10N+2jBERE+rnEH3Fmtp+Z/cLM7oru98tx/JzY8317GqQIwOZWeK2lYxLS7nDtio7HvXd0+SYg29phWxvMGqoZKiLS/yVKQszsZOCvwGDgqej+L9H2rhwXe35vjyIUiSxt6jyu4aENYYxIShVwSpLh0iWopR02tsBbh8HQMipFLyLSU0k/6s4HjnL3v6Q2mNmvgKuA67s4J94TrrH40mPN7fDqNhiZNi3XM7SCHDIKdijD5eVbPbTyzBra8WcUEenPknbHjAMeiG17EMg29C9eLqrI5aOknK1uDr9A6YM8H9sEzzd0PO7UiQUNq0+0O6xthj0Hw/huTuEVESlnSVtC/gB8BLgxbduHgDuynFNnZunFyAbEnuPu30t4falg7mHV2WGx39Z4K8i7RsCMgQULq0+4h4X4dhkIO5ZZ7CIivZU0CakCrjOzM4FFwDRgP+CW9AGo7n5G2jmPEqbipjwWe+6AkhDJaX1rKDE+Lu239enN8OSWjsedVoatIGtbYIf6MJVXRKTSdKdianp59gXRDSBjD7a7H9jzsES2W9gYFlRLF28Fedsw2KMPinoV0roWGFMDuw9RyXQRqUxJ64Sc2puLmNnOhHEla9z95d68llSWhrZQCXVsWqr7wlZ4ZFPH404tsxkxG9KKkfWkmJmISH/QrYmAZjYAGEvaTBd3fzXL8QcTqqXOiM5xM3sFOMvdVTFVclrWFL6k01sKro1VR33TYHhLEVek7a4tbVBNiFml1EWkkiWtE7KTmT0CbCWMCVmYduvqnP2BPwKPEMaC7B7dPwz8Idov0qXWdlgYWydmYSM8sL7jcadNLJ/ujG1tYbrxrKEwQMXIRKTCJW0JmQ0sAc4gJBHvAL5NSDK6cgHwPXe/KG3bC8Bfo9aQC4HDux2xVIw1zdDmoQx7yq9Wdpzr/YZB8I7hBQ+tR5rbYVMr7D8chqgYmYhI4iTkbcA0d99sZrj7c2b2SeBvwHVdnLMfcEIX+34OnNOtSKWiuMMr22BYWmvB0ib4v9c6HnfqhPJoBWn1MBD1rUNhhIqRiUg/c9KdJwIw9+gbunVe0h7pdqAxerzFzEYA64CpWc6pSTsnrpHQLZ5XZnatma02s2fTtn3TzJaZ2dPR7ci0fV8zs5fN7EUzOyzf8UnXNrbC5raOXRa/XgltacdMGwDvGVnw0LotVYzsTUNgnIqRiYi8LmkS8hzbV8J9DPgRcAVZxoQALwLv62Lf+4D5Ca/dG9eRucvnR+6+V3S7B8DMdiesd7NHdM7PzUy99kWyeBvUp/12rmmGO9d2POaUCVBV4q0gqWJkMweVZzl5EZF8SpqEfI7Q8gGhG2UyMAv4ZJZzrgB+aWYfNbMaADOrMbMTgV9E+/PK3R9ie9y5HAPc5O5N7r4QeBnQyr9FsK0Nljd17Ir5zSpoSRsMMqkODh9V+Ni6a00L7Dig/Cq5iogUQtI6Ic+kPV4AHJrgnLlRfZBfAdea2Vq2rzVzqbt3tfBdIZxlZicBjwNfcvf1hMTq0bRjlkbbOjGzMwiDdJk6NVuPlPTE8ti03A0tcOuajsecNAFqukihN7VCU3t+Y0zCPXS/7Da4PMatiFSS9M/xwWNVsrhYEiUhZvZp4DF3fyJt2yxglrv/oqvz3P1CM7uWkLSMBdYA97n7ol5F3TtXAhcRJllcBFwOnNadF3D3OcAcgFmzZmlhvj7U5rAgNi33xtWwLS2pGFMLR3WxdGJrO7S0hwqqpdBVM6S6NOIQkY7SP8dHzxitz/EiSTo75qvA3rFtC4HfE7pWuuTui4Gruh9afrj7qtRjM7sKuCt6ugzYIe3QKdE2KYA2h61t8FpzSCJqohkkW1rh5tUdj/3o+I7jRdKtb4XdBsFIzUARESl5SceEjHD3+NiKdcDork4wszPM7K4u9v3RzLrV+tBXzCx9mbMPAKmZM3cCx5lZvZlNB3YB/lXo+CqBOzS2hRkjLzfAPzfAfevgHxthfmPHBOJ3a0KF0ZTh1fC/YzO/blN7SE4mawaKiEhZSNoSstjM9nf3f6ZtexvQZcl24BTCgNZMvkkYmHptwuv3iJndCBwIjDGzpYQCaQea2V6E7phFRINro9ontwDPA63AZ9y9LcPLSje1tsPWqFDX2mZY17p9kGmNhcXpxtR2HjexrQ1+u6rjtuPHw6Au5ixtbIW9h3Q9VkREREpLdyqm/s7MvgO8RGglOA/4fpZzdnb3xzPtcPcnzGxGtyLtAXc/PsPma7Ic/13gu/mLqP9zh4b20LWyrgVeawn1PiAsHjSgKlQLrUkwTuKOtaF7JWVwFXx4XOZjt7aFVpLxdb3+EUREpECSzo6ZE9XM+CwwjdCCcLG7X5nltMFmNsjdG+I7zGwwUGYLr0s2jW1hnZel28L4DrOwONvAKhjbg8SgpT0UJ0t37DgY1sVv7JY22H+YZqGIiJSTxCtYRAlHtqQj7gXgYMJYi7iDCMXMpMw1t4fCYq80hmm1I2r7Zmn6u1+DVS3bn9cbnDA+87EbWmFCnQajioiUm3wuo3UN8DMzW+fuD6c2mtk7gJ+SvStHSlxre1jL5aXG0AUzqo+SDwjrrFwXawV5/1gYnSHJaPeQCL1B0/xFRMpO3pIQd7/SzPYFHooGhS4jFP+aDFyfrb6IlK42h5VN8EJDSBZG1PT9QND714UEJ6XG4GMTMh+7rgWmD4DBKrAvIlJ28rqguLufambXAEcQipX9FbjH3f+Rz+tK33OH1c0h+djaDiNroC4Ps1DaHa5d0XHbe0eH7pa41vZQCGy6SqKLiJSlvCYhAFFXzMM5D5SS5B6m1L6wFTa1hYGh4/P4W/PQhlAxNaUKOLmLVpBUYbKuCpeJiEhpS/x1Es2OeRuwg7vfbGaDAHf3xrxFJ0W1oQXmN8DaFhhaA+PyPP3VM7SCHDwKpmZYfVaFyUREyl/StWN2JpQ3nxidczNhPZhjgY/mLTopii2toXLpyqYw1mJ8gb7oH9sEz8cmdJ/WRSuICpOJiJS/pB/hPwVuAkYBqYmTDwLvzENMUiSNbfDcFnhoY+jqGFcXCovlW2s7LGuCq2OtIAeMgBkZZr2oMJmISP+Q9CtmX+Bod283Mwdw9w1mNiJvkUlBLdsG/90aZqKMy1BCvTe2tcGK5u23lU0dH69pgfYM53XVCqLCZCIi/UPSJGQTMAJYm9pgZpOAVZkONrOTkryou89NeH3Joy2tIQEZ2Yvptk3toTtleVPnZCO99HpS+w6FPYd03q7CZCIi/UfSJOQ24Foz+zSAmY0GfkzoosnkG7HnU6P71YSpugYsBpSEFFmbwzNbwpouPU1Ant4MX10Q1onpC9XAJyd33q7CZCIi/UvSJOQbwNVsXzV3NfBb4HuZDnb3XVKPzewrhPVmvuzuDdG6MZcS1p+RIlvUCBvbej7z5d+b4Asvw7ZM/SndMLoGJtTDlHo4ajS8OUMryHoVJhMR6VeSLmDXCJxoZp8nJBSL3X1NwmucDUx396botbaa2ZeBV4DLuh2x9JkNLfBiA4zpYQLyyEY452Vo8uzHVROSnIl1IdGYmHpcBxPrw32uWh+t7WEMiAqTiYj0H92a++Dua0kbF5JQNTAJWJi2LTXVV4qkpR3+syXU/+jJmi8ProdzF4TS7ekOGQkzBobkIpVsjKkLA157Q4XJRET6ny4TATO7D8jxNy64+6E5DrkB+JOZXUwYBzINOCfaLkXyUmPoQulJK8if18E3FobxJOm+MAVO7GJGS2+oMJmISP+UrTWir0qtfwVYD5wHTCEsZPdrtIpu0axthoWNPauzcdda+PaizlNqz50Kx47ri+g6U2EyEZH+qcskxN2/1RcXcPdW4KLoJkXWFHXDjOhBLZDb1sD3FnfcZsD50+CYMX0VYUcqTCYi0n91Z+2YIcD7CK0ZS4G73X1zwnOHA+8FJrv7ZWY2Aahy9+U9iFl6yB3mbQ2tGAO62apw0yr4wZKO26qBb02Hw0f3VYSdqTCZiEj5OOnOEwGYe3SyERdJ146ZBdwDNBKm6U4FrjCzI9398RznvgW4F1gBTCfMiHkT8Engg4milD6xsikUE+vuWjDXrYDZyzpuqzH47k5w0Mi+iy9OhclERPq3pC0hPwcud/dLUhui+h9XAm/Nce6Pga+4+6/MbH207RHgV12dEFVjTcLdfUXuw6ShLVRFHdWNL3R3uGoFzIm1V9UZXLoz/M+IPg2xAxUmExHp/5ImIbsBl8e2/ZDOlVEz2QO4LnqcWndmS1S0rCtLyT0zxwgtM9leRwhf6M9ugeoqqE3YDeMeWj+uX9lxe30V/HAGvG1Y38eZToXJRET6v6RJyNPAntF9yhtjz7uyhtB98/qQRjObQZgl05UGQvKSjSW8fsVbsi2UVB+XsBvGHS5fAjet7rh9UBX8eBfYZ2jfx5hOhclERCpDtjohJ6Q9/TNwl5ldzfZaH6cBcxJc43rgJjM7J7ysvYXQqnJVlnNudPfFWfanYuxq7ZrU/msJg2lXu/ue0bbLgKOAZkLV1lOjFYGnAfOAF6PTH3X3M3PFUOo2t8JzW2F0wtkl7Q4XvxpmwqQbUg0/3QXemKGcel9TYTIRkcqQrSXku7HnLcDJac9bgVPJPfX2EkKXyT3AEOAB4CfAT7s6wd0/keM1MbNxCZKE64DZdFwo7z7ga+7eamaXAF8Dvhrte8Xd98p17XKRWpxucHWyiqWtDhctgrtf67h9eDX8bCbsmoeOL/fQ7+bR42ZXYTIRkUqRrU7I9L64gLu3AV8Hvm5mY6LS771iZvWE2TZZRwy4+0NRC0f6tj+nPX0UOLa38ZSqBY2wuQ3GJmgFaW0PVVDvW99x+6ga+PlMmJFggOjG1jCY1Og4oCfbcwOqCKXjzcLjPQepMJmISCXI+/otZnavux8Or689k9p+t7u/tzcv3evgQpfSzWnPp5vZU8Am4Hx3/3sfXKMo1rfA/IZkq+M2t8PXFsDfNnTcPrYWrpwJ0xKMzWhsC/8g+w0LCUWVheev36IEI/U4fbuIiFSmpHVCBgLnAwcBY0lLANx9pxynv72L7fsluXYWOde1ycbMvk7oUkpVVFkBTHX316JxK3eY2R7uvinDuWcAZwBMnTq1N2HkRUs7PL0FhteEZCCbhjY49xV4JPZTTqiDX8yEKQNyX6/dQyvI24eHSqwiIqUu/XN88FjVAiiWpC0hPwL+h1AX5BLCGIqzyLIIXdrA1hozO56OLRe7ENaTKQozO4UwYPUgd09NG24CmqLHT5jZK8BMoFMxNnefQzQod9asWb1KhvLhxYaQiAzL0QqyrAm+9DK83Nhx+5T6kIBMSDguY20LzBykomIiUj7SP8dHzxhdcp/jlSJpEnIU8E53X2Bm33X3n5nZA4TBpd/p4pzUwNZ64Htp29uBlcBns13QzM7LsrvH3UhmdjhhUb13uXtD2vaxwDp3bzOznQiJ0oKeXqdYVjfB4m2511p5fBN8dUFowUg3bUDogkkyjgRgUyuMqIGdNJ1WRES6KemX+RB3T30hN5tZnbs/b2ZdVktNDWw1szvd/egexHZIjv0P5XoBM7sROBAYY2ZLgQsJs2HqgfssDEhITcU9APi2mbUQEqUz3X1dD+Iumm1t8MzW0CLR1VgLd7hlDfzwVWiL7Zs5EGbPTF5VtaU9jCfZNxoHIiIi0h1Jk5CFZrabu88DXgBOM7MNwMZcJ/YwAcHd392T82KvcXyGzdd0ceytwK29vWaxuMPzUbtOV/U1mtvh0lfhjgzzkw4ZCRdMg4EJK5S6w7oW2GuIqpqKiEjPJE1Cvk+oejqPUBfkdqAO+FSuEy00N5xO5kGt78ly3vPAbcDt7v5Ewjgr1vImWNncdTfMay1wzsuhpSSdAZ+aDKdO6N5MlfWtYezIRNXzEBGRHspZjSFKIh4gFPnC3e8DRgIj3T1jq0LMdwmJyxLCjJgngN3JXXL9E8AA4GYze9XMZpvZQWamv7tjUovTje4ipZy3FU56vnMCMrgKLp8Bp03sXgLS2BaKn+06WFNsRUSk55KUhDJCqfbXj3X3Fnff2vUpHZwAHObu5wDN0f37CaXfu+Tu/3D3L7v7DOC9wCrgUmC1mc01s/81M82rAlY2hWQgU4Gve1+D01+AVS0dt0+ph1/tBgeM6N612j0MRt1rCNSpoJiIiPRCzq8Rd28nzBIZ2cNrjHL3/0SP28ys2t0fBRKP+XD3/7r7Re7+FmBvwrTZzwIrzey0HsbVbzS2Q32sRaLN4adL4fyF0BSbfPa2YXD9bj2b0bK2BXbRdFwREekDSceEXA781sy+SWgVaU/tcPflOc5dZmZT3f1VQjJzhJmtJaxFk5OZjXT312uKRK9zBXCFmY0CRif8Gfqtre0d14bZ0gpfXwj/yDBs+ITx8LkpydaSiUtNx91Z03FFRKQPJE1Cro7uD6Ljsh9OjvVbCAXO3gK8Sih6dkd07oXZTjKzNwF3AjuY2QLg6Gh2zuuiKbRlNY02HxraYGDUprV4G3zx5XCfrtbg6zvC+8b07Brp03FzVWEVERFJImkS0uPF7Nz9irTHN5rZ3wl1R17IceplwL+AzxBW6/0+YSyJpHGHpnYYUg2PbITzFsCWWAGQMbXwg51hzyE9v4am44qISF9LlIS4++K+uqC7L0146N7Azu6+2cweAf7bVzH0Jy0eBov+ehXMXprWTxbZY3BIQJJWQM1E03FFRCQfki5gV02oNHoyMM7dh5vZYcB0d/9FjnNfoovF5tx9ZpZTB7j75ui49ZoJk9nGFrhsCTywofO+I0eHLpiuipcloem4IiKSL0m7Yy4CDiYsXHdttO0l4GIgaxJC57VlJhOKl12d4dh0VWa2P9uLm1XHnuPuj+QOvf/a3AqHPwNPbum4vYow+PTE8b1LHFLTcfcfrum4IiLS95ImIScA+7v7CjNLJQ8LyVHrA8Ddr49vM7M/Aj+g48J2cYOAf8S2pT9PMii2X7tqeecEZGg1fG+nkDj01hpNxxURkTxKmoQMAlbHttUB2zIcm8RzwP7ZDnB3/e2dw7OxcnHTB4QKqFMH9P61N7XCSE3HFRGRPEr6Rf8kYYZKuhMIs1eyMrNJsdsuhFLur+Y47+6EsVWsV5s6Pv/slL5JQFLTcd80RNNxRUQkf5K2hHwZeNDMjgMGRd0ps0hW9XQpHQemGrCIMMg1m3cmjK1iLYm1Q03oxQyYFPew2N3emo4rIiJ5lnSK7rNmtjvwMeAFQtXU0919VYLT4zVGNkdFxqQX3Du3hPRFErK+FXbQdFwREemBuUff0K3jk07RHejuqwnl27ulFzVGas3sY6TNhsnw2nN7+Npl77UW2JZWFGRQVRiU2huajisiIoWUtDtmlZndDFwTLT6XlZmdl+RF3T3b7Jg64IJspwMVm4TEW0HG12k6roiIlJekScjRwCnA/Wa2BPgVcH2W7phDErymk32K7lZ33yVhfBXn1V6OB2ltDy0p29rDP4QDb9B0XBERKaCkY0IeJAxMPQv4CGFQ6UVmdq+7H5Ph+CQDVqUXlmRoCcnEHZo9JBst7dtHCNdXwahaGFUTBqAOqoIBGogqIiIFlLQlBAB33wJcY2ZPEiqhvi8vUQUalZBFppaQ9mgxu6b2sKZMytBqmFgLw2tgcE1IOGrV5SIiIkWWOAkxs9HARwndMjOB24HDEpw3EDgfOAgYS8ey6ztlOfWIpLFVonhLyMAqWN8CI2phXB2MiJKNgdVQrXRORERKUKK/h83sdmAZcBxhrZhJ7v5Rd78/wek/Ao4Bfg2MJ8ywaWL7GjRdqTazfdNimGJmD5rZBjO708xGJYm9v4q3hLx9OBwyCvYdBjMHhURkSI0SEBERKV1JG+VfAvZ29/3d/ZfuvrEb1zgKONrdfwa0RvcfJHehs+8AI9Kez46enw9MICyqV7HiLSEzBmparYiIlJekA1O/0otrDHH3BdHjZjOrc/fnzeytOc7bFXgYwMwGAYcTFtF7ysz+D/hzL2Iqay3tsDyWhEzXGi8iIlJmukxCzOwKd/9c9HhOV8e5+xk5rrHQzHZz93mEaqunmdkGIFdrSp27N0SP9yFM2X0quuZL0RiVnMzsWsIA2tXuvme0bRRwM2EV4EXAh919vZkZ8BPgSKABOMXdn0xynUJa3gxpdcoYUQPDNLNFRETKTLbumNrY465uuXwfmBo9vgj4AaHI2DdznLfKzGZGj/8H+Gdqh5kNI4wrSeI6QitKunOBv0R1SP4SPYcwGHaX6HYGcGXCaxRUfM2Y8bXqihERkfLTZUuIu38q7XF8Bd3E3P3mtMf3mdlIQivH1iynQRjIenu0WN7pwOfS9r0dmJ/w+g+Z2bTY5mOAA6PH1wMPAl+Nts91dwceNbMRZjbR3VckuVahxKulTtI6LyIiUobyXi3CzL5hZlNSz929JUECAmFg6m+A3YGL3f23aft2I/fsmmzGpyUWKwmzdgAmA0vSjlsabSsp8ZaQKUpCRESkDGUbE/IS2wtsdsndZ+Y45CDgG2b2AHANcIe7Nyd4XSd05WTa96Nc5yfl7m5mOX/OdGZ2BqG7hqlTp+Y4uu/FW0KmDSh4CCIiZS39c3zw2EFFjqZyZZsd852+uIC7H2hmOxGKnF0KXGlmNwLXFnHQ56pUN4uZTQRWR9uXATukHTcl2taBu88B5gDMmjWrWwlMX4jXCNlRSYiISLekf46PnjG64J/jEmQbE3J9X10kmqJ7AXCBmb2HMP7i30Cx5nTcSVj/5uLo/g9p288ys5uAtwEbS208CHSuEaKWEBERKUfdKdu+E6Fi6iR3PyuauVLr7s8lPL+aULjsVEKhsn9mP6NvRK0uBwJjzGwpcCEh+bjFzD4OLAY+HB1+D2F67suEKbo9HpCbT/GWkJ1UI0RERMpQoiTEzA4BbgMeIHyhn0VYB+Z8cqzxYmZvInTFnAi0EKbnnuPuiWa3ZHndfd39X7mOc/fju9h1UIZjHfhMb+LKty2tsL51+/NqYAcNTBURkTKUtCXkYuBD7n6vma2Ptj1JKCKWy2OE7o6TgT+7e3uO43Mys3pCS0rFleiKd8WMrYMarYgrIiJlKGkSsrO73xs9dgB3bzSzJMXKJrn7+tyHdVtFlueKd8VMrCtOHCIiIr2V9G/oJWa2Z/oGM3szoeR5VlE59J3M7Dwz+1l07hvMbI9uRxt76V6eX5biLSGqESIiIuUqaRJyBXCbmX0UqDazDxIKieWs1xGNJ/kPsB/wsWjzGEL5dummeEvIVM2MERGRMpV0Fd2rwtpufJUwDuNbwI/d/dcJTu/ReJJsi+ZRgWNBUuItIVPVEiIiImUq8RRdd78KuKoH1+jpeJJc++f2IJayF28Jma7puSIiUqaylW1PVI/c3V/NccgSM9vT3Z9Ne+2c40l6s2hefxZvCZmu7hgRESlT2VpCFpFs8GeurpHUeJJvs308yTcJJdylG9w7rxuzk5IQEREpU9mSkPQ1VA4nFBz7FrAQmA58A8hZ2r0n40nM7L/u/sZcr21mT7v7XrmO6y/WtEBTWpWVQVUwMskkaRERkRzmHn1Dwa+Zbe2Y1xduM7NzgAPcPbXQ2ytm9l/gb8C1uS7Sg/EkM8zseHLXApnWjdcse/GumPF1YBVZLUVERPqDpANTJxDWUknXEG3Ph1XA9xIctzJP1y9J8UGpkzUzRkREyljSJOQh4Hoz+xLwKrAjYUzH3/MRlLtPy8frljtNzxURkf4kabGyTwAjgAWEReheBkYBp+cnLMlEhcpERKQ/SVqsbBVwkJlNAqYAy9LHjEhhxJOQHZWEiIhIGUtcrAzA3ZcDy/MUi+QQ746ZpiRERETKWN4XgTezdjNry3BrNLMXzOwCM9NasAnEW0J2VrVUEREpY91qCemhswljSn4ELCYMav08oez6FuAcYDChjoh0oaUdVjR33KYxISIiUs4KkYScChzl7otSG8zsAeA2d9/bzP4J/AElIVkta+pYvnZUDdTnvR1LREQkfwrxNbYTnceRLAd2BnD3Z4CxBYijrMXLtU/S9FwRESlziVpCzGww8DlgFjA0fZ+7H5rj9KeAS8zsXHdvMrN64PvRdsxsJ+C17gZeaZbExoNMURIiIiJlLml3zFxgV+AuOldOzeUTwB+BM81sNaHV41Xg6Gj/RNQVk1O8JUSFykREpNwlTUIOAqa5+4buXsDdXzKzPYD9gUnAMuBRd2+L9v+ju69ZieItIRqUKiIi5S5pErKEjuMiuyVKOB7u6fnSuSVkupIQEREpc0mTkLOBX5rZpcQWjYsKmHWpl+NJJBJvCZmuGiEiIlLmkiYhDrwT+FDaNou2V+c491pgb+AOYGs348sLM3sDcHPapp2ACwjr43wCWBNtP8/d7ylsdJnFW0JULVVERMpd0iTkl8B1wG/o/sDUQ4GZ7r4m55EF4u4vAnsBmFk1YZzK7YSaJj9y9x8UL7rONrfChtbtz2sMxqvGrIiIlLmkSch44Hx378m4kNcIlVFL1UHAK+6+2MyKHUtG8TVjJtRBVWmGKiIikljSYmX3A2/p4TXOA64ws1E9PD/fjgNuTHt+lpk9Y2bXmtnITCeY2Rlm9riZPb5mTf4beOJrxkxWK4iISK+kf443bdqW+wTJi6QtIQuBu83sFmBF+g53/16Oc28gjBs5zczaYucW9es0WjjvaOBr0aYrgYsIY10uAi4HTouf5+5zgDkAs2bN6vGsoaTiLSE7aDyIiEivpH+Oj54xOu+f45JZ0iRkH+B5YM/oluJAriTk4B7EVShHAE+6+yqA1D2AmV1FKM5WdPGWEBUqExGR/iBREuLu7+7pBdz9bz09twCOJ60rxswmunuqpecDwLNFiSpGM2NERKQ/KsQqupjZB4HTgR0Ihc+udvdbC3HtLDENBg4BPpm2+VIz24vQwrMotq9o4jVClISIiEh/kHQBuxa6qJiaa1yHmZ1BWLDul8DvCavn/tLMxrj7L7sXbt9x963A6Ni2jxUpnKw6VUtVoTIREekHkraExMd1TAa+APwqwblnA0e6+2OpDWZ2B3A9ITGRLNodlmpgqoiI9ENJx4R0GtdhZo8ANwE/z3H6JODfsW1PABOSXLvSrWmBpvbtz4dUw/CCdKKJiIjkV9I6IZksA3ZPcNwLwImxbccD83tx7YoRnxkzSTVCRESkn0g6JuTtsU2DgZOBeQlO/yrwp2hsyEJgGqHw2ZHJw6xc8RohU9QVIyJSsuYefUOxQygrSRv2H44930LoUulUyCvO3f9mZnsQKpPuAPwJOMndF3UjzoqlGiEiItJfJR0T0ptuG9x9IWGGjHRTvCVkqlpCRESknyhUnZD9gVnA0PTtCUq+V7x4S8h0JSEiItJPJB0TMhj4HJkTiUNznPsd4MvAf4CG9FPJXfK94sVbQpSEiIhIf5G0JWQusCthLZWGHMfGfRLY192f6eZ5QueWENUIERGR/iJpEnIQMM3dN/TgGo2Exe+km5rbYWXz9ucGTNbAVBER6SeSDjhdQhdl2xP4IXB+D8+taMuaOr7pY2qhvldDhEVEREpH0paQswnrvVwKrEzf4e7Lc5z7O+CvZnY2sDp27syE169I8a6YKWoFERGRfiRpEuLAO4EPpW2zaHt1jnNvBpYCP6b740kqWnxQ6g5KQkREpB9JmoT8ErgO+A3dTyT2Asa4+7ZcB0pHnQqVaVCqiIj0I0mTkPHA+e7ek3Eh84CRwIoenFvRXo21hExTEiIiIv1I0iTkfsJ6L4/34BrXAbea2Q/oPJ7kkR68XsVYEmsJURIiIiL9SdIkZCFwt5ndQqxFI0HV059G97+PbU8ynqSixVtC1B0jIiL9SdIkZB9CrY89o1tKzqqnvV13ppJpYKqIiPRnSRewe3e+A5GONraGW0qtwbi64sUjIiLS1/K+gJ2ZGXA6oerqWMLUXgDc/T35vn65io8HmVwPVZb5WBERkXLUZVeJmT2V9vglM5uf6ZbgGt8FLiJUXd0PeALYHXi6d6H3b/GuGBUqExGR/iZbS8hlaY+/04trnAAc5u7/MbPT3f0cM7sV+EovXrPf67RwnZIQERHpZ7pMQtz9t2mPr+/FNUa5+3+ix21mVu3uj5qZxplkEW8J2VEzY0REpJ9JNHPFzE7NsM3M7FsJTl9mZlOjxwuAI8xsP6AleZj5YWaLzOy/Zva0mT0ebRtlZvdFXVD3mdnIYsQWbwlRjRAREelvkk6f/Y6ZXWtmAwDMbBzwF+CYBOdeSSh0BvAj4A7gH8AV3Qs1b97t7nu5+6zo+bnAX9x9F8LPeG4xglJLiIiI9HdJk5C9ganAv8zsZOA/wCuEgaZZufsV7n579PhGYBqwh7v3ZpxJPh0DpLqfrgfeX4wgOo0JURIiIiL9TKIkxN1XA4cDrcC1wO/d/RM9WZTO3Ze6+wvdPS9PHPizmT1hZmdE28a7e6oq7ErCujkF1e4qVCYiIv1fojohZjYGuAGoA74EfNPM1rj7t/MZXAH8j7svi7qX7jOzDsmRu7uZdVq0L0pYzgCYOnVqfHevrW6GlrSrDquGYXmv6CIiUjnSP8cHjx1U5GgqV9LumP8Ay4G3uvuPCd0wx5rZn/MVWCG4+7LofjVwO7AvsMrMJgJE96sznDfH3We5+6yxY8f2eVzxNWNUI0REpG+lf47XD1N/d7EkTUK+4e6nunsjQNSdsh+wNG+R5ZmZDTazoanHwKHAs8CdwMnRYScDfyh0bPFqqRoPIiIi/VHStWOuzbCtATitzyMqnPHA7aGqPDXAb939XjP7N3CLmX0cWAx8uNCBdVo9Vy0hIiLSDyUeaWBmuwIH0nn9l7IcF+LuC4A3Z9j+GmGdm6JRjRARkdIw9+gbih1Cv5Z0YOrxwHXAM8Cbovs3Aw/lLbIKFp8ZM1VJiIiI9ENJx4R8HfiYu78VaIjuzwSezFtkFSzeEqIkRERE+qOkSchU4HexbXOBj/VtOAKqESIiIpUhaRKyARgePV5lZrsBo4DB+QiqkjW1w8rm7c8NmKwkRERE+qGkScj9wAeix7dEz/8F/CkfQVWypbFWkPF1UJf0X0lERKSMJJ2imz4V90LgRWAo29dYkT4SrxGiQmUiItJfdbsYuLs7oYS75EG8RohWzxURkf4q6RTdKuAjwCxCC8jr3P2MjCdJj8RbQlSoTERE+qukLSG/BI4GHgQa8haNqCVEREQqRtIk5FjgTe6+JJ/BSIaWECUhIiLSTyWdd7EWWJPPQCSIt4SoRoiIiPRXiVfRBX5sZqPyGYyoWqqIiFSOpEnIc8DBwBoza06/5TG2irOxFTa3bX9ebzC2tnjxiIiI5FPSMSG/Af4JfBYNTM2beCvI5Howy3ysiIhIuUuahOwE7OPubTmPlB7T6rkiIlJJknbH/BvYOZ+BSIbxIBqUKiIi/VjSlpC/AH80sznAivQd7v7bPo+qQsWTENUIERGR/ixpEnJ6dH9WbLsDSkL6SLw7ZgclISIi0o8lXcBuer4DEXXHiIhIZdEi8SVELSEiIlJJlISUiDaHpaqWKiIiFURJSIlY1Qwtvv35iBoYmnTEjoiISBlSElIi4gvXqRVERET6OyUhJSK+cJ0KlYmISH9XkUmIme1gZg+Y2fNm9pyZfT7a/k0zW2ZmT0e3IwsVU7wlRDNjRESkv6vUUQetwJfc/UkzGwo8YWb3Rft+5O4/KHRAagkREZFKU5FJiLuvIKr86u6bzWweMLmYMWndGBGR0jD36BuKHULFqMjumHRmNg3YG3gs2nSWmT1jZtea2cguzjnDzB43s8fXrFnTJ3HEC5VpYKqISP6kf443bdqW+wTJi4pOQsxsCHArcLa7bwKuJCzUtxehpeTyTOe5+xx3n+Xus8aOHdsnsXSqlqqWEBGRvEn/HK8fpg/cYqnYJMTMagkJyA3ufhuAu69y9zZ3bweuAvYtRCzb2mB1S1pswKS6QlxZRESkeCoyCTEzA64B5rn7D9O2T0w77APAs4WIJ14pdWId1Fbkv4yIiFSSihyYCrwD+BjwXzN7Otp2HnC8me1FWB14EfDJQgSjQakiIlKJKjIJcfeHCb0ecfcUOhbQeBAREalMavQvAZ1qhGhmjIiIVAAlISWg07oxagkREZEKoCSkBKglREREKpGSkBKglhAREalESkKKzF0tISIiUpmUhBTZhlbY0rb9+YAqGFNbvHhEREQKRUlIkcVrhOxQD5Zp8rCIiEg/oySkyFQjREREKpWSkCLL1BIiIiJSCZSEFJlaQkREpFIpCSmyTuvGqCVEREQqhJKQIou3hKhGiIiIVAolIUWmGiEiIlKplIQUUZvDsvjAVLWEiIhIhVASUkQrm6HVtz8fVQODq4sXj4iISCEpCSmi+JoxmhkjIiKVRElIEcXHg6hGiIiIVBIlIUWkGiEiIlLJlIQUkaqliohIJVMSUkRqCRERkUqmJKSI1BIiIiKVrKbYAVQytYSIiBTf9BHTmXv0DcUOoyKpJaRIGttgTcv251XApLqihSMiIlJwSkIyMLPDzexFM3vZzM7NxzWWxrpiJtVDjf41RESkguhrL8bMqoGfAUcAuwPHm9nufX2dTl0xGg8iIiIVRklIZ/sCL7v7AndvBm4Cjunri3QalKrxICIiUmGUhHQ2GViS9nxptK1PqSVEREQqnWbH9ICZnQGcET3dYmYvdvtFJu60I0NHjkk9vWztslcvW7dyTR+FmC9jgLXFDiKBcokTyifWcokTyifWcokTyiPWHbtzcBef48OBjbFD07dlepzpHqCW3O9ZOV9vI7l/L9JfL/O/j7vrlnYD9gf+L+3514Cv9fl1/upX8FdfwV/do9sxxf7ZE7w3jxc7hv4UZznFWi5xllOs5RJnucXay59zTrZtmR5nuo9uOd+zcr5ekt+LTNeL39QS0tm/gV3MbDqwDDgOOKGvL+Lv5nPA56xuwBP837ZjKf2/MkRE+rs/5tiW6XFX9/v08+slkfNYi7IVSWNmRwI/BqqBa939u3m81uPuPitfr9+XyiXWcokTyifWcokTyifWcokTyivWUlHo96wY/0Z9cU21hGTg7vcA9xTocnMKdJ2+UC6xlkucUD6xlkucUD6xlkucUF6xlopCv2fF+Dfq9TXVEiIiIiJFoSm6IiIiUhRKQgokVyl4M6s3s5uj/Y+Z2bQihImZ7WBmD5jZ82b2nJl9PsMxB5rZRjN7OrpdUKRYF5nZf6MYHs+w38zsiug9fcbMkgzcykecb0h7r542s01mdnbsmKK8p2Z2rZmtNrNn07aNMrP7zOyl6H5kF+eeHB3zkpmdXKRYLzOzF6J/39vNbEQX52b9XSlAnN80s2Vp/75HdnFu3peMSBDrzWlxLjKzp7s4t2DvqfRjxZ4SVQk3wgDXV4CdgDrgP8DusWM+DfwienwccHORYp0I7BM9HgrMzxDrgcBdJfC+LgLGZNl/JPAnwID9gMdKIOZqYCWwYym8p8ABhFH1z6ZtuxQ4N3p8LnBJhvNGAQui+5HR45FFiPVQoCZ6fEmmWJP8rhQgzm8CX07wu5H1c6IQscb2Xw5cUOz3VLf+e1NLSGEkKQV/DHB99Pj3wEFmZgWMEQB3X+HuT0aPNwPzyEPF2AI5BpjrwaPACDObWOSYDgJecffFRY4DAHd/CFgX25z+u3g98P4Mpx4G3Ofu69x9PXAfcHi+4oTMsbr7n929NXr6KDAlnzEk0cV7mkRBloxIly3W6PPnw8CN+YyhUpjZ+83sqqil6dACXG83M/uFmf3ezD6V7+tF1xxsZo+b2fuSnqMkpDCSlIJ//ZjoQ3UjMLog0XUh6hLaG3gsw+79zew/ZvYnM9ujsJG9zoE/m9kTFqofxhWkBH83HUfXH+ql8J4CjHf3FdHjlcD4DMeU4nt7GqHlK5NcvyuFcFbUbXRtF11cpfaevhNY5e4vdbG/FN7TosrUnRVt79St5u53uPsngDOBjxTgevPc/UxCIvmOfF8v8lXglu5cQ0mIZGRmQ4BbgbPdfVNs95OE7oQ3Az8F7ihweCn/4+77EFY8/oyZHVCkOBIxszrgaOB3GXaXynvagbs74cumpJnZ14FW4IYuDin278qVwM7AXsAKQjdHqTue7K0gxX5PS8F1xFoALfdK7OdH+/N+PTM7GribnpecSHw9MzsEeB5Y3Z0LKAkpjGXADmnPp0TbMh5jZjWEmvuvFSS6GDOrJSQgN7j7bfH97r7J3bdEj+8Bas1sTPy4fHP3ZdH9auB2QnN2uiTveyEdATzp7qviO0rlPY2sSnVbRfeZPlRK5r01s1OA9wEnRklTJwl+V/LK3Ve5e5u7twNXdXH9UnpPa4D/BW7u6phiv6eloIvurIzdahZcAvwp1eWdz+tFx9/p7kcAJxbgegcSxt6dAHzCzBLlF0pCCuP1UvDRX8PHAXfGjrkTSM0wOBb4a1cfqPkU9QNfA8xz9x92ccyE1HgVM9uX8HtU0IQp6nscmnpMGKD4bOywO4GTov/8+wEb07oZiqHLvyxL4T1Nk/67eDLwhwzH/B9wqJmNjLoWDo22FZSZHQ58BTja3Ru6OCbJ70pexcYifaCL6yf5nCiUg4EX3H1ppp2l8J6WsK661T5LeF+PNbMz8309CzPurjCzX9K3xTczXs/dv+7uZwO/Ba6KEu6cVDG1ANy91czOInxIp0rBP2dm3yYsAHQn4Yv/12b2MiHzPK5I4b4D+BjwX9s+Ne88YCqAu/+CkCR9ysxagUbguCIkTOOB26Pv7Rrgt+5+b+o/dxTnPYQZMi8DDcCpBY7xddEH9SHAJ9O2pcdalPfUzG4k/AUzxsyWAhcCFwO3mNnHgcWEPmXMbBZwpruf7u7rzOwiwhcnwLfdvSeDMXsb69eAeuC+6HfhUXc/08wmAVe7+5F08btS4DgPNLO9CF1bi4h+D9Lj7OpzIl9xdhWru19DhrFLxXxP+wN3vwK4ooDXexB4sFDXS7vudd05XhVTRUREesjCAP673H3P6Pn+wDfd/bDo+dcA3P37ul5n6o4RERHpO4XuVivr6ykJERER6YGoO+ufwBvMbKmZfTwqsZDqVpsH3NJX3Wr98XrqjhEREZGiUEuIiIiIFIWSEBERESkKJSEiZcbC6qVfLnYcXTGzWWbmVqSVoEWkfCgJEcnAzMaaWXNUlKnWzLaa2dRix5UvZnaKmW0p8DV3NrNrzGyJmTWZ2WILi229PXbcYWb2FzPbZGaNFtbX+Xy8ImOU+MRvTxfyZxKR7lESIpLZ/sB/3H0rYanzde7+apFj6jeiwmdPAnsAnyKsQXEU8ARh7ZzUcZ8mFJ17Anh7dNzPgW+ReZ2YTwAT024H5e2HEJFeUxIiktnbgX9Ej/8n7XFWZnaUhVVFt5nZQjP7bjSXHjP7npk9keGcR8zsiujxW83sz2a2NvrL/+GoOFC2a7qZHRvb1qHLxsy+aGEF161mtszMrjazEdG+A4FfAYPTWhC+Ge2rM7NLoul5DWb2bzM7LHatw83shehn/jswM0e8RlgYawHwDne/y91fcfdnooJHB0XHTQF+BPzU3b/i7s+6+0J3/yVwCnCcmX0o9vIb3H1l2u21tOteELW2NJnZSjObmy1OEck/JSEiETObamYbzGwD8EXgk9Hj7wHvj/b9PMv5hxH+Op9N+Av/NEI59u9Fh/wG2MfMdk07ZydCq8tvok1DgV8TllHfF3gauMfMRvfyx2sHzo7iOiF67VSLwyPRvga2tyD8INr3K+Bd0Tl7AtcDfzSzN0fx70BY8fc+wgqxPwUuzRHLXlEcl7l7W3ynu2+IHn4IqMv0eu5+B/BSFFdOZvZB4MvAp4FdCAve/SvJuSKSR+6um266uUNYA2Ma8CagObrfGdgMHBDtG5Pl/IeAb8S2vR/YwvaaPE8CF6XtPx94MctrGmHp94+mbVsEfDntuQPHxs7rcEyG1z0caAKqouenAFtix+xMSF6mxrbfAfw8evw9YH7q50v7mRyY1sW1Pxzt3zvHv8eVhEUHu9r/B+D52PvQGL3fqduJ0b4vAi8CtcX+PdOt8m6EJH929PgHwLkJzpkV/V9bA2yK/p/9GJiYdszuwO8Ji102AM9Fv+tVacecEv0/3hK7VRf7fXF3tYSIpLh7q7svAnYF/u3uzwATgFXu/pC7L3L3tVle4i3A181sS+pGWFFycPQ6EFo80v96P5G0sQ1mNs7Mfmlm881sIyEBGke0gGBPmdl7zOy+qFtlM3AboZVhQpbT9iEkQc/Hfqb3EhIUgN0Ii8alVz38Z65wuhF6d6spnkNoaUndUuWkfwcMABZGg2E/ZGb13XxtkZ56G/Bo9Hj/tMcZmdkhwMOExHkvdx9GaJF8LbrHzN4EPEZIUvYERhCSnS8SWjDTLXD3IbFbp1bIYtAquiIRM3sO2BGoBaqiL9waoCZ6vNjd98jyElWEAZO/y7BvTXR/I3BpNM6jiZDw/CbtuOsJK5R+gdCa0QT8hZAwdMXp/MVem/Zz7QjcDVwFXED4INsniiXb61ZFr/1WoCW2rzHLebnMj+53A57KcdxwM5vs7ssy7N+d8JdfupXu/nL8QHdfYmZvIIw3ORi4HLjQzN7mYfCxSD7tB1wQjQ97M/B4juN/TliZ+KupDe6+Argo7ZgfElZh/1TatvvM7KPAA2Z2lbs/nO0iUSL+U0KL7QBgFXCeu2f6DMsLtYSIbHck4a/nlcBHo8fPEv662Cvan82TwK7u/nKGWyu8/kHyV0ILyInAP919Qdpr/A9hIObdHtZj2EwYo5HNmvRjzGx87JxZhGTjC+7+T3efD0yKvUYzYfn4dE8RkpsJGX6eVFIwD3hbNNg0Zb8c8T4NPA+cY2bxa5IaMEtoZm4htG7Ej/kAMIPMM2Qycvdt0fv6BUJitQfwjqTni3SHmb0rGke2kfDHzb8JfwAMAJZGf/RkOm8m4Xf7t1leeyBwIB3/gAHA3R8ElgJHJAjzZML/hd2i1pb30Dmxzyu1hIhE3H2xmU0gtET8gdAKsAdwa5Q85PJt4C4zWwzcArQSmkn3dfevpB33G8Jf4s3Ad2OvMR/4qJk9RujGuTQ6Lpu/Ap8xs0eANsI4jW1p+18i/MFxtpndRkgSzo69xiJgQNQM/BTQ4O7zzewG4Doz+xIhyRpF+PBb4O63Ab8AvgT8OBq0+0bgzGzBurub2anA/cDDZvZdQjIziPDB+WFgVtR68SXgJ2bWTGglagAOid6Xm5P+xWZmpxA+7x4j9Id/hJDgvJTkfJHucve/ASPM7GTgCHc/zswuAIbEPg/ixkb3mVr/UkYR/mjo6pjlhG7clOnRIPuUue7+OcJnyxBgdzP7p7svyXLNvFBLiEhHBxLGg2wjzCBZmjABwd3/jzBe4t2EmRf/As4F4vVFbiN84Y4Fbo7tO43wofAEcBNwLSFByOZLhOmuDxJaD64GVqfF9QzweUJf8fPA6YSZIumxP0JIKG4ktKykPiRPJfQvXwq8ANxFGKS7ODrvVeB/CQNd/0PoRjo3R7y4+78IY2heiK47L3rtfQkrdKaO+ylhJstbCf3o86L9F5JwZkxkA/Bx4O+E1q0PAv/r7gu78RoiPfEe4IHo8YGE/6fZpLpuJ2c5Zh3hD46ujpmU9joAC919RNrtc9H23xA+L34EvGZmt5nZjBzx9SmtoisiItLH0loehhFa35wwBX9z9Hh/d5/XxbkvAQ+5+8ezvP5fCQ2LB8W2HwD8DTjA3f8etQKe7+5Zk4uoG3Q2YTbcATl/wD6ilhAREZE+5u4jCGO8XozGW5wI3O/uw6PWiIwJSOTTwIkWChxOgjDWy8y+ZmbHRcd8iTAea7aZTYgKCx5EaN34rbv/PVeM0ay5t5hZLWGw+VZCC0vBKAkRERHJjw8Sul9Tj3+f5CR3v4+QwOwO/DeaVv8wYZzHg9ExTxHGd00idLNuILRk/BQ4KWF84wnFEdcT6hHtCJyR8Nw+oe4YERERKQq1hIiIiEhRKAkRERGRolASIiIiIkWhJERERESKQkmIiIiIFIWSEBERESkKJSEiIiJSFEpCREREpCiUhIiIiEhR/D+pglQ8T9oAhwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, gridspec_kw={'width_ratios': [4, 1]}, figsize=[1.2 * 6.4, 4.8], sharey=True)\n", + "axs[0].plot(np.arange(bo_res['nb_iterations']), y_max_mu, label='BO', color=search_to_color['BO'], lw=4, clip_on=False)\n", + "axs[0].fill_between(np.arange(bo_res['nb_iterations']), y_max_mu - y_max_sigma, \n", + " y_max_mu + y_max_sigma, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['BO'])\n", + "\n", + "axs[0].set_xlabel('# evaluated COFs', fontsize=14)\n", + "axs[0].set_ylabel('maximum deliverable capacity\\namong evaluated COFs\\n[L STP/L]', fontsize=13)\n", + "axs[0].legend(fontsize=14)\n", + "\n", + "\n", + "# axs[0].set_xlim([0, ])\n", + "axs[0].set_ylim(ymin=0.0)\n", + "\n", + "axs[1].hist(y, orientation=\"horizontal\", color=cool_colors[7])\n", + "axs[1].set_xlabel(\"# COFs\", fontsize=13)\n", + "axs[1].set_xscale(\"log\")\n", + "plt.tight_layout()\n", + "plt.savefig(\"search_efficiency_max_found.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "furnished-purple", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 8db9404a573c03fbb8344f2bbcf47c444287dfd9 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Tue, 29 Jun 2021 23:59:23 -0700 Subject: [PATCH 08/29] k --- BO_run.ipynb | 239 ++++++++++++++++++++++++-------- random_forest_run.ipynb | 107 +++++++++++---- viz.ipynb | 293 ++++++++++++++++++++++++++++++---------- 3 files changed, 477 insertions(+), 162 deletions(-) diff --git a/BO_run.ipynb b/BO_run.ipynb index 0d72bfe..c30edba 100644 --- a/BO_run.ipynb +++ b/BO_run.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "structured-glass", + "id": "received-startup", "metadata": {}, "source": [ "# BO runs" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "banned-consequence", + "id": "starting-bennett", "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "stone-adrian", + "id": "resident-innocent", "metadata": {}, "source": [ "load data from `prepare_Xy.ipynb`" @@ -38,7 +38,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "adjacent-alignment", + "id": "settled-cheat", "metadata": {}, "outputs": [ { @@ -62,7 +62,7 @@ }, { "cell_type": "markdown", - "id": "divided-release", + "id": "painted-profile", "metadata": {}, "source": [ "convert to torch tensors" @@ -71,7 +71,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "textile-cabinet", + "id": "comprehensive-robin", "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "christian-subdivision", + "id": "legal-cosmetic", "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "martial-devon", + "id": "roman-envelope", "metadata": {}, "outputs": [ { @@ -123,7 +123,7 @@ }, { "cell_type": "markdown", - "id": "southwest-tobacco", + "id": "checked-jerusalem", "metadata": {}, "source": [ "number of COFs for initialization" @@ -132,7 +132,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "coated-fault", + "id": "aquatic-korean", "metadata": {}, "outputs": [], "source": [ @@ -142,11 +142,12 @@ { "cell_type": "code", "execution_count": 7, - "id": "stunning-public", + "id": "controlled-renaissance", "metadata": {}, "outputs": [], "source": [ "def bo_run(nb_iterations):\n", + " assert nb_iterations > nb_COFs_initialization\n", " # select initial COFs for training data randomly\n", " ids_acquired = np.random.choice(np.arange((nb_data)), size=nb_COFs_initialization, replace=False)\n", "\n", @@ -175,22 +176,31 @@ " break\n", "\n", " # acquire this COF\n", - " ids_acquired = np.concatenate((ids_acquired, np.array([id_max_aquisition])))\n", + " ids_acquired = np.concatenate((ids_acquired, [id_max_aquisition]))\n", "\n", " # update X, y acquired\n", " X_acquired = torch.cat([X_acquired, X[id_max_aquisition, :].unsqueeze(0)])\n", " y_acquired = y[ids_acquired, :] # start over to normalize y properly\n", " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", "\n", - " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition])\n", - " print(\"\\tbest y acquired:\", y[ids_acquired].max())\n", + " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition].item())\n", + " print(\"\\tbest y acquired:\", y[ids_acquired].max().item())\n", + " assert np.size(ids_acquired) == nb_iterations\n", " return ids_acquired" ] }, + { + "cell_type": "markdown", + "id": "broadband-nothing", + "metadata": {}, + "source": [ + "`ids_acquired[r, i]` will give ID of COF acquired during iteration `i` from run `r`." + ] + }, { "cell_type": "code", - "execution_count": 9, - "id": "innovative-peeing", + "execution_count": 22, + "id": "computational-portfolio", "metadata": {}, "outputs": [ { @@ -200,61 +210,172 @@ "\n", "\n", "RUN 0\n", - "torch.Size([10, 1])\n", "iteration: 10\n", - "\n", - "\tacquired COF 66825 with y = tensor([182.3584], dtype=torch.float64)\n", - "\tbest y acquired: tensor(182.3584, dtype=torch.float64)\n", + "\tacquired COF 16415 with y = 174.654915912\n", + "\tbest y acquired: 179.569572506\n", "iteration: 11\n", - "\n", - "\tacquired COF 44897 with y = tensor([183.6189], dtype=torch.float64)\n", - "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", + "\tacquired COF 27798 with y = 166.69585219\n", + "\tbest y acquired: 179.569572506\n", "iteration: 12\n", + "\tacquired COF 27035 with y = 178.57489196900002\n", + "\tbest y acquired: 179.569572506\n", + "iteration: 13\n", + "\tacquired COF 21314 with y = 194.053101714\n", + "\tbest y acquired: 194.053101714\n", + "iteration: 14\n", + "\tacquired COF 66263 with y = 185.76857139\n", + "\tbest y acquired: 194.053101714\n", + "iteration: 15\n", + "\tacquired COF 33370 with y = 196.720247142\n", + "\tbest y acquired: 196.720247142\n", + "iteration: 16\n", + "\tacquired COF 25862 with y = 167.849327414\n", + "\tbest y acquired: 196.720247142\n", + "iteration: 17\n", + "\tacquired COF 33366 with y = 204.811726149\n", + "\tbest y acquired: 204.811726149\n", + "iteration: 18\n", + "\tacquired COF 33330 with y = 195.58268240799998\n", + "\tbest y acquired: 204.811726149\n", + "iteration: 19\n", + "\tacquired COF 33355 with y = 122.363855499\n", + "\tbest y acquired: 204.811726149\n", + "iteration: 20\n", + "\tacquired COF 33332 with y = 205.963467853\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 21\n", + "\tacquired COF 25951 with y = 196.579974938\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 22\n", + "\tacquired COF 12402 with y = 175.504448723\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 23\n", + "\tacquired COF 33343 with y = 196.58076384900002\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 24\n", + "\tacquired COF 33347 with y = 208.43022665700002\n", + "\tbest y acquired: 208.43022665700002\n", + "\n", "\n", - "\tacquired COF 7312 with y = tensor([169.0128], dtype=torch.float64)\n", - "\tbest y acquired: tensor(183.6189, dtype=torch.float64)\n", + "RUN 1\n", + "iteration: 10\n", + "\tacquired COF 68952 with y = 195.657779278\n", + "\tbest y acquired: 195.657779278\n", + "iteration: 11\n", + "\tacquired COF 56517 with y = 194.530496788\n", + "\tbest y acquired: 195.657779278\n", + "iteration: 12\n", + "\tacquired COF 12392 with y = 185.480447434\n", + "\tbest y acquired: 195.657779278\n", "iteration: 13\n", + "\tacquired COF 34761 with y = 177.06386607099998\n", + "\tbest y acquired: 195.657779278\n", + "iteration: 14\n", + "\tacquired COF 19518 with y = 176.468362255\n", + "\tbest y acquired: 195.657779278\n", + "iteration: 15\n", + "\tacquired COF 33330 with y = 195.58268240799998\n", + "\tbest y acquired: 195.657779278\n", + "iteration: 16\n", + "\tacquired COF 33338 with y = 129.689513234\n", + "\tbest y acquired: 195.657779278\n", + "iteration: 17\n", + "\tacquired COF 33332 with y = 205.963467853\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 18\n", + "\tacquired COF 33347 with y = 208.43022665700002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 19\n", + "\tacquired COF 25951 with y = 196.579974938\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 20\n", + "\tacquired COF 33344 with y = 199.90463220799998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 21\n", + "\tacquired COF 33349 with y = 206.74476888599997\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 22\n", + "\tacquired COF 29861 with y = 199.72030120099998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 23\n", + "\tacquired COF 26565 with y = 207.39578187\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 24\n", + "\tacquired COF 16404 with y = 171.299812707\n", + "\tbest y acquired: 208.43022665700002\n", "\n", - "\tacquired COF 64842 with y = tensor([191.1083], dtype=torch.float64)\n", - "\tbest y acquired: tensor(191.1083, dtype=torch.float64)\n", - "iteration: 14\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_runs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\\nRUN\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mids_acquired\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbo_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_iterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids_acquired\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'ids_acquired'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mids_acquired\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bo_run'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.pkl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mbo_run\u001b[0;34m(nb_iterations)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleTaskGP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_acquired\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_acquired\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mmll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExactMarginalLogLikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mfit_gpytorch_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;31m# compute aquisition function at each COF in the database\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/botorch/fit.py\u001b[0m in \u001b[0;36mfit_gpytorch_model\u001b[0;34m(mll, optimizer, **kwargs)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_state_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m 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\u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/botorch/optim/fit.py\u001b[0m in \u001b[0;36mfit_gpytorch_scipy\u001b[0;34m(mll, bounds, method, options, track_iterations, approx_mll, scipy_objective, module_to_array_func, module_from_array_func)\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0mjac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m 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\u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 361\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb'NEW_X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;31m# new iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mfun_and_grad\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m \u001b[0;31m# pragma: nocover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0mparam_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m inputs=inputs)\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 145\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 146\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "\n", + "RUN 2\n", + "iteration: 10\n", + "\tacquired COF 15267 with y = 178.83787913\n", + "\tbest y acquired: 178.83787913\n", + "iteration: 11\n", + "\tacquired COF 14751 with y = 181.18376571\n", + "\tbest y acquired: 181.18376571\n", + "iteration: 12\n", + "\tacquired COF 12392 with y = 185.480447434\n", + "\tbest y acquired: 185.480447434\n", + "iteration: 13\n", + "\tacquired COF 66860 with y = 182.910685964\n", + "\tbest y acquired: 185.480447434\n", + "iteration: 14\n", + "\tacquired COF 66075 with y = 199.84356436299998\n", + "\tbest y acquired: 199.84356436299998\n", + "iteration: 15\n", + "\tacquired COF 66117 with y = 202.21921792700002\n", + "\tbest y acquired: 202.21921792700002\n", + "iteration: 16\n", + "\tacquired COF 33366 with y = 204.811726149\n", + "\tbest y acquired: 204.811726149\n", + "iteration: 17\n", + "\tacquired COF 33338 with y = 129.689513234\n", + "\tbest y acquired: 204.811726149\n", + "iteration: 18\n", + "\tacquired COF 66263 with y = 185.76857139\n", + "\tbest y acquired: 204.811726149\n", + "iteration: 19\n", + "\tacquired COF 25951 with y = 196.579974938\n", + "\tbest y acquired: 204.811726149\n", + "iteration: 20\n", + "\tacquired COF 33332 with y = 205.963467853\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 21\n", + "\tacquired COF 33330 with y = 195.58268240799998\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 22\n", + "\tacquired COF 33370 with y = 196.720247142\n", + "\tbest y acquired: 205.963467853\n", + "iteration: 23\n", + "\tacquired COF 33347 with y = 208.43022665700002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 24\n", + "\tacquired COF 33374 with y = 185.76111369\n", + "\tbest y acquired: 208.43022665700002\n" ] } ], "source": [ - "nb_runs = 2\n", - "nb_iterations = 50\n", - "for r in range(nb_runs):\n", + "bo_res = dict()\n", + "bo_res['nb_runs'] = 3\n", + "bo_res['nb_iterations'] = 25\n", + "bo_res['ids_acquired'] = []\n", + "ids_acquired = -np.ones((nb_runs, nb_iterations), dtype=int)\n", + "for r in range(bo_res['nb_runs']):\n", " print(\"\\n\\nRUN\", r)\n", - " ids_acquired = bo_run(nb_iterations)\n", - " print(ids_acquired)\n", - " torch.save({'ids_acquired': ids_acquired}, 'bo_run' + str(r) + '.pkl')" + " ids_acquired = bo_run(bo_res['nb_iterations'])\n", + " bo_res['ids_acquired'].append(ids_acquired)\n", + "\n", + "with open('bo_results.pkl', 'wb') as file:\n", + " pickle.dump(bo_res, file)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "given-chosen", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/random_forest_run.ipynb b/random_forest_run.ipynb index cc1a365..3244221 100644 --- a/random_forest_run.ipynb +++ b/random_forest_run.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "gothic-guest", + "id": "indoor-confusion", "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "saving-visibility", + "id": "tamil-payment", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "wireless-developer", + "id": "stunning-forth", "metadata": {}, "outputs": [ { @@ -75,7 +75,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "current-diamond", + "id": "universal-seeker", "metadata": {}, "outputs": [], "source": [ @@ -100,7 +100,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "twelve-fruit", + "id": "catholic-bulgarian", "metadata": {}, "outputs": [], "source": [ @@ -109,18 +109,18 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "entire-terminal", + "execution_count": 11, + "id": "wrong-drilling", "metadata": {}, "outputs": [], "source": [ - "diversify_training = True" + "diversify_training = False" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "naval-partnership", + "execution_count": 12, + "id": "funny-saint", "metadata": {}, "outputs": [], "source": [ @@ -166,39 +166,86 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "ahead-albania", + "execution_count": 16, + "id": "driving-result", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", - "RUN 0\n", - "diverse RF run\n", + "budget for evals: 200\n", + "\trun 0\n", + "RF run\n", + "\teval budget 50 = 25 training data and 25 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 1\n", + "RF run\n", "\teval budget 50 = 25 training data and 25 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", - "diverse RF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n" + "\tmax y acquired = 181.95215032099998\n", + "budget for evals: 50\n", + "\trun 0\n", + "RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 193.360391942\n", + "\trun 1\n", + "RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.938530808\n", + "budget for evals: 100\n", + "\trun 0\n", + "RF run\n", + "\teval budget 150 = 75 training data and 75 acquired.\n", + "\tmax y acquired = 195.928348822\n", + "\trun 1\n", + "RF run\n", + "\teval budget 150 = 75 training data and 75 acquired.\n", + "\tmax y acquired = 193.25083398700002\n", + "budget for evals: 150\n", + "\trun 0\n", + "RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 193.620114578\n", + "\trun 1\n", + "RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.120454446\n" ] } ], "source": [ - "nb_runs = 2\n", - "nb_evals_budgets = [50 * i for i in range(1, 5)]\n", - "for r in range(nb_runs):\n", - " print(\"\\n\\nRUN\", r)\n", - " for nb_evals_budget in nb_evals_budgets:\n", - " # decide how to spend the evals budget here. say 50/50\n", - " nb_training_data = nb_evals_budget // 2\n", - " nb_acquire = nb_evals_budget // 2\n", - " assert nb_training_data + nb_acquire == nb_evals_budget\n", - " \n", + "rf_res = dict()\n", + "rf_res['nb_runs'] = 2\n", + "rf_res['nb_evals_budgets'] = [50 * i for i in range(1, 5)]\n", + "rf_res['ids_acquired'] = [[] for b in rf_res['nb_evals_budgets']]\n", + "for b in range(len(rf_res['nb_evals_budgets'])):\n", + " print(\"budget for evals:\", nb_evals_budget)\n", + " nb_evals_budget = rf_res['nb_evals_budgets'][b]\n", + " # decide how to spend the evals budget here. say 50/50\n", + " nb_training_data = nb_evals_budget // 2\n", + " nb_acquire = nb_evals_budget // 2\n", + " assert nb_training_data + nb_acquire == nb_evals_budget\n", + " for r in range(rf_res['nb_runs']):\n", + " print(\"\\trun\", r)\n", " ids_acquired = rf_run(nb_training_data, nb_acquire)\n", - "# torch.save({'ids_acquired': ids_acquired}, 'rf_run' + str(r) + '.pkl')" + " rf_res['ids_acquired'][b].append(ids_acquired)\n", + "# torch.save({'ids_acquired': ids_acquired}, 'rf_run' + str(r) + '.pkl')\n", + "\n", + "if diversify_training:\n", + " with open('rf_results.pkl', 'wb') as file:\n", + " pickle.dump(rf_res, file)\n", + "else:\n", + " with open('rf_div_results.pkl', 'wb') as file:\n", + " pickle.dump(rf_res, file)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "informed-lighting", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/viz.ipynb b/viz.ipynb index c5551be..56a9174 100644 --- a/viz.ipynb +++ b/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "optional-crowd", + "id": "dimensional-influence", "metadata": {}, "source": [ "# viz" @@ -10,32 +10,10 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "chicken-academy", + "execution_count": 77, + "id": "breeding-piano", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt\n", @@ -51,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "advisory-neighborhood", + "id": "correct-judgment", "metadata": {}, "source": [ "load data" @@ -59,8 +37,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "backed-slide", + "execution_count": 78, + "id": "wrapped-activity", "metadata": {}, "outputs": [ { @@ -76,7 +54,7 @@ "69839" ] }, - "execution_count": 2, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -92,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "soviet-travel", + "id": "nonprofit-finding", "metadata": {}, "source": [ "load search results" @@ -100,27 +78,27 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "experienced-jerusalem", + "execution_count": 98, + "id": "private-harmony", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'ids_acquired': array([[16274, 36458, 68938, 28867, 1614, 31160, 32144, 32632, 22648,\n", - " 16501, 66097, 15142, 25981, 31975, 20704, 26565, 13582, 26507,\n", - " 26332, 29870],\n", - " [60245, 20040, 9607, 65519, 27430, 13330, 66251, 44068, 49512,\n", - " 8563, 23494, 13310, 26188, 14998, 20580, 66097, 25981, 26268,\n", - " 20723, 33044],\n", - " [52580, 38118, 40421, 50304, 57391, 55540, 28843, 8079, 25990,\n", - " 42676, 44551, 65585, 65338, 7267, 30056, 58680, 66103, 55829,\n", - " 37344, 65232]]),\n", - " 'nb_runs': 3,\n", - " 'nb_iterations': 20}" + "{'nb_runs': 3,\n", + " 'nb_iterations': 25,\n", + " 'ids_acquired': [array([57214, 54824, 63946, 40684, 5488, 37520, 46393, 50134, 67586,\n", + " 53501, 16415, 27798, 27035, 21314, 66263, 33370, 25862, 33366,\n", + " 33330, 33355, 33332, 25951, 12402, 33343, 33347]),\n", + " array([ 1739, 54992, 1072, 7535, 28352, 57664, 2012, 17766, 15085,\n", + " 68802, 68952, 56517, 12392, 34761, 19518, 33330, 33338, 33332,\n", + " 33347, 25951, 33344, 33349, 29861, 26565, 16404]),\n", + " array([53849, 18172, 24262, 11369, 69252, 100, 54, 43031, 11810,\n", + " 44138, 15267, 14751, 12392, 66860, 66075, 66117, 33366, 33338,\n", + " 66263, 25951, 33332, 33330, 33370, 33347, 33374])]}" ] }, - "execution_count": 18, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -130,9 +108,148 @@ "bo_res" ] }, + { + "cell_type": "code", + "execution_count": 107, + "id": "selected-excess", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'nb_runs': 2,\n", + " 'nb_evals_budgets': [50, 100, 150, 200],\n", + " 'ids_acquired': [[array([10053, 10048, 10055, 10050, 10195, 47861, 14579, 2835, 10075,\n", + " 19649, 19498, 5601, 10051, 2691, 9927, 67, 147, 15331,\n", + " 2857, 14755, 10184, 8225, 15461, 25449, 15460, 54113, 1468,\n", + " 16753, 31407, 1904, 51445, 34974, 6790, 31804, 25930, 51295,\n", + " 21591, 41049, 45937, 42528, 66738, 2901, 30411, 14342, 56069,\n", + " 36376, 29915, 33878, 28686, 11132]),\n", + " array([13759, 32661, 65995, 32203, 19230, 19231, 19220, 19221, 16567,\n", + " 17433, 26639, 19241, 19238, 68549, 29862, 32566, 68389, 32636,\n", + " 31975, 25971, 12422, 31130, 31082, 23184, 13763, 57945, 30037,\n", + " 19875, 27850, 34542, 39703, 33117, 19471, 12762, 12151, 34077,\n", + " 12787, 12269, 65975, 1074, 32528, 60687, 15196, 11411, 13206,\n", + " 8192, 6477, 64915, 27400, 23756])],\n", + " [array([59010, 57791, 54590, 37273, 53498, 15480, 58956, 13899, 15227,\n", + " 15482, 39133, 53020, 5281, 15412, 15475, 2359, 15232, 37176,\n", + " 15416, 15418, 15233, 15420, 34794, 58992, 15422, 15235, 34784,\n", + " 35096, 34797, 35165, 15484, 15230, 62271, 34792, 34432, 35094,\n", + " 13894, 61641, 36861, 34816, 34799, 55763, 15481, 34795, 21937,\n", + " 15413, 62223, 66061, 36878, 13890, 49253, 31108, 36137, 19222,\n", + " 49806, 38330, 7189, 54699, 19835, 27116, 34598, 66177, 48803,\n", + " 13580, 38609, 66317, 13358, 69358, 10158, 41782, 13596, 29363,\n", + " 52557, 3304, 54452, 45768, 64038, 54875, 55073, 52207, 54900,\n", + " 69502, 32686, 49984, 54069, 1593, 9130, 22520, 39269, 49899,\n", + " 3274, 41509, 5985, 42153, 51827, 57531, 8497, 56787, 19690,\n", + " 44493]),\n", + " array([22904, 44502, 42094, 21882, 22621, 14296, 1776, 42091, 42085,\n", + " 2070, 42123, 22948, 39079, 42095, 18331, 19589, 19592, 1722,\n", + " 38515, 40585, 40603, 40646, 39104, 32168, 54674, 38542, 54692,\n", + " 5045, 17397, 37172, 31073, 31387, 18327, 22392, 2782, 52538,\n", + " 14290, 32185, 58948, 16739, 17268, 17574, 59660, 5044, 40707,\n", + " 40696, 22976, 36974, 32193, 32194, 30617, 68581, 12206, 5498,\n", + " 62535, 936, 19212, 7223, 8294, 15197, 58302, 36720, 17026,\n", + " 11720, 16145, 58801, 65720, 64383, 61116, 68610, 53398, 43033,\n", + " 43643, 26494, 67438, 23380, 16311, 26291, 68102, 44719, 46406,\n", + " 23512, 38417, 32909, 7099, 6578, 38495, 64016, 20191, 65069,\n", + " 50709, 58833, 32734, 30719, 43865, 442, 16203, 36388, 42145,\n", + " 46489])],\n", + " [array([ 8889, 29365, 35137, 4563, 9962, 25731, 49368, 49371, 46842,\n", + " 5437, 16632, 19769, 35126, 29378, 3029, 10824, 29299, 35196,\n", + " 8910, 10827, 8856, 30460, 5387, 16739, 49435, 37286, 10708,\n", + " 29080, 9706, 8270, 29336, 4031, 8787, 8778, 8855, 69809,\n", + " 3035, 12113, 35149, 31987, 63834, 64045, 17256, 66103, 11945,\n", + " 23338, 16937, 68386, 30119, 11999, 11472, 8876, 21924, 40626,\n", + " 40625, 22831, 5388, 37338, 53203, 55323, 53557, 55348, 14251,\n", + " 5362, 4401, 9959, 56597, 10702, 55381, 11695, 4181, 16726,\n", + " 7298, 7280, 45149, 58841, 2961, 56269, 10736, 16091, 3768,\n", + " 48024, 54275, 41932, 52297, 8445, 20338, 42890, 38565, 5226,\n", + " 67338, 18568, 27821, 41113, 47567, 29561, 24702, 47897, 68630,\n", + " 64028, 240, 55267, 15007, 7245, 15878, 15044, 44191, 1698,\n", + " 67082, 538, 22616, 45125, 38482, 10590, 15250, 69600, 49715,\n", + " 11023, 54014, 9912, 30356, 7522, 63624, 1451, 50277, 61388,\n", + " 25228, 26508, 61491, 68672, 37651, 62291, 28390, 52313, 13608,\n", + " 61959, 2283, 68048, 22118, 32534, 34645, 44751, 3220, 15456,\n", + " 67618, 11154, 48821, 53767, 19108, 37343]),\n", + " array([69698, 5145, 5142, 53139, 442, 26303, 5792, 6034, 6035,\n", + " 15354, 6033, 6036, 45771, 5144, 25981, 26188, 14587, 23338,\n", + " 13757, 25978, 21842, 18382, 2748, 19214, 24047, 32566, 14999,\n", + " 32560, 19927, 46432, 25973, 32371, 21662, 23429, 17269, 24961,\n", + " 14798, 16416, 29862, 16674, 5839, 4272, 17039, 20707, 16419,\n", + " 24636, 23286, 15004, 16823, 5902, 16841, 5127, 2784, 23333,\n", + " 58996, 31992, 14795, 32589, 17402, 28831, 17253, 29865, 17383,\n", + " 23180, 32069, 14592, 16533, 5843, 23203, 31991, 19900, 26165,\n", + " 32037, 25984, 16409, 28327, 4705, 60335, 31414, 42071, 67272,\n", + " 22839, 51316, 30329, 34280, 56609, 62487, 46759, 28329, 64518,\n", + " 34012, 11888, 51902, 39570, 18438, 48608, 29888, 15693, 41726,\n", + " 19705, 83, 4999, 28827, 162, 36306, 34577, 68271, 31896,\n", + " 58350, 48542, 47306, 11148, 6895, 38396, 5562, 9758, 1340,\n", + " 2509, 20776, 58019, 21951, 32497, 66551, 39971, 47800, 55527,\n", + " 10032, 2122, 33113, 15580, 45857, 30699, 55851, 41296, 67206,\n", + " 12461, 29266, 59124, 25100, 20011, 17357, 59567, 28010, 23216,\n", + " 49006, 2820, 33177, 7494, 27821, 23965])],\n", + " [array([17258, 17397, 47865, 17400, 17268, 16417, 17264, 21850, 26514,\n", + " 21662, 21847, 26396, 16411, 26562, 65680, 17293, 17257, 64439,\n", + " 17278, 42091, 42085, 18327, 21859, 58996, 40628, 40633, 17079,\n", + " 40587, 42094, 17514, 29415, 32020, 42113, 4952, 26505, 40629,\n", + " 40598, 32084, 1029, 24636, 31035, 31197, 26507, 19334, 24565,\n", + " 30394, 32159, 50485, 47857, 26553, 16854, 16623, 36977, 21882,\n", + " 17535, 23153, 40579, 40589, 42116, 17295, 32185, 63649, 26387,\n", + " 69756, 32579, 21981, 17455, 40635, 25978, 2070, 29257, 26145,\n", + " 1179, 22904, 31107, 31391, 22222, 26517, 51595, 21852, 36923,\n", + " 15177, 26106, 26565, 53605, 61182, 17039, 32146, 18072, 29822,\n", + " 26399, 21975, 40585, 40603, 30331, 25104, 22521, 22215, 2198,\n", + " 30518, 46487, 20621, 36679, 17402, 1523, 65105, 54341, 68502,\n", + " 42264, 10560, 40538, 26089, 6707, 51585, 63632, 41424, 21195,\n", + " 27520, 31643, 54496, 48149, 62931, 52191, 34472, 16939, 40902,\n", + " 68544, 15108, 39535, 33520, 7810, 12215, 42027, 15672, 17796,\n", + " 16019, 69008, 64405, 54397, 60491, 20318, 34480, 32775, 37214,\n", + " 34376, 32466, 2840, 64444, 56144, 48059, 60611, 22015, 17265,\n", + " 59737, 16389, 53658, 60924, 68348, 64088, 58051, 52859, 34490,\n", + " 65154, 31039, 7872, 32302, 12737, 6320, 12987, 62944, 68066,\n", + " 35592, 34195, 42644, 14411, 66926, 49845, 63447, 29735, 3186,\n", + " 61113, 40478, 181, 56943, 1236, 56873, 62576, 28523, 53538,\n", + " 49985, 14438, 40592, 56040, 64137, 47249, 56634, 61895, 7017,\n", + " 56221, 30155]),\n", + " array([ 5468, 5351, 5469, 5526, 5525, 5470, 5386, 35055, 5471,\n", + " 19475, 35099, 5352, 441, 5283, 5175, 5388, 5193, 6063,\n", + " 19688, 5284, 13912, 5482, 59654, 52774, 5303, 427, 35032,\n", + " 5425, 5330, 5286, 11551, 26726, 5174, 5329, 6371, 5177,\n", + " 5285, 444, 5489, 6370, 5578, 5312, 5483, 5424, 16701,\n", + " 6062, 34802, 55829, 55828, 5263, 59655, 5385, 6392, 5311,\n", + " 5264, 59694, 34817, 5362, 63622, 5173, 5512, 9126, 6247,\n", + " 3035, 35028, 56376, 68492, 49887, 56369, 32131, 64402, 13915,\n", + " 56539, 5361, 34997, 66825, 35116, 35083, 5192, 49773, 6206,\n", + " 19543, 25375, 56370, 50796, 5252, 5338, 5457, 53565, 6090,\n", + " 19743, 35227, 53298, 32155, 34758, 63623, 17294, 35086, 36999,\n", + " 2401, 14919, 52082, 13128, 48863, 60986, 10191, 66853, 22381,\n", + " 8883, 47886, 34619, 62394, 20698, 16483, 56054, 20049, 21469,\n", + " 10706, 45147, 8163, 68339, 29296, 51466, 32386, 338, 43709,\n", + " 69715, 37309, 7023, 37443, 5789, 38126, 51239, 45152, 34496,\n", + " 48409, 41722, 52038, 49245, 9888, 33070, 61028, 882, 13280,\n", + " 62313, 37916, 13317, 57880, 36947, 69561, 41642, 34140, 67109,\n", + " 7733, 16106, 55507, 17031, 25121, 4992, 25493, 44894, 65861,\n", + " 5247, 19995, 4456, 30313, 59683, 16897, 67167, 20685, 42162,\n", + " 4446, 23054, 35658, 47091, 35017, 57598, 47747, 64605, 16122,\n", + " 30903, 8793, 11444, 49962, 64939, 65039, 23894, 48162, 42444,\n", + " 12428, 69757, 6757, 63476, 27997, 67454, 51675, 39709, 44497,\n", + " 17764, 46063])]]}" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf_res = pickle.load(open('rf_results.pkl', 'rb'))\n", + "rf_res" + ] + }, { "cell_type": "markdown", - "id": "bridal-antique", + "id": "purple-nirvana", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -140,8 +257,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "equivalent-glory", + "execution_count": 80, + "id": "cardiac-russia", "metadata": {}, "outputs": [], "source": [ @@ -152,8 +269,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "standard-bangladesh", + "execution_count": 81, + "id": "weekly-wrestling", "metadata": {}, "outputs": [ { @@ -185,13 +302,13 @@ }, { "cell_type": "code", - "execution_count": 38, - "id": "rational-frequency", + "execution_count": 85, + "id": "electric-myanmar", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -206,7 +323,7 @@ "which_BO_run = 0\n", "\n", "fig, ax = plt.subplots(1, 4, sharey=True, sharex=True, figsize=[3*6.4, 4.8])\n", - "nb_acquired = [10, 12, 15, 19]\n", + "nb_acquired = [10, 12, 15, 20]\n", "# gray background\n", "for a in ax:\n", " a.set_aspect('equal', 'box')\n", @@ -232,38 +349,57 @@ }, { "cell_type": "markdown", - "id": "appropriate-breakfast", + "id": "visible-scratch", "metadata": {}, "source": [ "# search efficiency\n", - "first, max $y$ among acquired set." + "### max $y$ among acquired set." ] }, { "cell_type": "code", - "execution_count": 57, - "id": "signal-fortune", + "execution_count": 103, + "id": "collect-accommodation", "metadata": {}, "outputs": [], "source": [ - "y_max_mu = np.zeros(bo_res['nb_iterations'])\n", - "y_max_sigma = np.zeros(bo_res['nb_iterations'])\n", - "for i in range(1, bo_res['nb_iterations']):\n", - " y_maxes = np.max(y[bo_res['ids_acquired'][:, :i]], axis=1) # among runs\n", - " \n", - " y_max_mu[i] = np.mean(y_maxes)\n", - " y_max_sigma[i] = np.std(y_maxes)" + "def y_max(res):\n", + " y_max_mu = np.zeros(res['nb_iterations'])\n", + " y_max_sigma = np.zeros(res['nb_iterations'])\n", + " for i in range(1, res['nb_iterations']):\n", + " y_maxes = [np.max(y[res['ids_acquired'][r]][:i]) for r in range(res['nb_runs'])]# among runs\n", + " assert np.size(y_maxes) == res['nb_runs']\n", + " y_max_mu[i] = np.mean(y_maxes)\n", + " y_max_sigma[i] = np.std(y_maxes)\n", + " return y_max_mu, y_max_sigma\n", + "\n", + "y_max_mu, y_max_sigma = y_max(bo_res)" ] }, { "cell_type": "code", - "execution_count": 61, - "id": "awful-emperor", + "execution_count": 104, + "id": "brazilian-theme", "metadata": {}, "outputs": [ + { + "ename": "TypeError", + "evalue": "missing a required argument: 'x'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# RF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# axs[0].set_xlim([0, ])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0;32mdef\u001b[0m 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required argument: {arg!r}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2911\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2912\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2913\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2914\u001b[0m \u001b[0;31m# We have a positional argument to process\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: missing a required argument: 'x'" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -276,6 +412,7 @@ ], "source": [ "fig, axs = plt.subplots(1, 2, gridspec_kw={'width_ratios': [4, 1]}, figsize=[1.2 * 6.4, 4.8], sharey=True)\n", + "# BO\n", "axs[0].plot(np.arange(bo_res['nb_iterations']), y_max_mu, label='BO', color=search_to_color['BO'], lw=4, clip_on=False)\n", "axs[0].fill_between(np.arange(bo_res['nb_iterations']), y_max_mu - y_max_sigma, \n", " y_max_mu + y_max_sigma, \n", @@ -285,6 +422,8 @@ "axs[0].set_ylabel('maximum deliverable capacity\\namong evaluated COFs\\n[L STP/L]', fontsize=13)\n", "axs[0].legend(fontsize=14)\n", "\n", + "# RF\n", + "axs[0].scatter(rf_res['nb_evals_budgets'], ref_res[]\n", "\n", "# axs[0].set_xlim([0, ])\n", "axs[0].set_ylim(ymin=0.0)\n", @@ -297,12 +436,20 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "furnished-purple", + "cell_type": "markdown", + "id": "greenhouse-antibody", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### max rank among acquired set" + ] + }, + { + "cell_type": "markdown", + "id": "trained-multiple", + "metadata": {}, + "source": [ + "### fraction of top 100 COFs recovered" + ] } ], "metadata": { From f16aaf067607670e0144e80eff55ce796a285523 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Wed, 30 Jun 2021 21:44:51 -0700 Subject: [PATCH 09/29] evol search --- evol_search.ipynb | 287 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 287 insertions(+) create mode 100644 evol_search.ipynb diff --git a/evol_search.ipynb b/evol_search.ipynb new file mode 100644 index 0000000..a364642 --- /dev/null +++ b/evol_search.ipynb @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "celtic-hormone", + "metadata": {}, + "outputs": [], + "source": [ + "import cma\n", + "import pickle\n", + "import numpy as np\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "unsigned-three", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "frank-static", + "metadata": {}, + "outputs": [], + "source": [ + "# so evol search is bounded by [0, 1]^12\n", + "assert np.max(X) <= 1.0\n", + "assert np.min(X) >= 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "residential-google", + "metadata": {}, + "outputs": [], + "source": [ + "# return id of COF closest to x0 in feature space.\n", + "def closest_COF(x0):\n", + " distances_to_xs = np.linalg.norm(X - x0, axis=1)\n", + " assert np.size(distances_to_xs) == nb_data\n", + " return np.argmin(distances_to_xs)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "distant-radio", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=257488, Wed Jun 30 21:44:06 2021)\n", + "\t# acquired COFs: 10\n", + "\t# max y value: 171.117194584\n", + "\t# acquired COFs: 20\n", + "\t# max y value: 188.242123191\n", + "\t# acquired COFs: 30\n", + "\t# max y value: 188.242123191\n", + "\t# acquired COFs: 40\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 49\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 58\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 76\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 87\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 98\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 107\n", + "\t# max y value: 191.507774129\n", + "\t# acquired COFs: 117\n", + "\t# max y value: 192.015638731\n", + "\t# acquired COFs: 128\n", + "\t# max y value: 197.87398978299998\n", + "\t# acquired COFs: 138\n", + "\t# max y value: 202.21921792700002\n", + "\t# acquired COFs: 148\n", + "\t# max y value: 202.21921792700002\n", + "\t# acquired COFs: 158\n", + "\t# max y value: 202.21921792700002\n", + "\t# acquired COFs: 169\n", + "\t# max y value: 202.21921792700002\n", + "\t# acquired COFs: 180\n", + "\t# max y value: 202.21921792700002\n", + "\t# acquired COFs: 191\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 205\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 211\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 213\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 216\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 225\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 228\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 232\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 234\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 236\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n", + "\t# acquired COFs: 238\n", + "\t# max y value: 208.120454446\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mids_ask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx_ask\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxs_ask\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mid_ask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosest_COF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_ask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mids_ask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid_ask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# warning: these aren't necessarily unique. and they could have been acquired before. so then they wuldn't count as extra inquires\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mclosest_COF\u001b[0;34m(x0)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# return id of COF closest to x0 in feature space.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclosest_COF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdistances_to_xs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances_to_xs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnb_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances_to_xs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "nb_iterations = 500\n", + "x_init = np.random.rand(np.shape(X)[1])\n", + "\n", + "# initialize evolutionary algo\n", + "ids_acquired = []\n", + "es = cma.CMAEvolutionStrategy(x0=x_init, sigma0=0.5, inopts={'bounds': [0.0, 1.0]})\n", + "\n", + "while len(ids_acquired) < nb_iterations:\n", + " # ask for a set of COFs to evaluate.\n", + " xs_ask = es.ask()\n", + "\n", + " # find IDs of these COFs (the closest COFs in feature space to them.)\n", + " ids_ask = []\n", + " for x_ask in xs_ask:\n", + " id_ask = closest_COF(x_ask)\n", + " ids_ask.append(id_ask)\n", + " # warning: these aren't necessarily unique. and they could have been acquired before. so then they wuldn't count as extra inquires\n", + "\n", + " # tell the ES algo about these COFs.\n", + " es.tell(X[ids_ask, :], -y[ids_ask]) # cuz we wanna minimize\n", + "\n", + " # updated ids acquired\n", + " for id_ask in ids_ask:\n", + " if not id_ask in ids_acquired:\n", + " ids_acquired.append(id_ask)\n", + "\n", + " print(\"\\t# acquired COFs:\", len(ids_acquired))\n", + " print(\"\\t# max y value: \", np.max(y[ids_acquired]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 97a6625b6110eaa598161043710a3befcbf5a302 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Thu, 1 Jul 2021 13:40:29 -0700 Subject: [PATCH 10/29] k --- new/BO_run.ipynb | 433 ++++++++++ new/evol_search.ipynb | 1601 +++++++++++++++++++++++++++++++++++ new/prepare_Xy.ipynb | 759 +++++++++++++++++ new/random_forest_run.ipynb | 225 +++++ new/random_search.ipynb | 96 +++ new/viz.ipynb | 766 +++++++++++++++++ 6 files changed, 3880 insertions(+) create mode 100644 new/BO_run.ipynb create mode 100644 new/evol_search.ipynb create mode 100644 new/prepare_Xy.ipynb create mode 100644 new/random_forest_run.ipynb create mode 100644 new/random_search.ipynb create mode 100644 new/viz.ipynb diff --git a/new/BO_run.ipynb b/new/BO_run.ipynb new file mode 100644 index 0000000..7ea9f09 --- /dev/null +++ b/new/BO_run.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "rocky-blackberry", + "metadata": {}, + "source": [ + "# BO runs" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "grateful-trade", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from botorch.models import FixedNoiseGP, SingleTaskGP\n", + "from gpytorch.kernels import ScaleKernel\n", + "from gpytorch.mlls import ExactMarginalLogLikelihood\n", + "from botorch import fit_gpytorch_model\n", + "from botorch.acquisition.analytic import ExpectedImprovement\n", + "import numpy as np\n", + "import pickle\n", + "import sys\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "white-optimization", + "metadata": {}, + "source": [ + "load data from `prepare_Xy.ipynb`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "characteristic-marker", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "y = np.reshape(y, (np.size(y), 1)) # for the GP\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "markdown", + "id": "worth-realtor", + "metadata": {}, + "source": [ + "convert to torch tensors" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "official-provision", + "metadata": {}, + "outputs": [], + "source": [ + "X = torch.from_numpy(X)\n", + "y = torch.from_numpy(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "running-nudist", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([69839, 12])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.size()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "abstract-announcement", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([69839, 1])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.size()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "alive-theta", + "metadata": {}, + "outputs": [], + "source": [ + "X_unsqueezed = X.unsqueeze(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "absolute-madrid", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_size = 10000\n", + "acquisition_values = np.zeros((nb_data))\n", + "acquisition_values[:] = np.NaN\n", + "nb_batches = nb_data // batch_size\n", + "for ba in range(nb_batches+1):\n", + " id_start = ba * batch_size\n", + " id_end = id_start + batch_size\n", + " if id_end > nb_data:\n", + " id_end = nb_data\n", + " acquisition_values[id_start:id_end] = range(id_start, id_end)\n", + " \n", + "np.sum(np.isnan(acquisition_values))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "hydraulic-salvation", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nb_data" + ] + }, + { + "cell_type": "markdown", + "id": "sticky-climb", + "metadata": {}, + "source": [ + "number of COFs for initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "piano-indicator", + "metadata": {}, + "outputs": [], + "source": [ + "nb_COFs_initialization = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "legitimate-phone", + "metadata": {}, + "outputs": [], + "source": [ + "def bo_run(nb_iterations):\n", + " assert nb_iterations > nb_COFs_initialization\n", + " \n", + " # select initial COFs for training data randomly.\n", + " # idea is to keep populating this ids_acquired and return it for analysis.\n", + " ids_acquired = np.random.choice(np.arange((nb_data)), size=nb_COFs_initialization, replace=False)\n", + "\n", + " # initialize acquired y, since it requires normalization\n", + " y_acquired = y[ids_acquired]\n", + " # standardize outputs\n", + " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", + " \n", + " for i in range(nb_COFs_initialization, nb_iterations):\n", + " print(\"iteration:\", i)\n", + " # construct and fit GP model\n", + " model = SingleTaskGP(X[ids_acquired, :], y_acquired)\n", + " mll = ExactMarginalLogLikelihood(model.likelihood, model)\n", + " fit_gpytorch_model(mll)\n", + "\n", + " # set up acquisition function\n", + " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", + " \n", + " # compute aquisition function at each COF in the database. need to do in batches to avoid mem issues\n", + " batch_size = 20000\n", + " acquisition_values = torch.zeros((nb_data))\n", + " acquisition_values[:] = np.NaN # for safety\n", + " nb_batches = nb_data // batch_size\n", + " for ba in range(nb_batches+1):\n", + " id_start = ba * batch_size\n", + " id_end = id_start + batch_size\n", + " if id_end > nb_data:\n", + " id_end = nb_data\n", + " acquisition_values[id_start:id_end] = acquisition_function.forward(X_unsqueezed[id_start:id_end])\n", + "# acquisition_values = acquisition_function.forward(X_unsqueezed)\n", + " assert acquisition_values.isnan().sum().item() == 0 # so that all are filled properly.\n", + " del acquisition_function\n", + "\n", + " # select COF to acquire with maximal aquisition value, which is not in the acquired set already\n", + " ids_sorted_by_aquisition = acquisition_values.argsort(descending=True)\n", + " for id_max_aquisition_all in ids_sorted_by_aquisition:\n", + " if not id_max_aquisition_all.item() in ids_acquired:\n", + " id_max_aquisition = id_max_aquisition_all.item()\n", + " break\n", + "\n", + " # acquire this COF\n", + " ids_acquired = np.concatenate((ids_acquired, [id_max_aquisition]))\n", + " assert np.size(ids_acquired) == i + 1\n", + "\n", + " # update y aquired; start over to normalize properly\n", + " del y_acquired\n", + " y_acquired = y[ids_acquired, :] # start over to normalize y properly\n", + " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", + "\n", + " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition].item())\n", + " print(\"\\tbest y acquired:\", y[ids_acquired].max().item())\n", + " \n", + " del model\n", + " del mll\n", + " del acquisition_values\n", + " \n", + " assert np.size(ids_acquired) == nb_iterations\n", + " return ids_acquired" + ] + }, + { + "cell_type": "markdown", + "id": "eight-class", + "metadata": {}, + "source": [ + "`ids_acquired[r, i]` will give ID of COF acquired during iteration `i` from run `r`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "premium-acceptance", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "RUN 0\n", + "iteration: 10\n", + "\tacquired COF 56259 with y = 182.416471606\n", + "\tbest y acquired: 182.416471606\n", + "iteration: 11\n", + "\tacquired COF 44551 with y = 188.642146113\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 12\n", + "\tacquired COF 59749 with y = 183.06633314099997\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 13\n", + "\tacquired COF 43434 with y = 166.762639788\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 14\n", + "\tacquired COF 57294 with y = 166.196918004\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 15\n", + "\tacquired COF 65585 with y = 173.44669686900002\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 16\n", + "\tacquired COF 441 with y = 186.034221186\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 17\n", + "\tacquired COF 20675 with y = 167.532168988\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 18\n", + "\tacquired COF 12418 with y = 176.910634695\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 19\n", + "\tacquired COF 13260 with y = 153.441277223\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 20\n", + "\tacquired COF 12402 with y = 175.504448723\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 21\n", + "\tacquired COF 66379 with y = 178.99445053\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 22\n", + "\tacquired COF 33381 with y = 82.0114448965\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 23\n", + "\tacquired COF 50163 with y = 167.486661746\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 24\n", + "\tacquired COF 440 with y = 183.14184687099998\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 25\n", + "\tacquired COF 3595 with y = 173.92050685200002\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 26\n", + "\tacquired COF 30136 with y = 178.774758072\n", + "\tbest y acquired: 188.642146113\n", + "iteration: 27\n", + "\tacquired COF 25951 with y = 196.579974938\n", + "\tbest y acquired: 196.579974938\n", + "iteration: 28\n", + "\tacquired COF 52297 with y = 157.061528528\n", + "\tbest y acquired: 196.579974938\n", + "iteration: 29\n", + "\tacquired COF 21607 with y = 205.171240133\n", + "\tbest y acquired: 205.171240133\n", + "iteration: 30\n", + "\tacquired COF 26565 with y = 207.39578187\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 31\n", + "\tacquired COF 32021 with y = 189.34518854799998\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 32\n", + "\tacquired COF 26507 with y = 200.44080272099998\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 33\n", + "\tacquired COF 37482 with y = 174.718514791\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 34\n", + "\tacquired COF 13582 with y = 183.60112018900003\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 35\n", + "\tacquired COF 29861 with y = 199.72030120099998\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 36\n" + ] + } + ], + "source": [ + "bo_res = dict()\n", + "bo_res['nb_runs'] = 1\n", + "bo_res['nb_iterations'] = 200\n", + "bo_res['ids_acquired'] = []\n", + "for r in range(bo_res['nb_runs']):\n", + " print(\"\\n\\nRUN\", r)\n", + " t0 = time.time()\n", + " ids_acquired = bo_run(bo_res['nb_iterations'])\n", + " bo_res['ids_acquired'].append(ids_acquired)\n", + " print(\"took time t = \", (time.time() - t0) / 60, \"min\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "impressed-investigation", + "metadata": {}, + "outputs": [], + "source": [ + "with open('bo_results.pkl', 'wb') as file:\n", + " pickle.dump(bo_res, file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "young-working", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/new/evol_search.ipynb b/new/evol_search.ipynb new file mode 100644 index 0000000..55a82d7 --- /dev/null +++ b/new/evol_search.ipynb @@ -0,0 +1,1601 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "yellow-lounge", + "metadata": {}, + "outputs": [], + "source": [ + "import cma\n", + "import pickle\n", + "import numpy as np\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "integral-documentation", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "adequate-scholar", + "metadata": {}, + "outputs": [], + "source": [ + "# so evol search is bounded by [0, 1]^12\n", + "assert np.max(X) <= 1.0\n", + "assert np.min(X) >= 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ranging-improvement", + "metadata": {}, + "outputs": [], + "source": [ + "# return id of COF closest to x_ask in feature space, which is not in the set of acquired COFs\n", + "def closest_COF(x_ask, ids_acquired):\n", + " distances_to_xs = np.linalg.norm(X - x_ask, axis=1)\n", + " assert np.size(distances_to_xs) == nb_data\n", + " ids_sorted_by_closeness = np.argsort(distances_to_xs)\n", + " for id_closest in ids_sorted_by_closeness:\n", + " if not id_closest in ids_acquired:\n", + " return id_closest" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "individual-moral", + "metadata": {}, + "outputs": [], + "source": [ + "# two tests\n", + "assert closest_COF(X[3, :], []) == 3\n", + "\n", + "ids = [i for i in range(nb_data) if not i == 3]\n", + "distances = np.linalg.norm(X - X[3, :], axis=1)\n", + "assert distances[closest_COF(X[3, :], [3])] == np.min(distances[ids])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "emerging-portland", + "metadata": {}, + "outputs": [], + "source": [ + "nb_iterations = 500\n", + "\n", + "def evol_run(nb_iterations):\n", + " x_init = np.random.rand(np.shape(X)[1]) # random initial point\n", + "\n", + " # initialize evolutionary algo\n", + " ids_acquired = []\n", + " es = cma.CMAEvolutionStrategy(x0=x_init, sigma0=0.5, inopts={'bounds': [0.0, 1.0]})\n", + "\n", + " while len(ids_acquired) < nb_iterations:\n", + " print(\"ask/tell sesh\")\n", + " # ask for a set of COFs to evaluate.\n", + " xs_ask = es.ask()\n", + "\n", + " # find IDs of these COFs (the closest unacquired COFs in feature space to them.)\n", + " ids_ask = []\n", + " for x_ask in xs_ask:\n", + " assert np.min(x_ask) >= 0.0\n", + " assert np.max(x_ask) <= 1.0\n", + " id_ask = closest_COF(x_ask, ids_acquired)\n", + " ids_ask.append(id_ask)\n", + " # officially acquire this COF\n", + " ids_acquired.append(id_ask)\n", + "\n", + " # tell the ES algo about these most recently acquired COFs.\n", + " es.tell(X[ids_ask, :], -y[ids_ask]) # cuz we wanna minimize\n", + "\n", + " print(\"\\t# acquired COFs:\", len(ids_acquired))\n", + " print(\"\\t# max y value: \", np.max(y[ids_acquired]))\n", + " assert np.size(np.unique(ids_acquired[:nb_iterations]) == nb_iterations)\n", + " return ids_acquired[:nb_iterations]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "dried-animation", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "RUN 0\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=305343, Thu Jul 1 13:07:43 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 200.420314123\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 1\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=231156, Thu Jul 1 13:07:47 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 2\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=292258, Thu Jul 1 13:07:51 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 3\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=200476, Thu Jul 1 13:07:55 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 164.593994065\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 170.049898364\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 4\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=225273, Thu Jul 1 13:07:59 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 199.72030120099998\n", + "\n", + "\n", + "RUN 5\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=192469, Thu Jul 1 13:08:02 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 149.316186302\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 181.99863318099997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 6\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=268625, Thu Jul 1 13:08:06 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 134.638026751\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.46977255299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.06469784200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 202.848493155\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 202.848493155\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 202.848493155\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 202.848493155\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 7\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=284032, Thu Jul 1 13:08:10 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 144.391389813\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 148.112761288\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 175.820374733\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 193.51655534\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 193.51655534\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 8\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=241457, Thu Jul 1 13:08:14 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 179.81664061900003\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 195.58268240799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 209.36697147400002\n", + "\n", + "\n", + "RUN 9\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=212735, Thu Jul 1 13:08:18 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 186.09094075099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 186.09094075099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 186.09094075099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 264\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 275\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 286\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 297\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 308\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 319\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 330\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 341\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 352\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 363\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 374\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 385\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 396\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 407\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 418\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 429\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 440\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 451\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 462\n", + "\t# max y value: 208.120454446\n", + "ask/tell sesh\n", + "\t# acquired COFs: 473\n", + "\t# max y value: 208.120454446\n", + "ask/tell sesh\n", + "\t# acquired COFs: 484\n", + "\t# max y value: 208.120454446\n", + "ask/tell sesh\n", + "\t# acquired COFs: 495\n", + "\t# max y value: 208.120454446\n", + "ask/tell sesh\n", + "\t# acquired COFs: 506\n", + "\t# max y value: 208.120454446\n" + ] + } + ], + "source": [ + "es_res = dict()\n", + "es_res['nb_runs'] = 10\n", + "es_res['nb_iterations'] = 500\n", + "es_res['ids_acquired'] = []\n", + "for r in range(es_res['nb_runs']):\n", + " print(\"\\n\\nRUN\", r)\n", + " ids_acquired = evol_run(es_res['nb_iterations'])\n", + " es_res['ids_acquired'].append(ids_acquired)\n", + " \n", + "with open('es_results.pkl', 'wb') as file:\n", + " pickle.dump(es_res, file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "focal-cologne", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/new/prepare_Xy.ipynb b/new/prepare_Xy.ipynb new file mode 100644 index 0000000..73b6dfa --- /dev/null +++ b/new/prepare_Xy.ipynb @@ -0,0 +1,759 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "discrete-greece", + "metadata": {}, + "source": [ + "# import and prepare data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "alpha-reputation", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pickle\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "indonesian-label", + "metadata": {}, + "source": [ + "load data from Mercado et al., drop outlier" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cheap-minnesota", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dimensionsbond typenamevoid fraction [widom]supercell volume [A^3]density [kg/m^3]heat desorption high P [kJ/mol]heat desorption error high P [kJ/mol]absolute methane uptake high P [molec/unit cell]absolute methane uptake error high P [molec/unit cell]...num sulfurnum siliconverticesedgesgenuslargest included sphere diameter [A]largest free sphere diameter [A]largest included sphere along free sphere path diameter [A]absolute methane uptake high P [v STP/v]absolute methane uptake low P [v STP/v]
02amidelinker91_CO_linker87_NH_hcb_relaxed0.90012049204.128057260.21322810.951620.315667233.28922.789059...0011017.1901415.6496117.19004176.16049820.988007
12amidelinker91_CO_linker88_NH_hcb_relaxed0.87923449390.074419297.96338711.817560.478028250.61643.464625...0011017.3491615.7694317.34916188.53207322.643282
22amidelinker91_CO_linker7_NH_hcb_relaxed0.85826950036.985281289.39724911.863780.140491255.15100.921036...0011016.8403215.6190716.84024189.46176422.566022
32amidelinker91_CO_linker8_NH_hcb_relaxed0.85706549135.924517370.06363312.488420.823728257.33682.377728...0011013.9308512.3216713.93085194.58896227.373459
42amidelinker91_CO_linker10_NH_hcb_relaxed0.85801649540.680132367.04015112.259240.191371253.26203.177484...0011016.0692313.4879116.06921189.94309224.774006
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5 rows × 53 columns

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" + ], + "text/plain": [ + " dimensions bond type name \\\n", + "0 2 amide linker91_CO_linker87_NH_hcb_relaxed \n", + "1 2 amide linker91_CO_linker88_NH_hcb_relaxed \n", + "2 2 amide linker91_CO_linker7_NH_hcb_relaxed \n", + "3 2 amide linker91_CO_linker8_NH_hcb_relaxed \n", + "4 2 amide linker91_CO_linker10_NH_hcb_relaxed \n", + "\n", + " void fraction [widom] supercell volume [A^3] density [kg/m^3] \\\n", + "0 0.900120 49204.128057 260.213228 \n", + "1 0.879234 49390.074419 297.963387 \n", + "2 0.858269 50036.985281 289.397249 \n", + "3 0.857065 49135.924517 370.063633 \n", + "4 0.858016 49540.680132 367.040151 \n", + "\n", + " heat desorption high P [kJ/mol] heat desorption error high P [kJ/mol] \\\n", + "0 10.95162 0.315667 \n", + "1 11.81756 0.478028 \n", + "2 11.86378 0.140491 \n", + "3 12.48842 0.823728 \n", + "4 12.25924 0.191371 \n", + "\n", + " absolute methane uptake high P [molec/unit cell] \\\n", + "0 233.2892 \n", + "1 250.6164 \n", + "2 255.1510 \n", + "3 257.3368 \n", + "4 253.2620 \n", + "\n", + " absolute methane uptake error high P [molec/unit cell] ... num sulfur \\\n", + "0 2.789059 ... 0 \n", + "1 3.464625 ... 0 \n", + "2 0.921036 ... 0 \n", + "3 2.377728 ... 0 \n", + "4 3.177484 ... 0 \n", + "\n", + " num silicon vertices edges genus \\\n", + "0 0 1 1 0 \n", + "1 0 1 1 0 \n", + "2 0 1 1 0 \n", + "3 0 1 1 0 \n", + "4 0 1 1 0 \n", + "\n", + " largest included sphere diameter [A] largest free sphere diameter [A] \\\n", + "0 17.19014 15.64961 \n", + "1 17.34916 15.76943 \n", + "2 16.84032 15.61907 \n", + "3 13.93085 12.32167 \n", + "4 16.06923 13.48791 \n", + "\n", + " largest included sphere along free sphere path diameter [A] \\\n", + "0 17.19004 \n", + "1 17.34916 \n", + "2 16.84024 \n", + "3 13.93085 \n", + "4 16.06921 \n", + "\n", + " absolute methane uptake high P [v STP/v] \\\n", + "0 176.160498 \n", + "1 188.532073 \n", + "2 189.461764 \n", + "3 194.588962 \n", + "4 189.943092 \n", + "\n", + " absolute methane uptake low P [v STP/v] \n", + "0 20.988007 \n", + "1 22.643282 \n", + "2 22.566022 \n", + "3 27.373459 \n", + "4 24.774006 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('properties.csv')\n", + "# we remove row 48225 since it is an outlier for void fraction feature # can be seen by df[df[' void fraction [widom]'] > 1]\n", + "df = df.drop(48225)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "fitted-tyler", + "metadata": {}, + "source": [ + "define new feature as density of elements" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "experimental-ethics", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dimensionsbond typenamevoid fraction [widom]supercell volume [A^3]density [kg/m^3]heat desorption high P [kJ/mol]heat desorption error high P [kJ/mol]absolute methane uptake high P [molec/unit cell]absolute methane uptake error high P [molec/unit cell]...largest included sphere along free sphere path diameter [A]absolute methane uptake high P [v STP/v]absolute methane uptake low P [v STP/v]density of carbondensity of fluorinedensity of hydrogendensity of nitrogendensity of oxygendensity of sulfurdensity of silicon
02amidelinker91_CO_linker87_NH_hcb_relaxed0.90012049204.128057260.21322810.951620.315667233.28922.789059...17.19004176.16049820.9880070.0073160.00.0043900.0029270.0014630.00.0
12amidelinker91_CO_linker88_NH_hcb_relaxed0.87923449390.074419297.96338711.817560.478028250.61643.464625...17.34916188.53207322.6432820.0072890.00.0043730.0029160.0029160.00.0
22amidelinker91_CO_linker7_NH_hcb_relaxed0.85826950036.985281289.39724911.863780.140491255.15100.921036...16.84024189.46176422.5660220.0086340.00.0071950.0028780.0014390.00.0
32amidelinker91_CO_linker8_NH_hcb_relaxed0.85706549135.924517370.06363312.488420.823728257.33682.377728...13.93085194.58896227.3734590.0073270.00.0029310.0043960.0043960.00.0
42amidelinker91_CO_linker10_NH_hcb_relaxed0.85801649540.680132367.04015112.259240.191371253.26203.177484...16.06921189.94309224.7740060.0072670.00.0029070.0043600.0043600.00.0
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5 rows × 60 columns

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" + ], + "text/plain": [ + " dimensions bond type name \\\n", + "0 2 amide linker91_CO_linker87_NH_hcb_relaxed \n", + "1 2 amide linker91_CO_linker88_NH_hcb_relaxed \n", + "2 2 amide linker91_CO_linker7_NH_hcb_relaxed \n", + "3 2 amide linker91_CO_linker8_NH_hcb_relaxed \n", + "4 2 amide linker91_CO_linker10_NH_hcb_relaxed \n", + "\n", + " void fraction [widom] supercell volume [A^3] density [kg/m^3] \\\n", + "0 0.900120 49204.128057 260.213228 \n", + "1 0.879234 49390.074419 297.963387 \n", + "2 0.858269 50036.985281 289.397249 \n", + "3 0.857065 49135.924517 370.063633 \n", + "4 0.858016 49540.680132 367.040151 \n", + "\n", + " heat desorption high P [kJ/mol] heat desorption error high P [kJ/mol] \\\n", + "0 10.95162 0.315667 \n", + "1 11.81756 0.478028 \n", + "2 11.86378 0.140491 \n", + "3 12.48842 0.823728 \n", + "4 12.25924 0.191371 \n", + "\n", + " absolute methane uptake high P [molec/unit cell] \\\n", + "0 233.2892 \n", + "1 250.6164 \n", + "2 255.1510 \n", + "3 257.3368 \n", + "4 253.2620 \n", + "\n", + " absolute methane uptake error high P [molec/unit cell] ... \\\n", + "0 2.789059 ... \n", + "1 3.464625 ... \n", + "2 0.921036 ... \n", + "3 2.377728 ... \n", + "4 3.177484 ... \n", + "\n", + " largest included sphere along free sphere path diameter [A] \\\n", + "0 17.19004 \n", + "1 17.34916 \n", + "2 16.84024 \n", + "3 13.93085 \n", + "4 16.06921 \n", + "\n", + " absolute methane uptake high P [v STP/v] \\\n", + "0 176.160498 \n", + "1 188.532073 \n", + "2 189.461764 \n", + "3 194.588962 \n", + "4 189.943092 \n", + "\n", + " absolute methane uptake low P [v STP/v] density of carbon \\\n", + "0 20.988007 0.007316 \n", + "1 22.643282 0.007289 \n", + "2 22.566022 0.008634 \n", + "3 27.373459 0.007327 \n", + "4 24.774006 0.007267 \n", + "\n", + " density of fluorine density of hydrogen density of nitrogen \\\n", + "0 0.0 0.004390 0.002927 \n", + "1 0.0 0.004373 0.002916 \n", + "2 0.0 0.007195 0.002878 \n", + "3 0.0 0.002931 0.004396 \n", + "4 0.0 0.002907 0.004360 \n", + "\n", + " density of oxygen density of sulfur density of silicon \n", + "0 0.001463 0.0 0.0 \n", + "1 0.002916 0.0 0.0 \n", + "2 0.001439 0.0 0.0 \n", + "3 0.004396 0.0 0.0 \n", + "4 0.004360 0.0 0.0 \n", + "\n", + "[5 rows x 60 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elements = ['carbon', 'fluorine', 'hydrogen', 'nitrogen', 'oxygen', 'sulfur', 'silicon']\n", + "for el in elements:\n", + " df[\"density of \" + el] = df[' num ' + el] / df[' supercell volume [A^3]']\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "photographic-bookmark", + "metadata": {}, + "source": [ + "get feature matrix and target vector" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "greenhouse-authorization", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# features: 12\n" + ] + } + ], + "source": [ + "features = [' void fraction [widom]', ' density [kg/m^3]', ' largest included sphere diameter [A]', ' largest free sphere diameter [A]', ' surface area [m^2/g]',\n", + " 'density of carbon', 'density of fluorine', 'density of hydrogen', 'density of nitrogen', 'density of oxygen', 'density of sulfur', 'density of silicon']\n", + "print(\"# features: \", len(features))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "equal-digest", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of Y: (69839,)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([155.17249134, 165.88879162, 166.8957419 , ..., 161.97256899,\n", + " 155.38761768, 155.48070341])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = df[' deliverable capacity [v STP/v]'].values\n", + "print(\"shape of Y: \", np.shape(y))\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "employed-browse", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (69839, 12)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[9.00120000e-01, 2.60213228e+02, 1.71901400e+01, ...,\n", + " 1.46329186e-03, 0.00000000e+00, 0.00000000e+00],\n", + " [8.79234000e-01, 2.97963387e+02, 1.73491600e+01, ...,\n", + " 2.91556556e-03, 0.00000000e+00, 0.00000000e+00],\n", + " [8.58269000e-01, 2.89397249e+02, 1.68403200e+01, ...,\n", + " 1.43893561e-03, 0.00000000e+00, 0.00000000e+00],\n", + " ...,\n", + " [8.85007000e-01, 2.59378413e+02, 1.60821800e+01, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [7.37251000e-01, 5.14847059e+02, 1.15594900e+01, ...,\n", + " 1.43384585e-03, 0.00000000e+00, 0.00000000e+00],\n", + " [7.77576000e-01, 5.01030978e+02, 1.15140800e+01, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = df[features].values\n", + "print(\"shape of X: \", np.shape(X))\n", + "X" + ] + }, + { + "cell_type": "markdown", + "id": "automatic-shark", + "metadata": {}, + "source": [ + "Min-Max normalize the features so that they lie in $[0, 1]$. this is ok to do over all data as opposed to just training because in BO we will compute the cheap features of every COF in the database." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "pharmaceutical-premises", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature 0 in [ 0.0 , 1.0 ]\n", + "feature 1 in [ 0.0 , 1.0 ]\n", + "feature 2 in [ 0.0 , 1.0 ]\n", + "feature 3 in [ 0.0 , 1.0 ]\n", + "feature 4 in [ 0.0 , 1.0 ]\n", + "feature 5 in [ 0.0 , 1.0 ]\n", + "feature 6 in [ 0.0 , 1.0 ]\n", + "feature 7 in [ 0.0 , 1.0 ]\n", + "feature 8 in [ 0.0 , 1.0 ]\n", + "feature 9 in [ 0.0 , 1.0 ]\n", + "feature 10 in [ 0.0 , 1.0 ]\n", + "feature 11 in [ 0.0 , 1.0 ]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0.90081723, 0.20318731, 0.15909104, ..., 0.15284004, 0. ,\n", + " 0. ],\n", + " [0.8775969 , 0.23561078, 0.16100571, ..., 0.30452924, 0. ,\n", + " 0. ],\n", + " [0.85428875, 0.22825336, 0.15487906, ..., 0.15029605, 0. ,\n", + " 0. ],\n", + " ...,\n", + " [0.88401512, 0.20247029, 0.14575075, ..., 0. , 0. ,\n", + " 0. ],\n", + " [0.71974518, 0.42189136, 0.0912957 , ..., 0.14976442, 0. ,\n", + " 0. ],\n", + " [0.7645771 , 0.41002478, 0.09074894, ..., 0. , 0. ,\n", + " 0. ]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in range(np.shape(X)[1]):\n", + " X[:, i] = (X[:, i] - np.min(X[:, i])) / (np.max(X[:, i]) - np.min(X[:, i]))\n", + " print(\"feature\", i, \" in [\", np.min(X[:, i]), \",\", np.max(X[:, i]), \"]\")\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "alleged-midwest", + "metadata": {}, + "outputs": [], + "source": [ + "with open('inputs_and_outputs.pkl', 'wb') as file:\n", + " pickle.dump({'X': X, 'y': y}, file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/new/random_forest_run.ipynb b/new/random_forest_run.ipynb new file mode 100644 index 0000000..3cd1103 --- /dev/null +++ b/new/random_forest_run.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "roman-pastor", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pickle\n", + "import torch\n", + "import time\n", + "\n", + "from sklearn.metrics import r2_score, mean_absolute_error, explained_variance_score, mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "import autosklearn.regression\n", + "from sklearn.ensemble import RandomForestRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "behind-grace", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (69839, 12)\n" + ] + }, + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "print(\"shape of X: \", np.shape(X))\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "electrical-geology", + "metadata": {}, + "outputs": [], + "source": [ + "def diverse_train_test_split(X, train_size):\n", + " ids_train = [np.random.randint(0, nb_data)] # initialize with one random point; pick others in a max diverse fashion\n", + " # select remaining training points\n", + " for j in range(train_size - 1):\n", + " # for each point, compute its min distance to training set\n", + " min_distances_to_train_set = np.zeros((nb_data, ))\n", + " for i in range(nb_data):\n", + " # compute its distance to all points in the training set\n", + " distances_to_train_set = np.linalg.norm(X[i, :] - X[ids_train, :], axis=1)\n", + " assert np.size(distances_to_train_set) == len(ids_train)\n", + " min_distances_to_train_set[i] = np.min(distances_to_train_set)\n", + " # select point with max min distance to train set (Furthest from train set)\n", + " ids_train.append(np.argmax(min_distances_to_train_set))\n", + " assert np.size(np.unique(ids_train)) == train_size\n", + " ids_test = [i for i in range(nb_data) if not i in ids_train]\n", + " assert np.size(np.unique(ids_test)) == nb_data - train_size\n", + " return np.array(ids_train), np.array(ids_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "filled-better", + "metadata": {}, + "outputs": [], + "source": [ + "diversify_training = True" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "frozen-height", + "metadata": {}, + "outputs": [], + "source": [ + "def rf_run(nb_training_data, nb_acquire):\n", + " if diversify_training:\n", + " print(\"\\tdiverse RF run\")\n", + " else:\n", + " print(\"\\tRF run\")\n", + " print(\"\\teval budget\", nb_training_data + nb_acquire, \"=\", nb_training_data, \"training data and\", nb_acquire, \"acquired.\")\n", + " # test/train split\n", + " if diversify_training:\n", + " ids_train, ids_test = diverse_train_test_split(X, nb_training_data)\n", + " else:\n", + " ids_train, ids_test = train_test_split(np.arange(nb_data), train_size=nb_training_data)\n", + " \n", + " X_train = X[ids_train, :]\n", + " y_train = y[ids_train]\n", + " \n", + " X_test = X[ids_test, :]\n", + " \n", + " # train random forest on training data\n", + " rf = RandomForestRegressor()\n", + " rf.fit(X_train, y_train)\n", + "\n", + " # hv random forest make predictions on test data\n", + " y_pred = rf.predict(X_test)\n", + "\n", + " # rank the test predictions\n", + " ids_test_ranked = np.flip(np.argsort(y_pred))\n", + " \n", + " # acquire the COFs in the test set with highest predicted property\n", + " ids_acquire = ids_test[ids_test_ranked[:nb_acquire]]\n", + "\n", + " # return the acquired COFs but also the trained COFs which count.\n", + " ids_acquire_incld_training = np.concatenate((ids_acquire, ids_train))\n", + " \n", + " assert np.size(np.unique(ids_acquire_incld_training)) == nb_training_data + nb_acquire\n", + " \n", + " print(\"\\tmax y acquired = \", np.max(y[ids_acquire_incld_training]))\n", + " return ids_acquire_incld_training" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "collect-breathing", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eval budgets: [10, 20, 30, 50]\n", + "budget for evals: 10\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 10 = 5 training data and 5 acquired.\n", + "\tmax y acquired = 159.96862082799998\n", + "budget for evals: 20\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 173.880645123\n", + "budget for evals: 30\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 30 = 15 training data and 15 acquired.\n", + "\tmax y acquired = 178.997150426\n", + "budget for evals: 50\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 50 = 25 training data and 25 acquired.\n", + "\tmax y acquired = 192.95415281799998\n" + ] + } + ], + "source": [ + "rf_res = dict()\n", + "rf_res['nb_runs'] = 1\n", + "rf_res['nb_evals_budgets'] = [10, 20, 30, 50] #[10 * i for i in range(1, 21)]\n", + "print(\"eval budgets: \", rf_res['nb_evals_budgets'])\n", + "rf_res['ids_acquired'] = [[] for b in rf_res['nb_evals_budgets']]\n", + "for b in range(len(rf_res['nb_evals_budgets'])):\n", + " nb_evals_budget = rf_res['nb_evals_budgets'][b]\n", + " print(\"budget for evals:\", nb_evals_budget)\n", + " # decide how to spend the evals budget here. say 50/50\n", + " nb_training_data = nb_evals_budget // 2\n", + " nb_acquire = nb_evals_budget // 2\n", + " assert nb_training_data + nb_acquire == nb_evals_budget\n", + " for r in range(rf_res['nb_runs']):\n", + " print(\"\\trun\", r)\n", + " ids_acquired = rf_run(nb_training_data, nb_acquire)\n", + " rf_res['ids_acquired'][b].append(ids_acquired)\n", + "\n", + "if not diversify_training:\n", + " with open('rf_results.pkl', 'wb') as file:\n", + " pickle.dump(rf_res, file)\n", + "else:\n", + " with open('rf_div_results.pkl', 'wb') as file:\n", + " pickle.dump(rf_res, file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "periodic-toolbox", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/new/random_search.ipynb b/new/random_search.ipynb new file mode 100644 index 0000000..1243dca --- /dev/null +++ b/new/random_search.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dated-collect", + "metadata": {}, + "source": [ + "# random search" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "lesser-finnish", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pickle\n", + "import torch\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "registered-apparel", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (69839, 12)\n" + ] + }, + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "print(\"shape of X: \", np.shape(X))\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "extreme-trace", + "metadata": {}, + "outputs": [], + "source": [ + "rs_res = dict()\n", + "rs_res['nb_runs'] = 10\n", + "rs_res['nb_iterations'] = 500\n", + "rs_res['ids_acquired'] = []\n", + "for r in range(rs_res['nb_runs']):\n", + " ids_acquired = np.random.choice(range(nb_data), replace=False, size=rs_res['nb_iterations'])\n", + " rs_res['ids_acquired'].append(ids_acquired)\n", + "with open('rs_results.pkl', 'wb') as file:\n", + " pickle.dump(rs_res, file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/new/viz.ipynb b/new/viz.ipynb new file mode 100644 index 0000000..ecbcfbc --- /dev/null +++ b/new/viz.ipynb @@ -0,0 +1,766 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "acceptable-scale", + "metadata": {}, + "source": [ + "# viz" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "lasting-reproduction", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" + ] + } + ], + "source": [ + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "import pickle\n", + "import pandas as pd\n", + "import torch\n", + "from sklearn.decomposition import PCA\n", + "\n", + "plt.matplotlib.style.use(\"https://gist.githubusercontent.com/JonnyCBB/c464d302fefce4722fe6cf5f461114ea/raw/64a78942d3f7b4b5054902f2cee84213eaff872f/matplotlibrc\")\n", + "cool_colors = ['#00BEFF', '#D4CA3A', '#FF6DAE', '#67E1B5', '#EBACFA', '#9E9E9E', '#F1988E', '#5DB15A', '#E28544', '#52B8AA']\n", + "cool_colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7']\n", + "\n", + "search_to_color = {'BO': cool_colors[0], 'random': cool_colors[1], 'evolutionary': cool_colors[2], 'RF': cool_colors[5], 'RF (div)': cool_colors[3]}" + ] + }, + { + "cell_type": "markdown", + "id": "rubber-viking", + "metadata": {}, + "source": [ + "load data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cardiovascular-venice", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (69839, 12)\n" + ] + }, + { + "data": { + "text/plain": [ + "69839" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", + "print(\"shape of X:\", np.shape(X))\n", + "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", + "nb_data = np.size(y)\n", + "nb_data" + ] + }, + { + "cell_type": "markdown", + "id": "august-retrieval", + "metadata": {}, + "source": [ + "for rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "activated-handbook", + "metadata": {}, + "outputs": [], + "source": [ + "ids_to_rank = np.argsort(y.squeeze())\n", + "y_ranks = np.arange(nb_data)[np.flip(ids_to_rank).argsort()] + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "functioning-sleep", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([216.8941107])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[y_ranks == 1] # ranked first" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "accessible-certificate", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.47994999])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[y_ranks == nb_data] # ranked last" + ] + }, + { + "cell_type": "markdown", + "id": "balanced-infrared", + "metadata": {}, + "source": [ + "load search results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "naval-italy", + "metadata": {}, + "outputs": [], + "source": [ + "bo_res = pickle.load(open('bo_results.pkl', 'rb'))\n", + "rf_res = pickle.load(open('rf_results.pkl', 'rb'))\n", + "rf_div_res = pickle.load(open('rf_div_results.pkl', 'rb'))\n", + "rs_res = pickle.load(open('rs_results.pkl', 'rb'))\n", + "es_res = pickle.load(open('es_results.pkl', 'rb'))" + ] + }, + { + "cell_type": "markdown", + "id": "ambient-singer", + "metadata": {}, + "source": [ + "# PCA and viz of acquisition of BO" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "loving-empty", + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=2)\n", + "pca.fit(X)\n", + "X_2D = pca.transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "greater-skating", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#low dimensional (PCA) visualization of the entire dataset\n", + "plt.hexbin(X_2D[:, 0], X_2D[:, 1], C=y)\n", + "plt.xlabel('PCA dimension 1')\n", + "plt.ylabel('PCA dimension 2')\n", + "cb = plt.colorbar(fraction=0.02, pad=0.04)\n", + "cb.set_label(label=\"deliverable capacity\\n[L STP/L]\")\n", + "plt.xticks()\n", + "plt.yticks()\n", + "plt.gca().set_aspect('equal', 'box')\n", + "plt.tight_layout()\n", + "plt.savefig('feature_space_colored_by_DC.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "overall-bearing", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "which_BO_run = 0\n", + "\n", + "fig, ax = plt.subplots(1, 4, sharey=True, sharex=True, figsize=[3*6.4, 4.8])\n", + "nb_acquired = [10, 12, 15, 20]\n", + "# gray background\n", + "for a in ax:\n", + " a.set_aspect('equal', 'box')\n", + " a.hexbin(X_2D[:, 0], X_2D[:, 1], C=0.3 * np.ones(nb_data), cmap=\"binary\", vmin=0, vmax=1)\n", + " \n", + "for i in range(4):\n", + " ids_acquired = bo_res['ids_acquired'][which_BO_run][:nb_acquired[i]]\n", + " assert len(ids_acquired) == nb_acquired[i]\n", + " # use above colorbar to assign color!\n", + " ax[i].scatter(X_2D[ids_acquired, 0], X_2D[ids_acquired, 1], \n", + " c=y[ids_acquired], marker=\"+\", s=55, vmin=cb.vmin, vmax=cb.vmax)\n", + " ax[i].set_title('{} acquired COFs'.format(nb_acquired[i]))\n", + " ax[i].tick_params(axis='x', labelsize=10)\n", + "ax[0].set_ylabel('PCA dimension 2', fontsize=14)\n", + "\n", + "ax[2].tick_params(axis='y', labelsize=0)\n", + "\n", + "\n", + "fig.text(0.5, 0.2, 'PCA dimension 1', ha='center', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig(\"feature_space_acquired_COFs.pdf\")" + ] + }, + { + "cell_type": "markdown", + "id": "therapeutic-carter", + "metadata": {}, + "source": [ + "# search efficiency\n", + "### max $y$ among acquired set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "smoking-enforcement", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:234: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " keepdims=keepdims)\n", + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:195: RuntimeWarning: invalid value encountered in true_divide\n", + " arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n", + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# get y_max acquired up to iteration i for i = 1,2,...\n", + "def y_max(res, rank=False):\n", + " y_max_mu = np.zeros(res['nb_iterations'])\n", + " y_max_sig_bot = np.zeros(res['nb_iterations'])\n", + " y_max_sig_top = np.zeros(res['nb_iterations'])\n", + " for i in range(1, res['nb_iterations']+1):\n", + " # max value acquired up to this point\n", + " if rank:\n", + " y_maxes = np.array([np.min(y_ranks[res['ids_acquired'][r]][:i]) for r in range(res['nb_runs'])]) # among runs\n", + " else:\n", + " y_maxes = np.array([np.max(y[res['ids_acquired'][r]][:i]) for r in range(res['nb_runs'])]) # among runs\n", + " assert np.size(y_maxes) == res['nb_runs']\n", + " y_max_mu[i-1] = np.mean(y_maxes)\n", + " y_max_sig_bot[i-1] = np.std(y_maxes[y_maxes < y_max_mu[i-1]])\n", + " y_max_sig_top[i-1] = np.std(y_maxes[y_maxes > y_max_mu[i-1]])\n", + " return y_max_mu, y_max_sig_bot, y_max_sig_top\n", + "\n", + "y_max_mu_BO, y_max_sig_bot_BO, y_max_sig_top_BO = y_max(bo_res)\n", + "y_max_mu_es, y_max_sig_bot_es, y_max_sig_top_es = y_max(es_res)\n", + "y_max_mu_rs, y_max_sig_bot_rs, y_max_sig_top_rs = y_max(rs_res)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "million-assets", + "metadata": {}, + "outputs": [], + "source": [ + "def y_max_rf(rf_res, rank=False):\n", + " # one for each # of evaluation budgets\n", + " y_max_mu = np.zeros(len(rf_res['nb_evals_budgets']))\n", + " y_max_sig_bot = np.zeros(len(rf_res['nb_evals_budgets']))\n", + " y_max_sig_top = np.zeros(len(rf_res['nb_evals_budgets']))\n", + " for b in range(len(rf_res['nb_evals_budgets'])):\n", + " # get y max over runs.\n", + " if rank:\n", + " y_maxes = np.array([np.min(y_ranks[rf_res['ids_acquired'][b][r]]) for r in range(rf_res['nb_runs'])])\n", + " else:\n", + " y_maxes = np.array([np.max(y[rf_res['ids_acquired'][b][r]]) for r in range(rf_res['nb_runs'])])\n", + " assert np.size(y_maxes) == rf_res['nb_runs']\n", + " y_max_mu[b] = np.mean(y_maxes)\n", + " y_max_sig_bot[b] = np.std(y_maxes[y_maxes < y_max_mu[b]])\n", + " y_max_sig_top[b] = np.std(y_maxes[y_maxes > y_max_mu[b]])\n", + " return y_max_mu, y_max_sig_bot, y_max_sig_top " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "focused-finance", + "metadata": {}, + "outputs": [], + "source": [ + "y_max_mu_rf, y_max_sig_bot_rf, y_max_sig_top_rf = y_max_rf(rf_res)\n", + "y_max_mu_rf_div, y_max_sig_bot_rf_div, y_max_sig_top_rf_div = y_max_rf(rf_div_res)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "furnished-twenty", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_asarray.py:83: UserWarning: Warning: converting a masked element to nan.\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, gridspec_kw={'width_ratios': [4, 1]}, figsize=[1.2 * 6.4, 4.8], sharey=True)\n", + "axs[0].plot(np.arange(bo_res['nb_iterations'])+1, y_max_mu_BO, label='BO', color=search_to_color['BO'], clip_on=False)\n", + "axs[0].fill_between(np.arange(bo_res['nb_iterations'])+1, y_max_mu_BO - y_max_sig_bot_BO, \n", + " y_max_mu_BO + y_max_sig_top_BO, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['BO'])\n", + "\n", + "# ES\n", + "axs[0].plot(np.arange(es_res['nb_iterations'])+1, y_max_mu_es, label='CMA-ES', color=search_to_color['evolutionary'], clip_on=False)\n", + "axs[0].fill_between(np.arange(es_res['nb_iterations'])+1, y_max_mu_es - y_max_sig_bot_es, \n", + " y_max_mu_es + y_max_sig_top_es, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['evolutionary'])\n", + "\n", + "# RS\n", + "axs[0].plot(np.arange(rs_res['nb_iterations'])+1, y_max_mu_rs, label='random search', color=search_to_color['random'], clip_on=False)\n", + "axs[0].fill_between(np.arange(rs_res['nb_iterations'])+1, y_max_mu_rs - y_max_sig_bot_rs, \n", + " y_max_mu_rs + y_max_sig_top_rs, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['random'])\n", + "\n", + "axs[0].set_xlabel('# evaluated COFs')\n", + "axs[0].set_ylabel('maximum deliverable capacity\\namong evaluated COFs\\n[L STP/L]')\n", + "\n", + "\n", + "# RFs\n", + "axs[0].errorbar(rf_res['nb_evals_budgets'], y_max_mu_rf, yerr=np.vstack((y_max_sig_bot_rf, y_max_sig_top_rf)), color=search_to_color['RF'], marker=\"s\", label=\"random forest\", linestyle=\"none\")\n", + "axs[0].errorbar(rf_div_res['nb_evals_budgets'], y_max_mu_rf_div, yerr=np.vstack((y_max_sig_bot_rf_div, y_max_sig_top_rf_div)), color=search_to_color['RF (div)'], marker=\"s\", label=\"random forest\\n(diverse train set)\", linestyle=\"none\")\n", + "\n", + "axs[0].axhline(y=np.max(y), color=\"0.5\", linestyle=\"--\", label=\"$\\max y_i$\")\n", + "# axs[0].set_ylim(ymin=0.0)\n", + "axs[0].set_xlim([0, 500])\n", + "axs[0].legend()\n", + "\n", + "axs[1].hist(y, orientation=\"horizontal\", color=cool_colors[7])\n", + "axs[1].set_xlabel(\"# COFs\", fontsize=13)\n", + "axs[1].set_xscale(\"log\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"search_efficiency_max_found.pdf\")\n", + "\n", + "ylims_for_below = plt.gca().get_ylim()" + ] + }, + { + "cell_type": "markdown", + "id": "intellectual-companion", + "metadata": {}, + "source": [ + "show distribution for context." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "circular-pattern", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(2.5, 4.8))\n", + "plt.hist(y, orientation=\"horizontal\", color=cool_colors[7])\n", + "plt.xlabel(\"# COFs\")\n", + "plt.ylabel(\"deliverable capacity [L STP/L]\")\n", + "plt.xscale(\"log\")\n", + "plt.tight_layout()\n", + "plt.savefig(\"y_distn.pdf\", format=\"pdf\")" + ] + }, + { + "cell_type": "markdown", + "id": "parallel-summer", + "metadata": {}, + "source": [ + "### max rank among acquired set" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "gorgeous-walter", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:234: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " keepdims=keepdims)\n", + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:195: RuntimeWarning: invalid value encountered in true_divide\n", + " arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n", + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "y_max_mu_BO, y_max_sig_bot_BO, y_max_sig_top_BO = y_max(bo_res, rank=True)\n", + "y_max_mu_es, y_max_sig_bot_es, y_max_sig_top_es = y_max(es_res, rank=True)\n", + "y_max_mu_rs, y_max_sig_bot_rs, y_max_sig_top_rs = y_max(rs_res, rank=True)\n", + "\n", + "y_max_mu_rf, y_max_sig_bot_rf, y_max_sig_top_rf = y_max_rf(rf_res, rank=True)\n", + "y_max_mu_rf_div, y_max_sig_bot_rf_div, y_max_sig_top_rf_div = y_max_rf(rf_div_res, rank=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "tutorial-burns", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_asarray.py:83: UserWarning: Warning: converting a masked element to nan.\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "\n", + "plt.plot(np.arange(bo_res['nb_iterations'])+1, y_max_mu_BO, label='BO', color=search_to_color['BO'], clip_on=False)\n", + "plt.fill_between(np.arange(bo_res['nb_iterations'])+1, y_max_mu_BO - y_max_sig_bot_BO, \n", + " y_max_mu_BO + y_max_sig_top_BO, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['BO'])\n", + "\n", + "plt.plot(np.arange(es_res['nb_iterations'])+1, y_max_mu_es, label='CMA-ES', color=search_to_color['evolutionary'], clip_on=False)\n", + "plt.fill_between(np.arange(es_res['nb_iterations'])+1, y_max_mu_es - y_max_sig_bot_es, \n", + " y_max_mu_es + y_max_sig_top_es, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['evolutionary'])\n", + "\n", + "plt.plot(np.arange(rs_res['nb_iterations'])+1, y_max_mu_rs, label='random search', color=search_to_color['random'], clip_on=False)\n", + "plt.fill_between(np.arange(rs_res['nb_iterations'])+1, y_max_mu_rs - y_max_sig_bot_rs, \n", + " y_max_mu_rs + y_max_sig_top_rs, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['random'])\n", + "\n", + "# RFs\n", + "plt.errorbar(rf_res['nb_evals_budgets'], y_max_mu_rf, yerr=np.vstack((y_max_sig_bot_rf, y_max_sig_top_rf)), color=search_to_color['RF'], marker=\"s\", label=\"random forest\")\n", + "plt.errorbar(rf_div_res['nb_evals_budgets'], y_max_mu_rf_div, yerr=np.vstack((y_max_sig_bot_rf_div, y_max_sig_top_rf_div)), color=search_to_color['RF (div)'], marker=\"s\", label=\"random forest\\n(diverse train set)\")\n", + "\n", + "plt.xlabel('# evaluated COFs')\n", + "plt.ylabel('highest rank\\namong evaluated COFs')\n", + "plt.xlim([0, 500])\n", + "# plt.ylim(ymin=1)\n", + "# plt.legend(fontsize=1/4)\n", + "# plt.axhline(y=0) # to see the band bleed into negative zone.\n", + "plt.yticks()\n", + "plt.xticks()\n", + "plt.yscale(\"log\")\n", + "plt.gca().invert_yaxis()\n", + "plt.tight_layout()\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=15)\n", + "plt.savefig(\"search_efficiency_rank.pdf\")#, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "id": "decreased-agent", + "metadata": {}, + "source": [ + "### fraction of top 100 COFs recovered" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "serious-riverside", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 100 COFs range from y = 195.43880373 to 216.894110699\n" + ] + } + ], + "source": [ + "top_100_COF_ids = np.flip(np.argsort(y))[:100]\n", + "assert np.size(top_100_COF_ids) == 100\n", + "print(\"top 100 COFs range from y =\", np.min(y[top_100_COF_ids]), \"to\", np.max(y[top_100_COF_ids]))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "handy-shore", + "metadata": {}, + "outputs": [], + "source": [ + "def fraction_in_top100_cofs(ids_acquired):\n", + " nb_in_top_100 = 0\n", + " for id_acquired in ids_acquired:\n", + " if id_acquired in top_100_COF_ids:\n", + " nb_in_top_100 += 1\n", + " return nb_in_top_100 / 100" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "plain-thirty", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:234: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " keepdims=keepdims)\n", + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:195: RuntimeWarning: invalid value encountered in true_divide\n", + " arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n", + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# get fraction of aquired COFs in top 100 for\n", + "def y_top_100(res):\n", + " y_top100_mu = np.zeros(res['nb_iterations'])\n", + " y_top100_sig_bot = np.zeros(res['nb_iterations'])\n", + " y_top100_sig_top = np.zeros(res['nb_iterations'])\n", + " for i in range(1, res['nb_iterations']):\n", + " # max value acquired up to this point\n", + " y_top100s = np.array([fraction_in_top100_cofs(res['ids_acquired'][r][:i]) for r in range(res['nb_runs'])]) # among runs\n", + " assert np.size(y_top100s) == res['nb_runs']\n", + " y_top100_mu[i] = np.mean(y_top100s)\n", + " y_top100_sig_bot[i] = np.std(y_top100s[y_top100s < y_top100_mu[i]])\n", + " y_top100_sig_top[i] = np.std(y_top100s[y_top100s > y_top100_mu[i]])\n", + " return y_top100_mu, y_top100_sig_bot, y_top100_sig_top\n", + "\n", + "y_top100_mu_BO, y_top100_sig_bot_BO, y_top100_sig_top_BO = y_top_100(bo_res)\n", + "y_top100_mu_es, y_top100_sig_bot_es, y_top100_sig_top_es = y_top_100(es_res)\n", + "y_top100_mu_rs, y_top100_sig_bot_rs, y_top100_sig_top_rs = y_top_100(rs_res)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "amateur-action", + "metadata": {}, + "outputs": [], + "source": [ + "def y_top_100_rf(rf_res, rank=False):\n", + " # one for each # of evaluation budgets\n", + " y_top100_mu = np.zeros(len(rf_res['nb_evals_budgets']))\n", + " y_top100_sig_bot = np.zeros(len(rf_res['nb_evals_budgets']))\n", + " y_top100_sig_top = np.zeros(len(rf_res['nb_evals_budgets']))\n", + " for b in range(len(rf_res['nb_evals_budgets'])):\n", + " # get y max over runs.\n", + " y_top100s = np.array([fraction_in_top100_cofs(rf_res['ids_acquired'][b][r]) for r in range(rf_res['nb_runs'])])\n", + " assert np.size(y_top100s) == rf_res['nb_runs']\n", + " y_top100_mu[b] = np.mean(y_top100s)\n", + " y_top100_sig_bot[b] = np.std(y_top100s[y_top100s < y_top100_mu[b]])\n", + " y_top100_sig_top[b] = np.std(y_top100s[y_top100s > y_top100_mu[b]])\n", + " return y_top100_mu, y_top100_sig_bot, y_top100_sig_top \n", + "\n", + "y_top100_mu_rf, y_top100_sig_bot_rf, y_top100_sig_top_rf = y_top_100_rf(rf_res)\n", + "y_top100_mu_rf_div, y_top100_sig_bot_rf_div, y_top100_sig_top_rf_div = y_top_100_rf(rf_div_res)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "intended-civilization", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_asarray.py:83: UserWarning: Warning: converting a masked element to nan.\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "data": { + "image/png": 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03CeAk4HvKuVsBW7yjx8BHAr8l99GEARBELpKRxV1lXJeAJwBHOYf2gpcqrX7zQBtWzWyD8qO7NmwI9rYEW3siDZt6co+qIEq+Z7NLnqjoyP9NiOUZLOLiDatEW3siDZ2RJu2BJ/NXClnf+BtwJu0duebzk0AHwLep7V7VzeMWS+FQlH+YSyINnZEGzuijR3RJnhWGp++ESg0OycArd05TLqjNwVhmCAIgrCxWclBPRm4tM35S2kfht5T0mn5NmNDtLEj2tgRbeyINsGzkoM6ALinzfl7gf27Zs06GaT1tG4j2tgRbeyINnZEm+BZyUFlgQPbnD/QbxMKstl2SS82NqKNHdHGjmhjR7QJnpUc1C+Bs9qcfznwq+6ZIwiCIAiGlTbqfhj4f0o5c8AHtXbvB1DK2RO4AHgxcFqwJnaOFBCzI9rYEW3siDZ2RJvg6bRg4ccwiWMbCxaWAKW1++lALVwFlUrFi8Vi/TYjlFQqFUSb1og2dkQbO6JNW3q3UVcpZx/gBcAh/oW3At/W2r27G0Z0i+npWW/Tpsl+mxFKpqdnEW1aI9rYEW3siDZtCX6jbg2t3XsAvd6LKeVsAi7GhKbvAN6qtdsyjF0p5zhMqfnjMIEY79fa/dh6bRAEQRB2DXqdSOqTQBHYApwJfFop56jmRko5uwE/Aj4LbMaM3Fas2isFxOyINnZEGzuijR3RJnh6VmxQKWcUeD7wN1q7GeAXSjnfA16CCbho5A3Aj7V2v+Y/L1DPpG5FCojZEW3siDZ2RBs7ok3w9LIa7mFAWWu3scDh9cCpLdo+GvijUs41mNHTr4DXae1ua26olHM2cDbAU55yOieeeAIAw8Mp4vEYCwtmm1YiESedHmVmZg6ASASmpiaZn1+gXK4AMDExRqFQJJ8vADAyMkw0GiWTMX0MDSUYHR1Z6iMajTA5OcHc3PxSbZiJiTHy+QKFQhGA0dFhIpEImcwiYCJ/hodTzM6aeJNYLMrExPiyPiYnx8nl8kt9pNMjeJ63tO8imRwilUoyN7ewrI/Z2TmqVbOmODU1QTa7SLFYAsyC7thYmsVF00cqlSSZHFrqIx6PMT4+xszMLLVlyampCTKZLKVSGYCxsVHK5Qq5XH5J40Qizvx8ZknjsbH0Uh81jRcWMkt9jI+nKZXKy/ro9/u0sLDAnnvuEYr3KZ0epVqthuZ92r79QcbGxkLxPkF4Pk/mb58lkUiE4n0K0+cJ6NraXM+ymSvlnAJ8S2t3z4ZjrwLO1Np9fFPbrcAemBD2P2KS0j5Ka/fkdteQIAk7sqBrR7SxI9rYEW3a0rsgCVjKbF5zLvdr7d65ymtlMOHpjYwDCy3a5oDvaO1e51/7PcAOpZwJP0mtIAiCMOCsuMqnlKOUcu4Cbgeu9X9uV8q5Synn9au41lYgrpRzaMOxY4AbW7S9AWgc2nU0zJuamliFORsL0caOaGNHtLEj2gRP2yk+pZx3AA7wQeDHwAP+qS2YUPG3AK7W7vs6uZhSzn9inM0rgWOBK4DHau3e2NTuiZhy8k/AOLAPAcdr7Z7Srv9MJuul06OdmLLhyGSyiDatEW3siDZ2RJu29GSK72zgZVq7lzUdvwv4jVLOzcBFQEcOCngt8EVgO/AQcI7W7o3++tQPtXbTAFq7Vyrl/AtwOTAC/AJTcr4ttQVMYWdEGzuijR3Rxo5oEzwrOajNtA/vvgWY6vRiWrvTwHNaHL8KSDcd+zQQmjRKgiAIQm9ZaQ3q18A7lXJ2yoroH3ub3yYUyHDbjmhjR7SxI9rYEW2CZ6UR1LnA/wDblXKuYvka1CnAIiHKZl6tVvttQmgRbeyINnZEGzuiTfC0HUFp7f4Js8H2rcA08DD/ZxqT/eHw5gCHflLb3CjsjGhjR7SxI9rYEW2Cp2cbdXuBbNS1I5sK7Yg2dkQbO6JNW3q3Udffu/RYGjbqAtdo7d7aDSO6RSqV7LcJoUW0sSPa2BFt7Ig2wbPSPqgJ4D+AZ2JKXmz3T+2BCf/+PvBSrd351j30FilYaEeKq9kRbeyINnZEm7b0ZAR1EXAwcIrW7tWNJ5RyHgt8xm9zVjeMWS9zcwsy5LYg2tgRbeyINnZEm+BZKcz8WcA/NTsnAK3da4BXA88OwjBBEARhY9NJxa12URShirCIx2W4bUO0sSPa2BFt7Ig2wbOSg/o+cLFSzqObT/jHPgt8LwjD1sL4+Fi/TQgtoo0d0caOaGNHtAmeldag/hn4OnCNUs4C8KB/fHdMaqIf+21CwczMLFNTk/02I5SINnZEGzuijR3RJnjaOiit3VngaUo5hwOPYXmY+bVauzcHa97qGKAtXV1HtLEj2tgRbeyINsHT0T4o3xGFyhkJgiAIg826Mkko5UwBz9Ta/Y/umbR2PM/zIpGuhN8PHJ7nIdq0RrSxI9rYEW3a0hVhOonia8d+wCXdMKQbZDLZfpsQWkQbO6KNHdHGjmgTPG2n+JRy9lvh9Xt30ZZ1UyqV+21CaBFt7Ig2dkQbO6JN8Ky0BnUH7fc6RVY4LwiCIAhrYiUHNQe8C9gpk4TPw4GvdNWidTA2JgXEbIg2dkQbO6KNHdEmeFZyUL8HhrV2f9vqpFJOmS4thnWDcrlCIpHotxmhRLSxI9rYEW3siDbBs1KQxNeBfJvz9wPv6Z456yOXa2fqxka0sSPa2BFt7Ig2wSMFCzcIUlzNjmhjR7SxI9q0JRRh5qFieDjVbxNCi2hjR7SxI9rYEW2CZ6AcVCLRUWKMDYloY0e0sSPa2BFtgmegHNT8fKbfJoQW0caOaGNHtLEj2gTPQDkoQRAEYXAYKAclQ247oo0d0caOaGNHtAmeVUXxKeU8ETjSf3qT1u5PA7Fq7QxOSKIgCMKuS1ei+DpyUEo5BwL/BTwCuNc/vDfwR+D5Wru3d8OY9TIzM+tJAbHWSHE1O6KNHdHGjmjTlp6GmV8MLAAHae3up7W7H3AQMAt8oRuGdIMB2tLVdUQbO6KNHdHGjmgTPJ06qMcA52ntbqsd8B8r/1wokNIsdkQbO6KNHdHGjmgTPJ06qG3AcIvjKeCu7pmzPmS4bUe0sSPa2BFt7Ig2wdNpGMobgY8r5ZwHXIcJRjgR+Kh/LhQsLGQYG0v324xQItrYEW3siDZ2RJvg6dRBfR1IYspuVP1jUaACfE0pZ6mh1u54Nw1cDVJAzI5oY0e0sSPa2BFtgqdTB3VuoFYIgiAIQhM9zWaulLMJExF4OrADeKvW7qVt2g8B1wNjWrv7rtR/uVz24nHZPNeKcrmMaNMa0caOaGNHtGlLV0JIOlZXKScJnInZqOsBNwJf19otrOJ6nwSKwBbgWOBypZzrtXZvtLR3gAeBsU46L5XkH8aGaGNHtLEj2tgRbYKnoyg+pZwjgVuBjwAnAY/GBEhsVco5osM+RoHnA+/Q2s1o7f4C+B7wEkv7A4EXAx/opH+QAmLtEG3siDZ2RBs7ok3wdOr+P4Yp//4Srd15AKWcceCrGEf1lA76OAwoa+1ubTh2PXCqpf1FwL8AuXadKuWcDZwNcPrpp3HSSScCplZLPB5jYSELmLxZ6fQoMzNzgNnDMDU1yfz8AuVyBYCJiTEKhSL5vBkUjowME41GyWRMH0NDCUZHR5b6iEYjTE5OMDc3T6VSXeojny9QKBQBGB0dJhKJkMksApBMDjE8nGJ2dh6AWCzKxMT4sj4mJ8fJ5fJLfaTTI3ieRzabW+ojlUoyN7ewrI/Z2TmqVTNlOzU1QTa7SLFYAsy3vXy+wOKi6SOVSpJMDi31EY/HGB8fY2ZmdmkD4tTUBJlMdmkxeGxslHK5svTBHB5OkUjEl7I6JxJxxsbSS33UNF5YyCz1MT6eplQqL+uj3+/T3Nx8aN6ndHqUarUamvdpbm4+NO8ThOfzlE6PUiwWmZ6eDcX7FKbPE9C1Qo6dpjpaBE5onopTyjka+KXW7mgHfZwCfEtrd8+GY68CztTafXxT2+cCZ2vtPk0p5/HAVztZg8rl8p4UEWtNLpeXAmsWRBs7oo0d0aYtPV2DygOTLY5P+Oc6IQM0h6CPY1IoLeFPBX4IeHqH/S4Rj8dW+5INg2hjR7SxI9rYEW2Cp1MH9X3g8/6I55f+sccAn8WsI3XCViCulHOo1u6t/rFjMMEWjRwKHABc5e+vGgImlHLuBx6ttXuH7QILC9muDS0HDdHGjmhjR7SxI9oET6epjs7HBElchRkx5YH/wzgd1UkHWrtZ4DLgvUo5o0o5JwPPBr7S1PRPwMMwUX7HAq8EHvAfhyatkiAIghAsHY2gtHZngWcr5RwC1KL2btLa/csqr/da4IvAduAh4Byt3Rv99akfau2mtXbLwP21FyjlTANVrd37W/bYgBQQsyPa2BFt7Ig2dkSb4Ok0SOKdwIVau4tNx4cBR2v3vQHZtyo8z/MikmK4JZ7nIdq0RrSxI9rYEW3a0tN6UO8CWmVFHPHPhYJaGKSwM6KNHdHGjmhjR7QJnk4dVITW5dQfCUx3zxxBEARBMLSdRFXKWcA4Jg+4XSmn0UnFMPWgPhOceatDRtt2RBs7oo0d0caOaBM8K63ynYsZPX0ReBvQOKYtAndo7V4bkG2rRgqI2RFt7Ig2dkQbO6JN8HQaJHEqcLUfYRda5ucXvPHxjvLKbjjm5xcQbVoj2tgRbeyINm3pXSYJrd3/68bFgqaWW0rYGdHGjmhjR7SxI9oET6dBEoIgCILQU3pasDBoKpWKF4tJfqxWVCoVRJvWiDZ2RBs7ok1bgt0HpZSzn1LOLhWnUkv1LuyMaGNHtLEj2tgRbYKn3RTfX4HdAZRyrlTKmeyJReugVs9E2BnRxo5oY0e0sSPaBE87B7UA7OY/fjyQCNwaQRAEQfCxrkEp5Xwb+FvgJkzV22swe592Qmv3iUEZuBry+YKXSiX7bUYoyecLiDatEW3siDZ2RJu2BB5m/hLgFcAhGAd1C7DYpn3fiUYlKNGGaGNHtLEj2tgRbYKn0426PwOe65fdCC3T07OeFBBrzfT0rBRXsyDa2BFt7Ig2benpRt0n1B4r5aQBzy9AKAiCIAiB0PE+KKWc1wFvAfbxD90NfFBr91MB2bZqMpmsl06P9tuMUJLJZBFtWiPa2BFt7Ig2benKCKrTKb5/Ad4KXAj8wj98CvAG4P1au//eDWPWixQstCPF1eyINnZEGzuiTVt6N8UHvAY4W2v36w3HfqqUcyvwfiAUDmpmZk7mhC2INnZEGzuijR3RJng6DUPZA7iuxfFfA1u6Z44gCIIgGDp1UFuBM1ocPwMTfh4KolEZbtsQbeyINnZEGzuiTfB0OsX3buCbSjmPA672j52M2R/1DwHYtSYmJyf6bUJoEW3siDZ2RBs7ok3wdDSC0tq9DDgJuB/4O//nfuBErd3/Dsy6VTI3N99vE0KLaGNHtLEj2tgRbYJnoMptyEZdO7Kp0I5oY0e0sSPatCXYchuCIAiC0E8GagQlBQvtSHE1O6KNHdHGjmjTFhlBNSP1WeyINnZEGzuijR3RJngGykFJhUs7oo0d0caOaGNHtAmegXJQgiAIQp8pV+G87wx3o6tO90GhlPNC4EmYrBLLHJvW7rO6Ycx6GR3tiiYDiWhjR7SxI9rY2fDaVKpQrECxbH4vliARhUwR4DDg+vVeoiMHpZTjAq8HfgbcC4QyskISN9oRbeyINnZEGzsDq02lan4iEdiegXxx5zt+LAqFMlT9E9EIxKNQ8Iyzgnw3TOl0BPVS4EVau9/uxkWDIpNZZNOmoX6bEUpEGzuijR3Rxs4uq43nQcWDUsU8BuNgolHIl2D7gu9kIiYWL9XCTVQ9SMaNo6r1uVAw03t3zgA8ki6kwevUQUWBP6z3YoIgCEIPKJahUIHFIuRKsEcahuJmZPTAgj8N1zQsiviHEjFIJ1fovwQPZmBHFmZzcNtDMLds0HQG8J/r/TM6dVCfA16MyckXWpLJXfDbTI8QbeyINnZEGzuh0KZSNaOWZMOtvFCGO6br03TxKPx1uu6A4lFIW2z3PJgvwN1zMLtoXpcrL29TrRrH1Dztl4obO8aScPfctd348zp1UJPAGUo5pwE3AKXGk1q753XDmPUyPJzqtwmhRbSxI9rYEW3s9E2bctU4iVIVdmSgChy4yZyrerBtxkzZDTeMgpJNt/qZRRPUUOtvRwYeWoS7Z2E6t7IN0QhsScPUMEwOwwGbYK9xczxXgk9efVk3/tROHdSR1Kf4Dm86F5qAidnZecmNZUG0sSPa2BFt7PREm6pnRkKeBw9loViFxUL9rhuL1kdR8SjkimZtqXGKruxH2JWrcNMDcMuDML1ov2Y8aqYEp4ZhvynYNLJzm6lhSCW6+qe2NKWTRlq7T+jGxZRyNgEXA6cDO4C3au1e2qKdA5wF7O+3+5TWrtsNGwRBEPqC50G2WI98K1dNUMKmkfrNvuZswLS7Z9YPZsAEMcQjMDxkRio1MkW4a9YcK5XrgQsPZY0z+u3dZtqvkVS87ngimMeTw7DbKDxs0qxXhYBVWaGUkwIOwch1m9buakMJPwkUMVV4jwUuV8q5Xmv3xqZ2EUzk4A3AwcBPlHLu0tptu+gWi8m+YxuijR3Rxo5oY6elNp4H+bJxKtkiZBpGOx7GgUSj9QPRCMznjYMqVKBSi57zaheB0RUCFkbixqlVgAezcOuDxmE9kKm3ScVN8MPUMJy4n3FCu8B721GyWKWcBPB+4FxgCONACsBFwNu0dkttXl7rYxSYAf5Ga3erf+wrwD1auxes8NqPAxGt3X9e4TKhmW4UBGEDUSwbh7BY8p0MxgEMxZanTY22cArlqhktxSOtz9uYWYRr74R75szrPa+2Sda/VgSO2AJH7AH7T5mAiV5g1qAO5+PP7VmY+QeBFwGvAX7hHzsF+AAmBP1NHfRxGFCuOSef6zFVea0o5UT8a33Wcv5s4GyApz71dE444QTALGDG4zEWFrIAJBJx0ulRZmbmAPNeTU1NMj+/QLls/qEmJsYoFIpLSSBHRoaJRqNkMqaPoaEEo6MjS31EoxEmJyeYm5unUqku9ZHPF5bydI2ODhOJRMhkzJxvMjnE8HCK2VlT7CwWizIxMb6sj8nJcXK5/FIf6fQInueRzeaW+kilkszNLSzrY3Z2jqo/fTA1NUE2u0ixaL47VKtV0ulRFhdNH6lUkmRyaKmPeDzG+PgYMzOzS1sjpqYmyGSylEpmemBsbJRyuUIul1/SOJGIMz+fWdJ4bCy91EdN44WFzFIf4+NpSqXysj76/T5lMhm2bNk9FO9TOj1KtVoNzfv04IM7SKfToXifIDyfp3QqxeyOGeJxM3pJzRVJxuPMlQoQixCPxRgfHmUms0BtEDCVHiOTW6RU9t+n4RHK1Qq5gtFn2EuSKMNCxrxPQ0WP0bxHJpcjki0Sy5RIlaE8nYVChXiLYAYvGqF69J4s7jNKeSpFfHKEdGqYmcyC/z5FmEqPMb+Ypew70omRUQrlEvmi0WckmTLvU85oPBRPMJpKLfURjUSZTKeZy2apVP0+RtPki0UKpSLky2zaybK10ekI6n7gFVq7VzQdfwbwBa3dvTro4xTgW1q7ezYcexVwptbu49u87j3AczDVe9umD5aChXakuJod0cbOhtLGd2gUymYja5T6iGY2Z4ITwETNlSpMZxfYNDpmjiX9KbR25Er1vUKeV99DNJ835x5aNJtdOyUehYfvDsc/rL5mVAv17id9GEFNALe1OH4bJgS9EzLAeNOxcWDB9gKlnHMxa1GnrOScBEEQOqbqmY2mwwkz/FvIG2dRIxIxCwaNs2LJuHke9R97heXRcp5npvnumIaZnPmda1j9qHawAhGL1B1MLGqCFmpThZtHIB6D3f1je6RXdoq7OJ06qOuB84DXNR0/n84zTGwF4ko5h2rt3uofOwZoDpAAQCnnFcAFwOO0du/u5AKTk83+T6gh2tgRbeyESpvaOkuNaKT1ukqlatZnqsBCDlJD5mZerprMCrW+tmeMQ6j1MTq0qnWaydG02Y901yz8apsJUMi1WY6PRoyTqUXgjafqkXOxqHE846mBdzqroVMH9WbgCqWcJwO/9I89GtgbeFonHWjtZpVyLgPeq5TzSkwU37OBxza3Vco5ExOU8QSt3ds7tJFcLs/oaIuYfUG0aYNoYycU2uRKcO+8CURodFAjQ+YGP+KPgqqecUKZgnE+CX/kMZfzp9a8BgfkmWwK7SLZiv5U32wObn+oPgKaycG2GSK18O/GkVF6CA7eDFMjJjChcQ9RJLI8PFxYkU73Qf1cKecwzAiqtlH3W5j9Sfeu4nqvBb4IbAceAs7R2r3RX5/6odZu2m/3PmAzcJ1STu21X9XafU27zguFYv8/TCFFtLEj2tjpuzYzi2Zkgld3RDVyJRPBFqGexgffUY0N1deP0iuMSDzPbFytVOG+Bbhv3qwFmaSnVpYs2TQMD98DDt/DOKRBzXLeBzoKkthVkCAJOxtqsXuViDZ2AtUmV6qPiCqeWQeaz5upuSiQTEC2YBb+u7VxtOoZp7YjC7ftMCOzStVcvxXJuJkGPGBTfTSUisMBm5j2CmwaGzPrQkKdXgRJKOUcB/xBa7fqP7aitfu79RrSDdJp+RZsQ7SxI9rYWZU2nmfyw4FJuZNtUxK94hln1DjlFo+aDavRhum6seT6RiQLebhjxmyc3TYL987Vc9A1MjpkAiYmUmZqbigO+06YKUQL6VJcnFPAtPta8htgT8x03G/YOaalhgeE4l0apNFgtxFt7Ig2dpa0qa2zNKbiMQ3qdYWyJbPeA37Zhmh755JuE5QQjZj1o9UynzeZFAoVM233lx3L7QXjiB42CQdtMiOjoZgJTFilI/QkL8Byqv5ItFJduW2HtHNQBwIPNjwOPdlsjmRyhbQgGxTRxo5o00ClavbjFMwoIzs7R3J41CQprd2QG2/ktZ2+np+2Z5WRcOuiUDbrRAU/eOK+BZMMtdkhPWzSjIT2SJtR0dSIGa2tk2w+TzIRgpIb/aD2xaTRIcWifrmNFMD93biM1UFp7d7ZaA5wl9buTl8ZlHL264YhQn+45JJLAHj5y1/eZ0uEUPBQFrZnIeWPXqqeCTZI92HzZ7a4vCheY0G8UsWsH7XaW7TfJOw9YYIqDtrcdppOaEGt5HstBZNt3mxkyPxfjAyZEWgyXo9S/Phz57phSqf/dX8F9sJM9y2hlLPZPxeKKb5QFBALKaKNnQ2tjef5JR08E5CwPWum3vwbTbKa6spoY0UqVRO8MF8wj2/ZbtaM2hHBjIgmfAc0kTKjpIM29ySce5cePVX9EVCNSrXu7KN+xvSJhFkTrG1QbiQa6Umy2U4d1FIQZxNpYLUZzQMjlZJpGhuijZ0NqU2pYn52ZM2+oRqjiWU399TQKm7CD2ZMBFelakLD22VOWCjU0/pUPRPa3VwSIhaBLWNmSm7K39Basy2COTfSPyexKm26heeZzcZexGjQHHrveSYIJOI/rnmWpZGQfyuPRpd9ESEeq2c8H4qFJtN5WwflZxEH45w+oJTTWOUqBpxI55kkAmdubkHChS2INnYGXptixRStq206LVdMSYjaulHa7qDnshk2RVP+eoNf6ru25uBhNq3mSyYgYaURz0psHjFOB8xo6FH79qQo3lqZy2bYNOZn2qhUjXOOYG72QzGj9zIn7Ucm1g7V2pb9Db81h9I4WmkO4PGoV7FdKJj3I0LdSVWrxqFH/ZRJtfdqYrjudGrrhrvApuGVRlBH+78jwBGYWk41isDvgAsDsEsQNhaNIdoR6jeTxptILdVP7VzteW26Jl+uR9Glk/UpnLmc+caNZ74hRyMmkq35BlX14Pp7Tclwz4NChckHM6bfTtlr3EwJblqh4moiZkZENROmRlpXbg0LuVLDXin/92IJIv4tsTbai0TMiDRThKEoJJpuseMJ0xZMf4tFM5KJR82+r1jEvicrHjXvWe09rGXSKFbq07AR/Cm5ds4n/I6pRlsHVaukq5RzCXC+1u58T6xaI1JczY5oY6ev2sznTTBAqbJ8qo2IuVlNpMzNsUo91U8yZorTlSs795eMm/vnjmz9ppVKtJ6yqdUPqq3//PE+uHv52nYUTD+j/nTW1LBxbjVGhoyNkQjsM2HWgLqN55npv6HY6uolrfVaxUp9H1btXp5OLg+2SMSILaZg3B/xxaJ1h99LR5sMQfbyAOn0L3srJvP4MgellLMvUNLafaDbhq2FiYkQJbYMGaKNnZ5pU6nC9oX6iKRahXzFOKJWU22Vqolci0WNpxjxHUPVLwWRbLN3p3EPkecZZ3bXrHFcRX/taXtmeWRc7XWPO8g4pGjEjHLSyf6uSSyWzPpIvrx8yiwarWvSSK2qbW10WVtfaaZQ9kPSvYZpNz9UfsiPSpsctmo8MTy53r9MWIFOHdRXgW8An286/hTghcDp3TRqrczOzjE5OdFvM9hRhE0tZlD6SVi0CSM90cbzAwHm8/WbZTRq8sTZnEwsCsMtHEOsw3+sQhmuuQN+f489YCEVNzfjqRE4ZDc4bLdlJcZnMxkm+z36rnpm+qw2OgTjvG/b0bp9tmRy8SUT5u/bvmAiFJuWgxgZqpesqE2drWJtRj5TwdOpgzqenUttAFwFuN0zZ31UO6m3EjAVD55+A6Si8LUj4WGpfltkCIM2YaUn2szmzc94QBGD5Qrc/KBf+C5rRksLheWOabdRs2k17tcZmhqGPcfb3pCrXveyAqxIpbq8hEaxbDYIx/0NoI2OsrZm05jPL+IHGMQiZh9Urf3IkNGn2tCulqliHZuK5TMVPJ06qDjQ6pOVshzfkNy6CKddD3fmYY8ETAzu1LCwGipVeGDBhHB3i1pF1lt3mHQ+2zOt2+0zDicfCPtNde/aNqpePVS8k5t3c4RaPGbW4mo+IxU3m24TlrDn3UdN9GAt1LxaNVN7W8Z2dma92MsldJ1Ob6G/As7xfxp5HXBdVy1aB1NT/Rtuf3s7nPFnKPmfuQv2h/EQOah+ahN2AtdmNrc8+m4t5EqmJtG2GbNucv/CzutHu42aG/poEg6cMqHF61xAn0qPLT9QW9dZek49WKNYNdF70Uh9hNOORENgQcSfYquNiCKR5ZkJWjGeMj99Qj5TwdPpf+/bgCuVch4BXOkfeyLwSODJQRi2FrLZRdLp0Z5f9+YsnOk7p9EofOUIeM7uPTejLf3SZlcgUG1yJeNMVjN6ml40UXVbHzR58RbyrbfJDyfMutEhu9VHGmulFr1Wqhjn4G8AzWYXSceG6k6p6i0PwIhigigiEeNwNo+uLxffcHj3PTUjn6ng6bRg4S+Vch4DOMDz/MO/B16rtXt9UMatlmKxTbnlALnwLih6cOQIfPkIOD6EAXP90mZXIDBtciUztZeItg6Prlbhnvm6M8qXzbFMizIV0YhJ63PIbsZ5jKXMnqP1ROJ4HuQaot3Gk2Z6LFMwoz6gWCzC7mkTRBHxQ9+7VZtpF0c+U8HT8X+a74heHKAtuyTlKnzXDyb6yCHhdE5CgNRSy9TWXh7MwnDcX+Cv1MOWK1WzXrQ9Y15z9xzcP996ZJSMw97jZl/RgZvqKX7WmyW8tkZTy1hQ9UzfI0PG8dTWcsZTJsgAYHoWBjnLhhBqVv1VSClnT2BZEiqt3W1ds2gd9GO4fdkO2FGChyXhST1Yh14rgz4VsZ6s7GvWxvPgzmnIlMwN3sPsTSqW/Z3+CTNyum8efnKLX7q8ialh2HfSOKSHTZoAgW7vOyqU61Fsu/lh1fGo+b3ClNqg/9+sB9EmeDpyUEo5E8DHgRfQ5Jx8QpHNvFrtYUgsJqT8jX8xj8/cEu5AoV5rsyuxJm0qVbM2lC21Dh2vRdn9/Hb467Q5NpGCI/1/lKE4HL5HcGsupUp9VJeKw27jxiG1ybvXCvm/sSPaBE+nI6gLgWOA5wCXAa8A9gHOB94YiGVrYHEx19PM1NfMwd0F2CcJ79i/Z5ddE83alMtl4vF4yxFH7VwvaXfNoO1Z9f9NqQK3T5vfjZkMHsqa9aRa+HctW3c0AsfvC485YH2BDDZqwQ1QH8mVq7DXmElzlFr7rvFef6Z2JUSb4On0U/804EVau1cp5VSA32rtfkMp5z7g1cC3A7MwpPxyDh73e/P4iZMwsoutG8fj8aVpsWb6Ubywb/ZkCvDAPGT8hKtVDw7cvPNweDZnshEk4iZ3XrUKXtVM8W19ELbuWF5fB0wf+03B6YeteuRipVZIzvNrOHmeGYVN+tFzhYqJrBtL1fPnCcIuSqe31UmgVmF3DtgM/AW4FvhC981aG736NlOowstvrj9/8ZaeXHZdDOo3vXWNBBfycNcsqWq0vm5UqpjyEelkPbQ6V4K7Z014dbVgHNKtO0y58UZScdh/yuRvO3izibJbb2BDpWrsqWUjj8f8tS0/+KJWzTQgBvX/phuINsHTqYO6DTgI2AbcBPyjUs6vMSHn0wHZtmp6URn1hgw844b61N4vj4N9Q5LOqB2DWjV2zSOvWgbvVIJkJA4x/yYfq5rRUqli9i95npmqu+kBsz8pV4IH/KwNMT+D926j8Ii9TT2jtTokz8+eDfXyGR7GIe2WNmtWQ37S0/U6vVUwqP833UC0CZ5OHdSXgEcA/wv8O/AD4FzMZML5QRi2FoIuPFesmg25d/tLC+89cNdwTrBcG8/ziPTwJrcS+XyeVKr7QrZd16pUiFc9iEeZ3zHNVDlh1o5qFWYXCv7IBVNPqbFGTzxqiuk9Ym8T+NAJFX9aLl9hKba8ti5UxRyrBUwk4/Ukpn2sGAsboJjjOhBtgqfTjbq64fGVSjmHYxLI3qq1+8egjAsb7jb4UxbGY/CVI+GZm/tt0eqpVqtce+21nHzyyW3btXNi3QxoqFQqXHnllTz96U/v+DWdsuLo6o5puOYOJucLLdss49Dd4Kg9jXPaMtZ59F2tem00ApGoCVxIxIyPyvrXHUuakdIA1/URhLWw4idCKScB/AJ4qdbuLbC07ykUe58aiceDmYuvevCJe+DtfzXPP3QwPGu3QC4VGPF4jGq1ylVXXcXtt9/e1kF14oC6EdDgeR7XXHMNDzywtnJiO3bsYLfd1vFG/OgWwM9NumnYpOmZ9MtpJ+Nmyg7MKKbTgINK1fyU/NLeUcw04FhqeWluMI4p5AT1mRoERJvgWdFBae2WlHIOpPWe91AxPj62cqM1cOE2eMvt5vEzN8Or9g7kMl2n0dHUtDn11FM59dRTKZfLbZ1JLyLq/vSnP/GXv/yFeDxOqVSy9r2wsMA999zD4YcfDhjHdtNNN3Hddddx1llnrd2AVBxO3A+O28eMYNZC1TMOqVA2n5CEv0405Ts1WybuXYSgPlODgGgTPJ3OKXwZeBUmF19omZmZZWpqsqt9/ny2PnI6dRI+dmi4ChG2ox+h26uZ/jv66KM5+uijl861YuvWrey1115LzgkgEolw5JFHcuSRR67P2LOOh2ScmYdmmRr1S5XHovZAhFKlHt5dbtikOZKAvSZMQtha0bsBIYjP1KAg2gRPpw5qFDhTKec04LfAspwtWrvndduwtdBcXma9XHQ3nHerefy0TfD9R3RezHSjspJTXK3DPOywwwD7iO4lL3nJOpxtBKJRvD3SsPsm44Dm/cKC8Ygf2g1LkwfDCTPSisdMcESlah7vQhm4V0u3P1ODhGgTPJ06qCOA3/mPD2o6N5Bv019zcMFt5vGhw3DhIbuWcyoUCiST4V/jWC/xeBy++yfjWK6/12RVqPHx57Z97YMjZXY/cE+TELUWLZdO1kueJxtGQ80VXQVBCByrg1LKeRxwjdZuWWv3CT20ac10o4DYzVl43p/gpkXz/PRNcEWIR062KbVd2jnVMjoMxUz6oN3S7dv/9Nb6480jfmaFYcr5onV0VSqV2P3AfQGWhwrHopTHh3qe6imsSFE+O6JN8LT7FP4M2AvYrpRzO3CC1u5DvTFrbWQyWcbGVriZtaHqwT/dXHdOmxPwoYPC65ygf+tMgfR954xZ4MuX/CqtEbjhPnjhse1f9/DdjTM7ZHc4aNPSqCd+z7zZWDs6ZCLzhhMQi1KNRUgkEqFK9RRW1vuZGmREm+Bp56BmgAOB7cABmIDZUFMqtV5o74Q78/CcP8IfMjARg+8eDY8c613Z9n4mS211PdtNeidbPK9ephvq02NroeTXULrhPlM36Z450/dKDuqZR9VtKZTrm2ojEZNXrylEPPT/yCFiPZ+pQUe0CZ52d73/Av7PTwjrAb/xE8XuhNZu87pU6Gm+0e6fgt+fYB7PFctMBFA1tJ2j6XZwQe16HTuaJltsLJ0rV02S1WIVFov+ZiLgyD2tr12Rn/4FbtthNrbW2KuDCpCZgj9q8sx+o1r27nRyeXlyQRB2KdrdhV8DfA84FPgIcAmw0Auj1srYWOcFxPoxNdbrazY6mlKpRCKRaHluTWQKMJMzG1ILpaVptXLOvu5TbrPXqTyTJX7j/ebJWBIevb9J97N5hHKhfZ/xA/xpvaFde89RGFnNZ2qjIdoEj/UupbXrAZcDKOUcA3xYa3ddDkopZxNwMXA6sAN4q9bupS3aRTA5/17pH/oCcIFvk5VyubLsJlysVhiKtv4GXaxW7AvoXgXPo+1r13Ku5LUcgPaEZm2WkSnUM3eDGcH88V64ebvJSbeT6p5xTvmdpzjiX7PbEI9Hd07nEwE2jRAfHjJOaUsa9hyvj4iqEC964JVabnqNJxIwuFHePaNcmqNaNYuvHh6lwr2USw9RLJQYGlq7wNXKIvncbXhesVumdoznVSjm76JaDebaXrVKJCpfiFpRyN3670edcPUF6+2n01x83fp6/0mgCGwBjgUuV8q5Xmv3xqZ2Z2OKIx6DuT3+D/BX4DO2ji/5xhfHjj7oUEZG6sk7jzzqBF6z7YqW7T+z39PbniNC2/PtRkJt++0xD11tJCsWiizmKsuTnoIpOTGbs3dg2yxfm3mLRc1G1VabU6MRGE+ZKJORIZgaab1nKLWAl6iQX/yLqbMUBa9SIZ+7jUohYwq7dJnDH/n9tudv/v0zu3/R0FKhUp7rtxHCYHH4yk1Wpmcr70o5o8Dzgb/R2s0Av1DK+R7wEqDZ056FGbHd7b/2w5hMFlYHFfcqjxzmArzFxqNXd/EvWM6vntS6CFSv47/arTPlc9u5f+gr5skQrZ3NXoGZtjNF/6eZPtwb87ntbXWrlENTRaY3ROJEo8NLT2OxURLJvSmVKgwl1nGbiERIpg4gGuvPdNhQcm/iQ3sE0vfCQkai+Cxs26re1o1+ernZ4zCgrLW7teHY9cCpLdoe5Z9rbHdUq06Vcs4Gzh4ZGx5+5OY9ifjFC3wCG3+f9FNLgtMee6h4PM7d33jlDIAXrRS8WKXUeH6MgwHIlSKjyVTxoWqitCwLiBfxqsWx2fuqyfyy433Aw4yStzccuwsIJFv+bX+qb+L90c8Of8FTn3DzN4O4zi6DV77niON+lGk+rJRzttbu5/phUth557tFGxtfuPSxZ+sTaJ4ZWzW9dFBpYL7p2Bytv9enWf69eg5IK+VEmteh/H+QzwEo5fxGa/f4xvPtptVWmnJrd75dUMNaX7fWc/u+cOWixq20EQxfuNR59lEnXP2v/bYjpJyN//kSdkK0sdMVbXq5wpehvnJRY5zWkYHNbceBzEpBEoIgCMLg0EsHtRWIK+Uc2nDsGGg5DLzRP7dSO0EQBGFA6dkUn9ZuVinnMuC9SjmvxETxPRt4bIvm/wG8QSnnCszaxBuBizq4jAy37Yg2dkQbO6KNHdHGTle0iXg9zBnv74P6InAa8BBmb9OlSjmnAD/U2k377SLAB1m+D+otMsUnCIKwceipgxIEQRCETpFt0IIgCEIoEQclCIIghJKBqMrWaY6/QUQp51zgZcDRwNe1dl/WcO5JmPRS+wG/Al6mtXunfy4JfBr4e2AR+JDW7kd6anzA+H/jp4AnA5uA2zD/Gz/0z290fb4KPAkYBe7H/I1f8M9taG1q+FHHfwS+rbX7Yv/YGcAHgN0wadheobU77Z8b+HuRUs7/Ao8Gask479Hafbh/rqvaDMoIqjHH35nAp5VyWmaeGEDuBd6HCT5ZQilnN+Ay4B2Ym/NvgG80NHk3JlP9/sATgDcr5Ty1B/b2kjgmG8WpwATwduCbSjkHiD6AuZEcoLU7DjwLeJ9SzqNEm2V8Eriu9sS/r3wWk6JtC8ZBf6qp/Ua4F52rtZv2f2rOqeva7PIjqFXm+Bs4tHYvA1DKOR7Yt+HU84AbtXa/5Z9/N7BDKedwrd2bMfkOX6a1OwPMKOV8HjMS+1EPzQ8Urd0s5mZa4wdKOX8FHgVsRvRp3Fvo+T8HY/TZ0NoAKOX8IzALXAMc4h8+E/i+1u7P/TbvAG5SyhnDpFnbsPciAtBmEEZQthx/g/itZTUsy2fo36xvA45SypnCpIntKN/hoKCUswXz/3Ijog8ASjmfUspZBG4G7gOuQLRBKWcceC/whqZTzdrchhkVHMbGuhd9QClnh1LO1Uo5j/ePdV2bQXBQq8nxt5FozmcIdV3SDc+bzw0kSjkJ4GvAl/1RgOgDaO2+FvN3nYKZ1isg2gD8K3BxraJCAytpsxHuRW8BDgL2wWzI/b5SzsEEoM0uP8XH6nL8bSTa6ZJpeJ5vOjdwKOVEga9gvs2d6x8WfXy0diuYKZcXA+ewwbVRyjkWE1jzyBan22lTbXNuYNDa/VXD0y8r5bwIeDoBaDMII6jV5PjbSCzLZ+iv1R2MWVuYwUznDHy+Qz8rycWYhdnna+3WypGIPjsTx9eAja3N44EDgG1KOfcDbwKer5TzO3bW5iAgibkPbdR7kYepjd11bQYik4RSzn9iRKrl+LsCeGyLSr0Dh1JOHHNjeRcmSOJVmPDPKUwt2lcAlwPvAU7V2n20/7p/Bx6DqVy8BfgZ8HKt3UFb6P4M5n/iyf7ibO347mxgfZRy9gCeCPwAyGFGDJcBLwKuZWNrM8Lyb/tvwjisc4A9MPo8A/gdJmotrrX7j/5rB/pepJQzCZwE/B/mPvNCzDTfI4EEXdZmEKb4AF6LCbPejsnxd86g/EN0wNsxzqnGi4H3aO2+Wynn+cAngK9i9rL8Y0O7d2H2styJuUF9cFBuMDWUcvYHXo1ZV7lfKad26tVau1/b4Pp4mBvuZzAzKXcCr9fa/R7ARtZGa3cREyINgFJOBshr7T4IPKiU8xrMeuZm4P+xvEzpoN+LEphtLYcDFUxwzXNqwQ/d1mYgRlCCIAjC4DEIa1CCIAjCACIOShAEQQgl4qAEQRCEUCIOShAEQQgl4qAEQRCEUCIOShAEQQglg7IPShD6glLOHcAntHYv7LctrfCz3F8HHKi1e0efzRGEVSEOSgg9ftaHezDZMYqYEghHaO1u66ddQaGU8zKM00uv1LaL1zwY+BdMMbk9MAUMrwM+orV7TUO7pwBvBk7AbNrcitl8eZHWbrWhXasNltdr7R4b1N8gDB4yxSfsCjwGc3PLAscB04PqnPqBP8r6Hab0wTnAkcAzgd8CFzW0ey0mPc1vgcf67T6FSYX0tRZdvwpTmqP286TA/ghhIJERlLAr8Fjgav/x3zY8botSzjMxBQuPwiQ4vRSTBqqolPN+4Clau49qes01wG+0ds9TyjkB+DeMUxwCbgAcrd1r21zTA/5Ba/fbDcfuoGEaUCnnDZgCfwdjRoM/BN6ktTvr19a5pKEvqKeuGsKUgTgTU+n2RuDtWrs/brjWU4GPYnLHXYdJSdROowjwJeB24GQ/s3mNG5RyPu232xfQmJHSmxvafFYp5wHgO0o5l9WKHPrMau3eb7nuO4F/AvYEZoCfaO2+tJ2twsZDRlBCKFHK2U8pZ1YpZxZTNO7V/uP3A8/xz32qzeufgvlW/wmMg3oF8Pf+68HkmDtOKefwhtcchBmtfdU/NIYp03EKcCLwB+AKpZzN6/zzqsDrfbvO8PuujVSu8c8tUh951Na3LsGUrz8D+Bvgy5haPMf49j8M+G/gfzDJOC8CPrSCLcf6drhNzgkArd1Z/+E/YJz0Tv1p7f43cKtv14r4ef7ehMnNdijwd8CvO3mtsLGQEZQQVu7F3DzHgd9gMihnMU7iGcA26rWJWvE2zE33Ev/5bUo5bwG+qpTjaO3+WSnn95jRyDv8NmcAW7V2fw2gtXtlY4dKOf+MKVv9NOpObNVo7X604ekdSjlvBr6rlHOWP7qbA7zG0Ye/RvQi4ICG6c1PKOU8GZMQ97WY6bltwHlaux5ws1LOYZhRl41a+YObVjD7MGBea/dey/mbgIc3HfuKUs6XGp6/Wmv3a8D+mBHtT/zyJ9sw77EgLEMclBBKtHbLmJv3C4DrtHZvUMo5GXhAa/fnHXTxKOBE3ynViALDmGml+zBO5nXUHdSZNKyl+CUp/hV4AqasRMx//X7r+duUcp4IvBU4Apjw+x3y7bI5gOMwNXf+3JCVHUy9nZojPQL4pe+calinI30iqzB9tZmlHaAxy/kD/u9vAecDf1XK+bHf5ntau4VV9i8MOOKghFCilHMj5pt2Aoj6JQ/imKJnGeBOrd2j2nQRxSzef6vFuQf9318HPqSU8xhMSY7DWT4y+jLGMSngDr/NTzHOxEateFsjiYa/a39MjaXPA+/ElB04zrelXb9Rv+8TgFLTuVyb163EVv/3EcDvV2g3oZSzj9buPS3OH8nOxefu19r9S3NDrd27lHIejgmaeDLwYeBdSjkn+YEwggCIgxLCy9MxN/afYsKafwv8J2ZB/0fsfJNu5nfA4a1ukDW0du9TyrkSM3IqANdq7d7e0ORvMdNllwMo5WzBrAm148HGNi1eczzGEanamo9Szt819VHEjKoa+T3G8e2ptfszy7VvwlR+jTSMoh69gr1/AP4MOEo532heh1LKmfTXob4NfBAzKnp9U5vnAodgwtQ7Qms3j3HUl/sFEO8HTgZ+0mkfwuAjDkoIJVq7dyrl7IkZwXwXM3o4Cvgvrd37OujivcAPlHLuBL6Jqf75N8CJTVFoX8V8gy9iIvYa2Qq8WCnnV8AoJkCguMJ1rwRe50cDVjBBGfmG87diRkOvV8q5DONAXt/Uxx1ASinnNIxjWtTa3aqU8zXgS0o5b8Q44E2Y8uS3a+1ehik++Ebgo34AydHAa9oZq7XrKeW8HFNc7hdKOf+GcXQjmLW2FwDH+6OeNwIfU8opYkaXi8Bpvi7faIrgs+Lv84pjCiFmMFVZS742grCERPEJYebxmPWnPCbS7e4OnRN+6PUzMOtHv/Z/LsAsyDdyGeZmvDvwjaZzrwDS1EdvX8Q4j3a8EROy/b+YUccXMBVEa3bdgFl/eQNm5PJKTERbo+3XYJzN1zEjsppDfTkmku9DmEqmPwAeh6lsix888TzgqcD1mKnJC1awFz8o5FF+n5/BOKgfYDQ/t6HdRZiIuxOAX/rtzsVU2O0ogs9nFhNifhXwJ0zgyfO0dv+6ij6EDYBU1BUEQRBCiYygBEEQhFAiDkoQBEEIJeKgBEEQhFAiDkoQBEEIJeKgBEEQhFAiDkoQBEEIJeKgBEEQhFAiDkoQBEEIJf8fzkBmcYMGghIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.ylabel('fraction of top 100 COFs found')\n", + "plt.xlabel('# evaluated COFs')\n", + "\n", + "plt.plot(np.arange(1, bo_res['nb_iterations']), y_top100_mu_BO[1:], label='BO', color=search_to_color['BO'], clip_on=False)\n", + "plt.fill_between(np.arange(1, bo_res['nb_iterations']), y_top100_mu_BO[1:] - y_top100_sig_bot_BO[1:], \n", + " y_top100_mu_BO[1:] + y_top100_sig_top_BO[1:], \n", + " alpha=0.2, ec=\"None\", color=search_to_color['BO'])\n", + "\n", + "plt.plot(np.arange(1, es_res['nb_iterations']), y_top100_mu_es[1:], label='CMA-ES', color=search_to_color['evolutionary'], clip_on=False)\n", + "plt.fill_between(np.arange(1, es_res['nb_iterations']), y_top100_mu_es[1:] - y_top100_sig_bot_es[1:], \n", + " y_top100_mu_es[1:] + y_top100_sig_top_es[1:], \n", + " alpha=0.2, ec=\"None\", color=search_to_color['evolutionary'])\n", + "\n", + "plt.plot(np.arange(1, rs_res['nb_iterations']), y_top100_mu_rs[1:], label='random search', color=search_to_color['random'], clip_on=False)\n", + "plt.fill_between(np.arange(1, rs_res['nb_iterations']), y_top100_mu_rs[1:] - y_top100_sig_bot_rs[1:], \n", + " y_top100_mu_rs[1:] + y_top100_sig_top_rs[1:], \n", + " alpha=0.2, ec=\"None\", color=search_to_color['random'])\n", + "\n", + "# RFs\n", + "plt.errorbar(rf_res['nb_evals_budgets'], y_top100_mu_rf, yerr=np.vstack((y_top100_sig_bot_rf, y_top100_sig_top_rf)), color=search_to_color['RF'], marker=\"s\", label=\"random forest\")\n", + "plt.errorbar(rf_div_res['nb_evals_budgets'], y_top100_mu_rf_div, yerr=np.vstack((y_top100_sig_bot_rf_div, y_top100_sig_top_rf_div)), color=search_to_color['RF (div)'], marker=\"s\", label=\"random forest\\n(diverse train set)\")\n", + "\n", + "plt.xlim([0, 500])\n", + "plt.ylim([0, 1])\n", + "plt.xticks()\n", + "plt.yticks()\n", + "plt.tight_layout()\n", + "plt.savefig(\"search_efficiency_top100.pdf\", format=\"pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "worldwide-radiation", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e0743c8ddd05c2f50db257c3532a4630d3db7e61 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Thu, 1 Jul 2021 14:57:38 -0700 Subject: [PATCH 11/29] select COF not random point in evol search --- new/BO_run.ipynb | 4902 ++++++++++++++++++++++++++++++++++- new/evol_search.ipynb | 1345 +++------- new/prepare_Xy.ipynb | 26 +- new/random_forest_run.ipynb | 526 +++- new/random_search.ipynb | 18 +- new/viz.ipynb | 68 +- 6 files changed, 5728 insertions(+), 1157 deletions(-) diff --git a/new/BO_run.ipynb b/new/BO_run.ipynb index 7ea9f09..ff043d6 100644 --- a/new/BO_run.ipynb +++ b/new/BO_run.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "rocky-blackberry", + "id": "relative-poland", "metadata": {}, "source": [ "# BO runs" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "grateful-trade", + "id": "thermal-wichita", "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "white-optimization", + "id": "solar-norman", "metadata": {}, "source": [ "load data from `prepare_Xy.ipynb`" @@ -38,7 +38,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "characteristic-marker", + "id": "charming-barrel", "metadata": {}, "outputs": [ { @@ -62,7 +62,7 @@ }, { "cell_type": "markdown", - "id": "worth-realtor", + "id": "progressive-update", "metadata": {}, "source": [ "convert to torch tensors" @@ -71,7 +71,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "official-provision", + "id": "novel-sydney", "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "running-nudist", + "id": "equivalent-sudan", "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "abstract-announcement", + "id": "convertible-curtis", "metadata": {}, "outputs": [ { @@ -124,7 +124,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "alive-theta", + "id": "important-button", "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "absolute-madrid", + "id": "acoustic-marketing", "metadata": {}, "outputs": [ { @@ -166,7 +166,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "hydraulic-salvation", + "id": "separate-leather", "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "sticky-climb", + "id": "ongoing-estimate", "metadata": {}, "source": [ "number of COFs for initialization" @@ -195,7 +195,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "piano-indicator", + "id": "intellectual-steering", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "legitimate-phone", + "id": "black-lancaster", "metadata": {}, "outputs": [], "source": [ @@ -275,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "eight-class", + "id": "chief-robertson", "metadata": {}, "source": [ "`ids_acquired[r, i]` will give ID of COF acquired during iteration `i` from run `r`." @@ -284,7 +284,7 @@ { "cell_type": "code", "execution_count": null, - "id": "premium-acceptance", + "id": "minor-bahrain", "metadata": {}, "outputs": [ { @@ -295,91 +295,4861 @@ "\n", "RUN 0\n", "iteration: 10\n", - "\tacquired COF 56259 with y = 182.416471606\n", - "\tbest y acquired: 182.416471606\n", + "\tacquired COF 38686 with y = 181.997321887\n", + "\tbest y acquired: 181.997321887\n", "iteration: 11\n", - "\tacquired COF 44551 with y = 188.642146113\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 14592 with y = 173.52788820799998\n", + "\tbest y acquired: 181.997321887\n", "iteration: 12\n", - "\tacquired COF 59749 with y = 183.06633314099997\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 13446 with y = 156.582607879\n", + "\tbest y acquired: 181.997321887\n", "iteration: 13\n", - "\tacquired COF 43434 with y = 166.762639788\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 35153 with y = 185.721713331\n", + "\tbest y acquired: 185.721713331\n", "iteration: 14\n", - "\tacquired COF 57294 with y = 166.196918004\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 442 with y = 177.769833484\n", + "\tbest y acquired: 185.721713331\n", "iteration: 15\n", - "\tacquired COF 65585 with y = 173.44669686900002\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 2782 with y = 190.660502314\n", + "\tbest y acquired: 190.660502314\n", "iteration: 16\n", - "\tacquired COF 441 with y = 186.034221186\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 776 with y = 164.249792893\n", + "\tbest y acquired: 190.660502314\n", "iteration: 17\n", - "\tacquired COF 20675 with y = 167.532168988\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 19351 with y = 191.120614308\n", + "\tbest y acquired: 191.120614308\n", "iteration: 18\n", - "\tacquired COF 12418 with y = 176.910634695\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 27950 with y = 177.605961385\n", + "\tbest y acquired: 191.120614308\n", "iteration: 19\n", - "\tacquired COF 13260 with y = 153.441277223\n", - "\tbest y acquired: 188.642146113\n", + "\tacquired COF 21852 with y = 189.50649556599998\n", + "\tbest y acquired: 191.120614308\n", "iteration: 20\n", + "\tacquired COF 14294 with y = 173.051146072\n", + "\tbest y acquired: 191.120614308\n", + "iteration: 21\n", + "\tacquired COF 26054 with y = 182.507837752\n", + "\tbest y acquired: 191.120614308\n", + "iteration: 22\n", + "\tacquired COF 26178 with y = 174.67636941799998\n", + "\tbest y acquired: 191.120614308\n", + "iteration: 23\n", + "\tacquired COF 66349 with y = 197.34635625599998\n", + "\tbest y acquired: 197.34635625599998\n", + "iteration: 24\n", + "\tacquired COF 23904 with y = 167.02190882600001\n", + "\tbest y acquired: 197.34635625599998\n", + "iteration: 25\n", + "\tacquired COF 16420 with y = 181.36312997299999\n", + "\tbest y acquired: 197.34635625599998\n", + "iteration: 26\n", + "\tacquired COF 5167 with y = 172.089578014\n", + "\tbest y acquired: 197.34635625599998\n", + "iteration: 27\n", + "\tacquired COF 20264 with y = 149.691664914\n", + "\tbest y acquired: 197.34635625599998\n", + "iteration: 28\n", + "\tacquired COF 12457 with y = 150.65472743200002\n", + "\tbest y acquired: 197.34635625599998\n", + "iteration: 29\n", + "\tacquired COF 2055 with y = 183.718553378\n", + "\tbest y acquired: 197.34635625599998\n", + "iteration: 30\n", + "\tacquired COF 26188 with y = 201.17983227599998\n", + "\tbest y acquired: 201.17983227599998\n", + "iteration: 31\n", + "\tacquired COF 25981 with y = 205.492194009\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 32\n", + "\tacquired COF 66078 with y = 190.67549353299998\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 33\n", + "\tacquired COF 21609 with y = 197.517412165\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 34\n", + "\tacquired COF 20696 with y = 197.86041748099998\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 35\n", + "\tacquired COF 37482 with y = 174.718514791\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 36\n", + "\tacquired COF 6455 with y = 188.927621488\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 37\n", + "\tacquired COF 3611 with y = 165.29457165899998\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 38\n", + "\tacquired COF 65232 with y = 182.26397528\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 39\n", + "\tacquired COF 33370 with y = 196.720247142\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 40\n", + "\tacquired COF 24403 with y = 191.501640273\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 41\n", + "\tacquired COF 20704 with y = 186.04049377\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 42\n", + "\tacquired COF 20724 with y = 164.306177151\n", + "\tbest y acquired: 205.492194009\n", + "iteration: 43\n", + "\tacquired COF 26565 with y = 207.39578187\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 44\n", + "\tacquired COF 26507 with y = 200.44080272099998\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 45\n", + "\tacquired COF 29861 with y = 199.72030120099998\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 46\n", + "\tacquired COF 33349 with y = 206.74476888599997\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 47\n", + "\tacquired COF 33319 with y = 188.836430211\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 48\n", + "\tacquired COF 33347 with y = 208.43022665700002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 49\n", + "\tacquired COF 33374 with y = 185.76111369\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 50\n", + "\tacquired COF 26399 with y = 206.54342821400002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 51\n", + "\tacquired COF 14463 with y = 174.252641138\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 52\n", + "\tacquired COF 16532 with y = 182.44953930000003\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 53\n", + "\tacquired COF 2193 with y = 173.11358177099999\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 54\n", + "\tacquired COF 14774 with y = 147.53148126899998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 55\n", + "\tacquired COF 14998 with y = 177.83641089900001\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 56\n", + "\tacquired COF 25984 with y = 190.04507896200002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 57\n", + "\tacquired COF 12479 with y = 161.279690414\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 58\n", + "\tacquired COF 33332 with y = 205.963467853\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 59\n", + "\tacquired COF 33343 with y = 196.58076384900002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 60\n", + "\tacquired COF 33330 with y = 195.58268240799998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 61\n", + "\tacquired COF 33364 with y = 209.36697147400002\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 62\n", + "\tacquired COF 33336 with y = 193.51655534\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 63\n", + "\tacquired COF 33351 with y = 132.880543055\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 64\n", + "\tacquired COF 25951 with y = 196.579974938\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 65\n", + "\tacquired COF 33321 with y = 209.88488105599998\n", + "\tbest y acquired: 209.88488105599998\n", + "iteration: 66\n", + "\tacquired COF 33375 with y = 216.894110699\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 67\n", + "\tacquired COF 12399 with y = 180.694709163\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 68\n", + "\tacquired COF 17545 with y = 118.580961855\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 69\n", + "\tacquired COF 28804 with y = 136.895703138\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 70\n", + "\tacquired COF 31021 with y = 27.4640999878\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 71\n", + "\tacquired COF 20426 with y = 150.449620805\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 72\n", + "\tacquired COF 28176 with y = 91.2868958191\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 73\n", + "\tacquired COF 35228 with y = 88.8427551498\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 74\n", + "\tacquired COF 29872 with y = 164.779984057\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 75\n", + "\tacquired COF 19622 with y = 16.8277012796\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 76\n", + "\tacquired COF 16404 with y = 171.299812707\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 77\n", + "\tacquired COF 28174 with y = 140.287860152\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 78\n", + "\tacquired COF 33366 with y = 204.811726149\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 79\n", + "\tacquired COF 20453 with y = 137.12480602899998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 80\n", + "\tacquired COF 26838 with y = 122.05731206600001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 81\n", + "\tacquired COF 29847 with y = 145.586994563\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 82\n", + "\tacquired COF 6448 with y = 171.117194584\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 83\n", + "\tacquired COF 17732 with y = 74.3808458788\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 84\n", + "\tacquired COF 33358 with y = 201.148834085\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 85\n", + "\tacquired COF 25961 with y = 163.27637386700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 86\n", + "\tacquired COF 14980 with y = 129.786115264\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 87\n", + "\tacquired COF 28819 with y = 59.766436766400005\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 88\n", + "\tacquired COF 14381 with y = 96.91691595350001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 89\n", + "\tacquired COF 17563 with y = 172.95669094599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 90\n", + "\tacquired COF 68777 with y = 204.958050668\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 91\n", + "\tacquired COF 67256 with y = 191.462943805\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 92\n", + "\tacquired COF 49009 with y = 77.9186309557\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 93\n", + "\tacquired COF 20435 with y = 164.46390475200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 94\n", + "\tacquired COF 20796 with y = 195.89774693900003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 95\n", + "\tacquired COF 30535 with y = 179.81664061900003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 96\n", + "\tacquired COF 7632 with y = 132.299049421\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 97\n", + "\tacquired COF 33345 with y = 192.672521193\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 98\n", + "\tacquired COF 67346 with y = 106.712912755\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 99\n", + "\tacquired COF 66117 with y = 202.21921792700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 100\n", + "\tacquired COF 29868 with y = 185.02377713400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 101\n", + "\tacquired COF 29866 with y = 181.753139212\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 102\n", + "\tacquired COF 43466 with y = 159.265144223\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 103\n", + "\tacquired COF 29976 with y = 145.607547351\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 104\n", + "\tacquired COF 33333 with y = 153.73519809\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 105\n", + "\tacquired COF 68871 with y = 205.189199744\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 106\n", + "\tacquired COF 19661 with y = 106.883992865\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 107\n", + "\tacquired COF 33022 with y = 50.3929032032\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 108\n", + "\tacquired COF 33317 with y = 158.63690306200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 109\n", + "\tacquired COF 20713 with y = 176.60991058599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 110\n", + "\tacquired COF 28807 with y = 140.053244392\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 111\n", + "\tacquired COF 18525 with y = 122.18995916\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 112\n", + "\tacquired COF 66413 with y = 148.908011197\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 113\n", + "\tacquired COF 66319 with y = 17.5631469711\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 114\n", + "\tacquired COF 33325 with y = 57.866248363800004\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 115\n", + "\tacquired COF 33371 with y = 199.75064711099998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 116\n", + "\tacquired COF 29870 with y = 196.796070915\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 117\n", + "\tacquired COF 35890 with y = 101.10875821\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 118\n", + "\tacquired COF 21314 with y = 194.053101714\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 119\n", + "\tacquired COF 30520 with y = 119.28128983399999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 120\n", + "\tacquired COF 33018 with y = 168.039155365\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 121\n", + "\tacquired COF 31014 with y = 196.752963258\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 122\n", + "\tacquired COF 16406 with y = 181.708538572\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 123\n", + "\tacquired COF 33365 with y = 198.020772317\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 124\n", + "\tacquired COF 14413 with y = 162.896085164\n", + "\tbest y acquired: 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185\n", + "\tacquired COF 19231 with y = 193.528032337\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 186\n", + "\tacquired COF 20683 with y = 170.544003696\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 187\n", + "\tacquired COF 33368 with y = 194.708308113\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 188\n", + "\tacquired COF 33372 with y = 178.271330036\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 189\n", + "\tacquired COF 17080 with y = 192.721952423\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 190\n", + "\tacquired COF 24065 with y = 164.12962684299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 191\n", + "\tacquired COF 20723 with y = 199.76380567299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 192\n", + "\tacquired COF 28172 with y = 63.7611262321\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 193\n", + "\tacquired COF 66118 with y = 190.44111299099998\n", + "\tbest y acquired: 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"\tacquired COF 30570 with y = 170.049898364\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 24\n", + "\tacquired COF 3555 with y = 177.71587614\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 25\n", + "\tacquired COF 52711 with y = 167.896319499\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 26\n", + "\tacquired COF 66860 with y = 182.910685964\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 27\n", + "\tacquired COF 10827 with y = 171.965999767\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 28\n", "\tacquired COF 66379 with y = 178.99445053\n", - "\tbest y acquired: 188.642146113\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 29\n", + "\tacquired COF 16405 with y = 181.898341554\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 30\n", + "\tacquired COF 14519 with y = 168.78203060299998\n", + "\tbest y acquired: 194.530496788\n", + "iteration: 31\n", + "\tacquired COF 6437 with y = 191.077676114\n", + "\tbest y 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"\tbest y acquired: 209.36697147400002\n", + "iteration: 66\n", + "\tacquired COF 30040 with y = 112.66838398799999\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 67\n", + "\tacquired COF 33336 with y = 193.51655534\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 68\n", + "\tacquired COF 14744 with y = 179.200127285\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 69\n", + "\tacquired COF 4389 with y = 179.85869594599998\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 70\n", + "\tacquired COF 25951 with y = 196.579974938\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 71\n", + "\tacquired COF 29856 with y = 191.48812323400003\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 72\n", + "\tacquired COF 35228 with y = 88.8427551498\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 73\n", + "\tacquired COF 31021 with y = 27.4640999878\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 74\n", + "\tacquired COF 14772 with y = 136.705692508\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 75\n", + "\tacquired COF 16404 with y = 171.299812707\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 76\n", + "\tacquired COF 10825 with y = 101.282551766\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 77\n", + "\tacquired COF 66117 with y = 202.21921792700002\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 78\n", + "\tacquired COF 33317 with y = 158.63690306200002\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 79\n", + "\tacquired COF 29868 with y = 185.02377713400003\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 80\n", + "\tacquired COF 28804 with y = 136.895703138\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 81\n", + "\tacquired COF 12382 with y = 185.162057723\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 82\n", + "\tacquired COF 31000 with y 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108\n", + "\tacquired COF 28809 with y = 135.602906107\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 109\n", + "\tacquired COF 49009 with y = 77.9186309557\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 110\n", + "\tacquired COF 29847 with y = 145.586994563\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 111\n", + "\tacquired COF 26605 with y = 41.357730881500004\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 112\n", + "\tacquired COF 20724 with y = 164.306177151\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 113\n", + "\tacquired COF 67351 with y = 119.31858380799999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 114\n", + "\tacquired COF 66401 with y = 148.112761288\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 115\n", + "\tacquired COF 17563 with y = 172.95669094599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 116\n", + "\tacquired COF 67256 with y = 191.462943805\n", + "\tbest y acquired: 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"\tacquired COF 40794 with y = 194.352667969\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 158\n", + "\tacquired COF 25972 with y = 156.692698503\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 159\n", + "\tacquired COF 29868 with y = 185.02377713400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 160\n", + "\tacquired COF 66097 with y = 185.043611707\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 161\n", + "\tacquired COF 16828 with y = 164.057948476\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 162\n", + "\tacquired COF 33354 with y = 178.47060025599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 163\n", + "\tacquired COF 26565 with y = 207.39578187\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 164\n", + "\tacquired COF 29856 with y = 191.48812323400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 165\n", + "\tacquired COF 17547 with y = 175.95207892099998\n", + "\tbest y acquired: 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168.484688957\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 175\n", + "\tacquired COF 14328 with y = 125.071396416\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 176\n", + "\tacquired COF 47232 with y = 53.479961007\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 177\n", + "\tacquired COF 19414 with y = 160.410266282\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 178\n", + "\tacquired COF 13271 with y = 142.578948125\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 179\n", + "\tacquired COF 33367 with y = 179.49445295799998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 180\n", + "\tacquired COF 28944 with y = 173.13774821200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 181\n", + "\tacquired COF 33384 with y = 157.546634643\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 182\n", + "\tacquired COF 33344 with y = 199.90463220799998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 183\n", + "\tacquired COF 26399 with y = 206.54342821400002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 184\n", + "\tacquired COF 65232 with y = 182.26397528\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 185\n", + "\tacquired COF 33368 with y = 194.708308113\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 186\n", + "\tacquired COF 28172 with y = 63.7611262321\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 187\n", + "\tacquired COF 2558 with y = 135.195839703\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 188\n", + "\tacquired COF 66078 with y = 190.67549353299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 189\n", + "\tacquired COF 26557 with y = 181.38750411\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 190\n", + "\tacquired COF 28802 with y = 134.638026751\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 191\n", + "\tacquired COF 33016 with y = 134.2717009\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 192\n", + "\tacquired COF 13847 with y = 106.33030028200001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 193\n", + "\tacquired COF 6455 with y = 188.927621488\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 194\n", + "\tacquired COF 33084 with y = 118.543383319\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 195\n", + "\tacquired COF 20663 with y = 192.274825215\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 196\n", + "\tacquired COF 16414 with y = 180.689671062\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 197\n", + "\tacquired COF 10708 with y = 171.473108903\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 198\n", + "\tacquired COF 35220 with y = 185.162425567\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 199\n", + "\tacquired COF 20614 with y = 192.178789156\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 200\n", + "\tacquired COF 55772 with y = 183.508848648\n", + "\tbest y acquired: 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193.330338992\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 210\n", + "\tacquired COF 67353 with y = 133.26622008\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 211\n", + "\tacquired COF 408 with y = 177.24746089400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 212\n", + "\tacquired COF 43043 with y = 87.2187888356\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 213\n", + "\tacquired COF 6448 with y = 171.117194584\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 214\n", + "\tacquired COF 67458 with y = 78.1507683647\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 215\n", + "\tacquired COF 26602 with y = 120.59630225\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 216\n", + "\tacquired COF 25623 with y = 133.706482995\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 217\n", + "\tacquired COF 33372 with y = 178.271330036\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 218\n", + "\tacquired COF 35773 with y = 120.458592721\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 219\n", + "\tacquired COF 20702 with y = 151.130277572\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 220\n", + "\tacquired COF 66310 with y = 180.91859135400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 221\n", + "\tacquired COF 28449 with y = 124.73143811\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 222\n", + "\tacquired COF 19676 with y = 107.41428795\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 223\n", + "\tacquired COF 29806 with y = 97.8342570276\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 224\n", + "\tacquired COF 19351 with y = 191.120614308\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 225\n", + "\tacquired COF 24893 with y = 83.8313413838\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 226\n", + "\tacquired COF 14799 with y = 174.145800528\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 227\n", + "\tacquired COF 14519 with y = 168.78203060299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 228\n", + "\tacquired COF 485 with y = 173.32037772700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 229\n", + "\tacquired COF 33369 with y = 171.712054876\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 230\n", + "\tacquired COF 12243 with y = 172.81117932200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 231\n", + "\tacquired COF 6454 with y = 188.76981126599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 232\n", + "\tacquired COF 68794 with y = 166.465002949\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 233\n", + "\tacquired COF 21609 with y = 197.517412165\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 234\n", + "\tacquired COF 19974 with y = 190.62920648\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 235\n", + "\tacquired COF 49216 with y = 123.317884276\n", + "\tbest y acquired: 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"iteration: 176\n", + "\tacquired COF 33368 with y = 194.708308113\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 177\n", + "\tacquired COF 33372 with y = 178.271330036\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 178\n", + "\tacquired COF 33369 with y = 171.712054876\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 179\n", + "\tacquired COF 13271 with y = 142.578948125\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 180\n", + "\tacquired COF 28802 with y = 134.638026751\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 181\n", + "\tacquired COF 33377 with y = 180.960807015\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 182\n", + "\tacquired COF 6437 with y = 191.077676114\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 183\n", + "\tacquired COF 26845 with y = 146.112788019\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 184\n", + "\tacquired COF 20696 with y = 197.86041748099998\n", + "\tbest y acquired: 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219\n", + "\tacquired COF 20384 with y = 120.603149838\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 220\n", + "\tacquired COF 29866 with y = 181.753139212\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 221\n", + "\tacquired COF 19207 with y = 124.570109011\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 222\n", + "\tacquired COF 29844 with y = 146.958980683\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 223\n", + "\tacquired COF 33022 with y = 50.3929032032\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 224\n", + "\tacquired COF 33354 with y = 178.47060025599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 225\n", + "\tacquired COF 2147 with y = 168.484688957\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 226\n", + "\tacquired COF 16889 with y = 177.906393671\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 227\n", + "\tacquired COF 14788 with y = 160.115266743\n", + "\tbest y acquired: 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COF 19746 with y = 123.96392410899999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 246\n", + "\tacquired COF 25623 with y = 133.706482995\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 247\n", + "\tacquired COF 28172 with y = 63.7611262321\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 248\n", + "\tacquired COF 1079 with y = 161.141779998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 249\n", + "\tacquired COF 2863 with y = 166.765915445\n", + "\tbest y acquired: 216.894110699\n", + "took time t = 8.913509527842203 min\n", + "\n", + "\n", + "RUN 4\n", + "iteration: 10\n", + "\tacquired COF 44374 with y = 174.559120314\n", + "\tbest y acquired: 175.60800624400002\n", + "iteration: 11\n", + "\tacquired COF 9701 with y = 167.156453713\n", + "\tbest y acquired: 175.60800624400002\n", + "iteration: 12\n", + "\tacquired COF 15270 with y = 177.67472951099998\n", + "\tbest y acquired: 177.67472951099998\n", + "iteration: 13\n", + "\tacquired COF 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"\tbest y acquired: 216.894110699\n", + "iteration: 48\n", + "\tacquired COF 33358 with y = 201.148834085\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 49\n", + "\tacquired COF 1079 with y = 161.141779998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 50\n", + "\tacquired COF 33371 with y = 199.75064711099998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 51\n", + "\tacquired COF 16404 with y = 171.299812707\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 52\n", + "\tacquired COF 68777 with y = 204.958050668\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 53\n", + "\tacquired COF 67256 with y = 191.462943805\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 54\n", + "\tacquired COF 66117 with y = 202.21921792700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 55\n", + "\tacquired COF 20426 with y = 150.449620805\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 56\n", + "\tacquired COF 13853 with y = 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"iteration: 144\n", + "\tacquired COF 16828 with y = 164.057948476\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 145\n", + "\tacquired COF 66413 with y = 148.908011197\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 146\n", + "\tacquired COF 20704 with y = 186.04049377\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 147\n", + "\tacquired COF 16532 with y = 182.44953930000003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 148\n", + "\tacquired COF 26838 with y = 122.05731206600001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 149\n", + "\tacquired COF 6437 with y = 191.077676114\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 150\n", + "\tacquired COF 20695 with y = 58.6512593787\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 151\n", + "\tacquired COF 19242 with y = 28.999934997199997\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 152\n", + "\tacquired COF 24065 with y = 164.12962684299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 153\n", + "\tacquired COF 18528 with y = 118.773803339\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 154\n", + "\tacquired COF 28831 with y = 196.653171762\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 155\n", + "\tacquired COF 20720 with y = 190.806274437\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 156\n", + "\tacquired COF 28802 with y = 134.638026751\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 157\n", + "\tacquired COF 11652 with y = 161.035530703\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 158\n", + "\tacquired COF 20427 with y = 107.85636725200001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 159\n", + "\tacquired COF 26399 with y = 206.54342821400002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 160\n", + "\tacquired COF 18107 with y = 143.570175369\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 161\n", + "\tacquired COF 29868 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"iteration: 170\n", + "\tacquired COF 30787 with y = 111.697103794\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 171\n", + "\tacquired COF 28819 with y = 59.766436766400005\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 172\n", + "\tacquired COF 33354 with y = 178.47060025599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 173\n", + "\tacquired COF 20668 with y = 187.511040491\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 174\n", + "\tacquired COF 68806 with y = 178.13002528400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 175\n", + "\tacquired COF 66379 with y = 178.99445053\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 176\n", + "\tacquired COF 10825 with y = 101.282551766\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 177\n", + "\tacquired COF 20381 with y = 153.163375554\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 178\n", + "\tacquired COF 66078 with y = 190.67549353299998\n", + 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"\tacquired COF 29844 with y = 146.958980683\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 232\n", + "\tacquired COF 12243 with y = 172.81117932200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 233\n", + "\tacquired COF 7632 with y = 132.299049421\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 234\n", + "\tacquired COF 17079 with y = 200.40213550099998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 235\n", + "\tacquired COF 35422 with y = 137.94533655799998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 236\n", + "\tacquired COF 30103 with y = 175.691061558\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 237\n", + "\tacquired COF 68888 with y = 166.212484089\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 238\n", + "\tacquired COF 19660 with y = 99.0812907516\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 239\n", + "\tacquired COF 29806 with y = 97.8342570276\n", + "\tbest y acquired: 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y acquired: 216.894110699\n", + "iteration: 249\n", + "\tacquired COF 28173 with y = 140.45889762\n", + "\tbest y acquired: 216.894110699\n", + "took time t = 9.394897739092508 min\n", + "\n", + "\n", + "RUN 5\n", + "iteration: 10\n", + "\tacquired COF 55719 with y = 174.051874717\n", + "\tbest y acquired: 174.051874717\n", + "iteration: 11\n", + "\tacquired COF 38711 with y = 179.57463563599998\n", + "\tbest y acquired: 179.57463563599998\n", + "iteration: 12\n", + "\tacquired COF 14560 with y = 167.799449748\n", + "\tbest y acquired: 179.57463563599998\n", + "iteration: 13\n", + "\tacquired COF 60596 with y = 147.50601540600002\n", + "\tbest y acquired: 179.57463563599998\n", + "iteration: 14\n", + "\tacquired COF 16702 with y = 169.771050097\n", + "\tbest y acquired: 179.57463563599998\n", + "iteration: 15\n", + "\tacquired COF 3993 with y = 168.81826538299998\n", + "\tbest y acquired: 179.57463563599998\n", + "iteration: 16\n", + "\tacquired COF 60127 with y = 185.76553294400003\n", + "\tbest y acquired: 185.76553294400003\n", + "iteration: 17\n", + "\tacquired COF 441 with y = 186.034221186\n", + "\tbest y acquired: 186.034221186\n", + "iteration: 18\n", + "\tacquired COF 35100 with y = 173.659295298\n", + "\tbest y acquired: 186.034221186\n", + "iteration: 19\n", + "\tacquired COF 34457 with y = 172.19117704599998\n", + "\tbest y acquired: 186.034221186\n", + "iteration: 20\n", + "\tacquired COF 35153 with y = 185.721713331\n", + "\tbest y acquired: 186.034221186\n", + "iteration: 21\n", + "\tacquired COF 59572 with y = 168.71551414799998\n", + "\tbest y acquired: 186.034221186\n", + "iteration: 22\n", + "\tacquired COF 35173 with y = 166.630093588\n", + "\tbest y acquired: 186.034221186\n", + "iteration: 23\n", + "\tacquired COF 66095 with y = 196.9895885\n", + "\tbest y acquired: 196.9895885\n", + "iteration: 24\n", + "\tacquired COF 14465 with y = 174.29660494599997\n", + "\tbest y acquired: 196.9895885\n", + "iteration: 25\n", + "\tacquired COF 26443 with y = 178.98932437\n", + "\tbest y acquired: 196.9895885\n", + "iteration: 26\n", + "\tacquired COF 66106 with y = 192.026373675\n", + "\tbest y acquired: 196.9895885\n", + "iteration: 27\n", + "\tacquired COF 66379 with y = 178.99445053\n", + "\tbest y acquired: 196.9895885\n", + "iteration: 28\n", + "\tacquired COF 29861 with y = 199.72030120099998\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 29\n", + "\tacquired COF 29845 with y = 183.95419856799998\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 30\n", + "\tacquired COF 29856 with y = 191.48812323400003\n", + "\tbest y acquired: 199.72030120099998\n", "iteration: 31\n", - "\tacquired COF 32021 with y = 189.34518854799998\n", - "\tbest y acquired: 207.39578187\n", + "\tacquired COF 30535 with y = 179.81664061900003\n", + "\tbest y acquired: 199.72030120099998\n", "iteration: 32\n", + "\tacquired COF 3611 with y = 165.29457165899998\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 33\n", + "\tacquired COF 33258 with y = 147.962275284\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 34\n", + "\tacquired COF 30780 with y = 168.744310959\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 35\n", + "\tacquired COF 33395 with y = 69.24723185180001\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 36\n", + "\tacquired COF 20696 with y = 197.86041748099998\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 37\n", + "\tacquired COF 16415 with y = 174.654915912\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 38\n", + "\tacquired COF 33370 with y = 196.720247142\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 39\n", + "\tacquired COF 20724 with y = 164.306177151\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 40\n", + "\tacquired COF 40794 with y = 194.352667969\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 41\n", + "\tacquired COF 21314 with y = 194.053101714\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 42\n", + "\tacquired COF 29866 with y = 181.753139212\n", + "\tbest y acquired: 199.72030120099998\n", + "iteration: 43\n", "\tacquired COF 26507 with y = 200.44080272099998\n", + "\tbest y acquired: 200.44080272099998\n", + "iteration: 44\n", + "\tacquired COF 26565 with y = 207.39578187\n", "\tbest y acquired: 207.39578187\n", - "iteration: 33\n", - "\tacquired COF 37482 with y = 174.718514791\n", + "iteration: 45\n", + "\tacquired COF 33410 with y = 175.74685276\n", "\tbest y acquired: 207.39578187\n", - "iteration: 34\n", - "\tacquired COF 13582 with y = 183.60112018900003\n", + "iteration: 46\n", + "\tacquired COF 25981 with y = 205.492194009\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 47\n", + "\tacquired COF 26399 with y = 206.54342821400002\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 48\n", + "\tacquired COF 66078 with y = 190.67549353299998\n", "\tbest y acquired: 207.39578187\n", + "iteration: 49\n", + "\tacquired COF 1079 with y = 161.141779998\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 50\n", + "\tacquired COF 20704 with y = 186.04049377\n", + "\tbest y acquired: 207.39578187\n", + "iteration: 51\n", + "\tacquired COF 33347 with y = 208.43022665700002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 52\n", + "\tacquired COF 33349 with y = 206.74476888599997\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 53\n", + "\tacquired COF 68777 with y = 204.958050668\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 54\n", + "\tacquired COF 67206 with y = 206.864600037\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 55\n", + "\tacquired COF 33319 with y = 188.836430211\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 56\n", + "\tacquired COF 68871 with y = 205.189199744\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 57\n", + "\tacquired COF 66075 with y = 199.84356436299998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 58\n", + "\tacquired COF 33332 with y = 205.963467853\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 59\n", + "\tacquired COF 66117 with y = 202.21921792700002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 60\n", + "\tacquired COF 21609 with y = 197.517412165\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 61\n", + "\tacquired COF 67256 with y = 191.462943805\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 62\n", + "\tacquired COF 16404 with y = 171.299812707\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 63\n", + "\tacquired COF 66145 with y = 180.398214003\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 64\n", + "\tacquired COF 17563 with y = 172.95669094599998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 65\n", + "\tacquired COF 33330 with y = 195.58268240799998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 66\n", + "\tacquired COF 33343 with y = 196.58076384900002\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 67\n", + "\tacquired COF 28174 with y = 140.287860152\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 68\n", + "\tacquired COF 33364 with y = 209.36697147400002\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 69\n", + "\tacquired COF 33336 with y = 193.51655534\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 70\n", + "\tacquired COF 25951 with y = 196.579974938\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 71\n", + "\tacquired COF 2497 with y = 172.46977255299998\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 72\n", + "\tacquired COF 19228 with y = 183.50656627400002\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 73\n", + "\tacquired COF 30570 with y = 170.049898364\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 74\n", + "\tacquired COF 33321 with y = 209.88488105599998\n", + "\tbest y acquired: 209.88488105599998\n", + "iteration: 75\n", + "\tacquired COF 33375 with y = 216.894110699\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 76\n", + "\tacquired COF 33358 with y = 201.148834085\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 77\n", + "\tacquired COF 33371 with y = 199.75064711099998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 78\n", + "\tacquired COF 20623 with y = 187.081474988\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 79\n", + "\tacquired COF 6439 with y = 172.71569396400002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 80\n", + "\tacquired COF 28806 with y = 139.315962979\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 81\n", + "\tacquired COF 13977 with y = 170.538199345\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 82\n", + "\tacquired COF 12382 with y = 185.162057723\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 83\n", + "\tacquired COF 27409 with y = 5.50577517293\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 84\n", + "\tacquired COF 33366 with y = 204.811726149\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 85\n", + "\tacquired COF 56517 with y = 194.530496788\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 86\n", + "\tacquired COF 33084 with y = 118.543383319\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 87\n", + "\tacquired COF 33365 with y = 198.020772317\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 88\n", + "\tacquired COF 26835 with y = 164.102328521\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 89\n", + "\tacquired COF 15218 with y = 121.807847324\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 90\n", + "\tacquired COF 28812 with y = 85.1282514861\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 91\n", + "\tacquired COF 20381 with y = 153.163375554\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 92\n", + "\tacquired COF 36108 with y = 92.6111273782\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 93\n", + "\tacquired COF 18528 with y = 118.773803339\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 94\n", + "\tacquired COF 14125 with y = 135.19162469100002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 95\n", + "\tacquired COF 28175 with y = 147.372826457\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 96\n", + "\tacquired COF 33382 with y = 25.9065014902\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 97\n", + "\tacquired COF 3566 with y = 140.56865170700001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 98\n", + "\tacquired COF 20427 with y = 107.85636725200001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 99\n", + "\tacquired COF 67351 with y = 119.31858380799999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 100\n", + "\tacquired COF 28804 with y = 136.895703138\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 101\n", + "\tacquired COF 33354 with y = 178.47060025599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 102\n", + "\tacquired COF 9704 with y = 183.77337184599997\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 103\n", + "\tacquired COF 66810 with y = 161.40209431\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 104\n", + "\tacquired COF 15571 with y = 105.444896447\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 105\n", + "\tacquired COF 33374 with y = 185.76111369\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 106\n", + "\tacquired COF 19763 with y = 48.9057466283\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 107\n", + "\tacquired COF 28181 with y = 115.431947175\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 108\n", + "\tacquired COF 20614 with y = 192.178789156\n", + "\tbest y acquired: 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106.883992865\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 118\n", + "\tacquired COF 35321 with y = 131.843034991\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 119\n", + "\tacquired COF 12425 with y = 178.63841840799998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 120\n", + "\tacquired COF 31023 with y = 91.0515080749\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 121\n", + "\tacquired COF 28184 with y = 65.9805000421\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 122\n", + "\tacquired COF 26841 with y = 139.04109103\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 123\n", + "\tacquired COF 14413 with y = 162.896085164\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 124\n", + "\tacquired COF 14701 with y = 181.598451053\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 125\n", + "\tacquired COF 30998 with y = 159.98157682299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 126\n", + "\tacquired COF 27336 with y = 112.73343318399999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 127\n", + "\tacquired COF 19622 with y = 16.8277012796\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 128\n", + "\tacquired COF 14934 with y = 129.591613927\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 129\n", + "\tacquired COF 28809 with y = 135.602906107\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 130\n", + "\tacquired COF 29847 with y = 145.586994563\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 131\n", + "\tacquired COF 20195 with y = 173.83279745299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 132\n", + "\tacquired COF 28814 with y = 122.52670242200001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 133\n", + "\tacquired COF 33317 with y = 158.63690306200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 134\n", + "\tacquired COF 57913 with y = 160.10405245799998\n", + "\tbest y acquired: 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"\tbest y acquired: 216.894110699\n", + "iteration: 144\n", + "\tacquired COF 20796 with y = 195.89774693900003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 145\n", + "\tacquired COF 55772 with y = 183.508848648\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 146\n", + "\tacquired COF 33512 with y = 164.067845055\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 147\n", + "\tacquired COF 27035 with y = 178.57489196900002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 148\n", + "\tacquired COF 16828 with y = 164.057948476\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 149\n", + "\tacquired COF 20663 with y = 192.274825215\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 150\n", + "\tacquired COF 26825 with y = 104.501303156\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 151\n", + "\tacquired COF 6437 with y = 191.077676114\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 152\n", + "\tacquired COF 20668 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161\n", + "\tacquired COF 29868 with y = 185.02377713400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 162\n", + "\tacquired COF 16532 with y = 182.44953930000003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 163\n", + "\tacquired COF 31014 with y = 196.752963258\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 164\n", + "\tacquired COF 20384 with y = 120.603149838\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 165\n", + "\tacquired COF 65232 with y = 182.26397528\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 166\n", + "\tacquired COF 20564 with y = 158.956229351\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 167\n", + "\tacquired COF 30787 with y = 111.697103794\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 168\n", + "\tacquired COF 4389 with y = 179.85869594599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 169\n", + "\tacquired COF 16567 with y = 194.20146897700002\n", + "\tbest y 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187\n", + "\tacquired COF 20675 with y = 167.532168988\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 188\n", + "\tacquired COF 66097 with y = 185.043611707\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 189\n", + "\tacquired COF 14518 with y = 160.90768417200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 190\n", + "\tacquired COF 33369 with y = 171.712054876\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 191\n", + "\tacquired COF 50029 with y = 81.5586385924\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 192\n", + "\tacquired COF 26853 with y = 109.390770156\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 193\n", + "\tacquired COF 9712 with y = 164.42518012\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 194\n", + "\tacquired COF 67268 with y = 176.81609524900003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 195\n", + "\tacquired COF 6435 with y = 188.242123191\n", + "\tbest y acquired: 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"\tacquired COF 66310 with y = 180.91859135400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 214\n", + "\tacquired COF 35811 with y = 156.495687255\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 215\n", + "\tacquired COF 30103 with y = 175.691061558\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 216\n", + "\tacquired COF 16889 with y = 177.906393671\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 217\n", + "\tacquired COF 6448 with y = 171.117194584\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 218\n", + "\tacquired COF 12384 with y = 152.458282239\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 219\n", + "\tacquired COF 17550 with y = 166.581672788\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 220\n", + "\tacquired COF 68793 with y = 185.423165449\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 221\n", + "\tacquired COF 14328 with y = 125.071396416\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 222\n", + "\tacquired COF 26673 with y = 134.231325481\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 223\n", + "\tacquired COF 29844 with y = 146.958980683\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 224\n", + "\tacquired COF 28819 with y = 59.766436766400005\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 225\n", + "\tacquired COF 20699 with y = 186.32524988400002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 226\n", + "\tacquired COF 16511 with y = 176.24564903700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 227\n", + "\tacquired COF 21662 with y = 189.901093629\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 228\n", + "\tacquired COF 7387 with y = 115.670221404\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 229\n", + "\tacquired COF 47232 with y = 53.479961007\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 230\n", + "\tacquired COF 635 with y = 145.925084609\n", + "\tbest y 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"iteration: 59\n", + "\tacquired COF 25981 with y = 205.492194009\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 60\n", + "\tacquired COF 20704 with y = 186.04049377\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 61\n", + "\tacquired COF 66078 with y = 190.67549353299998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 62\n", + "\tacquired COF 26507 with y = 200.44080272099998\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 63\n", + "\tacquired COF 21609 with y = 197.517412165\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 64\n", + "\tacquired COF 28804 with y = 136.895703138\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 65\n", + "\tacquired COF 20724 with y = 164.306177151\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 66\n", + "\tacquired COF 30570 with y = 170.049898364\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 67\n", + "\tacquired COF 19351 with y = 191.120614308\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 68\n", + "\tacquired COF 14513 with y = 150.13648807\n", + "\tbest y acquired: 208.43022665700002\n", + "iteration: 69\n", + "\tacquired COF 33364 with y = 209.36697147400002\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 70\n", + "\tacquired COF 33330 with y = 195.58268240799998\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 71\n", + "\tacquired COF 33343 with y = 196.58076384900002\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 72\n", + "\tacquired COF 33336 with y = 193.51655534\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 73\n", + "\tacquired COF 33319 with y = 188.836430211\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 74\n", + "\tacquired COF 27409 with y = 5.50577517293\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 75\n", + "\tacquired COF 24505 with y = 144.012004141\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 76\n", + "\tacquired COF 17563 with y = 172.95669094599998\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 77\n", + "\tacquired COF 17115 with y = 107.343414815\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 78\n", + "\tacquired COF 28174 with y = 140.287860152\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 79\n", + "\tacquired COF 20706 with y = 37.0540543817\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 80\n", + "\tacquired COF 6448 with y = 171.117194584\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 81\n", + "\tacquired COF 13582 with y = 183.60112018900003\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 82\n", + "\tacquired COF 35228 with y = 88.8427551498\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 83\n", + "\tacquired COF 33366 with y = 204.811726149\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 84\n", + "\tacquired COF 21314 with y = 194.053101714\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 85\n", + "\tacquired COF 66401 with y = 148.112761288\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 86\n", + "\tacquired COF 26318 with y = 171.27410334799998\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 87\n", + "\tacquired COF 28175 with y = 147.372826457\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 88\n", + "\tacquired COF 29847 with y = 145.586994563\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 89\n", + "\tacquired COF 33358 with y = 201.148834085\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 90\n", + "\tacquired COF 33352 with y = 191.102564445\n", + "\tbest y acquired: 209.36697147400002\n", + "iteration: 91\n", + "\tacquired COF 33321 with y = 209.88488105599998\n", + "\tbest y acquired: 209.88488105599998\n", + "iteration: 92\n", + "\tacquired COF 33375 with y = 216.894110699\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 93\n", + "\tacquired COF 14125 with y = 135.19162469100002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 94\n", + "\tacquired COF 31017 with y = 69.9242996732\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 95\n", + "\tacquired COF 30030 with y = 140.521698493\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 96\n", + "\tacquired COF 28176 with y = 91.2868958191\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 97\n", + "\tacquired COF 67049 with y = 146.484660578\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 98\n", + "\tacquired COF 19555 with y = 93.4670109684\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 99\n", + "\tacquired COF 28809 with y = 135.602906107\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 100\n", + "\tacquired COF 50029 with y = 81.5586385924\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 101\n", + "\tacquired COF 33371 with y = 199.75064711099998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 102\n", + "\tacquired COF 20379 with y = 162.873978548\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 103\n", + "\tacquired COF 13948 with y = 139.587940417\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 104\n", + "\tacquired COF 31014 with y = 196.752963258\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 105\n", + "\tacquired COF 29870 with y = 196.796070915\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 106\n", + "\tacquired COF 10827 with y = 171.965999767\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 107\n", + "\tacquired COF 30998 with y = 159.98157682299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 108\n", + "\tacquired COF 31021 with y = 27.4640999878\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 109\n", + "\tacquired COF 29868 with y = 185.02377713400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 110\n", + "\tacquired COF 16416 with y = 177.130147413\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 111\n", + "\tacquired COF 30520 with y = 119.28128983399999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 112\n", + "\tacquired COF 17544 with y = 151.327703601\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 113\n", + "\tacquired COF 2193 with y = 173.11358177099999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 114\n", + "\tacquired COF 30535 with y = 179.81664061900003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 115\n", + "\tacquired COF 14413 with y = 162.896085164\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 116\n", + "\tacquired COF 25961 with y = 163.27637386700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 117\n", + "\tacquired COF 28807 with y = 140.053244392\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 118\n", + "\tacquired COF 29856 with y = 191.48812323400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 119\n", + "\tacquired COF 20440 with y = 123.382918247\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 120\n", + "\tacquired COF 19622 with y = 16.8277012796\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 121\n", + "\tacquired COF 33345 with y = 192.672521193\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 122\n", + "\tacquired COF 68777 with y = 204.958050668\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 123\n", + "\tacquired COF 20796 with y = 195.89774693900003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 124\n", + "\tacquired COF 68871 with y = 205.189199744\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 125\n", + "\tacquired COF 17669 with y = 126.295568998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 126\n", + "\tacquired COF 26841 with y = 139.04109103\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 127\n", + "\tacquired COF 33022 with y = 50.3929032032\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 128\n", + "\tacquired COF 33508 with y = 170.264908225\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 129\n", + "\tacquired COF 67256 with y = 191.462943805\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 130\n", + "\tacquired COF 27035 with y = 178.57489196900002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 131\n", + "\tacquired COF 67351 with y = 119.31858380799999\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 132\n", + "\tacquired COF 12243 with y = 172.81117932200002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 133\n", + "\tacquired COF 6437 with y = 191.077676114\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 134\n", + "\tacquired COF 33354 with y = 178.47060025599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 135\n", + "\tacquired COF 66310 with y = 180.91859135400003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 136\n", + "\tacquired COF 31023 with y = 91.0515080749\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 137\n", + "\tacquired COF 20720 with y = 190.806274437\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 138\n", + "\tacquired COF 33016 with y = 134.2717009\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 139\n", + "\tacquired COF 35773 with y = 120.458592721\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 140\n", + "\tacquired COF 26606 with y = 60.3163108521\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 141\n", + "\tacquired COF 19228 with y = 183.50656627400002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 142\n", + "\tacquired COF 16567 with y = 194.20146897700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 143\n", + "\tacquired COF 68879 with y = 174.930216574\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 144\n", + "\tacquired COF 66145 with y = 180.398214003\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 145\n", + "\tacquired COF 6435 with y = 188.242123191\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 146\n", + "\tacquired COF 12425 with y = 178.63841840799998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 147\n", + "\tacquired COF 33365 with y = 198.020772317\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 148\n", + "\tacquired COF 26188 with y = 201.17983227599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 149\n", + "\tacquired COF 2497 with y = 172.46977255299998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 150\n", + "\tacquired COF 20713 with y = 176.60991058599998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 151\n", + "\tacquired COF 28173 with y = 140.45889762\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 152\n", + "\tacquired COF 69405 with y = 69.5967411775\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 153\n", + "\tacquired COF 33326 with y = 95.1787030835\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 154\n", + "\tacquired COF 26838 with y = 122.05731206600001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 155\n", + "\tacquired COF 30278 with y = 178.4514143\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 156\n", + "\tacquired COF 12348 with y = 159.76088175799998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 157\n", + "\tacquired COF 16533 with y = 192.863029816\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 158\n", + "\tacquired COF 33018 with y = 168.039155365\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 159\n", + "\tacquired COF 35220 with y = 185.162425567\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 160\n", + "\tacquired COF 33340 with y = 155.15457032700002\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 161\n", + "\tacquired COF 14381 with y = 96.91691595350001\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 162\n", + "\tacquired COF 20696 with y = 197.86041748099998\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 163\n", + "\tacquired COF 33372 with y = 178.271330036\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 164\n", + "\tacquired COF 2051 with y = 198.138166855\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 165\n", + "\tacquired COF 21662 with y = 189.901093629\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 166\n", + "\tacquired COF 15642 with y = 105.875950103\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 167\n", + "\tacquired COF 2147 with y = 168.484688957\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 168\n", + "\tacquired COF 26661 with y = 181.08434414\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 169\n", + "\tacquired COF 66097 with y = 185.043611707\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 170\n", + "\tacquired COF 47232 with y = 53.479961007\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 171\n", + "\tacquired COF 67589 with y = 172.332089688\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 172\n", + "\tacquired COF 40794 with y = 194.352667969\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 173\n", + "\tacquired COF 17550 with y = 166.581672788\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 174\n", + "\tacquired COF 28812 with y = 85.1282514861\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 175\n", + "\tacquired COF 67206 with y = 206.864600037\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 176\n", + "\tacquired COF 5165 with y = 193.408466045\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 177\n", + "\tacquired COF 20689 with y = 96.7601788104\n", + "\tbest y acquired: 216.894110699\n", + "iteration: 178\n" ] } ], "source": [ "bo_res = dict()\n", - "bo_res['nb_runs'] = 1\n", - "bo_res['nb_iterations'] = 200\n", + "bo_res['nb_runs'] = 10\n", + "bo_res['nb_iterations'] = 250\n", "bo_res['ids_acquired'] = []\n", "for r in range(bo_res['nb_runs']):\n", " print(\"\\n\\nRUN\", r)\n", @@ -391,8 +5161,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "impressed-investigation", + "execution_count": null, + "id": "played-albania", "metadata": {}, "outputs": [], "source": [ @@ -403,7 +5173,7 @@ { "cell_type": "code", "execution_count": null, - "id": "young-working", + "id": "thorough-catalog", "metadata": {}, "outputs": [], "source": [] diff --git a/new/evol_search.ipynb b/new/evol_search.ipynb index 55a82d7..60b7ad3 100644 --- a/new/evol_search.ipynb +++ b/new/evol_search.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "yellow-lounge", + "id": "short-estonia", "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "integral-documentation", + "id": "atlantic-edward", "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "adequate-scholar", + "id": "athletic-devon", "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "ranging-improvement", + "id": "breeding-organic", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "individual-moral", + "id": "brilliant-mistake", "metadata": {}, "outputs": [], "source": [ @@ -81,17 +81,36 @@ "assert distances[closest_COF(X[3, :], [3])] == np.min(distances[ids])" ] }, + { + "cell_type": "code", + "execution_count": 9, + "id": "seventh-faith", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "48442" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, { "cell_type": "code", "execution_count": 10, - "id": "emerging-portland", + "id": "delayed-treatment", "metadata": {}, "outputs": [], "source": [ "nb_iterations = 500\n", "\n", "def evol_run(nb_iterations):\n", - " x_init = np.random.rand(np.shape(X)[1]) # random initial point\n", + " x_init = X[np.random.choice(np.arange((nb_data)), size=1, replace=False)[0], :] # random initial COF\n", "\n", " # initialize evolutionary algo\n", " ids_acquired = []\n", @@ -124,7 +143,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "dried-animation", + "id": "catholic-musical", "metadata": {}, "outputs": [ { @@ -134,309 +153,317 @@ "\n", "\n", "RUN 0\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=305343, Thu Jul 1 13:07:43 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=282932, Thu Jul 1 14:53:00 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 161.035530703\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 171.117194584\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.72030120099998\n", + "\n", + "\n", + "RUN 1\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=219189, Thu Jul 1 14:53:02 2021)\n", "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 209.36697147400002\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 209.88488105599998\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 200.44080272099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 200.44080272099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 216.894110699\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 200.44080272099998\n", "\n", "\n", - "RUN 1\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=231156, Thu Jul 1 13:07:47 2021)\n", + "RUN 2\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=207560, Thu Jul 1 14:53:04 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.81117932200002\n", + "\t# max y value: 146.427775435\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.81117932200002\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 178.99445053\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 181.18376571\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 182.910685964\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.530496788\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.530496788\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.530496788\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 3\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=339397, Thu Jul 1 14:53:05 2021)\n", "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 201.66490141\n", "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 201.66490141\n", "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 205.492194009\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", - "\t# acquired COFs: 495\n", + "\t# acquired COFs: 242\n", "\t# max y value: 207.39578187\n", "ask/tell sesh\n", - "\t# acquired COFs: 506\n", + "\t# acquired COFs: 253\n", "\t# max y value: 207.39578187\n", "\n", "\n", - "RUN 2\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=292258, Thu Jul 1 13:07:51 2021)\n", + "RUN 4\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=331179, Thu Jul 1 14:53:07 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.81117932200002\n", + "\t# max y value: 168.172206806\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", "\t# max y value: 194.37058873700002\n", @@ -454,131 +481,135 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", + "\n", + "\n", + "RUN 5\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=294680, Thu Jul 1 14:53:09 2021)\n", "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 194.37058873700002\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 204.811726149\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 204.811726149\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 178.63841840799998\n", "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 205.171240133\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 178.63841840799998\n", "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 178.63841840799998\n", "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.108264299\n", "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 207.39578187\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 196.720247142\n", "\n", "\n", - "RUN 3\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=200476, Thu Jul 1 13:07:55 2021)\n", + "RUN 6\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=258871, Thu Jul 1 14:53:11 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 164.593994065\n", + "\t# max y value: 171.144256451\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 170.049898364\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", "\t# max y value: 194.37058873700002\n", @@ -590,267 +621,129 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "\n", "\n", - "RUN 4\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=225273, Thu Jul 1 13:07:59 2021)\n", + "RUN 7\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=291157, Thu Jul 1 14:53:13 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 173.92050685200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 188.76981126599998\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 199.72030120099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "\n", "\n", - "RUN 5\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=192469, Thu Jul 1 13:08:02 2021)\n", + "RUN 8\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=204744, Thu Jul 1 14:53:15 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 149.316186302\n", + "\t# max y value: 172.95669094599998\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 172.95669094599998\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", "\t# max y value: 177.71587614\n", @@ -862,617 +755,122 @@ "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 181.99863318099997\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 242\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 253\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 204.811726149\n", - "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 207.39578187\n", - "ask/tell sesh\n", - "\t# acquired COFs: 484\n", "\t# max y value: 207.39578187\n", "ask/tell sesh\n", - "\t# acquired COFs: 495\n", + "\t# acquired COFs: 242\n", "\t# max y value: 207.39578187\n", "ask/tell sesh\n", - "\t# acquired COFs: 506\n", + "\t# acquired COFs: 253\n", "\t# max y value: 207.39578187\n", "\n", "\n", - "RUN 6\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=268625, Thu Jul 1 13:08:06 2021)\n", + "RUN 9\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=315141, Thu Jul 1 14:53:16 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 134.638026751\n", + "\t# max y value: 165.80833295899998\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.46977255299998\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.06469784200002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 202.848493155\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 202.848493155\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 202.848493155\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 202.848493155\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 231\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 216.894110699\n", - "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 216.894110699\n", - "\n", - "\n", - "RUN 7\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=284032, Thu Jul 1 13:08:10 2021)\n", - "ask/tell sesh\n", - "\t# acquired COFs: 11\n", - "\t# max y value: 144.391389813\n", - "ask/tell sesh\n", - "\t# acquired COFs: 22\n", - "\t# max y value: 148.112761288\n", - "ask/tell sesh\n", - "\t# acquired COFs: 33\n", - "\t# max y value: 172.81117932200002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 44\n", - "\t# max y value: 175.820374733\n", - "ask/tell sesh\n", - "\t# acquired COFs: 55\n", - "\t# max y value: 188.242123191\n", - "ask/tell sesh\n", - "\t# acquired COFs: 66\n", - "\t# max y value: 188.242123191\n", - "ask/tell sesh\n", - "\t# acquired COFs: 77\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 88\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 99\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 110\n", - "\t# max y value: 193.51655534\n", - "ask/tell sesh\n", - "\t# acquired COFs: 121\n", - "\t# max y value: 193.51655534\n", - "ask/tell sesh\n", - "\t# acquired COFs: 132\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 143\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 154\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 165\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 176\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 187\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 198\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 209\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 220\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 231\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 242\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 253\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 208.43022665700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 208.43022665700002\n", - "\n", - "\n", - "RUN 8\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=241457, Thu Jul 1 13:08:14 2021)\n", - "ask/tell sesh\n", - "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", - "ask/tell sesh\n", - "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", - "ask/tell sesh\n", - "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", - "ask/tell sesh\n", - "\t# acquired COFs: 44\n", - "\t# max y value: 179.81664061900003\n", - "ask/tell sesh\n", - "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 66\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 77\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 88\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 99\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 110\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 121\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 132\n", - "\t# max y value: 191.507774129\n", - "ask/tell sesh\n", - "\t# acquired COFs: 143\n", - "\t# max y value: 195.58268240799998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 154\n", - "\t# max y value: 202.21921792700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 165\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 176\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 187\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 198\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 209\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 220\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 231\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 242\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 253\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 209.36697147400002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 209.36697147400002\n", - "\n", - "\n", - "RUN 9\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=212735, Thu Jul 1 13:08:18 2021)\n", - "ask/tell sesh\n", - "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", - "ask/tell sesh\n", - "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", - "ask/tell sesh\n", - "\t# acquired COFs: 33\n", - "\t# max y value: 186.09094075099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 44\n", - "\t# max y value: 186.09094075099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 55\n", - "\t# max y value: 186.09094075099998\n", - "ask/tell sesh\n", - "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", - "ask/tell sesh\n", - "\t# acquired COFs: 165\n", - "\t# max y value: 205.189199744\n", - "ask/tell sesh\n", - "\t# acquired COFs: 176\n", - "\t# max y value: 205.189199744\n", - "ask/tell sesh\n", - "\t# acquired COFs: 187\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 198\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 209\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 220\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", "\t# max y value: 206.864600037\n", @@ -1481,83 +879,14 @@ "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 264\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 275\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 286\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 297\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 308\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 319\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 330\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 341\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 352\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 363\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 374\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 385\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 396\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 407\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 418\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 429\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 440\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 451\n", - "\t# max y value: 206.864600037\n", - "ask/tell sesh\n", - "\t# acquired COFs: 462\n", - "\t# max y value: 208.120454446\n", - "ask/tell sesh\n", - "\t# acquired COFs: 473\n", - "\t# max y value: 208.120454446\n", - "ask/tell sesh\n", - "\t# acquired COFs: 484\n", - "\t# max y value: 208.120454446\n", - "ask/tell sesh\n", - "\t# acquired COFs: 495\n", - "\t# max y value: 208.120454446\n", - "ask/tell sesh\n", - "\t# acquired COFs: 506\n", - "\t# max y value: 208.120454446\n" + "\t# max y value: 206.864600037\n" ] } ], "source": [ "es_res = dict()\n", "es_res['nb_runs'] = 10\n", - "es_res['nb_iterations'] = 500\n", + "es_res['nb_iterations'] = 250\n", "es_res['ids_acquired'] = []\n", "for r in range(es_res['nb_runs']):\n", " print(\"\\n\\nRUN\", r)\n", @@ -1571,7 +900,7 @@ { "cell_type": "code", "execution_count": null, - "id": "focal-cologne", + "id": "binary-retention", "metadata": {}, "outputs": [], "source": [] diff --git a/new/prepare_Xy.ipynb b/new/prepare_Xy.ipynb index 73b6dfa..a83bde3 100644 --- a/new/prepare_Xy.ipynb +++ b/new/prepare_Xy.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "discrete-greece", + "id": "systematic-married", "metadata": {}, "source": [ "# import and prepare data." @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "alpha-reputation", + "id": "exceptional-trail", "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "indonesian-label", + "id": "persistent-scope", "metadata": {}, "source": [ "load data from Mercado et al., drop outlier" @@ -31,7 +31,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "cheap-minnesota", + "id": "outer-austria", "metadata": {}, "outputs": [ { @@ -292,7 +292,7 @@ }, { "cell_type": "markdown", - "id": "fitted-tyler", + "id": "another-sigma", "metadata": {}, "source": [ "define new feature as density of elements" @@ -301,7 +301,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "experimental-ethics", + "id": "directed-pontiac", "metadata": {}, "outputs": [ { @@ -562,7 +562,7 @@ }, { "cell_type": "markdown", - "id": "photographic-bookmark", + "id": "split-northern", "metadata": {}, "source": [ "get feature matrix and target vector" @@ -571,7 +571,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "greenhouse-authorization", + "id": "intimate-adult", "metadata": {}, "outputs": [ { @@ -591,7 +591,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "equal-digest", + "id": "formal-cleveland", "metadata": {}, "outputs": [ { @@ -622,7 +622,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "employed-browse", + "id": "handled-heaven", "metadata": {}, "outputs": [ { @@ -663,7 +663,7 @@ }, { "cell_type": "markdown", - "id": "automatic-shark", + "id": "authentic-reply", "metadata": {}, "source": [ "Min-Max normalize the features so that they lie in $[0, 1]$. this is ok to do over all data as opposed to just training because in BO we will compute the cheap features of every COF in the database." @@ -672,7 +672,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "pharmaceutical-premises", + "id": "physical-original", "metadata": {}, "outputs": [ { @@ -726,7 +726,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "alleged-midwest", + "id": "corresponding-rescue", "metadata": {}, "outputs": [], "source": [ diff --git a/new/random_forest_run.ipynb b/new/random_forest_run.ipynb index 3cd1103..becf540 100644 --- a/new/random_forest_run.ipynb +++ b/new/random_forest_run.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "roman-pastor", + "id": "chinese-orientation", "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "behind-grace", + "id": "partial-block", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "electrical-geology", + "id": "after-terry", "metadata": {}, "outputs": [], "source": [ @@ -79,17 +79,17 @@ { "cell_type": "code", "execution_count": 4, - "id": "filled-better", + "id": "suburban-desperate", "metadata": {}, "outputs": [], "source": [ - "diversify_training = True" + "diversify_training = False" ] }, { "cell_type": "code", "execution_count": 5, - "id": "frozen-height", + "id": "fuzzy-healing", "metadata": {}, "outputs": [], "source": [ @@ -134,42 +134,514 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "collect-breathing", + "execution_count": 6, + "id": "exclusive-reggae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "eval budgets: [10, 20, 30, 50]\n", - "budget for evals: 10\n", - "\trun 0\n", - "\tdiverse RF run\n", - "\teval budget 10 = 5 training data and 5 acquired.\n", - "\tmax y acquired = 159.96862082799998\n", + "eval budgets: [20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240]\n", "budget for evals: 20\n", "\trun 0\n", - "\tdiverse RF run\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 182.34542236299998\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 182.416471606\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 178.44859215099999\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 190.40496791\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 187.41220189\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 184.504347793\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 179.089815979\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 187.973529905\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 166.306286228\n", + "\trun 9\n", + "\tRF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 173.880645123\n", - "budget for evals: 30\n", + "\tmax y acquired = 176.020860814\n", + "budget for evals: 40\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 188.20542767400002\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 190.96105800200002\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 176.501690325\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 181.238096277\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 197.87398978299998\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 183.784918482\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 181.202599364\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 177.191166175\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 189.546131494\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 187.973529905\n", + "budget for evals: 60\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 192.9359556\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 183.478905752\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 195.915962745\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 186.79890652900002\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.80467023\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 186.800115493\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.88923220900003\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 174.428712513\n", + "budget for evals: 80\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.264226622\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 197.35770853900001\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 193.408466045\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 183.56630674599998\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 185.382350173\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 201.17983227599998\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 197.35770853900001\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 187.822691684\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "budget for evals: 100\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 195.448316445\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 190.91146585599998\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 189.186508129\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 198.034754095\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 193.05167775400002\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 188.37269533\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 186.577611083\n", + "budget for evals: 120\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.568968117\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 193.41397958\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 194.58787007400002\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 189.937977395\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 203.35670863099998\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 193.25083398700002\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 193.28874060799998\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 187.72054369999998\n", + "budget for evals: 140\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 197.918308448\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 191.108264299\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 194.766126231\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.625762218\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 193.72992463\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.37724838900002\n", + "budget for evals: 160\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 196.88923220900003\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 192.532706025\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 191.108264299\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 195.289662613\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 191.779475121\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 201.17983227599998\n", + "budget for evals: 180\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 201.17983227599998\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 191.45735074700002\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 188.63741461200001\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 193.675158092\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 193.562944445\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 193.949996568\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "budget for evals: 200\n", + "\trun 0\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 201.66490141\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.55088119400003\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 202.848493155\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 190.42701679299998\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 198.138166855\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 193.620114578\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 202.848493155\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.698499548\n", + "budget for evals: 220\n", "\trun 0\n", - "\tdiverse RF run\n", - "\teval budget 30 = 15 training data and 15 acquired.\n", - "\tmax y acquired = 178.997150426\n", - "budget for evals: 50\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 195.978854341\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 194.938530808\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 201.66490141\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "budget for evals: 240\n", "\trun 0\n", - "\tdiverse RF run\n", - "\teval budget 50 = 25 training data and 25 acquired.\n", - "\tmax y acquired = 192.95415281799998\n" + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 199.80359465400002\n", + "\trun 1\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 196.88923220900003\n", + "\trun 2\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 195.289662613\n", + "\trun 3\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 4\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 198.034754095\n", + "\trun 5\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 6\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 7\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 200.40213550099998\n", + "\trun 8\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 194.38766055\n", + "\trun 9\n", + "\tRF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 195.289662613\n" ] } ], "source": [ "rf_res = dict()\n", - "rf_res['nb_runs'] = 1\n", - "rf_res['nb_evals_budgets'] = [10, 20, 30, 50] #[10 * i for i in range(1, 21)]\n", + "rf_res['nb_runs'] = 10\n", + "rf_res['nb_evals_budgets'] = [20 * i for i in range(1, 13)]\n", "print(\"eval budgets: \", rf_res['nb_evals_budgets'])\n", "rf_res['ids_acquired'] = [[] for b in rf_res['nb_evals_budgets']]\n", "for b in range(len(rf_res['nb_evals_budgets'])):\n", @@ -195,7 +667,7 @@ { "cell_type": "code", "execution_count": null, - "id": "periodic-toolbox", + "id": "powered-definition", "metadata": {}, "outputs": [], "source": [] diff --git a/new/random_search.ipynb b/new/random_search.ipynb index 1243dca..95e9e55 100644 --- a/new/random_search.ipynb +++ b/new/random_search.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "dated-collect", + "id": "sapphire-conviction", "metadata": {}, "source": [ "# random search" @@ -10,8 +10,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "lesser-finnish", + "execution_count": 1, + "id": "dynamic-universal", "metadata": {}, "outputs": [], "source": [ @@ -23,8 +23,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "registered-apparel", + "execution_count": 2, + "id": "whole-caution", "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ "69839" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -55,14 +55,14 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "extreme-trace", + "execution_count": 3, + "id": "functional-mechanism", "metadata": {}, "outputs": [], "source": [ "rs_res = dict()\n", "rs_res['nb_runs'] = 10\n", - "rs_res['nb_iterations'] = 500\n", + "rs_res['nb_iterations'] = 250\n", "rs_res['ids_acquired'] = []\n", "for r in range(rs_res['nb_runs']):\n", " ids_acquired = np.random.choice(range(nb_data), replace=False, size=rs_res['nb_iterations'])\n", diff --git a/new/viz.ipynb b/new/viz.ipynb index ecbcfbc..16a9fc1 100644 --- a/new/viz.ipynb +++ b/new/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "acceptable-scale", + "id": "lesser-publicity", "metadata": {}, "source": [ "# viz" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "lasting-reproduction", + "id": "romance-clinton", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "rubber-viking", + "id": "amino-african", "metadata": {}, "source": [ "load data" @@ -62,7 +62,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "cardiovascular-venice", + "id": "based-climate", "metadata": {}, "outputs": [ { @@ -93,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "august-retrieval", + "id": "fatty-conditions", "metadata": {}, "source": [ "for rankings" @@ -102,7 +102,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "activated-handbook", + "id": "statewide-genesis", "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "functioning-sleep", + "id": "behavioral-palace", "metadata": {}, "outputs": [ { @@ -134,7 +134,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "accessible-certificate", + "id": "imperial-mixture", "metadata": {}, "outputs": [ { @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "balanced-infrared", + "id": "several-ballot", "metadata": {}, "source": [ "load search results" @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "naval-italy", + "id": "interpreted-tucson", "metadata": {}, "outputs": [], "source": [ @@ -176,7 +176,7 @@ }, { "cell_type": "markdown", - "id": "ambient-singer", + "id": "technological-family", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -185,7 +185,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "loving-empty", + "id": "mechanical-mouse", "metadata": {}, "outputs": [], "source": [ @@ -197,7 +197,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "greater-skating", + "id": "technical-tribe", "metadata": {}, "outputs": [ { @@ -230,7 +230,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "overall-bearing", + "id": "global-azerbaijan", "metadata": {}, "outputs": [ { @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "therapeutic-carter", + "id": "burning-celebrity", "metadata": {}, "source": [ "# search efficiency\n", @@ -286,7 +286,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "smoking-enforcement", + "id": "descending-charger", "metadata": {}, "outputs": [ { @@ -328,7 +328,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "million-assets", + "id": "creative-grill", "metadata": {}, "outputs": [], "source": [ @@ -353,7 +353,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "focused-finance", + "id": "qualified-basic", "metadata": {}, "outputs": [], "source": [ @@ -364,7 +364,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "furnished-twenty", + "id": "infinite-constraint", "metadata": {}, "outputs": [ { @@ -377,7 +377,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -432,7 +432,7 @@ }, { "cell_type": "markdown", - "id": "intellectual-companion", + "id": "copyrighted-miniature", "metadata": {}, "source": [ "show distribution for context." @@ -441,7 +441,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "circular-pattern", + "id": "ceramic-morgan", "metadata": {}, "outputs": [ { @@ -469,7 +469,7 @@ }, { "cell_type": "markdown", - "id": "parallel-summer", + "id": "honey-packing", "metadata": {}, "source": [ "### max rank among acquired set" @@ -478,7 +478,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "gorgeous-walter", + "id": "medical-australia", "metadata": {}, "outputs": [ { @@ -506,7 +506,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "tutorial-burns", + "id": "unexpected-discipline", "metadata": {}, "outputs": [ { @@ -519,7 +519,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -569,7 +569,7 @@ }, { "cell_type": "markdown", - "id": "decreased-agent", + "id": "emotional-andrew", "metadata": {}, "source": [ "### fraction of top 100 COFs recovered" @@ -578,7 +578,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "serious-riverside", + "id": "circular-money", "metadata": {}, "outputs": [ { @@ -598,7 +598,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "handy-shore", + "id": "physical-rehabilitation", "metadata": {}, "outputs": [], "source": [ @@ -613,7 +613,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "plain-thirty", + "id": "prepared-stroke", "metadata": {}, "outputs": [ { @@ -652,7 +652,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "amateur-action", + "id": "lucky-tampa", "metadata": {}, "outputs": [], "source": [ @@ -677,7 +677,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "intended-civilization", + "id": "pleasant-anatomy", "metadata": {}, "outputs": [ { @@ -690,7 +690,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "worldwide-radiation", + "id": "thirty-steel", "metadata": {}, "outputs": [], "source": [] From e09be616dc677ba6a11ef0f543017fdbd9b1521c Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Fri, 2 Jul 2021 09:03:56 -0700 Subject: [PATCH 12/29] k --- new/BO_run.ipynb | 5027 +---------------------------------- new/evol_search.ipynb | 3403 ++++++++++++++++++++++-- new/random_forest_run.ipynb | 773 +++--- new/random_search.ipynb | 10 +- new/viz.ipynb | 106 +- 5 files changed, 3634 insertions(+), 5685 deletions(-) diff --git a/new/BO_run.ipynb b/new/BO_run.ipynb index ff043d6..ea88410 100644 --- a/new/BO_run.ipynb +++ b/new/BO_run.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "relative-poland", + "id": "variable-triple", "metadata": {}, "source": [ "# BO runs" @@ -11,9 +11,31 @@ { "cell_type": "code", "execution_count": 1, - "id": "thermal-wichita", + "id": "retained-equity", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" + ] + } + ], "source": [ "import torch\n", "from botorch.models import FixedNoiseGP, SingleTaskGP\n", @@ -21,15 +43,28 @@ "from gpytorch.mlls import ExactMarginalLogLikelihood\n", "from botorch import fit_gpytorch_model\n", "from botorch.acquisition.analytic import ExpectedImprovement\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pickle\n", "import sys\n", "import time" ] }, + { + "cell_type": "code", + "execution_count": 37, + "id": "romantic-mistake", + "metadata": {}, + "outputs": [], + "source": [ + "which_acquisition = \"EI\"\n", + "# which_acquisition = \"max y_hat\"\n", + "# which_acquisition = \"max sigma\"" + ] + }, { "cell_type": "markdown", - "id": "solar-norman", + "id": "hindu-startup", "metadata": {}, "source": [ "load data from `prepare_Xy.ipynb`" @@ -37,8 +72,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "charming-barrel", + "execution_count": 38, + "id": "flush-samba", "metadata": {}, "outputs": [ { @@ -47,7 +82,7 @@ "69839" ] }, - "execution_count": 2, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -62,7 +97,7 @@ }, { "cell_type": "markdown", - "id": "progressive-update", + "id": "mineral-update", "metadata": {}, "source": [ "convert to torch tensors" @@ -70,8 +105,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "novel-sydney", + "execution_count": 39, + "id": "south-coordination", "metadata": {}, "outputs": [], "source": [ @@ -81,8 +116,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "equivalent-sudan", + "execution_count": 40, + "id": "weird-pleasure", "metadata": {}, "outputs": [ { @@ -91,7 +126,7 @@ "torch.Size([69839, 12])" ] }, - "execution_count": 4, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -102,8 +137,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "convertible-curtis", + "execution_count": 41, + "id": "occupational-tracker", "metadata": {}, "outputs": [ { @@ -112,7 +147,7 @@ "torch.Size([69839, 1])" ] }, - "execution_count": 5, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -123,8 +158,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "important-button", + "execution_count": 42, + "id": "attended-secondary", "metadata": {}, "outputs": [], "source": [ @@ -133,40 +168,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "acoustic-marketing", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "batch_size = 10000\n", - "acquisition_values = np.zeros((nb_data))\n", - "acquisition_values[:] = np.NaN\n", - "nb_batches = nb_data // batch_size\n", - "for ba in range(nb_batches+1):\n", - " id_start = ba * batch_size\n", - " id_end = id_start + batch_size\n", - " if id_end > nb_data:\n", - " id_end = nb_data\n", - " acquisition_values[id_start:id_end] = range(id_start, id_end)\n", - " \n", - "np.sum(np.isnan(acquisition_values))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "separate-leather", + "execution_count": 43, + "id": "criminal-support", "metadata": {}, "outputs": [ { @@ -175,7 +178,7 @@ "69839" ] }, - "execution_count": 8, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -186,7 +189,7 @@ }, { "cell_type": "markdown", - "id": "ongoing-estimate", + "id": "unexpected-theorem", "metadata": {}, "source": [ "number of COFs for initialization" @@ -194,8 +197,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "intellectual-steering", + "execution_count": 44, + "id": "varying-workstation", "metadata": {}, "outputs": [], "source": [ @@ -204,8 +207,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "black-lancaster", + "execution_count": 45, + "id": "wanted-motion", "metadata": {}, "outputs": [], "source": [ @@ -229,22 +232,29 @@ " fit_gpytorch_model(mll)\n", "\n", " # set up acquisition function\n", - " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", - " \n", - " # compute aquisition function at each COF in the database. need to do in batches to avoid mem issues\n", - " batch_size = 20000\n", - " acquisition_values = torch.zeros((nb_data))\n", - " acquisition_values[:] = np.NaN # for safety\n", - " nb_batches = nb_data // batch_size\n", - " for ba in range(nb_batches+1):\n", - " id_start = ba * batch_size\n", - " id_end = id_start + batch_size\n", - " if id_end > nb_data:\n", - " id_end = nb_data\n", - " acquisition_values[id_start:id_end] = acquisition_function.forward(X_unsqueezed[id_start:id_end])\n", - "# acquisition_values = acquisition_function.forward(X_unsqueezed)\n", - " assert acquisition_values.isnan().sum().item() == 0 # so that all are filled properly.\n", - " del acquisition_function\n", + " if which_acquisition == \"EI\":\n", + " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", + " \n", + " # compute aquisition function at each COF in the database. need to do in batches to avoid mem issues\n", + " batch_size = 15000\n", + " acquisition_values = torch.zeros((nb_data))\n", + " acquisition_values[:] = np.NaN # for safety\n", + " nb_batches = nb_data // batch_size\n", + " for ba in range(nb_batches+1):\n", + " id_start = ba * batch_size\n", + " id_end = id_start + batch_size\n", + " if id_end > nb_data:\n", + " id_end = nb_data\n", + " acquisition_values[id_start:id_end] = acquisition_function.forward(X_unsqueezed[id_start:id_end])\n", + " assert acquisition_values.isnan().sum().item() == 0 # so that all are filled properly.\n", + " del acquisition_function\n", + " # acquisition_values = acquisition_function.forward(X_unsqueezed) # runs out of memory\n", + " elif which_acquisition == \"max y_hat\":\n", + " acquisition_values = model.posterior(X_unsqueezed).mean.squeeze()\n", + " elif which_acquisition == \"max sigma\":\n", + " acquisition_values = model.posterior(X_unsqueezed).variance.squeeze()\n", + " else:\n", + " raise Exception(\"not a valid acquisition function\")\n", "\n", " # select COF to acquire with maximal aquisition value, which is not in the acquired set already\n", " ids_sorted_by_aquisition = acquisition_values.argsort(descending=True)\n", @@ -275,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "chief-robertson", + "id": "modern-elimination", "metadata": {}, "source": [ "`ids_acquired[r, i]` will give ID of COF acquired during iteration `i` from run `r`." @@ -283,8 +293,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "minor-bahrain", + "execution_count": 46, + "id": "upset-bristol", "metadata": {}, "outputs": [ { @@ -295,4861 +305,28 @@ "\n", "RUN 0\n", "iteration: 10\n", - "\tacquired COF 38686 with y = 181.997321887\n", - "\tbest y acquired: 181.997321887\n", - "iteration: 11\n", - "\tacquired COF 14592 with y = 173.52788820799998\n", - "\tbest y acquired: 181.997321887\n", - "iteration: 12\n", - "\tacquired COF 13446 with y = 156.582607879\n", - "\tbest y acquired: 181.997321887\n", - "iteration: 13\n", - "\tacquired COF 35153 with y = 185.721713331\n", - "\tbest y acquired: 185.721713331\n", - "iteration: 14\n", - "\tacquired COF 442 with y = 177.769833484\n", - "\tbest y acquired: 185.721713331\n", - "iteration: 15\n", - "\tacquired COF 2782 with y = 190.660502314\n", - "\tbest y acquired: 190.660502314\n", - "iteration: 16\n", - "\tacquired COF 776 with y = 164.249792893\n", - "\tbest y acquired: 190.660502314\n", - "iteration: 17\n", - "\tacquired COF 19351 with y = 191.120614308\n", - "\tbest y acquired: 191.120614308\n", - "iteration: 18\n", - "\tacquired COF 27950 with y = 177.605961385\n", - "\tbest y acquired: 191.120614308\n", - "iteration: 19\n", - "\tacquired COF 21852 with y = 189.50649556599998\n", - "\tbest y acquired: 191.120614308\n", - "iteration: 20\n", - "\tacquired COF 14294 with y = 173.051146072\n", - "\tbest y acquired: 191.120614308\n", - "iteration: 21\n", - "\tacquired COF 26054 with y = 182.507837752\n", - "\tbest y acquired: 191.120614308\n", - "iteration: 22\n", - "\tacquired COF 26178 with y = 174.67636941799998\n", - "\tbest y acquired: 191.120614308\n", - "iteration: 23\n", - "\tacquired COF 66349 with y = 197.34635625599998\n", - "\tbest y acquired: 197.34635625599998\n", - "iteration: 24\n", - "\tacquired COF 23904 with y = 167.02190882600001\n", - "\tbest y acquired: 197.34635625599998\n", - "iteration: 25\n", - "\tacquired COF 16420 with y = 181.36312997299999\n", - "\tbest y acquired: 197.34635625599998\n", - "iteration: 26\n", - "\tacquired COF 5167 with y = 172.089578014\n", - "\tbest y acquired: 197.34635625599998\n", - "iteration: 27\n", - "\tacquired COF 20264 with y = 149.691664914\n", - "\tbest y acquired: 197.34635625599998\n", - "iteration: 28\n", - "\tacquired COF 12457 with y = 150.65472743200002\n", - "\tbest y acquired: 197.34635625599998\n", - "iteration: 29\n", - "\tacquired COF 2055 with y = 183.718553378\n", - "\tbest y acquired: 197.34635625599998\n", - "iteration: 30\n", - "\tacquired COF 26188 with y = 201.17983227599998\n", - "\tbest y acquired: 201.17983227599998\n", - "iteration: 31\n", - "\tacquired COF 25981 with y = 205.492194009\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 32\n", - "\tacquired COF 66078 with y = 190.67549353299998\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 33\n", - "\tacquired COF 21609 with y = 197.517412165\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 34\n", - "\tacquired COF 20696 with y = 197.86041748099998\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 35\n", - "\tacquired COF 37482 with y = 174.718514791\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 36\n", - "\tacquired COF 6455 with y = 188.927621488\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 37\n", - "\tacquired COF 3611 with y = 165.29457165899998\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 38\n", - "\tacquired COF 65232 with y = 182.26397528\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 39\n", - "\tacquired COF 33370 with y = 196.720247142\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 40\n", - "\tacquired COF 24403 with y = 191.501640273\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 41\n", - "\tacquired COF 20704 with y = 186.04049377\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 42\n", - "\tacquired COF 20724 with y = 164.306177151\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 43\n", - "\tacquired COF 26565 with y = 207.39578187\n", - "\tbest y acquired: 207.39578187\n", - "iteration: 44\n", - "\tacquired COF 26507 with y = 200.44080272099998\n", - "\tbest y acquired: 207.39578187\n", - "iteration: 45\n", - "\tacquired COF 29861 with y = 199.72030120099998\n", - "\tbest y acquired: 207.39578187\n", - "iteration: 46\n", - "\tacquired COF 33349 with y = 206.74476888599997\n", - "\tbest y acquired: 207.39578187\n", - "iteration: 47\n", - "\tacquired COF 33319 with y = 188.836430211\n", - "\tbest y acquired: 207.39578187\n", - "iteration: 48\n", - "\tacquired COF 33347 with y = 208.43022665700002\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 49\n", - "\tacquired COF 33374 with y = 185.76111369\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 50\n", - "\tacquired COF 26399 with y = 206.54342821400002\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 51\n", - "\tacquired COF 14463 with y = 174.252641138\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 52\n", - "\tacquired COF 16532 with y = 182.44953930000003\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 53\n", - "\tacquired COF 2193 with y = 173.11358177099999\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 54\n", - "\tacquired COF 14774 with y = 147.53148126899998\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 55\n", - "\tacquired COF 14998 with y = 177.83641089900001\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 56\n", - "\tacquired COF 25984 with y = 190.04507896200002\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 57\n", - "\tacquired COF 12479 with y = 161.279690414\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 58\n", - "\tacquired COF 33332 with y = 205.963467853\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 59\n", - "\tacquired COF 33343 with y = 196.58076384900002\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 60\n", - "\tacquired COF 33330 with y = 195.58268240799998\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 61\n", - "\tacquired COF 33364 with y = 209.36697147400002\n", - "\tbest y acquired: 209.36697147400002\n", - "iteration: 62\n", - "\tacquired COF 33336 with y = 193.51655534\n", - "\tbest y acquired: 209.36697147400002\n", - "iteration: 63\n", - "\tacquired COF 33351 with y = 132.880543055\n", - "\tbest y acquired: 209.36697147400002\n", - "iteration: 64\n", - "\tacquired COF 25951 with y = 196.579974938\n", - "\tbest y acquired: 209.36697147400002\n", - "iteration: 65\n", - "\tacquired COF 33321 with y = 209.88488105599998\n", - "\tbest y acquired: 209.88488105599998\n", - "iteration: 66\n", - "\tacquired COF 33375 with y = 216.894110699\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 67\n", - "\tacquired COF 12399 with y = 180.694709163\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 68\n", - "\tacquired COF 17545 with y = 118.580961855\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 69\n", - "\tacquired COF 28804 with y = 136.895703138\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 70\n", - "\tacquired COF 31021 with y = 27.4640999878\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 71\n", - "\tacquired COF 20426 with y = 150.449620805\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 72\n", - "\tacquired COF 28176 with y = 91.2868958191\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 73\n", - "\tacquired COF 35228 with y = 88.8427551498\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 74\n", - "\tacquired COF 29872 with y = 164.779984057\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 75\n", - "\tacquired COF 19622 with y = 16.8277012796\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 76\n", - "\tacquired COF 16404 with y = 171.299812707\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 77\n", - "\tacquired COF 28174 with y = 140.287860152\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 78\n", - "\tacquired COF 33366 with y = 204.811726149\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 79\n", - "\tacquired COF 20453 with y = 137.12480602899998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 80\n", - "\tacquired COF 26838 with y = 122.05731206600001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 81\n", - "\tacquired COF 29847 with y = 145.586994563\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 82\n", - "\tacquired COF 6448 with y = 171.117194584\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 83\n", - "\tacquired COF 17732 with y = 74.3808458788\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 84\n", - "\tacquired COF 33358 with y = 201.148834085\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 85\n", - "\tacquired COF 25961 with y = 163.27637386700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 86\n", - "\tacquired COF 14980 with y = 129.786115264\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 87\n", - "\tacquired COF 28819 with y = 59.766436766400005\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 88\n", - "\tacquired COF 14381 with y = 96.91691595350001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 89\n", - "\tacquired COF 17563 with y = 172.95669094599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 90\n", - "\tacquired COF 68777 with y = 204.958050668\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 91\n", - "\tacquired COF 67256 with y = 191.462943805\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 92\n", - "\tacquired COF 49009 with y = 77.9186309557\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 93\n", - "\tacquired COF 20435 with y = 164.46390475200002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 94\n", - "\tacquired COF 20796 with y = 195.89774693900003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 95\n", - "\tacquired COF 30535 with y = 179.81664061900003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 96\n", - "\tacquired COF 7632 with y = 132.299049421\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 97\n", - "\tacquired COF 33345 with y = 192.672521193\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 98\n", - "\tacquired COF 67346 with y = 106.712912755\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 99\n", - "\tacquired COF 66117 with y = 202.21921792700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 100\n", - "\tacquired COF 29868 with y = 185.02377713400003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 101\n", - "\tacquired COF 29866 with y = 181.753139212\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 102\n", - "\tacquired COF 43466 with y = 159.265144223\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 103\n", - "\tacquired COF 29976 with y = 145.607547351\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 104\n", - "\tacquired COF 33333 with y = 153.73519809\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 105\n", - "\tacquired COF 68871 with y = 205.189199744\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 106\n", - "\tacquired COF 19661 with y = 106.883992865\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 107\n", - "\tacquired COF 33022 with y = 50.3929032032\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 108\n", - "\tacquired COF 33317 with y = 158.63690306200002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 109\n", - "\tacquired COF 20713 with y = 176.60991058599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 110\n", - "\tacquired COF 28807 with y = 140.053244392\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 111\n", - "\tacquired COF 18525 with y = 122.18995916\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 112\n", - "\tacquired COF 66413 with y = 148.908011197\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 113\n", - "\tacquired COF 66319 with y = 17.5631469711\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 114\n", - "\tacquired COF 33325 with y = 57.866248363800004\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 115\n", - "\tacquired COF 33371 with y = 199.75064711099998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 116\n", - "\tacquired COF 29870 with y = 196.796070915\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 117\n", - "\tacquired COF 35890 with y = 101.10875821\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 118\n", - "\tacquired COF 21314 with y = 194.053101714\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 119\n", - "\tacquired COF 30520 with y = 119.28128983399999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 120\n", - "\tacquired COF 33018 with y = 168.039155365\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 121\n", - "\tacquired COF 31014 with y = 196.752963258\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 122\n", - "\tacquired COF 16406 with y = 181.708538572\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 123\n", - "\tacquired COF 33365 with y = 198.020772317\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 124\n", - "\tacquired COF 14413 with y = 162.896085164\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 125\n", - "\tacquired COF 20440 with y = 123.382918247\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 126\n", - "\tacquired COF 27035 with y = 178.57489196900002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 127\n", - "\tacquired COF 14934 with y = 129.591613927\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 128\n", - "\tacquired COF 19178 with y = 85.6898688024\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 129\n", - "\tacquired COF 29856 with y = 191.48812323400003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 130\n", - "\tacquired COF 55772 with y = 183.508848648\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 131\n", - "\tacquired COF 16828 with y = 164.057948476\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 132\n", - "\tacquired COF 19763 with y = 48.9057466283\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 133\n", - "\tacquired COF 28181 with y = 115.431947175\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 134\n", - "\tacquired COF 12406 with y = 167.977744879\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 135\n", - "\tacquired COF 12402 with y = 175.504448723\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 136\n", - "\tacquired COF 30030 with y = 140.521698493\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 137\n", - "\tacquired COF 68793 with y = 185.423165449\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 138\n", - "\tacquired COF 67351 with y = 119.31858380799999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 139\n", - "\tacquired COF 17544 with y = 151.327703601\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 140\n", - "\tacquired COF 26454 with y = 155.815089078\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 141\n", - "\tacquired COF 3052 with y = 97.32770066260001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 142\n", - "\tacquired COF 10708 with y = 171.473108903\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 143\n", - "\tacquired COF 35886 with y = 109.40051975799999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 144\n", - "\tacquired COF 56259 with y = 182.416471606\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 145\n", - "\tacquired COF 28814 with y = 122.52670242200001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 146\n", - "\tacquired COF 16511 with y = 176.24564903700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 147\n", - "\tacquired COF 3201 with y = 141.59215378\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 148\n", - "\tacquired COF 20720 with y = 190.806274437\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 149\n", - "\tacquired COF 28809 with y = 135.602906107\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 150\n", - "\tacquired COF 66145 with y = 180.398214003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 151\n", - "\tacquired COF 26788 with y = 161.92578092600002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 152\n", - "\tacquired COF 20699 with y = 186.32524988400002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 153\n", - "\tacquired COF 26605 with y = 41.357730881500004\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 154\n", - "\tacquired COF 26850 with y = 127.468763452\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 155\n", - "\tacquired COF 35805 with y = 93.0131971127\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 156\n", - "\tacquired COF 32656 with y = 161.134340657\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 157\n", - "\tacquired COF 33016 with y = 134.2717009\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 158\n", - "\tacquired COF 66881 with y = 130.937727323\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 159\n", - "\tacquired COF 6435 with y = 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159\n", - "\tacquired COF 37565 with y = 157.145372095\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 160\n", - "\tacquired COF 20683 with y = 170.544003696\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 161\n", - "\tacquired COF 14125 with y = 135.19162469100002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 162\n", - "\tacquired COF 14934 with y = 129.591613927\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 163\n", - "\tacquired COF 26188 with y = 201.17983227599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 164\n", - "\tacquired COF 21314 with y = 194.053101714\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 165\n", - "\tacquired COF 16828 with y = 164.057948476\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 166\n", - "\tacquired COF 33368 with y = 194.708308113\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 167\n", - "\tacquired COF 66078 with y = 190.67549353299998\n", - "\tbest y acquired: 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"iteration: 23\n", - "\tacquired COF 33017 with y = 159.996244004\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 24\n", - "\tacquired COF 26507 with y = 200.44080272099998\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 25\n", - "\tacquired COF 24403 with y = 191.501640273\n", - "\tbest y acquired: 205.492194009\n", - "iteration: 26\n", - "\tacquired COF 33349 with y = 206.74476888599997\n", - "\tbest y acquired: 206.74476888599997\n", - "iteration: 27\n", - "\tacquired COF 33374 with y = 185.76111369\n", - "\tbest y acquired: 206.74476888599997\n", - "iteration: 28\n", - "\tacquired COF 33319 with y = 188.836430211\n", - "\tbest y acquired: 206.74476888599997\n", - "iteration: 29\n", - "\tacquired COF 33347 with y = 208.43022665700002\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 30\n", - "\tacquired COF 33332 with y = 205.963467853\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 31\n", - "\tacquired COF 67589 with y = 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195\n", - "\tacquired COF 20663 with y = 192.274825215\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 196\n", - "\tacquired COF 16414 with y = 180.689671062\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 197\n", - "\tacquired COF 10708 with y = 171.473108903\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 198\n", - "\tacquired COF 35220 with y = 185.162425567\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 199\n", - "\tacquired COF 20614 with y = 192.178789156\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 200\n", - "\tacquired COF 55772 with y = 183.508848648\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 201\n", - "\tacquired COF 14511 with y = 126.631206505\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 202\n", - "\tacquired COF 9704 with y = 183.77337184599997\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 203\n", - "\tacquired COF 14125 with y = 135.19162469100002\n", - "\tbest y acquired: 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230\n", - "\tacquired COF 12243 with y = 172.81117932200002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 231\n", - "\tacquired COF 6454 with y = 188.76981126599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 232\n", - "\tacquired COF 68794 with y = 166.465002949\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 233\n", - "\tacquired COF 21609 with y = 197.517412165\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 234\n", - "\tacquired COF 19974 with y = 190.62920648\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 235\n", - "\tacquired COF 49216 with y = 123.317884276\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 236\n", - "\tacquired COF 19660 with y = 99.0812907516\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 237\n", - "\tacquired COF 20686 with y = 184.837955642\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 238\n", - "\tacquired COF 26613 with y = 185.478431555\n", - "\tbest y acquired: 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acquired: 216.894110699\n", - "iteration: 215\n", - "\tacquired COF 3566 with y = 140.56865170700001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 216\n", - "\tacquired COF 2844 with y = 162.86087542\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 217\n", - "\tacquired COF 6454 with y = 188.76981126599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 218\n", - "\tacquired COF 16511 with y = 176.24564903700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 219\n", - "\tacquired COF 20384 with y = 120.603149838\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 220\n", - "\tacquired COF 29866 with y = 181.753139212\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 221\n", - "\tacquired COF 19207 with y = 124.570109011\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 222\n", - "\tacquired COF 29844 with y = 146.958980683\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 223\n", - "\tacquired COF 33022 with y = 50.3929032032\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 224\n", - "\tacquired COF 33354 with y = 178.47060025599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 225\n", - "\tacquired COF 2147 with y = 168.484688957\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 226\n", - "\tacquired COF 16889 with y = 177.906393671\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 227\n", - "\tacquired COF 14788 with y = 160.115266743\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 228\n", - "\tacquired COF 61065 with y = 131.667696802\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 229\n", - "\tacquired COF 29806 with y = 97.8342570276\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 230\n", - "\tacquired COF 19676 with y = 107.41428795\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 231\n", - "\tacquired COF 28144 with y = 109.288861671\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 232\n", - "\tacquired COF 635 with y = 145.925084609\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 233\n", - "\tacquired COF 67268 with y = 176.81609524900003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 234\n", - "\tacquired COF 23225 with y = 117.15633042200001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 235\n", - "\tacquired COF 16368 with y = 165.86007973600002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 236\n", - "\tacquired COF 26850 with y = 127.468763452\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 237\n", - "\tacquired COF 14517 with y = 147.244343044\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 238\n", - "\tacquired COF 67537 with y = 166.984948927\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 239\n", - "\tacquired COF 33367 with y = 179.49445295799998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 240\n", - "\tacquired COF 69362 with y = 73.9331775403\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 241\n", - "\tacquired COF 28449 with y = 124.73143811\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 242\n", - "\tacquired COF 19660 with y = 99.0812907516\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 243\n", - "\tacquired COF 67353 with y = 133.26622008\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 244\n", - "\tacquired COF 15571 with y = 105.444896447\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 245\n", - "\tacquired COF 19746 with y = 123.96392410899999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 246\n", - "\tacquired COF 25623 with y = 133.706482995\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 247\n", - "\tacquired COF 28172 with y = 63.7611262321\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 248\n", - "\tacquired COF 1079 with y = 161.141779998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 249\n", - "\tacquired COF 2863 with y = 166.765915445\n", - "\tbest y acquired: 216.894110699\n", - "took time t = 8.913509527842203 min\n", - "\n", - "\n", - "RUN 4\n", - "iteration: 10\n", - "\tacquired COF 44374 with y = 174.559120314\n", - "\tbest y acquired: 175.60800624400002\n", - "iteration: 11\n", - "\tacquired COF 9701 with y = 167.156453713\n", - "\tbest y acquired: 175.60800624400002\n", - "iteration: 12\n", - "\tacquired COF 15270 with y = 177.67472951099998\n", - "\tbest y acquired: 177.67472951099998\n", - "iteration: 13\n", - "\tacquired COF 16415 with y = 174.654915912\n", - "\tbest y acquired: 177.67472951099998\n", - "iteration: 14\n", - "\tacquired COF 13585 with y = 165.265766405\n", - "\tbest y acquired: 177.67472951099998\n", - "iteration: 15\n", - "\tacquired COF 16702 with y = 169.771050097\n", - "\tbest y acquired: 177.67472951099998\n", - "iteration: 16\n", - "\tacquired COF 19518 with y = 176.468362255\n", - "\tbest y acquired: 177.67472951099998\n", - "iteration: 17\n", - "\tacquired COF 66363 with y = 190.17935780099998\n", - "\tbest y acquired: 190.17935780099998\n", - "iteration: 18\n", - "\tacquired COF 30394 with y = 179.40758455099999\n", - "\tbest y acquired: 190.17935780099998\n", - "iteration: 19\n", - "\tacquired COF 66860 with y = 182.910685964\n", - "\tbest y acquired: 190.17935780099998\n", - "iteration: 20\n", - "\tacquired COF 5795 with y = 180.789647894\n", - "\tbest y acquired: 190.17935780099998\n", - "iteration: 21\n", - "\tacquired COF 30136 with y = 178.774758072\n", - "\tbest y acquired: 190.17935780099998\n", - "iteration: 22\n", - "\tacquired COF 30552 with y = 167.300911536\n", - "\tbest y acquired: 190.17935780099998\n", - "iteration: 23\n", - "\tacquired COF 30570 with y = 170.049898364\n", - "\tbest y acquired: 190.17935780099998\n", - "iteration: 24\n", - "\tacquired COF 29861 with y = 199.72030120099998\n", - "\tbest y acquired: 199.72030120099998\n", - "iteration: 25\n", - "\tacquired COF 29851 with y = 181.360479551\n", - "\tbest y acquired: 199.72030120099998\n", - "iteration: 26\n", - "\tacquired COF 29856 with y = 191.48812323400003\n", - "\tbest y acquired: 199.72030120099998\n", - "iteration: 27\n", - "\tacquired COF 3611 with y = 165.29457165899998\n", - "\tbest y acquired: 199.72030120099998\n", - "iteration: 28\n", - "\tacquired COF 33366 with y = 204.811726149\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 29\n", - "\tacquired COF 33355 with y = 122.363855499\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 30\n", - "\tacquired COF 33364 with y = 209.36697147400002\n", - "\tbest y acquired: 209.36697147400002\n", - "iteration: 31\n", - "\tacquired COF 33332 with y = 205.963467853\n", - "\tbest y acquired: 209.36697147400002\n", - "iteration: 32\n", - "\tacquired COF 33330 with y = 195.58268240799998\n", - "\tbest y acquired: 209.36697147400002\n", - "iteration: 33\n", - "\tacquired COF 33375 with y = 216.894110699\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 34\n", - "\tacquired COF 33343 with y = 196.58076384900002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 35\n", - "\tacquired COF 33336 with y = 193.51655534\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 36\n", - "\tacquired COF 33321 with y = 209.88488105599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 37\n", - "\tacquired COF 25953 with y = 197.03796965900003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 38\n", - "\tacquired COF 20195 with y = 173.83279745299998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 39\n", - "\tacquired COF 14512 with y = 160.133591285\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 40\n", - "\tacquired COF 12402 with y = 175.504448723\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 41\n", - "\tacquired COF 33349 with y = 206.74476888599997\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 42\n", - "\tacquired COF 33347 with y = 208.43022665700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 43\n", - "\tacquired COF 67589 with y = 172.332089688\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 44\n", - "\tacquired COF 33319 with y = 188.836430211\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 45\n", - "\tacquired COF 33370 with y = 196.720247142\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 46\n", - "\tacquired COF 25951 with y = 196.579974938\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 47\n", - "\tacquired COF 66145 with y = 180.398214003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 48\n", - "\tacquired COF 33358 with y = 201.148834085\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 49\n", - "\tacquired COF 1079 with y = 161.141779998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 50\n", - "\tacquired COF 33371 with y = 199.75064711099998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 51\n", - "\tacquired COF 16404 with y = 171.299812707\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 52\n", - 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"iteration: 61\n", - "\tacquired COF 28174 with y = 140.287860152\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 62\n", - "\tacquired COF 28811 with y = 116.60866129\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 63\n", - "\tacquired COF 29847 with y = 145.586994563\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 64\n", - "\tacquired COF 28176 with y = 91.2868958191\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 65\n", - "\tacquired COF 20796 with y = 195.89774693900003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 66\n", - "\tacquired COF 6448 with y = 171.117194584\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 67\n", - "\tacquired COF 12382 with y = 185.162057723\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 68\n", - "\tacquired COF 30030 with y = 140.521698493\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 69\n", - "\tacquired COF 16000 with y = 94.9682480517\n", - "\tbest y acquired: 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216.894110699\n", - "iteration: 192\n", - "\tacquired COF 26850 with y = 127.468763452\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 193\n", - "\tacquired COF 14306 with y = 153.914872897\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 194\n", - "\tacquired COF 13943 with y = 157.18240617200001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 195\n", - "\tacquired COF 69698 with y = 206.808591001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 196\n", - "\tacquired COF 34780 with y = 187.28305496599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 197\n", - "\tacquired COF 33372 with y = 178.271330036\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 198\n", - "\tacquired COF 33423 with y = 195.404718048\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 199\n", - "\tacquired COF 17462 with y = 157.28452629\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 200\n", - "\tacquired COF 408 with y = 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"iteration: 218\n", - "\tacquired COF 14328 with y = 125.071396416\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 219\n", - "\tacquired COF 21047 with y = 83.6992811651\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 220\n", - "\tacquired COF 20683 with y = 170.544003696\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 221\n", - "\tacquired COF 26613 with y = 185.478431555\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 222\n", - "\tacquired COF 66310 with y = 180.91859135400003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 223\n", - "\tacquired COF 8035 with y = 156.43401938899999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 224\n", - "\tacquired COF 56517 with y = 194.530496788\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 225\n", - "\tacquired COF 16414 with y = 180.689671062\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 226\n", - "\tacquired COF 33369 with y = 171.712054876\n", - "\tbest y 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"\tacquired COF 31021 with y = 27.4640999878\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 109\n", - "\tacquired COF 29868 with y = 185.02377713400003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 110\n", - "\tacquired COF 16416 with y = 177.130147413\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 111\n", - "\tacquired COF 30520 with y = 119.28128983399999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 112\n", - "\tacquired COF 17544 with y = 151.327703601\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 113\n", - "\tacquired COF 2193 with y = 173.11358177099999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 114\n", - "\tacquired COF 30535 with y = 179.81664061900003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 115\n", - "\tacquired COF 14413 with y = 162.896085164\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 116\n", - "\tacquired COF 25961 with y = 163.27637386700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 117\n", - "\tacquired COF 28807 with y = 140.053244392\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 118\n", - "\tacquired COF 29856 with y = 191.48812323400003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 119\n", - "\tacquired COF 20440 with y = 123.382918247\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 120\n", - "\tacquired COF 19622 with y = 16.8277012796\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 121\n", - "\tacquired COF 33345 with y = 192.672521193\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 122\n", - "\tacquired COF 68777 with y = 204.958050668\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 123\n", - "\tacquired COF 20796 with y = 195.89774693900003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 124\n", - "\tacquired COF 68871 with y = 205.189199744\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 125\n", - "\tacquired COF 17669 with y = 126.295568998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 126\n", - "\tacquired COF 26841 with y = 139.04109103\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 127\n", - "\tacquired COF 33022 with y = 50.3929032032\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 128\n", - "\tacquired COF 33508 with y = 170.264908225\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 129\n", - "\tacquired COF 67256 with y = 191.462943805\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 130\n", - "\tacquired COF 27035 with y = 178.57489196900002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 131\n", - "\tacquired COF 67351 with y = 119.31858380799999\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 132\n", - "\tacquired COF 12243 with y = 172.81117932200002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 133\n", - "\tacquired COF 6437 with y = 191.077676114\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 134\n", - "\tacquired COF 33354 with y = 178.47060025599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 135\n", - "\tacquired COF 66310 with y = 180.91859135400003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 136\n", - "\tacquired COF 31023 with y = 91.0515080749\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 137\n", - "\tacquired COF 20720 with y = 190.806274437\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 138\n", - "\tacquired COF 33016 with y = 134.2717009\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 139\n", - "\tacquired COF 35773 with y = 120.458592721\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 140\n", - "\tacquired COF 26606 with y = 60.3163108521\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 141\n", - "\tacquired COF 19228 with y = 183.50656627400002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 142\n", - "\tacquired COF 16567 with y = 194.20146897700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 143\n", - "\tacquired COF 68879 with y = 174.930216574\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 144\n", - "\tacquired COF 66145 with y = 180.398214003\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 145\n", - "\tacquired COF 6435 with y = 188.242123191\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 146\n", - "\tacquired COF 12425 with y = 178.63841840799998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 147\n", - "\tacquired COF 33365 with y = 198.020772317\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 148\n", - "\tacquired COF 26188 with y = 201.17983227599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 149\n", - "\tacquired COF 2497 with y = 172.46977255299998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 150\n", - "\tacquired COF 20713 with y = 176.60991058599998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 151\n", - "\tacquired COF 28173 with y = 140.45889762\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 152\n", - "\tacquired COF 69405 with y = 69.5967411775\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 153\n", - "\tacquired COF 33326 with y = 95.1787030835\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 154\n", - "\tacquired COF 26838 with y = 122.05731206600001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 155\n", - "\tacquired COF 30278 with y = 178.4514143\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 156\n", - "\tacquired COF 12348 with y = 159.76088175799998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 157\n", - "\tacquired COF 16533 with y = 192.863029816\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 158\n", - "\tacquired COF 33018 with y = 168.039155365\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 159\n", - "\tacquired COF 35220 with y = 185.162425567\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 160\n", - "\tacquired COF 33340 with y = 155.15457032700002\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 161\n", - "\tacquired COF 14381 with y = 96.91691595350001\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 162\n", - "\tacquired COF 20696 with y = 197.86041748099998\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 163\n", - "\tacquired COF 33372 with y = 178.271330036\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 164\n", - "\tacquired COF 2051 with y = 198.138166855\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 165\n", - "\tacquired COF 21662 with y = 189.901093629\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 166\n", - "\tacquired COF 15642 with y = 105.875950103\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 167\n", - "\tacquired COF 2147 with y = 168.484688957\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 168\n", - "\tacquired COF 26661 with y = 181.08434414\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 169\n", - "\tacquired COF 66097 with y = 185.043611707\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 170\n", - "\tacquired COF 47232 with y = 53.479961007\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 171\n", - "\tacquired COF 67589 with y = 172.332089688\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 172\n", - "\tacquired COF 40794 with y = 194.352667969\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 173\n", - "\tacquired COF 17550 with y = 166.581672788\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 174\n", - "\tacquired COF 28812 with y = 85.1282514861\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 175\n", - "\tacquired COF 67206 with y = 206.864600037\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 176\n", - "\tacquired COF 5165 with y = 193.408466045\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 177\n", - "\tacquired COF 20689 with y = 96.7601788104\n", - "\tbest y acquired: 216.894110699\n", - "iteration: 178\n" + "\tbest y acquired: 190.67549353299998\n", + "took time t = 0.6949090321858724 min\n" ] } ], "source": [ "bo_res = dict()\n", - "bo_res['nb_runs'] = 10\n", - "bo_res['nb_iterations'] = 250\n", + "bo_res['nb_runs'] = 1\n", + "bo_res['nb_iterations'] = 15\n", "bo_res['ids_acquired'] = []\n", "for r in range(bo_res['nb_runs']):\n", " print(\"\\n\\nRUN\", r)\n", @@ -5161,19 +338,19 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "played-albania", + "execution_count": 48, + "id": "tropical-poland", "metadata": {}, "outputs": [], "source": [ - "with open('bo_results.pkl', 'wb') as file:\n", + "with open('bo_results' + which_acquisition + '.pkl', 'wb') as file:\n", " pickle.dump(bo_res, file)" ] }, { "cell_type": "code", "execution_count": null, - "id": "thorough-catalog", + "id": "sexual-munich", "metadata": {}, "outputs": [], "source": [] diff --git a/new/evol_search.ipynb b/new/evol_search.ipynb index 60b7ad3..aa961d8 100644 --- a/new/evol_search.ipynb +++ b/new/evol_search.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "short-estonia", + "id": "pacific-detective", "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "atlantic-edward", + "id": "nasty-parcel", "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "athletic-devon", + "id": "acute-butler", "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "breeding-organic", + "id": "collectible-florida", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "brilliant-mistake", + "id": "verified-distributor", "metadata": {}, "outputs": [], "source": [ @@ -83,27 +83,16 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "seventh-faith", + "execution_count": null, + "id": "peripheral-quick", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "48442" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 10, - "id": "delayed-treatment", + "execution_count": 6, + "id": "dynamic-railway", "metadata": {}, "outputs": [], "source": [ @@ -142,8 +131,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "catholic-musical", + "execution_count": 7, + "id": "mysterious-launch", "metadata": {}, "outputs": [ { @@ -153,286 +142,286 @@ "\n", "\n", "RUN 0\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=282932, Thu Jul 1 14:53:00 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=299639, Thu Jul 1 19:34:01 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 161.035530703\n", + "\t# max y value: 172.81117932200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# 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"\t# max y value: 196.752963258\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.171240133\n", "\n", "\n", "RUN 1\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=219189, Thu Jul 1 14:53:02 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=267909, Thu Jul 1 19:34:03 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 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99\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 194.530496788\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 197.86041748099998\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 197.86041748099998\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 197.86041748099998\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 200.44080272099998\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 200.44080272099998\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 200.44080272099998\n", + "\t# max y value: 206.54342821400002\n", "\n", "\n", "RUN 2\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=207560, Thu Jul 1 14:53:04 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=274451, Thu Jul 1 19:34:05 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 146.427775435\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.71569396400002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 172.71569396400002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 178.99445053\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 180.249541863\n", + "\t# max y value: 182.111370584\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 181.18376571\n", + "\t# max y value: 182.910685964\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 185.76111369\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 185.76111369\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "\n", "\n", "RUN 3\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=339397, Thu Jul 1 14:53:05 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=254761, Thu Jul 1 19:34:07 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 172.81117932200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 201.66490141\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 201.66490141\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", "\t# max y value: 207.39578187\n", @@ -445,25 +434,25 @@ "\n", "\n", "RUN 4\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=331179, Thu Jul 1 14:53:07 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=267003, Thu Jul 1 19:34:09 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 168.172206806\n", + "\t# max y value: 164.067845055\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 183.77337184599997\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 183.77337184599997\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 183.77337184599997\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 183.77337184599997\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", "\t# max y value: 194.37058873700002\n", @@ -478,250 +467,3024 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 195.58268240799998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.708308113\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 216.894110699\n", "\n", "\n", "RUN 5\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=294680, Thu Jul 1 14:53:09 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=339643, Thu Jul 1 19:34:11 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 178.63841840799998\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 178.63841840799998\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 178.63841840799998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.108264299\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 207.39578187\n", "\n", "\n", "RUN 6\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=258871, Thu Jul 1 14:53:11 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=265112, Thu Jul 1 19:34:13 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 171.144256451\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 180.853194423\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 180.853194423\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 180.853194423\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 7\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=237361, Thu Jul 1 19:34:15 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 8\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=270969, Thu Jul 1 19:34:16 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 9\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=313865, Thu Jul 1 19:34:18 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 10\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=271733, Thu Jul 1 19:34:20 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 193.528032337\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 193.528032337\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 193.528032337\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 209.36697147400002\n", + "\n", + "\n", + "RUN 11\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=354633, Thu Jul 1 19:34:21 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 12\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=260980, Thu Jul 1 19:34:23 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 13\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=206220, Thu Jul 1 19:34:25 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.48474798200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.48474798200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.48474798200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.48474798200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.48474798200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.48474798200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 198.96574226299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 14\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=376715, Thu Jul 1 19:34:26 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.965999767\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.71569396400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.54342821400002\n", + "\n", + "\n", + "RUN 15\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=290338, Thu Jul 1 19:34:28 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 181.198814215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 181.198814215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.48474798200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 202.21921792700002\n", + "\n", + "\n", + "RUN 16\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=255573, Thu Jul 1 19:34:30 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 195.973650655\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 195.973650655\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 202.21921792700002\n", + "\n", + "\n", + "RUN 17\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=244493, Thu Jul 1 19:34:32 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 166.581672788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 174.833095498\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 174.833095498\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 178.63841840799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 183.50656627400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 183.50656627400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 190.17935780099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 199.72030120099998\n", + "\n", + "\n", + "RUN 18\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=365490, Thu Jul 1 19:34:33 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.9895885\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 19\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=310914, Thu Jul 1 19:34:35 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 20\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=240071, Thu Jul 1 19:34:37 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 21\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=291458, Thu Jul 1 19:34:38 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 173.92050685200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 173.92050685200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 173.92050685200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 22\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=306732, Thu Jul 1 19:34:40 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 23\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=293290, Thu Jul 1 19:34:42 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.963467853\n", + "\n", + "\n", + "RUN 24\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=328073, Thu Jul 1 19:34:43 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.171240133\n", + "\n", + "\n", + "RUN 25\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=340521, Thu Jul 1 19:34:45 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.32668546099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.32668546099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.32668546099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.32668546099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 200.420314123\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 200.420314123\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 200.420314123\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 200.420314123\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 200.420314123\n", + "\n", + "\n", + "RUN 26\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=257086, Thu Jul 1 19:34:47 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 199.72030120099998\n", + "\n", + "\n", + "RUN 27\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=287870, Thu Jul 1 19:34:48 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.642146113\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.720247142\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 204.811726149\n", + "\n", + "\n", + "RUN 28\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=327021, Thu Jul 1 19:34:50 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 204.958050668\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 204.958050668\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.189199744\n", + "\n", + "\n", + "RUN 29\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=276344, Thu Jul 1 19:34:52 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 197.517412165\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 197.517412165\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 197.517412165\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 197.517412165\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 197.517412165\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 197.517412165\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 30\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=359192, Thu Jul 1 19:34:53 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 173.92050685200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.189199744\n", + "\n", + "\n", + "RUN 31\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=263304, Thu Jul 1 19:34:55 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 32\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=311897, Thu Jul 1 19:34:57 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.75064711099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 33\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=319479, Thu Jul 1 19:34:58 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 175.646129915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 190.95759162299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 190.95759162299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 190.95759162299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 192.502149782\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.171240133\n", + "\n", + "\n", + "RUN 34\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=346557, Thu Jul 1 19:35:00 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 196.720247142\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 209.36697147400002\n", + "\n", + "\n", + "RUN 35\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=266321, Thu Jul 1 19:35:02 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 198.138166855\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 198.138166855\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 198.138166855\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 198.138166855\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 198.138166855\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 198.138166855\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 198.138166855\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.171240133\n", + "\n", + "\n", + "RUN 36\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=296151, Thu Jul 1 19:35:04 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 192.178789156\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 192.178789156\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 192.178789156\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 192.178789156\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.189199744\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.74476888599997\n", + "\n", + "\n", + "RUN 37\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=303660, Thu Jul 1 19:35:05 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 178.997150426\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 192.422391866\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 192.422391866\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.74476888599997\n", + "\n", + "\n", + "RUN 38\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=315731, Thu Jul 1 19:35:07 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 198.792072623\n", + "\n", + "\n", + "RUN 39\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=248182, Thu Jul 1 19:35:09 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 192.422391866\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 192.422391866\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 192.422391866\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.171240133\n", + "\n", + "\n", + "RUN 40\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=300872, Thu Jul 1 19:35:10 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 41\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=254513, Thu Jul 1 19:35:12 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 161.279690414\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.708308113\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.708308113\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 204.811726149\n", + "\n", + "\n", + "RUN 42\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=294321, Thu Jul 1 19:35:14 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 201.17983227599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 201.17983227599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 201.17983227599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 43\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=302811, Thu Jul 1 19:35:15 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 186.09094075099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.958050668\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.958050668\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.171240133\n", + "\n", + "\n", + "RUN 44\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=262833, Thu Jul 1 19:35:17 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 199.72030120099998\n", "\n", "\n", - "RUN 7\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=291157, Thu Jul 1 14:53:13 2021)\n", + "RUN 45\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=289860, Thu Jul 1 19:35:19 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 173.92050685200002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 188.76981126599998\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 193.51655534\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 193.51655534\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 193.51655534\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 193.51655534\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 196.579974938\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 197.86041748099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 197.86041748099998\n", + "\t# max y value: 206.22060552\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 197.86041748099998\n", + "\t# max y value: 206.22060552\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 197.86041748099998\n", + "\t# max y value: 206.22060552\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", "\t# max y value: 207.39578187\n", @@ -736,44 +3499,117 @@ "\t# max y value: 207.39578187\n", "\n", "\n", - "RUN 8\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=204744, Thu Jul 1 14:53:15 2021)\n", + "RUN 46\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=358462, Thu Jul 1 19:35:20 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.171240133\n", + "\n", + "\n", + "RUN 47\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=304630, Thu Jul 1 19:35:22 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 186.04049377\n", + "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 201.148834085\n", + "\t# max y value: 186.04049377\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 201.148834085\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 201.148834085\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", "\t# max y value: 206.864600037\n", @@ -800,92 +3636,165 @@ "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 206.864600037\n", "\n", "\n", - "RUN 9\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=315141, Thu Jul 1 14:53:16 2021)\n", + "RUN 48\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=307920, Thu Jul 1 19:35:24 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 165.80833295899998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 180.249541863\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 183.77337184599997\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 196.58076384900002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 199.90463220799998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 199.90463220799998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 199.90463220799998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.74476888599997\n", + "\n", + "\n", + "RUN 49\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=278723, Thu Jul 1 19:35:26 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 178.99445053\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 178.99445053\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 178.99445053\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.864600037\n" + "\t# max y value: 199.72030120099998\n" ] } ], "source": [ "es_res = dict()\n", - "es_res['nb_runs'] = 10\n", + "es_res['nb_runs'] = 50\n", "es_res['nb_iterations'] = 250\n", "es_res['ids_acquired'] = []\n", "for r in range(es_res['nb_runs']):\n", @@ -900,7 +3809,7 @@ { "cell_type": "code", "execution_count": null, - "id": "binary-retention", + "id": "logical-istanbul", "metadata": {}, "outputs": [], "source": [] diff --git a/new/random_forest_run.ipynb b/new/random_forest_run.ipynb index becf540..068bd6c 100644 --- a/new/random_forest_run.ipynb +++ b/new/random_forest_run.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "chinese-orientation", + "id": "metallic-quilt", "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "partial-block", + "id": "entertaining-kansas", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "after-terry", + "id": "prime-still", "metadata": {}, "outputs": [], "source": [ @@ -79,17 +79,17 @@ { "cell_type": "code", "execution_count": 4, - "id": "suburban-desperate", + "id": "duplicate-cornwall", "metadata": {}, "outputs": [], "source": [ - "diversify_training = False" + "diversify_training = True" ] }, { "cell_type": "code", "execution_count": 5, - "id": "fuzzy-healing", + "id": "amazing-counter", "metadata": {}, "outputs": [], "source": [ @@ -134,8 +134,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "exclusive-reggae", + "execution_count": null, + "id": "hourly-motivation", "metadata": {}, "outputs": [ { @@ -145,502 +145,363 @@ "eval budgets: [20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240]\n", "budget for evals: 20\n", "\trun 0\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 182.34542236299998\n", + "\tmax y acquired = 170.915156833\n", "\trun 1\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 182.416471606\n", + "\tmax y acquired = 174.168128644\n", "\trun 2\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 178.44859215099999\n", + "\tmax y acquired = 168.172206806\n", "\trun 3\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 190.40496791\n", + "\tmax y acquired = 179.257243776\n", "\trun 4\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 187.41220189\n", + "\tmax y acquired = 175.23753918900002\n", "\trun 5\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 184.504347793\n", + "\tmax y acquired = 194.20146897700002\n", "\trun 6\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.089815979\n", + "\tmax y acquired = 172.306975483\n", "\trun 7\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 187.973529905\n", + "\tmax y acquired = 187.96960308099997\n", "\trun 8\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.306286228\n", + "\tmax y acquired = 159.76088175799998\n", "\trun 9\n", - "\tRF run\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 177.734140541\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 147.372826457\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 180.36987107599998\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.690277919\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 161.01020700200002\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 164.19932299299998\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 159.702669558\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 186.36782056400003\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 161.493919017\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 169.901491297\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 171.70538126\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 155.191172441\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 180.51803323299998\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 189.901093629\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 162.964548393\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 166.15976674\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 194.20146897700002\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 172.79260676\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 178.35628773099998\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 172.37939513400002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 167.063296154\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 180.95214931\n", + "\trun 31\n", + "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.020860814\n", + "\tmax y acquired = 168.62852097299998\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 161.521853579\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 188.39498494\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 152.71130296\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 156.34060864100002\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 144.0749746\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 162.805857941\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 165.303562487\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 180.27321225900002\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 178.84845810599998\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 153.909520014\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.96001819999998\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 185.160077795\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 166.725680664\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 174.24511580599997\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 148.73144464700002\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 160.778133869\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 185.50953227099998\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 185.22656223799999\n", "budget for evals: 40\n", "\trun 0\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 188.20542767400002\n", + "\tmax y acquired = 187.02128687799998\n", "\trun 1\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 190.96105800200002\n", + "\tmax y acquired = 161.639932773\n", "\trun 2\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 176.501690325\n", + "\tmax y acquired = 191.11955720900002\n", "\trun 3\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.238096277\n", + "\tmax y acquired = 185.196241577\n", "\trun 4\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 197.87398978299998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 5\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 183.784918482\n", + "\tmax y acquired = 193.949996568\n", "\trun 6\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.202599364\n", + "\tmax y acquired = 196.752963258\n", "\trun 7\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 177.191166175\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 8\n", - "\tRF run\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 189.546131494\n", + "\tmax y acquired = 191.507774129\n", "\trun 9\n", - "\tRF run\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 191.077676114\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 181.735941442\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 179.489994305\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 180.77479322\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 173.0231062\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 191.48812323400003\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 180.789647894\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 17\n", + "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 187.973529905\n", - "budget for evals: 60\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 207.39578187\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 192.9359556\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 183.478905752\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 195.915962745\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 186.79890652900002\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.80467023\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.530496788\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 186.800115493\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.88923220900003\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 174.428712513\n", - "budget for evals: 80\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.264226622\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.530496788\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 197.35770853900001\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 193.408466045\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 183.56630674599998\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 185.382350173\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 201.17983227599998\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 197.35770853900001\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 187.822691684\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "budget for evals: 100\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 195.448316445\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 190.91146585599998\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 216.894110699\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 189.186508129\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 198.034754095\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 193.05167775400002\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 207.39578187\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 188.37269533\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 207.39578187\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 186.577611083\n", - "budget for evals: 120\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.568968117\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 193.41397958\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 194.58787007400002\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 189.937977395\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 203.35670863099998\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 193.25083398700002\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 193.28874060799998\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 187.72054369999998\n", - "budget for evals: 140\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 197.918308448\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 191.108264299\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 194.766126231\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 207.39578187\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.625762218\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", "\tmax y acquired = 209.88488105599998\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", "\tmax y acquired = 196.752963258\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 205.492194009\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 193.72992463\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.37724838900002\n", - "budget for evals: 160\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 196.88923220900003\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 192.532706025\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 191.108264299\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 195.289662613\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 207.39578187\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 191.779475121\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 201.17983227599998\n", - "budget for evals: 180\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 201.17983227599998\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 205.492194009\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 191.45735074700002\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 188.63741461200001\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 193.675158092\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 193.562944445\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 193.949996568\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", - "budget for evals: 200\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 201.66490141\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.55088119400003\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 202.848493155\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 190.42701679299998\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 198.138166855\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 193.620114578\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.9895885\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", "\tmax y acquired = 209.36697147400002\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.54342821400002\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 202.848493155\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.698499548\n", - "budget for evals: 220\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 194.530496788\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 195.978854341\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 205.492194009\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 194.938530808\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 201.66490141\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", "\tmax y acquired = 216.894110699\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", - "budget for evals: 240\n", - "\trun 0\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 199.80359465400002\n", - "\trun 1\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 196.88923220900003\n", - "\trun 2\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 195.289662613\n", - "\trun 3\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 194.530496788\n", - "\trun 4\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 198.034754095\n", - "\trun 5\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", - "\trun 6\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 207.39578187\n", - "\trun 7\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 200.40213550099998\n", - "\trun 8\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 194.38766055\n", - "\trun 9\n", - "\tRF run\n", - "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 195.289662613\n" + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 186.43690669900002\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 169.05009591299998\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 172.71569396400002\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 198.020772317\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n" ] } ], "source": [ "rf_res = dict()\n", - "rf_res['nb_runs'] = 10\n", + "rf_res['nb_runs'] = 50\n", "rf_res['nb_evals_budgets'] = [20 * i for i in range(1, 13)]\n", "print(\"eval budgets: \", rf_res['nb_evals_budgets'])\n", "rf_res['ids_acquired'] = [[] for b in rf_res['nb_evals_budgets']]\n", @@ -667,7 +528,7 @@ { "cell_type": "code", "execution_count": null, - "id": "powered-definition", + "id": "proud-percentage", "metadata": {}, "outputs": [], "source": [] diff --git a/new/random_search.ipynb b/new/random_search.ipynb index 95e9e55..fe8d2c1 100644 --- a/new/random_search.ipynb +++ b/new/random_search.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "sapphire-conviction", + "id": "perceived-winter", "metadata": {}, "source": [ "# random search" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "dynamic-universal", + "id": "empty-framework", "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "whole-caution", + "id": "broke-exhaust", "metadata": {}, "outputs": [ { @@ -56,12 +56,12 @@ { "cell_type": "code", "execution_count": 3, - "id": "functional-mechanism", + "id": "sufficient-australia", "metadata": {}, "outputs": [], "source": [ "rs_res = dict()\n", - "rs_res['nb_runs'] = 10\n", + "rs_res['nb_runs'] = 50\n", "rs_res['nb_iterations'] = 250\n", "rs_res['ids_acquired'] = []\n", "for r in range(rs_res['nb_runs']):\n", diff --git a/new/viz.ipynb b/new/viz.ipynb index 16a9fc1..dcc7b9a 100644 --- a/new/viz.ipynb +++ b/new/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "lesser-publicity", + "id": "driving-homeless", "metadata": {}, "source": [ "# viz" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "romance-clinton", + "id": "developing-space", "metadata": {}, "outputs": [ { @@ -48,12 +48,14 @@ "cool_colors = ['#00BEFF', '#D4CA3A', '#FF6DAE', '#67E1B5', '#EBACFA', '#9E9E9E', '#F1988E', '#5DB15A', '#E28544', '#52B8AA']\n", "cool_colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7']\n", "\n", + "plt.rcParams.update({'font.size': 14})\n", + "\n", "search_to_color = {'BO': cool_colors[0], 'random': cool_colors[1], 'evolutionary': cool_colors[2], 'RF': cool_colors[5], 'RF (div)': cool_colors[3]}" ] }, { "cell_type": "markdown", - "id": "amino-african", + "id": "electoral-advantage", "metadata": {}, "source": [ "load data" @@ -62,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "based-climate", + "id": "objective-poison", "metadata": {}, "outputs": [ { @@ -93,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "fatty-conditions", + "id": "soviet-forty", "metadata": {}, "source": [ "for rankings" @@ -102,7 +104,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "statewide-genesis", + "id": "mediterranean-jenny", "metadata": {}, "outputs": [], "source": [ @@ -113,7 +115,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "behavioral-palace", + "id": "occupational-stability", "metadata": {}, "outputs": [ { @@ -134,7 +136,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "imperial-mixture", + "id": "injured-pierce", "metadata": {}, "outputs": [ { @@ -154,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "several-ballot", + "id": "optional-drive", "metadata": {}, "source": [ "load search results" @@ -163,7 +165,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "interpreted-tucson", + "id": "worthy-federation", "metadata": {}, "outputs": [], "source": [ @@ -176,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "technological-family", + "id": "necessary-workplace", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -185,7 +187,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "mechanical-mouse", + "id": "unusual-league", "metadata": {}, "outputs": [], "source": [ @@ -197,12 +199,12 @@ { "cell_type": "code", "execution_count": 8, - "id": "technical-tribe", + "id": "applied-vision", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -230,12 +232,12 @@ { "cell_type": "code", "execution_count": 9, - "id": "global-azerbaijan", + "id": "ignored-alberta", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -263,20 +265,20 @@ " ax[i].scatter(X_2D[ids_acquired, 0], X_2D[ids_acquired, 1], \n", " c=y[ids_acquired], marker=\"+\", s=55, vmin=cb.vmin, vmax=cb.vmax)\n", " ax[i].set_title('{} acquired COFs'.format(nb_acquired[i]))\n", - " ax[i].tick_params(axis='x', labelsize=10)\n", + " ax[i].tick_params(axis='x')\n", "ax[0].set_ylabel('PCA dimension 2', fontsize=14)\n", "\n", "ax[2].tick_params(axis='y', labelsize=0)\n", "\n", "\n", - "fig.text(0.5, 0.2, 'PCA dimension 1', ha='center', fontsize=14)\n", + "fig.text(0.5, 0.2, 'PCA dimension 1', ha='center')\n", "plt.tight_layout()\n", "plt.savefig(\"feature_space_acquired_COFs.pdf\")" ] }, { "cell_type": "markdown", - "id": "burning-celebrity", + "id": "illegal-yemen", "metadata": {}, "source": [ "# search efficiency\n", @@ -286,7 +288,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "descending-charger", + "id": "promotional-measurement", "metadata": {}, "outputs": [ { @@ -328,7 +330,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "creative-grill", + "id": "daily-belly", "metadata": {}, "outputs": [], "source": [ @@ -353,7 +355,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "qualified-basic", + "id": "commercial-judges", "metadata": {}, "outputs": [], "source": [ @@ -364,7 +366,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "infinite-constraint", + "id": "complicated-royal", "metadata": {}, "outputs": [ { @@ -377,7 +379,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -408,20 +410,20 @@ " alpha=0.2, ec=\"None\", color=search_to_color['random'])\n", "\n", "axs[0].set_xlabel('# evaluated COFs')\n", - "axs[0].set_ylabel('maximum deliverable capacity\\namong evaluated COFs\\n[L STP/L]')\n", + "axs[0].set_ylabel('maximum deliverable capacity\\namong acquired COFs\\n[L STP/L]')\n", "\n", "\n", "# RFs\n", "axs[0].errorbar(rf_res['nb_evals_budgets'], y_max_mu_rf, yerr=np.vstack((y_max_sig_bot_rf, y_max_sig_top_rf)), color=search_to_color['RF'], marker=\"s\", label=\"random forest\", linestyle=\"none\")\n", "axs[0].errorbar(rf_div_res['nb_evals_budgets'], y_max_mu_rf_div, yerr=np.vstack((y_max_sig_bot_rf_div, y_max_sig_top_rf_div)), color=search_to_color['RF (div)'], marker=\"s\", label=\"random forest\\n(diverse train set)\", linestyle=\"none\")\n", "\n", - "axs[0].axhline(y=np.max(y), color=\"0.5\", linestyle=\"--\", label=\"$\\max y_i$\")\n", + "axs[0].axhline(y=np.max(y), color=\"k\", linestyle=\"--\", label=\"$\\max$ $y_i$\", zorder=0)\n", "# axs[0].set_ylim(ymin=0.0)\n", - "axs[0].set_xlim([0, 500])\n", + "axs[0].set_xlim([0, 250])\n", "axs[0].legend()\n", "\n", "axs[1].hist(y, orientation=\"horizontal\", color=cool_colors[7])\n", - "axs[1].set_xlabel(\"# COFs\", fontsize=13)\n", + "axs[1].set_xlabel(\"# COFs\")\n", "axs[1].set_xscale(\"log\")\n", "\n", "plt.tight_layout()\n", @@ -432,7 +434,7 @@ }, { "cell_type": "markdown", - "id": "copyrighted-miniature", + "id": "rising-replication", "metadata": {}, "source": [ "show distribution for context." @@ -441,12 +443,12 @@ { "cell_type": "code", "execution_count": 14, - "id": "ceramic-morgan", + "id": "answering-enough", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -469,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "honey-packing", + "id": "still-explorer", "metadata": {}, "source": [ "### max rank among acquired set" @@ -478,7 +480,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "medical-australia", + "id": "egyptian-steel", "metadata": {}, "outputs": [ { @@ -506,7 +508,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "unexpected-discipline", + "id": "crucial-buying", "metadata": {}, "outputs": [ { @@ -519,7 +521,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -549,27 +551,27 @@ " alpha=0.2, ec=\"None\", color=search_to_color['random'])\n", "\n", "# RFs\n", - "plt.errorbar(rf_res['nb_evals_budgets'], y_max_mu_rf, yerr=np.vstack((y_max_sig_bot_rf, y_max_sig_top_rf)), color=search_to_color['RF'], marker=\"s\", label=\"random forest\")\n", - "plt.errorbar(rf_div_res['nb_evals_budgets'], y_max_mu_rf_div, yerr=np.vstack((y_max_sig_bot_rf_div, y_max_sig_top_rf_div)), color=search_to_color['RF (div)'], marker=\"s\", label=\"random forest\\n(diverse train set)\")\n", + "plt.errorbar(rf_res['nb_evals_budgets'], y_max_mu_rf, yerr=np.vstack((y_max_sig_bot_rf, y_max_sig_top_rf)), color=search_to_color['RF'], marker=\"s\", label=\"random forest\", linestyle=\"None\")\n", + "plt.errorbar(rf_div_res['nb_evals_budgets'], y_max_mu_rf_div, yerr=np.vstack((y_max_sig_bot_rf_div, y_max_sig_top_rf_div)), color=search_to_color['RF (div)'], marker=\"s\", label=\"random forest\\n(diverse train set)\", linestyle=\"None\")\n", "\n", "plt.xlabel('# evaluated COFs')\n", - "plt.ylabel('highest rank\\namong evaluated COFs')\n", - "plt.xlim([0, 500])\n", + "plt.ylabel('highest rank\\namong acquired COFs')\n", + "plt.xlim([0, 250])\n", "# plt.ylim(ymin=1)\n", "# plt.legend(fontsize=1/4)\n", - "# plt.axhline(y=0) # to see the band bleed into negative zone.\n", + "plt.axhline(y=1, color=\"k\", linestyle=\"--\", zorder=0) # to see the band bleed into negative zone.\n", "plt.yticks()\n", "plt.xticks()\n", "plt.yscale(\"log\")\n", "plt.gca().invert_yaxis()\n", "plt.tight_layout()\n", - "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=15)\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n", "plt.savefig(\"search_efficiency_rank.pdf\")#, bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", - "id": "emotional-andrew", + "id": "instrumental-recruitment", "metadata": {}, "source": [ "### fraction of top 100 COFs recovered" @@ -578,7 +580,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "circular-money", + "id": "greenhouse-savannah", "metadata": {}, "outputs": [ { @@ -598,7 +600,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "physical-rehabilitation", + "id": "insured-certification", "metadata": {}, "outputs": [], "source": [ @@ -613,7 +615,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "prepared-stroke", + "id": "clinical-addiction", "metadata": {}, "outputs": [ { @@ -652,7 +654,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "lucky-tampa", + "id": "straight-albania", "metadata": {}, "outputs": [], "source": [ @@ -677,7 +679,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "pleasant-anatomy", + "id": "scientific-blackjack", "metadata": {}, "outputs": [ { @@ -690,7 +692,7 @@ }, { "data": { - "image/png": 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\n", 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eBNR24Ku75fU7jhaPp0rRgX1FWbh5TkrONbwMAmvmseZNrz8DnaRVJoS/Q8pgfwqwqh6P5xG9FfiMUuYjWlu/nOH8s2venq6UWRstFwSeCuw8jP7PK4FAb8849rr84OvgUOS3HZm/WR5uXSrg5jQctOHYGDy3psiL41UXPTMpXstMgQLzQa8/A52klQdkAv+htVVf8noXcJVXJ+itSLbsRtzO5DBto2PySDbqRUEmk+3psd9elx98HcxW/lxF8qsVHRho8W3juvC9/fL6OSumzskcLMPGOBzRBRXBe/0Z6CStDNCjgSub7P8a8Nom+49FJqs2A+chmamrlIB9Wlu9vbLPx2eJUHHFUwHIVCTb9J6SeCvLW5Q82F2ET+6AP41JZoJnLJfttgPpitTW2eCPfC05WhmgMOKlzES+WRtaW1u9l0vCZ41EOlOGdrHS6/KDr4OZ5C9U4JY0lD0D5LgyV7My3Di6rOhIpNv+kswN/W5Uggf6AvCh4ySL9MGShGMvC8Hpye5Zc9Prz0AnaWWAHgAuBWYKNHgqUqm0JUqZT0PqCm0AnuqV934VsEVr69dt9ndBSST6Wh+0hOl1+cHXwUzy7yxKWPOKuqCC0bIk+7z+oHhFVbJeGYIqIQMuHYJ/XivRbYWKzBddNNA9hqdKrz8DnaTVrf0K8GGlzGfV71DKfDbwIe+YpihlvhT4LvAgMixX/QkRBN4ymw4vJCMj6YXuwoLS6/KDr4ODw2lsz3BUQ51LDjxcgIEax+D+HPzbFnj6XfDpnZLTbX958i/nwIl98OJVcOWR8LPT4P0bxPiAhGyflug+4wP+M9BJWnlAn0aqi/5YKXMTcJ+3/STgeOAH3jGteAvwaq2tb3teT5U/Au+bXZd9fHwWiq0FuG1YUtZkKrIAdNSGb++TmjwgmQXuzclrA6mZ88JVcHzNHE7QG1prNDxnOzIPtMwf6VryNDVAWlsu8CKlzB8CL2FyIer9wLu1tr7b5nWOB/7QYHsGWVO0KAgEFjD2swvodfmht3VQcmB32WBlEmwksODurAQP/CUz9dhEEJ69XAzPUbOMXBurSBh2t6q6l5+BTtNWJgTP0LRrbBqxCzgB2Fq3/WLg4XYb8bJwfxmZlzoAvF1r69oGxykkvHsFYuS+A5haW7a3/xjgGuB8YBtwpdbWr1pdf3BwoN2uLkl6XX7obR3sKkI4NUAoAK4DH9g6WcxtRRiuWieZC0KGJAdtJ69alUJFDA/I0F79AtVuopefgU7TdIRVKfNopcwveilz6vcNKGV+QSnzqDau80Xgk0qZF3rvj1LKvAz4KPC5WfT3M0j49mrgpcDnlDJPaXDcT4Cztbb6gVOBM4A31uy/DvgLsBx4B/B9pcyV01qpI50ea3XIkqbX5Yfe1UG2IklCY/kxxmx4w4NifKIGvGgVfOMkeNpyuGQIHjc4O+NTdmC8IkXdLhmEc/u7u75Orz4Dc0GrKb6rgaLW1jSNa22lgSLw5lYX0dr6KPBD4H+BBPAb4PPA57W2PtNOR5UyE0g+undpbWW0tm5GDM3LGlzv4Zq8cwYS4bnRa+cE4GzgPVpbea2tHwB/89puSqXitDpkSdPr8kNv6qBQgb+OS92c3SW4/H64fVyG4L54Irx5Paxs4LEUHSlXXXElQi5TqfmzJcx6X1HmkM5MShvxYOO2uolefAbmila/M54M/HOT/dciQ1kt0dp6h1LmB4CTEcN3r9ZWpsVptZwA2FpbtWHfdwKPb3SwUuZLECOXQobrrvZ2nQJs1toar2unkSfl49Mz7ChIks9aXGDElg/snhK8fnuStCNZCT6xceYcbBlbfvWtCMsiUwxYGYLaLDb9ASmrHTKmFoXz6R1aGaBjaJ6rbRdwdLsX09rKAbcrZcaBC5UyH6xZrNqKJJPJUKukEQPT6FrXAtcqZR4PvBzYW9NOfRxlGjiyUTtKmVcAVwA8+9nP5IwzzgAgHo8RCgUZH88CEA6HSCYTEyGahgFDQ4OMjY1PlDEeGEhRLJYoFCRcqK8vTiAQIJORNiKRMIlE30QbgYDB4OAA6fTYxK+ugYEUhUKRYlG+KRKJOIZhkMlI2FE0GiEejzE6KqoKBgMMDPRPaWNwsJ98vjDRRjLZh+u6ZLP5iTZisSjp9PiUNlzXZXh4FIChoQGy2RylUtlrI4HjOORy0kYsFiUajUy0EQoF6e9PMTIyOhG+OzQ0QCaTpVyWgrupVALbrpDPFyZ0HA6HGBvLTOg4lUpOtFHV8fh4ZqKN/v4k5bI9pY1O3ifHqZDJZLv6Po2OpnG8lATJgQEy3n0ygMFUAtd1GM/kcQEnGmWMCMOj4+wuQX80SCSZopCW+wyQGhjgYCbLm7b0kXYCPDbl8O/rSkTyBXJ5CMdiBEIhihm5T8FQiGw4ySnOKClXxsuHBgcpZDOUS3X3KV2gCDiL6vOU6vh9Wmyfp3C4MyGKrQxQFlm3s22G/cd6xzRFKfOrwK1aW59VyowAf0LmZkpKmc/T2rqhjb42ipjrB8YbHDuB1taDSpn3AJ8Fnj/bdrxs318EyGZzbv0itPqcUPXv+/un2se+vvi0bLqt2hgYmNrdRKJv2mK4Zcside9n30Y0OvXnbH0bsVh0yjnJZIJ6YrHmbQwNTX2fSiWnvA+Hw8TjU8OmZttGKBRq2cah3qdsNjehg269T9VJ8jEbfpsG101MVm/JSHaBkiFtuCXxQALJQdYFJyPP+rw2x2z49n747r4k+2w4JV7h48cHCQdiSDJ7wXYh0zc4Md69JgJHp6b2KzyP96nZ+8O9T9lsrqP3qcpi+jx1ilYG6I/AZcDvZth/OWJMWvFU4JPe62cDA8Aa4JXAe4F2DNAmIKSUebzW1oPetjOAe9o4NwQc572+B9iglJmqGYY7AxlObEqxWOrpVdC9Lj8sLh1syYtxGaj7sWq7MGDIHM3/jcK+EtyVhZtGZV8ttbMdx8Tg31ZnCAemfnE6XlXRkxKSsw1kfdBSZTE9A91OKwP0MeBXSplp4CNaW3sAlDLXAG8D/gl4ShvXGQL2ea//Dvi+1tY+pcxvI1FoLdHaynrrkd7nLWY9E3gOcEH9sd7+n3jXOBl4O/ALr51NSpl/Bd6jlPlO4GnA6bQRhODjs1jI2LCrJLnY6jlYhu/vkyi2Ebt5OwZwXj/84ypZUFpMT6+gfKAsReP8ZKE+s6XVQtTfKmW+HvhP4I1KmbUF6crAG7S2ftPGdfYApypl7ka8oSu87UmvnXZ5HZL6Zx9wEHit1tY9SpkXATdobVV9xwuBDyhlJpEM3N8D3lXTzouArwIjyPDi32tt1Wbqbkgi0dufsF6XH7pTB7YDRVeG1iIBGTa7Y1wqjFYzDbgu/DUjGQt+OyIlqgFOiMuaneVhyUBdn7XaYOqC0EjdcNfBsgQXnNBDDkE3PgOLFcOtr13bAKXMI4EXIqHMBjIc9n2trR3tXEQp891IFNouIA6coLVVUsr8Z+CftbameTHdSKlUciORLo8RnUNKpRK9LD90nw5KDtw2NrmIMxGUNTsA39wrSUJB/j/k5bUPAk8cEq/mjGTjdDgzkc2XCEYixIIy7DYUgjNTYvh6hW57BhaIjqSDaDcTwk5AH+pFtLbe5wUCrAe+p7VVDfa0gY8carvzTSaTmzY52Uv0uvywcDqwHXg4L7VxKjW/GQuOvF8VES+n5NXkUQ/Bg3WFVIZC8PyV8IKVcvyhMJrJEeqPUEHmec7pX9jqpAuB/znoHG15QD7C8PCo28uVEIeHR3u+EuRC6eCeDGwrSIaAWo8lwFTv4/4sXPWQDI2tj8LrjpxcZ3NOBzyVHftHWbN8kN0luGiwdaG5pYj/OQDm0wPyEaLR3v7V0+vyw/zoIF+BHUXYXpgs8Fb1cpoNl+0rwRsfhGFb0tp89DjJWt0pKi5EIhFOTshQ37Ie/fbwPwedw/eAZoHjOG4g0EOD3XU4jkMvyw+d18HuopQ4qH4KXVfmcwIGDAQnh7dazdOM2WJ87s7CuSn45PFS0K2TZCsQx+HcQf8Z6PXPAR3ygHpei7Ohuhq6V+l1+eHQdbCzAA/lxFCA5Fd7KCfRarYr620cwDVkWGt5WIqxGUZz43PzKLxxE7z4XjE+qyPwwQ2dNz4g802xvP8M+J+DztG2E62UeTSyeBRgzyxS6Pj49DQ7C1IvJxqAB3OwNip51Qwk8WY7k/jZipS1vic76S0VHbhxZPJ9NT/b0BzNy1QcGXrz8ekULQ2QV1vnTcARTLpdrlLmLuBjWlufmLvudRfBYG87jL0uP8xeB/kK/C0rC0JDASk9MGKLhzNTXbNCBX45IkNzAGkb/ncYsjMkYX71Wnh0v5SwbhZkUPKyU5e8+aRQ3fWrWaunyeDIuUNh6Hf8Z8D/HHSOpnNASpnvAkwkVPoXTCb0XI0UhXsrYGltvb/BuVuY/HHWFK2tDbPr9oLhT5j5zIqteamjs7zBvPVwGX43KkNbVXaX4GcHJtf11HJ2Ei5dJgtMq2yIw8nTU4gB4iGN2pPj7NEAbIiJMdlSkD7lKuJduS4EA5Bs8N3aH5Kqpv3B2a0Z8lnSzEsU3BXAK7S2fli3fTuS1fp+4FPANAMEfLrmdRLxom5lsjT3Y4HzkHQ/i4J0emxaEsJeotflh9npwHVhaxGSdZ+ye7LwnX3i1ZRn+Elzch9cPOglCjXgMf2zzzaQLstC02XekFw0IG2VHNhSlFo8/SE4KyXX6Q+2njvynwFfB52klQFaDtzXZP8DSJ63aWhtTRgWLxv2R7S2Plh7jFLm21lEdXh6vRBVr8sPs9PBqC3eRXXR5/YCvGeLJP4E+Qn5uAFYV5PwOGzAk4bg1OS05mZFtiLGZW10+lBfJCBh2gYwGJqdV+M/A74OOkkrA3Qr8G6lzMtqshcA4JVVeId3TCuej1Qhred7SKJQH59FTdmRuRKQeZStBQmx7gvAj/bDLWmJeEtXxNN4zgr4+1Vw5AwF3Q6HjDfPc17/zPNMy3pwAalP99HKAF2JlNHep5R5E1PngC4CcrSXDTsLXAI8VLf9Eq+NRcHgYG+73b0uPzTWgePCn8fF4zEMGXqLBuRL/sPb4McHJo993AC8f8PclStwXZnXuWhw+tBfJ/CfAV8HnaRVNuy7lTJPQMouPAY4ytu1BynHcK3WVjtB8Rr4jFLmo5EaQ3jtXYbUA1oU5POFnq4D0uvyw1QduN76nZ0FGClLeekqO4rwtofh1nGIGvCGdRIwcE5qbnOnVYf85sL4gP8MgK+DTjJvmRCUMl8IXAWc5G26D/hPra3vzksHOoCfC87PgXXw4CjLlw+yJQcPeMk+95UkUSiGhDL/3yjcnJaQyeUh+NjGw5/TaZd9RQnJXjlH2WL8Z8DXgcf85YJTyjweKfw2sRAVuKWmMmlLPEOzaIyNj089uwtw5zicnoTvedWjfjkytb5OlbAhIdOvPVLKU88lhQoUXJmH6gv68zs+i4dW64AGgK8Dz0LmcapVTVcBfcBPgZe3MwynlBkDnomUxv6C1taoUuZxwIjW1vBhSTFP+PWAercOyuYc3J+HSKXEf+2L8N2a8oVBAy7ol4gygKNj8OwVc2MIChWpYVKl5Mj110Ul4GFVZG5r8/TyM1DF1wEwTx7QpxCDcZHW1u9rdyhlXgB83jvmsmaNKGVuBH6FrAcaRKLfRoHXeu9fNeueLwC9nri1l+TfWZAibmekZDHpg3kZ1tpWdPmRF1Rwfj+cnoDnHUZ9nWaUnUnPqlCRyLb+4KShAwgjc0uxeUqR00vPwEz4OugcrQzQs4Gnam39qX6H1tYtSpn/AtzQxnU+AfwSMTijNdt/AlzTVk8BpcxlwJeRLAwHgLdrbV3b4DgTMYpHe8d9VmvLqtn/CBLJV/1836K1dWmr62ezeaLROYibXSQsVfnTtqSoOSom77fmJbFnyJBkn0VHUun89wG4ZmeIsgvPWg7vOXbu+lQNpU55hmVNxMtGsMAlEJbqMzAbfB10jnYe52bmvt2fAhcAj9Haqihl1m7fhuSYa5fPACXEeJwJXK+UeafW1j11xxnAy4G7EA/ul0qZ27W2vl1zzLO0tn41i2v7LEEcF+7KSBRbpiLGZmcRVni50vIVwID3bZVkoBDk6Bi8fl1nrl9ypudfq6bmuWDAT/7ps7RpZYB+CnxZKfPVWlt/rN2hlPkY4AuIF9MOjUbE1wPpdk5WykwALwBO1drKADcrZf4EeBkSEj6B1tZHa94+oJT5Y+BCoNYAzZpeL0S1FOXfVYTxCqyJSuh00JCSBtX1PN/ZB/+1C4qu5GB7y9oiz1gTPeRQ6oID47asA0rb4tHE6+Zs1kYkg0E3Gp+l+AzMFl8HnaOVAXoDcB1wi1LmOFCdel2JzOf8wjumFb9EcsH9s/feVcrsB/4NuL7Nvp4A2Fpbm2q23Qk8vtlJSpkGsmj2C3W7vqWUGQD+AphaW3e26kAs1ttu91KTv+TAfTmp7Bk0pgcNfGQbfN974h/bD1eug+OjoRmzC7TCcWG0DMfFxfCdm5q6dmgxsNSegUPB10HnaLUQdRR4mlLmiUjy0Now7D9obd3f5nWuBm5UynwAiAHfATYimRVe2GYbSaA+2i4NpFqc914kIXDtXNNLgTuQobqrgF8oZZ7oyTsFpcwrkKSsXHrpUzj//PMAiMdjhEJBxsclsVc4HCKZTDAyIg6dYcDQ0CBjY+PYtkw1DQykKBZLFApFAPr64gQCATIZaSMSCZNI9E20EQgYDA4OkE6PTeSfGhhIUSgUKRYlM1IiEccwDDIZSSgRjUaIx2MTRbOCwQADA/1T2hgc7CefL0y0kUz24bou2Wx+oo1YLEo6PT6lje3bd9HfL+oeGhogm81RKpW9NhI4jkMuJ23EYlGi0chEG6FQkP7+FCMjo1TncIeGBshkspTLEteVSiWw7Qr5fGFCx+FwiLGxzISOU6nkRBtVHY+PZyba6O9PUi7bU9qov0+JRIIdB9PsLUGxBMtWDFLIjON49ymWSvH7YZvv748SMVz+/SibS4agmM0yenCMxLLlRPr6yKe9ex0wiPcPUBgfw/F0HEulsEtF7GIJXEiH44DBEU6OVQE4KhohHo4xPNz5+zQ6msZx3Dm5T+n0GOvWrZ2X+9Stn6dKxZn43633Ceb28xQOdybEcz4XosaBFyM54QKIAfiW1la+zfPPAn6vtdVXs+1q4BKtrWfNcM6ViPG7SGtrR5O270e8oJ8264O/EHVpLMDbX4Jbx8TraVSXp1CBl94n+dzecCRctnZyX250lL7BwVld72AJ1sXE84m0qHDa7SyVZ+Bw8HUAzOdC1JlQyhxCJvO/3uSYMFK+4UlaW18BvnKIl9sEhJQyj69ZAHsGUB+AUL3uK5G5oYubGR8PlzYU2uuFqJaK/DuLMr+SavD0HyjD1Q+J8VkfhRevnro/0EQH9b/lHGS+JxyQaqVzuT5nvlgqz8Dh4OugcxxuUOd6ZGhrRgOktVVWyixzmMXctLaySpk/BN6nlPkqJAruOUiE3RSUMl8KfBB4gtbW5rp965GcdrchntgbgBXA7+vbqafXa4AsBfnLjpTDXtFgBOGhPPzrg7L/iAj8x8bpRiOWmqoD25vXqbhTK4y6SDaE1RHP81ki31lL4Rk4XHwddI6mBsj7sm5GuyHUnwLerpR5udaW3fLomXkd4kHtAw4Cr9Xaukcp8yLgBq2tasat9yO1jG6rCfv+ptbWa5A5o88h4dkF4K/A07S2Dra6+OhomsHBgcPo/uJmKcg/Ynvubp2/e/MovGOzlL0+LSHGZ3kDI5UfSxPvH2C0LMYnaIh3c0QU4l0YtdZplsIzcLj4OugcrVLxODT3XAzA1dpq+tFTyvwpEq2WB+5G0vpMoLX17HY7vJD4c0CLe+y76MDv05Kdupo5YGcR3r0F7pR5WZ4yJAtMYzN4LLnRUeIDg+wvwcVDEkJ9qFFxi5HF/gx0Al8HwDzNAaWB9zDz8NSjgG+0cZ0DwA9m0S8fn45zd1bmaWLeU1+owJsfkjQ7ySC8fA28Yk1rg5LxSh504zodH5/FRCsD9BcgrrX150Y7lTJt2rCEWluXH0Lfuo6hod52uxez/PmKlE1YFoZr98LBMtw0Cpu9YIOvntRmmpvkAJkKnDZP5RW6jcX8DHQKXwedo9XU6HXIPMlM7EEWk/YE2eyiKd46Jywm+UvO1PcHyvJL6dq98PHt8LU9YnxWheGjG9szPpkK5HM5zk3J4tVeZDE9A3OFr4POMWfrgJQy7wIer7U1opT5N5rMJWltnT4nnegw/hzQ4hj73l+Eu3KSvaAvKBkI/pAWI/SSeyHnwEtWw6kJeMKghEm3wnHl/FOcUdavHJxrEbqWxfIMzCW+DoBuWAfUgh8ARe/19+fwOj4+E2RsuCMja3AeykEoANsKEn79se1ifC4ehDcd1aqlSdK2BDAcH4dks/EAHx+fWTFvmRCWAqVS2Y1EerfcZKlUppvltx34w5h4K4kg7C7JWpwVYfjKbvjcLqmn882TJWy6HcZsaePslHhT3a6DuabX5QdfBx5d7wEtORzHaX3QEqZb5C85YmSqBA35uycLuYqUUgDJKm0Y8EgB/mu3fGL+fcPsjA/AOanJNT7dooOFotflB18HnWReDNBSmQPK5fI9nQm3G+TfUZBw6nr6QzJUVluZNOvADQfh5wdl0ehzVsCFTQKYHFdCsG1HFqwmglOND3SHDhaSXpcffB10kvnygOrngMJIKp0LkSJzPj4t2VuUBaPVYnFVHBfyXtXSKruKklZnszdnkwzC649s3G7B8bwdd7IO0CkJ8ZRCSySFjo9PNzIrA6SU+UTgZO/tfVpbv27nPK2thqHaXunso2fTh4Wk13/1LKT8FRfuzcFQeKrxAfFaaheFZmy4chNsK8KxMQk6uGhger2fKmM2nJGU1DsP5+HIKAzM8Mnwn4Helh98HXSStgyQUuaxSFTb6cAub/MR3tDaC+oTfs6CHwK3A1ce4vnzSq9XQlxI+XcWxVNptV6n5MC7tojxOSEOX3wUJJuc47oyN7QyLOHYJyeat+8/A70tP/g66CTtDjB8GRgHNmhtrdfaWg9sAEaBLx3G9S8GFs2qrmoxqF5loeR3XPFMBlsYn1EbXr8JbkpDKggfPa658QGZJ6oan3bwn4Helh98HXSSdofgHgs8RmtrW3WD1tY2pUwF/KHVyUqZP6nbZABrgbPooUwKPofGqN3a+9legDc+CNuLYlD0RikCNxMVVwITxmw40c+u7+OzILRrgLYB8QbbY0ixuVbUlzpwkEJy/09r65dt9mHBCYV6O/vkQsm/vTBzdmqQFDlXecbnUX1ifFY1GSUZt2WoLhaE0xONawPNhP8M9Lb84Ougk7S1EFUp85nAu4E3IoXcXOA84BPAB1qVsl5C+Kt25wHHFQ8FJJrt7qwUdmtUyrpQgbduljILG+PwlRNlwWgz9pXg3NTkeiEfH59Z05GFqO0aoHEgCgQR7wVk/qhCXbJSra0lO6AxMjLqDg0NLnQ3FoyRkVHmWn7HhT+OTS4CdVxY7oVd/3Ucvr9fKpZW2V+CnSWZ8/naSbC+ybAbiGEbteFJQ7J4dbbMhw66mV6XH3wdeMxrJoTDilJTytxCm96D1taGw7nWXNLrWYvmQ/79JUiXYVVdpOsvh+H/zRBruTYCnzi+tfEBGa47InJoxgf8Z6DX5QdfB52kLQOktfW1w7zOV4E3AbcyGbTwWGQY7+NA5jDb91kCOC48kJ8ebJCtSAkFgH9cJd5L1X4YwIl9kxVOm5G2xQNqx1D5+PjMPW0vRFXKjAIvRRaiukgQwXVaW8WmJwrHAh/R2vpgXZtvB07R2vqnNvuwDAkJvxSpsvp2ra1rGxxnApchi1wPAJ/V2rJq9h8DXAOcjwRYXKm19atW1+/1QlRzLf94RYxNfQDBV3Z7pRAScPVRh1YCu1ARY/W4gampdWaL/wz0tvzg66CTtLX6QSnzZOBBxFs5H3gMEoCwSSnzpDaaeD7w3Qbbvwc8u62eCp8BSsBqxBh+TinzlAbHGcDLgSHg74ArlTJfVLP/OqTa63LgHcD3lTJXtrp4JtMgCVkP0Un5yw7sLcGBmvmc4fL0obFtBfjWXnltHoLxKToy7DZqw+nJwzM+4D8DvS4/+DroJO16QP+JfGG/TGtrDEApsx/4JmKIntri/CxwCfBQ3fZLaHMhqlJmAngBcKrWVga42Vtf9DLgbbXHam19tObtA0qZP0byzn1bKfME4GzgUq2tPPADpcx/9dr+fLM+lMt2O11dsnRKfseFu7KwuygG5aIBSIUk20FtSp2iAx/eKut1nrUcTm2zDLbtiMcU9FL0DARhXURS7Rwu/jPQ2/KDr4NO0q4BuhA4t2p8ALS2xpQy3wH8sY3zNfAZpcxH1xz/GGSY7L1t9uEEwNba2lSz7U7g8c1OUso0gIuAL3ibTgE2a23VLme+09vuM8fkKvBATkKh10ZhtCxeznFxGYK7YxxuGJaJ3l0lKaXQH4TXr2v/GiO2pNRZHzv0YAMfH5+5p10DVAAGG2wfoC4MuxFaWx9VynwEuAp4obf5PuAyra1GQ3ONSAJjddvSQKrFee9FhhqvqWkn3aCdhrmSlTKvAK4AeNaznsGZZ54JQDweIxQKMj4u7ng4HCKZTDAyIk0bBgwNDTI2No5tVwAYGEhRLJYoFGTarK8vTiAQmHDpI5EwiUTfRBuBgMHg4ADp9BiVijPRRqFQpFiUsatEIo5hGGQy4khGoxHi8Rijo6KqYDDAwED/lDYGB/vJ5wsTbSSTfbiuSzabn2gjFotOpByptuE4FYaHRwEZB89mc5RKZa+NBI7jkMtJG7FYlGg0MtFGKBSkL5ni/3aM4jji8bjhAaLFLA+N2uwJwngowQe2hsg7k1ZjTdjlQ2vG6cs6FEMhoskk+fSo5HAzID4wSDGToWLLr9JAXxKjaJNwC6Tznb9Ptl0mk8l29X0aHU3jeAWTDuU+9fenGBkZnYj2GhoaIJPJUi7b2HaZcrmMbVfI5wsTn4VwOMTYWGZCx6lUcqKNqo7HxzMT3kN/f5Jy2Z7SxmL5PKVSia6/TwCpVGLO7lM43JmCfO2uA/oacC7waiY9mMciXsWtWluXd6Q3zftwFvB7ra2+mm1XA5dobT1rhnOuBK4GLtLa2uFtex6yePbkmuM+BaC19YZmfcjnC2483rshVPl8gcORf3tBisatrAsyyFbkF8I39sIXd8HZSXj5GhmeOz0ppRSqZCqQr0xdhBAyYDA8WVbhnOT0MO5Ocbg6WOz0uvzg68CjI2ML7SYjvQoJQrgJ8XgKwO+ATYBqdbJS5uOVMqcNlXnbL26zD5uAkFLm8TXbzkCi8Rpd85XI3NCTqsbH4x5gg1Jmrec0Yzu1VH8F9CqHI3/ZkaG3RglFE0GZ7/nOPnn/L0fC4wbhgoGpxudgCaIGnJWSQnHVv2r76bJkOJgr4wP+M9Dr8oOvg07S7jqgUeA5SpkbgWrU231aW/VBBTM2AbyvwfZ+ZIjsnDb6kFXK/CHwPqXMVyEF7Z4DXFB/rFLmS4EPAk+oLxWhtbVJKfOvwHuUMt8JPA0pM/GCNmXxOQQeyEnwQaOs02VHFpmmbfF+zm4QbJCtyFqfR6emF4k7Lg5/ycCRET+9jo/PYqLdekDvBv7DMzgP1WyPA6bWViPjUsujkIn+eu729rXL64CvAPuQBKev1dq6RynzIuAGra3qV9f7kRDr25Qyq+d+U2vrNd7rFyGLY0eQdUB/r7W1v9XFe93tPlT59xVha0HyudXz0wPw6R1w0IZlIXj/huk531wXsjZcMNi4QumaiKTiOa5v+r5O4z8DvS0/+DroJO0GIbwHCVGuD5nu8/a1MkB5pPzClrrtRyLretpCa2sYeG6D7TchwQXV98e2aOcRJAR8VoTD81XBvDs5VPkf8rIbVA1LtiKLSx/MwS1eWMnGOLzz6MZZrMcqsLpZldIgnNsv0XJzjf8M9Lb84Ougk7Q7B2TQOJfbWcBwG+f/AviIUuZQdYOX1eBD3r5FQTV6pFc5FPnTNqQrkxmqXRf+7RH42h4xPkED3nE0XHdy43U+ritZDDY2KgZSw0CocbbsTuM/A70tP/g66CRNTbmXBdv1/jYrZdYaoSBSD6jp4k2PNwP/BzyilHmXt+10ZCjtH2fbaZ/Fw7YCRGp+5nxjL9w4IoEHbz4KTkvAMU2MS9qGddHWpbh9fHwWH60+1lci3s9XkJQ1tetnSsAjWlstK6Jqbe1WyjwDSZ9zprf5a8C1WluLpiR3r7ves5W/6Eh2g2oGgtvGZL4H4N+OgUuGGp83bkPJ+6njuHD8PMzttIv/DPS2/ODroJO0uw7o8cganF7PQeEnYp8FW/Nwf04i0/aW4J/ulSwFl6+ZObOB60oanZM9o5MISj0gHx+frmL+CtJ1AqXMEFJ+YT0w5StFa+vr89KJw8QvSNd+Ia6SAzenIe4Nv13xgFQ2PS8Fnzph5hQ5uQpEA3Bel5Y17PViZL0uP/g68JjXgnSHhVLmicBPkbIMBlJJNQSUgSKwKAxQrxeialf+kiPDba4r8z8f2SrGZ00EPriheX62XAWO6eIoV/8ZWOgeLDy+DjpHu1Fwh8sngD8jueNyyGLWRwN/ZREtAJ2PKKtupl3595UkdHowDD87AN/bD2EDPnKcbGuGw8zh1t2A/wwsdA8WHl8HnWNGA6SUud7LJN0JzgXer7WVRb5jQlpbdwBvAT7WoWvMOb3udrcr//aCLAx9IAcf2irb3rJeCso1o+KKW5ych/U8h4r/DAwudBcWHF8HnaOZB7QFWAmglHmjUubgYVzHYHIR634mM0/vADYeRrvzyvh4b8f/N5P/YBluS0uBuVEbyi685SEouvDsFfDcFa3bz1akRMOhVDydL/xnoLflB18HnaTZYMc4sAJZq3MJcDj5t+9GEn5uBm4F3qqUWUGya7ebT27B6fVCVDPJP27DrWOSlfqvGQkVfNcW2FmCk/rgreunD1s4roRa216anWRIslmv6fKIN/8Z6G35wddBJ2lmgH4F3KiUeZ/3/kdKmQ3T5mhtPbHFdT4AVAdg3glcD/wGOMBkfSCfRcqoLauSl4WlXMJ39sHv01KJ9CPHSVRbFceV0tsgcz2xAGxIwt8y4vl08/yPj49PZ2n2cX8Z8EpkiOzxwAO0WT67Hq2tX9S83gyc5KXiGdHaWjQxJf39bdaEXqLMJP++kuRjA9iUgy/vljHX92+AI2pKI9iuDNGd0CeRbrWZsfeXZ86W3U34z0Bvyw++DjpJuwtRfwM8zyvL0LP4BemmF+KquPCrYfF+HGSx6UN5+Oe18NqaGrOuK4tRT0tKqex6ChWZN0p1uQfU68XIel1+8HXgMX/rgLS2nlB9rZSZBFwvoq2n6PUHr1b+XAVGyrKgy0Wexi/tEuNzRAQuXzv13OEyHBtvbHxAPKjFoFn/Geht+cHXQSdp+/emUubrgbfiRbApZe4APqK19dk56ptPF+K4sCUPm/ISQukiC0s/sBX++4Ac8+b1MrdTcWVfyZH5nVYZrX18fHqLtkbclTL/H/Bh4MvApd7fNcCHlTLfNnfd6y56/VdPPB5jbxHuy0mC0RURCRp41xYxPlEDPrwBLh6UiLbdJYmQGy7DqYmpWbEXK/4z0Nvyg6+DTtKuB/Qa4Aqtretqtv1aKfNBpPT1hzvesy4kFOriFZLzQCgUZFde5mmq6XSu3Qt/GpNqph/fOFnTZ8yG0/rgwbwMva2JztzuYsJ/BnpbfvB10EnaNUCrgNsabL8VWN3qZKXMi2fY5QIF4GGv2mmrdpYx6YUdAN6utXVtg+OeALwbOBuJtDumbv8jXr8r3qZbtLYubXX98fEsy5YNtjpsyTKczrLfGGSFtyJsX0ki3gD+fcOk8Sk5MgR3dFwqmcaWgOdTpdefgV6XH3wddJJ2DdAm4CVML739EiQ8uxW/ZbKUQTV6ova9o5T5E+BlLYIbPoPUIVqN1BW6XinzTq2te+qOyyI1jK4D/t8MbT1La+tXbfTdx2OsAq5XedR24d1bIO/AEwbh/Jrs1WlbhtyChpRT8PHx8WlEuwbovcB3PU/m9962C5H1Qf/QxvnPACxkQeqfvG3nA28H3oNE8GpkKO8NjRpQykwgiUtP1drKADdXjRYwZR5Ka+tW4FalzCe3KV9b9GohKteFOzKwsxBiaEAi3T6/E24fh+UhyfNWpezIXE+3ZzQ4VHr1GajS6/KDr4NO0m4Y9g+VMs8HFPBMb/N9wHlaW39po4n3A1dpbf26Zttmpcz9SCTdOV5qnk8xgwECTgBsra1NNdvuRIzgofAtpcwA8BfA1Nq6s9UJyWSLbJpLlJwD+4pwxFCCHUW47D6pdhox4EPHwVBYcsENhuT/6UkILaFht1p69Rmo0uvyg6+DTtK2Kdfa+jPwT4d4nZOBnQ227/T2AfwNWNOkjSQwVrctDaQOoT8vBe5Ahv+uAn6hlHlio4W2SplXAFcAXHrpUzj//PMAiYQJhYKMj8uIYTgcIplMMDIiVcsNQ7Lmjo2NY9sy1TQwkKJYLFEoFAHo64sTCATIZKSNSCRMItE30UYgYDA4OEA6PUal4ky0USgUKRYlK1IiEccwDDIZSVIRjUaIx2OMjoqqgsEAAwP9U9oYHOwnny9MtJFM9uG6LtlsfqKNWCxKOj0OwAE7QCDYz+jOHVhjayg6YS7od/nXlXnWVErsOwDL+xPsyTgc6eaJG5BzokSjkYk2QqEg/f0pRkZGJ+qpDA0NkMlkJ3JrpVIJbLtCPl+Y0HE4HGJsLDOh41QqOdFGVcfj45mJNvr7k5TL9pQ2Onmf0ukxVq5c3pX3qdrG6Ggax3EndJzN5iiVyl4bCRzHIZeTNmKx2d2ndHqMdevWdv19grn7PFUqzsT/br1PMLefp3D4cFKDTjIvFVGVMv8M3Au8Smur6G2LAl8CTvY8oMcB39DaOnaGNs5CyoL31Wy7GrhEa+tZM5zzZOBL9UEIDY67H/GCftrsuOHhUbcXJx9vG5O5nj/vzfCmXUkSAfjBabAiLMNz+0twyZAcG1/icz7Dw6M9PQHd6/KDrwOPxVMRFXgdUhF1p1Lm3d62U5G5n+qQ3gag2aLWTUBIKfN4ra0HvW1nAPUBCIdCdTF/U3qxEFXZkWG1FWH4xojEUl+2lolIuLQt+d6WuuGp0ovPQC29Lj/4Ougk8+IBwUQQwT8Bj/I23Q9c6wUUtNvGtxFj8SokCu7nwAX1UXDe3E4EeALwee+ajtZWSSlzPXAUElYeQOac3gKcqLV1sEUXFk3i1E7xcE6CDnaX4PL7Jart+tOkfMKoLQo8vx/6esQA+fj4AIvMA8ILr/7CYTbzOiS8eh9wEHit1tY9SpkXATdobVXT1F6MlHuokgd+h9Q1SgGfA45D1iD9FXhaG8aHsbFx+vsPZcppcTJuS8qd5WH4j+2y7R9WSt62PUVYFZa1P73i/UDvPQP19Lr84Ougk8ynB7QOMQyrqEsBpLX18XnpxGHSa3NA92RgT1nmeZ52Fziuy09Plx8+p/bB+h7M7dbr4/+9Lj/4OvCYXw9IKfMfgSfR2IA8u8W5L0U8FxspyV1r9VxgURigXqLiSkXTwRBct1cWnl7QZxMNhFkWgqP8dFg+Pj6HSVsGSCnTAv4VGdbaxeznQt4HfAx4l9ZWpdXB3crAQO+43aO2GCGDySzXz1sdoOTA0bHenYjtpWegEb0uPywtHVxzzTUAXH755TMfVHFgOAdjRVg/COHOjbm36wG9HHix1tb3D/E6q5Fw6EVrfACKxRJ9fUt/3Ml1YWdBSmnfnIZHCrA6DOfGShSNeE+Xze6VZ2Amel1+mB8dtGUY5gPXhd3jkM5LTZUDGVg70LHm212vHkAm6w+VnyOpdxY11QVvSxXbC7m+Pwfbi5AMwFe9ZKMvXQP5QpE1kclM2L3IUn8GWtHr8kMP6aBcgd1jkM5BKgp9YRjOQ9Hu2CXa/S37RSSE+r2HeJ3/BT6ilHkKkvGgXLtTa+uHh9iuTwfZWoB7s+L5rIrA/47AXVkYCMJzV8DwMKxdojnefHx8EKMzmoeRPNgVCAQg6dVSMQwvE7EDHSqv0q4BGgReopT5FOAuphuQN7Y4vxp+3SgztQssikDepTz0UHFhS0EMTygAm/Pw74/IvtceKSUVIn1xhnp4+A2W9jPQDr0uPyxRHeRK4tnsHZdv5FhI/uaYdq9wMpNDcCfW7WsZkKC1tSRSUwYCS0KMhgyXJetByEuv84GtUtX0acvgBSthvAJrooElm2S0XZbyM9AOvS4/LCEdlGqG0h4ZBgwxOvP4IW83G/YT5roji4FMZukWotpRmMxm8D/DcGdGqpy+db143QUHBipZxBnuXZbyM9AOvS4/LBId5MuwPwPBABzRPxm2WnEgV4axggQWVEl2aExtlszKx1LKjAEbEa/nYa2twizOfQbwVsSbcpHkpB/R2vr5bPrgMzeM2JAIQbYC/7lDtl25TlLuVFwIGdC/KAZKfXx6nHIFto5ItFDJFo/GdaFgQ6EMjiuGKbHwE7pt+VpKmWFvLdAIUoPnb8CIUuZHlTJb5uVWynwV8CPgYcQIvQ3YAvxIKfOVh9r5+SYS6UwK8m6j6MhfyIBrdsOBMpySgGcul/3jFTgyArHo0pR/NizVZ6Bdel1+6HIdlGzYmZYFfNEQJKJwMCuBBbYDsbB4O/FwVyzma9cD+gjwYuA1wM3etouADyFG7M0tzn8r8CatrU/XbPuyV6bhbUiWhK4nkehrfdAiJFeRZ/FnB+Abe2WbeZSE/QOUHFgbhURoaco/G5bqM9AuvS4/dKkO8mVZLJrOi3cT94xkwFiw4bV2aNcAvQR4Zd1w2cNeRdMv0doArQf+p8H2G4D/aLMPC87ISLr7x34PgVwFbhqFD26T9/+8VpKMgqTgCRswEFq68s+GXtdBr8sPXaCD6nBapgjZkszrFLyhtkSkKzybdmnXAA0gw2f1PEx7s9LbgKcAD9VtvxTY2mYffOaIHUX4orfg9Moj4RVrJ/elbTg2NukN+fj4LCC5koRK58tiaMJBGYNKda+X04x2DdCdwBuB19dtv4r2MiT8B/AppcyzgVu8bRcCL0Pq8SwKAkvkW7hQkZIKIPORn94p8z4n98HLa4qiu64EIBzpPdtLRf7Dodd10OvywzzowGmwsqVow660RLBFgl09rDYb2jVAbwF+7pW4/qO37THAEcDTWp2stfUFpcx9wNXA873N9wEv1Nr68ey6vHAMDnYuB9JCYTvwp3E4PeEVlzsA394n+8z1Uz2dTEVq/lTDs5eC/IdLr+ug1+WHOdBBdUgtVxLPJluaui9fhu2jEliwSD2dmWh3HdD/KWWegHhA1YWo3wM+q7W1q802foREwi1a0ukxBgb6F7obh8WBMhwowSNByW7w0e0yz/PM5XBacuqx+Qqcmph8vxTkP1x6XQe9Lj90SAfV9Ti5EqQLkvbGMGQeJ1qz3uGhAxJWHQ5KVNsSo22JPEPzjsO9oLeWqL6eUO5w250PKhVnobtw2GzOw8oI7CnBgzn44xjEAzL3U4vrijdUm/l6Kch/uPS6DnpdfjgMHbiu5Fg7mIVSRTyagAGRJmlvgoElaXiqzCiZN1/zV60tx3s9I1pbdzTbr5R5NPBJ4AlAosEh/hLHeSBjS1DBqqh8Fn7k1fn5+5Wwom5NWs6BFWEIL5GsIz4+c47twHhBhtNWJMS4VFzxdmwHRnIwXpSs0u0alSWe+6qZFm4H1gD7vNcujcuwtpNM9JtADAk42MvsC9oBoJS5DPgyEj13AHi71ta1DY57AvBu4GxgRGvrmLr9xwDXICUitgFXam39qtX1F3shqgPlyTme/WX43aiEWL9k9fRjcxXYWFf1dLHL3wl6XQe9Lj800EG5ImlvRr3UNoYhnk71a84w5BdfOAjJxRUmPdc0M0DHIuWzq68Ph7OAc7W27jvMdj4DlJACd2cC1ytl3qm1dU/dcVlkcet1NM7AfR3wB+Dp3t/3lTKP19ra3+DYCQqFYncuQmsD14VtRUiF5PXHtsvH49krZEhu2vHAUN2C78Usf6fodR30tPxesEAhkyURicnP8aItQ2oYU9fguK5vaNpgRgOktVW7PscFtmttTfNclDLXt3GdO4GVSOTbIaGUmQBeAJyqtZUBblbK/AkSyv22ur7fCtzqRe3Vt3MC4hldqrWVB36glPmvXtufb9aHYrG0aD98mQpkveG334zI3E8yCK85YvqxhYrkfYvX+bWLWf5O0es66Fn5C2XYNQaFMsVshkTC84IMIB6Sujm1LDXjU65Iip+HD8JgrGN55Nqd3doCrEWG4yZQylzu7Ws1BHcF8EmlzE8CdzO9ntC2NvpwAmBrbW2q2XYn8Pg2zq3lFGCz1tZ4XTunzLKdRYPrwkN5iARk0Wm1zs9rjpju5YAYq5MbzdT5+PQSjhcCXbKlLHXYK87mFmUorRfIleC27fC33TK3BXDccjiiM6Ho7Rogg8bzNkmgnYzYAWTY7Ed17VTbbScIIQmM1W1LA7MdlE5659W3c2SDY1HKvAIxoDzzmU/nrLPOAiAejxEKBRkfzwIQDodIJhOMjEjThgFDQ4OMjY1j2xVAxo6LxdJESd++vjiBQIBMRtqIRMIkEn0TbQQCBoODA6TTYxORNwMDKQqFIsWirBVIJOIYhkEmI4GE0WiEeDzG6KioKhgMkIn2s/XAGENBh7fvSDJWCfG4pM0zIxlyoxDp6wNcstk8hQpUwhEGU1GGh8cn2hgY6Me2ywwPjwIwNDRANpujVJLfEslkAsdxyOVkHDwWixKNRkinpY1QKEh/f4qRkVFc7wkYGhogk8lSLsuDnUolsO0K+XxhQsfhcIixscyEjlOp5EQbVR2Pj2cm2ujvT1Iu21Pa6OR9KpVKZDLZOblPAwP9U9oYHOwnny9MtJFM9uG6cp+qbcRi0QkdV9sYHU3jeIsZO32fSqUS5XK56+8THObnae8I7MsQDQSJRyKMVkrgGgTtAIlYjHQ2Q8Xx7lMiSb5UpFj2dByL4+KSLYhs0XCEWCRCOiv6CQaCDCQSjGYyOK60MZRMkS0UKNlTfpszPC7PRywSIRoKk86JbKFgkP6+BCOZcVzvRg0lU2QKecq293mK92E7FfJF0U88EiUcCjHmtREOhqZcx8BgKJViPJejnC8Su/cA8b/uwSiKvu3lcdwj+mFlH51Kx9rUAHkeC4iR+JBSZm24dBA4j/YyIXwN8Z7eyqEHIWSA+uD7fmC8wbEda0dr64tISXJKpZIbiUz95VOfE6r+fX//VPvY1xefVlGxVRv1aw4Sib5pwyDLljXu15gNd43CmmX9XH8Q7ivK4tJ/3xgiGZp6naIbZV1MAhNSEaCuH4ODA9TKn0xOd5NisakL5eplGRqa+j6Vmrr4KBwOE49PjX6YbRuhUKhlG4d6n0ql0oQOOnmfZtNGNNpcx/ULJTt5n0qlEuFwuOvvU7P3TXVccWA0z7K0C0ODE1Foy5i8RqlcZiAxVZZELE4iNrUf0XDdvU5Nve5gcmobyXgcqJOl7pz690PJqfpIxac+K2FCxCN19zpV//XnbcsW4dZtpO7dCweykzuPGYILjyW0th8yJVg/NO38Q6WVB3Sa998ATkICAKqUgDtoL5noicCZdcNns2UTEPKCBR70tp0B1AcgtOIeYINSZqpmGO4MYFo0XT2ZTG7aF0g3U3Hhz+NS06fswmd2yvarjpJghFpKjqTnOSUhZUQasdjknwt6XQdLUv7q+pz9GfnQGEAyPH1exyNTyLMs3MUlGdolW+LkfVGW54Lw9dtF/qprEDBgdQouOAaOXTZnXWhqgKqVUJUyrwGu0tqqHwJrl1uRSLpDNkBaW1mlzB8C7/PqC50JPAe4oP5YpcwAEAHCgOEtfnW0tkpaW5uUMv8KvEcp851IKqHTkSCEJcXBslQyXRWB/zkoRedO7oNLG/yASdvNjY+PTzdzzTXXAHD55Ze3Prhckbkd25nMKl22oS8iX7xLLYBgrCDGZawogQTpgsztjBc536l6XBmR/bjlcOoaMTrBuV+D1O4c0NuRYaopBkgpcx1Q1tra2+L8zwGfUMr8GFLMrj4IoelC1hpeh4RX7wMOAq/V2rpHKfMi4AatrapPezHwm5rz8sDvgEu89y8CvooU2NsG/H2rEGyQMffFxOa8RLoB/GpE/j99+fTPl+3Is7emhXiLTf65oNd1sOjkz5dlaKlgy0MeDMiCUMfzdAIBSe4Zi7VsCuCaX/83AJc/6blz1uXDxnVh9xjcs0cqo45On6a3/+1SQkONI43sUpnQnsxc9xJo3wB9E/gO8F91258K/COyMLQZ13n/v9hgX7tBCGhtDQPPbbD9JiS4oPr+tzReNFvd/wiTxqht6seru5V9RdhZguEyrI5KVNstXpHEJzXwfkZteFRf66wHi0X+uaTXdbBo5K84UrZgJC+Gp1qW2nElC8E8/Lqfd/JluG8v3LV76hxOJAhH9EsE35oUrEgQGkpMeI31tOVFdoh2DdCjmV6KAeAmwGrj/MNdyNoVjI6OdX0xrpIDd2YlkGCZN0z9swNQcuHs5PRFpwVHht2OaCPJ7mKQf67pdR0sCvkLZdgzDvnS0sw8UHEkO/aBrHhzw3n5n6mZou8Lwylr4ISVsDo5MZ9Vssts27+bjQvT82m0a4BCQKOvqNgM26dQt6jVZ45wXNiSlx96Kc/43JeFT+6Q1/+waurxtitzP4/plzVCPj6LklJl8vXD+2UldSgAiaVVugAQD+d3D081NlWCBqwbhNPXwsYVE15euWKzfc8utuzbyc6De6k4DhvPP2N++z0D7RqgPwGv9f5qeT1wWzsNKGWGkLDt9UiAwARaW19vsx8LSrBL3fayAzuLsKUgn73lnnZdFz6wVbyf56+Ep9QFs4yU4aS+SU+pFd0q/3zS6zroOvkPZmWorYoRmB7iuZhxXClEt2tMvJ4tw7J9WVzCoZf1wVAfDMWhf7J0sV2psGPfLrbs3cmOA3uwnUkjvWZw+QII0ph279Q7gBuVMk8HbvS2PRHJ8TYt3U09SpknAj9FhuIMoOJduwwUgUVhgLq1DsrOIvwtK9mr+2vu6J/G4P4cLA/Bm46aek7ZG3pbN4sfid0q/3zS6zqYrfyzik6bLcM5GWqrTQuzFLJHj+Y5dW+UgUIAvvRHiV6rEjTgiceLl1M3tFhxHHYd2MuWvTvYtn8P5Yo9sW/lwDKOXXUkx64+gr7o1LVGC0m7Ben+qJT5WMBksqLpX4DXaW3d2UYTnwD+jIRO7/H+DyDRce+cVY8XkG4txnWgLHV76ofRrtkj/1+8WorPVbFdCdE+PTm7cgvdKv980us6WHD5HVdCiLMlCS1ORqaW8V2MVBwJlth8EB7YB3sznDuxILUIAzE4ZpkEEBw9JJ6Oh+M47B45wJa9O9i6f/eUTArLU4Mcu/pIjl11JMm6BardwmwK0t0J/NMhXudc4PHeWh4HCGlt3aGU+RbgU8g6nK6nG4txua4MpQ3UDaPdPCqLUBNBqfdTxXbEYJ2amJ33A90p/3zT6zpYUPnzZVnHUrIny1Mv5gADuwJ/3QV/eASKNfNY4SAPJ/PsTdpc8NjHwlGDU+R0XJe9owfYsncnj+zbRbE8OR80lOyfMDr9fXUljmsvXSrP6JXapXL7huEwmfV1lDLXMH0Op1UyUQOopvHZj+RdewDYAV0TkLEoyTsynlm7gHS4DB/fLq9fvVYyIQAUHTFWZybhyEUSTevTw+TL8gfyS2vvuIQUJxdxcIHrwo403LsXNu2bNDz9UVjbj335uYT6ohwHHFd3aqlQ5I6b/sgj+3aSL00Oyw30JcXorF7HYCJFO1TX+TRa19TQKFQcyNvyv4O0ZYCUMgeQiqYvpM74eLRax3M3ku5mM5IV4a1KmRXg1cBDbfd2gRkc7L6hl2xlamK9/x2Gd2+R1DvHxOAfvci3kbKEZp+VhCMO0fh0o/zzTa/rYF7kHy9KcEG26A2veQXd4uHFu34nXYC7dkkUW+2czuokXHCsZCAAQn3Rputz7tuxGZCcb8euWsexq49kKNmPMZeeYNEWb21lEkZzTVZYzp52PaD/QAzIc4EfAq9EvJirgKvbOP8DTJbifidwPZKp4ABi1BYF+Xyh62qhpG0xLCCBBXq7GJ8LB0AdJXM8jivzPhcOTK/xMxu6Uf75ptd1MOfyH8hIYEEsBKlF7KY7rgRJpPPw4AHxeLwM5aSicPJqOGm1lO6eBaes38iG1UeyPDU4d0bHdSUnHojxCQVgwwpZwLusr6Nzbu0aoKcBL9bausnzXP6stfUdpczdwL8A3292stbWL2pebwZO8sprjzQqctetdFsxLteFXaVJo3LDMOwrw3Fx0Bsnn5O0Detjh2d8oPvkXwiWkg4OJUJtTuR3XPmFfTALB3MyxLbYAgtcF/ZnJQXO7jEJl87WrdU5cZVEr9XN6Uw24bZ0Ls47/tSOdXkKjiO/YEsVGc8Ph2TbioSEeVejCzvsgbZrgAaB6mLSNLAcGTr7A/ClQ7mwl1bH5zA4WJYhuGUh+MR2+IGXze6yNZOf37Ijn+9jF/GPSZ8lTKEM20YlOmYxBha4rkSu/XHb1PQ3ILIMxuHIAfF4ls1suPenh/nTpr/xzKOf27m+2RXRZTtGo+hIZdc1/fOaPaJdA/QwsAFJ3Hkf8CKlzFuRkOyeMSTJZPf88i05UuU0EYT/3AHXebVqnzA4Ndv1wTKckzp87we6S/6Fotd10FH582XYOiy/rhdbhVHXFYPz6wclqAAk/c3RQ/Ilvm4AViVbfpHnigVuf+geHt6zvXN9syuQK4tBKVUniV1wDa8EqMu0GqOPWtmwqbmmXQP0VSRU+rfAh4GfAVcilU6vmouOdSPVyoMLzXAZbh8HXFmAet0+8Zo/fTycWzNHnKvA8jCs6VDQULfIv5D0ug46Jn+hxvhEujxzQakCmw/A9jSXbk6QKAXgbzeJ1wYSHPG4Y6WMQZtDVHalwr3bH+bORzZhV2wCRoBT1x9iQLDryp/tSlkJIyCGMBWTkPX9WVmsGw4AnhEKBmSY5E+HdslO0e5CVF3z+kYvs8GjgQe1tv42V53rNrLZ/LRqlPON48LdWegLyHNkeT+crlo31fiADM+dPrs5zqZ0g/wLTa/roCPyVxzxGoJdbHxsBx46AI8MSxBBUbIKHDlRjNqRkPCTV8OFx4oRagPXddl2YA+3Pfg3xvOyMmX9yrWcd/yppOKJ9tbnlCpiWAAwxA0wAhANwrJ+WbhaNYSRkAwBdikt775SZhi4GXi51tYDMLHup9XaH585YFdRyiusjsAXdsLekpRS+MdV04913fbzvPn4zAulimQwKNvdmSz0YFbKGdy7R9a9VFnbDxtX8Mudd5GNODzvKU+XqLBZMJIZ49YH/8auYZmsHUykOP+E0zhimffhdV1CD8uMxjV//DnQYH1O0ZZfoeuHxOgZxuIL2KihpQa1tspKmccydblJT7LQxbgqLmzKS9DBuA3XemUA33zU1IWoFS87wvKwlNnuFAstfzfQ6zo4ZPmrZa/3jElpgL4u0aPjSBDEthHYOjo1semqpESuHbtM1sAAO3/t1c6chfEplkv8ZfN93L/zEVzXJRIKc9aGkzjxyGMIBAJSobVoA4YkFY3UfGhzJfnmrQ59RkJwzNCsjV+30q4UX0MWjZpz2JeuJxZb2F9se4sSrDIQgm/uhawD56XgrLrFzwfLcHSs85FvCy1/N9DrOpiV/LWr5jcflKqkfV2ymLTiyPDazVtkjqRKOCDrc05fC6tThxUN5jgOD+x6hL88fB9Fu4wBnLjuWM7acBKxUFg8LKcMoSAcMeDN09T9YlyWEE8nHBC91e8/TOaz+Fwj2jVACeClSplPQZKKTok31Np6Y6c71ghv7dCXkQqsB4C3a21d2+A4AwmWeJW36UvA26prjpQyXSQ1UNWr+7bW1qvq26knnR5fsGJcJQceyIvxyVXgOs/7uWzt9GMNYGO88zV+FlL+bqHXddCW/OWKZDPYX1PW2UXCkheSog0PH4SHvbmdahqcVFQ8nfWDcOTgVA/kENk9vJ8/Pfg3RjJjAKwZWsH5x5/GskhCPszFEixPyLVj4ZmH0VbNnM9tKdCuAToJ8HxPNtTtm8+huc8AJWA1klH7eqXMO7W27qk77goka8MZXv/+F9gCfL7mmDO0thZFGiDXhXuyEuTSH4BP74BhWxKKnlfn/RQq0B/0C8z5zAPVstfZ0kTFTXDki91AvlirdOBLfdaUK5OVQ/dn4KGDsq3KioQEEZx1ZGc8C9dlPJvhtofuYetBSUWfjPVx3vGnsn5wFUbBFp0MxOT/Quiky5jRACllXgzcorVla209YR77NFN/EsALgFO1tjLAzUqZPwFeBryt7vDLgI9pbe3wzv0YMoT4eQ6DhSrGdaAMu0sSePBIHr5VM/dTP0KQdWDjHC067bpiZAvAfOhgTmvoHCbBoDdnMZqXNDPlinyZTvwMDcjitIVaTFqqwFhBggju2i3DfrWsG4DjV8KGZbLCvxM4DuXxAnfteph7dm+h4jqEgiFOP/I4Tll9DCEjIBmDNyzvnrmvLqGZB/QbYC2wTylzM3Cu1tbB+elWQ04AbK2tTTXb7gQe3+DYU7x9tcedUnfM/yllBoBbgDdpbT3SqgMLUQfFdWFTTryaXAXe8rDkenv2Cji1gXdemcPIt16ug1OlZ3XguuC4DIRjsHVEDE8k2B2RbPkyK7NBNh6MwKdumjomsyopYcgrEpP/O4Xr4hbKbN6zg9t3PUiukAfguOOO45xzziERi3sZHgxZ77SIo9XmimYGaASpYLoPOAaJNl9IksBY3bY00Cj/eNLbV3tcUinT8OaBHg/8EegD3g/8TCnzTK0tu74hpcwrkCE9nvrUSznvvHMBiMdjhEJBxsdlOiwcDpFMJhgZkcsaBgwNDTI2No5ti9s/MJCiWCxRKEg23L6+OIFAgExG2ohEwiQSfRNtBAIGTmKAg6NjDAYc/m1vH5sLEY6JOryuf4zcKET64oBBKZdj3IZ4NEIiEGN4WFQVDAYYGOgnnR6bqOUyONhPPl+gWJRcVclkH67rks3KBygajRCLRUmnx6e0sW3bDpJJsXpDQwNkszlKpbLXRgLHccjlpI1YLEo0GploIxQK0t+fYmRkdCKgZ2hogEwmS7ksak+lEth2hXy+MKHjcDjE2FhmQsepVHKijaqOx8czE2309ycpl+0pbXTyPo2Pj7N8+bJp92lwcGCKjgcGUhQKxQkdJxJxDMMgk8lN6DgejzE6Ov0+VXEc55Du0+hoGsdLfNnqPgFUKpXG98lxYbzIkB0iM56hbJcZL+Q5YvkK7EiAfCELBYhHooRDIcZyno6DIVJ9fYyM10SUAeO53ESVzv6+BGXbnigrEI9GCQWCE2tjwqEQyVickcy4d58MhpIpxnJZ7Ip3nw6U4bZtBLaneaYrXwOuAU4ygr0mQeWMtcSPXsFIVp6fgOEyCKSzWSpeieqBRJJCqTRRUycRi2FgkPGMSTQcJh4IUVk3QDgWneaVGsCqI/rIbb6LwcEhTjvtNNavPwrXdRn27mU0GiEWjJIebv8+VRkeHgW67/MUDnfmV24zA/QD4HdewlEXuN1LRDoNra36eaG5IAPU//zsB8bbOLYfyFSDELS2/s/bXlLKvAoxbCcB0xbVam19EfgiwPDwqFs/AdvqfX//VPvY1xenr29qSdxmbdydgdRAPz8+CDdmZAHqf2wMsCI+9ZxCIMKAAY/ul4CZ+jbrf7knEn3TkkrWLzCsbyOZTE7ZlkxO/zVZHyVV38bQ0NT3qdRUNy4cDhOPTx1DnG0boVCoZRuHc5+qch+Kjpcti9S9n7mNQCBwSPdpcHDqwsNW9ykYDE7XcX8/7E5LuFEMUsv65RtqfIxwMk4YMTxT+pGaKv9QaqpOU31T5QgFg8TrZalro/59f6xPFojevh12ecY6YHAgViYdczjuORcSXJGYUh+mvo2BxFR9JGIxErGYl03AgVCAZWHPe7ENiBoEYs3LJDzucY9j48aNUzJUH859mmn4tVs+T52imQF6DfAT4Hjg48A1NP6yny82ASGlzOO1th70tp0B1Acg4G07A6k91Oy4KtXkSF2F48rcz5gNn9oh295zLBxTV9LddSXrwYWDEF1oP9VncVNxJI/Y/oxEjS105FqVkg1374E/75DaOgCxEPa7n0IoGWMFsKLuFLtUnii81pJ8GSoObiRIeTRLIQzFVJCCY1Msl9hI81xpxx9//KxF8mligDxv4XoApcwzkEn9BTNAXjnvHwLvU8p8FRIF9xzgggaHfx14k1LmzxHjcjVS+hulzFOAMOLtxJEhuJ1IktWmDA3Nb0qLMVsi3/5zh8z7PGM5PGlo+nHpChwRlRDtuWS+5e9GlqQOdozK5KGBfBHbjqR1SUyfMB9Ktldx85CpOFJHZzQvC1dH85Apwc7RybDpwRiccxScsppQMtbUMynZZW+IrUihLENtE/9LJYrFAsVSiYJTplgpUygWG+a727jRL9w8F7SbC65bwnFeB3wFmZc6CLxWa+sepcyLgBu0tqq+4xeQcPHqkNqXvG0gIdyfA9YhAwy3AM/U2po6+NqAbDbXcDhjrthXgr+Mw01pSATgDUc2Pq5UgWPmYbnAfMvfjSwZHdSGIxfKTGRHjgSb5jXLFgok4/EZ9x8yjgt374bfPzK9jk6VdQNwzjo4bkXbE/rf+t31s+5KKBQiFosRjUYn/vvMDYsqn4NXQ+i5DbbfhAQeVN+7wFu8v/pjbwQedSjXr58gnCsqLtyfhYdy8IVdsu1VR8CKBhGctiPDbnPt/cD8yd/NLHodlGzYmfaMjscsEoKW7DIycNABXFcWhm4ZFi/soAQg0B+VRZpDcamnk4pKyPSKBOWKzf7R/ewZOcCe0YM8ff2zm14iHAwRDUeIhSNEg2GiwbC8TsaJDSSJphIThqZqbIJBf33OfLGoDFAv4LoSeLCrCDeOwraipNV5UYNkowBjFTgmtrhqePksEEVbQqhxF3Y9il2B+/eLx7OjJli1PwoXb4BHrZp4oMu2zb70QfaMbGXPIwfYPzYyq5IQ/3TupfKhCgYhFRGDVk3i6bPg+AZoFszH0MvOovwFDfjSbtl29VFeKQ9kXqjkTC51CBqwZp6+S5bE0NNhsmh1UC3+FgxA9NBDaJPxQ1y86bpSqnrzQbhnr6TqAYiFZFhtRQKOXUYJh70H97Jn9CB7Rg5wcHx0isExgOWpQdYMLWfNYH3YQQNWp8SDOsxMB7Ztz1wmwbYJhfyv0kPB19oscByn9UGHge3AAzlZSPrBrZBz4OJBuMCb9855w/bV5KMBIBWav8i3uZZ/MbAodOC6kgGg7FXDLNmwLyMZlA8z/cts5Y/YBuvTYfjGn6UPVVYm4MwjKR43yN58mj2je9l7xz1icGrONwyDFf1DrBlczpqhFaweXE4kFJ6M1mtFkzLYs6FqYBplqfCNz6Hja24W5HL5Oc2GvLMo0W57y3D9QfFu1DrZV3GlBMPjBqF/ge7aXMu/GOh6HZQrMqyVK00OMxkuJMI1+doOnVyxQCzSwOV2XfGyRvOwZxz7SRsJpeK89NWvmHZoKZfnL7fcyp7RBxj+U3rKPsMwWFlrcAaWEQ7VeWz5snwgjuj3PZNFjn93uoRqtuuhEHxsOzjAM5bBUd76r5GyFJ5bKOPj0+W4krWA3d7izCbrd+w1SUKRcMMv7lZrZ4x8GR7cISHSmaJErI17/x0XF5eKAaHnntY0PPre7ZsBCBgGK/uXsdobUls1uIxwsMFD7rpe+QIHklEZWouGJr7AfM9kceLfoVkwl798txfkM3bQhp8ckG0vXyP/i45ktz56jpKMtktX//KfJ7pWByN52JX2asc0H2YLRcJNjUO+VKRklynZZcp2mVK5TGlfmtKOYUrDGTYFHEpBl1IQSn0u5ZTrvZc/14BW6zbOPPZRrB5cwar+QUIlyTNHKAAEIFv0Jjm90HAM8eKWJSSIYIkUY/PxDdCsmKtqmBUXHi7AYBjevQWKLjx5CI7zol1HbTgn6X0+F5BerwYKc6uD6pBRQ89kpuGkiiMLwfaMQSKCGzCwbZuiXaJULlO0SxTLZUrV/+Uy56y/sGk/vn3TDY13RIA1reUItjHUd9YxJ8o8Vd6BZXFJajqSk3md1f1iSEOByWHEQHeUnr788supVBpmJPM5BHwDNAvmqhhZ2hYjdFcGfjksQQVXeXM/4zYsD8GqLvju7/VibDC3OgiFQk09k4ceeohisUipVJL/4zmKmRxFu0ypUqZo25TsEk6LMOVzaG6AohWDiG0QqRiEK/I/EggSGUriDMVJDQ0QCYWJhELe/zBh738kFCIYaCPQIVeG5X1TPZpUVDyhQyh5MZ+lK/zPQefwDVAXsL8EQcDaJu9fsQbWep/FnAPnHF5l4CXPfNXPmcvrlMvllhmGb7rpprbaCgaCRMNhIqEI0XCYaChMJByZ2NaKl9w1INFy6walSuj6QViZBMNgeHxsWnLPQ2Lj8ukLYA1DIm98egbfAM2CUKjzK6RdV6LffjUCD+ZhbQRe5g1zjJRhQ0xCrbuBuZC/l8nlcmzbto1t27axe/duLrvssqbHbzj6GKIliBhBotEokXiMaChMNByZMDKRUJhQ/Up+15V8amMFySTdipeeDauTDaPmQsHgZNZo151VFoUpHOp5XYD/Oegci/cpWADqU/Z3gkxF/r4qFXy5ah3EAjBchr4gbJiDtFuH+kt+LuTvJVzXZWRkhG3btrF9+3YOHGjDGNTw+NUnSZLQVl/eu8fgrzslXLnsJfesza/2skc3P39tAw/HdaFUob8SkuGzWAgIwJi3aro67Oc5MHaxtGTDo/3PQedYvE/BAjAyMjqtdsah4rgyp7q3BL8bhf1lCTp44hCMlmEwCGemJjMgdAOdlL9XcByHPXv2sH37drZt20YmMxniHAwGOWLlatavWMtRg83T/QMzh1a7rizy3DsuudUeblC4OBaS848cwM4XZzYOpfLkl4LjeKHPXrWyZISRhMvQmpXy8FZDv8uVyYCBSBCCAUJN5nEWs/EB/3PQSRb3kzDPzCIFVVP2FOG+HJyelKSj390v21+xRhaiusBpye4yPtA5+Zc6pVKJnTt3sm3bNnbs2EGpNOl9xEIRjhpaxfqh1RzRv4JQKCBDXbOZ5HNd2DY6GTW2bWRqTrWAAWevk+zRYa+swvK+iWuE9ueAXMOmQxVHotNcLxhgWd9kKptwEHd4dDIazTCgf4HXBiwA/uegc/gGaJ7JVeAvGRlmu2MMPrYDdhTh2Bg8ZRkMlyTVTswfZl5UZDIZtm8TL2fP3j1TUtYMxBKsX76G9WuOYMXAMgIzGBu7VJ7ZM8kWCf3hEZnL2ZWG/dmpB8RCcOwyGT47YaUs1qzieKHatiPDZTEv2MF1JYzbkeE1MORXz/KEGJ1oqCtCn32WLsZsMsv2Oq7rusZhhqMdKMHt47AyAl/dDZ/eCfEAfO0kWBeFggOPH5zbz/2hzgG5rsvhyt9Jms0ldHKeoVlbhXyBX97wPxxMj0xsM4BVqSGOWrmW9auPYKCvpliT48i8jO3Il3+5IsNY6YKUSChVZChtz7h4IjORiMBxy6EvDKkYnLhqMpzZdmT+x/AWcgaDkoonFoLxEuSKYHjudTQkRmkwLm21mF/qtmdgIfB1AHSogrTvAc2CTCY7rV76bBmzxbjcOgaf82r9vPsYWB2BA2U4K9m9Pzo7IX8nabVuZt++fRguGBgYhvwHyTdmAIYRwDBc2e64GI58qgzXwMiVMHJSqC126pFNr3MwPUIoEOSI5atYv3ItRy1fTSzieSB2BbaPyjDZ9lHYNSYeRztEg+LJJKOQjMj/RESGvY4emprxoGhLahwQg3PEAAx4w2O1D9QyF9J5mauJhWe95qbbnoGFwNdB5/AN0Cwol5v8Im2TnUX48X744m5ZfPr3K+F8L+jo0UlYPYeZXg5ppX0Ns5W/E+tmyuUy4+PjZDIZMpnMlNfPec5zmp57/fWzr4bZiMtPbd7/Jyc3srYUJ7TXhi1jkDsoyUBzZTEM9XgT9YQC8j8VFaNSTRi6MiFDaanozHNDRVuG46q5o+MRWNMvbUeCM58XMKS42yHSic/AYsfXQefwDdA88nAennM3jHjP70tWw+uOkGwk5/dL2PVc0spj6BSzMXS2bZPNZicMS72xKRaLh9yPlakhXFxc18X1EmW6rguui+syZR/VY1zvr+bYVhx1U5NwagOZyD92ORy/Qoa8agICZqSaYsfx8qTV9sNFCsqt7Z80YqFZBjL4+HQBi8oAKWUuA74MXAocAN6utXVtg+MM4MPAq7xNXwLe5pXqRinzTK+dk4D7gH/W2vprq+unUodWjGxnEW44CJ/cIcbn+Di8dT1cvla+S4oORFybmW7HbOYzXNfFtm3y+Tz5fJ5CoTDx+qyzzmp67o033kgoFCIYDE78TbwPBDEMGD64n2AwRCg0eUwwFCIUqL4O0pdINDV0v/vtb8XAZDLk8/mmfQoGAiQTSZLJJEkjQsoNkwxFSQVar+h/5i0G2F4yy84MWTfm7HWeF+MNl6WiMqcyGBdDUTsEtjMtJQuqIY4uMi9UtS+GF94cDk7mQ4t48zR4+dBaeTkeczG/m0z24brVAIv5mj925+9SbZBMRHGdznlBrjwEM+x0qQrvOEVKpd3QwWu3g4sL1XtuGMT6NhIMdqYw46IyQMBngBKwGjgTuF4p806trXvqjrsCeC5wBnL3/hfYAnxeKTMC/Bj4BPBZ4F+AHytlHq+1VaIJtl1pmS4FoOxWCBuT7syRUXjVEfK3s1ghQJC1NUNtfUEouzN/mbhBg2KxOM2gHLNxA32RqWGwhmEQDofJl4r8/Oc/n7KvlQHaunXrtG0veOE/0J9oPN49ls3wg+9+b9r2Vt7U5i1bJvsLJAiTIkKSMMlKmFQ5QLIUIFUwiOddjIoL5TLk6kKHn3hG0+tgex+aqpdQTWhZ/Qsasj0eFkORiMj/ZESMSX9cXrfAvezR5HObKBW2T35QAQouFGqOw4WQC9Hi5BdLwICoIfM21UcmEJh8DTJWW5P/0s3Xfxu3+d4zSG6r46adV/1XoVjIEgraOE4B1y3PcN5M7VW7Mbvjp25vcMwM7bmz7NeU7e4M23GxKw7BKXNnh9Oeg+OUcd0SrlsBHDHwta+9/65bxnXKuK7tteFOtDdVp27N/8l7707b5/13J9+7de1OdtWd+Ftz9JtZtvJZM8g8OxaNAVLKTAAvAE7V2soANytl/gR4GfC2usMvAz6mtbXDO/djwKuBzwOXIHJ/wvOIPqmU+WbgicD/NOvDvff+mXg10sidvElG9cvEu5EnnfE4XrPt5w3b+Pz6p5O99zdk3ckHA9clcdqTm55zzbVfn7b95JNPbnrOsohBNGgQDbhEg60rWZ6dHMZxoeIa8p3nQn8i2fQag0aGClAhgEOACq0ntTcuv41YKEs0nCEazGMYUz8UFQPSwOjEGTWr7Kt22oBH8bym17n/Ff81ebw79RpTv4TdhsdQAobhpGN/1fQ6995+MTP+gvXxWWq4nXvWF40BAk4AbK2tTTXb7gQe3+DYU7x9tcedUrPvrupwnMdd3vamBiheeBeuM31OYvrvn983a4ark9OHnT7f9Az405NWT9vWatbmhotWTXn/9W0/hwbtAPxp28/h/JOmbb+gxTVOOOXbDbb+S9Nzlq2dvDWHM5hQyO+b0dsq5PdRqaQb7uv0dcABI0TAiEyGN9cN+Rn1Q4DThs+Muv912yf+zeI8o/b4GY6boR8Nr2MEcJwY0WiSQDCOEYg3OK7uGjMME049bybvf3K7EQjOLMO0bTO0V9OXxv1u0k7Ny0K+TCwembJjxvaMVv0yCFZ1GQhjGCEMAmAEMYwghhHGMAJAECMQJRjswwhE5BjvHk/+MqvphWHUbPeOMYzGz4NRK4MxrV2DAIYRnHgfCHYuNf9iMkBJYKxuWxpolJgp6e2rPS7pzQ3V72vWDkqZVyBDelx+6Ro3GCpNOrvulJtpiA9kAM3dAPPe/8qBMfVn+PqnNx1UfcvdXx732q7+XHdY//TBFtfZA4aLi2tUQvaGZ353fbO5mc0/e+GfAAdcBwMHw62w/umXNLsG8O9AFvEXKt7fp1ucc6b3v87fn93/h+9+3qG2AeKy5GvOb+c6XPujsy97yfPu+GrdceVTHv276njUkkYp8wqtrS8udD8WEl8HndPBYjJAGaA+S2I/MN7Gsf1ARmvLVcqcTTt4Sv4igFLm7VpbLTI5Cp9f//QZ9x33dz+a9Tkbnv6Djlyn2fzMhmd+t+k1Gsl/yrkNvb3PNLvODOcsCnLXmq845dzftzKwS5kr8D4PPYyvgw7poMuyjTVlExBSyjy+ZtsZQH0AAt62M2Y47h7gdM8bqnL6DO34+Pj4+MwRi8YD0trKKmX+EHifUuarkGGc59B4muLrwJuUMn+ODJFcDXzK2/dbZJjojUqZn0eCEwBunLve+/j4+PjUs5g8IIDXAXFgH3Ad8FqtrXuUMi/yhtaqfAH4KfA34G7gem8bXqj1c4GXI4FWrwSe2yoE26PX3e5elx98HfS6/ODrADqkAz8ZqY+Pj4/PgrDYPCAfHx8fnyWCb4B8fHx8fBaERROEsJC0m4NuKaGU+VvgMUyuFd2ptfUob99LgA8BK5A0R6/U2hpeiH52CqXMK4FXAKcB12ltvaJm35OQNFDrgT8Br9Da2urtiwKfA/4eKTP6Ua2tj89r5zvETDpQyjwGSWVVWwXvI1pb/+7tXxI68OT4LPBkYBnwMPJZv8Hbv6Sfg2byz9Uz4Bug9mg3B91S40qtrS/VblDKPAUJ6HgGcAcyGflZ4EXz372Osgt4P/BUJNAFAKXMFcAPkcS2P0UW3n4HMc4A7wWOB44G1gC/Ucq8V2uraVaNLqWhDmoY1NpqlLzivSwNHYSA7Uh2lW3A04HvKmWehqwtXOrPQTP5q3T0GfANUAtmmYOuF3gp8FOtrf8DUMp8F3CfUmZKa6vhYt7FgNbWDwGUMh8NrKvZ9XzgHq2t73n73wscUMo8UWvrfiTv4Cu0tkaAEaXM/0K8iMX0xQM01UErloQOtLayyBdplZ8pZW4BzgGWs8Sfgxby/7nF6Yckvz8H1JqZctCdMsPxS4kPKWUeUMr8vVLmJd62KXn2tLYeRrzDExagf/NBvbxZZGjiFKXMIWAtM+cdXGpsVcrcoZR5jecZspR1oJS5Gnmu76EHn4M6+at09BnwDVBrZpODbinxVmADcCQyzPZTpczjmGUuvSVAM3mTNe/r9y0lDgDnIsMr5yDyfcvbtyR1oJQZRmT8mufh9NRz0ED+OXkG/CG41swqd9xSQWvrTzVvv6aU+WJkTLjX9NFM3kzN+0LdviWDN/R8u/d2rxessFspM8US1IFSZgD4BuLZX+lt7pnnoJH8c/UM+B5Qa2aTg24p4yLpuKfk2VPK3ABEET0tRerlTQDHIfMBI8BuZs47uFSprl4PLDUdeDkiv4wEHL1Aa6ua5bwnnoMm8tfTkWfA94BaMMscdEsCpcxB4Hzgd0gY9j8CFwNXAWHgD0qZFyFRcO8DfriYAxAAlDJDyOchCASVMmOI7D8CLKXMFyApnd6N1JO63zv168A7lTJvRz60r6Z1qaaupIkOzkHSVj0IDAGfBH6rtVUdclkyOkBCiU8Cnqy1VVu4q1eeg4byK2Wezxw8A74Bao/XAV9BctAdxMtBt7BdmlPCSDjuiUji1vuRfHmbAJQyX4OM/y4HfsXi/KDV807gPTXv/wn4N62t93pfOp8Gvoms/6gNOX8P8qHditQX+sgiC72tpaEOgAeADwKrkPnQ/wVeXHPcktCBUubRSDXFIrBHKbO661+0tr611J+DZvIj9bM6/gz4ueB8fHx8fBYEfw7Ix8fHx2dB8A2Qj4+Pj8+C4BsgHx8fH58FwTdAPj4+Pj4Lgm+AfHx8fHwWBN8A+fj4+PgsCP46IB+fOUQp8xHg01pb/7HQfWmEl/n6NuBYra1HFrg7Pj2Gb4B8Fj1KmSuBncgK7RKyYvskra1tC9mvuUIp8xWIUUu2OraD1zwO+H9IUcZVwB7EcH1ca+uWmuOeCrwFSVwZRlI0fQX4lNaWU3NcowWId2ptnTlXMvh0H/4QnM9S4LHIl1cWOBsYXqrGZyHwvKQ7kPT6rwVOBp6F1Ij5VM1xrwN+7m2/wDvus0g2hW8xnVcjafyrf0+aMyF8uhLfA/JZClwA/N57/bia101RynwWUoDrFCSZ4rVI+p2SUuYHgadqbZ1Td84twO1aW29UyjwX+ABi9CLAXYCptfWHJtd0gX/Q2vp+zbZHqBmmU8p8E1LM6zjEm7sBeLPW1qhXl+mamrZgMmVQBKnU+VKkpPI9wDu1tn5Rc62/Az4BHIN4MJ9roSMD+CqwGbhQa6tSs/supczPecetAzTi6byl5pgvKGXuBX6klPnDakE3j1GtrT0zXPfdwD8j1TVHgF9qbb28WV99Fh++B+SzKFHKXK+UOaqUOQq8CfgX7/UHged6+z7b5PynIr/KP40YoFci9ew/6B3yTeBspcwTa87ZgHhb3/Q2pZC09RcB5wF/BX6ulLn8MMVzgH/1+vUSr+2qp3GLty/HpOdQnV+6Bimn/BLgVOBrSB2nM7z+HwX8N5LH60yvzY+26MuZXj+sOuMDgNbWqPfyHxAjPK09ra3/RpJYvqTFtfD6+QLgzUgOxuOBZwK3tnOuz+LC94B8Fiu7kC/HfqROyflAFjECz0Bq2mdmOBfgHciX6jXe+4eVMt8KfFMp09Taulcp8y+IN/Eu75iXAJu0tm4F0Nq6sbZBpcw3IOXbn8akkZo1WlufqHn7iFLmW4AfK2Ve5nlnacCt9R68OZoXA8fUDD9+WinzyUgyydchw2fbgDdqbbnA/UqZJyBe00xUy5Dc16LbJwBjWlu7Zth/H/Coum3fUMr8as37f9Ha+hZS9Gw34vWUvT7fjs+SwzdAPosSrS0b+XJ+IXCb1tZdSpkXAnu1tv6vjSbOAc7zjE6VABBHhn12I0bk9UwaoJdSM5ehlLkK+fJ+ApKCPuidv/5wZFPKfCLwdiQt/oDXbsTr10xf8Gcj9ZrurcliDFKrqWooTwL+6BmfKjMOF3oYs+j6bDMbm0BtxuS93v/vIaU/tihl/sI75idaW8VZtu/T5fgGyGdRopR5D/JLOQwElDIzyPMc8l5v1dpqVpM+gEyOf6/Bvv3e/+uAjyplPhZJUX8iUz2bryGGRwGPeMf8GjEWM1Et7FdLuEauo5F6M/+F1Jw5iBiX61q0G/DaPheoLyKWn35421QLDZ4E/KXFcQNKmUdqbe1ssP9kphco26O19VD9gVpb25UyH4UEJTwZ+BjwHqXM871AE58lgm+AfBYrT0e+uH+NhP3+Gfg2MmH+P0z/Eq7nDuDERl+AVbS2ditl3oh4PkXgD1pbm2sOeRwynHU9gFLmamROphn7a49pcM6jEUOjqnMuSpnPrGujhHhFtfwFMWxrtLZ+M8O17wNeoJRp1HhBj2nR378C9wKmUuZ36ueBlDIHvXmg7wMfQbyaf6075nnARiSMuy20tgqIIb5eKfPDSNj3hcAv223Dp/vxDZDPokRra6tS5hrEA/kx8uv/FOAHWlu722jifcDPlDK3At9FKn+eCpxXF8X1TeQXeAmJeKtlE/BPSpl/AhLIBHypxXVvBF7vRdNVkKCHQs3+BxFv5l+9SryPoe4LHfG2YkqZT0EMT05ra5NS5reAryplXo0Y2GXAJcBmra0fAp8HrgY+4QVonAa8pllntbZcpczLkcKDNytlfgAxZH3IXNcLgUd7XsvVwH8qZZYQ7zAHPMXTy3fqIuBmxFvnFEKKvmWQirxlTzc+Swg/Cs5nMXMJMv9TQCLFdrRpfPBCk5+BzN/c6v29DZnwruWHyJftSuA7dfteCSSZ9L6+ghiHZlyNhDT/FvEavoRU2q326y5k/uNNiOfxKiQirLbvtyDG5DrEo6oazMuRSLiPIlVsf4aUUt/qnbcNeD7wd8CdyNDh21r0Fy/o4hyvzc8jBuhniM6vrDnuU0jE2rnAH73jrkSqZbYVAecxioRg3wTcjQR2PF9ra8ss2vBZBPgVUX18fHx8FgTfA/Lx8fHxWRB8A+Tj4+PjsyD4BsjHx8fHZ0HwDZCPj4+Pz4LgGyAfHx8fnwXBN0A+Pj4+PguCb4B8fHx8fBYE3wD5+Pj4+CwIvgHy8fHx8VkQ/j9H6otrni4sagAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -703,7 +705,7 @@ ], "source": [ "plt.figure()\n", - "plt.ylabel('fraction of top 100 COFs found')\n", + "plt.ylabel('fraction of top 100 COFs\\namong acquired set')\n", "plt.xlabel('# evaluated COFs')\n", "\n", "plt.plot(np.arange(1, bo_res['nb_iterations']), y_top100_mu_BO[1:], label='BO', color=search_to_color['BO'], clip_on=False)\n", @@ -725,8 +727,8 @@ "plt.errorbar(rf_res['nb_evals_budgets'], y_top100_mu_rf, yerr=np.vstack((y_top100_sig_bot_rf, y_top100_sig_top_rf)), color=search_to_color['RF'], marker=\"s\", label=\"random forest\")\n", "plt.errorbar(rf_div_res['nb_evals_budgets'], y_top100_mu_rf_div, yerr=np.vstack((y_top100_sig_bot_rf_div, y_top100_sig_top_rf_div)), color=search_to_color['RF (div)'], marker=\"s\", label=\"random forest\\n(diverse train set)\")\n", "\n", - "plt.xlim([0, 500])\n", - "plt.ylim([0, 1])\n", + "plt.xlim([0, 250])\n", + "plt.ylim(ymin=0.0)\n", "plt.xticks()\n", "plt.yticks()\n", "plt.tight_layout()\n", @@ -736,7 +738,7 @@ { "cell_type": "code", "execution_count": null, - "id": "thirty-steel", + "id": "third-briefing", "metadata": {}, "outputs": [], "source": [] From 0ab439b28c8ef861f01169a378b8f919d7745c0f Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Fri, 2 Jul 2021 10:35:40 -0700 Subject: [PATCH 13/29] verbose mode for BO --- new/BO_run.ipynb | 90 +++++++++++++++--------------------------------- 1 file changed, 27 insertions(+), 63 deletions(-) diff --git a/new/BO_run.ipynb b/new/BO_run.ipynb index ea88410..a2437a7 100644 --- a/new/BO_run.ipynb +++ b/new/BO_run.ipynb @@ -13,29 +13,7 @@ "execution_count": 1, "id": "retained-equity", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" - ] - } - ], + "outputs": [], "source": [ "import torch\n", "from botorch.models import FixedNoiseGP, SingleTaskGP\n", @@ -52,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 2, "id": "romantic-mistake", "metadata": {}, "outputs": [], @@ -72,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 3, "id": "flush-samba", "metadata": {}, "outputs": [ @@ -82,7 +60,7 @@ "69839" ] }, - "execution_count": 38, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -105,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 4, "id": "south-coordination", "metadata": {}, "outputs": [], @@ -116,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "id": "weird-pleasure", "metadata": {}, "outputs": [ @@ -126,7 +104,7 @@ "torch.Size([69839, 12])" ] }, - "execution_count": 40, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -137,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, "id": "occupational-tracker", "metadata": {}, "outputs": [ @@ -147,7 +125,7 @@ "torch.Size([69839, 1])" ] }, - "execution_count": 41, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -158,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "id": "attended-secondary", "metadata": {}, "outputs": [], @@ -168,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 8, "id": "criminal-support", "metadata": {}, "outputs": [ @@ -178,7 +156,7 @@ "69839" ] }, - "execution_count": 43, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 9, "id": "varying-workstation", "metadata": {}, "outputs": [], @@ -207,12 +185,12 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 27, "id": "wanted-motion", "metadata": {}, "outputs": [], "source": [ - "def bo_run(nb_iterations):\n", + "def bo_run(nb_iterations, verbose=False):\n", " assert nb_iterations > nb_COFs_initialization\n", " \n", " # select initial COFs for training data randomly.\n", @@ -225,7 +203,7 @@ " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", " \n", " for i in range(nb_COFs_initialization, nb_iterations):\n", - " print(\"iteration:\", i)\n", + " print(\"iteration:\", i, end=\"\\r\")\n", " # construct and fit GP model\n", " model = SingleTaskGP(X[ids_acquired, :], y_acquired)\n", " mll = ExactMarginalLogLikelihood(model.likelihood, model)\n", @@ -236,7 +214,7 @@ " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", " \n", " # compute aquisition function at each COF in the database. need to do in batches to avoid mem issues\n", - " batch_size = 15000\n", + " batch_size = 35000\n", " acquisition_values = torch.zeros((nb_data))\n", " acquisition_values[:] = np.NaN # for safety\n", " nb_batches = nb_data // batch_size\n", @@ -271,9 +249,10 @@ " del y_acquired\n", " y_acquired = y[ids_acquired, :] # start over to normalize y properly\n", " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", - "\n", - " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition].item())\n", - " print(\"\\tbest y acquired:\", y[ids_acquired].max().item())\n", + " \n", + " if verbose:\n", + " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition].item())\n", + " print(\"\\tbest y acquired:\", y[ids_acquired].max().item())\n", " \n", " del model\n", " del mll\n", @@ -293,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "upset-bristol", "metadata": {}, "outputs": [ @@ -304,29 +283,14 @@ "\n", "\n", "RUN 0\n", - "iteration: 10\n", - "\tacquired COF 67403 with y = 188.512939628\n", - "\tbest y acquired: 188.512939628\n", - "iteration: 11\n", - "\tacquired COF 57716 with y = 171.85467019900003\n", - "\tbest y acquired: 188.512939628\n", - "iteration: 12\n", - "\tacquired COF 11529 with y = 177.102249387\n", - "\tbest y acquired: 188.512939628\n", - "iteration: 13\n", - "\tacquired COF 66078 with y = 190.67549353299998\n", - "\tbest y acquired: 190.67549353299998\n", - "iteration: 14\n", - "\tacquired COF 65338 with y = 180.51981098299999\n", - "\tbest y acquired: 190.67549353299998\n", - "took time t = 0.6949090321858724 min\n" + "iteration: 22\r" ] } ], "source": [ "bo_res = dict()\n", - "bo_res['nb_runs'] = 1\n", - "bo_res['nb_iterations'] = 15\n", + "bo_res['nb_runs'] = 50\n", + "bo_res['nb_iterations'] = 250\n", "bo_res['ids_acquired'] = []\n", "for r in range(bo_res['nb_runs']):\n", " print(\"\\n\\nRUN\", r)\n", @@ -338,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 25, "id": "tropical-poland", "metadata": {}, "outputs": [], @@ -372,7 +336,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.5" } }, "nbformat": 4, From 755d2d611dd0ad644b972743f5c392d06ce3fadc Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Fri, 2 Jul 2021 17:23:17 -0700 Subject: [PATCH 14/29] no grad for acquisition functions. vectorize diverse train set selection --- new/BO_run.ipynb | 137 ++++---- new/random_forest_run.ipynb | 651 ++++++++++++++++++++++++++++++------ 2 files changed, 632 insertions(+), 156 deletions(-) diff --git a/new/BO_run.ipynb b/new/BO_run.ipynb index a2437a7..130ce35 100644 --- a/new/BO_run.ipynb +++ b/new/BO_run.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "variable-triple", + "id": "sound-seeking", "metadata": {}, "source": [ "# BO runs" @@ -10,8 +10,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "retained-equity", + "execution_count": 49, + "id": "colored-search", "metadata": {}, "outputs": [], "source": [ @@ -30,8 +30,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "romantic-mistake", + "execution_count": 50, + "id": "joined-gauge", "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "markdown", - "id": "hindu-startup", + "id": "institutional-baghdad", "metadata": {}, "source": [ "load data from `prepare_Xy.ipynb`" @@ -50,8 +50,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "flush-samba", + "execution_count": 51, + "id": "herbal-spread", "metadata": {}, "outputs": [ { @@ -60,7 +60,7 @@ "69839" ] }, - "execution_count": 3, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "mineral-update", + "id": "accessible-economics", "metadata": {}, "source": [ "convert to torch tensors" @@ -83,8 +83,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "south-coordination", + "execution_count": 52, + "id": "distributed-triple", "metadata": {}, "outputs": [], "source": [ @@ -94,8 +94,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "weird-pleasure", + "execution_count": 53, + "id": "eastern-capacity", "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ "torch.Size([69839, 12])" ] }, - "execution_count": 5, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -115,8 +115,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "occupational-tracker", + "execution_count": 54, + "id": "absolute-ministry", "metadata": {}, "outputs": [ { @@ -125,7 +125,7 @@ "torch.Size([69839, 1])" ] }, - "execution_count": 6, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -136,8 +136,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "attended-secondary", + "execution_count": 55, + "id": "precise-strike", "metadata": {}, "outputs": [], "source": [ @@ -146,8 +146,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "criminal-support", + "execution_count": 56, + "id": "polyphonic-cigarette", "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ "69839" ] }, - "execution_count": 8, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -167,7 +167,7 @@ }, { "cell_type": "markdown", - "id": "unexpected-theorem", + "id": "latin-liverpool", "metadata": {}, "source": [ "number of COFs for initialization" @@ -175,8 +175,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "varying-workstation", + "execution_count": 57, + "id": "played-commonwealth", "metadata": {}, "outputs": [], "source": [ @@ -185,8 +185,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "wanted-motion", + "execution_count": 58, + "id": "advanced-insulation", "metadata": {}, "outputs": [], "source": [ @@ -213,24 +213,27 @@ " if which_acquisition == \"EI\":\n", " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", " \n", - " # compute aquisition function at each COF in the database. need to do in batches to avoid mem issues\n", - " batch_size = 35000\n", - " acquisition_values = torch.zeros((nb_data))\n", - " acquisition_values[:] = np.NaN # for safety\n", - " nb_batches = nb_data // batch_size\n", - " for ba in range(nb_batches+1):\n", - " id_start = ba * batch_size\n", - " id_end = id_start + batch_size\n", - " if id_end > nb_data:\n", - " id_end = nb_data\n", - " acquisition_values[id_start:id_end] = acquisition_function.forward(X_unsqueezed[id_start:id_end])\n", - " assert acquisition_values.isnan().sum().item() == 0 # so that all are filled properly.\n", - " del acquisition_function\n", - " # acquisition_values = acquisition_function.forward(X_unsqueezed) # runs out of memory\n", + " # compute aquisition function at each COF in the database. \n", + "# batch_size = 35000 # need to do in batches to avoid mem issues\n", + "# acquisition_values = torch.zeros((nb_data))\n", + "# acquisition_values[:] = np.NaN # for safety\n", + "# nb_batches = nb_data // batch_size\n", + "# for ba in range(nb_batches+1):\n", + "# id_start = ba * batch_size\n", + "# id_end = id_start + batch_size\n", + "# if id_end > nb_data:\n", + "# id_end = nb_data\n", + "# with torch.no_grad():\n", + "# acquisition_values[id_start:id_end] = acquisition_function.forward(X_unsqueezed[id_start:id_end])\n", + "# assert acquisition_values.isnan().sum().item() == 0 # so that all are filled properly.\n", + " with torch.no_grad(): # to avoid memory issues; we arent using the gradient...\n", + " acquisition_values = acquisition_function.forward(X_unsqueezed) # runs out of memory\n", " elif which_acquisition == \"max y_hat\":\n", - " acquisition_values = model.posterior(X_unsqueezed).mean.squeeze()\n", + " with torch.no_grad():\n", + " acquisition_values = model.posterior(X_unsqueezed).mean.squeeze()\n", " elif which_acquisition == \"max sigma\":\n", - " acquisition_values = model.posterior(X_unsqueezed).variance.squeeze()\n", + " with torch.no_grad():\n", + " acquisition_values = model.posterior(X_unsqueezed).variance.squeeze()\n", " else:\n", " raise Exception(\"not a valid acquisition function\")\n", "\n", @@ -246,7 +249,6 @@ " assert np.size(ids_acquired) == i + 1\n", "\n", " # update y aquired; start over to normalize properly\n", - " del y_acquired\n", " y_acquired = y[ids_acquired, :] # start over to normalize y properly\n", " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", " \n", @@ -254,17 +256,13 @@ " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition].item())\n", " print(\"\\tbest y acquired:\", y[ids_acquired].max().item())\n", " \n", - " del model\n", - " del mll\n", - " del acquisition_values\n", - " \n", " assert np.size(ids_acquired) == nb_iterations\n", " return ids_acquired" ] }, { "cell_type": "markdown", - "id": "modern-elimination", + "id": "friendly-virgin", "metadata": {}, "source": [ "`ids_acquired[r, i]` will give ID of COF acquired during iteration `i` from run `r`." @@ -272,8 +270,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "upset-bristol", + "execution_count": 59, + "id": "russian-parent", "metadata": {}, "outputs": [ { @@ -283,7 +281,32 @@ "\n", "\n", "RUN 0\n", - "iteration: 22\r" + "iteration: 27\r" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\\nRUN\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mids_acquired\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mfit_gpytorch_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# set up acquisition function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/botorch/fit.py\u001b[0m in \u001b[0;36mfit_gpytorch_model\u001b[0;34m(mll, optimizer, **kwargs)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_state_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/botorch/optim/utils.py\u001b[0m in \u001b[0;36m_scipy_objective_and_grad\u001b[0;34m(x, mll, property_dict)\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m \u001b[0;31m# pragma: nocover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0mparam_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m inputs=inputs)\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 145\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 146\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -302,8 +325,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "tropical-poland", + "execution_count": null, + "id": "accessory-commodity", "metadata": {}, "outputs": [], "source": [ @@ -314,7 +337,7 @@ { "cell_type": "code", "execution_count": null, - "id": "sexual-munich", + "id": "devoted-thanks", "metadata": {}, "outputs": [], "source": [] @@ -336,7 +359,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/new/random_forest_run.ipynb b/new/random_forest_run.ipynb index 068bd6c..15ad99f 100644 --- a/new/random_forest_run.ipynb +++ b/new/random_forest_run.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "metallic-quilt", + "id": "convertible-sauce", "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "entertaining-kansas", + "id": "cultural-chase", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "prime-still", + "id": "convinced-attribute", "metadata": {}, "outputs": [], "source": [ @@ -61,15 +61,13 @@ " ids_train = [np.random.randint(0, nb_data)] # initialize with one random point; pick others in a max diverse fashion\n", " # select remaining training points\n", " for j in range(train_size - 1):\n", - " # for each point, compute its min distance to training set\n", - " min_distances_to_train_set = np.zeros((nb_data, ))\n", - " for i in range(nb_data):\n", - " # compute its distance to all points in the training set\n", - " distances_to_train_set = np.linalg.norm(X[i, :] - X[ids_train, :], axis=1)\n", - " assert np.size(distances_to_train_set) == len(ids_train)\n", - " min_distances_to_train_set[i] = np.min(distances_to_train_set)\n", + " # for each point in data set, compute its min distance to training set\n", + " distances_to_train_set = np.linalg.norm(X - X[ids_train, None, :], axis=2)\n", + " assert np.shape(distances_to_train_set) == (len(ids_train), nb_data)\n", + " min_distances_to_a_training_pt = np.min(distances_to_train_set, axis=0)\n", + " assert np.size(min_distances_to_a_training_pt) == nb_data\n", " # select point with max min distance to train set (Furthest from train set)\n", - " ids_train.append(np.argmax(min_distances_to_train_set))\n", + " ids_train.append(np.argmax(min_distances_to_a_training_pt))\n", " assert np.size(np.unique(ids_train)) == train_size\n", " ids_test = [i for i in range(nb_data) if not i in ids_train]\n", " assert np.size(np.unique(ids_test)) == nb_data - train_size\n", @@ -79,7 +77,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "duplicate-cornwall", + "id": "little-heater", "metadata": {}, "outputs": [], "source": [ @@ -89,7 +87,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "amazing-counter", + "id": "operating-alcohol", "metadata": {}, "outputs": [], "source": [ @@ -135,7 +133,7 @@ { "cell_type": "code", "execution_count": null, - "id": "hourly-motivation", + "id": "single-authorization", "metadata": {}, "outputs": [ { @@ -147,276 +145,276 @@ "\trun 0\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 170.915156833\n", + "\tmax y acquired = 153.625944043\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 174.168128644\n", + "\tmax y acquired = 184.021162787\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.172206806\n", + "\tmax y acquired = 164.770180227\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.257243776\n", + "\tmax y acquired = 147.353016507\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 175.23753918900002\n", + "\tmax y acquired = 169.90979067700002\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 194.20146897700002\n", + "\tmax y acquired = 173.817499665\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 172.306975483\n", + "\tmax y acquired = 152.536423272\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 187.96960308099997\n", + "\tmax y acquired = 176.513893577\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 159.76088175799998\n", + "\tmax y acquired = 162.709413637\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 177.734140541\n", + "\tmax y acquired = 161.397636793\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 147.372826457\n", + "\tmax y acquired = 184.638370983\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.36987107599998\n", + "\tmax y acquired = 164.770180227\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.690277919\n", + "\tmax y acquired = 168.437513404\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.01020700200002\n", + "\tmax y acquired = 163.178770291\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 164.19932299299998\n", + "\tmax y acquired = 176.446039031\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 159.702669558\n", + "\tmax y acquired = 163.092877048\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 186.36782056400003\n", + "\tmax y acquired = 127.95124794700001\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.493919017\n", + "\tmax y acquired = 173.817499665\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 169.901491297\n", + "\tmax y acquired = 158.404940415\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 171.70538126\n", + "\tmax y acquired = 173.817499665\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 155.191172441\n", + "\tmax y acquired = 149.12912969299998\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.51803323299998\n", + "\tmax y acquired = 191.507774129\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 189.901093629\n", + "\tmax y acquired = 155.099764061\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.964548393\n", + "\tmax y acquired = 164.602426982\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.15976674\n", + "\tmax y acquired = 176.910634695\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 194.20146897700002\n", + "\tmax y acquired = 161.526826114\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 172.79260676\n", + "\tmax y acquired = 166.270189296\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 178.35628773099998\n", + "\tmax y acquired = 179.19628339599998\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 172.37939513400002\n", + "\tmax y acquired = 130.245621135\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 167.063296154\n", + "\tmax y acquired = 170.108224599\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.95214931\n", + "\tmax y acquired = 161.61358860200002\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.62852097299998\n", + "\tmax y acquired = 168.115468225\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.521853579\n", + "\tmax y acquired = 162.805857941\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 188.39498494\n", + "\tmax y acquired = 166.561509961\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 152.71130296\n", + "\tmax y acquired = 167.83151530700002\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 156.34060864100002\n", + "\tmax y acquired = 174.608792433\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 144.0749746\n", + "\tmax y acquired = 160.046189397\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.805857941\n", + "\tmax y acquired = 177.490009055\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 165.303562487\n", + "\tmax y acquired = 176.690277919\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.27321225900002\n", + "\tmax y acquired = 194.20146897700002\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 178.84845810599998\n", + "\tmax y acquired = 186.181282165\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 153.909520014\n", + "\tmax y acquired = 169.39598068799998\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.96001819999998\n", + "\tmax y acquired = 180.068856751\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 185.160077795\n", + "\tmax y acquired = 182.533285496\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.725680664\n", + "\tmax y acquired = 183.595039227\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 174.24511580599997\n", + "\tmax y acquired = 176.690277919\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 148.73144464700002\n", + "\tmax y acquired = 165.570982585\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 160.778133869\n", + "\tmax y acquired = 184.403510347\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 185.50953227099998\n", + "\tmax y acquired = 163.01020185\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 185.22656223799999\n", + "\tmax y acquired = 160.604816103\n", "budget for evals: 40\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 187.02128687799998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 161.639932773\n", + "\tmax y acquired = 182.620846882\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.11955720900002\n", + "\tmax y acquired = 189.190920955\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 185.196241577\n", + "\tmax y acquired = 216.894110699\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 193.949996568\n", + "\tmax y acquired = 196.796070915\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 181.533434354\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 180.79675965799998\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 188.57709109299998\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.077676114\n", + "\tmax y acquired = 196.796070915\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.735941442\n", + "\tmax y acquired = 175.85998613599997\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 179.489994305\n", + "\tmax y acquired = 216.894110699\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 180.77479322\n", + "\tmax y acquired = 216.894110699\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 173.0231062\n", + "\tmax y acquired = 196.796070915\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.48812323400003\n", + "\tmax y acquired = 181.546669268\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 180.789647894\n", + "\tmax y acquired = 196.752963258\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 180.789647894\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -424,23 +422,23 @@ "\trun 19\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 185.412554964\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.9895885\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 191.077676114\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -448,31 +446,31 @@ "\trun 25\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 179.489994305\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 192.539600494\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 186.43690669900002\n", + "\tmax y acquired = 184.111351023\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 182.98036740599997\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -480,22 +478,477 @@ "\trun 33\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 169.05009591299998\n", + "\tmax y acquired = 205.963467853\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 172.71569396400002\n", + "\tmax y acquired = 166.580111006\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 198.020772317\n", + "\tmax y acquired = 216.894110699\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", "\tmax y acquired = 216.894110699\n", "\trun 37\n", "\tdiverse RF run\n", - "\teval budget 40 = 20 training data and 20 acquired.\n" + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 188.57709109299998\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 182.26397528\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 184.686971958\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 179.81664061900003\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 185.31228748599997\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 181.885991327\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 180.764849285\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 190.461820465\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 191.02071475\n", + "budget for evals: 60\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.901093629\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.901093629\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 180.76506026200002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 186.58427506799998\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 185.59509969799998\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 193.408466045\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 192.539600494\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 193.949996568\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 180.279527455\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 187.813295088\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.644969217\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 183.508848648\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 188.242123191\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 200.420314123\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 192.697076125\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 188.927621488\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.053559538\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "budget for evals: 80\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 193.72992463\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 187.832756447\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.90463220799998\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 185.76111369\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 183.77337184599997\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 187.244243495\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 192.43303832400002\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 181.885991327\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 185.901678884\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 193.949996568\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 197.07304941400002\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 197.07304941400002\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 195.289662613\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 184.154236099\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "budget for evals: 100\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n" ] } ], @@ -528,7 +981,7 @@ { "cell_type": "code", "execution_count": null, - "id": "proud-percentage", + "id": "adjustable-formula", "metadata": {}, "outputs": [], "source": [] From 332ab913de068ea13a41ee48281f484c187dbbb1 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Fri, 2 Jul 2021 21:27:49 -0700 Subject: [PATCH 15/29] toy example for illustration --- synthetic_example.ipynb | 92 ++++++++++++++++++++++++++++++++++------- 1 file changed, 78 insertions(+), 14 deletions(-) diff --git a/synthetic_example.ipynb b/synthetic_example.ipynb index 7ff11db..571c27b 100644 --- a/synthetic_example.ipynb +++ b/synthetic_example.ipynb @@ -14,9 +14,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" + ] + } + ], "source": [ "import math\n", "import torch\n", @@ -35,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -45,22 +67,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -85,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -139,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -169,12 +191,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -216,6 +238,48 @@ " plt.savefig(\"gp_example.pdf\")" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with torch.no_grad():\n", + " # Initialize plot\n", + " # Get upper and lower confidence bounds\n", + " lower, upper = observed_pred.confidence_region()\n", + " fig = plt.figure()\n", + " # Plot training data as black stars\n", + " plt.scatter(train_x.numpy(), train_y.numpy(), marker=\"+\", color=\"k\", label='observations $\\{(x_i, y_i)\\}$', s=95, zorder=2000, lw=5)\n", + "# plt.plot(ground_x.numpy(), ground_y.numpy(), linestyle=\"--\", color=cool_colors[2], label='truth, $f(x)$', lw=3)\n", + " # Plot predictive means as blue line\n", + " plt.plot(test_x.numpy(), observed_pred.mean.numpy(), color=cool_colors[0], label='model, $\\hat{y}(x)$', lw=3)\n", + " # Shade between the lower and upper confidence bounds\n", + " plt.fill_between(test_x.numpy(), lower.numpy(), upper.numpy(), alpha=0.15, color=cool_colors[0], edgecolor=\"none\")\n", + "# plt.ylim(the_ylims)\n", + " plt.xlim([0, 1])\n", + "# plt.xlabel('NPM feature space, $x$', fontsize=15)\n", + "# plt.ylabel('property, $y$', fontsize=15)\n", + " fig.patch.set_visible(False)\n", + " plt.gca().axis('off')\n", + " plt.yticks([])\n", + " plt.xticks([])\n", + "# plt.legend(fontsize=12)\n", + " the_ylims = plt.gca().get_ylim()\n", + " plt.savefig(\"gp_example_for_illustration.pdf\")" + ] + }, { "cell_type": "code", "execution_count": 13, @@ -485,7 +549,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.6.9" } }, "nbformat": 4, From 01b0319f13092684a79ecc27d50723efd31bef6e Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Sat, 3 Jul 2021 10:38:54 -0700 Subject: [PATCH 16/29] done! --- new/BO_run.ipynb | 541 +++- new/evol_search.ipynb | 5876 ++++++++++++++++++++++++++++------- new/random_forest_run.ipynb | 4516 +++++++++++++++++++++++++-- new/random_search.ipynb | 10 +- new/viz.ipynb | 145 +- 5 files changed, 9572 insertions(+), 1516 deletions(-) diff --git a/new/BO_run.ipynb b/new/BO_run.ipynb index 130ce35..5bd7b25 100644 --- a/new/BO_run.ipynb +++ b/new/BO_run.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "sound-seeking", + "id": "essential-performer", "metadata": {}, "source": [ "# BO runs" @@ -10,10 +10,32 @@ }, { "cell_type": "code", - "execution_count": 49, - "id": "colored-search", + "execution_count": 1, + "id": "municipal-wedding", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", + "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", + "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" + ] + } + ], "source": [ "import torch\n", "from botorch.models import FixedNoiseGP, SingleTaskGP\n", @@ -30,8 +52,8 @@ }, { "cell_type": "code", - "execution_count": 50, - "id": "joined-gauge", + "execution_count": 2, + "id": "rapid-healthcare", "metadata": {}, "outputs": [], "source": [ @@ -42,7 +64,7 @@ }, { "cell_type": "markdown", - "id": "institutional-baghdad", + "id": "convinced-internet", "metadata": {}, "source": [ "load data from `prepare_Xy.ipynb`" @@ -50,8 +72,8 @@ }, { "cell_type": "code", - "execution_count": 51, - "id": "herbal-spread", + "execution_count": 3, + "id": "perfect-gravity", "metadata": {}, "outputs": [ { @@ -60,7 +82,7 @@ "69839" ] }, - "execution_count": 51, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -75,7 +97,7 @@ }, { "cell_type": "markdown", - "id": "accessible-economics", + "id": "romance-juice", "metadata": {}, "source": [ "convert to torch tensors" @@ -83,8 +105,8 @@ }, { "cell_type": "code", - "execution_count": 52, - "id": "distributed-triple", + "execution_count": 4, + "id": "arabic-crown", "metadata": {}, "outputs": [], "source": [ @@ -94,8 +116,8 @@ }, { "cell_type": "code", - "execution_count": 53, - "id": "eastern-capacity", + "execution_count": 5, + "id": "worldwide-brand", "metadata": {}, "outputs": [ { @@ -104,7 +126,7 @@ "torch.Size([69839, 12])" ] }, - "execution_count": 53, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -115,8 +137,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "absolute-ministry", + "execution_count": 6, + "id": "sexual-angel", "metadata": {}, "outputs": [ { @@ -125,7 +147,7 @@ "torch.Size([69839, 1])" ] }, - "execution_count": 54, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -136,8 +158,8 @@ }, { "cell_type": "code", - "execution_count": 55, - "id": "precise-strike", + "execution_count": 7, + "id": "instant-hearts", "metadata": {}, "outputs": [], "source": [ @@ -146,8 +168,8 @@ }, { "cell_type": "code", - "execution_count": 56, - "id": "polyphonic-cigarette", + "execution_count": 8, + "id": "fancy-catalyst", "metadata": {}, "outputs": [ { @@ -156,7 +178,7 @@ "69839" ] }, - "execution_count": 56, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -167,7 +189,7 @@ }, { "cell_type": "markdown", - "id": "latin-liverpool", + "id": "general-nightlife", "metadata": {}, "source": [ "number of COFs for initialization" @@ -175,8 +197,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "id": "played-commonwealth", + "execution_count": 9, + "id": "professional-ceiling", "metadata": {}, "outputs": [], "source": [ @@ -185,8 +207,8 @@ }, { "cell_type": "code", - "execution_count": 58, - "id": "advanced-insulation", + "execution_count": 10, + "id": "unlimited-retreat", "metadata": {}, "outputs": [], "source": [ @@ -262,7 +284,7 @@ }, { "cell_type": "markdown", - "id": "friendly-virgin", + "id": "consecutive-biography", "metadata": {}, "source": [ "`ids_acquired[r, i]` will give ID of COF acquired during iteration `i` from run `r`." @@ -270,8 +292,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "id": "russian-parent", + "execution_count": 11, + "id": "frank-debate", "metadata": {}, "outputs": [ { @@ -281,38 +303,439 @@ "\n", "\n", "RUN 0\n", - 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This is likely due to numerical instabilities. Rounding negative variances up to 1e-10.\n", + " NumericalWarning,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "took time t = 7.055034768581391 min\n", + "\n", + "\n", + "RUN 29\n", + "took time t = 7.425979002316793 min\n", + "\n", + "\n", + "RUN 30\n", + "took time t = 7.601202404499054 min\n", + "\n", + "\n", + "RUN 31\n", + "took time t = 7.254582150777181 min\n", + "\n", + "\n", + "RUN 32\n", + "took time t = 7.32819838921229 min\n", + "\n", + "\n", + "RUN 33\n", + "took time t = 7.508639041582743 min\n", + "\n", + "\n", + "RUN 34\n", + "took time t = 7.175657308101654 min\n", + "\n", + "\n", + "RUN 35\n", + "took time t = 7.168862398465475 min\n", + "\n", + "\n", + "RUN 36\n", + "took time t = 7.118225149313608 min\n", + "\n", + "\n", + "RUN 37\n", + "took time t = 7.218559682369232 min\n", + "\n", + "\n", + "RUN 38\n", + "took time t = 7.178347424666087 min\n", + "\n", + "\n", + "RUN 39\n", + "took time t = 7.149571132659912 min\n", + "\n", + "\n", + "RUN 40\n", + "took time t = 7.397369893391927 min\n", + "\n", + "\n", + "RUN 41\n", + "took time t = 7.374075202147166 min\n", + "\n", + "\n", + "RUN 42\n", + "took time t = 7.3341242591540015 min\n", + "\n", + "\n", + "RUN 43\n", + "took time t = 7.261066178480784 min\n", + "\n", + "\n", + "RUN 44\n", + "took time t = 7.141440697511038 min\n", + "\n", + "\n", + "RUN 45\n", + "took time t = 7.313784658908844 min\n", + "\n", + "\n", + "RUN 46\n", + "took time t = 7.267547098795573 min\n", + "\n", + "\n", + "RUN 47\n", + "took time t = 7.20398888985316 min\n", + "\n", + "\n", + "RUN 48\n", + "took time t = 7.437757857640585 min\n", + "\n", + "\n", + "RUN 49\n", + "took time t = 7.218947410583496 min\n", + "\n", + "\n", + "RUN 50\n", + "took time t = 7.493029769261678 min\n", + "\n", + "\n", + "RUN 51\n", + "took time t = 7.450812164942423 min\n", + "\n", + "\n", + "RUN 52\n", + "took time t = 7.184896246592204 min\n", + "\n", + "\n", + "RUN 53\n", + "took time t = 7.236911598841349 min\n", + "\n", + "\n", + "RUN 54\n", + "took time t = 7.327038383483886 min\n", + "\n", + "\n", + "RUN 55\n", + "took time t = 7.282283405462901 min\n", + "\n", + "\n", + "RUN 56\n", + "took time t = 7.380854411919912 min\n", + "\n", + "\n", + "RUN 57\n", + "took time t = 7.353207222620646 min\n", + "\n", + "\n", + "RUN 58\n", + "took time t = 7.448291130860647 min\n", + "\n", + "\n", + "RUN 59\n", + "took time t = 7.5363772710164385 min\n", + "\n", + "\n", + "RUN 60\n", + "took time t = 7.202328856786092 min\n", + "\n", + "\n", + "RUN 61\n", + "took time t = 7.447040545940399 min\n", + "\n", + "\n", + "RUN 62\n", + "took time t = 7.211400802930196 min\n", + "\n", + "\n", + "RUN 63\n", + "took time t = 7.560369396209717 min\n", + "\n", + "\n", + "RUN 64\n", + "took time t = 7.230643598238627 min\n", + "\n", + "\n", + "RUN 65\n", + "took time t = 7.261888154347738 min\n", + "\n", + "\n", + "RUN 66\n", + "took time t = 7.355716152985891 min\n", + "\n", + "\n", + "RUN 67\n", + "took time t = 7.425435543060303 min\n", + "\n", + "\n", + "RUN 68\n", + "took time t = 7.367885267734527 min\n", + "\n", + "\n", + "RUN 69\n", + "took time t = 7.487645892302195 min\n", + "\n", + "\n", + "RUN 70\n", + "took time t = 7.497790106137594 min\n", + "\n", + "\n", + "RUN 71\n", + "took time t = 7.336937848726908 min\n", + "\n", + "\n", + "RUN 72\n", + "took time t = 7.299442613124848 min\n", + "\n", + "\n", + "RUN 73\n", + "took time t = 7.724779530366262 min\n", + "\n", + "\n", + "RUN 74\n", + "took time t = 7.473151163260142 min\n", + "\n", + "\n", + "RUN 75\n", + "took time t = 7.4461992104848225 min\n", + "\n", + "\n", + "RUN 76\n", + "took time t = 7.6278742750485735 min\n", + "\n", + "\n", + "RUN 77\n", + "took time t = 7.1177510023117065 min\n", + "\n", + "\n", + "RUN 78\n", + "took time t = 7.31965833902359 min\n", + "\n", + "\n", + "RUN 79\n", + "took time t = 7.430999302864075 min\n", + "\n", + "\n", + "RUN 80\n", + "took time t = 7.2871841311454775 min\n", + "\n", + "\n", + "RUN 81\n", + "iteration: 82\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cokes/.local/lib/python3.6/site-packages/gpytorch/distributions/multivariate_normal.py:263: NumericalWarning: Negative variance values detected. 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\u001b[0mfit_gpytorch_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# set up acquisition function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/botorch/fit.py\u001b[0m in \u001b[0;36mfit_gpytorch_model\u001b[0;34m(mll, optimizer, **kwargs)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_state_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m 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\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_if_needed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 75\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m_compute_if_needed\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/botorch/optim/utils.py\u001b[0m in \u001b[0;36m_scipy_objective_and_grad\u001b[0;34m(x, mll, property_dict)\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m \u001b[0;31m# pragma: nocover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0mparam_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m inputs=inputs)\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 145\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 146\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "took time t = 7.436510316530863 min\n", + "\n", + "\n", + "RUN 82\n", + "took time t = 7.304804150263468 min\n", + "\n", + "\n", + "RUN 83\n", + "took time t = 7.327514203389486 min\n", + "\n", + "\n", + "RUN 84\n", + "took time t = 7.376877991358439 min\n", + "\n", + "\n", + "RUN 85\n", + "took time t = 7.5617250045140585 min\n", + "\n", + "\n", + "RUN 86\n", + "took time t = 7.26043134133021 min\n", + "\n", + "\n", + "RUN 87\n", + "took time t = 7.4054002404212955 min\n", + "\n", + "\n", + "RUN 88\n", + "took time t = 7.160198378562927 min\n", + "\n", + "\n", + "RUN 89\n", + "took time t = 7.317469696203868 min\n", + "\n", + "\n", + "RUN 90\n", + "took time t = 7.337529293696085 min\n", + "\n", + "\n", + "RUN 91\n", + "took time t = 7.506365013122559 min\n", + "\n", + "\n", + "RUN 92\n", + "took time t = 7.476064924399058 min\n", + "\n", + "\n", + "RUN 93\n", + "took time t = 7.318663656711578 min\n", + "\n", + "\n", + "RUN 94\n", + "took time t = 7.3077609499295555 min\n", + "\n", + "\n", + "RUN 95\n", + "took time t = 8.31069944302241 min\n", + "\n", + "\n", + "RUN 96\n", + "took time t = 7.400353403886159 min\n", + "\n", + "\n", + "RUN 97\n", + "took time t = 7.146096642812093 min\n", + "\n", + "\n", + "RUN 98\n", + "took time t = 7.348718508084615 min\n", + "\n", + "\n", + "RUN 99\n", + "took time t = 7.142310913403829 min\n" ] } ], "source": [ "bo_res = dict()\n", - "bo_res['nb_runs'] = 50\n", + "bo_res['nb_runs'] = 100\n", "bo_res['nb_iterations'] = 250\n", "bo_res['ids_acquired'] = []\n", "for r in range(bo_res['nb_runs']):\n", @@ -325,8 +748,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "accessory-commodity", + "execution_count": 12, + "id": "congressional-extent", "metadata": {}, "outputs": [], "source": [ @@ -337,7 +760,7 @@ { "cell_type": "code", "execution_count": null, - "id": "devoted-thanks", + "id": "abroad-intermediate", "metadata": {}, "outputs": [], "source": [] diff --git a/new/evol_search.ipynb b/new/evol_search.ipynb index aa961d8..c0b0383 100644 --- a/new/evol_search.ipynb +++ b/new/evol_search.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "pacific-detective", + "id": "combined-method", "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "nasty-parcel", + "id": "powered-forge", "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "acute-butler", + "id": "royal-terminal", "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "collectible-florida", + "id": "measured-investing", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "verified-distributor", + "id": "naval-sullivan", "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ { "cell_type": "code", "execution_count": null, - "id": "peripheral-quick", + "id": "comparable-flavor", "metadata": {}, "outputs": [], "source": [] @@ -92,7 +92,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "dynamic-railway", + "id": "affecting-entrepreneur", "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "mysterious-launch", + "id": "emerging-browse", "metadata": {}, "outputs": [ { @@ -142,156 +142,156 @@ "\n", "\n", "RUN 0\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=299639, Thu Jul 1 19:34:01 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=354564, Fri Jul 2 18:51:06 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.81117932200002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 182.44953930000003\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 182.44953930000003\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 182.910685964\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 182.910685964\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 192.026373675\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 192.026373675\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 192.026373675\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 192.026373675\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "\n", "\n", "RUN 1\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=267909, Thu Jul 1 19:34:03 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=337814, Fri Jul 2 18:51:08 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 146.958980683\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 188.927621488\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.530496788\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 201.148834085\n", "\n", "\n", "RUN 2\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=274451, Thu Jul 1 19:34:05 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=448572, Fri Jul 2 18:51:09 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 159.98157682299998\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", "\t# max y value: 177.71587614\n", @@ -300,80 +300,80 @@ "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 189.445550442\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 182.111370584\n", + "\t# max y value: 189.445550442\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 189.445550442\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 185.76111369\n", + "\t# max y value: 189.445550442\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 185.76111369\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 201.148834085\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 205.963467853\n", "\n", "\n", "RUN 3\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=254761, Thu Jul 1 19:34:07 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=424356, Fri Jul 2 18:51:11 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.81117932200002\n", + "\t# max y value: 173.92050685200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", "\t# max y value: 194.37058873700002\n", @@ -397,31 +397,31 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 200.420314123\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 206.54342821400002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 201.40394484\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", "\t# max y value: 207.39578187\n", @@ -434,25 +434,25 @@ "\n", "\n", "RUN 4\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=267003, Thu Jul 1 19:34:09 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=386385, Fri Jul 2 18:51:13 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 164.067845055\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", "\t# max y value: 194.37058873700002\n", @@ -461,28 +461,28 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 195.58268240799998\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", "\t# max y value: 216.894110699\n", @@ -507,137 +507,137 @@ "\n", "\n", "RUN 5\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=339643, Thu Jul 1 19:34:11 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=319507, Fri Jul 2 18:51:15 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 172.81117932200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 185.01909968400003\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 185.01909968400003\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 206.808591001\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 206.808591001\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.808591001\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.808591001\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.808591001\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.808591001\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.492194009\n", "\n", "\n", "RUN 6\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=265112, Thu Jul 1 19:34:13 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=355323, Fri Jul 2 18:51:17 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 183.77337184599997\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 202.848493155\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.189199744\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.189199744\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.189199744\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.189199744\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", "\t# max y value: 207.39578187\n", @@ -653,80 +653,80 @@ "\n", "\n", "RUN 7\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=237361, Thu Jul 1 19:34:15 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=376222, Fri Jul 2 18:51:19 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 199.32668546099998\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 199.32668546099998\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.774937788\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "\n", "\n", "RUN 8\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=270969, Thu Jul 1 19:34:16 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=369252, Fri Jul 2 18:51:21 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", "\t# max y value: 177.71587614\n", @@ -735,13 +735,13 @@ "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", "\t# max y value: 194.37058873700002\n", @@ -750,40 +750,40 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 195.973650655\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 195.973650655\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 195.973650655\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", "\t# max y value: 205.492194009\n", @@ -799,359 +799,359 @@ "\n", "\n", "RUN 9\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=313865, Thu Jul 1 19:34:18 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=366825, Fri Jul 2 18:51:23 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 206.864600037\n", "\n", "\n", "RUN 10\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=271733, Thu Jul 1 19:34:20 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=335913, Fri Jul 2 18:51:25 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 174.145800528\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 193.528032337\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 193.528032337\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 193.528032337\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 193.528032337\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 193.528032337\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 195.812801966\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "\n", "\n", "RUN 11\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=354633, Thu Jul 1 19:34:21 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=341808, Fri Jul 2 18:51:27 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 201.148834085\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 201.148834085\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 201.148834085\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "\n", "\n", "RUN 12\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=260980, Thu Jul 1 19:34:23 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=360780, Fri Jul 2 18:51:29 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 179.81664061900003\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 184.36123354\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 209.36697147400002\n", "\n", "\n", "RUN 13\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=206220, Thu Jul 1 19:34:25 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=391162, Fri Jul 2 18:51:31 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 173.817499665\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 173.817499665\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 173.817499665\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 173.817499665\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.48474798200002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.48474798200002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.48474798200002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.48474798200002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.48474798200002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.48474798200002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 198.96574226299998\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 199.76380567299998\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", "\t# max y value: 207.39578187\n", @@ -1164,332 +1164,332 @@ "\n", "\n", "RUN 14\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=376715, Thu Jul 1 19:34:26 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=422063, Fri Jul 2 18:51:33 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 171.965999767\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.71569396400002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 192.539600494\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 192.539600494\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 192.539600494\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 192.539600494\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 192.539600494\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 192.539600494\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.530496788\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.530496788\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.530496788\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 197.86041748099998\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.54342821400002\n", + "\t# max y value: 202.21921792700002\n", "\n", "\n", "RUN 15\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=290338, Thu Jul 1 19:34:28 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=343308, Fri Jul 2 18:51:35 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 172.81117932200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 181.198814215\n", + "\t# max y value: 183.06633314099997\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 181.198814215\n", + "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.48474798200002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 199.72030120099998\n", "\n", "\n", "RUN 16\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=255573, Thu Jul 1 19:34:30 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=338298, Fri Jul 2 18:51:37 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 144.391389813\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 189.445550442\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 192.274825215\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 192.274825215\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 192.274825215\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 192.274825215\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 192.274825215\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 192.274825215\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 195.973650655\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 195.973650655\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 201.66490141\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "\n", "\n", "RUN 17\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=244493, Thu Jul 1 19:34:32 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=377544, Fri Jul 2 18:51:39 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 166.581672788\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 174.833095498\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 174.833095498\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 178.63841840799998\n", + "\t# max y value: 196.58076384900002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 183.50656627400002\n", + "\t# max y value: 196.58076384900002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 183.50656627400002\n", + "\t# max y value: 196.58076384900002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 190.17935780099998\n", + "\t# max y value: 196.58076384900002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 196.58076384900002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 196.58076384900002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 208.43022665700002\n", "\n", "\n", "RUN 18\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=365490, Thu Jul 1 19:34:33 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=378444, Fri Jul 2 18:51:41 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 173.92050685200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.9895885\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", "\t# max y value: 206.74476888599997\n", @@ -1498,38 +1498,38 @@ "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "\n", "\n", "RUN 19\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=310914, Thu Jul 1 19:34:35 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=315837, Fri Jul 2 18:51:43 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", "\t# max y value: 177.71587614\n", @@ -1538,83 +1538,83 @@ "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 192.539600494\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.492194009\n", + "\t# max y value: 216.894110699\n", "\n", "\n", "RUN 20\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=240071, Thu Jul 1 19:34:37 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=433862, Fri Jul 2 18:51:44 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", "\t# max y value: 191.507774129\n", @@ -1626,244 +1626,244 @@ "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 192.178789156\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 192.178789156\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 192.422391866\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 196.579974938\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 205.171240133\n", "\n", "\n", "RUN 21\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=291458, Thu Jul 1 19:34:38 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=360401, Fri Jul 2 18:51:46 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 173.92050685200002\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 173.92050685200002\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 173.92050685200002\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.530496788\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 207.39578187\n", "\n", "\n", "RUN 22\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=306732, Thu Jul 1 19:34:40 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=405633, Fri Jul 2 18:51:48 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 179.85869594599998\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 185.189018713\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 185.189018713\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 185.189018713\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 185.189018713\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 185.480447434\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 208.120454446\n", "\n", "\n", "RUN 23\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=293290, Thu Jul 1 19:34:42 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=321132, Fri Jul 2 18:51:50 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 175.02369428900002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 182.26397528\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 180.853194423\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 180.853194423\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", "\t# max y value: 205.963467853\n", @@ -1894,192 +1894,192 @@ "\n", "\n", "RUN 24\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=328073, Thu Jul 1 19:34:43 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=354223, Fri Jul 2 18:51:52 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 172.95669094599998\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.720247142\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 201.40394484\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "\n", "\n", "RUN 25\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=340521, Thu Jul 1 19:34:45 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=391079, Fri Jul 2 18:51:54 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 187.902075939\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 197.87398978299998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 197.87398978299998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 197.87398978299998\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 197.87398978299998\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 197.87398978299998\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 199.32668546099998\n", + "\t# max y value: 197.87398978299998\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 199.32668546099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 199.32668546099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 199.32668546099998\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 200.420314123\n", + "\t# max y value: 216.894110699\n", "\n", "\n", "RUN 26\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=257086, Thu Jul 1 19:34:47 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=331012, Fri Jul 2 18:51:57 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 172.81117932200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 172.95669094599998\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 172.95669094599998\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", "\t# max y value: 199.72030120099998\n", @@ -2097,111 +2097,111 @@ "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 207.39578187\n", "\n", "\n", "RUN 27\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=287870, Thu Jul 1 19:34:48 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=339889, Fri Jul 2 18:51:59 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 188.642146113\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 194.48474798200002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 197.517412165\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 197.517412165\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 205.171240133\n", "\n", "\n", "RUN 28\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=327021, Thu Jul 1 19:34:50 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=379394, Fri Jul 2 18:52:01 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 171.117194584\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", "\t# max y value: 194.37058873700002\n", @@ -2210,259 +2210,259 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 204.958050668\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 204.958050668\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 201.17983227599998\n", "\n", "\n", "RUN 29\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=276344, Thu Jul 1 19:34:52 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=344044, Fri Jul 2 18:52:03 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 172.81117932200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 187.945004404\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 197.517412165\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 197.517412165\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 197.517412165\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 197.517412165\n", + "\t# max y value: 206.864600037\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 197.517412165\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 197.517412165\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 197.517412165\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.963467853\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 209.88488105599998\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 207.39578187\n", "\n", "\n", "RUN 30\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=359192, Thu Jul 1 19:34:53 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=316100, Fri Jul 2 18:52:05 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.81117932200002\n", + "\t# max y value: 178.63841840799998\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.81117932200002\n", + "\t# max y value: 178.63841840799998\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 173.92050685200002\n", + "\t# max y value: 178.63841840799998\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 178.63841840799998\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 185.76111369\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 199.698499548\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 200.40213550099998\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 200.40213550099998\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 200.40213550099998\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 205.492194009\n", "\n", "\n", "RUN 31\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=263304, Thu Jul 1 19:34:55 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=345910, Fri Jul 2 18:52:07 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 200.40213550099998\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 196.796070915\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", "\t# max y value: 207.39578187\n", @@ -2478,86 +2478,86 @@ "\n", "\n", "RUN 32\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=311897, Thu Jul 1 19:34:57 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=425859, Fri Jul 2 18:52:09 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 171.117194584\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 191.077676114\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.75064711099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 216.894110699\n", + "\t# max y value: 202.848493155\n", "\n", "\n", "RUN 33\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=319479, Thu Jul 1 19:34:58 2021)\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=317196, Fri Jul 2 18:52:12 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 175.646129915\n", + "\t# max y value: 172.81117932200002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", "\t# max y value: 188.57709109299998\n", @@ -2572,71 +2572,3575 @@ "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 34\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=362808, Fri Jul 2 18:52:14 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 198.751812898\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 199.72030120099998\n", + "\n", + "\n", + "RUN 35\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=399414, Fri Jul 2 18:52:15 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 36\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=475130, Fri Jul 2 18:52:17 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 159.60432238200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 37\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=351325, Fri Jul 2 18:52:20 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 160.628846081\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.76981126599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 38\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=337701, Fri Jul 2 18:52:22 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 39\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=356487, Fri Jul 2 18:52:24 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.71569396400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 194.37058873700002\n", + "\n", + "\n", + "RUN 40\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=320778, Fri Jul 2 18:52:26 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 41\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=355786, Fri Jul 2 18:52:28 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 179.81664061900003\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 179.81664061900003\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 182.843902245\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 182.843902245\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 182.843902245\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 182.843902245\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 182.843902245\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 190.17935780099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 42\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=379684, Fri Jul 2 18:52:30 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 43\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=311928, Fri Jul 2 18:52:32 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 44\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=407052, Fri Jul 2 18:52:34 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 45\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=366760, Fri Jul 2 18:52:36 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 46\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=326974, Fri Jul 2 18:52:38 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 196.721230593\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 202.004818298\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 202.08883754099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 202.08883754099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 202.08883754099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 202.08883754099998\n", + "\n", + "\n", + "RUN 47\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=340747, Fri Jul 2 18:52:40 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.471904046\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.471904046\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 48\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=417814, Fri Jul 2 18:52:42 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 170.569561285\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 192.539600494\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.579974938\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.720247142\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 196.720247142\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 196.720247142\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 196.720247142\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.80359465400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.80359465400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.80359465400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.80359465400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 201.40394484\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 49\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=396793, Fri Jul 2 18:52:44 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 171.712054876\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 202.21921792700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 50\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=408700, Fri Jul 2 18:52:46 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 192.539600494\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 192.539600494\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 51\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=394029, Fri Jul 2 18:52:48 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 52\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=370271, Fri Jul 2 18:52:50 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 179.81664061900003\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 179.81664061900003\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 195.58268240799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 195.58268240799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 195.58268240799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 195.58268240799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 201.17983227599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 201.17983227599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 201.17983227599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 209.88488105599998\n", + "\n", + "\n", + "RUN 53\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=346376, Fri Jul 2 18:52:52 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.58076384900002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 54\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=452624, Fri Jul 2 18:52:53 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 188.76981126599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 198.020772317\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 209.88488105599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 55\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=430836, Fri Jul 2 18:52:55 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 183.005063212\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.120454446\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.120454446\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.120454446\n", + "\n", + "\n", + "RUN 56\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=402161, Fri Jul 2 18:52:57 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.808591001\n", + "\n", + "\n", + "RUN 57\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=365749, Fri Jul 2 18:52:59 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 202.08883754099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 58\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=396851, Fri Jul 2 18:53:00 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 204.811726149\n", + "\n", + "\n", + "RUN 59\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=349650, Fri Jul 2 18:53:02 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.965999767\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 200.44080272099998\n", + "\n", + "\n", + "RUN 60\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=372009, Fri Jul 2 18:53:04 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 136.871973316\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 199.698499548\n", + "\n", + "\n", + "RUN 61\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=294829, Fri Jul 2 18:53:06 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 166.331636592\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.698499548\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 199.698499548\n", + "\n", + "\n", + "RUN 62\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=376885, Fri Jul 2 18:53:07 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 182.910685964\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 192.178789156\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 192.178789156\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 192.178789156\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.74476888599997\n", + "\n", + "\n", + "RUN 63\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=398213, Fri Jul 2 18:53:09 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 192.672521193\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 192.672521193\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 192.672521193\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 192.672521193\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 64\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=384861, Fri Jul 2 18:53:11 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 65\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=400280, Fri Jul 2 18:53:12 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.175658139\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.175658139\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.175658139\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 66\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=386021, Fri Jul 2 18:53:14 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 178.63841840799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 201.148834085\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 206.74476888599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 67\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=377880, Fri Jul 2 18:53:16 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.95669094599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 208.43022665700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 208.43022665700002\n", + "\n", + "\n", + "RUN 68\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=292072, Fri Jul 2 18:53:18 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 197.86041748099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 197.86041748099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 197.86041748099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 197.86041748099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.864600037\n", + "\n", + "\n", + "RUN 69\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=416440, Fri Jul 2 18:53:19 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 181.99863318099997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 70\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=354545, Fri Jul 2 18:53:21 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 197.87398978299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 197.87398978299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 197.87398978299998\n", + "\n", + "\n", + "RUN 71\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=332935, Fri Jul 2 18:53:23 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 191.077676114\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.171240133\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 72\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=385842, Fri Jul 2 18:53:24 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 191.48812323400003\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 204.811726149\n", + "\n", + "\n", + "RUN 73\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=382269, Fri Jul 2 18:53:26 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 199.72030120099998\n", + "\n", + "\n", + "RUN 74\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=388861, Fri Jul 2 18:53:28 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 171.473108903\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.24746089400003\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 192.274825215\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.530496788\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 195.915962745\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 195.915962745\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 195.915962745\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 195.915962745\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 195.915962745\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.54342821400002\n", + "\n", + "\n", + "RUN 75\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=325865, Fri Jul 2 18:53:30 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 157.420767442\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 187.945004404\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 192.539600494\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 196.579974938\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.54342821400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.54342821400002\n", + "\n", + "\n", + "RUN 76\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=442086, Fri Jul 2 18:53:31 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 178.63841840799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 178.63841840799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 178.63841840799998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 183.77337184599997\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.864600037\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.864600037\n", + "\n", + "\n", + "RUN 77\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=395339, Fri Jul 2 18:53:33 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.71569396400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.71569396400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 188.57709109299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", "\t# max y value: 188.57709109299998\n", "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 191.507774129\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.963467853\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.963467853\n", + "\n", + "\n", + "RUN 78\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=403518, Fri Jul 2 18:53:35 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 198.792072623\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 199.72030120099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 79\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=384949, Fri Jul 2 18:53:37 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 180.853194423\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 196.796070915\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 200.44080272099998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 201.17983227599998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 205.492194009\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 205.492194009\n", + "\n", + "\n", + "RUN 80\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=378031, Fri Jul 2 18:53:38 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 188.242123191\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 199.76380567299998\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 207.39578187\n", + "\n", + "\n", + "RUN 81\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=430329, Fri Jul 2 18:53:40 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 178.108111318\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 182.26397528\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 196.752963258\n", + "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 188.57709109299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 190.95759162299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 190.95759162299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 190.95759162299998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 192.502149782\n", + "\t# max y value: 196.796070915\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 199.72030120099998\n", "\n", "\n", - "RUN 34\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=346557, Thu Jul 1 19:35:00 2021)\n", + "RUN 82\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=379674, Fri Jul 2 18:53:42 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 171.117194584\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 172.95669094599998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", "\t# max y value: 194.37058873700002\n", @@ -2672,114 +6176,187 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 196.720247142\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 197.513082545\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.171240133\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 209.36697147400002\n", + "\t# max y value: 205.492194009\n", "\n", "\n", - "RUN 35\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=266321, Thu Jul 1 19:35:02 2021)\n", + "RUN 83\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=332218, Fri Jul 2 18:53:44 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", + "\t# max y value: 171.117194584\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 172.81117932200002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 195.973650655\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 207.39578187\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 209.36697147400002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 216.894110699\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 216.894110699\n", + "\n", + "\n", + "RUN 84\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=397933, Fri Jul 2 18:53:45 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 173.92050685200002\n", + "ask/tell sesh\n", "\t# acquired COFs: 22\n", "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 198.138166855\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 198.138166855\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 198.138166855\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 198.138166855\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 198.138166855\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 198.138166855\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 198.138166855\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 208.43022665700002\n", "\n", "\n", - "RUN 36\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=296151, Thu Jul 1 19:35:04 2021)\n", + "RUN 85\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=396911, Fri Jul 2 18:53:47 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 160.16278228299998\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", "\t# max y value: 191.507774129\n", @@ -2788,16 +6365,89 @@ "\t# max y value: 191.507774129\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 192.178789156\n", + "\t# max y value: 199.90463220799998\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 192.178789156\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 192.178789156\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 192.178789156\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 110\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 121\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 132\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 143\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 154\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 165\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 176\n", + "\t# max y value: 204.811726149\n", + "ask/tell sesh\n", + "\t# acquired COFs: 187\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 198\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 209\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 220\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 231\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 242\n", + "\t# max y value: 206.808591001\n", + "ask/tell sesh\n", + "\t# acquired COFs: 253\n", + "\t# max y value: 206.808591001\n", + "\n", + "\n", + "RUN 86\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=393143, Fri Jul 2 18:53:49 2021)\n", + "ask/tell sesh\n", + "\t# acquired COFs: 11\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 22\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 33\n", + "\t# max y value: 177.71587614\n", + "ask/tell sesh\n", + "\t# acquired COFs: 44\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 55\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 66\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 77\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 88\n", + "\t# max y value: 194.37058873700002\n", + "ask/tell sesh\n", + "\t# acquired COFs: 99\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", "\t# max y value: 194.37058873700002\n", @@ -2812,111 +6462,111 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.189199744\n", + "\t# max y value: 199.80359465400002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.80359465400002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.80359465400002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.80359465400002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 202.848493155\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 202.848493155\n", "\n", "\n", - "RUN 37\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=303660, Thu Jul 1 19:35:05 2021)\n", + "RUN 87\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=356324, Fri Jul 2 18:53:51 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 175.051425862\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 175.051425862\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 178.997150426\n", + "\t# max y value: 175.051425862\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 180.012074097\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 181.145551234\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 181.145551234\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 187.945004404\n", + "\t# max y value: 181.145551234\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 187.945004404\n", + "\t# max y value: 185.76857139\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 192.422391866\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 192.422391866\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 216.894110699\n", "\n", "\n", - "RUN 38\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=315731, Thu Jul 1 19:35:07 2021)\n", + "RUN 88\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=409036, Fri Jul 2 18:53:52 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", "\t# max y value: 177.71587614\n", @@ -2925,71 +6575,71 @@ "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 198.751812898\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 200.44080272099998\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 207.39578187\n", "\n", "\n", - "RUN 39\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=248182, Thu Jul 1 19:35:09 2021)\n", + "RUN 89\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=395784, Fri Jul 2 18:53:54 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", "\t# max y value: 177.71587614\n", @@ -2998,165 +6648,165 @@ "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 180.960807015\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 180.960807015\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 180.960807015\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 180.960807015\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 185.162057723\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 192.422391866\n", + "\t# max y value: 192.274825215\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 192.422391866\n", + "\t# max y value: 192.274825215\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 192.422391866\n", + "\t# max y value: 192.274825215\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.695494435\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 206.808591001\n", "\n", "\n", - "RUN 40\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=300872, Thu Jul 1 19:35:10 2021)\n", + "RUN 90\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=355713, Fri Jul 2 18:53:56 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 155.38284843899999\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 172.71569396400002\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 216.894110699\n", "\n", "\n", - "RUN 41\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=254513, Thu Jul 1 19:35:12 2021)\n", + "RUN 91\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=378744, Fri Jul 2 18:53:58 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 161.279690414\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 187.945004404\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 188.242123191\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", "\t# max y value: 194.37058873700002\n", @@ -3168,80 +6818,80 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.708308113\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.708308113\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 207.39578187\n", "\n", "\n", - "RUN 42\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=294321, Thu Jul 1 19:35:14 2021)\n", + "RUN 92\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=427780, Fri Jul 2 18:53:59 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 180.853194423\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 198.020772317\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 198.020772317\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 198.020772317\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 198.020772317\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 201.17983227599998\n", + "\t# max y value: 198.020772317\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 201.17983227599998\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 201.17983227599998\n", + "\t# max y value: 206.74476888599997\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", "\t# max y value: 208.43022665700002\n", @@ -3253,123 +6903,123 @@ "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 208.43022665700002\n", + "\t# max y value: 216.894110699\n", "\n", "\n", - "RUN 43\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=302811, Thu Jul 1 19:35:15 2021)\n", + "RUN 93\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=372258, Fri Jul 2 18:54:01 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 170.376486196\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 175.640236293\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 186.09094075099998\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 204.958050668\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 204.958050668\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 216.894110699\n", "\n", "\n", - "RUN 44\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=262833, Thu Jul 1 19:35:17 2021)\n", + "RUN 94\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=413367, Fri Jul 2 18:54:03 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 180.249541863\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", "\t# max y value: 194.37058873700002\n", @@ -3378,357 +7028,357 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 198.792072623\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 197.03796965900003\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 197.03796965900003\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 197.716305842\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 197.716305842\n", "\n", "\n", - "RUN 45\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=289860, Thu Jul 1 19:35:19 2021)\n", + "RUN 95\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=335360, Fri Jul 2 18:54:05 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 161.035530703\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 180.853194423\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 191.507774129\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 193.51655534\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 193.51655534\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 193.51655534\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 193.51655534\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.22060552\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.22060552\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.22060552\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 208.43022665700002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 207.39578187\n", + "\t# max y value: 208.43022665700002\n", "\n", "\n", - "RUN 46\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=358462, Thu Jul 1 19:35:20 2021)\n", + "RUN 96\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=403291, Fri Jul 2 18:54:06 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 183.048069832\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 183.048069832\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 183.048069832\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 182.26397528\n", + "\t# max y value: 183.048069832\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 183.048069832\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 183.048069832\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 183.048069832\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 188.39942697200001\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 196.752963258\n", + "\t# max y value: 202.004818298\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.004818298\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.004818298\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.004818298\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.004818298\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.004818298\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 202.004818298\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 202.08883754099998\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 204.811726149\n", + "\t# max y value: 202.08883754099998\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.08883754099998\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 202.08883754099998\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 205.171240133\n", + "\t# max y value: 205.492194009\n", "\n", "\n", - "RUN 47\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=304630, Thu Jul 1 19:35:22 2021)\n", + "RUN 97\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=393691, Fri Jul 2 18:54:08 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 171.117194584\n", + "\t# max y value: 177.71587614\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 180.853194423\n", + "\t# max y value: 188.242123191\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 182.910685964\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 186.04049377\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 186.04049377\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 194.708308113\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 202.21921792700002\n", + "\t# max y value: 204.811726149\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.864600037\n", + "\t# max y value: 216.894110699\n", "\n", "\n", - "RUN 48\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=307920, Thu Jul 1 19:35:24 2021)\n", + "RUN 98\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=414555, Fri Jul 2 18:54:10 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 165.29457165899998\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 171.117194584\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 185.480447434\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", - "\t# max y value: 177.71587614\n", + "\t# max y value: 192.672521193\n", "ask/tell sesh\n", "\t# acquired COFs: 55\n", - "\t# max y value: 183.77337184599997\n", + "\t# max y value: 192.672521193\n", "ask/tell sesh\n", "\t# acquired COFs: 66\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 192.672521193\n", "ask/tell sesh\n", "\t# acquired COFs: 77\n", - "\t# max y value: 191.077676114\n", + "\t# max y value: 192.672521193\n", "ask/tell sesh\n", "\t# acquired COFs: 88\n", - "\t# max y value: 196.58076384900002\n", + "\t# max y value: 192.672521193\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 199.90463220799998\n", + "\t# max y value: 192.672521193\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 199.90463220799998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 199.90463220799998\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 196.752963258\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 198.792072623\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.72030120099998\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 206.74476888599997\n", + "\t# max y value: 199.72030120099998\n", "\n", "\n", - "RUN 49\n", - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=278723, Thu Jul 1 19:35:26 2021)\n", + "RUN 99\n", + "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=328048, Fri Jul 2 18:54:12 2021)\n", "ask/tell sesh\n", "\t# acquired COFs: 11\n", - "\t# max y value: 178.99445053\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 22\n", - "\t# max y value: 178.99445053\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 33\n", - "\t# max y value: 178.99445053\n", + "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 44\n", "\t# max y value: 194.37058873700002\n", @@ -3746,55 +7396,55 @@ "\t# max y value: 194.37058873700002\n", "ask/tell sesh\n", "\t# acquired COFs: 99\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 110\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 202.21921792700002\n", "ask/tell sesh\n", "\t# acquired COFs: 121\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 204.958050668\n", "ask/tell sesh\n", "\t# acquired COFs: 132\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 205.963467853\n", "ask/tell sesh\n", "\t# acquired COFs: 143\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 154\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 165\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 176\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 187\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 198\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 209\n", - "\t# max y value: 194.37058873700002\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 220\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 231\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 242\n", - "\t# max y value: 199.72030120099998\n", + "\t# max y value: 209.36697147400002\n", "ask/tell sesh\n", "\t# acquired COFs: 253\n", - "\t# max y value: 199.72030120099998\n" + "\t# max y value: 209.36697147400002\n" ] } ], "source": [ "es_res = dict()\n", - "es_res['nb_runs'] = 50\n", + "es_res['nb_runs'] = 100\n", "es_res['nb_iterations'] = 250\n", "es_res['ids_acquired'] = []\n", "for r in range(es_res['nb_runs']):\n", @@ -3809,7 +7459,7 @@ { "cell_type": "code", "execution_count": null, - "id": "logical-istanbul", + "id": "proper-seattle", "metadata": {}, "outputs": [], "source": [] diff --git a/new/random_forest_run.ipynb b/new/random_forest_run.ipynb index 15ad99f..f9dde07 100644 --- a/new/random_forest_run.ipynb +++ b/new/random_forest_run.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "convertible-sauce", + "id": "competent-reserve", "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "cultural-chase", + "id": "photographic-immigration", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "convinced-attribute", + "id": "sound-penguin", "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "little-heater", + "id": "aggregate-monster", "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "operating-alcohol", + "id": "introductory-mouth", "metadata": {}, "outputs": [], "source": [ @@ -132,8 +132,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "single-authorization", + "execution_count": 6, + "id": "collaborative-constitution", "metadata": {}, "outputs": [ { @@ -145,264 +145,464 @@ "\trun 0\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 153.625944043\n", + "\tmax y acquired = 159.39879764100002\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 184.021162787\n", + "\tmax y acquired = 198.792072623\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 164.770180227\n", + "\tmax y acquired = 179.120429293\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 147.353016507\n", + "\tmax y acquired = 158.40819035299998\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 169.90979067700002\n", + "\tmax y acquired = 162.964548393\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 173.817499665\n", + "\tmax y acquired = 171.186997696\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 152.536423272\n", + "\tmax y acquired = 166.73698836399998\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.513893577\n", + "\tmax y acquired = 146.373534281\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.709413637\n", + "\tmax y acquired = 176.690277919\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.397636793\n", + "\tmax y acquired = 156.867337628\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 184.638370983\n", + "\tmax y acquired = 172.95669094599998\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 164.770180227\n", + "\tmax y acquired = 181.033618038\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.437513404\n", + "\tmax y acquired = 146.373534281\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 163.178770291\n", + "\tmax y acquired = 159.76088175799998\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.446039031\n", + "\tmax y acquired = 168.207357801\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 163.092877048\n", + "\tmax y acquired = 169.059493776\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 127.95124794700001\n", + "\tmax y acquired = 166.15976674\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 173.817499665\n", + "\tmax y acquired = 159.790178937\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 158.404940415\n", + "\tmax y acquired = 179.85869594599998\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 173.817499665\n", + "\tmax y acquired = 185.03190212400003\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 149.12912969299998\n", + "\tmax y acquired = 154.048838695\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 176.60896076400002\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 155.099764061\n", + "\tmax y acquired = 160.74528706200002\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 164.602426982\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.910634695\n", + "\tmax y acquired = 186.116016225\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.526826114\n", + "\tmax y acquired = 154.272072388\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.270189296\n", + "\tmax y acquired = 168.169424843\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.19628339599998\n", + "\tmax y acquired = 177.71587614\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 130.245621135\n", + "\tmax y acquired = 158.258882165\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 170.108224599\n", + "\tmax y acquired = 144.903301149\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.61358860200002\n", + "\tmax y acquired = 167.987216544\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.115468225\n", + "\tmax y acquired = 162.805857941\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.805857941\n", + "\tmax y acquired = 173.103734578\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.561509961\n", + "\tmax y acquired = 176.97717943400002\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 167.83151530700002\n", + "\tmax y acquired = 161.61358860200002\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 174.608792433\n", + "\tmax y acquired = 178.742181407\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 160.046189397\n", + "\tmax y acquired = 185.353421999\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 177.490009055\n", + "\tmax y acquired = 156.408222469\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.690277919\n", + "\tmax y acquired = 182.914858305\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 194.20146897700002\n", + "\tmax y acquired = 152.323996663\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 186.181282165\n", + "\tmax y acquired = 163.27718761100002\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 169.39598068799998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.068856751\n", + "\tmax y acquired = 180.91526314\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 182.533285496\n", + "\tmax y acquired = 159.08417302799998\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 183.595039227\n", + "\tmax y acquired = 162.805857941\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.690277919\n", + "\tmax y acquired = 154.207955664\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 165.570982585\n", + "\tmax y acquired = 159.738065448\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 184.403510347\n", + "\tmax y acquired = 161.61358860200002\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 163.01020185\n", + "\tmax y acquired = 199.90463220799998\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 160.604816103\n", + "\tmax y acquired = 180.114479152\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 183.618908037\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.22595719\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 192.340730027\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 157.529651595\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.372242861\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 174.67727233\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 145.231871343\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 170.874711129\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 179.19628339599998\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 184.403510347\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 166.286404403\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 161.751385809\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 144.184208777\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 138.200963434\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 173.817499665\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 181.23803981900002\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 160.7072373\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 162.964548393\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 181.533434354\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 161.61358860200002\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 180.36987107599998\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 186.74455223599998\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.688826993\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 172.95669094599998\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 201.148834085\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.142548094\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 179.36818937799998\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 172.096141322\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 172.95669094599998\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 154.272072388\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 157.529651595\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 179.19628339599998\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 153.981563906\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 168.169424843\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 164.602426982\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 147.372826457\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 162.805857941\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 146.373534281\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.297530607\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 168.99697320200002\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 139.438058268\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 164.602426982\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 190.102542686\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 192.43303832400002\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 188.901132522\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 180.068856751\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 187.604176965\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 162.436742786\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 20 = 10 training data and 10 acquired.\n", + "\tmax y acquired = 176.97717943400002\n", "budget for evals: 40\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 157.693301403\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 182.620846882\n", + "\tmax y acquired = 196.796070915\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 189.190920955\n", + "\tmax y acquired = 188.02562188299999\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 187.451829608\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.533434354\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 180.79675965799998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 188.57709109299998\n", + "\tmax y acquired = 183.95419856799998\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 174.661028847\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 175.85998613599997\n", + "\tmax y acquired = 184.154236099\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 189.053559538\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 181.197342546\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.546669268\n", + "\tmax y acquired = 216.894110699\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -410,67 +610,67 @@ "\trun 16\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 181.197342546\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 180.789647894\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 185.412554964\n", + "\tmax y acquired = 216.894110699\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 216.894110699\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.077676114\n", + "\tmax y acquired = 196.752963258\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 179.489994305\n", + "\tmax y acquired = 179.875457882\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 192.539600494\n", + "\tmax y acquired = 180.789647894\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 184.111351023\n", + "\tmax y acquired = 198.751812898\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 198.020772317\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 182.26397528\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 182.98036740599997\n", + "\tmax y acquired = 196.752963258\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -478,160 +678,360 @@ "\trun 33\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 205.963467853\n", + "\tmax y acquired = 175.7386644\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 166.580111006\n", + "\tmax y acquired = 196.752963258\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 188.57709109299998\n", + "\tmax y acquired = 191.077676114\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 182.26397528\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 184.686971958\n", + "\tmax y acquired = 216.894110699\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 179.81664061900003\n", + "\tmax y acquired = 216.894110699\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 185.31228748599997\n", + "\tmax y acquired = 192.539600494\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 192.393334386\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.885991327\n", + "\tmax y acquired = 183.95419856799998\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 180.764849285\n", + "\tmax y acquired = 186.89961704700002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 190.461820465\n", + "\tmax y acquired = 196.796070915\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 196.796070915\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.02071475\n", - "budget for evals: 60\n", - "\trun 0\n", + "\tmax y acquired = 189.190920955\n", + "\trun 50\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", - "\trun 1\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 181.36312997299999\n", + "\trun 51\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", - "\trun 2\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 180.789647894\n", + "\trun 52\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", "\tmax y acquired = 216.894110699\n", - "\trun 3\n", + "\trun 53\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.901093629\n", - "\trun 4\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 193.949996568\n", + "\trun 54\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 206.864600037\n", - "\trun 5\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 55\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 167.75125141200002\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", "\tmax y acquired = 194.37058873700002\n", - "\trun 6\n", + "\trun 57\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.901093629\n", - "\trun 7\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.796070915\n", + "\trun 58\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 180.76506026200002\n", - "\trun 8\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 189.325235236\n", + "\trun 59\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 198.792072623\n", - "\trun 9\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 171.353905639\n", + "\trun 60\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 186.58427506799998\n", - "\trun 10\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 168.64153222299998\n", + "\trun 61\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 185.59509969799998\n", - "\trun 11\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 184.19832833599997\n", + "\trun 62\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 193.408466045\n", - "\trun 12\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 63\n", "\tdiverse RF run\n", - "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 192.539600494\n", - "\trun 13\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 178.997150426\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 183.95419856799998\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 193.72992463\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 191.11955720900002\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 182.98036740599997\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 178.158343282\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 179.967947498\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.796070915\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 181.885991327\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 160.599617962\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 184.686971958\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 195.58268240799998\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 196.796070915\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 193.61022285099997\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 40 = 20 training data and 20 acquired.\n", + "\tmax y acquired = 179.81664061900003\n", + "budget for evals: 60\n", + "\trun 0\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", "\tmax y acquired = 194.37058873700002\n", - "\trun 14\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 179.967947498\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.22739577599998\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 180.805755057\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 195.58268240799998\n", + "\trun 8\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", "\tmax y acquired = 199.72030120099998\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 191.11955720900002\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 206.74476888599997\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.36951723599998\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.503247339\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 185.442482271\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 194.708308113\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 193.949996568\n", + "\tmax y acquired = 188.709824186\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 180.279527455\n", + "\tmax y acquired = 196.752963258\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 183.01453012599998\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -643,11 +1043,11 @@ "\trun 24\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 192.539600494\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 196.796070915\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -655,7 +1055,7 @@ "\trun 27\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 191.507774129\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -663,43 +1063,43 @@ "\trun 29\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 194.38766055\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.644969217\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 183.508848648\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 188.242123191\n", + "\tmax y acquired = 190.04507896200002\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 200.420314123\n", + "\tmax y acquired = 196.752963258\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 180.853194423\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 192.697076125\n", + "\tmax y acquired = 208.120454446\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -707,31 +1107,31 @@ "\trun 40\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 192.43303832400002\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 187.01688480400003\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 191.077676114\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 193.620114578\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 188.927621488\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -739,172 +1139,372 @@ "\trun 48\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.053559538\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", - "budget for evals: 80\n", - "\trun 0\n", - "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", - "\trun 1\n", - "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", "\tmax y acquired = 194.37058873700002\n", - "\trun 2\n", + "\trun 50\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", - "\trun 3\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 51\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", - "\trun 4\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 193.05167775400002\n", + "\trun 52\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", - "\trun 5\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.325235236\n", + "\trun 53\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 193.72992463\n", - "\trun 6\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 54\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", - "\trun 7\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 182.381507756\n", + "\trun 55\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", "\tmax y acquired = 194.37058873700002\n", - "\trun 8\n", + "\trun 56\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", - "\trun 9\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 57\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", "\tmax y acquired = 194.37058873700002\n", - "\trun 10\n", + "\trun 58\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", "\tmax y acquired = 198.792072623\n", - "\trun 11\n", + "\trun 59\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 187.832756447\n", - "\trun 12\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 180.54061992400003\n", + "\trun 60\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.90463220799998\n", - "\trun 13\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 191.02071475\n", + "\trun 61\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", "\tmax y acquired = 209.36697147400002\n", - "\trun 14\n", + "\trun 63\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", - "\trun 15\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 185.162057723\n", + "\trun 64\n", "\tdiverse RF run\n", - "\teval budget 80 = 40 training data and 40 acquired.\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 185.32763518599998\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 184.686971958\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 184.541440663\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 197.35770853900001\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 187.01688480400003\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 180.54061992400003\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 192.02091057099997\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 195.142218812\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 180.789647894\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 192.33523148900002\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 189.069812594\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 184.154236099\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 182.26397528\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 60 = 30 training data and 30 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "budget for evals: 80\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.503247339\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 183.95419856799998\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 187.945004404\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 206.74476888599997\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 206.74476888599997\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", "\tmax y acquired = 196.752963258\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 185.76111369\n", + "\tmax y acquired = 193.72992463\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 183.77337184599997\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 187.244243495\n", + "\tmax y acquired = 194.708308113\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 192.43303832400002\n", + "\tmax y acquired = 198.751812898\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 181.885991327\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 185.901678884\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 197.03796965900003\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 193.949996568\n", + "\tmax y acquired = 196.752963258\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 188.76981126599998\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 195.142218812\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 197.07304941400002\n", + "\tmax y acquired = 191.507774129\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 194.38766055\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 197.07304941400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 195.289662613\n", + "\tmax y acquired = 193.949996568\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", @@ -912,49 +1512,3453 @@ "\trun 41\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 184.154236099\n", + "\tmax y acquired = 196.752963258\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 198.792072623\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.213573499\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", "\tmax y acquired = 194.37058873700002\n", - "budget for evals: 100\n", - "\trun 0\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 183.718553378\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 195.289662613\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 200.420314123\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 187.780313647\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 183.718553378\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.38766055\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 179.955561987\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 195.142218812\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 181.398957492\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 184.686971958\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.38766055\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 197.34635625599998\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 195.289662613\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 192.539600494\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 188.76981126599998\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 188.76981126599998\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 80 = 40 training data and 40 acquired.\n", + "\tmax y acquired = 190.660502314\n", + "budget for evals: 100\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 180.954411655\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 183.94813059400002\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 205.963467853\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 197.03796965900003\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 185.901678884\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.74476888599997\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 187.81611316400003\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 205.963467853\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.796070915\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 202.004818298\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 193.003059026\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 199.320637918\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.74476888599997\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 193.002639377\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 193.244990632\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 199.698499548\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 195.58268240799998\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 190.102542686\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 202.004818298\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.58076384900002\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 191.700524655\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 186.04049377\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 190.29267501400003\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 197.03796965900003\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 196.720247142\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 198.568968117\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 190.17935780099998\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 100 = 50 training data and 50 acquired.\n", + "\tmax y acquired = 186.040369533\n", + "budget for evals: 120\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 185.76111369\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 197.07304941400002\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 182.038043442\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 205.963467853\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 193.949996568\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 188.63741461200001\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 195.58268240799998\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 190.17935780099998\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 196.213573499\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 191.97029438599998\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 190.29267501400003\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 188.239279769\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 197.35770853900001\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 197.07304941400002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 201.71891966299998\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 193.002639377\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 199.90463220799998\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 199.72030120099998\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 192.882732714\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 196.88923220900003\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 196.720247142\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 190.17935780099998\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 202.08883754099998\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 193.72992463\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 194.80467023\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 205.963467853\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 188.666049397\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 188.76981126599998\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 197.07304941400002\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 120 = 60 training data and 60 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "budget for evals: 140\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 188.77987954900001\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.796070915\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.74476888599997\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.104126559\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 193.620114578\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 192.539600494\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 197.07304941400002\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 197.918308448\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 199.80359465400002\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 193.72992463\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 195.89774693900003\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 199.410130367\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.9895885\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 188.242123191\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.695494435\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 195.915962745\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 199.80359465400002\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 196.88923220900003\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 195.928348822\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 140 = 70 training data and 70 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "budget for evals: 160\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 193.41397958\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.171240133\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 197.07304941400002\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 196.9895885\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 194.38766055\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.963467853\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 194.938530808\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 202.848493155\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 198.020772317\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 196.9895885\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.80359465400002\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.698499548\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.80359465400002\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 197.86041748099998\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.410130367\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 204.811726149\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 203.35670863099998\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.320637918\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 201.17983227599998\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 194.157140046\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 160 = 80 training data and 80 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "budget for evals: 180\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 193.002639377\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 199.698499548\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 194.708308113\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 195.289662613\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 196.579974938\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 199.698499548\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 193.675158092\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.74476888599997\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 196.491162041\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 197.34635625599998\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 199.698499548\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 204.811726149\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 195.657779278\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 197.918308448\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 180 = 90 training data and 90 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "budget for evals: 200\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.320637918\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 196.720247142\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.320637918\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 195.978854341\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 191.507774129\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 202.08883754099998\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.410130367\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.698499548\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.963467853\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 200 = 100 training data and 100 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "budget for evals: 220\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 193.949996568\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 198.96574226299998\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 197.918308448\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 191.852225648\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 194.37058873700002\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 203.35670863099998\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 202.848493155\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 195.289662613\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 201.40394484\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 205.189199744\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 200.44080272099998\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 195.89774693900003\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 196.752963258\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 220 = 110 training data and 110 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "budget for evals: 240\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 196.491162041\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 194.30370504400003\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 203.35670863099998\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 194.530496788\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 203.35670863099998\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 196.327147635\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 199.80359465400002\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 202.08883754099998\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 205.963467853\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.55088119400003\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 202.848493155\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 201.17983227599998\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 202.848493155\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 205.492194009\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 99\n", "\tdiverse RF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n" + "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 216.894110699\n" ] } ], "source": [ "rf_res = dict()\n", - "rf_res['nb_runs'] = 50\n", + "rf_res['nb_runs'] = 100\n", "rf_res['nb_evals_budgets'] = [20 * i for i in range(1, 13)]\n", "print(\"eval budgets: \", rf_res['nb_evals_budgets'])\n", "rf_res['ids_acquired'] = [[] for b in rf_res['nb_evals_budgets']]\n", @@ -981,7 +4985,7 @@ { "cell_type": "code", "execution_count": null, - "id": "adjustable-formula", + "id": "leading-costs", "metadata": {}, "outputs": [], "source": [] diff --git a/new/random_search.ipynb b/new/random_search.ipynb index fe8d2c1..ca067bd 100644 --- a/new/random_search.ipynb +++ b/new/random_search.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "perceived-winter", + "id": "placed-thread", "metadata": {}, "source": [ "# random search" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "empty-framework", + "id": "olive-launch", "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "broke-exhaust", + "id": "knowing-explorer", "metadata": {}, "outputs": [ { @@ -56,12 +56,12 @@ { "cell_type": "code", "execution_count": 3, - "id": "sufficient-australia", + "id": "major-roulette", "metadata": {}, "outputs": [], "source": [ "rs_res = dict()\n", - "rs_res['nb_runs'] = 50\n", + "rs_res['nb_runs'] = 100\n", "rs_res['nb_iterations'] = 250\n", "rs_res['ids_acquired'] = []\n", "for r in range(rs_res['nb_runs']):\n", diff --git a/new/viz.ipynb b/new/viz.ipynb index dcc7b9a..3e1149c 100644 --- a/new/viz.ipynb +++ b/new/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "driving-homeless", + "id": "growing-perth", "metadata": {}, "source": [ "# viz" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "developing-space", + "id": "proud-consolidation", "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "electoral-advantage", + "id": "historic-oliver", "metadata": {}, "source": [ "load data" @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "objective-poison", + "id": "palestinian-robert", "metadata": {}, "outputs": [ { @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "soviet-forty", + "id": "extended-manitoba", "metadata": {}, "source": [ "for rankings" @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "mediterranean-jenny", + "id": "dressed-degree", "metadata": {}, "outputs": [], "source": [ @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "occupational-stability", + "id": "bright-animal", "metadata": {}, "outputs": [ { @@ -136,7 +136,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "injured-pierce", + "id": "given-occurrence", "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "optional-drive", + "id": "neural-school", "metadata": {}, "source": [ "load search results" @@ -165,20 +165,31 @@ { "cell_type": "code", "execution_count": 6, - "id": "worthy-federation", + "id": "demonstrated-metallic", "metadata": {}, "outputs": [], "source": [ - "bo_res = pickle.load(open('bo_results.pkl', 'rb'))\n", + "bo_res = pickle.load(open('bo_resultsEI.pkl', 'rb'))\n", "rf_res = pickle.load(open('rf_results.pkl', 'rb'))\n", "rf_div_res = pickle.load(open('rf_div_results.pkl', 'rb'))\n", "rs_res = pickle.load(open('rs_results.pkl', 'rb'))\n", "es_res = pickle.load(open('es_results.pkl', 'rb'))" ] }, + { + "cell_type": "code", + "execution_count": 7, + "id": "educated-citizen", + "metadata": {}, + "outputs": [], + "source": [ + "for res in [bo_res, rf_res, rf_div_res, rs_res, es_res]:\n", + " assert res['nb_runs'] == 100" + ] + }, { "cell_type": "markdown", - "id": "necessary-workplace", + "id": "hindu-laundry", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -186,8 +197,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "unusual-league", + "execution_count": 8, + "id": "advanced-theta", "metadata": {}, "outputs": [], "source": [ @@ -198,8 +209,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "applied-vision", + "execution_count": 9, + "id": "weird-musical", "metadata": {}, "outputs": [ { @@ -231,13 +242,13 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "ignored-alberta", + "execution_count": 10, + "id": "color-wisconsin", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -252,7 +263,7 @@ "which_BO_run = 0\n", "\n", "fig, ax = plt.subplots(1, 4, sharey=True, sharex=True, figsize=[3*6.4, 4.8])\n", - "nb_acquired = [10, 12, 15, 20]\n", + "nb_acquired = [20, 40, 60, 80]\n", "# gray background\n", "for a in ax:\n", " a.set_aspect('equal', 'box')\n", @@ -278,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "illegal-yemen", + "id": "urban-component", "metadata": {}, "source": [ "# search efficiency\n", @@ -287,8 +298,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "promotional-measurement", + "execution_count": 11, + "id": "orange-proposition", "metadata": {}, "outputs": [ { @@ -329,8 +340,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "daily-belly", + "execution_count": 12, + "id": "perfect-disabled", "metadata": {}, "outputs": [], "source": [ @@ -354,8 +365,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "commercial-judges", + "execution_count": 13, + "id": "global-transmission", "metadata": {}, "outputs": [], "source": [ @@ -365,21 +376,13 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "complicated-royal", + "execution_count": 14, + "id": "invalid-people", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_asarray.py:83: UserWarning: Warning: converting a masked element to nan.\n", - " return array(a, dtype, copy=False, order=order)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -434,7 +437,7 @@ }, { "cell_type": "markdown", - "id": "rising-replication", + "id": "adequate-funeral", "metadata": {}, "source": [ "show distribution for context." @@ -442,8 +445,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "answering-enough", + "execution_count": 15, + "id": "departmental-kitchen", "metadata": {}, "outputs": [ { @@ -471,7 +474,7 @@ }, { "cell_type": "markdown", - "id": "still-explorer", + "id": "ignored-nickel", "metadata": {}, "source": [ "### max rank among acquired set" @@ -479,8 +482,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "egyptian-steel", + "execution_count": 16, + "id": "horizontal-tours", "metadata": {}, "outputs": [ { @@ -507,21 +510,13 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "crucial-buying", + "execution_count": 17, + "id": "ceramic-chicken", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_asarray.py:83: UserWarning: Warning: converting a masked element to nan.\n", - " return array(a, dtype, copy=False, order=order)\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -571,7 +566,7 @@ }, { "cell_type": "markdown", - "id": "instrumental-recruitment", + "id": "alien-director", "metadata": {}, "source": [ "### fraction of top 100 COFs recovered" @@ -579,8 +574,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "greenhouse-savannah", + "execution_count": 18, + "id": "established-carpet", "metadata": {}, "outputs": [ { @@ -599,8 +594,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "insured-certification", + "execution_count": 19, + "id": "respective-forge", "metadata": {}, "outputs": [], "source": [ @@ -614,8 +609,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "clinical-addiction", + "execution_count": 20, + "id": "welcome-hostel", "metadata": {}, "outputs": [ { @@ -653,8 +648,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "straight-albania", + "execution_count": 21, + "id": "descending-fields", "metadata": {}, "outputs": [], "source": [ @@ -678,21 +673,13 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "scientific-blackjack", + "execution_count": 22, + "id": "stretch-fleece", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/cokes/.local/lib/python3.6/site-packages/numpy/core/_asarray.py:83: UserWarning: Warning: converting a masked element to nan.\n", - " return array(a, dtype, copy=False, order=order)\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -734,14 +721,6 @@ "plt.tight_layout()\n", "plt.savefig(\"search_efficiency_top100.pdf\", format=\"pdf\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "third-briefing", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 371504d58da1c67e571fd96053e595c1e46fbddf Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Sat, 3 Jul 2021 11:11:31 -0700 Subject: [PATCH 17/29] print stats, rf run at 250 --- new/random_forest_run.ipynb | 16 ++-- new/viz.ipynb | 172 ++++++++++++++++++++++++++++-------- 2 files changed, 144 insertions(+), 44 deletions(-) diff --git a/new/random_forest_run.ipynb b/new/random_forest_run.ipynb index f9dde07..ddf9835 100644 --- a/new/random_forest_run.ipynb +++ b/new/random_forest_run.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "competent-reserve", + "id": "pending-reservoir", "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "photographic-immigration", + "id": "eight-likelihood", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "sound-penguin", + "id": "foster-brass", "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "aggregate-monster", + "id": "paperback-revolution", "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "introductory-mouth", + "id": "mounted-density", "metadata": {}, "outputs": [], "source": [ @@ -133,7 +133,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "collaborative-constitution", + "id": "automatic-replication", "metadata": {}, "outputs": [ { @@ -4959,7 +4959,7 @@ "source": [ "rf_res = dict()\n", "rf_res['nb_runs'] = 100\n", - "rf_res['nb_evals_budgets'] = [20 * i for i in range(1, 13)]\n", + "rf_res['nb_evals_budgets'] = [20 * i for i in range(1, 13)] + [250]\n", "print(\"eval budgets: \", rf_res['nb_evals_budgets'])\n", "rf_res['ids_acquired'] = [[] for b in rf_res['nb_evals_budgets']]\n", "for b in range(len(rf_res['nb_evals_budgets'])):\n", @@ -4985,7 +4985,7 @@ { "cell_type": "code", "execution_count": null, - "id": "leading-costs", + "id": "still-bangkok", "metadata": {}, "outputs": [], "source": [] diff --git a/new/viz.ipynb b/new/viz.ipynb index 3e1149c..a2963f1 100644 --- a/new/viz.ipynb +++ b/new/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "growing-perth", + "id": "sharp-thriller", "metadata": {}, "source": [ "# viz" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "proud-consolidation", + "id": "sunset-treaty", "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "historic-oliver", + "id": "charitable-upper", "metadata": {}, "source": [ "load data" @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "palestinian-robert", + "id": "electoral-teddy", "metadata": {}, "outputs": [ { @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "extended-manitoba", + "id": "operational-wholesale", "metadata": {}, "source": [ "for rankings" @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "dressed-degree", + "id": "native-plumbing", "metadata": {}, "outputs": [], "source": [ @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "bright-animal", + "id": "bulgarian-tokyo", "metadata": {}, "outputs": [ { @@ -136,7 +136,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "given-occurrence", + "id": "urban-concentrate", "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "neural-school", + "id": "abandoned-beaver", "metadata": {}, "source": [ "load search results" @@ -165,7 +165,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "demonstrated-metallic", + "id": "lesser-backup", "metadata": {}, "outputs": [], "source": [ @@ -178,18 +178,19 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "educated-citizen", + "execution_count": 35, + "id": "fundamental-blink", "metadata": {}, "outputs": [], "source": [ + "nb_runs = 100\n", "for res in [bo_res, rf_res, rf_div_res, rs_res, es_res]:\n", - " assert res['nb_runs'] == 100" + " assert res['nb_runs'] == nb_runs" ] }, { "cell_type": "markdown", - "id": "hindu-laundry", + "id": "alpha-deficit", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -198,7 +199,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "advanced-theta", + "id": "fourth-savage", "metadata": {}, "outputs": [], "source": [ @@ -210,7 +211,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "weird-musical", + "id": "descending-scientist", "metadata": {}, "outputs": [ { @@ -243,7 +244,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "color-wisconsin", + "id": "typical-marshall", "metadata": {}, "outputs": [ { @@ -289,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "urban-component", + "id": "rocky-progressive", "metadata": {}, "source": [ "# search efficiency\n", @@ -299,7 +300,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "orange-proposition", + "id": "sonic-penguin", "metadata": {}, "outputs": [ { @@ -341,7 +342,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "perfect-disabled", + "id": "designed-buffalo", "metadata": {}, "outputs": [], "source": [ @@ -366,7 +367,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "global-transmission", + "id": "touched-chancellor", "metadata": {}, "outputs": [], "source": [ @@ -377,7 +378,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "invalid-people", + "id": "specialized-ghost", "metadata": {}, "outputs": [ { @@ -437,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "adequate-funeral", + "id": "pretty-vehicle", "metadata": {}, "source": [ "show distribution for context." @@ -446,7 +447,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "departmental-kitchen", + "id": "angry-blowing", "metadata": {}, "outputs": [ { @@ -474,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "ignored-nickel", + "id": "imperial-party", "metadata": {}, "source": [ "### max rank among acquired set" @@ -483,7 +484,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "horizontal-tours", + "id": "trying-vocabulary", "metadata": {}, "outputs": [ { @@ -510,13 +511,13 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "ceramic-chicken", + "execution_count": 24, + "id": "fitting-sweden", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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THwHe6TiZYrh8BLDbcTJB+yP7i5oD6pwJDz6+E649KJefOjpd+YCs0bNcsxwoFIr+sdSPhTcBjcmT7gSOXuJzdo1EIt7vLvSduWTw9gek8jE1eMMW+MejZ+4TiNaVS1cK6jqQKDkoGXSbpXZCaH7srIjHUN0E9/KXv5SzznpEv7vTV3R99neUnAuf2yM/f/A4meWgFQErWwG1k8FaQslByaDbqLj0FigT3BSFwuwFuL68H2oCzk7NrnxAzhNZK/i+bSeDtYSSg5JBt1lqBSSAEdtOew3Lw7adHm3cqcErTrGC+K+98u/T5nAu8JFxQQqFQtFIL0xwdzYt/6lpWbDMnk91E9zll1/G6OjZ/e5OX4lEWueM3VaEP06Apcnicu1Y6SOg2WSw1lByUDLoNkutgJ6wxO13jG2nh4CfAKcCFzpO5vbZ9q2b4IQQa94El0wmZqyb8OAdD8o3h78YgcE5rqKVPgfUSgZrESUHJYNus6QKyHEyv1rK9udJCXgGU2mA5mR8PLfm7b2NMqgFMt7nniJ8J3S7vnLz3G2sdC84dR1IlByUDLpNT5wQbDt9OPB84KRw1T3A/zhOZncvzg/gOBkXOGDb6Tn3bQhEZa0HojbyiV3w1X3w5wJ4Ah47BCd08EIYIE11CoVC0ciSKyDbTr8a+CgQRSYlBRgEPmjb6Tc5Tuaaebb3euBK4Azgq46TubJh2yjweeAS4CDwNsfJfGW+fa6b4LLZ3Jo3welhArf/PQBvvi9ch1Q+bz2qszbECh8B6Ss1iV2XUXJQMug2Szo1bNvppwKfAD4NHOE4mWHHyQwDRwDXAJ+w7fQl82x2N/A+4Asttn0CqAGbgMuBT9l2+rQF9PtS205fs3PnrvkeuuoYHh6i6MHr75XLl22EH5wJzgmwKdJZG4KVW4oBpAwUSg6wemTgitnLSrTb1m2WegSUBj7kOJm3Nq4MTW9vsu10GXgLMlt2RzhO5loA206fi1RkhMtJpJnvdMfJFIDf2nb6O8BLgbe2aqvNOb4LfDeXm1jzI6BcboLX7xpkZxWOicEbj1iYR9tKfnHM5SYYGhrsdzf6jpLD6pGBpRm8ZscPWm779Nan96wfS62AzgVe32b7fwCv7dK5TgQ8x8lsa1h3C/C4+oJtp38AnAWcZNvpzzhO5t+bG7Ht9NXA1QCXXPJkLrhAzgHF4zFM0yCfLwJgWSapVJLx8Rwga8WPjAwzMZGfLFo1NDRAtVqjUqkCMo2HrusUCrKNSMQimUxMtqHrGsPDQ+RyE/h+MNlGpVKdzMKbTMbRNI1CoQRANBohHo+RzUrrpmHoDA0NTmtjeHiQcrky2UYqlUAIQbFYnmwjFouSy+WntZHN5nC2e3xpHCxN8PbNFdyJKi4QTSYRQUCtLNuwolGMSIRKXrahmwax1ADlXJZaDQo+bNgwRKFQxHVlWNjAQBLP8ymXK5MytiyTiYnCpIwHBlKMj2cRYkrG+Xxhso3BwRSu601ro9u/U6lUZnBwYFn/TkEgkwqPjAxRLJao1dywjSRBEFAqyTZisSjRaGSyDdM0wu8mZVxvo9XvNDaWxfeDZfs79eJ+yuUmGBhI9eZ3Mk1y4zmwzHn9TtPuJ9NgItf0O2WzjIy2j50YG8tO/50OZmGshKXppGJxtI/+5nA+9txFz+EvtQKygHKb7eUu9iHF1BxTnRwwWcDDcTJzqvZwTuoagLGxrGj2eJlreXBwer2QRCI+I3/UXG00v2Elk4kZ7p+jo5Gm5fm3EY1G27Zxqxjin8fl1f4PR2k8cn0cmP5dzKY2EsPT24gPDROpwcigfKgMDKSmbbcsi3h8ek3C5n6MjExfbm7DNM0521js76Rp2rL9nZrNQqlUkmZisfZtzCVjy7IYGhqcdtxy/J1aLXf7d9J1fel/p6oHu3KMVnw4alBm8616jEQSchvAoSIDAtAtiJhQE1hoxM0YlGpwKA+uz+hkBrQaUGIkbsLojFNP7/u4VIzsr0IgGBWAFQdDr5+/K4WRlloB3YN0CJjN0eApwLZZts2XAtK5oZFBYMFF3Ndy8Skh4KV3gY/GZRvhmesX195KNsGt5eugESWHLsugPpQR4eeKC7kKVDwou1LpWAY8cEjeQPWwfZBvczC1Tgim7aBrUilFWzziy+7cfdN02VzcmDpXHa97xQyWOj79C8D/C92ap2Hb6WcB76e1M8FC2AaYtp0+oWHdmcAdC22wPtRfi4x7sKMKcU3wpiMX396ySnUxT9byddCIksMCZSAEjJelInnwEOwYh20H4K598t/d++Ge/bB9HApVuf9AFOIWRAwYjEEqCsmo/JuKQjIi/yXCv6kopCJT2xOR2WugxDvI5mDqcrRTVz7ZMtx3UPb73gMAj5y/IFqcphuNtOHjwKOB/7Xt9DbgrnD9KcAJwP+E+3SMbadNZL8NwLDtdAw591O07fS1wHttO/1K5FzPs4GLFtr5arW2ZiOf94aFH0eMAFNbvPpYySOgtXwdNKLk0CSDmg+uJ/96wdTopJEgkCOaQlU++AXge9KTJ7aMc0H/cQe4vhwtlV2pdKZ/v5cA/73Y0yx1JgQBvDhUDC9hKhD1buAfHSfz9QU0+w7gXQ3LVwDvAd4NvA45otoPHAJe6ziZBY+A1jJTCqg7FXNXcCo4hUIihFQkQVH+LdWYNHvV37CazVUgYxAGoq23zZdAgOdPmd0CAcUalFz5OQigUINcGbIVadLzA7mvQPZVgPu3F8/q7ebmSli/eWD6Sg04ekSaBIWA+w7duvgvo0pyt6VarYrmicW1wlf2weV3wmMHfJyTFjcCOlCTCUtXakLSarU6Y4J5LbKm5eD6vGbPjwD4dPEceTFHlvD9vVSDW3ZDvion/WvhaORgsatzMJOkIrA+BQlLjswMXZrh4pb8vHUYRsKRX9mFT/zuZD723HsWe9qlLsl9FPAPwN85TmaiadsQ8EHgfY6TeXgp+7FQtG68saxQ9oam7tEuJf9dySa4tXwdNLJm5CAEuIEcTbiBNEUdKspcLiDnXLqNF+BuGcCKNrR98qbJj26uhPVOqQAx9dApIXRMiFty3scI1yUjMBSD4bj8axnTnRbqxxk6r0/chG/Ap7QFz1QsiqU2Qv4tUG1WPgCOk8nZdroK/B2ydPeyo1AozXDPXCtMmuBEjWbX6/kghLzWV+jgB1jb10Ejq1YOgZBv9a4vzVXZMlR90KRnmXv8KNboBj7NTJOV63lYuzt0tK15cNse2JGV56orN9eHUg3rw89uHxz64rNgXbIzJ4IO8fpcpHqpFdCTgFe02f4V4ItL3AfFAqgroHXm4ky0noCY3h3zt0LRFUSocOpzIgeKch6lPjKIGNIkFWJZVnvFsGdCKq4gnGcJRDjnEs7R7M3D7XvlORbDEcOLO34ZstQK6GigXUK13UCHKS17TzS6Ct/2OqSugDZGFzd2cQWkVrIPNmv7OmhkRcohEFOBm3UOFeTkvBZe23UngYXy5Zs6208DDhuEsw6XLtX1OB9Lbx2vswZY6m9dBI4Bdsyy/Zhwn2VJc9T2WqKugA5bpL27JmDDCldAa/k6aGS5yqE+Opnh1eX6sDMH5drMIfhAF7/LppRUJprWMDcTftaBqAWPOAwOH1SmgCaWWgH9AXg5MFthuquAPy5xHxZMNjuxZotP1RXQQDUPAwvPAOwGMLDCFdBavg4aWW5ycIWPpRkt3YndwMd6aEx6jKXmObqpetLpYKwEE1XYOkfN+ZeeO7/2FZMstQL6MPBT207ngA84TmYvgG2nNyMzVF8BPHmJ+zBv1npBOjeAg658eRvuQhzQIq14ihXKrCOThVKPZQlDRyxjjozOXiAzArTDD+D+QzBeknM0B4qwMzs96PLFXQn6V7SgIwVk2+lnOk7me7Nse7vjZP6l1TbHyfzSttN/Dfwr8EbbTjcWpHOBNzhO5hcL6PeSstbLMeyuyvtv2ATLWLz2WOkKyOiCDFYDSyoH15cjjmwFGSzZ9OITINc35js7dY568K2UT9WDX90vzwVyLijflF5H12BDEkYTMNw7s6PrebMHh3oe3fN9Wz50OgL6sm2nn+44md81rrTt9D8gXa1bKiAAx8l8xrbT3wNeCByPvIK2Ad90nMzOhXW7N6yGuh8L4TqZzZ5RE2KDi5dBZIU/v9fqddBMp3JoaxoLt00SCDn62C9LBhAzp8esNDLf+ZNSDW7bK92f6/rswUNylNPIaAKOWyfjZwaicNQIxKYe971SDHV37tdwHQCf5lFT27p0juVGpwror4Hv2Hb6CY6TuRXAttPvAN6MzGjdFsfJ7AKcBfeyT6yW4lPz4Vfj8Kaw9PbGCFQmJhashOoxQJEVPu+6Fq+DVnQqhzmLnRWr4AtpIsuWZRbohAV6l99Uvvin1pmfh+PwxOPBNKTzwKaBtpHSa1Ex9IqOFJDjZL5k2+l1wI9sO/0YZF63NwOXOE7mT0vZwX5SL2K1lnjDvbDfhU0WvHAjBMHCZeAKSLTI5r7SWIvXQSu6Joft42FEPtINudFJoB47I8SU2a0xvoZwvevP7RxQduHIYTmiITyfacApG+eeG1L0hI6dEBwn86+2nV4P/Al5OTzJcTI3LlnPFD3nhgm4rSi91q49Q87dlLKdHVsJoOTLkAohZACqJ+CINZo6bDkyw/zV4bZ5EYi58y41x9wcKsJ1D0lnANfv/Fwfe2777Y8/Ds45YuW/Aa1iZlVAtp1+c4vV48jCb78BHmfb6ccBOE7mI0vTvf4yPLx2zC7VAP75Ifn56aNTjgPxDs1vOReOj4fPHx2SumxjpQehwuq5DuY0jc3BDDnUlUVjQbWDRThuHtULx0rwtZtlNuc6GtNznU2LrwmXTR03X559bqbmYp3bhUJWiiWl3QjoDbOs95E1durZ6wSwqhRQ3Q37iitewjnnrH4XTC+Av7oLvn1QLj97w9Q2t1IhkmhfA6bgw3oLTpxZYXhVUC5X1l4dnEBAvgL7CjASh6E45VKRZCQWFlgLY2Q0mPRME3SWITpXkSUFDpXgp9uk8jlqBC45EYY6zztojVdgvKLmZlYws14tjpM5ppcdWU7U3bDHxrJrwg37i3vgK/vB1OA1h8OJDc9ar1abUwGVfDh9dQwSWrJmCrFlS7KWjOvLZJyBgKghRzUHilSLEySTA0AY4T/fAM86n/3D9OWtw/Ds02UONsWaYskCUW07/SCtawTOwHEyxy5VPxQzKftyfiZpwIQ3ZXp759HwjHXzaysQsjTtyNpMZbW62J2Xecl0TbpD1+dyrFAxiMjcSmfHOO76ePtiZ3EL4qZs98SNcN6RK7teR7+pJz6F0ByKDLBtt78f7l+3WhSaYqHqP4do/F3COCx5rqYEewuj48eGbadfBDwR2EhTdn3HyTyrxSGNpbZTSK+56yEcL8OjgPOR2RKWJanU6nnrFQJyHuyqwo6KXJf34cv74KEqbInAU0ZnHjfX6KccSPPban5+rKbroC2p9p5hqVhoHvMCGU9TrMk3ED+QI6btY7A3j/X1WyaPKUUhcfioTLZp6VhHDsNrL1rdF0y38ALpyTdp5oSZ7/ThvJiuT+1iaO1LNui6HG3qmpzRB2kCDefWZjht1ON/63NwkqaSqQuj00wIGeBvgF8gM1jPObJxnMykYrHt9L8jU/FMC1i17fTbgNM6725vWS3VYks+3FKAcVc6BsQM+Oo+qXxK4YvSa7dIE9xM2sug7MMxyzNHZddY0deBH0xlg16I6/GBAvx5F9R8zGJVVubMV6c7DTQSNWXizbjFhw97mPsP1/ik8YiF938tU3FlAtNUVCqLuiJoV/57vtQVUHKe5tSPPbcrN0WnI6CXAZc5TuabCzzP84CzW6z/BvC2Bba55BSL5VVRgvj+snQU8IEv7YNvHYBc6MD0qEF4xWFw1kDrY2ulMmZkdhkEAgZXufltxV4HZVfmNav5oIF7wrq5I/r9QL55HyzKUc4NOydLQE8ze6xPyoejEZrsDB02D8DRI5OOCPe2rcSiaEk9ehukiXIovqpHi50+OnTg5kWcpwg8Hrivaf3jgdIi2l0SVlMy0pIvvdt+dAh+PzE1njk7Ba/bMrvi6QQRhnysBlfrVUXVg/156W0WNSfjbqw98nX3NVzHcTsD0tsPnwz8tA4VZTqcQouiaadtgqNGyYsaA6ND8sG4LqHia+aDH0AlHImKppx2NCzqGtQtvpvbZ2hYDXSqgK5BZq5+9wLP4wCfsO30ucgSDQAXIks1LLTNJaPuBVcsllasF1zOg3uK8I0D8KGH5TpLgyeNwF9uhEckO3t+mG2KkGU9OCwiTc6rmWVRiE0IOVFc906refJvEAANtnsvkHMz9SJrLX7k4x8OsL/ugnho5nk05GhmJA6HD8GJ6+EoOTmoVyoQW+X21qVACPmbbEhJr8JoOGnaaNqtxzpZxlT1tG7WLJqFC362T364aslP1ZJOFdAw8BLbTj8ZuBWZyXoSx8m8sd3BjpP5oG2ntwNvQiYlBbgLeLnjZL4+nw73klhsBZpdgIM1uD4vU+F8Zrdcd9VmeMkmGJlngESj+U0IGG/I62gAJ63S2J9GenYdjJdltc66J5OlywnjIDSLVcNUE3UlUZ8YFmIqKFQjzKsWKp5COG9zsF7fpsJr7nUxBHDiBpn1WdNkfrRNKfl3ljeTWGQZKOLlQKHGjNHLbAhN/n6bBqQCWiZ4nodpmlx11UzNU9/WCzo9y6lMmeBObtrWqav114Flq2xakcvll1UBrk7wBdxRhPtL8Mnd0tPt/AFpbluIxaSSz5MYHgZkjaAtURg0pBv3Omvll1rohCW7DurlooWQk/p7J6TyqP9QgQDfn1I485lsGyvBrx+A+w7O2JQC7jxK49RnnjKvBKC5YoHRgVUc8NVI3Y25fs+UXCk4gKOGpdzMDmUnxJQrexvqD/5W83TdVgqmafLFL36x5bZWSmmp6DQZ6RMWeyLbTseAZwLHAZ9xnEzWttPHAeOOkxlbbPvdZKXOAQkhFc/vc/DOB6XTwToT3nLU4sz1QsAhVyqeU5Or3+S2JNR8OSqpP7QqLuyekH/rtv9UZLpC6GRuzfOlEnMD+dAcL8N122UGA5DnS0bkyGZjCgaifHjDQ9y3ReNT3c4+vVJpjIsRQs7VWMbUvIxAmiTrdp/5eox1yHJRCr2kJ+Ms204fD/wU+Q4xjPR+ywKvDZdf2Yt+dMpKLUi3rQS/zcH7d0jl84IN8IYjZEbqhVIUOgUXjorJXG9rUfksuhCbH8COMUCDwwZk0bVsWZrYOskmIATccwBu3S0fjm6odKre1IOzmagBx2+Axxw9Yy7hXh5e0Ncw9FXibeL5UPamm86i4XfTdTn3NRyb+da2A0WXmU8g6hOAy4CtwDRjsONk/mKOwz8K/BipcLIN678DtFb5y4CVVAMm58G9JfjkLml2u3gY0lsX50QjBGjJQR4zBKlV7mrdjgVdB/W3ateX2Z7dQP4Y28flqCQVmf6AO1jkuutvJFGFM8WwPLauZLxgZtXOOvWsBaYh3w4sA07eCGdvkeu6yFByBU74BUIqnEYqnizTUM9bZxmdm9MUXaXTQNQrgU8D30K6Tv8vcCJwDPClDpq4CLjQcTK+bacb1+8ADu+8u72hboJ78YtfyAUXnNfv7syJENLj7edZuKkgK5n+41GL9+AMAD+fI7VuqBvdXLFkszmGhzuQgRfAREV6qOWqcv4GpKJpnNsB+WB88JCM1TlUhD/v4lFuPX1KC4t0xIDHHCPfziOG/Bc1W0euLxHZQoHh1PKZSG+LEHKUEwTSZNYoog0DMKS8+ZYDnb7X/h3wesfJfM6203ngbY6TecC20x9nKpZ2Llr5X20Fch0e3zNWWjLSvA8PVuDze+Ty32+F4S6kAxaA3pmPyaomCJpk4AdyhBIEcmSTrwJC/vUD6TAQMcCY5fbKleFH98CO7LTV15+ic+NJOq/VTpamoJgp2zF1mcWgg4nspSQQS1SYr25SnKYl2lx3dYUbujFf8MfQlfjChlGiQM57rU+pJKfLmE4V0LHIORyAKlP+IB8Hfgm8dY7jf4zMBfeKcFnYdnoQeA/w/U47q2hNzoOv7IOJ0OPtiXMUiuwUIdbmnE9bDhbhYEGOYOqT1HWXaEufPQeXH8h5nHv2wwOH5HEJC44exX3Z2ViJGOcjkyM24nreZEnoFUux7rbcHPsSro5ZsHlQKty5aBzt1T/+Mfx77Prp2zopDdEBvfRO6xWe583q2LAc3bAPAfWY+V3A6ch4oHVAJwU8/hb4uW2n7wFiwNeA44F9TMUFLSm2nT4f+FekL8su4GWOk2mZ0KpugnvFK65cEW7Y1+Xge4dkuoo3b+2eRSYAEoNr2/wGMDISyqDqwb48JK15uS+zLw/fvxPGynJZ1+DkDbJiZzKKlYgtqlDcgqm4YZlrporKwVR8kaGHJRlkZuyRVIdpM8LszJ8unQ2DMdjUFD9Uz7TdGHy5AOaMZVlQqzPplXdaL5VCva3692o8by8Vaqdn+g1wCXAbMpbnY2FQ6hOBn8x1sONkdtl2+iykE8PZyGflNcCXHSdTXkC/F8LDwF84TqZs2+n3A88GWua2q5vgCoXisjfB+YH0eguQXm/Hd17Pa06EgKBSYipn+9qkWCyRSiXl/I6hz0/5lF349u3SPDcclyWiT1i/8Fo63cQLpGs2TMW1BIFURl6YxFTXZPE5y6BYrZCKxwEtNEPOYpKrK6/RhAzAXKI5qtXmtrxclEIv6fRbvR45cgF4P7IWxKORyuh97Q607bSFfPg/0XEyXwC+sLCuLg7HyexpWKwhn9ltqdVmyfjbRyZfUsN7+ufjcHNBxui8ekt3zxUAuMtPBr2kPjL5dPypMrgz2uZt3QugXJPK5mBRPsDv3i+XNw/Aix+5vLyt4pacI5mLgSjkq9T8CljhIyMRkTV96rpFqycl1aRJbZXnMFN0hzkVkG2nTeDFwLcBHCcTAB/o9ASOk3FtO+3SYcaEDvrzeuBK4Azgq46TubJh2yjweeRo7SDSWeIrTccfFW6fVXEu10BUEWY58AU8IiWV0P8ckNueuR6Gu/ySFNCjQLHlSqPzwbZQ0IYuTVeuLx0QchXpVDBehlv3TKXEaSRuwTNPXV7KB9rXjGlkICb/xQTMwyTd6k1eoWhkzueL42S8sB7QYpwF/g14m22nr3KczGIr6e1GKo+nMHP+6RPI0c0m4Czg+7advsVxMncAhI4P/wVcOdv8D0yZ4Go1d9mY4IIwy0G9mJwZmtB/GHrsPnYppmoEJAfWoPnNC3C1AMtoPfHs5kpY7/xR62OTEflg35iScycbU9LkFuuCW2K36WTSv4FUag1eC4olpdMr8A/AOUCL9Lkd8VjgccAu207fjizPMMksFVVb4jiZawHCzNpH1NfbdjoJPB843XEyBeC3tp3+DvBS4K3hSO6/gfc4Tuaedueoj4Be9rIreOQjz+y0a0uGG8CNeRjzYH04n7u7BvtrsKMKSR3OWoLwjADQgiVyvV1u+IEsvhYGfVqnbGrvGJCKhAGlURhJyIf5iRvgsJUTvDxpTuuQYK1cC4qe0ekV+FngQ7ad3grcyEwFctMcxx8E/mf+3ZsXJwKe42S2Nay7Ban4QDpAXAC807bT7wQ+5TiZr7VqaDnFAQkBtxelq3WjM9GoBd/YLz9fOCQ9gLtNAHjVMrAMJsyXmv0FOccTMTozTb3moq6d2vW8uQvFLQXz9D4rlcorNkP8YlguLsurkU4lV59H+UiLbYI50iY6TqYXRuAUMNG0LkfoPu44mf9Cmt/aYtvpq4GrAS655MlccIGcA4rHY5imQT4vda9lmaRSScbHZRytpsHIyDATE3m8MPXH0NAA1WqNSkUGyCUScXRdp1CQbUQiFslkYrINXdcYHh4il5vADz2McpEBHs5WGaFGqQSRRJybizrf3i/4v7zUSE8bCShlJ8I2dGKDg1QmJibfWOODg7iVCl5NFhuLJBKAoFaSDohmNIIZiVLJy3gT3dCJDQxSzeWoVSYYi0lX5GKxNOmYkUolCYKAUthGLBYlGo2Qy8k2TNNgcHCA8fHspOPEyMgQhUIR15VW2IGBJJ7nUy5XJmVsWSYTE4VJGQ8MpCbbqMs4ny9MtjE4mMJ1vWltdPQ7jefwSjUoVhmKxqmO5alYQE0jocXoJE4+VyziB+FvnUxRqdWoulLGyVgMDY1CRconalnEI1GyRfndDF1nKJkiVyzg3yN/u7cP3AHAv+RllfpULI5AMFaphG1EiEUi5CbbMBhKJskWCpNBoiOpAYqVCjUv/J3iCfk7VWUbsUhk8n1ibGICM2J2/DvlchMd/U4DA0ksy2r50HZdl3y+2JX7aXAwNaticF2XYrE0434aGhqgUqlSrYa/UzKOpmkUCrIuZjQaIR6PkQ3vJ8PQGRoaJJeb4Npr5Tv0y1/+csrlymQbqVQCIQTFYnmyjVgsOnkv1NvIZnOTQc1z3U8Avu8v+f1UZ2wsOynTTu4nrUuejZ0qoGO6cralpQA02z8GgXlF8TlO5hqkizilUlkkEtOnmZrjgpqXBwenx0okEnHm20Y991jOg7tzsGkogaklEGF9n8+F/nw68Laj4PGjOjKn6xSxwemiiCQSoeKZornUdr3swuT2wSFGByOMjsr+t5oDaH4jbv4uIyPTlwcGptsKLcsiHp/+uO+kjUnvtNGnY5rmnG1MWy5UGdxbCUu6WlALSAwPkJhn0tHm3GjJWIxkU8G2UWv6+KW5nMFQcqbttHmfqBVpu705PY50lQ6vuUBAoBGL1vs65Vgxun4qYrmT32nTpg3TruV2v1M79+jG47pxP7VydrAsazJ9UnMuv2QyQTI5/V4YHW2S8Sz3JMiXvFZtNJdtb26jOZ3TbPdT4/foxf3UvE8n91O36LQcw7znfmw7fSvwOMfJjNt2+jbaeME5TuYR822/BdsA07bTJzhO5t5w3ZnAHQttsN+VMO8qykzWpgbVAN6zHX48JhXP5ZvgGeu7G/fTjBAQi62yImR+IMsgRIy+p7bpCvXX4cm/4b96miAExGXiU++wFGbE4tMsLKK/3/eDYvWxlMbL/0Gm7YFZAj4XQuhMYCLNfkZYZ8hznEzRttPXAu+17fQrkV5wz0YmQl0Q/SxIV/Jl9dGNEfjhITny2Rk6HPzLcfDoHiQoEMiCdCSHl/5kvWKsJLMjt5vLqC3WUXOR1BrKztYVSxBm19ZgKocN0o6iMRUcqyHfUExDOkcMxiYVrUn7kclcrMQCjYrlzZIpIMfJvKfV5y7wDuBdDctXIHPKvRt4HTLQdT8yfdBr6y7YK439Nfkc+dGYLC4HsDUKHzwOjk+0PbSrrKp4wqonPd0STW/ygYCbdsqs1NkK7MrhvueS7jsGCCG97AIxpVAat9WtTVFzykCva1M/wmBsujv3qvpxFP2g3zFaK859w3Ey70Yqm1bbxoDndOtcZpfrqXSKEPBQBQ648L7tct3rtsDLNktzXC+x+iSD2XCFj6UZreNzwm0zCAQUqlLBmGHEfhDI3GwTFbhzn8xY0ID1yd/Do47mNcdKp8pP86ipbQvpeBBAwZV55KKmHJVETbB0vIiBaZkLNo31in7dD4rVS68qovZiDqjrNE+A9oqcBw9X4e/uh3IATx2Fqzb3rOzLNAb6JIPZsDRjfok7AwG7s7I+j6nLUdB37pDzQF5DXIulw2OOlWlnjhzuPEtAp5RcWQ11NDHjh1ysaaxX9Ot+UKxeOi1I93PgeY6TyTatHwS+3UFF1OY5IAs5R/NoZPaCZcn4eLalx8hSs7MqK5vuq8EjkvCOo/ujfADy2SzrNgz35+QLoepBzYdiVSYCrYbzKQNR+fna2+Q8EMBgVCoEy4Dzty59EOlAizLPK4hO7wcVN6PolE6vhMfTVIY7JIbMctCW2eaAbDudBo7qsA89R8w6Zls6aoEsr3DdBEQ1+ODxEOtjCjFtpdWju/+g/FtP819PjBkI+N6dUvmsT8ILzpRpc3pBEIBhrPjCaJ3eD2sxq7NiYbS9Gmw7fXbD4iNsO91YK9hA5mPbtYjzXwvcgMy2rQB2VuBb4TP0GethfZ9TiC2bF/bmNOCz0VzmIBByjufeA/DgmMzg/JzTe6d8QI7Ienm+WVAjE8VyY64r7gamIgt+3GJ7GXjDIs5/MVBaxPFLymQhsh5R9uH6vCyxAPCSjT09fUtGhnsrg5aUXVm+2vfh1M2dHSOEzEz9s3vhjrBks67Bs06XdXl6iSeWRf2fxY5Men0/KFY/c111xyAjCx5AVgs+0LCtBux3nEyL/PPTCZOCNqIBhwGPRLpQL0sKheKMSOOl5IGyzG5dFTK79dE9fk62olwqMjDUOxlMwwukl9q+idCc1sEo4qadcMtuWSah7mRg6nDBVjh2nSyQ1smpN7cJ2qy5mHsL7RsIhAx6rVcaja780UWv7wfF6qftXdGQAWGxsxCHmpYDZIaCtztOptXIallQz7HUCyo+3FeG74Xmt8s39ezUbfF7KAN5wkBW4Kx4UvmAnMcJa+m0TdyZK2H9/L6pFaYuY2eefKL0bJsHZsRq75lWDqt5NGYiEA0BomjS3KeHfWijgFaKaayX98Ny5KqrrprMmaboDp16wb0QyNaVhW2n/xGZsPMOZG2dPe2O71Ey0q7Rj4J0u6sy6HTMgxPjcM4y8Xjt+RTQeAn25GV5g2RkxpyPtX0cSi7/UrmJVBneuH0T3LhTbhuNy9o7J2+EY9Yt7aT/UJgrq56BwAxLWtf7G59SmnNRVzCT+e0aFOxyUT7LmeXkqq6YH51e3e8G/gYmHRPeDvwj8FTgw8BLlqBvfaNejsF1e1OQLhBwSwG+Ek5VXH348pn8H+xlQTrXhwNFGIhMPdiFkNVI9+alB9sDh0DIC1CyU2rJRx0NF26dOm6p2byC6v50iYG1WJywidUkAyE8RODRGKIpECB8hJg+2g2CCl7tIEG4/qF7Xn/Eaef9budi+9CpAjoKqBdxey4y9ueDtp3+MTBLacgpbDv9IB2W5HaczLEd9mnJ8Twfy1p6N7S8D187ADlfjnweN7zkp+wYEfjMJ/a/1Vt8S8qu9A4jTElTcSFflZpX12Xmgt0TcNc+uPfg1HG6BgNRHopXKcXglMQGeOQWOFxNkC81vbofljMrVQZCBIighkBO2Qd+lWrlgVABdYKGbsTQdYvALwF0RRN3qoAqTGWqeiIy3xo01NuZg38H3gxcD1wXrnsU0rHhI8hSCsuOcrkyIy35UnCwCr/Oys/LafQDUClVSHZbBqUabB9jMqmmBhi6HO38ZBvszE3NsYB0QDjnCFmF9IQNkIzw/vAy+jSndrdvilnp1f2wnFlqGQR+mVrtALCA+bag7rBcRyAICIIqgV9m8l4DEALdSGFYC3Aq0bpnZehUAf0G+LBtp38LnAv8Zbj+RODhDo4/BviA42T+pXGlbaffBpzmOJkrOuzHquR3E7CnBiPm0pTWXggivFY7VYZtc7T5HtZYRZrYqp5MSxMzZ5ZD+NE9U6OdiCEzExw2CKdv7r3rtEIxD4QQgI8IPAT1FE8CEbiAkEogqE7uH3h5AlFtasMH4YOmo2nzH2W1vlU10EwMc7BrReS6SacK6PXAp5CK5zWOk9kdrn8aHZjggOcBZ7dY/w3gbR32oef04m2v5MPPs/LzY4bAWCbXSIDsS6cymDNH26GiNJ8ZunQuqGdyDgK475Cc27l9rzzpix8Jmwf6OhT0au6K8EzrJWtl9OPWxnDdfa1TPwQupcLuhhWCwC9J5dHINIdIAeho9cLRGmiaha43y1ND6+LoYiXQaUG6ncClLdb/TYfnKSLT+dzXtP7xLONAVMta+ofMgRr8QVYhXl5zP0Lqgq7JoF4CwQ/g5/fK7NN+MLMswUVHL31Otg6ox/m8pm7qK54DEROOW7cmlQ/05n7oBb5fwq3tlWYpETRtFfheHt1ItDQ1GUaAEO60dbqRXNWKQ4gAt7obzxsHIfCDMsApTPkFLJiOr6iw8NszgeOAzzhOJmvb6eOA8bAMQjsc4BO2nT4X+EO47kLg5cxSWqGf9MoNWwj4Yx7uKUNUhwv6/9ydRCBLP0xMFLpThOwX90nHggPFqWSgdUbicPphcOTQ8nUmGI7PrCO0xljstdA4ShCBOzkhHq4AhJwsF9M9s6aOdxeUoDEQPggPhCAQVXwvh6aZaHqEVoYrwxqd1VxVKpUYHu5/Vov5EAQunnsQ35NvukL4lAu3UynfByKQnm/IoGmBj+/lmpwTZnrFAX8FfHuxfes0Duh44KdAChhGms6ywGvD5Ve2Oz70mNsOvAl4Ybj6LuDljpP5+vy7vbTU3bDHxrJL6oad9+FHYYjuxUMQX0a5KgOgq925scFjMxmBZ50GG5LSzNYYPzMHi85QsFCWwahssYjwIT+5HHhoeutHQBC4CPmmO7XOL+B7OpoWAc1gMktXqBQCUcNzs8B0c1S5uA3fy88YOUxNiDcsi/qKFteDpqEtKCZemxzNaJqBYQ71fMQSBNVJBRz4Jbza/gaFLBDCpVbdS6V0F26tMeHMlGPBlO4VTdub1jUoaUFA4C/+njDMESLRwwAdgU+leNeiRz/Q+Qjoo8hccK9FKp463wFah4s3ESqaZads2rHUJoddVfh1aH67ZHRJTzVvhJAjoK7J4OJjZT60gShsSklz1gKYM0OBYhIRePhBkcAr4PkT+O4EjQ+qwsQNaJoxbcJbxoE0mJka9IDr1igVrHDl1CSH1nCsppkzTFeBX0Q3omjayoih8dwsQTDLzICoUatOjYQDv4TnjiNElSCoIYQr3Z2FSxBUEEEVt7pPjjY6i0RZAnRMax2mNUw9qY0V3Uxq4Fw0IxYqdW1SwRvmYDg6rKOhaZHJUaHvF3jonjd9rhs96/QpcBFwoeNkfNtON67fARw+18G2nX4cgONkftVivXCczK877EdPWcq8VxUffpeT6XeSOly0zCxPdSeErsng/K3daaeXBGLxSajmwQU/CyORQz065VElH/ZBUAndaX10I4UWPvrlW26NyTfloIbvT+B7ocLRdHQtMsMTyrSGw1HR1DyI3Kq3VBYLzUurG8vLg7Fa2YHvjtelFa6Vf8uF25kY/2Xb4+eab2iNgR4+1DU9ghXZiKZNmfI0zcCMbCAWP5Zo7OgmJa417KfNWNfyc8PvbBgptFaVgpcB83kNbeUXuBUZCzQXDvDeFusHkXNA58yjHz1jKQvSPVSB34aSe/yInANaLtQCqAlpb51TBoGA3TncTcnZc7R53sLKWPcbP+ipAqoT+BXKpW0y4K/RmypUJvXPmqgroLqZRi5rmoGmW6Gpqb1pU5qiOvuS2WyJ4eHEPL9NdwiCWoPZqq46ggaT3XRFIkQwbRkEbu0ghdwfqJTunuNsBlakdSp6PxAYeoNC0COY1jp0PYamR9A1C02PoGkRdD2KpkcxzAHiiZOXnSJeDnSqgH6MDCR9Rbgswmqo7wG+38HxJwG3tFh/e7htWbJUBenyHtxfht9k5fJyM79lPflIilhy7rb9zmXIlbE8H755K+zI8oMLDZ7+6MdMvoWtSOUDYWDf0iKEQAQVBAGXvfjxeO4YxfzN4YNteMnP3wvKxbvJZ387adabNFvhh84HQRhHEyCEPxUP04A8tju/h67HiSaOBwhHkVMKRTfiDK17KtHYkS2P7acSXo10qoDeDPzCttP3IKugfg04HtjHlFNBO8rI8gsPNq3fgizrsKxYSi84X8CtBdjnwgMVGDLggmWSeLSOBlw8LE1wRbfNjq4vSyUkI/DQOOzIUorCT881ePoyDHqbN36zi+7ikR5JY4BP4JXxvOzUw1UzMIwE6PGuT5ILEch5DSEwjTKaJvDccaaPGARC+AR+gWplRz3lyiSViotf9fC97ORIREwqEF/mCvOL4cgk4OxTXDQEex7qRhZtXZqtJj3XtOnKY5ppKtymTS0D6EaCePJUUkMXSjkr+k6ncUC7bTt9FnAZMqBUB64Bvuw4mXK7Y0N+BHzAttPPcpzMOIBtp0eB99NZIGtPqXvBAV33gpvwYMKXma8B/mKk46TJPaEWQNKY8siLtDO/ZcuABlVfptABfniBQSm2CpQPQCD49ODjocM3XulC7BP4RTlf4+Xx/AIg5ESvRjhBHUgFo5lLMjkfBDVqlYepVrZTLW+nVt2FW909OQI5+xS53457vzLvtju52euY9bhLPc7Q6JOJROV0sR6ardDMSROg/KtJ92jNCE2NjXMfhnRw6DNq9NNdOv5FQ0XzBabywM2HvwN+DWy37fSt4bpHAPuBFy2gvZ6Qzxe67ohwwJXzP9/cL7X4Xy6DqqeNlAM4rMFmNqsM/EDG9Pz2AbhjL967L8EcSfJ84PlNu3bTPbpthoKaO69JzY4wjRkyEMKbZioSwqdW3TMZZwGApqFr1uTEc3ggmpFa0OimWtlBceIGfG8C388jglrYZIDvjRMElXDZn9XtVtfjoBlUqx5CaMTjDaMJTQN06bCgx4lEt2BaI9OOr9U8otEEpjUces7pU67RmvS0MszBcLnuMq2FcyLLcxJ8vhQKVVLLoLrtamE+gahHIEtob6Rp1tJxMh9pd6zjZPbYdvpM4HLgrHD1fwBfcZzMss2EsBQFuHZV4OM7ZaTEizfCScvshaoWwGjDM3NSBoGQVUbHCrLzQQC/e1Cmz9E1zJFkT9yjZ2Qo4FFT27p2FokgIKBCpTJBPFafvyhTrT48Y4JQ062552w6GBgK4ZM9+H+UCjc3uPO6+N58fK8MrOgmovFjiMaOJho7kkh0i4zuXwRq/kNmw1Z0j04DUS9Hjnw8ZFnu5kiotgoIIFQ0n11AH3vOUs0BlXy49iDcUYL1FrxmS9eaBuQz0QtT6OjztIIJAQVf6plk8wv6WAkOFOSoJ2pCRIPtObhtjzzZ8x/Rte/QT4QI8EVe/g2yuN4+qAxRKbmUrehkTi/DSHbNHFQp3Uetugevtp9S8Q45wvHGZ+ynaREGhh9DJLYFwxhA0+tv4RqmORwqFxnLYRgDqzo1jGL10Old9F5k4bl3Ok5mQa8Atp02keUXtgLTcpo4TuY/F9LmUlGfA/I8r6tzQDvK8O975ee/OxJSXbJKuAGMh55rMV0qOl2DmCFfuuP63Aop58GAAYdFINHQr0FPh/25sDqpCTUPXAG/fkDucNExsHWkdaPLjEC4NEfpgzSp+UGBWnAAISrISWwLgxRadIRhK8A0u/NjBUEVr3YAt7afQu4PFPM3ztjHMEdYv/kyzMiGMADQxDAH0PX+mn6U6UnJoNt0qoA2AZ9bhPI5GTmpfwzymeiH53aBKrCsFFAd1+1u1uNrD8pRxhlJeGKXntlCwCFXlnHYHJUDkpIvk5yOuVLQ+2qwMdJeCdUEnJSEofrXdX04WMDdk8WMRWQqnRselqUU6gxE4ZwuD+OWACECXP8AVf9hEK1SvMj/dC2OoYc/TCDA8EDX8GqdKyAhAtzafrzaflz3AG7tAL43IU1pfolq+f5pKWk0LUJy8Gx0Y4BE6hGY5iBmZBO6vvyc1z2ve4p4pbJSZTDp+NKwHATlme7uYSYMYHp6LDF9L7rkvdzp0/UHwAXAAws8z0eBG5HzP3vDv0PIEg/vWGCbS8ZSmOAmPLg+Lz9fOLi4SgPVQI5YNORz8rg4bGnI7J4w4Ki4/CcE3FuScUemLhWMoclRUyG89iwdEjoM6uE8T64iE4cK0G58GG4/MHUBRgzZgGnAk06ccnXqE16QJwiKAFPVHkWVQJTRkGljfFEEXAxtEE2fpb9BIH3O9fp9JSAmlUCl4hKLzVQIMnZFyOj57K8RQYVq+aHZ07iEWJFNmNY6ovHjGRh+NFZk/QK+ee+ZTQ5riYXKIAjchqA6QRDUGtzEaRF0ON8HRHj8NKXR0Kamh4plyl3diqxHN2aW2JBBtdFpfdDQw7yBk0ps+zw72JJZFZBtp5/XsPgTpBv1acBtyJHLJI6TuXaO85wHPM5xMkXbTgeA6TiZm2w7/ffAvyE94pYNS5GMdFcV7pDPSc7uMO5nf3XqetI0iGlSWYy7cHYKoobMoJBoY+7XNDgxCYdFYXcVHqpKpRXVYWtMXmI7SoITdA9td0m6VkcNqWh+eDexew/KodPRI7L09TGjfS/ZKoTPBT+XaWvKj7EAo54DIPxf5jcT4WVqaHE0bQ5vxpovs3KPJKSAPB8/KONVHqZW2U8xbyACl2L+BkqF28Jswa0NAoY5SiS6CdPaiBXZgGEOo+kWuhYhEtu6agJMVzNiRpmGqfXTt8kUSUJ48gGvaS3jZXUjhm5Mudtb0REMc7r7vYbeMpfePHrdYt2Ul2P9ham+fjEF6k4773ddiQpuNwL6Zot1b2+xTjB34mSNqbo/B5ABqPcAO5EBrcuSbhXgmjYK0aQJbi6qgRzJnDcoXaMLHmR9OZI6OQGHzbNrAyacZMLxCRkMa2oyry1ewHH5LFrJDYvFWfBwFn63HfZMIKIG2nPOgCOH27bfTffoaVmCw0zBgajKhI/4eMGUR5ipz94vbR5nFb5HSdxP7dBefG+CYv7GaW7VhVmd0DQMc5Ch0UuIxI7AimzAimzq+LwribUy+vH9EiKotXQdj0Q8Ar/YsEaXTiDmALpuhrWBjGnbF/uw7xayD/3vRyOz3qGOk+mmG83twJlIE971wFtsO+0jAz2bi9QtCbad3gR8Czl684HLHSezZ5Z9LwUuvfLKl3LmmYsfnFUCuLkgNfWpCekcMBcTHpyWlEooYcA6C45aRB/qLtJXXXklRtWDbAmyFQgEuhl6KfziXlkS2wvf8AaieM85FWvT3Fkou+EeHYgKVW9PqGAac/TXP9Vv5vk/CCvedmre7hnrA2q43h6KtVsJ9k8Ps9S0KGZkHbo+hGHIEgSRyGYGRh6HaQ6BZqwpbzPDWBvfVQRV4smTZ8RBAURdF8taG4q4F/QqtPifgfp7/zuQ+eN+ARyks1Q+3eAg8BjHyQS2nb4Smdfufa127LYJLu/Db8KX6XM6ML95Qj52N3Wx/tkfnyjfyq+6/6A0N+kaxE2pcG7dA7tyU4pnXQJO2ghnHk4+qLCUqeqECAhECdcfww32o2HKuZoFvjEKEeAGBwiCkiyuJQoUazdTrN0857GR6JHEU6eh6zESqTOIxI5C0zQV/xJSLFbXjBxmi5nK54vdKdCoADqPA/rHWTYJoIIcxfzfbGl5HCfzo4bPDwCnhKl4xh0n05MiGU0efAPAHbPt220nhLsL8MusVCrP7mC+ecyFk+MQ6cILpxv4WLrRMlO1mythfffOqRVHj0jHguGGrL35yuI7QRihL6r4QZlAFAhEGYFcB0GD4un0SwekEvsZL++UbtSiiOvvo+rtRLRw0NEwSVpnytGTF0hzYyDQMDG1YRLDZxHZeGxXvqti5RIELrqR6LvL+1qh0xHQC5DxO0mgbsc4HCgi53SOBPbbdvpxoYKZkw7KeLfEttOvB64EzgC+6jiZKxu2jQKfBy5Bjnje5jiZrzRsPwv4DLKK6yVt+vZd4Lv5fKErI6DP7ZWjmicMwxFt5m7q3m0pA7Z2I3N7xcWKWbxmxw9abv701qfDYBTO2wonrJcF45qw5umGPlnT5olhpmdkNH/ZexAhqtRjbDTNRMPA0OaXmsYLcmTLP+WRJ/8Jy6ww3uKVx9SGMRgM84pFiGlHkkpdiBUMQ9WDdTFYn5yewb9NF1ai2+1SsFrkIIRPEFRbzoYEQYVIdPbaVUtdpHKt0ak0PwxcAVzpOJmdMJma5wvAl5Amta8jMyI8p/vdnMZupOnsKUDzY/oTSP/0TUhX7+/bdvoWx8ncAeA4mZuBC2w7/ULgbcBr2p0olVp8ksiKDz8NA9tfNEfet5wn54g2RaSn84Iou1B1oViT7tSnbm6//9WPars5FetcE4oGt08/KFD19uAHsiiarscw2jgM1I+vetvxRC6sQe8TiAoV70Gq3sN4wTj14mmWCZXaABsHHomhp9BFHMsfIqofgREfgaGYjGWqeVKxJkJ7Zs2fKgHeoYyTyS7aQlcwi5GDLMk9Rxihps1dA2VeplnR8iOagWWOMr2OkMQwBjEjsxudu/FMUEzRqQJ6F/DsuvIBcJzMztCN+tuOk/lP207/A/C/S9HJRuou37adPhc4or7ettNJZB7M0x0nUwB+a9vp7wAvBd5q2+mI42TqtpkcU155M+imCW5bCfbUZHqbs9rM/4hw3mdLtMPs2PVaNXUTWdSEiYpMEKohG+lCOYHxQp7RgcE23aji+RMEooQnprzGSu490rxldFZGs+rtZKz8PcruXMXCNBL6aVy/7XhKlXW8+NyTpx5aWwYgbrV/SEXm/xafy5VXxdyH7xUmY6XaPug1IQN2ten7ZHNVhgfDIfy0QnmNx7ZYJwJMc5RI7IiG/dr8RrP1rQueZBpGmBx1Yfbt8fGcmgPqIvPJhNDKeBRFJicFWRuon3fpiYDnOJltDetuAR4Xfj7LttMfQnrAVYC/mq2hbjoh1MsunJmSrs+zURUwaHSofHJl2J1regCEb/TJCBwqwp8ehrv3w0efs+C+N1NXNm6wHyHcsBKnT92spmtTb8im3l7x1M1zFe9BcpVfUXbvAkAnTjx2Eppphen6zdA54CQsa6P0PBMapVv/KBs6YijU3tqClMtawffy6EYC09rQsFZD0/XJ0t6N61tRqU4QTw6C1nhM475ihk4KK/GE7snLywVY0X86VUA/BT5j2+mrkRkNQJbR/hQySBXknExzwblekgImmtblkA4HOE7memQ277aE3/FqgEsueTIXXCBHQPF4DNM0yOdlDIBlmaRSScbH5Vu/psHIyDATE/nJjLmDgwP88pAATE63ynhVHU3XqRZlG4ZlEUkkKOdyTHhweEyD4SFyuQn8cPQyNDRApVKlWpWDt6Sroe2ZoKD7YGhELYt4JEq2KP28o3fuI/mj+6H+EjvH9x3LTxC1IsQiEXJF6Upt6AaDiSiH8gfJFYvU/P2k4hUKlQKuC7oeJRWLEQio1OR3jVomkQbtmS/VGEhEyE6U8at7qGl3YJjjVINDeOIQQmsqGSAskvr5DMefRHlwncywbRqkUlGy2RKlKlD1GB6OUChUJw/zdPA8QaVcg5KMVTEMnWJR7mOaBslkhFwunCzSYHgoQT5fmZTxwECMWs2jWvXC3zqCrmuTbZQrNYZEfLINTdMYGoozka8QNLRRrXrUalNtaBqUSvJ3i0RMYjGLiQnZhq5rDA7GmZgoE4Sj2cHBOJWKO9lGIhFBCCiXZRumJYiYHhP5CmhgajoDAxFyE5XJAfHQYIRS2cN1Zb+SSYsgEFQqGrHERhJ6img0Qi6Xn5TP4OAA4+PZyYHHyMgQhUJxMhP6wEASz/MplvIYpkY8HsWyTCYm5G9oWSYDA6nJNur3Qj5fCNsIGBz0cV2PcrkSymf+99PQ0ADVao1KpRrKJ46u6xQKxVDGFslkYrINXdcYnut+SsbRNI1CQRpEotEI8XiMbFY+SgxDZ2hocLKNiYkJhocHKZcrk22kUgmEEBSL5ck2YrHopIzrbWSzucnfemRkiGKxRK3mhm0kCYKAUkm2EYtFF/w7Ncp4fr8TDA6mOvqduvUy0akCeiUyX9sfmQr/1pGluuujhDyy7s8MbDs924O/7kV3/0KdEhooAM22osGwXx3jOJlrkMX26v2bRvPwu3l5cHDKzlby4bbQQnbhhjhmOMefGJ5+TGJ4mEINjgwPHRqa/jWSyQTJWEya1w4WYCjBaFNMxujAIIyX4BcPyV6fvhntwrkjhxrNa6MDg2HetEMUa/cSjXpsjNSLgcUYSsx04YtFWsdEpAzBRPE31IIHKRi3AH59+qZBK+pEIltIxB/BUPJiDD0FcYuoNX0k02z+akwIaZoGpmnMCJJsPqZ5eWBg+oA+Ho+E9XFmHjMcDuyb2xhsaiORiJBITG8jEpl+i81oY3D6HFsiESEetybnwAAsS2ZhAIEV2UI8aaIbiclRSLwpyUNyQGbFlhm7w+wQmj4tg3fzdTvSVHiwuQaUZVkcffT0yfn5tmGa5ozg7vncTyCVTiIxXWZztdHyfkpO/x1GRyNNy63bqK9v1UY0Ot2Jp7mN4eHpVoFW80mxWPs2Ovmd5pJxN36nbtFpRdT9wFNtO30ScFK4+u5Gc5fjZH7RpolfMjOysHE5qM/XOE6myMLYBpi2nT7BcTL3huvOpI279VxMTORn3ADz4b4SPFyVaW9O6cA4mZzNglTzYcc4uJ5MANqc7+m+g3D/Ibhrn0xzcNw6eMpJoGm4wm/pgg3gCp/6YzsQNVz/IG4whggqGHoKTTMnRzJzEgQN/RIc8L5Jofqnyc2poUcRS5yIFamnpxlZVBDni1/Y3nmim+TzlRkKq1uIwMX38jPSr+hGYirgVtPQ9QTR2BHoRjfcIxfGYu+H1YCSQXeZl0+h42TuQabQmS/PADLIgNTQeM8FSE+0dyHfjR3g/wFvaNdQWNbBRKb/MWw7HUPO/RRtO30t8F7bTr8S6QX3bOCiBfQXWHzxqR/W53+SMofbbARCfqFY8z77wsFboSodCpJNbtLZMvz0XtjeMHg8aQM8+cRJZaC5waxzI1rNo+LtAOHjBePSG1mLYxjDk/v4weyODCIIcMu7qQV7cTlEybudR5yQwzBqFKoVNC3C6MbnEUueTDR25OwCWOb4XXDmaERWUg0QCAIvTyx5EoY5gMZUKerlOF+iirEpGXSbdslIP4aMoymGn2fFcTJvnOM87wPe5DiZnzWse8C20weADzhO5pwwNc+/MYcCQmZSeFfD8hXAe4B3A69DuobvBw4Br627YPcaL4Dfh05hcyUfrQUyV9u0Z062LIvAGbr0yY6Hb8MTFbjuIdhfgP15OY6MmXDekXDcehnf0kBzipwP14YJRCkcfwo0zUJDR9eac1jNRIiAsnsXZe8BAlGiXLsbT0wvnla3IOhGko2Hv4rEwBntv/wqJAiqBH6JaTNwDV5jmmah6SYaOtHEMSsmG7ZC0W3ajYDOgEkLTbunSCeZDE4FdrVYvyvcBjLL9hxBK+A4mXcjlU2rbWN0MQ5paGjhQ+2cD7d1mP26KuCw+i8hhMzRticsAlef6ylUpWfbLbunUuboGpy6CR5zjDTNdUAQFDCN9sWIhBDU/D0EoohmjHOotJeav4eatwtfTPfz0PUUseTxGOYwidQZ/PhnewkCk+c99y9WTZ60ZvObzIYctFgnZICjZhFLnIhuxMP8dUwb2XSrmmqvWcz9sFpQMugu7ZKRPqHV5wVyJ/APtp1+peNkqgC2nY4is2vXc8EciawVtGyoVmszJjw7ZVsJtldk9erT5ohdc8MREELIUc/+osxKbehy3fUPw3XbpxTPiRvg7C2wLjk1MmqBED6+KMraI+Evbeizx/QI4TFR/R25yq/xgkMt9zG0YQasc9FJEDOPJrr19Gk1dqo1OdJaicpHCEHgF2WszGTtFEG54hGfdHIQ0g05nJ+pKxhNM0AzsawBIrEjl2VBucWymPthtaBk0F169Sr2OmRF1F22nb49XHc68jXymeHyscAne9SfjqhUqgu+2H6TDZ3RktIJoR0CiBNIxXOgCAMRaY+refCje+CeA3LHE9bDBUfB5tnfwoTwcf0DYXnpGpN5ZsJfupWZzQsmGC//gGLtNgIRuohrQ1jGenw/Qco4jIi+GSt6OJaxcUq5DMWgqcBbL50DuoU0mZWBADOyAcMYknMwmnwBKFfzxBJS5ppmoBupValg5mIx98NqQcmgu3SsgGw7/SLgicjA02mPVMfJPKvdsY6T+aNtp49BztfUvei+AnwlzFqA42SWZVnuhfKrrPx73uwDDgBu/aYskxB/7KVS4dSVz/0H4SfboFCTTgTPPBWOXde2LSF8yu69+EFBujRrsYbJ7AMgBK5/AD/IU/V3UPEewguyuP4+OS8EWMZhjMafTsI6DU3TZSboWASOGG5f03sZIYQ/I+2LEC4icGcEXQoCNCNONH4chhHFMGf+YKZlYkWGl7LLCsWapNNs2Bngb5AlFHbT2bzPNEL36s/M97h+stA3naoPfwinSi4enr5N8wKsbInaaHLaAz3q+jJnmRDw++3yH8jRzlNOgg0zK3oGwsX38/iUQbgEoowvypgNXmyTCMFTc9fycLV1qpu4eSLrYs/CMg6Tj2g3AM+TyfbWJ5ed8pGVKd2Zk/0INC2CrkemrTeMIcz4+hYjQE3O1bRxwFBvvBIlByWDbtPpCOhlwGWOk2lVJbUjwuSlF9N6BPWRhba7lOj6wuYxfj8BYx5sjsCJTderUa6S2JdD933cgamNRiI06dzwsFQ+GvDYY6V3WwuXXD8oUna3IYQfzkdoaJqO2TzHEwQQCE72buOE6t1oWES0zUSMw4gaW7GMTegiQkQ/DM0wpbu3rstaQVET3QvazjP1CqlwZFR9EFRA6BhGnFjieExzJNQ1S+PCvNDrYLWh5KBk0G06VUA6cPNCT2Lb6cuR7tEesnxD4whKILNoLzsKhYUVn/ruQfn34uGZusMqVPFiFlaugpVrqLWjaXDzLvhVWM3iGafAybKIXD1vGkDN24sXjCHw0LUERlPdkkBUKJfvxvOy1MRuamIPPkWeHJaxXr/5CgZGHt1xYsditsRwj2JSfL+ACLxZtmoYRgL0GFFrA1Z0U8+8yRZ6Haw2lByUDLpNp3fwNcj5m3cv8DzvRZZ0eGdTYbhVST3+56Lm6YRAYOar+AmLY44dIBa1OPGcq6a2bx2BZ52OV6xgHiojhMD191P1d0uvrFAPGFp9Qnz629hE9Q8cKn4TwcyHuI/OHfGzePY8lE83qZvMCN2V63M0gmAyKNM0BommjkZrMJ9p1NPKqESjCsVqY65A1Do6cLltp58M3Aq4jft2EIi6CfjcSlE+9XIMl19+GaOjZ8/7+PvDgc2xzea3qouGzAIYi1p88YtfbHn8VVddRW3fQ5OlpQ19cJqrcyPC9xCaS83fy8HiNwCfaOw4IvEjsKwNcnLdHORt+TyBZvCceSofy1r4g18IQRCUEUENTdPQjYEwALMhJb6mSROipmOaQ8syRiYyS767tYaSg5JBt5krELWRm8O/Jzet78Qh4QfI1DsdVUvtN/VyDEKIeZdjyHtw0AVLk4XlGomMlwgMnUIHarjqPYSuJ1sHjdY8arU9jHs/oRjcTmNQ5MDgxWw44uUzDgkKs5Y/aktzcs35EPh5dCOFGd2Maa0PHQNWHs1JJ9cqSg5KBt2mo0DULvAT4AO2nT4NmfGgeQR1bRfP1TUWUnzqvjDr/5bo9KqmesXFmqjgJiNUZ5vmaMDQh6dPptc88ALc4BBl/X4Ouf8rTVpoaFo0jL4/gXWHv3he/Z2LxRRjE8InGtuKYc704FtJqCJkEiUHJYNu0yt7R939+u0ttglkYtFVwd1h+p2tTcmTo4cKBJZOQWicdNtD8Ij26XAalY8olfGsAuP6TyhUrp9cnxq6kNGNf4lptW+rHwjhSXdoQ5UwVigUremJAnKczIr0XdQXEPtyTzgC2trgnGYUKlj5Ct5AjJHb9rDhjw/C5WfN2Zbr7idb+hkF/2ZEWIRL0yLEEseTHDyfgeHHLHnW5E7aDwIX8KeVUg6CMlb0iGWZ1Xm+LOQ6WI0oOSgZdJvlN+O7jGguINUJ28KpliPrI6BAEDuQJ29Z+IfKnPO7bbMe28j+iS9T8G6iXpTMMEeIRA9n3eYXE4kePu9+LZShIelJIYRAzjUJRFALiy8LmV1Aj6AbCaTPmizXbFhDRCIb27S8cljIdbAaUXJQMug2PVNAtp1+BvAWZPZrgUxC+gHHyfygV32YL7ncxIxqinNxf9MIyCxV0as+lViUs257CD0QcOomvJrLVVdd1bKNank/Be9PgEZq6CKG1z+tZ0pHJuTM1as1kM/XGBiIyAScYb0aw0ih6TL4VdejWJENy9J7rVss5DpYjSg5KBl0m548NcICcZ8Evgz8R7j6scC3bDv9WsfJfKEX/eiUuhv2i170Ai688Px5HftA6IJdHwFFxkpULIORYpnE3ftkeMujjpqs0/PFn30bgBc99jQ87yC78h8hoEwydS6jm56HFd3UnS81B0J4+H4RhIcZ2UQ0diQaBjU/S2podEVmt+4W3S5It1JRclAy6Da9em19C/Bmx8l8vGHd5207fSPwVmSWhGVD3Q17bCw7LzfsQoML9kZDBp2a5RrZSIRH/uYOWfr01E0EwxZBkCMIqpPHamWXQ+63CCiTSJ7JxiNfvSQPfRG4MjGnXJL/hEAIl0hsK7oRx2wol61p5ppWPgqFYunolQLaCvxfi/U/BD7Uoz7Mm/kWn9ols+WwwRKkdo9jlGr4UZMtN2wn9vA4xC1qjxqmVrtVzqk0TNAXtTsp+Xei6VHWH/6yJXvo+34ey9oUJhfVqKfls6yRlpmgVQEuJYM6Sg5KBt2mVwpoB/Bk4L6m9ZcAD/WoD/OmUqnOK/BsZ2h+26wHGKUqfipGfMcYR/75IQRQfcogbvxhDG2oIbPBDrZsvJF95VsAGFn/LExruKvfoxErsplY4tiO95+vDFYjSgYSJQclg27TKwX0IeDfbDt9NvD7cN2jgZcCb+hRH+ZNtVqbnwIKLWpbvBr+YASjXGPLT+9EA6oXRPC36hiMTLomCyHYuvmPbF5/O6AxvP6ZDK27ZNH9lvVwZJBqPc9anfnOKc1XBqsRJQOJkoOSQbfpiXHfcTKfAV4EnIJURh9CpvR5oeNkrulFH+aDbacvte30Ndu2deYyXefhCZniYJMlwNDZ+Lv7sCoelS0WwQUb0LXoNOUzVv5fNq+/nSDQ2XTEXzO68bmLNr15Xo7AL6PpMjuCZsQxjSnXUUMFhioUimWCJsS8a8utGarVqohGo3PvCFDzePVNNa4pJ/j3iYd4wW33kNhfJDAg95LNxEblYDMQVXKVX1Ko/AlXHCQIdO57+Ik89WmLT6EjhMD3ciQHz56Rd+01O6S3+6e3Pn1ebVarVTqWwSpFyUCi5KBk0EBXInJ7Hrxh2+kYMwvSLSxT5hLTcRS/ELBrgp1BkhPzeV7yq5uxvAChwY7zh9kQKp+at4e9hWvwgnEAdD3JtocuIps/qiv9FaKGYaRaJv2cr+KpsxoyGSwWJQOJkoOSQbfpVRzQUcDHgCcArWxAyzIXXKFQYnS0gwzO+SqUauz0BvjSDTdgeQG540x2XLQRPa6zUQiK7m0cKP43gSgRMbYwaj2D+DHn8odbr5+7/Q4J/ArR+NFdaw/mIYNVjJKBRMlByaDb9GoE9CUghnQ42EdnJRxWBoGAvXnKEZPHPLCT87JZKkmN3Y9dT0HX2BT8ll0T11HzdwGQsE5nY+Ry9HgcFllkLQiqMqZnsoqowGzhSq1QKBTLkV4poEcC5zlO5q4enW9R1DMhXHHFSxgdfWT7nbNl8HzudQ3efufdAOw/f5iqaZD0vkvZ/TkAupZgJP40BqOPRqv4EO1M9EIIhKjJCqIiQIgpZWMYKQxrPbqeQNct0Ax0I962vfkSjaq3PSUDiZKDkkG36ZUCugXYAKwIBVTPhBAEQftMCF4A+/MUXMER376Z0UqFW4YHMY8rotX+E0PcA+hsSL6IZORsdC2spig8sNqLXpawrhF4JQxzCMMaAAxMcyD0cDPR9aWfDI3HY3PvtMpRMpAoOSgZdJteKaCrgY+FZb5vZ2ZBuh096se8yGYn2hefKlQJvADth3czeqjA/ckk773wBN5beyOmOICmxdiQvIxU5MyZx5rSD+OPT5RxOXUfOCE8fDcPuomhx4klT8GK9K/ez5wyWAMoGUiUHJQMuk2vFJAObAK+xfT5H41lWJCu02SkYrzMwfvG2bg7y0TE5KKLL+YfRz4B4gBom9k69AYMvZXPhTapgKa1JwS+N0E0cTxWZL3KwaZQKFY1vVJA/wHsRyYlXfZOCHUTXC43MbsJzvXZv6fIyHUPAPC3p5/CG4Y+wuON7yPQGIy/qLXy8QOIaGEutimk8sliRTYRiS6fOjqGoZSgkoFEyUHJoNv0SgGdDJzlOJn5pRboM+3qfhw6VMW47j4iZZcb1g0THP8Hnmt9HzCpGs9jMHJM6wNrPgzHMeLHENGjfDp58oxdXOFjLdJDrluo2idKBnWUHJQMuk2vFND1wDFAXxWQbacvAz7mOJkNc+x3KXDpZZe9iPPPP3fGdt8LGLthFyc8lMU1Na489zQ+G/kAAEHkrxDaqURne1ESApIWET06mZ2gmYUGjS4FqgCXkkEdJQclg27TKwX0KeCjtp3+MHAbM50QblrqDth22gBeADw8175z1QPav7fEurtlM58/8RieMvwNBrU86MfhaaeQaFY+QoDnS8NjxIDIyqkeqgpwKRnUUXJQMug2vXoSfjX82yrxaK+cEC4DvgH87Vw7tnNCCHzBwfsPcfrDOXwNfnhcjvdZX0WgoVmX4qIx2vhtggDKLsQjgIDBGIFf7ub3UigUihVJrxTQLBMi88O2068HrgTOAL7qOJkrG7aNAp9H1hg6CLzNcTJfCbcZwAuB59CBAmoXBzRRcBm59yE0AT88NsLfD/0/dE2A9TQ042gCD2KNCqjqw7okDMsAUc+bQNNWTjDb8LAyNygZSJQclAy6TU8UkONkulV0bjfwPuApQHPI/yeAGtLd+yzg+7advsVxMncAVwBfd5xMYNvpjk9WLldm1P44sDfPEfeO45s14ud9jWEtB/opYD55ch+r7uAmhBzfpWTAqOflMYwkscSJHfeh37SSwVpDyUCi5KBk0G16Nhlh22kTOB9ZnnvaEMBxMv/ZSRuOk7k2bOtc4IiGtpPA84HTHSdTAH5r2+nvIAvevRU4FXikbaevAE6w7fTHHCfzxjZ9bWmC8zyBefM9xMsBd5x/M5sjD4O2CaLTS2hHNKTZTQhEMoLQqgReDV2PE0+chKavnDkgVYBLyaCOkoOSQbfpVTbsk4HvIk1xGuCH53aBKtCRAmrDiYDX5OZ9C/A4AMfJvKWhLzfMoXyuRmZuYHw8y9hYFpApOAp7J4jeOYabmMA94Q9YQCV4HkFJA61CLBYjqNXIV12IGqQOH6Ts5fAmEmhGkqGhrbieoFDIkhxKzertVvU9irkCIGvQVypVqtUaAMlkHE3TKBRkBYtoNEI8HiObnQBknMLQ0CC53MTkhOnw8CDlcmWyjVQqgRCCYrE82UYsFiWXy09rI5vNkcvJdkdGhigWS9RqbthGkiAIKJVkG7FYlGg0MtmGaRoMDg4wPp6lXnJqZGSIQqGI68p8dgMDSTzPp1yuTMrYskwmJuR3tyyTgYHUZBuaBiMjw+Tzhck2BgdTuK43rQ3TNMjni5NtpFJJxsdzwFQbExN5PM+flHG1WqNSkSVtE4k4uq5TKMg2SqUyIyNDk23ousbw8NA0Gff7dwoCseS/U/1aWK6/UyRikUwmlvR3yuUmlv3v1Iv7qVtlKXpSkM620/8HZIFXAHuRJrIhpHfcOxwn85N5tvc+4Ij6HJBtpx8LfMNxMpsb9nkVcLnjZB6/0H7XajURiYSDtXyFXX+8ky3feYgHLv4e5aPuBuMstOiVk/sXfBjSA7ZqPv5hEYTuEo0fhRU5bNYfbKGF4npFrVZjUgZrFCUDiZKDkkEDXdFAvQrrPQ94n+NkikAAmKHr9d8DH+5C+wWgeXZwEMgvptFpyrlYY+LBQ+QPf4DyUXcjiID1rKnNPkQ12ISPSEnlkxh4BJHo4Su6iJWqmKtkUEfJQcmg2/RKAWlAverpAWBL+HkncHwX2t8GmLadPqFh3ZnAHYtptD6kBijksoxuL7Pn/J8BoFlPQ9NHJ7f7Ao6KQVQIRDzAsEYwjFZ54FYWjTJYqygZSJQclAy6Ta9mw29HKoQHkFkR3mLbaR94FXBfp42EjgwmMm7ICMt7e46TKdp2+lrgvbadfiXSxPds4KKu9D4QVB/YQ2TjzbgDOUpsJmFePLnZD2Ru0SgCdI3A8olHNrdpUKFQKBS9GgH9M1M2w3cgPeF+gYzZmdUhoAXvAMpIz7Yrws/vCLe9DumavR8Z+Pra0AV7wUwWn6p5BLft58AZfwAgYj0ZrSFXWxUYdV388gReooxuJDDMgcWcetmgCnApGdRRclAy6DY9cUJoRRg4Ou44mWVrVPV9XxiGgbh3H4Vr/4sdf/EtDvqHsT6VnuZ2XSq6HDEoSG00iQ+ejm4kVvS8TyO+72MYyyMxar9QMpAoOSgZNNCVB1zfAlIcJzPWr3N3Si6XZ3R0GO/67eSOugeAXcZj2VBXPkGAVqnhmxWiGwaJD56KYa78eZ9G6jJYyygZSJQclAy6zcqJiOwjwc6D5B8jp6qGrdPlSiHQK2UOJgIO23wcQyNHoutWH3upUCgUKwulgNpgGDq4PmXzToJIjfu94zkuPiiVT7nEWNxn8LBHcuzIKKvE4jYDVYBLyaCOkoOSQbdR0mzD0NAgPDxOadODAGzTLkTTQC/XyMZ84oc9gtNWsfIBVYALlAzqKDkoGXQbpYDakM3mCO7fT2nTTgBixrFoQYCvlymNHsXpIxswVrHyASmDtY6SgUTJQcmg2ygF1IYgELgP7aQ6fAgvsDjeWo9WKTOWTHDcyFFYa0B69dxVaxklA4mSg5JBt1FzQC1ozIZ9fPUuAHa4x3NM3MOniBh6FEfElcOBQqFQLIa+xQGtBITni71f+jvGTrue33rP42LrkYxFBjn+pHPZFO1373qDEGLVxDQtFCUDiZKDkkEDKyoZ6YqktPMQlfW7AIgYR+Hhom84ko1rKBi6WCzNvdMqR8lAouSgZNBtlAmuDd6eLJWRAwAcKdYxMWRw+MDAqvZ6a6Zer2Qto2QgUXJQMug2agTUhurYHoJIDddNcJhmUDMtRqKxfndLoVAoVgVKAbWhXHkYgInqFjTdRURGGTDXlshSqdWVWmghKBlIlByUDLrN2nqazhPP3QFANthCOSIYjo2u+rifZoIg6HcX+o6SgUTJQcmg2ygF1AbN2A3AhL6FStRiUzzV5x71nnqN+rWMkoFEyUHJoNsoJ4QW2Hb60qjQn3P1k/cB4Ec24Zlx1qn5H4VCoegaagTUAsfJfPf9nPeR6vAhAFJ6ioHUBmJrzf4GxGJrJOCpDUoGEiUHJYNuoxTQLPzsRV8rBJEq+WCA9abOloHRfnepL6gKkEoGdZQclAy6jVJAs/BZ7yVbv1i7jB94T8OKm4xE16b3Sy6X73cX+o6SgUTJQcmg26g5oFn4uvfcP/3g6HHu230PkUSciKFEpVAoFN1EPVVbYNvpS/8GLh1+4V9yfmScoYGt/e5S3zBNo99d6DtKBhIlByWDbqOSkbahVquJm+/4PceefB7r42vTBKdQKBQtUMlIl5rsRB7WbWQgkuh3V/rG+Hi2313oO0oGEiUHJYNuo0xwbdA1gw3rTyW6hkfdaoCsZFBHyUHJoNsoBdSCxoJ0F154fr+7o1AoFKsSNQfUBiGEWOvFp1QBLiWDOkoOSgYNqDmgpaZQKPa7C31HyUDJoI6Sg5JBt1EKqA2u6/W7C31HyUDJoI6Sg5JBt1FzQC1Qc0AKhUKx9Kg5oDa4rissy+p3N/qK67ooGSgZgJIDKBk0oOaAlhrP8/vdhb6jZKBkUEfJQcmg2ygF1ALbTl9q2+lrbrnl1n53pe+Uy5V+d6HvKBlIlByUDLqNMsG1YWwsK0ZHh/vdjb4yNpZFyUDJAJQcQMmgAWWCW2r+6Z/++dVIQa/Zf0oGSgZKDkoGzf9sO301XUApoPZ0RcgrHCUDJYM6Sg5KBnWUAlIoFArFykUpIIVCoVD0BaWA2nNNvzuwDFAyUDKoo+SgZFCnK3JQXnAKhUKh6AtqBKRQKBSKvqAUkEKhUCj6gkpG2gLbTo8CnwcuAQ4Cb3OczFf626ulxbbTvwQuBOrpfnc5TuakcNtLgPcD64GfAH/lOJmxfvSzm9h2+vXAlcAZwFcdJ3Nlw7YnAp8AtgJ/BK50nMxD4bYo8CngL4ES8EHHyXykp53vIrPJwbbTRwMPAo01CD7gOJl/CrevGjmE3+WTwJOAUeB+5H3/w3D7qr8e2slgqa4FpYBa8wmgBmwCzgK+b9vpWxwnc0dfe7X0vN5xMp9rXGHb6dOAzwDPAG5CTj5+Enhx77vXdXYD7wOeAsTrK207vR64Fngl8F3gn4CvIRU0wLuBE4CjgM3AL2w7fafjZP6vZz3vLi3l0MCw42Ra1SF4N6tHDibwMPA4YAfwdODrtp0+AyiwNq6HdjKo09VrQSmgJmw7nQSeD5zuOJkC8FvbTn8HeCnw1r52rj9cDnzXcTK/BrDt9DuBu2w7PeA4mXx/u7Y4HCdzLYBtp88FjmjY9DzgDsfJfCPc/m7goG2nT3aczN3Ay5FvwOPAuG2nP4scQay0Bw7QVg5zsWrk4DiZIvIhWud7tp1+EDgHWMcauB7mkMGNcxy+IBmoOaCZnAh4jpPZ1rDuFuC0PvWnl7zfttMHbTv9O9tOPz5cdxry+wPgOJn7kaPDE/vQv17R/J2LSHPEabadHgEOa9zO6r8+HrLt9E7bTn8xHB2y2uVg2+lNyGv8Dtbo9dAkgzpdvRaUAppJCphoWpcDBvrQl17yFuBYYAvSzPZd204fh5RHrmnf1S6Pdt851bDcvG21cRA4D2lWOQf5Hb8cblu1crDttIX8nv8RjnDW3PXQQgZLci0oE9xMCsBg07pBYEWbm+bCcTJ/bFj8D9tOX4a0Aa9FebT7zoWG5UrTtlVFaIK+IVzcFzor7LHt9ACrVA62ndaB/0KO8l8frl5T10MrGSzVtaBGQDPZBpi2nT6hYd2ZTB+GrgUEMvPtHcjvD4Btp48Fokg5rVaav3MSOA45DzAO7Gncztq5PupR6/pqlINtpzWk9+sm4PmOk3HDTWvmemgjg2a6ci2oEVATjpMp2nb6WuC9tp1+JdIL7tnARX3t2BJi2+lh4ALgV0g37BcBFwNvAizgOttOPxbpBfde4NqV7oAAYNtpE3kPGIBh2+kY8vt/C8jYdvr5wPeBfwRuDU0RAP8JvMO20zcgb9RXAVf1uv/doo0czgGywL3ACPAx4JeOk6mbWlaVHJBuxKcAT3KcTLlh/Vq6HlrKwLbTF7AE14JSQK15HfAFYD9wCHjtKnfBtpBuuCcDPnA38Jy6I4Ztp1+DtPeuA37Kyr25mnkH8K6G5SuA9zhO5t3hw+bjwJeQcR+NbufvQt6oDwFlZDzEivJ4aqKlHIB7gH8BNiLnRX8CXNaw36qRg22njwJeDVSBvbadrm96teNkvrwWrod2MgACluBaULngFAqFQtEX1ByQQqFQKPqCUkAKhUKh6AtKASkUCoWiLygFpFAoFIq+oBSQQqFQKPqCUkAKhUKh6AsqDkihWAJsO70d+LjjZD7U7760Isx8/SfgGMfJbO9zdxRrFKWAFCsW205vAHYhI7NryEjtUxwns6Of/VoqbDt9JVKppebat4vnPA54O7I440ZgL1JxfcRxMr9v2O8pwN8jE1ZayFRNXwD+zXEyQcN+rQIPb3GczFlL9R0UyxdlglOsZB6FfHgVgbOBsdWqfPpBOEq6CZlW/7XAqcClyNow/9aw3+uAH4TrLwr3+yQym8KXmcmrkOn76/+euGRfQrGsUSMgxUrmIuB34efHNHxui22nL0UW3joNmUTxK8gUPDXbTv8L8BTHyZzTdMzvgRscJ/NG206fB/wzUulFgFuBtONkrmtzTgG8wHEy32xYt50GM51tp9+MLOJ1HHI090Pg7xwnkw3rM32xoS2YShsUQVbpvBxZSvkO4B2Ok/lRw7meCnwUOBo5gvnUHDLSgH8HHgAe7TgZv2Hzrbad/lS43xGAgxzp/H3DPp+x7fQ+4Fu2nb62XswtJOs4mb2znPcfgVcgq2qOAz92nMzL2vVVsXJRIyDFisK201ttO5217XQWeDPw6vDzvwDPCbd9ss3xT0G+lX8cqYD+ClnH/l/CXb4EnG3b6ZMbjjkWOdr6UrhqAJmu/rHA+cDNwA9sO71ukV8vAP4m7NdLwrbrI43fh9tKTI0c6vNLX0SWUX4JcDrwH8h6TmeG/T8S+DYyf9dZYZsfnKMvZ4X9yDQpHwAcJ5MNP74AqYRntOc4mW8jk1e+ZI5zEfbz+cDfIXMxngA8E7i+k2MVKxM1AlKsNHYjH46DyPokFwBFpBJ4BrKWfWGWYwH+AflQ/WK4fL9tp98CfMm202nHydxp2+k/I0cT7wz3eQmwzXEy1wM4TubnjQ3advoNyDLuT2NKSc0bx8l8tGFxu22n/x74X9tOvzwcneUA0Th6COdoLgOObjA/fty2009CJpF8HdJ8tgN4o+NkBHC3badPRI6aZqNejuSuObp9IjDhOJnds2y/Czipad1/2Xb63xuWX+04mS8ji53tQY563LDPN6BYtSgFpFhROE7GQz6cXwj8yXEyt9p2+tHAPsfJ/LqDJs4Bzg+VTh0diCPNPnuQSuSvmVJAl9Mwl2Hb6Y3Ih/cTkKnnjfD4rYv5brad/gvgbch0+ENhu5GwX7M94M9G1m26syF7MciaTXVFeQrwh1D51JnVXBiizaPr881onAYaMyXvC/9+A1kC5EHbTv8o3Oc7jpOpzrN9xQpBKSDFisK203cg35QtQLftdAF5HZvh54ccJ9OuFr2OnBz/RottB8K/XwU+aNvpRyFT05/M9JHNfyAVjw1sD/f5GVJZzEa9wF8jVsP3OgpZa+azyHozh5DK5atztKuHbZ8HNBcPK8/cvWPqBQdPAf48x35Dtp3e4jiZXS22n8rMwmR7HSdzX/OOjpN52LbTJyGdEp4EfBh4l22nLwgdTRSrDKWAFCuNpyMf3D9Duv3eCPw3csL8/5j5EG7mJuDkVg/AOo6T2WPb6Z8jRz5V4DrHyTzQsMtjkOas7wPYdnoTck6mHQca92lxzLlIRWPX51xsO/3MpjZqyFFRI39GKrbNjpP5xSznvgt4vm2ntYZR0IVz9Pdm4E4gbdvprzXPA9l2ejicB/om8AHkqOZvmvZ5LnA80o27IxwnU0Eq4u/bdvr/Id2+Hw38uNM2FCsHpYAUKwrHyTxk2+nNyBHI/yLf/k8D/sdxMns6aOK9wPdsO/0Q8HVk5c/TgfObvLi+hHwDryE93hrZBlxh2+k/AknkBHxtjvP+HPjr0JvORzo9VBq234sczfxNWJH3Qpoe6MjRVsy2009GKp6S42S22Xb6y8C/23b6b5EKdhR4PPCA42SuBT4N/C3w0dBB4wzgNe066zgZYdvpq5AFCH9r2+l/RiqyBHKu64XAueGo5W+Bf7XtdA05OiwBTw7l8rUmD7hZCeOcTGTBtwKyMq8bykaxClFecIqVyOOR8z8VpKfYzg6VD6Fr8jOQ8zfXh//eipzwbuRa5MN2A/C1pm1/BaSYGn19Aakc2vG3SJfmXyJHDZ9DVtyt9+tW5PzHm5Ejj1ciPcIa+/57pDL5KnJEVVeYVyE94T6IrGb7PWRJ9YfC43YAzwOeCtyCNB2+dY7+EjpdnBO2+WmkAvoeUuavb9jv35Aea+cBfwj3ez2ySmZHHnAhWaQL9m+A25GOHc9znMyD82hDsYJQFVEVCoVC0RfUCEihUCgUfUEpIIVCoVD0BaWAFAqFQtEXlAJSKBQKRV9QCkihUCgUfUEpIIVCoVD0BaWAFAqFQtEXlAJSKBQKRV9QCkihUCgUfeH/A911Igu58jaIAAAAAElFTkSuQmCC\n", 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" ] @@ -560,13 +561,85 @@ "plt.yscale(\"log\")\n", "plt.gca().invert_yaxis()\n", "plt.tight_layout()\n", - "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n", + "#plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n", "plt.savefig(\"search_efficiency_rank.pdf\")#, bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", - "id": "alien-director", + "id": "under-orange", + "metadata": {}, + "source": [ + "print stats to report in paper" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "exciting-cutting", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# evals by BO req'd to reach top COF in all runs: 174\n" + ] + } + ], + "source": [ + "print(\"# evals by BO req'd to reach top COF in all runs:\", np.argmax(y_max_mu_BO == 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "clean-anaheim", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_max_mu_BO[np.argmax(y_max_mu_BO == 1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "imposed-contamination", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "after 240 iterations, top-ranked COF found by RF: 53.13\n", + "after 240 iterations, top-ranked COF found by RF: 10.67\n", + "after 250 iterations, top-ranked COF found by CMA-ES: 12.92\n", + "after 250 iterations, top-ranked COF found by rs: 308.26\n" + ] + } + ], + "source": [ + "print(\"after \", rf_res['nb_evals_budgets'][-1], \"iterations, top-ranked COF found by RF:\", y_max_mu_rf[-1])\n", + "print(\"after \", rf_div_res['nb_evals_budgets'][-1], \"iterations, top-ranked COF found by RF:\", y_max_mu_rf_div[-1])\n", + "\n", + "print(\"after \", np.size(y_max_mu_es), \"iterations, top-ranked COF found by CMA-ES:\", y_max_mu_es[-1])\n", + "print(\"after \", np.size(y_max_mu_rs), \"iterations, top-ranked COF found by rs:\", y_max_mu_rs[-1])" + ] + }, + { + "cell_type": "markdown", + "id": "executed-leisure", "metadata": {}, "source": [ "### fraction of top 100 COFs recovered" @@ -575,7 +648,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "established-carpet", + "id": "going-storm", "metadata": {}, "outputs": [ { @@ -595,7 +668,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "respective-forge", + "id": "handed-package", "metadata": {}, "outputs": [], "source": [ @@ -610,7 +683,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "welcome-hostel", + "id": "musical-argument", "metadata": {}, "outputs": [ { @@ -649,7 +722,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "descending-fields", + "id": "continuing-tuition", "metadata": {}, "outputs": [], "source": [ @@ -674,7 +747,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "stretch-fleece", + "id": "chemical-seeking", "metadata": {}, "outputs": [ { @@ -721,6 +794,33 @@ "plt.tight_layout()\n", "plt.savefig(\"search_efficiency_top100.pdf\", format=\"pdf\")" ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "acceptable-motorcycle", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "after 240 iterations, fraction top 100 COFs found by RF: 0.0691\n", + "after 240 iterations, fraction top 100 COFs found by RF (div): 0.1436\n", + "after 250 iterations, fraction top 100 COFs found by BO: 0.36119999999999997\n", + "after 250 iterations, fraction top 100 COFs found by CMA-ES: 0.1107\n", + "after 250 iterations, fraction top 100 COFs found by RS: 0.0025\n" + ] + } + ], + "source": [ + "print(\"after \", rf_res['nb_evals_budgets'][-1], \"iterations, fraction top 100 COFs found by RF:\", y_top100_mu_rf[-1])\n", + "print(\"after \", rf_div_res['nb_evals_budgets'][-1], \"iterations, fraction top 100 COFs found by RF (div):\", y_top100_mu_rf_div[-1])\n", + "\n", + "print(\"after \", np.size(y_top100_mu_BO), \"iterations, fraction top 100 COFs found by BO:\", y_top100_mu_BO[-1])\n", + "print(\"after \", np.size(y_top100_mu_es), \"iterations, fraction top 100 COFs found by CMA-ES:\", y_top100_mu_es[-1])\n", + "print(\"after \", np.size(y_top100_mu_rs), \"iterations, fraction top 100 COFs found by RS:\", y_top100_mu_rs[-1])" + ] } ], "metadata": { From 476b1a7cf83bfd3385a2d370f63f274a52c37219 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Sat, 3 Jul 2021 17:15:00 -0700 Subject: [PATCH 18/29] include 250 in rf --- new/random_forest_run.ipynb | 2639 ++++++++++++++++++++--------------- new/viz.ipynb | 110 +- 2 files changed, 1575 insertions(+), 1174 deletions(-) diff --git a/new/random_forest_run.ipynb b/new/random_forest_run.ipynb index ddf9835..61ac3c3 100644 --- a/new/random_forest_run.ipynb +++ b/new/random_forest_run.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "pending-reservoir", + "id": "looking-release", "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "eight-likelihood", + "id": "structured-lemon", "metadata": {}, "outputs": [ { @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "foster-brass", + "id": "invisible-commodity", "metadata": {}, "outputs": [], "source": [ @@ -74,10 +74,18 @@ " return np.array(ids_train), np.array(ids_test)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecological-webmaster", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 4, - "id": "paperback-revolution", + "id": "promotional-channels", "metadata": {}, "outputs": [], "source": [ @@ -87,7 +95,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "mounted-density", + "id": "continental-occasions", "metadata": {}, "outputs": [], "source": [ @@ -133,179 +141,179 @@ { "cell_type": "code", "execution_count": 6, - "id": "automatic-replication", + "id": "funny-melissa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "eval budgets: [20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240]\n", + "eval budgets: [20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 250]\n", "budget for evals: 20\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 159.39879764100002\n", + "\tmax y acquired = 168.05583815399999\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 175.471266044\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.120429293\n", + "\tmax y acquired = 171.494803798\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 158.40819035299998\n", + "\tmax y acquired = 166.6328691\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.964548393\n", + "\tmax y acquired = 172.874325845\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 171.186997696\n", + "\tmax y acquired = 176.97717943400002\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.73698836399998\n", + "\tmax y acquired = 171.155147188\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 146.373534281\n", + "\tmax y acquired = 179.87305674400002\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.690277919\n", + "\tmax y acquired = 190.005803698\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 156.867337628\n", + "\tmax y acquired = 170.65544931600002\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 172.95669094599998\n", + "\tmax y acquired = 164.602426982\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 181.033618038\n", + "\tmax y acquired = 180.853194423\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 146.373534281\n", + "\tmax y acquired = 182.843902245\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 159.76088175799998\n", + "\tmax y acquired = 161.61358860200002\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.207357801\n", + "\tmax y acquired = 185.14487359400002\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 169.059493776\n", + "\tmax y acquired = 172.917360884\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.15976674\n", + "\tmax y acquired = 134.396735683\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 159.790178937\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.85869594599998\n", + "\tmax y acquired = 177.29642561\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 185.03190212400003\n", + "\tmax y acquired = 173.59215796799998\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 154.048838695\n", + "\tmax y acquired = 172.95669094599998\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.60896076400002\n", + "\tmax y acquired = 176.33658526\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 160.74528706200002\n", + "\tmax y acquired = 165.270546096\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 172.23118901799998\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 186.116016225\n", + "\tmax y acquired = 180.36987107599998\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 154.272072388\n", + "\tmax y acquired = 172.95669094599998\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.169424843\n", + "\tmax y acquired = 160.604816103\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 177.71587614\n", + "\tmax y acquired = 173.817499665\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 158.258882165\n", + "\tmax y acquired = 182.49392183900002\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 144.903301149\n", + "\tmax y acquired = 178.538299495\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 167.987216544\n", + "\tmax y acquired = 173.356951765\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.805857941\n", + "\tmax y acquired = 155.503906304\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 173.103734578\n", + "\tmax y acquired = 144.693187552\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.97717943400002\n", + "\tmax y acquired = 162.805857941\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.61358860200002\n", + "\tmax y acquired = 163.178770291\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 178.742181407\n", + "\tmax y acquired = 167.83151530700002\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 185.353421999\n", + "\tmax y acquired = 147.504269974\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 156.408222469\n", + "\tmax y acquired = 146.33274175\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 182.914858305\n", + "\tmax y acquired = 177.76671742\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 152.323996663\n", + "\tmax y acquired = 140.510211632\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 163.27718761100002\n", + "\tmax y acquired = 179.81280954099998\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", @@ -313,264 +321,264 @@ "\trun 42\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.91526314\n", + "\tmax y acquired = 139.198990162\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 159.08417302799998\n", + "\tmax y acquired = 162.420647939\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.805857941\n", + "\tmax y acquired = 167.242532504\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 154.207955664\n", + "\tmax y acquired = 158.115999441\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 159.738065448\n", + "\tmax y acquired = 180.996230494\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.61358860200002\n", + "\tmax y acquired = 216.894110699\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 199.90463220799998\n", + "\tmax y acquired = 187.01688480400003\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.114479152\n", + "\tmax y acquired = 165.381970925\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 183.618908037\n", + "\tmax y acquired = 159.08417302799998\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.22595719\n", + "\tmax y acquired = 198.792072623\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 192.340730027\n", + "\tmax y acquired = 173.817499665\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 157.529651595\n", + "\tmax y acquired = 154.399212648\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.372242861\n", + "\tmax y acquired = 185.22656223799999\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 174.67727233\n", + "\tmax y acquired = 162.805857941\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 145.231871343\n", + "\tmax y acquired = 140.705200351\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 170.874711129\n", + "\tmax y acquired = 176.690277919\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.19628339599998\n", + "\tmax y acquired = 162.525608684\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 184.403510347\n", + "\tmax y acquired = 161.250331381\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 166.286404403\n", + "\tmax y acquired = 164.102328521\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 183.77337184599997\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.751385809\n", + "\tmax y acquired = 175.734922474\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 144.184208777\n", + "\tmax y acquired = 148.44263643\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 138.200963434\n", + "\tmax y acquired = 169.901491297\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 173.817499665\n", + "\tmax y acquired = 162.964548393\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 181.23803981900002\n", + "\tmax y acquired = 159.790178937\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 160.7072373\n", + "\tmax y acquired = 162.805857941\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.964548393\n", + "\tmax y acquired = 165.043815086\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 181.533434354\n", + "\tmax y acquired = 146.373534281\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 161.61358860200002\n", + "\tmax y acquired = 176.690277919\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.36987107599998\n", + "\tmax y acquired = 199.90463220799998\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 186.74455223599998\n", + "\tmax y acquired = 157.529651595\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.688826993\n", + "\tmax y acquired = 155.525090885\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 172.95669094599998\n", + "\tmax y acquired = 176.35219957299998\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 201.148834085\n", + "\tmax y acquired = 167.701114875\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.142548094\n", + "\tmax y acquired = 146.373534281\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.36818937799998\n", + "\tmax y acquired = 162.805857941\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 172.096141322\n", + "\tmax y acquired = 166.580111006\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 172.95669094599998\n", + "\tmax y acquired = 163.456138677\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 154.272072388\n", + "\tmax y acquired = 153.924524651\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 157.529651595\n", + "\tmax y acquired = 186.587513319\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 179.19628339599998\n", + "\tmax y acquired = 150.259841903\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 153.981563906\n", + "\tmax y acquired = 198.792072623\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.169424843\n", + "\tmax y acquired = 166.6328691\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 164.602426982\n", + "\tmax y acquired = 156.160013661\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 147.372826457\n", + "\tmax y acquired = 178.039358767\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.805857941\n", + "\tmax y acquired = 166.765915445\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 146.373534281\n", + "\tmax y acquired = 143.374620351\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.297530607\n", + "\tmax y acquired = 155.94846083299998\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 168.99697320200002\n", + "\tmax y acquired = 174.669990774\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 139.438058268\n", + "\tmax y acquired = 143.567110987\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 164.602426982\n", + "\tmax y acquired = 169.89520874\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 190.102542686\n", + "\tmax y acquired = 162.805857941\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 192.43303832400002\n", + "\tmax y acquired = 156.856656669\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 188.901132522\n", + "\tmax y acquired = 170.127336228\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 180.068856751\n", + "\tmax y acquired = 168.883648397\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 187.604176965\n", + "\tmax y acquired = 161.21126762\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 162.436742786\n", + "\tmax y acquired = 176.690277919\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 20 = 10 training data and 10 acquired.\n", - "\tmax y acquired = 176.97717943400002\n", + "\tmax y acquired = 162.964548393\n", "budget for evals: 40\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 157.693301403\n", + "\tmax y acquired = 181.533434354\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 178.321343978\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 188.02562188299999\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 178.63634382200001\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 187.451829608\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -578,63 +586,63 @@ "\trun 8\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 183.95419856799998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 174.661028847\n", + "\tmax y acquired = 175.646129915\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 184.154236099\n", + "\tmax y acquired = 189.69003171\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 189.053559538\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 180.789647894\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.197342546\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.38766055\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.197342546\n", + "\tmax y acquired = 186.43690669900002\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 176.34249782\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 184.154236099\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 195.142218812\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 189.69003171\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -642,27 +650,27 @@ "\trun 24\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 179.875457882\n", + "\tmax y acquired = 216.894110699\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 180.789647894\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 176.34249782\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 198.020772317\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 182.26397528\n", + "\tmax y acquired = 192.393334386\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -670,239 +678,239 @@ "\trun 31\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.27665315299998\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.796070915\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 175.7386644\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.077676114\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 192.539600494\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 192.393334386\n", + "\tmax y acquired = 201.148834085\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 183.95419856799998\n", + "\tmax y acquired = 180.789647894\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 186.89961704700002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 196.752963258\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 184.686971958\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 183.77337184599997\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 188.262003905\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 189.190920955\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.36312997299999\n", + "\tmax y acquired = 216.894110699\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 180.789647894\n", + "\tmax y acquired = 168.098644143\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 186.022890236\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 193.949996568\n", + "\tmax y acquired = 180.54061992400003\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 180.387219514\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 167.75125141200002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 194.20146897700002\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 187.813295088\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 189.325235236\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 171.353905639\n", + "\tmax y acquired = 196.796070915\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 168.64153222299998\n", + "\tmax y acquired = 216.894110699\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 184.19832833599997\n", + "\tmax y acquired = 184.749858846\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 176.372242861\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 185.02054252599999\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 187.01688480400003\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 178.997150426\n", + "\tmax y acquired = 192.539600494\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 183.95419856799998\n", + "\tmax y acquired = 184.849339348\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 173.149927035\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 180.789647894\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 178.95953659900002\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 193.72992463\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.11955720900002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 182.98036740599997\n", + "\tmax y acquired = 182.52512803\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 178.158343282\n", + "\tmax y acquired = 174.833095498\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 179.967947498\n", + "\tmax y acquired = 196.752963258\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 179.51492700900002\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 202.004818298\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 191.077676114\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 181.885991327\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 160.599617962\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 178.997150426\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 179.694467972\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -910,27 +918,27 @@ "\trun 91\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 184.686971958\n", + "\tmax y acquired = 185.31228748599997\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 195.58268240799998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 182.76216997\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 193.61022285099997\n", + "\tmax y acquired = 196.752963258\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 184.19832833599997\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", @@ -938,11 +946,11 @@ "\trun 98\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 40 = 20 training data and 20 acquired.\n", - "\tmax y acquired = 179.81664061900003\n", + "\tmax y acquired = 194.37058873700002\n", "budget for evals: 60\n", "\trun 0\n", "\tdiverse RF run\n", @@ -955,7 +963,7 @@ "\trun 2\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 179.967947498\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -963,127 +971,127 @@ "\trun 4\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 181.533434354\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.22739577599998\n", + "\tmax y acquired = 191.507774129\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 180.805755057\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 195.58268240799998\n", + "\tmax y acquired = 179.81664061900003\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 191.11955720900002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 206.74476888599997\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.36951723599998\n", + "\tmax y acquired = 216.894110699\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 188.714874977\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.503247339\n", + "\tmax y acquired = 193.05167775400002\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 185.442482271\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 182.98036740599997\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 188.709824186\n", + "\tmax y acquired = 190.461820465\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 181.885991327\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 183.01453012599998\n", + "\tmax y acquired = 187.813295088\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 192.539600494\n", + "\tmax y acquired = 181.63890316799998\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 206.74476888599997\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 179.376429437\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 199.90463220799998\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 187.813295088\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.38766055\n", + "\tmax y acquired = 196.752963258\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 181.63890316799998\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 206.864600037\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 198.792072623\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 190.04507896200002\n", + "\tmax y acquired = 196.752963258\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -1091,47 +1099,47 @@ "\trun 36\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 180.789647894\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 180.853194423\n", + "\tmax y acquired = 216.894110699\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 188.02562188299999\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 192.43303832400002\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 192.43303832400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 187.01688480400003\n", + "\tmax y acquired = 196.37724838900002\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 190.04507896200002\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 191.077676114\n", + "\tmax y acquired = 216.894110699\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 193.620114578\n", + "\tmax y acquired = 194.708308113\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -1139,39 +1147,39 @@ "\trun 48\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 189.053559538\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 193.05167775400002\n", + "\tmax y acquired = 196.752963258\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.325235236\n", + "\tmax y acquired = 198.792072623\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 182.381507756\n", + "\tmax y acquired = 198.751812898\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.796070915\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 190.461820465\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -1179,123 +1187,123 @@ "\trun 58\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 187.282999897\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 180.54061992400003\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 191.02071475\n", + "\tmax y acquired = 192.539600494\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.796070915\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 187.813295088\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 185.162057723\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 185.32763518599998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 184.686971958\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 206.864600037\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 180.789647894\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 196.752963258\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 184.541440663\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 195.58268240799998\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 197.35770853900001\n", + "\tmax y acquired = 196.796070915\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 187.01688480400003\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 194.090852802\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 180.54061992400003\n", + "\tmax y acquired = 186.35826006599999\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 191.507774129\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 192.02091057099997\n", + "\tmax y acquired = 205.171240133\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 201.17983227599998\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 195.142218812\n", + "\tmax y acquired = 198.792072623\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 191.507774129\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 180.789647894\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 192.33523148900002\n", + "\tmax y acquired = 189.46195930099998\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -1307,39 +1315,39 @@ "\trun 90\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 189.069812594\n", + "\tmax y acquired = 196.752963258\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 184.154236099\n", + "\tmax y acquired = 191.507774129\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 182.26397528\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 191.02071475\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 182.98036740599997\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.720247142\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 60 = 30 training data and 30 acquired.\n", @@ -1348,103 +1356,103 @@ "\trun 0\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.503247339\n", + "\tmax y acquired = 198.751812898\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 183.95419856799998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 187.945004404\n", + "\tmax y acquired = 188.76981126599998\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 196.720247142\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 198.792072623\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 206.74476888599997\n", + "\tmax y acquired = 197.918308448\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 185.901678884\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 201.66490141\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 206.74476888599997\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 216.894110699\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 193.72992463\n", + "\tmax y acquired = 194.20146897700002\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 198.751812898\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 216.894110699\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 198.751812898\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", @@ -1456,11 +1464,11 @@ "\trun 27\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 197.03796965900003\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 191.97523738900003\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", @@ -1468,11 +1476,11 @@ "\trun 30\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 188.76981126599998\n", + "\tmax y acquired = 190.17935780099998\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 195.142218812\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", @@ -1480,23 +1488,23 @@ "\trun 33\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 191.507774129\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.38766055\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 205.189199744\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 191.48812323400003\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 193.949996568\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", @@ -1508,15 +1516,15 @@ "\trun 40\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 190.17935780099998\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 196.752963258\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", @@ -1524,107 +1532,107 @@ "\trun 44\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.213573499\n", + "\tmax y acquired = 206.55088119400003\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.792072623\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 183.718553378\n", + "\tmax y acquired = 198.751812898\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 195.289662613\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 200.420314123\n", + "\tmax y acquired = 191.108264299\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 196.720247142\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 187.780313647\n", + "\tmax y acquired = 216.894110699\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 208.120454446\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 190.079670689\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 183.718553378\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.38766055\n", + "\tmax y acquired = 196.752963258\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 179.955561987\n", + "\tmax y acquired = 208.120454446\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.708308113\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 186.27516017599999\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 194.530496788\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 194.708308113\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 188.927621488\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", @@ -1632,208 +1640,208 @@ "\trun 71\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 198.751812898\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 196.58076384900002\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 187.02128687799998\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 188.39498494\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 195.142218812\n", + "\tmax y acquired = 206.864600037\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 188.76981126599998\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 181.398957492\n", + "\tmax y acquired = 198.792072623\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 181.197342546\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 184.686971958\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 216.894110699\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.38766055\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 184.686971958\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 197.34635625599998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.808591001\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 198.568968117\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 195.289662613\n", + "\tmax y acquired = 193.562944445\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 192.539600494\n", + "\tmax y acquired = 199.90463220799998\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 188.76981126599998\n", + "\tmax y acquired = 196.752963258\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 182.843902245\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 194.20146897700002\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 185.59509969799998\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 188.76981126599998\n", + "\tmax y acquired = 190.17935780099998\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 80 = 40 training data and 40 acquired.\n", - "\tmax y acquired = 190.660502314\n", + "\tmax y acquired = 190.17935780099998\n", "budget for evals: 100\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.808591001\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 199.410130367\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 208.120454446\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 189.190920955\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 195.978854341\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 180.954411655\n", + "\tmax y acquired = 206.808591001\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 183.94813059400002\n", + "\tmax y acquired = 196.752963258\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 205.963467853\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 197.03796965900003\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 185.901678884\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 195.289662613\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.74476888599997\n", + "\tmax y acquired = 191.507774129\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 187.81611316400003\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 208.120454446\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 208.120454446\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", @@ -1841,11 +1849,11 @@ "\trun 23\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", @@ -1857,23 +1865,23 @@ "\trun 27\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 197.918308448\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 202.004818298\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.708308113\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", @@ -1881,7 +1889,7 @@ "\trun 33\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.74476888599997\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", @@ -1889,171 +1897,171 @@ "\trun 35\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 198.751812898\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 188.642146113\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 193.003059026\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 199.320637918\n", + "\tmax y acquired = 206.864600037\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 192.43303832400002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.74476888599997\n", + "\tmax y acquired = 194.708308113\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 216.894110699\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.189199744\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 196.9895885\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 191.108264299\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 196.752963258\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 207.39578187\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 193.002639377\n", + "\tmax y acquired = 193.459708345\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 193.244990632\n", + "\tmax y acquired = 205.963467853\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 199.698499548\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 185.76553294400003\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 195.58268240799998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 188.76981126599998\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 208.120454446\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 190.102542686\n", + "\tmax y acquired = 216.894110699\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 190.17935780099998\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 208.120454446\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 206.864600037\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 202.004818298\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 202.848493155\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.58076384900002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 208.120454446\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 191.700524655\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", @@ -2061,15 +2069,15 @@ "\trun 78\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 216.894110699\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 186.04049377\n", + "\tmax y acquired = 196.752963258\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 190.29267501400003\n", + "\tmax y acquired = 195.915962745\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", @@ -2077,136 +2085,136 @@ "\trun 82\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 197.03796965900003\n", + "\tmax y acquired = 191.507774129\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 196.720247142\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 198.568968117\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 198.568968117\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 181.197342546\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 190.17935780099998\n", + "\tmax y acquired = 206.864600037\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 196.58076384900002\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 205.963467853\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 196.491162041\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 196.88923220900003\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 186.040369533\n", + "\tmax y acquired = 206.808591001\n", "budget for evals: 120\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 196.752963258\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 187.945004404\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 216.894110699\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 194.609105533\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 195.978854341\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.708308113\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 193.949996568\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 185.76111369\n", + "\tmax y acquired = 216.894110699\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 197.07304941400002\n", + "\tmax y acquired = 193.949996568\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 195.978854341\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", @@ -2214,103 +2222,103 @@ "\trun 16\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 191.108264299\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 190.17935780099998\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 182.910685964\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 182.038043442\n", + "\tmax y acquired = 199.76380567299998\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 205.963467853\n", + "\tmax y acquired = 206.808591001\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 193.949996568\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 188.63741461200001\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 206.808591001\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 206.808591001\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 207.39578187\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 195.58268240799998\n", + "\tmax y acquired = 216.894110699\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 188.220318021\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 190.17935780099998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 196.213573499\n", + "\tmax y acquired = 205.189199744\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", @@ -2318,31 +2326,31 @@ "\trun 42\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 191.97029438599998\n", + "\tmax y acquired = 198.792072623\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 190.29267501400003\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.120454446\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.189199744\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 188.239279769\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", @@ -2358,27 +2366,27 @@ "\trun 52\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 197.35770853900001\n", + "\tmax y acquired = 206.864600037\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 197.07304941400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 194.530496788\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 201.71891966299998\n", + "\tmax y acquired = 195.915962745\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 196.104126559\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", @@ -2386,67 +2394,67 @@ "\trun 59\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 187.06812721\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 198.751812898\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 198.568968117\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 196.579974938\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 188.981525033\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.751812898\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 208.120454446\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 196.720247142\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 193.002639377\n", + "\tmax y acquired = 206.864600037\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 199.90463220799998\n", + "\tmax y acquired = 193.562944445\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 199.72030120099998\n", + "\tmax y acquired = 205.189199744\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", @@ -2454,128 +2462,128 @@ "\trun 76\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 216.894110699\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 192.882732714\n", + "\tmax y acquired = 206.864600037\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 187.147423045\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.751812898\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 196.88923220900003\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 196.720247142\n", + "\tmax y acquired = 205.189199744\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 190.17935780099998\n", + "\tmax y acquired = 207.39578187\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 202.08883754099998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 193.72992463\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 206.864600037\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 194.80467023\n", + "\tmax y acquired = 196.752963258\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 205.963467853\n", + "\tmax y acquired = 198.751812898\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 196.752963258\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 188.666049397\n", + "\tmax y acquired = 216.894110699\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 188.76981126599998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 197.07304941400002\n", + "\tmax y acquired = 181.197342546\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 196.752963258\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 120 = 60 training data and 60 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 206.74476888599997\n", "budget for evals: 140\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 196.752963258\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 188.77987954900001\n", + "\tmax y acquired = 216.894110699\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 198.751812898\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.492194009\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", @@ -2583,63 +2591,63 @@ "\trun 8\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 206.808591001\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 196.752963258\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.796070915\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 193.002639377\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.74476888599997\n", + "\tmax y acquired = 205.189199744\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.751812898\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 198.792072623\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.698499548\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", @@ -2651,55 +2659,55 @@ "\trun 25\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 198.792072623\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.104126559\n", + "\tmax y acquired = 185.59509969799998\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 206.864600037\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 202.004818298\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 205.189199744\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 199.333447425\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 193.949996568\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 206.864600037\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", @@ -2707,59 +2715,59 @@ "\trun 39\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 193.620114578\n", + "\tmax y acquired = 216.894110699\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 195.448316445\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 205.189199744\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 192.539600494\n", + "\tmax y acquired = 216.894110699\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.9895885\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 197.07304941400002\n", + "\tmax y acquired = 198.751812898\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 216.894110699\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 216.894110699\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 195.289662613\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", @@ -2767,119 +2775,119 @@ "\trun 54\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.320637918\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 198.792072623\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 206.55088119400003\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 199.90463220799998\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 197.918308448\n", + "\tmax y acquired = 198.792072623\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.54342821400002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 199.80359465400002\n", + "\tmax y acquired = 206.55088119400003\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 193.72992463\n", + "\tmax y acquired = 216.894110699\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 195.89774693900003\n", + "\tmax y acquired = 202.08883754099998\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 198.792072623\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 202.08883754099998\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 199.410130367\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.808591001\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 199.410130367\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.492194009\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 198.792072623\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.9895885\n", + "\tmax y acquired = 196.720247142\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 205.189199744\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", @@ -2887,104 +2895,104 @@ "\trun 84\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 208.120454446\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 205.492194009\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 188.242123191\n", + "\tmax y acquired = 207.39578187\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.695494435\n", + "\tmax y acquired = 206.808591001\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 195.915962745\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 199.80359465400002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 196.88923220900003\n", + "\tmax y acquired = 202.848493155\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.751812898\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 195.928348822\n", + "\tmax y acquired = 193.675158092\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 196.752963258\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 140 = 70 training data and 70 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "budget for evals: 160\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 194.708308113\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 196.720247142\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 193.41397958\n", + "\tmax y acquired = 206.808591001\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 196.752963258\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 205.171240133\n", + "\tmax y acquired = 196.491162041\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", @@ -2992,51 +3000,51 @@ "\trun 10\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.189199744\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 196.752963258\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 206.864600037\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.752963258\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 197.07304941400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.120454446\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 196.9895885\n", + "\tmax y acquired = 205.189199744\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 194.38766055\n", + "\tmax y acquired = 206.864600037\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 197.34635625599998\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 208.120454446\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", @@ -3044,19 +3052,19 @@ "\trun 23\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 202.08883754099998\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 205.963467853\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 194.938530808\n", + "\tmax y acquired = 205.963467853\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.189199744\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", @@ -3064,91 +3072,91 @@ "\trun 28\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 202.848493155\n", + "\tmax y acquired = 205.189199744\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 198.020772317\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 199.698499548\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 196.9895885\n", + "\tmax y acquired = 206.864600037\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 205.492194009\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.80359465400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 201.71891966299998\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 196.752963258\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.698499548\n", + "\tmax y acquired = 195.928348822\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 203.35670863099998\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.189199744\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.189199744\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 199.90463220799998\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 193.244990632\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", @@ -3156,147 +3164,147 @@ "\trun 51\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 206.54342821400002\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 216.894110699\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 194.234159646\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 185.218512852\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.80359465400002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 185.31228748599997\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 197.86041748099998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.410130367\n", + "\tmax y acquired = 216.894110699\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 202.004818298\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.792072623\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 194.530496788\n", + "\tmax y acquired = 208.120454446\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.171240133\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 193.41397958\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 202.848493155\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 204.811726149\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 203.35670863099998\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 190.29267501400003\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.320637918\n", + "\tmax y acquired = 206.864600037\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 207.39578187\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 198.751812898\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 201.17983227599998\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", @@ -3304,19 +3312,19 @@ "\trun 88\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 196.752963258\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 198.792072623\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", @@ -3324,27 +3332,27 @@ "\trun 93\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 194.157140046\n", + "\tmax y acquired = 216.894110699\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.698499548\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 208.120454446\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 160 = 80 training data and 80 acquired.\n", @@ -3353,51 +3361,51 @@ "\trun 0\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.120454446\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 206.864600037\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 202.004818298\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 216.894110699\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 207.39578187\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.698499548\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3405,111 +3413,111 @@ "\trun 13\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 193.002639377\n", + "\tmax y acquired = 208.120454446\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 194.530496788\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 206.864600037\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 206.54342821400002\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 205.492194009\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 199.698499548\n", + "\tmax y acquired = 216.894110699\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 195.928348822\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 194.708308113\n", + "\tmax y acquired = 205.189199744\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.189199744\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 207.39578187\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 202.774937788\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 205.492194009\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 206.54342821400002\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 216.894110699\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3517,31 +3525,31 @@ "\trun 41\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.492194009\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 195.289662613\n", + "\tmax y acquired = 216.894110699\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 196.579974938\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 199.698499548\n", + "\tmax y acquired = 198.751812898\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.120454446\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3549,47 +3557,47 @@ "\trun 49\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 193.675158092\n", + "\tmax y acquired = 197.07304941400002\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 202.08883754099998\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 203.35670863099998\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.54342821400002\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 206.74476888599997\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 206.54342821400002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 198.034754095\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3597,11 +3605,11 @@ "\trun 61\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 207.39578187\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 208.120454446\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3609,79 +3617,79 @@ "\trun 64\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 208.120454446\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 195.928348822\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 199.698499548\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 196.491162041\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 197.34635625599998\n", + "\tmax y acquired = 197.918308448\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 199.698499548\n", + "\tmax y acquired = 205.189199744\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 206.864600037\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 204.811726149\n", + "\tmax y acquired = 207.39578187\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 190.17935780099998\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3689,15 +3697,15 @@ "\trun 84\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 208.120454446\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3705,31 +3713,31 @@ "\trun 88\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 206.74476888599997\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.568968117\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.503247339\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 195.657779278\n", + "\tmax y acquired = 203.35670863099998\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 197.918308448\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", @@ -3741,60 +3749,60 @@ "\trun 97\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 180 = 90 training data and 90 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.492194009\n", "budget for evals: 200\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 208.120454446\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.54342821400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 201.71891966299998\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 205.492194009\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 201.17983227599998\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 205.189199744\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -3802,87 +3810,87 @@ "\trun 12\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.320637918\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 202.848493155\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 207.39578187\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 208.120454446\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 201.40394484\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 196.720247142\n", + "\tmax y acquired = 208.120454446\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 206.808591001\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 208.120454446\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.320637918\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -3890,11 +3898,11 @@ "\trun 34\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 198.751812898\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -3902,23 +3910,23 @@ "\trun 37\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 216.894110699\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -3930,71 +3938,71 @@ "\trun 44\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 199.698499548\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 190.17935780099998\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.120454446\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.792072623\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 195.978854341\n", + "\tmax y acquired = 199.333447425\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 216.894110699\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 206.808591001\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -4002,23 +4010,23 @@ "\trun 62\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.120454446\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 196.491162041\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 206.864600037\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 191.507774129\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -4026,47 +4034,47 @@ "\trun 68\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 202.08883754099998\n", + "\tmax y acquired = 205.492194009\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.410130367\n", + "\tmax y acquired = 199.698499548\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 71\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 196.720247142\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 207.39578187\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 208.120454446\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.698499548\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 216.894110699\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.54342821400002\n", + "\tmax y acquired = 185.901678884\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -4074,71 +4082,71 @@ "\trun 80\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 205.189199744\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.492194009\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 208.120454446\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.410130367\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 206.54342821400002\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.963467853\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 208.120454446\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 197.918308448\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.963467853\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 188.611966941\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", @@ -4146,80 +4154,80 @@ "\trun 98\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 196.752963258\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 188.591408936\n", "budget for evals: 220\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 193.949996568\n", + "\tmax y acquired = 205.492194009\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 208.120454446\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 5\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 194.530496788\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 6\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 206.864600037\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 10\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 198.792072623\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 198.96574226299998\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 207.39578187\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 197.918308448\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4227,11 +4235,11 @@ "\trun 18\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 191.852225648\n", + "\tmax y acquired = 216.894110699\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 201.17983227599998\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4239,7 +4247,7 @@ "\trun 21\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 194.37058873700002\n", + "\tmax y acquired = 205.492194009\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4247,19 +4255,19 @@ "\trun 23\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 202.004818298\n", "\trun 24\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 205.492194009\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4271,11 +4279,11 @@ "\trun 29\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.792072623\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4283,23 +4291,23 @@ "\trun 32\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 196.579974938\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 203.35670863099998\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.120454446\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 194.530496788\n", "\trun 36\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 202.848493155\n", + "\tmax y acquired = 196.720247142\n", "\trun 37\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4311,39 +4319,39 @@ "\trun 39\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 195.289662613\n", + "\tmax y acquired = 216.894110699\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 208.120454446\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.120454446\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 201.40394484\n", + "\tmax y acquired = 216.894110699\n", "\trun 46\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4351,79 +4359,79 @@ "\trun 49\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.74476888599997\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.189199744\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 206.864600037\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 205.189199744\n", + "\tmax y acquired = 205.171240133\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 208.120454446\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.189199744\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 200.44080272099998\n", + "\tmax y acquired = 207.39578187\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 67\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.492194009\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4431,7 +4439,7 @@ "\trun 69\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 197.34635625599998\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4439,35 +4447,35 @@ "\trun 71\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 196.579974938\n", "\trun 72\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 195.89774693900003\n", + "\tmax y acquired = 200.420314123\n", "\trun 73\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 196.752963258\n", "\trun 74\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 196.752963258\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 198.792072623\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 200.420314123\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4475,27 +4483,27 @@ "\trun 80\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 216.894110699\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.120454446\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 207.39578187\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4503,39 +4511,39 @@ "\trun 87\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 203.35670863099998\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 206.864600037\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 186.61865843\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 208.120454446\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.120454446\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4543,7 +4551,7 @@ "\trun 97\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", @@ -4551,24 +4559,24 @@ "\trun 99\n", "\tdiverse RF run\n", "\teval budget 220 = 110 training data and 110 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 206.864600037\n", "budget for evals: 240\n", "\trun 0\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 199.333447425\n", "\trun 1\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 202.21921792700002\n", + "\tmax y acquired = 216.894110699\n", "\trun 2\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 3\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 4\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", @@ -4580,15 +4588,15 @@ "\trun 6\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 7\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 8\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 197.35770853900001\n", "\trun 9\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", @@ -4596,55 +4604,55 @@ "\trun 10\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 199.76380567299998\n", "\trun 11\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 196.491162041\n", + "\tmax y acquired = 199.410130367\n", "\trun 12\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 13\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 206.54342821400002\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 14\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 199.84356436299998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 15\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 200.40213550099998\n", "\trun 16\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 17\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 208.120454446\n", "\trun 18\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 202.08883754099998\n", "\trun 19\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 208.120454446\n", "\trun 20\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 207.39578187\n", "\trun 21\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 198.751812898\n", + "\tmax y acquired = 196.752963258\n", "\trun 22\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 208.120454446\n", "\trun 23\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", @@ -4652,47 +4660,47 @@ "\trun 24\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 194.30370504400003\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 25\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 197.918308448\n", "\trun 26\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 203.35670863099998\n", + "\tmax y acquired = 207.39578187\n", "\trun 27\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 216.894110699\n", "\trun 28\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 198.792072623\n", "\trun 29\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 194.530496788\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 30\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 31\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 32\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 33\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 216.894110699\n", "\trun 34\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 35\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", @@ -4704,35 +4712,35 @@ "\trun 37\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 38\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 39\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 40\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 41\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 42\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 207.39578187\n", "\trun 43\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.792072623\n", "\trun 44\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 201.17983227599998\n", "\trun 45\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", @@ -4740,83 +4748,83 @@ "\trun 46\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 199.333447425\n", "\trun 47\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 203.35670863099998\n", + "\tmax y acquired = 208.120454446\n", "\trun 48\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 49\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 196.327147635\n", + "\tmax y acquired = 207.39578187\n", "\trun 50\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 51\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 193.949996568\n", "\trun 52\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 206.864600037\n", "\trun 53\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 199.80359465400002\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 54\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 55\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 56\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 57\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 58\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 207.39578187\n", "\trun 59\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 216.894110699\n", "\trun 60\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 194.37058873700002\n", "\trun 61\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 202.21921792700002\n", "\trun 62\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 63\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 196.625762218\n", "\trun 64\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 208.120454446\n", "\trun 65\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 202.08883754099998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 66\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", @@ -4824,15 +4832,15 @@ "\trun 67\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 206.54342821400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 68\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 69\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 70\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", @@ -4852,106 +4860,507 @@ "\trun 74\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 75\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.43022665700002\n", + "\tmax y acquired = 199.84356436299998\n", "\trun 76\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 208.120454446\n", + "\tmax y acquired = 205.492194009\n", "\trun 77\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 205.963467853\n", + "\tmax y acquired = 199.72030120099998\n", "\trun 78\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 207.39578187\n", "\trun 79\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 207.39578187\n", + "\tmax y acquired = 206.808591001\n", "\trun 80\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 206.55088119400003\n", + "\tmax y acquired = 208.120454446\n", "\trun 81\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 82\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 83\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 84\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 206.808591001\n", + "\tmax y acquired = 216.894110699\n", "\trun 85\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 202.848493155\n", + "\tmax y acquired = 199.80359465400002\n", "\trun 86\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 87\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 88\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.88488105599998\n", + "\tmax y acquired = 201.17983227599998\n", "\trun 89\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 205.492194009\n", "\trun 90\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 201.17983227599998\n", + "\tmax y acquired = 202.08883754099998\n", "\trun 91\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 206.864600037\n", + "\tmax y acquired = 209.88488105599998\n", "\trun 92\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 208.120454446\n", "\trun 93\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 202.848493155\n", + "\tmax y acquired = 209.36697147400002\n", "\trun 94\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 198.792072623\n", "\trun 95\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 96\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 216.894110699\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 97\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 205.492194009\n", + "\tmax y acquired = 208.43022665700002\n", "\trun 98\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", - "\tmax y acquired = 209.36697147400002\n", + "\tmax y acquired = 208.120454446\n", "\trun 99\n", "\tdiverse RF run\n", "\teval budget 240 = 120 training data and 120 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "budget for evals: 250\n", + "\trun 0\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 1\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 2\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 3\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 4\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 5\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 6\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 7\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 8\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 9\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 10\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 11\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 12\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 13\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 14\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 15\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 16\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 17\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 18\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 191.07007078799998\n", + "\trun 19\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 20\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 21\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 193.244990632\n", + "\trun 22\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 23\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 24\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 25\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 26\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 27\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 28\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 29\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 30\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 31\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 32\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 33\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 34\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 35\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 36\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 37\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 38\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 199.410130367\n", + "\trun 39\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 40\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 41\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 42\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 43\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 44\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 45\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 46\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 47\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 48\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 202.21921792700002\n", + "\trun 49\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 50\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 51\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 52\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 53\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 54\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 55\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 199.76380567299998\n", + "\trun 56\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 57\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.43022665700002\n", + "\trun 58\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.808591001\n", + "\trun 59\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 199.80359465400002\n", + "\trun 60\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 61\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 62\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 202.08883754099998\n", + "\trun 63\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 64\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 65\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 66\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 67\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 68\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 203.35670863099998\n", + "\trun 69\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 70\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 71\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 72\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 73\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 74\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 75\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.54342821400002\n", + "\trun 76\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 77\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 78\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 79\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 207.39578187\n", + "\trun 80\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 81\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 82\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 83\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 84\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 85\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.22060552\n", + "\trun 86\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 87\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.88488105599998\n", + "\trun 88\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 198.792072623\n", + "\trun 89\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 199.84356436299998\n", + "\trun 90\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.55088119400003\n", + "\trun 91\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 92\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 197.918308448\n", + "\trun 93\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 198.751812898\n", + "\trun 94\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 216.894110699\n", + "\trun 95\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 209.36697147400002\n", + "\trun 96\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 97\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 206.864600037\n", + "\trun 98\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", + "\tmax y acquired = 208.120454446\n", + "\trun 99\n", + "\tdiverse RF run\n", + "\teval budget 250 = 125 training data and 125 acquired.\n", "\tmax y acquired = 216.894110699\n" ] } @@ -4981,14 +5390,6 @@ " with open('rf_div_results.pkl', 'wb') as file:\n", " pickle.dump(rf_res, file)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "still-bangkok", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/new/viz.ipynb b/new/viz.ipynb index a2963f1..798ec36 100644 --- a/new/viz.ipynb +++ b/new/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "sharp-thriller", + "id": "royal-needle", "metadata": {}, "source": [ "# viz" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "sunset-treaty", + "id": "absolute-cross", "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "charitable-upper", + "id": "musical-martial", "metadata": {}, "source": [ "load data" @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "electoral-teddy", + "id": "successful-approach", "metadata": {}, "outputs": [ { @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "operational-wholesale", + "id": "running-meeting", "metadata": {}, "source": [ "for rankings" @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "native-plumbing", + "id": "reserved-volunteer", "metadata": {}, "outputs": [], "source": [ @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "bulgarian-tokyo", + "id": "czech-negotiation", "metadata": {}, "outputs": [ { @@ -136,7 +136,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "urban-concentrate", + "id": "rural-therapy", "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "abandoned-beaver", + "id": "sustainable-developer", "metadata": {}, "source": [ "load search results" @@ -165,7 +165,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "lesser-backup", + "id": "narrative-suspect", "metadata": {}, "outputs": [], "source": [ @@ -178,8 +178,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "id": "fundamental-blink", + "execution_count": 7, + "id": "everyday-wellington", "metadata": {}, "outputs": [], "source": [ @@ -190,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "alpha-deficit", + "id": "legitimate-sarah", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -199,7 +199,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "fourth-savage", + "id": "sonic-private", "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "descending-scientist", + "id": "subtle-hebrew", "metadata": {}, "outputs": [ { @@ -244,7 +244,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "typical-marshall", + "id": "noticed-specification", "metadata": {}, "outputs": [ { @@ -290,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "rocky-progressive", + "id": "joint-track", "metadata": {}, "source": [ "# search efficiency\n", @@ -300,7 +300,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "sonic-penguin", + "id": "false-lotus", "metadata": {}, "outputs": [ { @@ -342,7 +342,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "designed-buffalo", + "id": "electrical-quantum", "metadata": {}, "outputs": [], "source": [ @@ -367,7 +367,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "touched-chancellor", + "id": "willing-estimate", "metadata": {}, "outputs": [], "source": [ @@ -378,12 +378,12 @@ { "cell_type": "code", "execution_count": 14, - "id": "specialized-ghost", + "id": "shared-nomination", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -438,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "pretty-vehicle", + "id": "rocky-member", "metadata": {}, "source": [ "show distribution for context." @@ -447,7 +447,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "angry-blowing", + "id": "incoming-champion", "metadata": {}, "outputs": [ { @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "imperial-party", + "id": "equivalent-prince", "metadata": {}, "source": [ "### max rank among acquired set" @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "trying-vocabulary", + "id": "monthly-restaurant", "metadata": {}, "outputs": [ { @@ -511,13 +511,13 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "fitting-sweden", + "execution_count": 17, + "id": "medieval-royalty", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -567,7 +567,7 @@ }, { "cell_type": "markdown", - "id": "under-orange", + "id": "temporal-principle", "metadata": {}, "source": [ "print stats to report in paper" @@ -575,8 +575,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "exciting-cutting", + "execution_count": 18, + "id": "moving-owner", "metadata": {}, "outputs": [ { @@ -593,8 +593,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "clean-anaheim", + "execution_count": 19, + "id": "organizational-chambers", "metadata": {}, "outputs": [ { @@ -603,7 +603,7 @@ "1.0" ] }, - "execution_count": 31, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -614,16 +614,16 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "imposed-contamination", + "execution_count": 20, + "id": "selected-waterproof", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "after 240 iterations, top-ranked COF found by RF: 53.13\n", - "after 240 iterations, top-ranked COF found by RF: 10.67\n", + "after 250 iterations, top-ranked COF found by RF: 63.94\n", + "after 250 iterations, top-ranked COF found by RF: 12.07\n", "after 250 iterations, top-ranked COF found by CMA-ES: 12.92\n", "after 250 iterations, top-ranked COF found by rs: 308.26\n" ] @@ -639,7 +639,7 @@ }, { "cell_type": "markdown", - "id": "executed-leisure", + "id": "completed-joint", "metadata": {}, "source": [ "### fraction of top 100 COFs recovered" @@ -647,8 +647,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "going-storm", + "execution_count": 21, + "id": "protected-secondary", "metadata": {}, "outputs": [ { @@ -667,8 +667,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "handed-package", + "execution_count": 22, + "id": "prescription-triumph", "metadata": {}, "outputs": [], "source": [ @@ -682,8 +682,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "musical-argument", + "execution_count": 23, + "id": "under-tourist", "metadata": {}, "outputs": [ { @@ -721,8 +721,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "continuing-tuition", + "execution_count": 24, + "id": "proof-convention", "metadata": {}, "outputs": [], "source": [ @@ -746,13 +746,13 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "chemical-seeking", + "execution_count": 25, + "id": "usual-biotechnology", "metadata": {}, "outputs": [ { "data": { - "image/png": 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LB6SUtgGJfH26s+he4LuGoR/ocP/rgGc3ZhxVSksC/88w9Md1XGMXF5dlQy0TJ8hcz80Zmd/pRkPJVuFno/DdI3WpsJE01S9ZKxGoOyVtSjTsig0ZS4brTnA0nIhXhtdG3XmdntFWACmlvQL4CBBiMiV2EvigUtrrDUP/bAfHeQzQLEJSGLiso5r2AYM89AJu+8Htg161v1gVY4CDZdgWlaGtHXnJuNmp8NldgC8fhp+POloK4tT5zNUy1Lauw5hspiX+P6YtUaUflpAhv6rdfOhv0K+BXtJyCE4p7UnAj4CPAXpN43E0on9D8vP8rWHoP59h//OdrzcBlwP11nI+4InAlYahnzi3ZiwM5XLFDgYH16ayXK4wyO0Htw/m2v5aCoQ78zKGE/VJjDavB4YDnSVj210Qa7afjcocEYjQeOYqcRztdL6oltrA75WkbWuCYtbdbphu0K8BhwUZgtOADxmG/ub6hY4ger1SWgERRE0FECJ4bOfTbJsCkpF0SZDNLs9kXJ0y6O0Htw9m0/6KBcfKou2MmWLdNhSYTFXQaUTqRsHj98AzVsGL1ooFWqfUHFlDXhmeW9elg+igXwO9pJ0AugB4bYv1XwJe1WL9SYik3AlchGQnrVEGjhiGPtiefS4uy5i0CbdkIG+JthP3d59y+qY0/O9huD41VfBcsQ7WdZFOoeIMtVk2nByBrZHujBxcek87ARRAtJSZKLQqwzD0Pc7XZTFtN+hq96C3H9w+6LT9piWRC3YUxH+n2xw5tg1/zcC3jsJ1Y7Is4IGnOYJnfYeCJ1eVyAQ2YkRwclj2jc7BbHrQr4Fe0k4A3YvM3cxkaPBEYEcnB1JKezKSV2gr8ETD0PcppV0J7DIM/VcdlrEC+LxTp2PA1Yahf63JdgoZ2lsFZIFvApph6Kaz/kTgGuBiYC/wWsPQf9nu+LFYtJNqLlsGvf3g9sFM7TctybMT9soDf0dB5lhWBrrTMkYrEirnB8fEDwjEHPof18PfrZZ5ok6oDbMlnAyiCd/chE49g34N9JJ2mskXgPcrpT2tcYVS2tOB9znbtEQp7UXAt4D7kGG52mXkA97URX0/gQzdrQVeBHxKKW1bk+2uBc43DD0JnA2cC7yubv3XgVuAlcBbgO8opa1ud/CxsVQXVV1+DHr7we2DZu1PmXBDWkyo/5yGO3Iyv7I62Lnwqdrwo2PwrDvh4/tF+KwJwJXr4bvnwMs3dC58ilVxRD0xLAnj1gZ7J3zAvQZ6STsN6OPApcAPlNJ2AHc7y88ETgW+62zTjjcBLzcM/RuO1lPjz8C7OqmoUloM8UU62zD0LPAHpbRrgRcDjUYSD9T99AAWkkocpbTTkEyulxuGXgC+q5T2L07Zn+6kLi4uLqLh3J2TNAgRX3fzMTVMG75/FL54cDLx28VJeMEauGSoO+2pUBXH0bBXBM8Kd6Ss72kpgAxDt4HnK6V9D3ghk46o9wBvNwz9Wx0e51TgT02WZxGfok44DTANQ68f8rsNeHSzjZXSXogIlAQyXPdGZ9U2YKdh6HVB2LnNWd4S74AHeRr09oPbB16vB9uGI2W4Mydvdp1asdWwbdiek2G2347DqBPqeGMIXrYenray8xhtpi2GDqYtJtTnxUTzms8goIN+DfSSjiIhOIKmU2HTjAOIANnTsPxRwAPTN29KnElH2BopRMBMw5kb+ppS2qnAS4DDdeU06tAp4IRm5SilXQVcBfDMZz6dc845B4BIJIzf7yOTkVx9gYCfeDw2oZ57PJK8K53OTISwHxpKUCqVKRZlcDsajeD1eslmpYxgMEAsFp0ow+v1MDw8RCqVplq1JsooFkuUSmUAYrEIHo+HbDYPQCgUJBIJMz4uXeXzeRkaSk4pY3g4SaFQnCgjHo9i2za5XGGijHA4RCqVmVIGSCgWgJGRIXK5POVyxSkjhmVZ5PNSRjgcIhQKTpTh9/tIJhOMjY1Tcz0bGRkim81RqcgTKJGIYZpVCoXiRB8HAn7S6exEHycS8Ykyan2cyWQnykgm41Qq5pQyen2estlcX5+n8fEUlmXPy3lKm3D78QrpcpWIWSTsgUo4jNfvp5SV8+Tz+wnF4xRSk+cpnBzmntE8v095+EU2yK7y5JjYpkCVV62r8tgRD5V8jkIKfAE/wWiMQmryPEWGhilmMxPpEIrhBKVymRM8JVYGYE1MzlN6fH7vp8Yy+vE8wfzeT4FAb9TLdo6oW5A5kn81DD3dsG4I+CDwbsPQ97U6iFLam4ArgCuB/wOeCpwIfAh4p2Hon2hXUaW0hwJ/NAw9WrfsjcBjDEOfNkfVsO/zgecZhv5spbRnAe8xDP2suvUfAzAMvaVPUiqVtmsP4kEklUozyO2Hwe6D/UX46+E0I0PJruZUfj8OH3lQrOJqjPjhqSvhKSvhlEhnGk8ttpsHMXhY44dt8YUPBDrI10AdPVED2ymqbwRKjcIHwDD0FFAC/rXdQQxD/yDwPeAXQAz4NTI89ulOhI/DDsDvaDQ1zgW2d7CvHzjZ+b4d2KqUVq85dVRO7Y1nUBn09sPg9IFtw6GSfI6V4fYM3JqFpMfqWPgcr8DVD4C6X4TPiF+G1z50MvzkIfD6TXBqtL3wMS0Z8kuZMF6R76dG4Pzk4kShHpRrYCFoNwT3eOCfWqz/GmLO3BbD0N+ilPYe4CxE8N3lGBN0hGHoOWcu6l2OIcN5wDOARzRu66y/1jD0I0ppZwFXAz9zytmhlHYr8A6ltLcCTwYeghghuLgMNLYtD/r7C5KLx+cRH5qgk2it0Mor0KFiwY+Ow8ceFMfPsBdetQH+fm13Tqi2LZETbBvOiYn/jmlL3LchN5fzsqDdaTwR2N9i/QFgS6cHMww9D9yklBYBLlVKu6/OWbUTXo2YfR8BjgOvMgx9u1LaZcBPDUOvxb29FHiPUlocib7wbeBtdeU8H/giMIb4AT3XMPT6KA1NcZORDXb7Yfn2wWhFBM7BEpRsERrNrNrCiZnbf6wC3zsqCeCOOxZtj0jCm7fAhi4MFWxbBFfJgi1hyeMTdjQdn2cyhM9isVyvgcWg3RzQUeTh/NsZ1j8a+I5h6C19aJTSvgjcYBj6J5XSgkiMuLMRn55nGYb+01nWf0HJ5fL2IDuh5XL5gXfCW259UKzCziLsKshDPu5tbUFWLuQJRqa2/04nB88vxkRDAQl1c8U6eOKK7rKO5qqS+O2EEJwU6T719kKw3K6BWbIgwUj/DLwUaCqAEMOCv3RwnCcC/+18fzowBKwDXga8E1gSAqhUKg/0hTfo7Yfl0wejFdhblAChPmd4rRNBYZbKBCNRKhb8ckwEz51idIYXeOww/P0aiU7djeCp+fBEvXDZsCSj61eWyzXQD7Q7zR8GfqmUlgI+YBj6IQCltHWI8+c/AE/o4DgjyLAZwJMQremIUto3ECs7FxeXecS0xN8m5wTjvCcPcR+sCkgqhG64Jw///gDsdULlJH0SHPTv1nQ+1GbbMG5K4jeQujw0LvWZTx8el/6inSPqb5TSXgN8FHidUlp9QroK8M+Gof+6g+McAs5WSjuIaENXOcvjTjlLglhssBNRDXr7Yen1gW1LWJo78zJ57/fIsm7C5NS4Nw9fOpbgV+NQBTaH4MXr4MkrJudoOqFiyRzRxpAM1dlIwNKl4t+51K6BfqatomsY+meU0n4EPA8JZ+NBTKK/Yxj6gx0e5wtIQNADyLVbCz56MRJVYUng6WZMYRky6O2HpdUHhapEHDhSlvw7s7Uce7Aofjy/GQfw4UMCg75+U3eps01L0mdXbdF21oe6G6brF5bSNdDvtDRC6CVKac8BNgPfrgkupbSXAuOGof9gQSoxR0ZHx+1BTkQ1Ojo+8Im4lkofHK9IcFCvB4ZnIXhsG358HP7nIOx3htoiXvjbRImXbg51nA4BRPNKVcRnZ1VAtJ5eBgddaJbKNTDP9EQKL5gAWg64Asi98fq5D0wLjpswVoEHChI9uhsNBUTb+cko/PT4ZDqEmBcePQL/fALE8uNEh4c7Lq9kSay28xOwOrA0NZ5G+vkaWEAWxArOpY5QqMusWsuMQW8/9Fcf5KqwIy8P9ootzqOmLYnbupnjuScPvx2Dv6Th9tzk8jUBePUJ8OSVk2WVzc7bXzOpvigJq/qn2+ZMP10DSx1XA+oCy7Jsr3dwTXQsy2KQ2w/90wdHS3BLVgRDxRYT6CF/dxZk9+Tgswfhd+OTy8JeMaX+25VwYXK6ELMtC0+b9hctSFfEh+eceH/68syFfrkGFhlXA1poxsfTA616D3r7YfH7oGLJ8NoDBRgJzC4qwM9H4XMHxAEVpIynr4QLknBJsvX8TCGdbjoEVwvhU7Zk//PisDa0dCzbumGxr4HlRMcCyImMvc75eajLEDouLi5zoGrL3M4dOXnIrwl2/3A3bRE8nzsov+M+eOYqMaVeOcvo+lVbHEjLVdgUlggGw/7lMdfjMv+0FUBKaQp4A7CBSbXLVko7AHzYMPSPzF/1+gufb7DV7kFvPyx8H5gW7CrCzoIkf0v6ux/SqljwqzH438Piy+MB/mUjPG8NBLpsTm3oybbF0s7jgY1B2JhYfkNtM+HeB72jXSy4twEa8AEkmnQtqdta4HLg3wDdMPR3N9l3F+Jj1hbD0Ld2V+1Fw50wc1kw0ibclpXJ/JWziFhg25Jx9KMPTlq0rQvCW7dIyurZYtmSouHEiOTy6VaIuSwLFmQO6CrgHw1D/17D8n1IVOt7gI8B0wQQ8PG673FEi7qBydTclwAXIeF+lgSDnohq0NsPC9cHYxWxSov4xKKtW+7Ng7EPbnISz28JwwvXStSCufjgjI6nqUSSnBLpLJfPcsS9D3pHOwG0Eri7xfp7kThv0zAMfUKwONGwP2AY+nvrt1FKuxrY1lFN+4BBT0Q16O2H+e+DXFVC59yTl4Cc3fjxZEwxTrj2OPzwmKjrQz646gR4zqq5xVirDbl5LYtLhwc7H497H/SOdpfRDcDbldJeahh6uX6Fk1bhLc427Xg2cH6T5d9GksW5uAw0haoMt42Z4EMm8jsd2tpXhK8chh8dk1w+IObTf78Grlw/t7kZ2xZn0pQJm8Ow1h5s4ePSW9pdSq9F0mgfUUr7PVPngC4D8nQWDTsHPAa4v2H5Y5wylgTDw4Otdg96+2F++uBYWYSPxyPWbZ1yZxa+dEhitNUmJ8+MytDYS9fJsNtsSZkieHxAwgcPiYuFmx11rwH3Pugd7aJh36mUdhqSduHhwCZn1SEkHcPXDENPz7R/fVHAJ5TSLkByDOGU91IkH9CSoFAoDnQekEFvP/S2D2ohcw6Xuwub82AR/nMP/NWZ3/F74Ckr4R/WwtY5BmrOViFniiA8PTE9L08+714D7n3QOxYyGOnzgNcDZzqL7gY+ahj6txakAj3AjQXnxsCaSx9kTEkCN2pKYM8jZUlDEO9iSOtno/De3ZCzxI/nOavh+WtmZ6hQw7IlQZ1li7XdyVFYMYMvj3sNuH3gsHDBSJXSTgUeQZ0jKnC9Yej39aISnaKUtgL4PGICfgy42jD0rzXZTkO0qy3Odp80DF2vW78bGUasOouuNwz98nbHdwWQe+PNpg8KVZmneaAIQa8In4olwqdTK7JDJfj4fvi/Ufn9uBExp56r700tN8+pEZnjaZfXx70G3D5wmH8zbKW0IeDLwNOQeZxaVtM1QFQp7YfASzoZhlNKCwNPBU4GPmMY+rhS2snAmGHoox3W9xNAGREe5wE/Vkq7zTD07Q3beYCXALc7x/u5Uto+w9C/UbfN0wxD/2WHxwUgHh9stXvQ2w/d98GREtyclQuyPvtopyF0siZ88RB87TCUbQh64A2bRPOZqwl02pnneVgC1nWYXsG9Btw+6CXt3p8+hjzALzMM/Y/1K5TSHgF82tnmpa0KUUo7Bfgl4g80jFi/jQOvcn5f2a6iSmkx4DnA2YahZ4E/KKVdC7wYmY+awDD0D9b9vFcp7QfApUC9AOqaQQ/cOujth+76YKwi8zTDAdF8uuWXo/D+vZK6GuAJI/CajZJJdC7UsqSuCsCFie6GAN1rwO2DXtLutng68E+NwgfAMPTrgVcAz+jgOB8Bfo5oLoW65dcCj+2opnAaYBqGvqNu2W208SNSSvMgFnuNWtJXldKOKqX9XCnt3E4qkMsV2m+0jBn09kPnfZAyxZE0OUvh880jcPVOET4PjcMXz4D3nTw34ZMxRSM7VIYtEQk+2o3wAfcaALcPekknl18rcd/pq8AjgIcbhl5VSqtfvheJMdcJcaBxqC8FJNrs905E0F5Tt+xFwM3IyMjrgZ8ppZ1hGPp4485KaVchESG4/PIncPHFFwEQiYTx+31kMpJAJRDwE4/HGBtLATI8MjIyTDqdwTRlqmloKEGpVKZYlLgo0WgEr9dLNitlBIMBYrHoRBler4fh4SFSqfSE89vQUIJisUSpJG5ZsVgEj8dDNivW7KFQkEgkzPi4dJXP52VoKDmljOHhJIVCcaKMeDyKbdsTN1YoFCQcDpFKZaaUkU5nJvplZGSIXC5PuVxxyohhWRb5vJQRDocIhYITZfj9PpLJBGNj49ReIEdGhshmc1Qq8oqfSMQwzSqFQnGijwMBP+l0dqKPE4n4RBm1Ps5kshNlJJNxKhVzShm9PE+pVLrtecpX4S4ShMwSVr5MHghGI4CHcl7Okz8YJBAOU0innTK8hJNJsqk01xwL8qUxsaF+5ZoyL0jk8ZhglqOATdnpY38oiD8YopiRPvb6vIQTSQrpFLYlnRwZGqKYz3MsV2FFAB6yMkbRtBgqFhgrdX+eUqn0kjhPMH/3E9CT+2l8PIXlnKeldj8FArOMXttAOwH0Q+DzSmkvNwz9z/UrlNIeDnwG0WI6oVmNNyNCpBOyQKMBfhLINNkWAKW01yJzQZcZhl6qLW/Q6N7npAa/DGnvFAxD/yzwWYBcLm83ml82TkY2/k4mp8rHaDRCNDrVVrZdGY1hP2Kx6DQz0BUrgg2/uy8jFJr6et1YxurVK6fsE4/HaCQcbl3GyMjU34lEfMrvQCBAJDLVgaXbMvx+f9syZnueQqHgRB/Ul5E1Ya8vyTELKj4IeSAejgJT+9gfnHqeaqkNLFuG3D59IMnuorwxveVEeMaqIBBsKCPUtIwakeRkoLdCFdL+GOdtEL+gyXmj2Z2nUChIIBDo+/PU6vdc76dcLj9t+9ncT8PDUwPyLaX7qVe0E0D/DHwduF4pLQMcdZavRjSSnznbtOPnSCy4f3J+20ppSeA/gB93WNcdgF8p7dQ667tzmT60BoBS2suQuaFHGYb+YJuybTqw6mi8GAaNQW8/NO+Do46hgd8LcS/gEd+cTrBtuD4Nn9wv8dtAHD7ftBkunUPAUJA5KJ8HLhmS3EG9wL0G3D7oJZ2aYZ+BBA+tN8P+k2Ho93RyEKW0E4DrnJ9bgVuAU5DICo8yDP3oTPs2lPMNRFhciVjB/QR4RKMVnFLai5Agp481DP3uhnWbEYfaG5EXzX8G3gScYRj68VbHd82wXfPT+j4wLUmTcN8sksNVLIlU/fUjEgUBJLX2lRvgGSvnFrfNsuFYBVb74ZzE7JLWzYR7Dbh94LBwGVEdQdORsJlh//1KaecBL0BiwnmRYa2vGobezYzeq4EvIObgx4FXGYa+XSntMuCnhqHXdMd3I4FUb6ybc/qKYeivROaMPoVY9xWBW4EntxM+Li71HC3BHXkRJN0khyta8MWD8J2jk9ZtQ364Yh08d013wUcbsWwp07Th5DCcEl2eGUldlg9zioSglDaC+NN8ucU2ASR9w9808ddZUqRSaXuQw7C7YeilD9KhJLdnu9N6yhb85LjEbqvl5jklIhlJn7pKohrMhlwVol7xERqrSCiezWFxcp0P3GvA7QOHnrzazFU538xU67JpGIZeASosg2Rug37RDWr7LRvuz8NtGciHktyRhVXBzoRP2YI/jMM/3AXv3iPCZ2sYPns6fP0seP7a2Qmfmi+PbUssuVwVLkrAmbH5Ez4wuNdAPW4f9I52kRA2t9m/UxPqjwFXK6VdYRi62eE+fcf4eGqa5cogMYjtt2yZozlYFsOC+4+mWL9yqK2RQcWCbxyRKAYp54rfHJI5nieMzC2LaE34bAzBWXHx7wl5JXndfDOI10Ajbh/0jnZzQLtprbl42qyvcRnwaGC/UtqdSFifCQxDf3oHZSw6NZv9QWUQ27+rIHHY1jqGT5GA3VbzSZugPTAZrfrkCDxxhWQkncscT42jFUmHfbozxzPcIwu3ThjEa6ARtw96RzsBlALeAUyLhOBwOvC/HRznGPDdLurl4rLoHCpJZtJucvT8egz0vXCkIqFu3n4iXJLsXerqjCnJ6k53DQxclgHtBNAtQMQw9L82W6mUZtLBZJRh6FfMom59x8jIYKvdg9T+tAm3ZCQ9Qf2DPjLUvA+KFnz8QRl2AzgrCh88ufMgn50wLk7ynBNbPOEzSNfATLh90DvaDQh8HTFVnolDiDPpQJDLLZnkrfPCoLS/asMdWYg2SYtdC6VTv+2PjsFz7hDh4/fAGzfBNWf2TviYFhwuwZBPnFO7jd/WSwblGmiF2we9Y94S0iml3Q482jD0MaW0O2gxV2QY+kPmpRI9xnVEXf4OeIdLkqU0XW2e5C0/Pj4R+uYvafjoPtjheLKdHoWrN8PZ8en7zQbbSRSHB86IwqZQ74byZssgXAPtcPsAWEhH1FnyXaAWf+0783gcF5eecKAIt2TFMbRVhtFCFT60D35wTH6vDcKrT4Anr+jd0FjVsXTbHILTY7OLqO3i0u8sWEru5UC5XLGDwQU0OeozyuUKy7X9KROuT0kq6lZhcDLFCm/cHeDmrCSHu3JD76zbali2pOs+IyqOpYut9dSznK+BTnH7AFgCGtCyw7Ksxa7CorJc229a4usT87UWPuMVUDt93JGHNQH471Ml3E0vqfn4nBLpP+EDy/ca6Aa3D3rHggig5TIHlM8XBjoS7nJt/46CDKutmmHYzbbhN+Ng7IMDZS9rg/Cp0yTkTS9Jm1CswkkROC3af8IHlu810A1uH/SOhdKAGueAAkg060uBTyxQHVxcABEoR8pwbwFG/LCvOLOvz7gJ790D143J79NCJh893d9yjmg2jJsQ9sDDhiHpjku4LBCv3PsTAD69+SmLcvyuLnWltMcBZzk/7zYM/Ved7GcYelNTbaU0DdjSTR0Wk0F/61kO7S9W4a48HCyJscHhMqwINNc2Mia8+l7RkGJeeM1GeFq8QiTYOwlh2zBqyhzSwxIQXoBwOnNhOVwDc8Xtg97R0Z2klHYSYtX2EOCAs3iDM7T2HMPQd87y+N8DbgJeO8v9F5RQqMevvUuMpd7+qi3hcQrWpI/OTPHTMia8/j4RPptD8LHTJFGcVZ17H9TSHxctmVdaSpZuS/0a6AVuH/SOTi/5zyOpr7cahr7ZMPTNSGK5ceBzczj+o4Al49VVy8c+qCzl9ttOROtMtX120LEKXHUv3J6DtQH4hCN8AIqZ2fWBbcPxsgz9Ha+IoUHVhgsSkjRuKQgfWNrXQK9w+6B3dDqWcAnwcMPQ99YWGIa+VylNAX9qt7NS2rUNizzAeuChDFAkBZfFoWpLuuvdRck62oq0Ca/ZIVlOt4ThE6fOLaJBxZL5HRspb2tYIldX7bllPXVxWQ50KoD2ApEmy8NIsrl2NGYbtYDtwL8bhv7zDuuw6Pj9fT5AP88sxfbnqrA9K/Msa2aY66nf9nV1w26fOV0Citbj7aIP0k520rOisDI4NU9Pu3QO/cpSvAZ6jdsHvaNTAfRG4L+V0l4H3Ii80F0EfMRZ15LlEow0mUwsdhUWlaXU/sMl2FOE46Y4jLazWitWQd0Hd+ZgfRA+2UT4AITjnfVBoSo3ySOHFiZPz0KxlK6B+cLtg97RqQD6OhBC0jLUvLC8QBX4qlLaxIaGoc9bukCltBXIfNTlSIqHqw1D/1qT7TTgpYiF3THgk4ah63XrT0QyuV6MaHevNQz9l+2OPzY2zsjI8NwbskRZKu3f74TUSfo7S6VQseBND8DNWRmi+9RpsG6G/QqpcSJDw23LS5vwiGUmfGDpXAPzidsHvaNTATQnKzWltF10mJLbMPStLVZ/AigDaxE/oh8rpd1mGPr2hu08wEuA24GTgZ8rpe0zDP0bzvqvI3NXT3E+31FKO9Uw9KOt6jboUYuWQvvHKxLVYHWgszkW24b/3APXp8Un6JOnwcYWDqat+iBflY+FmFQvZKK4hWIpXAPzjdsHvaMjAWQY+pfmeJwvAm8AbmDSaOESZBjvv4BsuwKU0mLAc4CzDUPPAn9wjBteDLy5ob4frPt5r1LaDxCn128opZ0GnA9cbhh6AfiuUtq/OGV/erYNdFl8UqZEqB5qE8+thmnBe/bAT45DxCuhdU5qNtPZhpJjTj3sh4fEIeqTOri4uLSm49tEKS0EvAhxRLURI4KvG4ZearmjcBLwAcPQ39tQ5tXANsPQ/6GDMk4DTMPQd9Qtuw1J9d2q3h4kJfhnnEXbgJ2GodfbUt7mLG/JoCei6tf22zYcKMHtWUj4O3PmvC8P/7lbnFJDXtBPhjNj7ferT0hn2WJSHfCIxrMm2J/hc3pJv14DC4nbB72jU0fUs4D/A5LAHc7ilwP/oZT2JMPQ725TxLMRraORbwNXd1jXOJBuWJYC2s0IvhOZr7qmrpxUk3JOaLazUtpVwFUAT37yk7jggocBEImE8ft9ZDI5AAIBP/F4jLExKdrjgZGRYdLpDKZZBWBoKEGpVKZYFJkdjUbwer1ks1JGMBggFotOlOH1ehgeHiKVSlOtWhNlFIslSqUyALFYBI/HQzYr7lShUJBIJMz4uHSVz+dlaCg5pYzh4SSFQnGijHg8im3b5HKFiTLC4dCEv0OtjAMHDhGJiIowMjJELpenXK44ZcSwLIt8XsoIh0OEQsGJMvx+H8lkgrGx8YkhjJGRIbLZHJWKCUAiEcM0qxQKxYk+DgT8pNPZiT5OJOITZXg84IkPc+uRLKmSyZAfgok4laJJpShlBMJhvD4fpZz0senz87+pGF8+BFU8rPZbfOhULyfbGfLjcp7CiQTVcplKSc5TMBLB4/VSyuUoF/JEkkP4I1H2H0uxJQybIx5WhfrnPI2Pp7Ase17OUy6XZ9261V2fp5GRYTKZ7MS5TibjVCrmlDKWyv3k9XqxLKuvzxN0dj/VGB0d7+o8BQK9GV/uVAP6KJKe+8WGoacBlNKSwFcQS7gnttk/BzwGuL9h+WPo3BE1iwjAepKIg2xTlNJei8wFXVanqXVVjmHonwU+C80T0rX73WgxE41GiEanjvO0K2NoaGp1Y7EosdjUMMwrVgQbfndfRig01eGlsYxIJDJlWTw+XWVoDFPSWEbj5G0iMTV7WyAQIBKZOgnTrAzbhp0FuDcF8VicjXXN8/n9BMJTy4gOD3N3Dt6yE/aWZJLw71bDazZ6ifug8T3GG4kQiESmlQEQisU4XIJt64Y5tS5oaL+cp+HhqW/ovT5PgUCg4/M0UxkAfr+/bRn9eD81S0jXj+cJOrifxqYv6+Q89YpOBdClwIU14QNgGHpaKe0twJ872N8APqGUdkHd9g9HLNXe2WEddgB+x1jgPmfZuchQ4DSU0l6GzA09yjD0B+tWbQe2KqUl6obhzgWmWdO59CemJVlL7y/IsFcnSeD+nALtAQnDc1IY3noinDvLzKXHyhIZoV74uLi4dE+nvthFYLjJ8iFnXUsco4AXA+cgRgf/5Xx/qWHoH+ikAoah55DYce9SSosppV0KPAP438ZtldJeBLwXeEJjnDpnDulW4B1KaWGltGchMe6+264OiUQHkwTLmH5of9YUi7Vdxc6ET9WGLx0UB9OCJVlLv3pW98LHtCBbhUIoRtIP22KDKXz64RpYbNw+6B2dakA/BP5HKe3lTGowlyAT+41hdppiGPq3gG91XcOpvBr4AnAEia7wKsPQtyulXQb81DD02mPl3cBK4MY6H6WvGIb+Suf78xHLvDHED+i57UywAUyz2rOxz6XIYrd/rAI3ZSRuWifpEHYX4O27xNAA4Ip18KoTuk+bnTJFAK0MgJ8q5ycCAxtGZ7GvgX7A7YPe0VFKbqW0YeBLwNMQ51MQ7ela4ArD0Mfb7P9oAMPQf9tkuW0Y+u+6rfhi0GwOaJBoNva9UOwvwm05GPK1t3KrWPCzUfjQPtFa1gbhzZvhsuHuj5syxcrtgoQ4lS5mH/QDg95+WF59MId8QAuXktsRMM9QSjsFONNZfLdh6I1GBTMWAbyryfIkMgf0sA7LcRkwbBvGTBE+KwPtY6jtLkpInX2OycnjRuCdJ4pvTrdkTXnLqgkfFxeX3tKpGfbbgQ85Auf+uuURQDMMvZlwqed0xNemkTuddUuC+bIEWSosdPuPliQ8TtV2nEvbCJ/fjMG7dkO6KpGnX7IOnray+yE3kMCkpg0Pbwin414Dg91+cPugl3Q6kv0OxH+mkaizrh0FJP1CIycgoXWWBPV284PIQrY/a4rwSfhhbaj1sFvZgvfvgX99QITPo4fhK2fCM1bNTviULAkmelFyagRrcK+BQW8/uH3QSzoVQB6ax3J7KDDawf4/Az6glDZSW+AEFn2fs25JUHPgGlQWqv2FKtyYEaETanOFHi5L8rjvHJW5mjdukqgGsx0yy5qSDfXCpAi/RtxrYLDbD24f9JKWolwpLYMIHhvYqZRWL4R8SD6gTuKn/SvwO2C3UtrtzrKHINZsf99tpV2WL/mqWLrZ4DiIzsw9eXjdDsn1sy4IHzwZzpqDhWzKlIv60iGIuy+5Li7zTrvb7LWI9vMF4C1MDWFTBnYbht42I6ph6AeV0s5FYsmd5yz+EvA1w9CXTEruQVe957v9xaoEE4XWwTxtG349LvM92SpclID3bp1b9OlcVYYDLkq21p7ca2Cw2w8L0wdzsE5bUnRqhv1o4I+GoZvzX6W+xg3EPk/YNvw1I1pIK0GyqwD6XrjBiWHx2GERPoE5+OVYNhwtw6XDbhRrl/5goQTQUjHD/m37rVqjlOZH0i9sBqa4ERqG/uW5lr8QDHoiqvlq/4NF2FmEnAlrQs23sW249jh8YA+UbUj44JUnwHNXg28Ot4JtS2idU6OdCR/3Ghjs9oPbB71kQd73lNLOQKIpnIRIzqpz7ApQApaEABr0RFTz0f60CXfkJJfOTMLngYIInpudud+nrYTXb5zbkFvRgrQEHmZTGE7pMA+Qew0sdg0WH7cPesdCDTh8BPgrMv9zyPk/BHwKeOsC1WHODGLsr3p63f6yJTl8oj4Jr9OMnQV4xb0wbkLSB2oTPG3V7I9ZqMq8kc8jlm4hr5had9o29xpY7BosPm4f9I4ZR86V0jY7ydx6wYXAu52AohbgNwz9ZuBNwId7dIx5Z9DV7l61P2PCngLckBZNZCZrt1+OwpX3iPB5RBKuPWduwidlQsWG8+LwyCFYFRRT6258hdxrYHixq7DouH3QO1pN3e4CVgMopV3nxIObLR4m8/4cZTL524PAKXMod0HJZAbb/r8X7c9X4c9puKcg47AjTYbRbBs+sx/evFMcSy8bgg+eMnvT6KoNR8qSdvvhSVgXmr2fkHsNDHb7we2DXtLqls4AqxBfnccAcwn/eieSc2cncAPwb0ppVSSraqfx5BadWpbAQWWu7TctuCUjIXWSM1x5VRs+tBe+fVTejt6wCf5+zeyHPcoWjFbg9ChsjcwuMkI97jUw2O0Htw96SSsB9EvgOqW0Wrrt7yulNQ2bYxj649oc5z1AzUXwrcCPgV8Dx4DndV5dl6WKbcP2nPjbrJwhlUKuCu/cJT4+QY+YVz9mpPm2nTBuQtWCCxMzGzi4uLgsHq0E0IuBlyFDZI8G7qXz9NlTMAz9Z3XfdwJnOqF4xgxDXzI2JcnkLFNoLhPm0v6dBdhfkrhuzUiZ8PJ7xBw75oP/OgUelmi+bTuqjl/P+hCcGe1tJGv3Ghjs9oPbB71kRgFkGHoB+ASAUtp5wBvb5f3pBsPQO4kh11dUKiZ+/+B6Ks62/YdLEjZnpiRyVRvetlOEz4lh+PApEs16thyriODZGp19GTPhXgOD3X5w+6CXdOqI+tjad6W0OJJELjdvtepTCoXiQIdi77b9FUs0kVtzsCLQ3GHUtiWywfVpcQT92KmiucyGqi3zPav8cGKHfj3d4l4Dg91+cPugl3QsxpXSXgP8G44Fm1Lag8AHDEP/5DzVzWUJU7Lgj+NQsmHEP7Ofz2cOSCTroAc+sHX2widtSiy506KiPc3V2MDFxWX+6SiCllLavwPvBz4PXO58rgHer5T25vmrXn8x6G893bR/b1ESuq0Jzix8/pSCzx2Ui/CDJ8MFydnV63gZwl541AicHAX/HOLCtcO9Bga7/eD2QS/pVAN6JXCVYehfr1v2K6W0+4D3IsJp3nEMF2pC8BhwtWHoX2uy3WOBtwPnI4YOJzas3w2sRVxRAK43DP3ydsf3+wc7L3On7S9WJXzOihaG+7uL8LZd8v2qDfDI4dnV6XhZrOrOjc2v4KnhXgOD3X5w+6CXdCqA1gA3Nll+A/Igb4lS2qNmWGUDReCBDo0SPoGkgViLhPP5sVLabYahb2/YLoekkPg68O8zlPU0w9B/2cExJ8hkcqxYMdzNLsuKTtpv23BnTpLDzRQk9HAZXrtjMsLBFc1y5bahZMn+q/wLJ3zAvQYGvf3g9kEv6VQA7QBeCLyrYfkLEfPsdvyGyVQGtcdS/W9LKe1a4MUzGTcopcWA5wBnG4aeBf5Q2weYMgxoGPoNwA1KaY/voG4uPWJHHsYqcLwys7n1uCnC51AZHhKDD5zcXTRryxYrt7BXBM/a4MIJHxcXl97SqQB6J/AtR5P5o7PsUsQ/6O862P9vAR1xSP2Ls+xi4GrgHUh8OAMZyvvnGco4DTANQ99Rt+w2pw6z4atKaV7gFkAzDP22ZhsppV0FXAXw5Cc/iQsueBgg48B+v49MRuRlIOAnHo8xNiY5+zweiRmVTmcwTRnpGxpKUCqVKRZLAESjEbxeL9mslBEMBojFohNleL0ehoeHSKXSVKvWRBnFYolSSXyCY7EIHo+HbFZctEKhIJFImPFxyezm83kZGkpOKWN4OEmhUJwoIx6PYts2uVxhooxwOEQqlZlSRqFQYNTRU0dGhsjl8pTLElLaDMe4N2URLheIe6BihfAFgxQzUobX76MUTvDqu012lfycFKzyX6d48RZz5B3P8lAshlWtUikWpU/DYbx+P6WshD6xfX4ywTgnlMfZGAJfAfzhYTKZ7IR3ejIZp1IxKRSK83Kecrl835+n8fEUlmU3PU/xeAzLssjnpYxwOEQoFJwow+/3kUwmGBsbn4j6PDIyRDabo1IxyeXyJBIxTLM6pY8DAf9EqupAwE8iEZ8oo9bHC3meYP7up0DAP+/nqcbo6PiszhPQ0XmqP0435ykQmEtgnEk6SkgHoJT2MEABZzqL7gY+bBj6LR3s+1fgTYah/6ph+eMRS7qHKaU9FfiYYegnzVDGZcC3DUNfV7fs5cCLDEN/zAz7PB74XJM5oEuBmxHt6/XO54x2fk62bdueAQ6Fa9s2M7X/xhTkrZnjtaVMeM0O8QfaFILPnC4GCp2SNsWse1sMTljEOeBWfTAIDHr7l0CiuI6o2FUCnuZzWa3W1bFwCekADEP/K/APszzOWcD+Jsv3O+sA7gDWNdmmRhZotJNKIjHrusIw9D/W/XyfUtpLgcuQnEUzMjaWGuix35nav68IRyoS5LMZuaoMu9WEz6e7FD4VSyzqLhvubVSD2eBeA4Pd/uVCwOObEHKNLGQa8IUaPb8LeItS2sQjyvn+7846gE1IrqCZ2AH4ldJOrVt2LtBogDAbbHok0QcJ24bdBcnps2oGgWLa8O874e48bHSEz9oOhU/JkvmeWmSDxRY+Li4uvWWh4km8GtEu9iul3eksOxuZ+3mq83srMKNTq2HoOaW07wHvUkq7ErGCewbwiMZtnbmdIBLB26OUFgYsw9DLSmmbEWF3IyKA/xmJ+v3HxnIaGeCRB2Bq+6u2CJ6DJRE+/iZ9U6yKqfUfUxLl4OOndi58xityvDMi8nawoU+CibrXwGLXwGW22LbNrvI412V2c+Wqh3a3c6UK6SJEgxDpzfwPLJAAMgz9L0ppJyFDeKc7i78GfM2xaMMw9E7Scr8aMa8+AhwHXmUY+nZnfuinhqHXogQ+Com2XaMA/BZJK5FAMrGejJiA3wo82TD04+0OPuiJqGrtt2y4Myvm1DNZu1Vt0B6AP6Uh4QPjFNjY4dxNyhQrtwucjKX9hHsNDC92FVy6xLQt/po/yHWZ3ewpi0HGlXQogEomjOXlY3tk2OOkFSKIekDHRggukE5n7GRyliGalwHpdIZkMsHOvMznzCR8QBLK/c9BGPbDZ0+XXDydcLwsQ20XJPpzyK3WB4PKoLd/KRkhpKslfpfdy+8ye0lbYikY8wZ4ZHwTzxo+o/UcUNWC0TwczYraGwlIfKtsGbaMQCy4sEYIc0UpbSOimayhYe7JMPT/Wqh6zIWa+eegYppVxipwb2HmOR+AG9MSYscDvGdr58Ina8pQ3fkJCPSZ5lPDvQYGu/1LgT2lca7L7OGv+YOYiKn4hkCcxyVO4qLoBoLeDt7s7j8GliWazjwGVuwmGOnfA39DcwHy9Db7vggZOjORlNz1apcNLAkBNOiULLgrA0nfzM6jYxWZ97GBK9fDxV3Ed8tX4Zx4/wofF5d+pWpb3JI/xHWZPewsjwHyAnhuZC2PS5zIaaEVU8znK3Z1Ru2qUqkQCPrA17u5npnoSAAppenAvyDzKgeYKkA64V3Ah4G3GYa+ZF+hhoYGd+ghY8IOj7Q/PMMLlGXDO3aJ1dpD43Dlhs7Lz1Yh5pfI2f3MIF8D0L/tX6ihsX4jUy3xh+w+fpvdy3jVcRb1+Lk0vonHJLawyu8kxWqYagl4fFCs8MojvwDg09nzIeiDoJ8AgG9h3gI7vd1fArzAMPTvzPI4axGH0CUrfABKpTLR6DwlmuljClX4cxq8lTLDsZnb/7XDktcn6YP/3NrcMq4ZR0oy9HZmtP+trAb1Gqgx6O3vFx4sp7kus5sbcgcmhtnW+eM8LrGFi2InEPb6RegUK5CvwLEcrI7DSEQMCw5nIFMSkyyA+OKYmXYqgLyItdhs+QkSemfnHMpYdIrF0sDdfLYtPjxeIFgpAc3bf1cOPu64Gr/9RFjXoZFM0RLN55Kh/hc+MJjXQD2D3v7FpGpb3F44wnWZ3dxXkphYHuCc8Boem9jCmeFVk8NsJRMOpSFXlq2CPjiQglwJ0iUZQ08svm9DpwLos4gJ9TtneZxfAB9QStuGRDyYEvDIMPTvzbJcl3lmzIRDJbF4y8+wzbgJV+8Up9PnrYHHjHReftqE82JLQ/i4uMw3tTA4zYYSU9USnzl2MwBhj59HxDfymPgW1gRisoFli4CpaTg+71TNxuuBfBligb654ToVQMPAC5XSngDcznQB8ro2+3/G+d8sNYIN9KHB7XQG8c1vT3HSHDoYmd7+igVXPwD7S3BGFF6/sfOyS5b4+azujUvBgjCI10A9g97++aZdiJw1/iiPTZzIJbGNMsxWI18WjadgiqCJBKbP4/i9fRc6vlMBdBaTQ3BnNKxra5BgGHp/tXqWeL3LohkdU6iK9lMTEJ6G9ps2vHUX3JiBlX748CndOY6Om3B+vO/uiZYM2jXQyKC3f7F55/pH4/V4RNvJOsNphZKEHQn6+mJYrRs6EkCGoT92viuyFMhmBycRVdWG+/IyVDwxrJzLER0entjmw3vhV2MQ88FHuwizY1owasIaf+f79AuDdA00Y9DbP5+kqyWSvtYCxLvzOIQDMrdTqcrbW8C75ARPja6MXp2YaqcgWs8DhqEXu9j3b4F/Q7QpGwlC+gHD0Jvrmy6LhmXDrVk4WoZVM7gCfOcIfPuoZD796ClwRqzz8o9XJK3CxnDfDEW7uCwaFbvKdZnd/DT1AB/ZdHnrjW1knsfnhfDSFDr1dOoHFADeC7wWCfLpAUpKaR8D3mIYeqXN/lcigUa/CnzJWXwZ8H2ltFcZhv6FWdZ/QQkG598xqx/YWXDivDVoJz4nCdXvxuGDe2XZW7bAeV24hoxXYH0ItizRqYRBuQZmYtDb30ts2+bWwmG+O34Px8yZTHwaCPpYIlPmHdGpBvQB4AXAK4E/OMsuA96HWOj+a5v9/w14g2HoH69b9nknUd2bkSgJfU8sFl3sKsw7uSrcV4DVTZ4zwWiUO7Ji8WYhkQ6euqrzsk0bKjacvoS7cTldA7Nx3lxO7V9M9pZTfHvs7glz6vWBOM8dPrPNXsuPTgXQC4GXNQyXPaCUdhT4HO0F0Gbg/5os/ynwoQ7rsOgMQjKuwyV5o2gW/uneoxn+5UCSkgXPWAWv6CLSAcBoGbbFIbqEX+AG4RpoxaC3f66kqiWuHbuX6/MPYgMxT4CnRbdymX89voxFJWDOHCLHNFlu+menAmgIeKDJ8gcQE+127AWeANzfsPxyYE+HdXCZZ2wb9jhRCRo5VoZ/PRAnZcIjh+DqLd3N3+SqUu7GpT9s7eLSNRW7yq9Su/hp+gFKVPHa8LjKGp5SWkcsEwDS4PUQSBXAA68M3gjAp7lkoozlJnygcwF0G/A64DUNy19PZxESPgR8TCntfOB6Z9mlwIuRhHBLAu88RoVdbPJVSS5XrEKy4aooW/DGB+CQ6WVbDN7XRZidGjkTLhqa18C6C8JyvgY6YdDb3y22ZXFz5gDfS+/guC02Ww+xhnmO90TWBiMyo75UGC/AH3fDC7pMZteCTgXQm4CfKKU9Hvizs+zhwAbgye12Ngz9M0ppR4A3As92Ft8NPM8w9B90V+XFY3h4aLGrMC9UbbgpI34/I01es/S9sD0H64PwkVO6z9OTd4Taij4PNNoJy/Ua6JRBb3837E0f51tjd3G/JwPABjvCcz0ncpZ3eHEr1i3HcnDnIbjlQXlY/GEnPHVbT4ru1A/od0pppyEaUM0R9dvAJw1DP9BhGd8Hvj+rWvYJqVSaoaEu8gssEQ6WJBp1M5+c7x+F7x+DkAfevT7LSCA+faMWVG0Jt7NUYr21Y7leA50y6O1viW1DpUoqn+MH6R38yT6K7YE4fp7OJi71rMXHErkJsiW49wjcdRgOZyeXn7YaLtjcs8N0/E7qCJq3zPWAji9RYz6hDm0QF5dq1VrsKvQc04J78821kx35SXPrq7fAqX6zq7JtW+aOzozCimUygL0cr4Fu6Lb9yzZNgmVPRpu2gGM5KsUiv/If4afBQ5Q8Fj48PJZ1PIWNRBcu9+fsSRfhvmOw4yjsT00uD/ngtDXwkPWQCPfU6XXGXnHma241DN1yvs+IYeg3t1qvlLYF+G/gsUAzl8WOBnWU0lYAn0eMF44BVxuG/rUm2z0WeDtwPjBmGPqJDetPBK5BInTvBV5rGPovO6nDcuNgGco2DDdEWDFt+M/dYjb9rFVibp0f767sYxUxOjhpifr8uLhMoVT3AnYwDaUKFExsj83NoTTfje5j1FMG4FxGeLZnC2tniB7fF1QtztldZcshG3b/FQ5mJtf5PHDiCjhrLZy8EvzOIzpb7mkVWonlm4B1wBHnuw1N9cdOgol+BQgjBgeH6T6hXY1PAGUkv9B5wI+V0m4zDH17w3Y5xLfo6zQPgPp14E/AU5zPd5TSTjUM/Wirg/drMq7ZUrXF56dZErivHZY0DGuD8C+bZFk40Xn7C1WI+8TsejkMvdVYbtdAtwxk+y0bRnNwJAu1Eeh0Ebwe9iQqfJvd3I88vE8gyt9xImfQp3Nl2ZKk296fgn3jvCZbE6oZCetz0go4fTVsXQnB+dfaWh3hJCR9du37XHgocKFh6HfPtgCltBjwHOBsw9CzwB+U0q5FLOneXL+tYeg3ADc4RhON5ZyGaEaXG4ZeAL6rlPYvTtmfblWHYrG0bBzxbFuG3ioWBBqugr1F+IyT2+fft0isNwCzXCIY6az9GRPOT8ycunupspyugdkwcO0vVOBQmsrGJIFVcT7N1KHELcAV5kref+APPJ3NXMoavP02z2PbInBuPwh3H57y+j8eqrJvqMI5D3+YaDzBhXXSm1EAGYZe759jA/sMQ5+muSildTIjdRuwGrF8my2nAaZh6Dsayn10l+VsA3Yahl6nb3Kbs7wlpVJ52dx8+0uwqzDd8MC04V27oWTDk1fApXUvcmap3JEAKlmStnsppVnolOV0DcyGgWm/bcPxnEzAB30EAoGWaRLexUOJ9NM8T8mEe47A7lHYNw5FR9PxIENqJ62AtQm+v/068MA5p61elGp22mO7gPXIcNwESmkrnXXtxOZVwH8rpf03cCfT8wnt7aAOcSDdsCzFZFLZTok7+zWWc0KzjZXSrkLqz+WXP4GLL74IgEgkjN/vI5PJARAI+InHY4yNSdEeD4yMDJNOZzBNyUQ+NJSgVCpTLJYAya3i9XrJZqWMYDBALBadKMPr9TA8PEQqlZ6Y/B0aSlAsliiVZCw2Fovg8XjIZsWOIxQKEomEGR+XrvL5vAwNJaeUEUsmufN4kWi1TKEgIXbAppwv8PnjYW7Nhlnpt3nVUJr8uI3X5yWcSFLKTsrsyNAQ5XyeakVOZSgWw7YsyoUCo2V4yIoQthVkNCX7+P0+kskEY2PjE+npR0aGyGZzVCpycyQSMUyzSqFQnOjjQMBPOp2d6ONEIj5RRq2PM5nsRBnJZJxKxZxSRi/PUyqVXrDzNDycpFAoTpQRj0exbZtcrjBRRjgcIuX0ca2M8fEUlmVP9HEul6dcrjhlxLAsi3y+MHEuq9XqRBntzlMqle7qPNUz3+cJoFyuzO1+ioTx5Ctk9x+DokkoGaMYsFhJa6xihdGKnNt4OIKNTa4obQsFgoSDQVI56R+f18dQLMZ4NotlSz1G4glyxSJl03k0Ok+10YxcH+FgkJA/QCovbfP7fCSjMcayGWznRI3EE2TzeXw37ydy8yE8lUmDkWoiiHXqKjznbiAdlOUBn39iUmU0k8aDh5FEgkw+T6XqnKdojIppUihLH0dML/5KhUCPHJg6FUAems/bxIFOImJ7kXmb7zeUUyu3E70vCzTafyaBTJNte1aOYeifRTLCUiqV7FBoqgVIY1iSxt/J5FT5GI1GpiX1aldGo9lrLBad9ha6YkWw4ffMZezMgyccJRmcWsaOSogvj8lJec9WDxuSU8exE2vW4A9Otj8Um25PUvKHWAVscZxOG+sxMjL1dyIx1aw7EAgQiYRbtqVdGX6/v20Zsz1PsViE2jUw3+dppjLaXYONvjrx+PTzFA6H5E4AfD5fx30ci0UIBAKdn6e6u2pez5PTlmAw0Pn9ZNng9Uz2ca4MD6YwKxWOxizuHM6xnQc5QIFP0zruVCwcIRaeer2EAg3nOjH13A7Hp/ZHPBKhMeV94z6Nv0fiTn+M5eEXO0jsPD5pKLBxCM5cC1tG8A2F8TmTsSua1L++3ER06vXm9/mI1K65bBkCvTNpbSmAHI0FREi8Tymt3lzaB1xEZ5EQvoRoT//G7I0QdgB+x1jgPmfZuUCjAUI7tgNbldISdcNw5wLTrOka8SyD2fRiFe4vwHDDNVS14f175MT8w1q4oKmrR+v2m5b4Ez0iufQjHszEcrgG5sKyaf9oXobYtoyADaNHjrO9cIQ7g1nuDaUpUp3YNIS3RUGLSLoow2v3H5NP7ak6FIYnnCZzOn1OOw3oHOe/BzgTsUCrUQZuprNgomcA5zXM33SFYeg5pbTvAe9y0jucBzwDeETjtkppXiTIRQDwOL5HlmHoZcPQdyil3Qq8QyntrUgkh4cgRggtyWbz095glxo7izKc0RhK5wfH4K48rAnAy2d42Svn8/iDzdtv22J2/dD4dOG2nFgO18BcWPLtr1pwJIN5PMv9wTzb9+1ju3ecA76i2Ok6rCfCNoY5mxFO7nqUfx6xbXgwBX99EB6oEzoe4Ox1cP5GWB1bMqanLQVQLROqUto1wOsNQ2+cg+mUGxBLulkLIIdXI+bVR4DjwKsMQ9+ulHYZ8FPD0Gs67aOAX9ftVwB+CzzG+f184IvAGOIH9Nx2JtjLgawJe4rTUy2MV+ATD8p3tWnS6q0bxk3YFIIN4fbburgsOGWT0XSaO1MH2e4Z555EhpJnco4khJczGGIbI2xjmJX0UdRc2xZN5z5H08k4c15eD2xdARuH4cw1EO+jOndIp3NAVyPzJFMEkFLaRqBiGPrhNvt/CviIUtqHgTuYboTQ0pG1brtR4JlNlv+eSQt9DEP/DS3GiwxD382kMOqYUGgJv/khwifomf5y9LH9kKrCRQl4/MjM+8+k/Zi2fE4ZAOOopX4NzJV+a3/FrhLw+JpGWihbVXbmj3Pn+AG2V0Y56CtOCSm9wdFytjHCKSTwtxhqq5iLkCbBtmHXcbh+Nxyqm0yLBSUqwXkbILb0hE49nQqgrwDfBP6nYfkTgb9HIhO04uvO/882WdepEcKi0zhhupQoVMW/p9E0+o6sDL/5PfCmNikWAuHp7S9aokGdt8Tz/HTKUr4GekG/tT/g8bU0j/7I6E3ywzep5ZztaDkrutByAgdEALySP0nZ85kmwbbZtqvKU6+vwqE7ZFkkIELnlFWwLrFkhtja0akAuoDpqRgAfg/oHew/V0fWvmB8PL1kk3HtKYqjc/11a1rwPsfb68Vr4cQ2z5ZCOk10eBgQA6JjFRmuuygBq5f2i1jHLOVroBf0VfsdK7ZWiJYzwtkMc3IbLWdRqVRh1yjsHYODaf75sOO3EwnARZvg3BMW3El0IehUAPmh6etCeIblU2hwanVZYIpV2F2cHhD0mkOwoyBpFl62vrsyj1bg5DCcFl02L2MuS4gjhTQ3HdrFU046t+V2b+e8hanQbCibkubg9oOS8qCOdAR+cZGP55z78GUpeGp0KoD+ArzK+dTzGuDGTgpQSvMjZtubaUjDZBj6lzusx6Li8/Xp21MLbFuEj5epYXHuz8PnDsr3d5zYWY4fr1faP26KtdypAyh8luI10EsWrf2mxVhqnL/mD3GjdZQ9nhz44Cm0FkB9SbYEN+0TwVN2zL09wNoEnLoKVsX4wQN/wEzTP8Knakld7dl40MxMpwLoLcB1SmkPAa5zlj0OifE2Ld5aI0ppZwA/RIbiPEDVOXYFKAFLQgAttTwoti2J5PY0zP3YtqRZqNrw3NUz+fxMJ5xMYloydHfWMvb1acVSuwZ6zUK3P1MpcsuxvdxYPMT93iy2B/BAyPZynqf//VymkCrCDXvhzoNy84E4i56/UYJ/+ieFu7l7cao4gWVLOJ+qBXgg4BXjh7FC2127odOEdH9WSrsE0JjMaHoL8GrD0G/roIiPAH9FfHcOOf+HEOu4t3ZV40VkqSXjOlQS4bM2OFVT+fkY3JyFYT+8umkAouYU02lS4SQPiQ2GwUEzFuIa6OccOgvR/oJV4db8YW7KHeDu0jEsAB/48XAOI1zAKs7xDBNcGrZLEqXgpn1wxyF5sINoOhdvEYOCfsC25c2ypuV4vZAMSf6fkG8yMvZIFEK9i3nXTUK624B/mOVxLgQe7TiTWoDfMPSbldLeBHwMcQTte5ZSMrKSBdvzMu9TL3zGK/BhJ/Lea06QVNmdULXhaNHipCScMCAGB81YStfATLQyXa6tm4n5an/ZqnJH8Qg35Q5wR+EopogdvDZs8wxzIas4l5FpAT8XxTy6Ew6mZYht75hoPuC486+Bh2+Blc3Soi0i+QqE/bAqJppOONB8iCPWWzP8rkWZUto6ps/htAsm6gFqYXyOIoE/7wUeBE7ptg4urbFtuCcniRqDDUP2xoMwasLDEvCMVZ2VZ1pwvAJbInDW0nGydpmBdqbLC4VpW9xdPMaNuQPcVjhMyZb5EI8Np1pxLvSs5nzvSuItxMiCmkfPRLkKRzJwPA8Pjsv/I3VprEN+iUB98eaFFzxVSyJhWxYEfCJYnNThE/NPAKevWZQx9Y4EkFLaEJLR9HnQNAxqO134TiTe2k4kKsK/KaVVgZcD93dc20VmeHhpDL8dKsGDpempFvaX4KfHxefnbVs6v96Om3BuHNYHk3gHew5+yVwDc+F/j9/BkC9Ewhck6QuR9IZIOt87TUjXSssqWBW+O3YPtxQOkbMmfdK3+JJcmE/wMP8aRnz95W80jVp0gu2HJYV1pTp1vd8L550AZ6yGNYmFfbjbtgRWBfB5YUUUokGpb74CloV58kr8oQBXnHXFtN1N08TvX5jUEp0e5UOIAHkm8D3gZYgW83rgjR3s/x4mU3G/FfgxEirnGCLUlgSFQrHvc6GkTLg1CysD0zWVbxwWrejJK2Bjh/f3eAXWBWFDCPL5/m//fLMUroGZyFXL/Dh9P88bOavldn/M7ZtxnR8vQ74QSUdADflCJOoElAirEGsCsZZa1h+cY2wIxLkgsp4LqiOsOWRCxA++/p3bSeRsHnFnFe74C4zXJQJYE4eVUdgwBGvjoun0cK6kY8qmaDwro5OCr/YgWJcQC7x1SfxBH9dcc03TIq64YrpQmi867aEnAy8wDP33jubyV8PQv6mUdhB4BfCdVjsbhv6zuu87gTOV0lYAY82S3PUr/Z6Mq2rDLRmI+8VopZ5xE649Jt9fuLaz8iqWlHmmY27d7+1fCJZiH5i2xW8ze/hx+n7yVqWtAHrhyDZS1RIZq0y6WpKP871sVzleLXC82toaqt1Q3pOSJ3NhdAMneKKSrTNXhmhA3tj7kUoVbtzHu28oEzIBqpAIwba1cNY60TIWs27lqhg4RINwQgyGIhNal23b5PN5UsUUqVyK1M338vCHP3zx6ltHpwJoGKg5k6aAlcjQ2Z+Az83mwE5cN5cecrAEBQvWNBkk/eg+yFlwcRJO7+BeOV6WGEnnxDrzEXLpP2zb5rbCYb47fg9HTZmCPT3ULrUaPCqxZcZ1B48fx58Mk7JKZKr1AqpE2vmdqZZm3L/GMysnwIEyFI/LDHGiDy1bqhYcSEuEgrsPQ6ZECNiXrLDp8edLuoOFHFozLalT0OcIHQuwJVrC6hhmyEe6UiA1fpjUnhSp1OTHNM0pRS01AfQAsBWJHH038HyltBsQk+yBESTxeP+++VYsuDcPI03O6A1p+OFxCUT6pg4SqI9WxG9oW2yqEUM/t3+hWCp9sLec4ttjd3NfSW7Ptf4Yzxk+g3Mia+ZU7spEgmAgyGrmOJl+PC/zJCFf/2k9pgW3H4A/75E5kxpr4vw0eYhDCZMrtrYX5LM69Lo4/mCg6TCYWSrjO5yjMJoiZRZJ+U3GC1nSD6ZJpVJks9kmJQqhUIjh4WGGhoZIJvtnHrNTAfRFxFT6N8D7gR8Br0Uc7F8/HxXrR+weewH3kgcKMlzWOPRWqMJ7dsv3f1oPW9rM/RSrEPDA2bHpZfVz+xeKfu+DMbPID1L38pfcfmwg5g3w1KFTeVR8Mz6PnNCKXZ3ZdLmNGXbP2h/ts6RRxQrsljhs7Dg6mfJgJCJOoietgM0jHPr1D+a1Gv5goOXczFd+9y0qlUrT9R6Ph2QyOSFkhoeHJ36HmwQS7gc6dUQ16r5f50Q2uAC4zzD0O+arcv1GLleYlg65HxivwM7C9EjXAJ86APvLcGoEXrqufVlpEx6amC58oH/bv5D0ax8ULZNfpHfy88xOKraFDw+PTZzIk4dOIead+rCvCZhmDq+thA900H4n4Rvrh2bepl/IlkTY3H9MkrxZdcJ1VQweeZKYT8+330HVwi6bpPJZhje3yIcCVCoVQqEQQ0NDE5+asEkkEhPhspYKbQWQUloA+APwEsPQ74UJv592vj8uC4DlhNuJ+6cPR9+ZFcs3L/C2E6dE+phGsQp5C8K+5nNILv2JZdv8OfcgP0jtIOXMvZwfWcezhk9ndWCBfU5yJdifBtOisjranw6iAKkC/HE33HNkUuh4gE3DkqJ7bUL+z9f8TsmESpWyWeFA+jj708fYnzpGrljgioee3HLXF7zgBT3RZkzTnNHara/MsA1DryilncRk8teBpd+ScQEcLonWsqbhpbRiwbt2i9n1i9eKA+lMmDakq7A1LEFGZ7rv+rH9C00/9cE9xWN8d+we9lUkT+SW4BB/N3wmp4TnL0bajO0fy8OBlDg6hoMEDsh8xEI5iF78Kycn5t/MsIFlS/22H4K7Dst4tQcJiXPqahlii8yvWLTzZY6lx9ifG2N/5jhHR49NGdLsRLPu1VBaTcDUhvvqhdFCCR/ofA7oS4jTqDaPdel7wuH+Gnqp2nBPAYab3DfXHIKdRUmT/YoNrcsZq8DpEdjaZn6939q/0EwMWcUWN0bboUqW743fw+2FIwCM+MI8c/h0LoxuwDvPw0VTrgHbluCUqQLky2IC3E8GBWUT9ozJENvOUSjUzZ2cuQYeuRWG5nduJF8ssP/IIfYfP8KBzHFKlfLEOo/Hw9q1aznhhBM44YQTWLlyfgwb+plOBVAMeJFS2hOQoKJTklcYhv66XlesGY7v0OeRDKzHgKsNQ/9ak+08iLHElc6izwFvrvkcKaXZSGig2uvHNwxDv7KxnEZSqUz/JOMCjpYlI2ljPLcHCvAFJ9XCW7bIsNpMFKoQ8cLmDu7Dfmv/oJGtlvlx6j5+m92LhU3I4+OJyZN5fOIkgt6FsZVPpTKsGBmSh/mxnEzWh/0QD/VPjKZ8GX67E+45PBl1GkTYnLIKzt0wb347VavK4fFR9h87zP7jhxnLZ6asj8fjEwJn/fr1BGdIcz8odCqAzgRudr5vbVi3kENznwDKwFokovaPldJuMwx9e8N2VyFRG8516vcLYBfw6bptzjUMfcmEAWrEsiWZXKPwqdrwn7tlWO1Zq1qnWrBsGb57+FDr+aF+p5+jR/eCil3lt5k9/CR1P3nbxAM8MraJpw2fxpBvgbVSy4bDWTieE0uVPvHf8diwdTQAX/0rHM2JKTXAhqQYEpy8SqIDzFJItjKPrhTL3PfnWzkwdpSD6eOY1mRYHp/Px/r16yeETjKZxNOiDv0yN7NQzNgapbRHAdcbhm4ahv7YBazTTPWJAc8BzjYMPQv8QSntWuDFwJsbNn8p8GHD0B909v0wMoT4aeZAPyUje7AEuep0g4FvHoE7c7A6AK/fOPP+YxWZHzotOj1T6kz0U/uXIzPFTwt4fDw0uo7vjN/DmeFVPHf4DE4ILoIvR6GC71AGvEGIBxdX46lacDQrERR2jvK87UmiphdwNI4tI/D4UyV9QA9oZx79l12T78AjIyMTAmfNmjVdCY1+mZtZKFq16NfAeuCIUtpO4ELD0I8vTLWachpgGoa+o27ZbcCjm2y7zVlXv922hm1+p5TmBa4H3mAY+u52FeiXXED5KtydgxUNZ29/CT65X76/eYtYxjUjWxUH00uSzc2tZ6Jf2r9caRel+rWrL2BbeHXLN+humJi4nyn0l23L+EHJhNEcjBcZCkUWJ8aZWYWDGTGdPpyBu49Aroz5H5fjf+ypRLl4+i7lCv5DzZ0zLcuiXDWpmBXKE5/63yZlszLx+7Gbn9iyeieeeCIbN25kw4YNxGJ9lmqhj2l1JY0hGUyPACci1ryLSRxINyxLAc3C88addfXbxZXSPM480KOBPwNR4N3Aj5TSzjMM3WwsSCntKmRIjyc+8XIuuuhCACKRMH6/j0xGpsMCAT/xeIyxMTmsxwMjI8Ok0xlMU1TyoaEEpVKZYlHMZaPRCF6vl2xWyggGA8Ri0YkyvF4Pw8NDpFLpiTwswXiCPx8rYZfLlH1ANAJ4KOXy/OeBGEUrwBNGbC4kRX5c0miHk0mK6TSWZWHaUAgnOT9UJDMuE6LxeBTbtsnlJL5XKBQkHA6RSsnbpM/nZWgoyd69DxKPxwEYGRkil8tTLlecMmJYlkU+L2WEwyFCoeBEGX6/j2QywdjY+ERW35GRIbLZHJWKdHsiEcM0qxQKxYk+DgT8pNPZiT5OJOITZdT6uMbo6DjJZJxKxZxSRq/OU41sNtf2PA0NJSgWS5RK0sexWASPx0M2m5/o40gkzPh4ekoft+I03zDlcrnteRofT2E55sXtzhNAtVqdfp4OHsU+nIWKyUgkTrZapuKzyYwX2LByFaZVpVCS/okEQwT8ftJ5p499fhLRKGOZDBffMGmdlsnnqVTlXCejMSqmSaHslBEK4ff6yBSkfwJ+P3F/iOw9+wk+MEZwTwpPZWouInsojH8k1lIz+fUdN5ArFjGrJpWqSdWyKJtlqlZv8xpddNHFlEplSqUKgUC5o/up1XmqMTo6Dszf/XTFFVcwNjbO6Oj4xL2QyWQnypjpfgoEemMx2EoAfRf4rRNw1AZucgKRTsMw9MZ5ofkgCzTeoUkmdO6W2yaBbM0IwTD03znLy0ppr0cE25nANKdaw9A/C3wWYHR03G6chG/3O5mcKh+j0QjRaKSrMmoPJtuGm9LgiURZnZg6tPCTdJCbCjDkA22zh2hgahnhZBLLhiNlyQW0LhRF5O8kjWagjfWIx+NTlsXj09/06q2kms3N1AsMgEQiPuV3IBAgEplqEdFYj8YyGrfz+/1ty5jNeapRa/dM56lGLBadFrh0xYqpY6bR4Tjbi0e5NX+If+KhTY9XozZh3e48DQ9PdQJtd558Pp+UYdmiYew8zkixCvH4RCbMBE5/ejwE/H4C+IkEG+qRmNr+kcTUPk1Ep/aF3+cj0tiWjC1m0vtTMFYgYVqUfBbHgxaZ9UEyCQ+ZsE02bJOx0jx3WsumsvvIgabLPUDAHyDo9zv/6777/ATxEfD4CEbCBJPth/GanetenKd21+183E+NZTS7n3pFKwH0SuBa4FTgv4BraP6wXyh2AH6ltFMNQ7/PWXYu0GiAgLPsXCT3UKvtatjINdnXjJlwtAJrG+Z9D5XBcCLov2HzzHM6oxWJiLCuP+aNB5ZstczthSPcWjjEXYVjE9k/2wmgeaVsSjSAYkWCXcbndpG0jGnWMDRmVatk9xwhs30fmWNjZEIW2ZhFZoVFJmxR9jbYOVlMprdsw6O3XUDQHyDg9ztCRgSN3+efOpRp2zLUaDrHSoYkpcI8+wYNOjMKIEdb+DGAUtq5yKT+ogkgJ53394B3KaVdiVjBPQN4RJPNvwy8QSntJ4hweSOS+hultG2IL9wdQAQZgtuPBFltycjI4oUXsWwJNhprOGMlC950P2SqcOkQPGUGH8SiJfM+WyOztxpbzPYvdcbMArcWDnNr/jD3lUax6oxHtwaHOS/aQZyk+SJXkmRlXk9bwTMS7ywhXbtJ+z/efQuZXJZMJkOuWsb2IAp5k2C5fp+PRCRGIhwlEYkRj8RIROR7O7aum8ESp1IVgeP3ikGDDSTCEvst5JfsoS7zTqex4BYuQ1FrXg18AZmXOg68yjD07UpplwE/NQy9pjt+BjEXrw2pfc5ZBmLC/SlgI+LPdD3wVMPQm0f4qyOXyzdVkxeCXQXRgBqznH5yP9yVhw1B+I+TZjZMSptwQWJu5taL2f6lyKFKllvzh7mlcIg95ckpSS8ezgyv4qGRtZwbXcvQYmf/3D0mieD87R+6uWKReKT50GQ37DiwZ8rvWMVLIhghsWqEeCIxIWASkRjhQLBnhhfYNhScqd4ThsSPKRoUc/IWQmehzaMXMincYrKk7PqcHELPbLL894jhQe23DbzJ+TRuex1w+myO3zhBuFCkTNF+VjUIn7ty8HUn1tv7TobhGc5mripzQ6vmOJqwWO1fKti2zd5Kmlvzh7glf5hD5uQwU8DjZVt4NQ+NruPsyJppAUJhblGqO8KyxZqsVBVnzRqxAJ3mWi+bFWTgoEkdzQr7jh1m15H9/M3mJ7Us55J9ERIlL/EVQ8TP3oTvtLXzG0XBrErAQ48NK2LiiBrwSeK2Dhg08+iFwu25JcCOHER94Kt7CcxV4R27ZDj8H9ZK7p5GLBuOlWVk5aJk/ziqLycs2+aB0hi3FA5xa/4Qo9XJNM1Rj59zIms5L7qWbeHVbaMVzCVK9TRsW5wxy1XIFOVNv1JnQ1Qf8G8OEZTLZoV9xw6x+/B+9o8e6di67Iytp0o4nNXx9hu3wCxXZtZMyhX8JVP6IOSTlNRtNB2XhcUVQF2w0MNPtg1HKtMNDywb3rkLdhUlgOhMsd6OV2TO56TI1MRys2VQh99mchAFGK8W+fCRP0/8HvKFODeylvMiazk9vHIiB8+8Y9kylxHwSZicIxlx0gQRNiF/T/x34pEopUpZhM6RA+w/fhirZgdsw9qcjxPHOggv86geGM5aFv59KfB5uObX1wJwxd88U9ZVqviLFYiGJBpCdJEdZ12a4gqgLrB67DvQCtuG27PiXDrUMFrzsQfh1+MQ84F+SvOU2TknxtvJkd6F2VnI9vcT7RxEV/ujnBdZx0OjazkxODzvAUGnUK6K5drRrEyq+7yi+QS8c7Zkq6dUKbP36EF2PriXQ5k6Iwob1mV9bBkPcuJ4gGg4DG1y2syaqiVanGXLx+uVOHT1w4nZEuCRN64tKyDWW8FzxRVXTPiHucwdVwB1QT5fWLCI0AdKkkiu0eT6p8fhfw/LcNwHtk7PcHq8DHhEgF3S4xhvC9n+xaBomRyqZDlQyXKwkuVQJcOBSpb3nNA6EtW71j+6d5Pk7bBtETq5sqRAKDkT6jXTacvqmelwqVJm79697Dqwj4OlFJbTRI8N67J+ThwPsKUUJXrCSnjoMGwehuEIeDzth8a6qojkz8HvtDHog1BAsqr6vJApcgVPEaGzMi7DbD7PvGk8y/0+WEhcAdSHlCyxbGsMtXOkDB900gC+abMEEa1ntCKGCjGvWMs1Bip1EYqWycFKloOOgDnofEarhfY7N6HXwmdaiBzbhqIpaQ9SJahW5eHazF9njhkxi5Uye48cYPfu3RwojIt5NOIktz7jZ0s2xInJNUS2rILLhmUyv0n7a34+1/zq/0lTakNjtHno1ASs6ZhGY4sWc8KQCNZmfZ0Iw2nBpR1Rd0BxH1FdsFBvPQ8U5N6rj9NWtODqnZP+Ps9eNXWfYlWs4bbFIDRP92G/vfW1mpup2FVM25oQLgcrWQ5UMhysZBmrMxSox4+XtYEY6wPxuk9nfi/zwoE0YIu2U6lOzuX45nbbtozsnMnwx9/dCojQ2ZDxc2JghC1rNxA+dzX5hI9IeA5m2BPGEaYIy3ptrWYwEA9KSuygTyIxdGI0sIDCp9/ug6WMK4C6YCGyYWZM2FOcajJt2vDvO+G2rGg2b9sy+SJo2pL9NF2V4KLzJXygv7KBQvu5mX/e97Om6/x4WTchaBITwma1P7pwRgPNqDlH1sg6gtLv64kBgW3blM0KoTZOohvSfk4qRti89STCj9wkWU4dQtWm0bjakyszEXAk7Ie1ScknVDZlcbkqQ2obh/s++kC/3QdLGVcAdcF8J2TLVeHGjJhc16xkbRvetwd+Ny6+PB8/ddIfqGhBqgIjATgvJv/nk8VOSGfbNuPVEvsqKR4sZ3jK0Ckttw94vKzzT9Vm1gfirPJHFkfQVB2zaNuWxE1lUyzWHMFjnjSCPx7qKHzNtPVVk3ypWPcpkC/X/S7I76ptccXJrZ0cn/iox844tJbK56bFfJtCbQitUgUbrnjkU0WgJEIQcYbJahd32C8ZSyMBWJ/sucHAfLHY98FywhVAfUK+CjekwQfE60YcPnUAfnBMNBvjVDGptm3ReEpVmQfqNJ/PUsKybQ6bOR4sp9lbTvNgJc2+cpqsNWnx1E4AfXTjE3tikTZrB1Hbsdaq2uKLczQnRgJ4ACecd+2BHA7gDwVbaia7j+yfImQK5SK5YpFCuUDZnBbIvSn+ThSYlV2Y25uWCFbTkvbikThqaxPtTb+jQTh9zVSfJJeBwhVAXeDvIFTJbKja8Fcnyl59Dp9vHpHU2j7g/VvhIXEYr0DZgvUh2JpYWEOD+Wp/2aqyv5JhX52gebCSpmJPN/uOegNsCiTZ1EFCtl6ZQ3ftIGrbkjH0eEEMBgDwSLgbX6BhU5t8qUgqk2UDMwTyc/j1HTfOuM6Lhyh+omUP0bwt/yteohUvkYqHGAGiK4cIbBhu3+AW+L1eMfuuZRwN+kSQBH0yhBYOdBfRYAkKn/m6DwYRVwB1QWPI/l5xsCRJ4uqzm/5iFD7kWLy95UQxOBo3IewV8+roItwDnba/lXFA2aryQHmMfeX0xOewmW2a132FL8ym4BAbAwk2BYfYFEyywhdeOJPnBmZM4FazUitURMOp2qLtxAITBgPFSpl0dpxUPkt6yic3kcL5ivNOa3n8zavWEbX8RIo20axFdLRC9HiZaAlCVQ+e+oDuK6LigLkhKcNbK2PdPexr81FejxgBOP43SU9QhsqSEcdIwLckhs16yXw9BwYRVwB1wdjY+Iy5aGaLaUmctxHnTNz+nWu4O7yej6x/EjbwmhPg6askHlzAI7l8wrMUPu2sxtqFe2nWftu2qWJTsatUbIuyXWWVP9rSOOCjR26YssyLh/WB2IRmszGYZFMgSczXH5O9tWCTTedmSmX8u8bAsqlYVdLFPOlCllS1SHpfbkLQlMyZ4+iFAyGS0fbDXn9zvQW5xnkgrwibkQisiU8KnPAcxmWzJdl/XVKEUL4i/j3JMGOFPCMrh2df9jJgPp4Dg4orgLrAbvaaPkcOlaFsw7AzarE9vIGPr3kcFRuevwb+cZ1Esg56JJr1bIUPtLca+97YPVRsyxEmVcoT30WwFM0yVsFD2Vkm21SnaS/t0jxsDQ5PCJrNgSTrA4m2cdKaUbZmnpspW9VZlTkN08Lv97ecm/np3TeQLmTJl5qbdwP4fX6GojGS0ThD0TjJ2icSIxQIyrBWO3JlmczfkBThsC4BayeTxnXcpFZOohUT/6mrZzR9tosdJuJZxszHc2BQcQXQImLZcF9hMor1D4+Bse4JWHh58grRfnKWzPlcNDw3zWdHcZRtkdUtt/t5ZmcHlZ6+yIuHgMdL0OMj0IF12ZvWNUvh1D1Br6+lYLj77ruxLItqtYplWTN/N6tUKyaWWcWqmLLcdraxLJ71/L9rWY9D48cAmXNKROJNBU0kGJocOrSdzKMHM7B3N+wdh9E8/PezWjf4ReeL0Kkf8qo4oXiqtnj/N2o+lpNorVozfgD//cfB55MhwnhIPo4fjT/gPhJcFg73auuCXidkO1wSB9KED/7nAHzmAICXc4cfQNt8MiVLjAzOiEjct244WslxZ/Eo2wtHubd0nIpttdVMnjl0uggSr6QjDni8dYLFRwAPQa9/Yl1N4MyXSbNpmuRyOXK5HNlsduJ/7ftzn9s6IfOf//znlut7xRPOu4ShaJxYKILX6xUBM16AI1k4kIf0GKSL8smURCA0vkX7PJipfOvwNesSInAqVSREgS0haeIhsTYbzYsw8nknzb29HkiGJ4WMzzv5fxa4SQndPuglrgDqgmw2Ny1f+mxJm3BbTqze3r1HTK29wCWrtnP20B5M+2QuSU61imtF2apyX2mUOwtH2V48whFz6lDJpkB7q7EnDZ3ccn0mk+1Z+23bplwuTxEojf8LhdmFxqlxxuateCsWXjz4PF68Xi9ejwefxyf/fT68Pi/eoB+v14fP68Xr8cp/57u3g4n7jfcWIDUKqaI8+McL8r8VYb+kItg0DFtGYF0Cf6okoXakgyZNmy0bf9WW79Gg5LMJ+cUAoH6oLB6azGy6OibbhgM9tTTr5T2wVHH7oHe4AqgLKpXOfC3aka/CjWkZEHnzA3B9GkIeyWi6yyOZIh/egfA5UsmxvXiUOwtH2eFoOTWiHj9nRlazLbyabZFVPcm62av2f//73yebzWK28V3xeDzEolHisRixSIx4pPY9SryDSftLNp4pD+l2b/uWLZpD0RTnUNOxZDOrMlR2VpsDXb97+rJ4UIwCnMl7+YQkblnYP71OpiVWdFWr1njZzusImWhgylBZU0J+OGXVzOt7QK+ugaWM2we9wxVAC0zKhFszEjj07bvhnjwkfXDNGTLMtisn2yWanJmyVWVH6TjbC0e5s3iUo020nG2R1ZwdXs1JoeGeD41ZlkU+n6dQKEz5NC57+rOf2dJxc3x8HJCJ+Xg4QjwUIRaU//I9TDwYJuIPy8u7Bxly8kwUAikLTmhT4b1jMlxVrvvUzItzZREuubIInlY8bVvr9Y88ScycV0VFSNi2xEiKNrFEs2wx1S5XRcBZyPZhHwyFJVpAyAm9M2DmzS6Dx5ISQEppK4DPA5cDx4CrDUP/WpPtPMD7gSudRZ8D3uyk6kYp7TynnDOBu4F/Mgz91nbHTyTmlpBtV15y/Pw6BZ87KKF3NgThB+fAQ+JitfXEldMf3NlqmWuO39ZGy1nNkK91kMR2Hv3HDh2ZUagUCgWKxZmtvOoJeFobBzz9lIuI+8MEPT48NU3DtCS0Q7YK5RJU8vKQLpmTH9Oa/FQt+M8nt67ID+/qqL6APPDDjue+3zv5iQYxc8WZ52ZME//zzpu6sFyF+49NJoSrN5vyeiDgB79HLNriwe6dNxeRud4Dy4Gl1geWVaGp9dAszfk83lDPfPGWlAACPgGUgbXAecCPldJuMwx9e8N2VwHPBM5Fpnt/AewCPq2UFgR+AHwE+CTwCuAHSmmnGoZepgWmWSUQmJ1/xb050PfCT0bhoHOURw7Bl8+Ak6IArU2ktxePArC6GmSLGWZTOcTqih8sG8s6xL3WASzLwrbtCcsuq1RxrLlsLNviCU95UkvB8H//939t2xH2Boh4A0Q8fiIEiOAj6gkQKXuIlCBSbW8tsfLbO9pu0wnmWG5mwZDO4z91lSNUAjIUFwlMCplkGEYc/5k2Q1v1N0mhUCQSmRzO9Pub3EJBH5yQFG0rFHAm/T0iZJaIoAGZp4MqtmVii6pGuVTC4/FhVsaRW2syMd2kZcXkg82eWF//sGuynW1h21Vs2wTbmlLCxP+JMiwarTjsKevr923YzrbArmDbtTZNa3WT+k5dVy6bBIIzRMBoWe/GdXX9A2BbWFYZ2y5j2RWwa+2s76f63/XHmPrbrv23ylhWAewqtl2dUp4NeOz63w31b3JObWxWrXsB8aGLprd/FiwZAaSUFgOeA5xtGHoW+INS2rXAi4E3N2z+UuDDhqE/6Oz7YeDlwKeBxyDt/oijEf23Utq/Ao8DWj6B7777r4QCAWyril21JBWxZWFXLapVi6ptY1ZtbGw82Ni2Tca0OVyy2FWw2USF19smIx6T86MWp1QtvL+3SNs2ySe9tGX7t25PMTRaJliWm+aA8+klK6sFQnaVECZhuyrf7SohKoTsKkGqTD4+6y5Wz+Qy0wfSlTNz9GE3TQ4v1R7M/skHtBWoUg0WwA+23yseuD6PaA8+wOPB9gKHboVDjvmxz+PUwHZutgr2E+uG1uzGm8yWV5nDNhxm2nay5fQHh1kx60KxOGXalhzPNpm4weseOlOOOXFj1/2m4eZveMjYtoltVZxtGh4+dsPvpseEVgJgUkg0rh/MDLgurUmOPLpnZS0ZAQScBpiGode/Pt8GNOuNbc66+u221a27vTYc53C7s7ylAAoX3oZdLVGblph4GPtmTlkyDGwCLmiybv+UCrcWQGcWb8MTs/DELTweC69H/ns8Fh7qvk/52FPWT48hM5WTz/3qtGU2UHQ+ndJuGvzIWb/pojQHy/n0wfxvSzV52eHB4/Ehkn8ioULD/JRn2t/JVZ6GZVPXTwkf5PHLsTzeqcsnvtfmAT3N96fuWJ76ZfVbeyaO45kx8oen4f/U8sxKFX/A13Ds5u2TOjepb9MhLC8ebwivJ4jHG2io39R+9DTtV8+UenqcZR6P3ynPz+RTq7auvpzG/psso77OscR5Teo+O5aSAIoD6YZlKaBZYKa4s65+u7gzN9S4rlU5KKVdhQzp8Y+Xr7N9/rKjmHqwbTk5kqrYU/c66bFrC7xge23LClIt+y3b9NieKmDhsS3bY1t1evamVo0/M3rPA3gskSZMPAimvMra9a+5Htt09GuQ918beGirYwDfaLJsoozDR+OnrF2dvZ9pr+FTty+Xxv/uiiuuaGp2Vy6NF4BvznQM538FOO78t5t8aLKsTv2gDJTqltFk32b/m9XHBuScATfdvunxFzxk3y8atrGAvHNcq2HfmcpsbEP9b8s5Zu13sa49rerfyf/GZdX69jVuu+3CP0xRg5TSrjIM/bMMMG4f9K4PlpIAygKNzixJINPBtkkgaxi6rZTWTTk4nfxZAKW0mwxDb6bM9IRWjqKnPPH/9eQYM82ZAGy78I8t9/2ctP9FHRzmJTOtCIaG2x6nn/nc17R/f+k/fePVi12PReQqnPthgHH7oEd9sHRmRGEH4FdKO7Vu2blAowECzrJzZ9huO/AQRxuq8ZAZynFxcXFxmSeWjAZkGHpOKe17wLuU0q5ErOCeATQLLPZl4A1KaT9BhhLeCHzMWfcbZMjhdUppn0aMEwCum7/au7i4uLg0spQ0IIBXAxHgCPB14FWGoW9XSrvMGVqr8Rngh8AdwJ3Aj51lOKbWz0SGicaBlwHPbGeC7TDoavegtx/cPhj09oPbB9CjPvDYbmxxFxcXF5dFYKlpQC4uLi4uywRXALm4uLi4LApLxghhMek0Bt1yQintN8DDmfQ92W8Y+unOuhcC70N8Tn8BvMww9NHFqGevUEp7LfCPwDnA1w1D/8e6dX+DhIHaDPwF+EfD0Pc460LAp4DnIr5AHzQM/b8WtPI9YqY+UEo7EQlllavb/AOGof+ns35Z9IHTjk8CjwdWAA8g9/pPnfXL+jpo1f75ugZcAdQZncagW2681jD0z9UvUErbhhh0/C1wMzIZ+Ung+QtfvZ5yAHg38ETE0AUApbRVwPeQwLY/BP4TcaR9uLPJO4FTgS3AOuDXSml3GYbePrBe/9G0D+oYNgy9WSyKd7I8+sAP7EOiq+wFngJ8SyntHMS3cLlfB63aX6On14ArgNrQZQy6QeBFwA8NQ/8dgFLa24C7ldIShqE3deZdChiG/j0ApbQLgI11q54NbDcM/dvO+ncCx5TSzjAM/R4k7uA/GoY+Bowppf0PokUspQcP0LIP2rEs+sAw9BzyIK3xI6W0XcDDgJUs8+ugTfv/2mb3WbXfnQNqz0wx6NokiVkWvE8p7ZhS2h+V0h7jLJsSZ88w9AcQ7fC0RajfQtDY3hwyNLFNKW0EWM/McQeXG3uU0h5USrvG0QxZzn2glLYWua63M4DXQUP7a/T0GnAFUHu6iUG3nPg3YCuS9u2zwA+V0k6my1h6y4BW7Y3X/W5ct5w4BlyIDK88DGlfLXLtsuwDpbQA0sYvORrOQF0HTdo/L9eAOwTXnq5ixy0XDEP/S93PLymlvQAZEx60/mjV3mzd72LDumWDM/R8k/PzsGOscFApLcEy7AOlNC/wv4hm/1pn8cBcB83aP1/XgKsBtaebGHTLmVok/ilx9pTStgIhpJ+WI43tjQEnI/MBY8BBZo47uFypea97l1sfODEiP48YHD3HMPSKs2ogroMW7W+kJ9eAqwG1ocsYdMsCpbRh4GLgt4gZ9t8DjwJeDwSAPymlXYZYwb0L+N5SNkAAUErzI/eDD/AppYWRtn8f0JXSnoOEdHo7kk/qHmfXLwNvVUq7CblpX067xEt9Sos+eBgStuo+YAT4b+A3hqHXhlyWTR8gpsRnAo83DL1Qt3xQroOm7VdKu5h5uAZcAdQZrwa+gMSgO44Tg25xqzSvBBBz3DOQwK33IPHydgAopb0SGf9dCfySpXmjNfJW4B11v/8B+A/D0N/pPHQ+DnwF8f+oNzl/B3LT7gEKiG/EkrF8aqBpHwD3Au8F1iDzob8AXlC33bLoA6W0LcArkNxLh5TSaqteYRj6V5f7ddCq/Ui+qJ5fA24sOBcXFxeXRcGdA3JxcXFxWRRcAeTi4uLisii4AsjFxcXFZVFwBZCLi4uLy6LgCiAXFxcXl0XBFUAuLi4uLouC6wfk4jKPKKXtBj5uGPqHFrsuzXAiX98InGQY+u5Fro7LgOEKIJclj1LaamA/4qFdRjy2zzQMfe9i1mu+UEr7R0Soxdtt28Njngz8O5KUcQ1wCBFc/2UY+vV12z0ReBMSuDKAhGj6AvAxw9Ctuu2aOSDeZhj6efPVBpf+wx2Cc1kOXII8vHLA+cDochU+i4GjJd2MhNd/FXAW8DQkR8zH6rZ7NfATZ/kjnO0+iURT+CrTeTkSxr/2+Zt5a4RLX+JqQC7LgUcAf3S+P7Lue0uU0p6GJODahgRT/BoSfqeslPZe4ImGoT+sYZ/rgZsMQ3+dUtqFwHsQoRcEbgc0w9D/1OKYNvB3hqF/p27ZbuqG6ZTS3oAk8zoZ0eZ+CvyrYejjTl6ma+rKgsmQQUEkU+eLkJTK24G3Gob+s7pjPQn4CHAiosF8qk0feYAvAjuBSw1Dr9atvl0p7VPOdhsBA9F03lS3zWeU0g4D31dK+14toZvDuGHoh2Y47tuBf0Kya44BPzcM/SWt6uqy9HA1IJcliVLaZqW0caW0ceANwCuc7+8Fnums+2SL/Z+IvJV/HBFAL0Py2b/X2eQrwPlKaWfU7bMV0ba+4ixKIGHrLwMuAm4FfqKUtnKOzbOAf3Hq9UKn7Jqmcb2zLs+k5lCbX7oGSaf8QuBs4EtIHqdznfpvAv4fEsfrPKfMD7apy3lOPfQG4QOAYejjzte/Q4TwtPIMQ/9/SBDLF7Y5Fk49nwP8KxKD8VTgqcANnezrsrRwNSCXpcoB5OGYRPKUXAzkECHwt0hO++wM+wK8BXmoXuP8fkAp7d+AryilaYah36WUdguiTbzN2eaFwA7D0G8AMAz9uvoCldL+GUnf/mQmhVTXGIb+kbqfu5XS3gT8QCntpY52lgLseu3BmaN5AXBi3fDjx5XSHo8Ek3w1Mny2F3idYeg2cI9S2mmI1jQTtTQkd7ep9mlA2jD0AzOsvxs4vWHZ/yqlfbHu9ysMQ/8qkvTsIKL1VJw634TLssMVQC5LEsPQTeTh/DzgRsPQb1dKuxQ4bBj67zoo4mHARY7QqeEFIsiwz0FEiLyGSQH0IurmMpTS1iAP78ciIeh9zv6b59I2pbTHAVcjYfGHnHKDTr1mesCfj+RruqsuijFIrqaaoDwT+LMjfGrMOFzo4Omi6t1GNtaA+ojJh53/30ZSf+xSSvuZs821hqGXuizfpc9xBZDLkkQpbTvyphwAvEppWeR69jvf9xiG3ionvReZHP92k3VHnf9fBz6olHYJEqL+DKZqNl9CBI8Cdjvb/AoRFjNRS+xXT6CuXVuQfDP/g+ScOY4Il6+3KdfrlH0h0JhErDB9846pJRo8E7ilzXZDSmknGIa+v8n6s5ieoOyQYej3N25oGPo+pbTTEaOExwMfBt6hlHaxY2jiskxwBZDLUuUpyIP7V4jZ71+BbyAT5v/H9IdwIzcDZzR7ANYwDP2gUtp1iOZTAv5kGPrOuk0eiQxn/RhAKW0tMifTiqP12zTZ5wJE0KjanItS2lMbyigjWlE9tyCCbZ1h6L+e4dh3A89RSvPUaUEPb1PfW4G7AE0p7ZuN80BKacPOPNB3gA8gWs2/NGzzLOAUxIy7IwxDLyKC+MdKae9HzL4vBX7eaRku/Y8rgFyWJIah71FKW4doID9A3v63Ad81DP1gB0W8C/iRUtoe4FtI5s+zgYsarLi+gryBlxGLt3p2AP+glPYXIIZMwJfbHPc64DWONV0VMXoo1q2/D9Fm/sXJxPtwGh7oiLYVVkp7AiJ48oah71BK+yrwRaW0NyICdgXwGGCnYejfAz4NvBH4iGOgcQ7wylaVNQzdVkq7Akk8+AeltPcggiyKzHU9D7jA0VreCHxUKa2MaId54AlOv3yzwQJuRhw/Jz+S9C2LZOStOH3jsoxwreBcljKPQeZ/ioil2IMdCh8c0+S/ReZvbnA+b0YmvOv5HvKwXQ18s2Hdy4A4k9rXFxDh0Io3IibNv0G0hs8hmXZr9bodmf94A6J5XIlYhNXX/XpEmHwd0ahqAvMKxBLug0gW2x8hqdT3OPvtBZ4NPAm4DRk6fHOb+uIYXTzMKfPTiAD6EdLnr63b7mOIxdqFwJ+d7V6LZMvsyALOYRwxwf49cCdi2PFsw9B3dVGGyxLAzYjq4uLi4rIouBqQi4uLi8ui4AogFxcXF5dFwRVALi4uLi6LgiuAXFxcXFwWBVcAubi4uLgsCq4AcnFxcXFZFFwB5OLi4uKyKLgCyMXFxcVlUXAFkIuLi4vLovD/ATcatJpBhJ4lAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -797,16 +797,16 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "acceptable-motorcycle", + "execution_count": 26, + "id": "responsible-capture", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "after 240 iterations, fraction top 100 COFs found by RF: 0.0691\n", - "after 240 iterations, fraction top 100 COFs found by RF (div): 0.1436\n", + "after 250 iterations, fraction top 100 COFs found by RF: 0.0724\n", + "after 250 iterations, fraction top 100 COFs found by RF (div): 0.14980000000000002\n", "after 250 iterations, fraction top 100 COFs found by BO: 0.36119999999999997\n", "after 250 iterations, fraction top 100 COFs found by CMA-ES: 0.1107\n", "after 250 iterations, fraction top 100 COFs found by RS: 0.0025\n" From f59feb75dd4aa8414207c6e66fa7207bf4c8f489 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Sun, 4 Jul 2021 11:16:46 -0700 Subject: [PATCH 19/29] bbox tight inches for accquire viz --- new/viz.ipynb | 107 +++++++++++++++++++++++++++++--------------------- 1 file changed, 63 insertions(+), 44 deletions(-) diff --git a/new/viz.ipynb b/new/viz.ipynb index 798ec36..5fb7121 100644 --- a/new/viz.ipynb +++ b/new/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "royal-needle", + "id": "genetic-leone", "metadata": {}, "source": [ "# viz" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "absolute-cross", + "id": "changing-collective", "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "musical-martial", + "id": "understood-essex", "metadata": {}, "source": [ "load data" @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "successful-approach", + "id": "raising-hybrid", "metadata": {}, "outputs": [ { @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "running-meeting", + "id": "thrown-korea", "metadata": {}, "source": [ "for rankings" @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "reserved-volunteer", + "id": "aggressive-tower", "metadata": {}, "outputs": [], "source": [ @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "czech-negotiation", + "id": "communist-irish", "metadata": {}, "outputs": [ { @@ -136,7 +136,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "rural-therapy", + "id": "bronze-stopping", "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "sustainable-developer", + "id": "secondary-thermal", "metadata": {}, "source": [ "load search results" @@ -165,7 +165,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "narrative-suspect", + "id": "egyptian-discretion", "metadata": {}, "outputs": [], "source": [ @@ -179,7 +179,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "everyday-wellington", + "id": "complicated-christmas", "metadata": {}, "outputs": [], "source": [ @@ -190,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "legitimate-sarah", + "id": "instrumental-bunny", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -199,7 +199,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "sonic-private", + "id": "assisted-enforcement", "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "subtle-hebrew", + "id": "legendary-training", "metadata": {}, "outputs": [ { @@ -243,8 +243,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "noticed-specification", + "execution_count": 28, + "id": "lined-conservation", "metadata": {}, "outputs": [ { @@ -285,12 +285,12 @@ "\n", "fig.text(0.5, 0.2, 'PCA dimension 1', ha='center')\n", "plt.tight_layout()\n", - "plt.savefig(\"feature_space_acquired_COFs.pdf\")" + "plt.savefig(\"feature_space_acquired_COFs.pdf\", bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", - "id": "joint-track", + "id": "first-writing", "metadata": {}, "source": [ "# search efficiency\n", @@ -300,7 +300,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "false-lotus", + "id": "interesting-lindsay", "metadata": {}, "outputs": [ { @@ -342,7 +342,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "electrical-quantum", + "id": "whole-combat", "metadata": {}, "outputs": [], "source": [ @@ -367,7 +367,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "willing-estimate", + "id": "radio-fellowship", "metadata": {}, "outputs": [], "source": [ @@ -378,7 +378,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "shared-nomination", + "id": "egyptian-assist", "metadata": {}, "outputs": [ { @@ -438,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "rocky-member", + "id": "olympic-remark", "metadata": {}, "source": [ "show distribution for context." @@ -447,7 +447,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "incoming-champion", + "id": "dutch-testimony", "metadata": {}, "outputs": [ { @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "equivalent-prince", + "id": "unique-emission", "metadata": {}, "source": [ "### max rank among acquired set" @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "monthly-restaurant", + "id": "excellent-phenomenon", "metadata": {}, "outputs": [ { @@ -512,7 +512,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "medieval-royalty", + "id": "identical-prescription", "metadata": {}, "outputs": [ { @@ -567,7 +567,7 @@ }, { "cell_type": "markdown", - "id": "temporal-principle", + "id": "manual-failure", "metadata": {}, "source": [ "print stats to report in paper" @@ -576,7 +576,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "moving-owner", + "id": "mediterranean-outdoors", "metadata": {}, "outputs": [ { @@ -588,13 +588,14 @@ } ], "source": [ - "print(\"# evals by BO req'd to reach top COF in all runs:\", np.argmax(y_max_mu_BO == 1))" + "nb_bo_runs_to_get_top_cof = np.argmax(y_max_mu_BO == 1)\n", + "print(\"# evals by BO req'd to reach top COF in all runs:\", nb_bo_runs_to_get_top_cof)" ] }, { "cell_type": "code", "execution_count": 19, - "id": "organizational-chambers", + "id": "weighted-craps", "metadata": {}, "outputs": [ { @@ -609,13 +610,13 @@ } ], "source": [ - "y_max_mu_BO[np.argmax(y_max_mu_BO == 1)]" + "y_max_mu_BO[nb_bo_runs_to_get_top_cof]" ] }, { "cell_type": "code", "execution_count": 20, - "id": "selected-waterproof", + "id": "expanded-compound", "metadata": {}, "outputs": [ { @@ -623,7 +624,7 @@ "output_type": "stream", "text": [ "after 250 iterations, top-ranked COF found by RF: 63.94\n", - "after 250 iterations, top-ranked COF found by RF: 12.07\n", + "after 250 iterations, top-ranked COF found by RF (div): 12.07\n", "after 250 iterations, top-ranked COF found by CMA-ES: 12.92\n", "after 250 iterations, top-ranked COF found by rs: 308.26\n" ] @@ -631,7 +632,7 @@ ], "source": [ "print(\"after \", rf_res['nb_evals_budgets'][-1], \"iterations, top-ranked COF found by RF:\", y_max_mu_rf[-1])\n", - "print(\"after \", rf_div_res['nb_evals_budgets'][-1], \"iterations, top-ranked COF found by RF:\", y_max_mu_rf_div[-1])\n", + "print(\"after \", rf_div_res['nb_evals_budgets'][-1], \"iterations, top-ranked COF found by RF (div):\", y_max_mu_rf_div[-1])\n", "\n", "print(\"after \", np.size(y_max_mu_es), \"iterations, top-ranked COF found by CMA-ES:\", y_max_mu_es[-1])\n", "print(\"after \", np.size(y_max_mu_rs), \"iterations, top-ranked COF found by rs:\", y_max_mu_rs[-1])" @@ -639,7 +640,7 @@ }, { "cell_type": "markdown", - "id": "completed-joint", + "id": "mexican-motion", "metadata": {}, "source": [ "### fraction of top 100 COFs recovered" @@ -648,7 +649,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "protected-secondary", + "id": "covered-gibson", "metadata": {}, "outputs": [ { @@ -668,7 +669,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "prescription-triumph", + "id": "surrounded-auction", "metadata": {}, "outputs": [], "source": [ @@ -683,7 +684,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "under-tourist", + "id": "manufactured-native", "metadata": {}, "outputs": [ { @@ -722,7 +723,25 @@ { "cell_type": "code", "execution_count": 24, - "id": "proof-convention", + "id": "fundamental-driving", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fraction of top 100 acquired after 174 runs: 0.3091\n" + ] + } + ], + "source": [ + "print(\"fraction of top 100 acquired after \", nb_bo_runs_to_get_top_cof, \"runs:\", y_top100_mu_BO[nb_bo_runs_to_get_top_cof])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "deluxe-office", "metadata": {}, "outputs": [], "source": [ @@ -746,8 +765,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "usual-biotechnology", + "execution_count": 26, + "id": "african-neighborhood", "metadata": {}, "outputs": [ { @@ -797,8 +816,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "responsible-capture", + "execution_count": 27, + "id": "representative-vegetation", "metadata": {}, "outputs": [ { From fd11b7f76355093c704e315d8819d8a04cd5fb43 Mon Sep 17 00:00:00 2001 From: SimonEnsemble Date: Mon, 5 Jul 2021 11:19:16 -0700 Subject: [PATCH 20/29] clean up --- COFS_figures.ipynb | 807 ---------------------------- COF_dataframe_to_methane_storage.py | 21 - README.md | 42 +- bo_run.py | 69 --- compile_results_in_one_file.py | 80 --- diverse_random_forest_run.py | 69 --- evolutionary_search_run.py | 55 -- new/viz.ipynb | 202 +++---- random_forest_run.py | 49 -- 9 files changed, 141 insertions(+), 1253 deletions(-) delete mode 100644 COFS_figures.ipynb delete mode 100644 COF_dataframe_to_methane_storage.py delete mode 100644 bo_run.py delete mode 100644 compile_results_in_one_file.py delete mode 100644 diverse_random_forest_run.py delete mode 100644 evolutionary_search_run.py delete mode 100644 random_forest_run.py diff --git a/COFS_figures.ipynb b/COFS_figures.ipynb deleted file mode 100644 index ae91072..0000000 --- a/COFS_figures.ipynb +++ /dev/null @@ -1,807 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### README\n", - "- methane_storage.pkl containts the entire dataset with two keys 'inputs' and 'outputs'\n", - "\n", - "- results for all methods are stored in corresponding [method_name]_results.pkl file\n", - " - for e.g. bo results are stored in 'bo_results.pkl'\n", - " - has three keys 'outputs', 'outputs_normalized', 'inputs_selected'\n", - " - requires pytorch to open https://pytorch.org/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import numpy as np \n", - "import matplotlib.pyplot as plt\n", - "import pickle\n", - "import pandas as pd\n", - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "cool_colors = ['#00BEFF', '#D4CA3A', '#FF6DAE', '#67E1B5', '#EBACFA', '#9E9E9E', '#F1988E', '#5DB15A', '#E28544', '#52B8AA']\n", - "\n", - "search_to_color = {'BO': cool_colors[0], 'random': cool_colors[1], 'evolutionary': cool_colors[2], 'RF': cool_colors[5], 'RF (div)': cool_colors[3]}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PCA low dimensional figure" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# all COFs \n", - "inputs = pickle.load(open('methane_storage.pkl', 'rb'))['inputs']\n", - "for i in range(len(inputs[0])):\n", - " inputs[:, i] = (inputs[:, i] - np.min(inputs[:, i]))/(np.max(inputs[:, i]) - np.min(inputs[:, i])) # same normalization as BO code\n", - "outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values\n", - "#outputs = ((outputs - np.min(outputs))/(np.max(outputs)-np.min(outputs)))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# 2 dim PCA\n", - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(inputs)\n", - "inputs_low_dim_pca = pca.transform(inputs)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#low dimensional (PCA) visualization of the entire dataset\n", - "plt.hexbin(inputs_low_dim_pca[:, 0], inputs_low_dim_pca[:, 1],C=outputs)\n", - "plt.xlabel('PCA dimension 1', fontsize=13)\n", - "plt.ylabel('PCA dimension 2',fontsize=13)\n", - "cb = plt.colorbar(fraction=0.02, pad=0.04)\n", - "cb.set_label(label=\"deliverable capacity\\n[L STP/L]\", fontsize=13)\n", - "plt.xticks(fontsize=13)\n", - "plt.yticks(fontsize=13)\n", - "plt.gca().set_aspect('equal', 'box')\n", - "plt.tight_layout()\n", - "plt.savefig('feature_space_colored_by_DC.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['outputs_normalized', 'inputs_selected', 'outputs'])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bo_data = torch.load('bo_results.pkl')\n", - "bo_data.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#low dimensional (PCA) visualization of the points selected by BO\n", - "which_BO_run = 3\n", - "# bo_results.pkl is a torch-compatible dictionary storing BO results\n", - "bo_data = torch.load('bo_results.pkl') \n", - "inputs_selected = bo_data['inputs_selected'][which_BO_run]\n", - "inputs_selected = pca.transform(inputs_selected) # PCA transform\n", - "bo_outputs = bo_data['outputs'][which_BO_run]\n", - "#print(len(bo_outputs))\n", - "fig, ax = plt.subplots(1, 4, sharey=True, sharex=True, figsize=[3*6.4, 4.8])\n", - "nb_to_show = [20, 40, 60, 80]\n", - "# nb_to_show = [10, 11, 12, 13]\n", - "for a in ax:\n", - " a.set_aspect('equal', 'box')\n", - " a.hexbin(inputs_low_dim_pca[:, 0], inputs_low_dim_pca[:, 1],C=0.3*np.ones(len(inputs_low_dim_pca[:, 1])), cmap=\"binary\", vmin=0, vmax=1)\n", - "for i in range(4):\n", - " ax[i].scatter(inputs_selected[:nb_to_show[i], 0], inputs_selected[:nb_to_show[i], 1], c=bo_outputs[:nb_to_show[i]], marker=\"+\", s=55, vmin=cb.vmin, vmax=cb.vmax)\n", - " ax[i].set_title('{} acquired COFs'.format(nb_to_show[i]))\n", - " ax[i].tick_params(axis='x', labelsize=10)\n", - "ax[0].set_ylabel('PCA dimension 2', fontsize=14)\n", - "\n", - "ax[2].tick_params(axis='y', labelsize=0)\n", - "\n", - "\n", - "fig.text(0.5, 0.2, 'PCA dimension 1', ha='center', fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig(\"feature_space_acquired_COFs.pdf\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def return_best_accumulated(data):\n", - " best_accumulated_data = []\n", - " for i in range(len(data)):\n", - " best_accumulated_data.append(np.maximum.accumulate(data[i]))\n", - " return np.array(best_accumulated_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### BO vs ES vs RS vs RF ranking" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "n_evals_array = range(1, 501) # 500 evals" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def return_best_accumulated_ranking(data):\n", - " outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values\n", - " outputs = ((outputs - np.min(outputs))/(np.max(outputs)-np.min(outputs)))\n", - " outputs = np.sort(outputs)[::-1]\n", - " best_ranked_data = []\n", - " for i in range(len(data)):\n", - " best_ranked_data.append(np.minimum.accumulate(np.minimum.accumulate([np.argwhere(x <= outputs)[-1][-1] for x in data[i]]) + 1))\n", - " return np.array(best_ranked_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def get_upper_error_bars(data):\n", - " u = data - np.mean(data, axis=0) \n", - " u[u < 0] = 0\n", - " upper_bars = np.std(u, axis=0)\n", - " return upper_bars\n", - "\n", - "def get_lower_error_bars(data):\n", - " l = data - np.mean(data, axis=0) \n", - " l[l > 0] = 0\n", - " lower_bars = np.std(-l, axis=0)\n", - " return lower_bars" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "bo_data = return_best_accumulated_ranking(torch.load('bo_results.pkl')['outputs_normalized'])\n", - "# exploitation_data = return_best_accumulated_ranking(torch.load('bo_exploitation_results.pkl')['outputs_normalized'])\n", - "# exploration_data = return_best_accumulated_ranking(torch.load('bo_exploration_results.pkl')['outputs_normalized'])\n", - "es_data = return_best_accumulated_ranking(torch.load('es_results.pkl')['outputs_normalized'])\n", - "rf_data = return_best_accumulated_ranking(torch.load('rf_single_acq_results.pkl')['outputs_normalized'])\n", - "diverse_rf_data = return_best_accumulated_ranking(torch.load('diverse_rf_single_acq_results.pkl')['outputs_normalized'])\n", - "\n", - "\n", - "# Random Search\n", - "all_outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values\n", - "max_DC = np.max(all_outputs)\n", - "min_DC = np.min(all_outputs)\n", - "range_DC = max_DC - min_DC\n", - "all_outputs = (all_outputs - min_DC) / range_DC\n", - "rs_data = []\n", - "for i in range(25):\n", - " initial_random_idxs = np.random.choice(np.arange((inputs.shape[0])), size=500, replace=False)\n", - " rs_data.append(all_outputs[initial_random_idxs])\n", - "rs_data = return_best_accumulated_ranking(rs_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# TODO: the error bars are not symmetric: see how the shaded region bleeds past 1.0? I think this is because the error bars are treated as symmetric but they are not.\n", - "plt.plot(n_evals_array, np.mean(bo_data, axis=0), label='BO', lw=4, clip_on=False, color=search_to_color['BO'])\n", - "plt.fill_between(list(range(500)), np.mean(bo_data, axis=0) - get_lower_error_bars(bo_data), \n", - " np.mean(bo_data, axis=0) + get_upper_error_bars(bo_data), \n", - " alpha=0.2, color=search_to_color['BO'], ec=\"None\")\n", - "# plt.plot(list(range(500)), np.mean(exploitation_data, axis=0), label='BO (Exploitation)')\n", - "# plt.fill_between(list(range(500)), np.mean(exploitation_data, axis=0) - np.std(exploitation_data, axis=0), np.mean(exploitation_data, axis=0) + np.std(exploitation_data, axis=0), alpha=0.5)\n", - "# plt.plot(list(range(500)), np.mean(exploration_data, axis=0), label='BO (Exploration)')\n", - "# plt.fill_between(list(range(500)), np.mean(exploration_data, axis=0) - np.std(exploration_data, axis=0), np.mean(exploration_data, axis=0) + np.std(exploration_data, axis=0), alpha=0.5)\n", - "\n", - "plt.plot(n_evals_array, np.mean(rs_data, axis=0), label='random search', lw=4, clip_on=False, color=search_to_color['random'])\n", - "plt.fill_between(list(range(500)), np.mean(rs_data, axis=0) - get_lower_error_bars(rs_data), #np.std(rs_data, axis=0), \n", - " np.mean(rs_data, axis=0) + get_upper_error_bars(rs_data), #np.std(rs_data, axis=0), \n", - " alpha=0.2, color=search_to_color['random'], ec=\"None\")\n", - "\n", - "plt.plot(n_evals_array, np.mean(es_data, axis=0), label='evolutionary search', lw=4, clip_on=False, color=search_to_color['evolutionary'])\n", - "plt.fill_between(list(range(500)), np.mean(es_data, axis=0) - get_lower_error_bars(es_data), #np.std(es_data, axis=0), \n", - " np.mean(es_data, axis=0) + get_upper_error_bars(es_data), #np.std(es_data, axis=0), \n", - " alpha=0.2, color=cool_colors[2], ec=\"None\")\n", - "\n", - "#plt.errorbar([50, 100, 150, 200, 300, 400, 500], np.mean(rf_data, axis=0), np.std(rf_data, axis=0), \n", - "# fmt='o', label='random forest', clip_on=False, color=search_to_color['RF'], ms=8, markerfacecolor=\"None\", lw=2, markeredgewidth=3)\n", - "#plt.errorbar([50, 100, 150, 200, 300, 400, 500], np.mean(diverse_rf_data, axis=0), np.std(diverse_rf_data, axis=0), \n", - "# fmt='s', label='random forest\\n(diverse train set)', clip_on=False, color=search_to_color['RF'], ms=8, markerfacecolor=\"None\", lw=2, markeredgewidth=3)\n", - "\n", - "plt.plot(n_evals_array, np.mean(rf_data, axis=0), label='random forest', color=search_to_color['RF'], lw=4, clip_on=False)\n", - "plt.fill_between(list(range(500)), np.mean(rf_data, axis=0) - get_lower_error_bars(rf_data), #np.std(rf_data, axis=0), \n", - " np.mean(rf_data, axis=0) + get_upper_error_bars(rf_data), #np.std(rf_data, axis=0), \n", - " alpha=0.2, ec=\"None\", color=search_to_color['RF'])\n", - "\n", - "plt.plot(n_evals_array, np.mean(diverse_rf_data, axis=0), label='random forest\\n(diverse train set)', color=cool_colors[3], lw=4, clip_on=False)\n", - "plt.fill_between(list(range(500)), np.mean(diverse_rf_data, axis=0) - get_lower_error_bars(diverse_rf_data), #np.std(diverse_rf_data, axis=0), \n", - " np.mean(diverse_rf_data, axis=0) + get_upper_error_bars(diverse_rf_data), #np.std(diverse_rf_data, axis=0), \n", - " alpha=0.2, ec=\"None\", color=search_to_color['RF (div)'])\n", - "\n", - "\n", - "\n", - "plt.xlabel('# evaluated COFs', fontsize=15)\n", - "plt.ylabel('highest rank\\namong evaluated COFs', fontsize=15)\n", - "plt.xlim([0, 500])\n", - "plt.ylim(ymin=1)\n", - "# plt.legend(fontsize=1/4)\n", - "# plt.axhline(y=0) # to see the band bleed into negative zone.\n", - "plt.yticks(fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yscale(\"log\")\n", - "plt.gca().invert_yaxis()\n", - "plt.tight_layout()\n", - "# plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=15)\n", - "plt.savefig(\"search_efficiency_rank.pdf\")#, bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "how many evals needed to reach top material?" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "110" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_evals_array[np.argmin(np.mean(bo_data, axis=0))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "best rank after 500 evals" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "29.2" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(diverse_rf_data, axis=0)[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.2" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(es_data, axis=0)[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "167.96" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(rs_data, axis=0)[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### BO vs ES vs RS vs RF best accumulated" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "bo_data = min_DC + range_DC * return_best_accumulated(torch.load('bo_results.pkl')['outputs_normalized'])\n", - "# exploitation_data = return_best_accumulated(torch.load('bo_exploitation_results.pkl')['outputs_normalized'])\n", - "# exploration_data = return_best_a/ccumulated(torch.load('bo_exploration_results.pkl')['outputs_normalized'])\n", - "es_data = min_DC + range_DC * return_best_accumulated(torch.load('es_results.pkl')['outputs_normalized'])\n", - "# rf_data = min_DC + range_DC * return_best_accumulated(torch.load('rf_results.pkl')['outputs_normalized'])\n", - "# diverse_rf_data = min_DC + range_DC * return_best_accumulated(torch.load('diverse_rf_results.pkl')['outputs_normalized'])\n", - "\n", - "rf_data = min_DC + range_DC * return_best_accumulated(torch.load('rf_single_acq_results.pkl')['outputs_normalized'])\n", - "diverse_rf_data = min_DC + range_DC * return_best_accumulated(torch.load('diverse_rf_single_acq_results.pkl')['outputs_normalized'])\n", - "\n", - "\n", - "\n", - "\n", - "# Random Search\n", - "all_outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values\n", - "all_outputs = ((all_outputs - np.min(all_outputs))/(np.max(all_outputs)-np.min(all_outputs)))\n", - "rs_data = []\n", - "for i in range(25):\n", - " initial_random_idxs = np.random.choice(np.arange((inputs.shape[0])), size=500, replace=False)\n", - " rs_data.append(all_outputs[initial_random_idxs])\n", - "rs_data = min_DC + range_DC * return_best_accumulated(rs_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 2, gridspec_kw={'width_ratios': [4, 1]}, figsize=[1.2 * 6.4, 4.8], sharey=True)\n", - "axs[0].plot(list(range(1, 501)), np.mean(bo_data, axis=0), label='BO', color=search_to_color['BO'], lw=4, clip_on=False)\n", - "axs[0].fill_between(list(range(500)), np.mean(bo_data, axis=0) - np.std(bo_data, axis=0), \n", - " np.mean(bo_data, axis=0) + np.std(bo_data, axis=0), \n", - " alpha=0.2, ec=\"None\", color=search_to_color['BO'])\n", - "# plt.plot(list(range(500)), np.mean(exploitation_data, axis=0), label='BO (Exploitation)')\n", - "# plt.fill_between(list(range(500)), np.mean(exploitation_data, axis=0) - np.std(exploitation_data, axis=0), np.mean(exploitation_data, axis=0) + np.std(exploitation_data, axis=0), alpha=0.5)\n", - "# plt.plot(list(range(500)), np.mean(exploration_data, axis=0), label='BO (Exploration)')\n", - "# plt.fill_between(list(range(500)), np.mean(exploration_data, axis=0) - np.std(exploration_data, axis=0), np.mean(exploration_data, axis=0) + np.std(exploration_data, axis=0), alpha=0.5)\n", - "\n", - "\n", - "axs[0].plot(n_evals_array, np.mean(rs_data, axis=0), label='random search', color=search_to_color['random'], lw=4, clip_on=False)\n", - "axs[0].fill_between(list(range(500)), np.mean(rs_data, axis=0) - np.std(rs_data, axis=0), \n", - " np.mean(rs_data, axis=0) + np.std(rs_data, axis=0), \n", - " alpha=0.2, ec=\"None\", color=search_to_color['random'])\n", - "\n", - "axs[0].plot(n_evals_array, np.mean(es_data, axis=0), label='evolutionary search', color=search_to_color['evolutionary'], lw=4, clip_on=False)\n", - "axs[0].fill_between(list(range(500)), np.mean(es_data, axis=0) - np.std(es_data, axis=0), \n", - " np.mean(es_data, axis=0) + np.std(es_data, axis=0), \n", - " alpha=0.2, ec=\"None\", color=search_to_color['evolutionary'])\n", - "\n", - "#axs[0].errorbar([50, 100, 150, 200, 300, 400, 500], np.mean(rf_data, axis=0), np.std(rf_data, axis=0), \n", - "# fmt='o', label='random forest', clip_on=False, color=search_to_color['RF'], ms=8, markerfacecolor=\"None\", lw=2, markeredgewidth=3)\n", - "#axs[0].errorbar([50, 100, 150, 200, 300, 400, 500], np.mean(diverse_rf_data, axis=0), np.std(diverse_rf_data, axis=0), \n", - "# fmt='s', label='random forest\\n(diverse train set)', clip_on=False, color=search_to_color['RF'], ms=8, markerfacecolor=\"None\", lw=2, markeredgewidth=3)\n", - "\n", - "\n", - "axs[0].plot(n_evals_array, np.mean(rf_data, axis=0), label='random forest', color=search_to_color['RF'], lw=4, clip_on=False)\n", - "axs[0].fill_between(list(range(500)), np.mean(rf_data, axis=0) - np.std(rf_data, axis=0), \n", - " np.mean(rf_data, axis=0) + np.std(rf_data, axis=0), \n", - " alpha=0.2, ec=\"None\", color=search_to_color['RF'])\n", - "\n", - "axs[0].plot(n_evals_array, np.mean(diverse_rf_data, axis=0), label='random forest\\n(diverse train set)', color=cool_colors[3], lw=4, clip_on=False)\n", - "axs[0].fill_between(list(range(500)), np.mean(diverse_rf_data, axis=0) - np.std(diverse_rf_data, axis=0), \n", - " np.mean(diverse_rf_data, axis=0) + np.std(diverse_rf_data, axis=0), \n", - " alpha=0.2, ec=\"None\", color=search_to_color['RF (div)'])\n", - "\n", - "axs[0].set_xlabel('# evaluated COFs', fontsize=14)\n", - "axs[0].set_ylabel('maximum deliverable capacity\\namong evaluated COFs\\n[L STP/L]', fontsize=13)\n", - "axs[0].legend(fontsize=14)# bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=15)\n", - "axs[0].tick_params(axis='both', labelsize=14)\n", - "axs[1].tick_params(axis='both', labelsize=14)\n", - "\n", - "axs[0].set_xlim([0, 500])\n", - "axs[0].set_ylim(ymin=0.0)\n", - "# axs[0].set_ylim([125, 250])\n", - "\n", - "axs[1].hist(min_DC + range_DC * all_outputs, orientation=\"horizontal\", color=cool_colors[7])\n", - "axs[1].set_xlabel(\"# COFs\", fontsize=13)\n", - "axs[1].set_xscale(\"log\")\n", - "plt.tight_layout()\n", - "plt.savefig(\"search_efficiency_max_found.pdf\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "109" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.argmax(np.mean(bo_data, axis=0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Top100 results" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def num_points_in_top100(data):\n", - " outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values\n", - " outputs = ((outputs - np.min(outputs))/(np.max(outputs)-np.min(outputs)))\n", - " top_hundredth_point = np.sort(outputs)[::-1][99]\n", - " top100_counts = []\n", - " for i in range(len(data)):\n", - " count = 0\n", - " count_list = []\n", - " for j in range(len(data[i])):\n", - " #print(data[i][j])\n", - " if data[i][j] >= top_hundredth_point:\n", - " count += 1\n", - " count_list.append(count)\n", - " top100_counts.append(count_list)\n", - " return np.array(top100_counts) / 100" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "num_points = 500\n", - "\n", - "bo_top100 = num_points_in_top100(torch.load('bo_results.pkl')['outputs_normalized'])\n", - "es_top100 = num_points_in_top100(torch.load('es_results.pkl')['outputs_normalized'])\n", - "rf_top100 = num_points_in_top100(torch.load('rf_single_acq_results.pkl')['outputs_normalized'])\n", - "diverse_rf_top100 = num_points_in_top100(torch.load('diverse_rf_single_acq_results.pkl')['outputs_normalized']) \n", - "# Random Search\n", - "all_outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values\n", - "all_outputs = ((all_outputs - np.min(all_outputs))/(np.max(all_outputs)-np.min(all_outputs)))\n", - "rs_data = []\n", - "for i in range(25):\n", - " initial_random_idxs = np.random.choice(np.arange((inputs.shape[0])), size=500, replace=False)\n", - " rs_data.append(all_outputs[initial_random_idxs])\n", - "rs_top100 = num_points_in_top100(rs_data)\n", - "\n", - "\n", - "plt.plot(n_evals_array, np.mean(bo_top100, axis=0), label='BO', color=search_to_color[\"BO\"], lw=4)\n", - "plt.fill_between(n_evals_array, np.mean(bo_top100, axis=0) - np.std(bo_top100, axis=0), np.mean(bo_top100, axis=0) + np.std(bo_top100, axis=0), \n", - " alpha=0.2, color=search_to_color[\"BO\"], ec=\"None\")\n", - "plt.plot(n_evals_array, np.mean(rs_top100, axis=0), label='Random Search', color=search_to_color[\"random\"], lw=4, clip_on=False)\n", - "plt.fill_between(n_evals_array, np.mean(rs_top100, axis=0) - np.std(rs_top100, axis=0), np.mean(rs_top100, axis=0) + np.std(rs_top100, axis=0), \n", - " alpha=0.2, color=search_to_color[\"random\"], ec=\"None\")\n", - "plt.plot(n_evals_array, np.mean(es_top100, axis=0), label='Evolutionary Search', color=search_to_color[\"evolutionary\"], lw=4)\n", - "plt.fill_between(n_evals_array, np.mean(es_top100, axis=0) - np.std(es_top100, axis=0), np.mean(es_top100, axis=0) + np.std(es_top100, axis=0),\n", - " alpha=0.2, color=search_to_color[\"evolutionary\"], ec=\"None\")\n", - "plt.plot(n_evals_array, np.mean(rf_top100, axis=0), label='random forest', color=search_to_color[\"RF\"], lw=4)\n", - "plt.fill_between(n_evals_array, np.mean(rf_top100, axis=0) - np.std(rf_top100, axis=0), np.mean(rf_top100, axis=0) + np.std(rf_top100, axis=0),\n", - " alpha=0.2, color=search_to_color[\"RF\"], ec=\"None\")\n", - "\n", - "plt.plot(n_evals_array, np.mean(diverse_rf_top100, axis=0), label='random forest\\n(diverse train set)', color=cool_colors[3], lw=4)\n", - "plt.fill_between(n_evals_array, np.mean(diverse_rf_top100, axis=0) - np.std(diverse_rf_top100, axis=0), np.mean(diverse_rf_top100, axis=0) + np.std(diverse_rf_top100, axis=0),\n", - " alpha=0.2, color=search_to_color[\"RF (div)\"], ec=\"None\")\n", - "\n", - "plt.ylabel('fraction of top 100 COFs found', fontsize=14)\n", - "plt.xlabel('# evaluated COFs',fontsize=14)\n", - "# plt.legend(fontsize=12)\n", - "plt.xlim([0, 500])\n", - "plt.ylim([0, 1])\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig(\"search_efficiency_top100.pdf\", format=\"pdf\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "109" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.argmin((np.mean(bo_top100, axis=0) - 0.25) ** 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.251" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# after 110 interations, wut fraction did BO recover?\n", - "np.mean(bo_top100, axis=0)[110]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "50.500000000000014" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# after 500 evals, wut fraction did BO recover?\n", - "np.mean(bo_top100, axis=0)[-1] * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.76" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# after 500 evals, wut fraction did RS recover?\n", - "np.mean(rs_top100, axis=0)[-1] * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.000000000000002" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# after 500 evals, wut fraction did RF recover?\n", - "np.mean(diverse_rf_top100, axis=0)[-1] * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "15.200000000000003" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# after 500 evals, wut fraction did ES recover?\n", - "np.mean(es_top100, axis=0)[-1] * 100" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/COF_dataframe_to_methane_storage.py b/COF_dataframe_to_methane_storage.py deleted file mode 100644 index 53b6439..0000000 --- a/COF_dataframe_to_methane_storage.py +++ /dev/null @@ -1,21 +0,0 @@ -# code to convert from the original dataframe dataset 'properties.csv' to 'methane_storage.pkl' - -import numpy as np -import pickle -import pandas as pd - -df = pd.read_csv('properties.csv') -# we remove Column 48225 since it is an outlier for void fraction feature # can be seen by df[df[' void fraction [widom]'] > 1] -df = df.drop(48225) - -features = [' void fraction [widom]', ' density [kg/m^3]', ' largest included sphere diameter [A]', - ' largest free sphere diameter [A]', ' surface area [m^2/g]'] -extra_features = [' num carbon', ' num fluorine', ' num hydrogen', ' num nitrogen', ' num oxygen', ' num sulfur', ' num silicon'] - -inputs = np.concatenate([df[features].values, (df[extra_features].values)/(df[' supercell volume [A^3]'].values.reshape(-1, 1))], axis=1) - -outputs = df[' deliverable capacity [v STP/v]'] - - -with open('methane_storage.pkl', 'wb') as file: - pickle.dump({'inputs': inputs, 'outputs': outputs}, file) diff --git a/README.md b/README.md index 0983a46..2a35f73 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,37 @@ -# Bayesian optimization of nanoporous materials +Python code to reproduce all plots in: -#### This repository contains source code for the paper [Bayesian optimization of nanoporous materials](). The details for reproducing the results are given below: +> ❝Bayesian optimization of nanoporous materials❞ +> A. Deshwal, C. Simon, J. R. Doppa. +> ChemRxiv. (2021) [DOI](https://chemrxiv.org/engage/chemrxiv/article-details/60d2c7d7e211337735e056e2) + +## requirements + +the Python 3 libraries required for the project are in `requirements.txt`. use Jupyter Notebook or Jupyter Lab to run Python 3 in the `*.ipynb`. + +# search methods + +## step 1: prepare the data + +our paper relies on data from Mercado et al. [here](https://pubs.acs.org/doi/10.1021/acs.chemmater.8b01425). visit [Materials Cloud](https://archive.materialscloud.org/record/2018.0003/v2) to download and untar `properties.tgz`. place `properties.csv` in the main directory. + +run the code in the Jupyter Notebook `prepare_Xy.ipynb` to prepare the data and write `inputs_and_outputs.pkl` to be read in by other Notebooks. + +## step 2: run the searches + +run the following Jupyter Notebooks, which will write search results to `.pkl` files. +* `random_search.ipynb` for random search +* `evol_search.ipynb` for evolutionary search (CMA-ES) +* `random_forest_run.ipynb` for one-shot supervised machine learning (via random forests). run twice, one with the flag `diversify_training = True`, the other with `diversify_training = False`. +* `BO_run.ipynb` for Bayesian optimization. run three times, with `which_acquisition` set to `"EI"`, `"max y_hat"`, and `max sigma`. + +each `.ipynb` can be run on a desktop computer. the BO code takes the longest, at ~10 min per run. -- To prepare the data from Mercado et al. [here](https://pubs.acs.org/doi/10.1021/acs.chemmater.8b01425), visit [Materials Cloud](https://archive.materialscloud.org/record/2018.0003/v2) and download and untar `properties.tgz`. run `COF_dataframe_to_methane_storage.py` to read in the data and write to `.pkl` files for convenience. -- The main code of Bayesian optimization can be run by ```python bo_run.py```. The core logic for this code is built using [GpyTorch](https://github.com/cornellius-gp/gpytorch) and [BoTorch](https://github.com/pytorch/botorch) libraries. -- Code for One shot supervised learning (Random Forest: with and without diverse training set) is provided in ```random_forest_run.py``` ```diverse_random_forest_run.py```. -The core logic for this code is written using [Scikit-learn](https://github.com/scikit-learn/scikit-learn) library. -- To run Evolutionary search (CMA-ES) baseline, run ```python evolutionary_search_run.py```. The core logic for this baseline requires installing [CMA-ES](https://github.com/CMA-ES/pycma) package. -This code iterates over different choices of ```sigma``` and ```population size``` (two key parameters for instantiating CMA-ES search). As mentioned in our paper, we found ```sigma=0.2``` and ```population size=20``` to be the best parameters. -- Since each code generates a single file for each different random run of the method, we provide a simple wrapper ```compile_results_in_one_file.py``` to combine all the results into a single file. -- The code for generating figures is shown given in jupyter notebook ```cofs_results.ipynb```. -All the libraries required for the entire repository are given in ```requirements.txt``` file. +## step 3: visualize the results + +finally, run `viz.ipynb` to read in the `*.pkl` files and visualize the results. +# toy GP illustrations +see `synthetic_example.ipynb` for the toy GP plots in the paper. diff --git a/bo_run.py b/bo_run.py deleted file mode 100644 index 95bc30b..0000000 --- a/bo_run.py +++ /dev/null @@ -1,69 +0,0 @@ -import torch -from botorch.models import FixedNoiseGP, SingleTaskGP -from gpytorch.kernels import ScaleKernel -from gpytorch.mlls import ExactMarginalLogLikelihood -from botorch import fit_gpytorch_model -from botorch.acquisition.analytic import ExpectedImprovement -import numpy as np -import pickle -import sys -import time - -def initialize_model(train_x, train_obj, covar_module=None, state_dict=None): - # define models for objective and constraint - if covar_module is not None: - model = SingleTaskGP(train_x, train_obj, covar_module=covar_module) - else: - model = SingleTaskGP(train_x, train_obj) - mll = ExactMarginalLogLikelihood(model.likelihood, model) - if state_dict is not None: - model.load_state_dict(state_dict) - return mll, model - -def main(): - n_init = 10 - n_evals = 500 - # Normalizing input features - inputs = pickle.load(open('methane_storage.pkl', 'rb'))['inputs'] - for i in range(len(inputs[0])): - inputs[:, i] = (inputs[:, i] - np.min(inputs[:, i]))/(np.max(inputs[:, i]) - np.min(inputs[:, i])) - outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values - outputs = ((outputs - np.min(outputs))/(np.max(outputs)-np.min(outputs))) - DATA_SIZE = inputs.shape[0] - - # print(f"Input data size: {inputs.shape}") - # print(f"Output data size: {outputs.shape}") - inputs = torch.from_numpy(inputs) - for nrr in range(25): - initial_random_idxs = np.random.choice(np.arange((DATA_SIZE)), size=n_init, replace=False) - train_x = inputs[initial_random_idxs] - train_y = torch.from_numpy(outputs[initial_random_idxs]).unsqueeze(-1) - mll_ei, model_ei = initialize_model(train_x, train_y)#, covar_module) - for num_iters in range(n_init, n_evals): - print("Fitting GP ....") - fit_gpytorch_model(mll_ei) - print("Fitting GP finished.") - EI = ExpectedImprovement(model_ei, best_f = train_y.max().item()) - # EI_vals = EI(torch.from_numpy(inputs).unsqueeze(1)).detach().numpy() # vectorized acq function computation - EI_vals = [] - for x in inputs: - with torch.no_grad(): - EI_vals.append(EI(x.unsqueeze(0)).item()) - indices = np.argsort(EI_vals)[::-1] - for idx in indices: - if not torch.all(inputs[idx].unsqueeze(0) == train_x, axis=1).any(): # pick topmost already not in the dataset - best_next_input = inputs[idx].unsqueeze(0) - break - train_x = torch.cat([train_x, best_next_input]) - train_y = torch.cat([train_y, torch.tensor(outputs[idx]).reshape(1, 1)]) - print(f"Iteration {num_iters}:") - print(f"{idx}th point selected ", end='') - print(f"with value: {train_y[-1].item()}") - print(f"Best value found till now: {train_y.max().item()}") - - mll_ei, model_ei = initialize_model(train_x, train_y, state_dict = model_ei.state_dict())# update model - torch.save({'inputs_selected':train_x, 'outputs':train_y}, 'bo_data_run'+str(nrr)+'.pkl') - -if __name__ == '__main__': - main() - diff --git a/compile_results_in_one_file.py b/compile_results_in_one_file.py deleted file mode 100644 index e5f4ac6..0000000 --- a/compile_results_in_one_file.py +++ /dev/null @@ -1,80 +0,0 @@ -### code to generate combinedresults -### since each program generates a single file for each run, this is a wrapper code to combine data for all runs into a single file - - -import numpy as np -import matplotlib.pyplot as plt -import pickle -import pandas as pd -import torch -import os - - -def main(): - outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values - - rf_data = {} - temp = [] - temp_idxs = [] - for i in range(10): - temp.append(np.array(pickle.load(open('mbo_rf_methane_data_tsp99_'+str(i)+'.pkl', 'rb'))['all_best_vals'])) - temp_idxs.append(np.array(pickle.load(open('mbo_rf_methane_data_tsp99_'+str(i)+'.pkl', 'rb'))['all_best_idxs'])) - rf_data['outputs_normalized'] = np.array(temp) - rf_data['outputs'] = outputs[np.array(temp_idxs)] - torch.save(rf_data, "rf_single_acq_results.pkl") - - ''' - # uncomment to combine files for 50% acquisition - rf_data = {} - temp = [] - temp_idxs = [] - for i in range(10): - temp.append(np.array(pickle.load(open('mbo_50percent_rf_methane_data_tsp50_'+str(i)+'.pkl', 'rb'))['all_best_vals'])) - temp_idxs.append(np.array(pickle.load(open('mbo_50percent_rf_methane_data_tsp50_'+str(i)+'.pkl', 'rb'))['all_best_idxs'])) - rf_data['outputs_normalized'] = np.array(temp) - rf_data['outputs'] = outputs[np.array(temp_idxs)] - torch.save(rf_data, "rf_50percent_acq_results.pkl") - ''' - - diverse_rf_data = {} - temp = [] - temp_idxs = [] - for i in range(10): - temp.append(np.array(pickle.load(open('mbo_diverse_rf_data_num_training_points_99_'+str(i)+'.pkl', 'rb'))['all_best_vals'])) - temp_idxs.append(np.array(pickle.load(open('mbo_diverse_rf_data_num_training_points_99_'+str(i)+'.pkl', 'rb'))['all_best_idxs'])) - diverse_rf_data['outputs_normalized'] = np.array(temp) - diverse_rf_data['outputs'] = outputs[np.array(temp_idxs)] - torch.save(diverse_rf_data, "diverse_rf_single_acq_results.pkl") - - ''' - # uncomment to combine files for 50% acquisition - diverse_rf_data = {} - temp = [] - temp_idxs = [] - for i in range(10): - temp.append(np.array(pickle.load(open('mbo_50percent_diverse_rf_data_num_training_points_50_'+str(i)+'.pkl', 'rb'))['all_best_vals'])) - temp_idxs.append(np.array(pickle.load(open('mbo_50percent_diverse_rf_data_num_training_points_50_'+str(i)+'.pkl', 'rb'))['all_best_idxs'])) - diverse_rf_data['outputs_normalized'] = np.array(temp) - diverse_rf_data['outputs'] = outputs[np.array(temp_idxs)] - torch.save(diverse_rf_data, "diverse_rf_50percent_acq_results.pkl") - ''' - - temp = [] - for i in range(10): - if os.path.exists('bo_data_run'+str(i)+'.pkl'): - temp.append(torch.load('bo_data_run'+str(i)+'.pkl')['outputs'].squeeze(1).numpy()) - bo_results = {} - bo_results['outputs_normalized'] = np.array(temp) - torch.save(bo_results, 'bo_results.pkl') - - normalized_es_data = [] - max_len = 0 - sigma = 0.2 - popsize = 20 - for i in range(10): - normalized_es_data.append((-1*np.matrix.flatten(np.array(pickle.load(open('es_run_'+str(i)+'_' + 'sigma_' + str(sigma) + '_popsize_'+ str(popsize) +'.pkl', 'rb'))['final_outputs'])))) - normalized_es_data = np.array(normalized_es_data) - torch.save({"outputs_normalized":normalized_es_data[:, :500]}, "es_results.pkl") - -if __name__ == '__main__': - main() diff --git a/diverse_random_forest_run.py b/diverse_random_forest_run.py deleted file mode 100644 index f256845..0000000 --- a/diverse_random_forest_run.py +++ /dev/null @@ -1,69 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import pickle -import torch -import time - -from sklearn.metrics import r2_score, mean_absolute_error, explained_variance_score, mean_squared_error -from sklearn.model_selection import train_test_split -import autosklearn.regression -from sklearn.ensemble import RandomForestRegressor - - -def main(): - inputs = pickle.load(open('methane_storage.pkl', 'rb'))['inputs'] - # normalizing input features - for i in range(len(inputs[0])): - inputs[:, i] = (inputs[:, i] - np.min(inputs[:, i]))/(np.max(inputs[:, i]) - np.min(inputs[:, i])) - outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values - # normalizing outpuuts - outputs = ((outputs - np.min(outputs))/(np.max(outputs)-np.min(outputs))) - rlist, maelist, mselist = [], [], [] - - - for i in range(10): - all_best_vals = [] - all_best_idxs = [] - for num_training_points in [50, 100, 150, 200, 300, 400, 500]: - print(f"num_training_points : {num_training_points}") - num_training_points = num_training_points - len(all_best_vals) - num_training_points = num_training_points - 1 - print(f"num_training_points : {num_training_points}") - train_idxs = [np.random.randint(0, len(inputs))] # initialize with one random point; pick others in a max diverse fashion - for j in range(num_training_points - 1): - if j >= 1: - distances = np.sort(np.sum((inputs - inputs[train_idxs][:, None, :])**2, axis=-1).T, axis=1)[:, 1] - else: - distances = np.sort(np.sum((inputs - inputs[train_idxs][:, None, :])**2, axis=-1).T, axis=1) - train_idxs.append(np.argmax(distances)) - print(f'train_idxs {len(train_idxs)}') - X_train = inputs[train_idxs] - y_train = outputs[train_idxs] - - all_best_vals.extend(y_train) - all_best_idxs.extend(train_idxs) - - test_idxs = np.setdiff1d(np.arange(len(inputs)), train_idxs) - - X_test = inputs[test_idxs] - y_test = outputs[test_idxs] - - start_time = time.time() - regr = RandomForestRegressor() - regr.fit(X_train, y_train) - #best_idx = np.argmax(regr.predict(X_test)) - best_indices = np.argsort(-regr.predict(X_test))[:1] - #best_val = y_test[best_idx] - best_vals = y_test[best_indices] - all_best_vals.extend(best_vals) - all_best_idxs.extend(test_idxs[best_indices]) - #all_best_vals.append(best_val) - #all_best_idxs.append(test_idxs[best_idx]) - #print(f"Best value {best_val} found at {test_idxs[best_idx]}") - - with open('mbo_diverse_rf_data_num_training_points_'+str(num_training_points)+'_'+str(i)+'.pkl', 'wb') as f: - pickle.dump({'all_best_vals': all_best_vals, 'all_best_idxs': all_best_idxs}, f) - - -if __name__ == '__main__': - main() diff --git a/evolutionary_search_run.py b/evolutionary_search_run.py deleted file mode 100644 index 4a9682d..0000000 --- a/evolutionary_search_run.py +++ /dev/null @@ -1,55 +0,0 @@ -import cma -import pickle -import numpy as np -import time -def closest_node(node, nodes): - nodes = np.asarray(nodes) - dist_2 = np.sum((nodes - node)**2, axis=1) - return np.argmin(dist_2) - - -def main(): - inputs = pickle.load(open('methane_storage.pkl', 'rb'))['inputs'] - for i in range(len(inputs[0])): - inputs[:, i] = (inputs[:, i] - np.min(inputs[:, i]))/(np.max(inputs[:, i]) - np.min(inputs[:, i])) - outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values - outputs = ((outputs - np.min(outputs))/(np.max(outputs)-np.min(outputs))) - # CMA-ES requires two main params: sigma and population size - sigma_list = [0.2]# [0.1, 0.2, 0.5] - pop_list = [20]# [20, 50, 100] - for i in range(25): - print(f"Iteration {i}**************") - x_init = np.random.rand(12) - for popsize in pop_list: - for sigma in sigma_list: - cont_bounds = [0, 1] - start_time = time.time() - es = cma.CMAEvolutionStrategy(x0=x_init,sigma0=sigma,inopts={'bounds': cont_bounds, "popsize": popsize},) - final_outputs = [] - final_inputs = [] - #while not es.stop(): - for iter in range(int(20000/popsize)): - xs = es.ask() - temp_time = time.time() - Y = [] - X = [] - for x in xs: - # find the closest input - idx = closest_node(x, inputs) - X.append(inputs[idx]) - Y.append(-1*outputs[idx]) - #Y.append(-1*outputs[closest_node(x, inputs)]) - es.tell(X, Y) # return the result to the optimizer - final_outputs.append(Y) - final_inputs.append(X) - if iter % 50 == 0: - print(f"CMA-ES iteration:{iter}") - print("current best") - print(f"{es.best.f}") - best_x = es.best.x - #print(best_x) - with open('es_run_'+str(i)+'_' + 'sigma_' + str(sigma) + '_popsize_'+ str(popsize) +'.pkl', 'wb') as f: - pickle.dump({'final_outputs':final_outputs, 'final_inputs': final_inputs}, f) - -if __name__ == '__main__': - main() diff --git a/new/viz.ipynb b/new/viz.ipynb index 5fb7121..a0b1128 100644 --- a/new/viz.ipynb +++ b/new/viz.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "genetic-leone", + "id": "cross-civilization", "metadata": {}, "source": [ "# viz" @@ -10,32 +10,10 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "changing-collective", + "execution_count": 52, + "id": "familiar-shirt", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n", - "In /home/cokes/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n", - "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt\n", @@ -50,12 +28,12 @@ "\n", "plt.rcParams.update({'font.size': 14})\n", "\n", - "search_to_color = {'BO': cool_colors[0], 'random': cool_colors[1], 'evolutionary': cool_colors[2], 'RF': cool_colors[5], 'RF (div)': cool_colors[3]}" + "search_to_color = {'BO': cool_colors[0], 'random': cool_colors[1], 'evolutionary': cool_colors[2], 'RF': cool_colors[3], 'RF (div)': cool_colors[5]}" ] }, { "cell_type": "markdown", - "id": "understood-essex", + "id": "addressed-georgia", "metadata": {}, "source": [ "load data" @@ -63,8 +41,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "raising-hybrid", + "execution_count": 53, + "id": "activated-monaco", "metadata": {}, "outputs": [ { @@ -80,7 +58,7 @@ "69839" ] }, - "execution_count": 2, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -95,7 +73,7 @@ }, { "cell_type": "markdown", - "id": "thrown-korea", + "id": "strategic-mayor", "metadata": {}, "source": [ "for rankings" @@ -103,8 +81,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "aggressive-tower", + "execution_count": 54, + "id": "committed-apparatus", "metadata": {}, "outputs": [], "source": [ @@ -114,8 +92,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "communist-irish", + "execution_count": 55, + "id": "coupled-developer", "metadata": {}, "outputs": [ { @@ -124,7 +102,7 @@ "array([216.8941107])" ] }, - "execution_count": 4, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -135,8 +113,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "bronze-stopping", + "execution_count": 56, + "id": "color-journal", "metadata": {}, "outputs": [ { @@ -145,7 +123,7 @@ "array([4.47994999])" ] }, - "execution_count": 5, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -156,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "secondary-thermal", + "id": "protected-spring", "metadata": {}, "source": [ "load search results" @@ -164,8 +142,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "egyptian-discretion", + "execution_count": 57, + "id": "young-controversy", "metadata": {}, "outputs": [], "source": [ @@ -178,19 +156,43 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "complicated-christmas", + "execution_count": 58, + "id": "greenhouse-wednesday", "metadata": {}, "outputs": [], + "source": [ + "# full exploration and exploitation\n", + "bo_res_explore = pickle.load(open('bo_resultsmax sigma.pkl', 'rb'))\n", + "bo_res_exploit = pickle.load(open('bo_resultsmax y_hat.pkl', 'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "thermal-bridal", + "metadata": {}, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mnb_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbo_res\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf_res\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf_div_res\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrs_res\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mes_res\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbo_res_explore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbo_res_exploit\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nb_runs'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnb_runs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], "source": [ "nb_runs = 100\n", - "for res in [bo_res, rf_res, rf_div_res, rs_res, es_res]:\n", + "for res in [bo_res, rf_res, rf_div_res, rs_res, es_res, bo_res_explore, bo_res_exploit]:\n", " assert res['nb_runs'] == nb_runs" ] }, { "cell_type": "markdown", - "id": "instrumental-bunny", + "id": "demonstrated-currency", "metadata": {}, "source": [ "# PCA and viz of acquisition of BO" @@ -198,8 +200,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "assisted-enforcement", + "execution_count": 60, + "id": "stuck-dietary", "metadata": {}, "outputs": [], "source": [ @@ -210,8 +212,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "legendary-training", + "execution_count": 61, + "id": "related-department", "metadata": {}, "outputs": [ { @@ -243,8 +245,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "lined-conservation", + "execution_count": 62, + "id": "rural-teaching", "metadata": {}, "outputs": [ { @@ -290,7 +292,7 @@ }, { "cell_type": "markdown", - "id": "first-writing", + "id": "micro-alexander", "metadata": {}, "source": [ "# search efficiency\n", @@ -299,8 +301,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "interesting-lindsay", + "execution_count": 63, + "id": "raised-franklin", "metadata": {}, "outputs": [ { @@ -336,13 +338,16 @@ "\n", "y_max_mu_BO, y_max_sig_bot_BO, y_max_sig_top_BO = y_max(bo_res)\n", "y_max_mu_es, y_max_sig_bot_es, y_max_sig_top_es = y_max(es_res)\n", - "y_max_mu_rs, y_max_sig_bot_rs, y_max_sig_top_rs = y_max(rs_res)" + "y_max_mu_rs, y_max_sig_bot_rs, y_max_sig_top_rs = y_max(rs_res)\n", + "\n", + "y_max_mu_BO_explore, y_max_sig_bot_BO_explore, y_max_sig_top_BO_explore = y_max(bo_res_explore)\n", + "y_max_mu_BO_exploit, y_max_sig_bot_BO_exploit, y_max_sig_top_BO_exploit = y_max(bo_res_exploit)" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "whole-combat", + "execution_count": 64, + "id": "senior-aquarium", "metadata": {}, "outputs": [], "source": [ @@ -366,8 +371,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "radio-fellowship", + "execution_count": 65, + "id": "unable-render", "metadata": {}, "outputs": [], "source": [ @@ -377,13 +382,13 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "egyptian-assist", + "execution_count": 66, + "id": "quality-estate", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -438,23 +443,23 @@ }, { "cell_type": "markdown", - "id": "olympic-remark", + "id": "magnetic-latvia", "metadata": {}, "source": [ - "show distribution for context." + "full explore vs exploit" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "dutch-testimony", + "execution_count": 72, + "id": "current-greene", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -464,18 +469,31 @@ } ], "source": [ - "plt.figure(figsize=(2.5, 4.8))\n", - "plt.hist(y, orientation=\"horizontal\", color=cool_colors[7])\n", - "plt.xlabel(\"# COFs\")\n", - "plt.ylabel(\"deliverable capacity [L STP/L]\")\n", - "plt.xscale(\"log\")\n", - "plt.tight_layout()\n", - "plt.savefig(\"y_distn.pdf\", format=\"pdf\")" + "plt.figure()\n", + "plt.plot(np.arange(bo_res['nb_iterations'])+1, y_max_mu_BO, label='exploitation/exploration balance', color=search_to_color['BO'], clip_on=False)\n", + "plt.fill_between(np.arange(bo_res['nb_iterations'])+1, y_max_mu_BO - y_max_sig_bot_BO, \n", + " y_max_mu_BO + y_max_sig_top_BO, \n", + " alpha=0.2, ec=\"None\", color=search_to_color['BO'])\n", + "\n", + "plt.plot(np.arange(bo_res_explore['nb_iterations'])+1, y_max_mu_BO_explore, label='exploration', color=\"C6\", clip_on=False)\n", + "plt.fill_between(np.arange(bo_res_explore['nb_iterations'])+1, y_max_mu_BO_explore - y_max_sig_bot_BO_explore, \n", + " y_max_mu_BO_explore + y_max_sig_top_BO_explore, \n", + " alpha=0.2, ec=\"None\", color=\"C6\")\n", + "\n", + "plt.plot(np.arange(bo_res_exploit['nb_iterations'])+1, y_max_mu_BO_exploit, label='exploitation', color=\"C7\", clip_on=False)\n", + "plt.fill_between(np.arange(bo_res_exploit['nb_iterations'])+1, y_max_mu_BO_exploit - y_max_sig_bot_BO_exploit, \n", + " y_max_mu_BO_exploit + y_max_sig_top_BO_exploit, \n", + " alpha=0.2, ec=\"None\", color=\"C7\")\n", + "\n", + "plt.xlabel('# evaluated COFs')\n", + "plt.ylabel('maximum deliverable capacity\\namong acquired COFs\\n[L STP/L]')\n", + "plt.legend()\n", + "plt.tight_layout()" ] }, { "cell_type": "markdown", - "id": "unique-emission", + "id": "destroyed-calcium", "metadata": {}, "source": [ "### max rank among acquired set" @@ -484,7 +502,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "excellent-phenomenon", + "id": "expanded-shaft", "metadata": {}, "outputs": [ { @@ -512,7 +530,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "identical-prescription", + "id": "ruled-killing", "metadata": {}, "outputs": [ { @@ -567,7 +585,7 @@ }, { "cell_type": "markdown", - "id": "manual-failure", + "id": "wireless-senator", "metadata": {}, "source": [ "print stats to report in paper" @@ -576,7 +594,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "mediterranean-outdoors", + "id": "irish-driver", "metadata": {}, "outputs": [ { @@ -595,7 +613,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "weighted-craps", + "id": "graduate-entertainment", "metadata": {}, "outputs": [ { @@ -616,7 +634,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "expanded-compound", + "id": "worldwide-portable", "metadata": {}, "outputs": [ { @@ -640,7 +658,7 @@ }, { "cell_type": "markdown", - "id": "mexican-motion", + "id": "senior-converter", "metadata": {}, "source": [ "### fraction of top 100 COFs recovered" @@ -649,7 +667,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "covered-gibson", + "id": "registered-cooperation", "metadata": {}, "outputs": [ { @@ -669,7 +687,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "surrounded-auction", + "id": "graphic-ontario", "metadata": {}, "outputs": [], "source": [ @@ -684,7 +702,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "manufactured-native", + "id": "scenic-shannon", "metadata": {}, "outputs": [ { @@ -723,7 +741,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "fundamental-driving", + "id": "quick-grammar", "metadata": {}, "outputs": [ { @@ -741,7 +759,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "deluxe-office", + "id": "labeled-stationery", "metadata": {}, "outputs": [], "source": [ @@ -766,7 +784,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "african-neighborhood", + "id": "homeless-sperm", "metadata": {}, "outputs": [ { @@ -817,7 +835,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "representative-vegetation", + "id": "acquired-costs", "metadata": {}, "outputs": [ { diff --git a/random_forest_run.py b/random_forest_run.py deleted file mode 100644 index 7280cfc..0000000 --- a/random_forest_run.py +++ /dev/null @@ -1,49 +0,0 @@ -import numpy as np -import pickle -import torch -import time - -from sklearn.metrics import r2_score, mean_absolute_error, explained_variance_score, mean_squared_error -from sklearn.model_selection import train_test_split -import autosklearn.regression -from sklearn.ensemble import RandomForestRegressor - -def main(): - inputs = pickle.load(open('methane_storage.pkl', 'rb'))['inputs'] - # normalizing input features - for i in range(len(inputs[0])): - inputs[:, i] = (inputs[:, i] - np.min(inputs[:, i]))/(np.max(inputs[:, i]) - np.min(inputs[:, i])) - outputs = pickle.load(open('methane_storage.pkl', 'rb'))['outputs'].values - outputs = ((outputs - np.min(outputs))/(np.max(outputs)-np.min(outputs))) - rlist, maelist, mselist = [], [], [] - - for i in range(10): - all_best_vals = [] - all_best_idxs = [] - for test_size in [50, 100, 150, 200, 300, 400, 500]: - test_size = test_size - len(all_best_vals) - # test_size = test_size // 2 - test_size = test_size - 1 - X_train, X_test, y_train, y_test, train_idxs, test_idxs = train_test_split(inputs, outputs, np.arange(len(outputs)), test_size=test_size/len(outputs), random_state=i) - - all_best_vals.extend(y_test) - all_best_idxs.extend(test_idxs) - - start_time = time.time() - regr = RandomForestRegressor() - regr.fit(X_test, y_test) - # best_indices = np.argsort(-regr.predict(X_train))[:test_size] - best_idx = np.argmax(regr.predict(X_train)) - best_val = y_train[best_idx] - # best_vals = y_train[best_indices] - #print(f"Best value {best_val} found at {train_idxs[best_idx]}") - all_best_vals.append(best_val) - all_best_idxs.append(train_idxs[best_idx]) - #all_best_vals.extend(best_vals) - #all_best_idxs.extend(train_idxs[best_indices]) - - with open('mbo_rf_methane_data_tsp'+str(test_size)+'_' + str(i)+'.pkl', 'wb') 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z(pOZZW;lt#xCK8AGKCjI-~(N8g4(>V)WUcwjO5Z+W+8xd#qk)p;HPn38Ba~FIHj4s zunL}mUD_#N%$nik=IvB)XfEwIPI9KjW-*v@j24ngJTfocOq0zIkw?7Qj5dP=840FB bxQnp=;=*9Dk!=V+_EC9iz<`L@m#KdN1m|ad From c47da594ba1e5a62d81d8b315e5458dc029069b8 Mon Sep 17 00:00:00 2001 From: Cory Simon Date: Mon, 5 Jul 2021 11:33:21 -0700 Subject: [PATCH 22/29] Delete BO_run.ipynb --- BO_run.ipynb | 402 --------------------------------------------------- 1 file changed, 402 deletions(-) delete mode 100644 BO_run.ipynb diff --git a/BO_run.ipynb b/BO_run.ipynb deleted file mode 100644 index c30edba..0000000 --- a/BO_run.ipynb +++ /dev/null @@ -1,402 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "received-startup", - "metadata": {}, - "source": [ - "# BO runs" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "starting-bennett", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from botorch.models import FixedNoiseGP, SingleTaskGP\n", - "from gpytorch.kernels import ScaleKernel\n", - "from gpytorch.mlls import ExactMarginalLogLikelihood\n", - "from botorch import fit_gpytorch_model\n", - "from botorch.acquisition.analytic import ExpectedImprovement\n", - "import numpy as np\n", - "import pickle\n", - "import sys\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "id": "resident-innocent", - "metadata": {}, - "source": [ - "load data from `prepare_Xy.ipynb`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "settled-cheat", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "69839" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", - "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", - "y = np.reshape(y, (np.size(y), 1)) # for the GP\n", - "nb_data = np.size(y)\n", - "nb_data" - ] - }, - { - "cell_type": "markdown", - "id": "painted-profile", - "metadata": {}, - "source": [ - "convert to torch tensors" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "comprehensive-robin", - "metadata": {}, - "outputs": [], - "source": [ - "X = torch.from_numpy(X)\n", - "y = torch.from_numpy(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "legal-cosmetic", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([69839, 12])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.size()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "roman-envelope", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([69839, 1])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.size()" - ] - }, - { - "cell_type": "markdown", - "id": "checked-jerusalem", - "metadata": {}, - "source": [ - "number of COFs for initialization" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "aquatic-korean", - "metadata": {}, - "outputs": [], - "source": [ - "nb_COFs_initialization = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "controlled-renaissance", - "metadata": {}, - "outputs": [], - "source": [ - "def bo_run(nb_iterations):\n", - " assert nb_iterations > nb_COFs_initialization\n", - " # select initial COFs for training data randomly\n", - " ids_acquired = np.random.choice(np.arange((nb_data)), size=nb_COFs_initialization, replace=False)\n", - "\n", - " # initialize acquired X, y\n", - " X_acquired = X[ids_acquired, :]\n", - " y_acquired = y[ids_acquired]\n", - " # standardize outputs\n", - " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", - " \n", - " for i in range(nb_COFs_initialization, nb_iterations):\n", - " print(\"iteration:\", i)\n", - " # construct and fit GP model\n", - " model = SingleTaskGP(X_acquired, y_acquired)\n", - " mll = ExactMarginalLogLikelihood(model.likelihood, model)\n", - " fit_gpytorch_model(mll)\n", - "\n", - " # compute aquisition function at each COF in the database\n", - " acquisition_function = ExpectedImprovement(model, best_f=y_acquired.max().item())\n", - " acquisition_values = acquisition_function.forward(X.unsqueeze(1))\n", - "\n", - " # select COF to acquire with maximal aquisition value, which is not in the acquired set already\n", - " ids_sorted_by_aquisition = acquisition_values.argsort(descending=True)\n", - " for id_max_aquisition_all in ids_sorted_by_aquisition:\n", - " if not id_max_aquisition_all.item() in ids_acquired:\n", - " id_max_aquisition = id_max_aquisition_all.item()\n", - " break\n", - "\n", - " # acquire this COF\n", - " ids_acquired = np.concatenate((ids_acquired, [id_max_aquisition]))\n", - "\n", - " # update X, y acquired\n", - " X_acquired = torch.cat([X_acquired, X[id_max_aquisition, :].unsqueeze(0)])\n", - " y_acquired = y[ids_acquired, :] # start over to normalize y properly\n", - " y_acquired = (y_acquired - torch.mean(y_acquired)) / torch.std(y_acquired)\n", - "\n", - " print(\"\\tacquired COF\", id_max_aquisition, \"with y = \", y[id_max_aquisition].item())\n", - " print(\"\\tbest y acquired:\", y[ids_acquired].max().item())\n", - " assert np.size(ids_acquired) == nb_iterations\n", - " return ids_acquired" - ] - }, - { - "cell_type": "markdown", - "id": "broadband-nothing", - "metadata": {}, - "source": [ - "`ids_acquired[r, i]` will give ID of COF acquired during iteration `i` from run `r`." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "computational-portfolio", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "RUN 0\n", - "iteration: 10\n", - "\tacquired COF 16415 with y = 174.654915912\n", - "\tbest y acquired: 179.569572506\n", - "iteration: 11\n", - "\tacquired COF 27798 with y = 166.69585219\n", - "\tbest y acquired: 179.569572506\n", - "iteration: 12\n", - "\tacquired COF 27035 with y = 178.57489196900002\n", - "\tbest y acquired: 179.569572506\n", - "iteration: 13\n", - "\tacquired COF 21314 with y = 194.053101714\n", - "\tbest y acquired: 194.053101714\n", - "iteration: 14\n", - "\tacquired COF 66263 with y = 185.76857139\n", - "\tbest y acquired: 194.053101714\n", - "iteration: 15\n", - "\tacquired COF 33370 with y = 196.720247142\n", - "\tbest y acquired: 196.720247142\n", - "iteration: 16\n", - "\tacquired COF 25862 with y = 167.849327414\n", - "\tbest y acquired: 196.720247142\n", - "iteration: 17\n", - "\tacquired COF 33366 with y = 204.811726149\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 18\n", - "\tacquired COF 33330 with y = 195.58268240799998\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 19\n", - "\tacquired COF 33355 with y = 122.363855499\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 20\n", - "\tacquired COF 33332 with y = 205.963467853\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 21\n", - "\tacquired COF 25951 with y = 196.579974938\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 22\n", - "\tacquired COF 12402 with y = 175.504448723\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 23\n", - "\tacquired COF 33343 with y = 196.58076384900002\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 24\n", - "\tacquired COF 33347 with y = 208.43022665700002\n", - "\tbest y acquired: 208.43022665700002\n", - "\n", - "\n", - "RUN 1\n", - "iteration: 10\n", - "\tacquired COF 68952 with y = 195.657779278\n", - "\tbest y acquired: 195.657779278\n", - "iteration: 11\n", - "\tacquired COF 56517 with y = 194.530496788\n", - "\tbest y acquired: 195.657779278\n", - "iteration: 12\n", - "\tacquired COF 12392 with y = 185.480447434\n", - "\tbest y acquired: 195.657779278\n", - "iteration: 13\n", - "\tacquired COF 34761 with y = 177.06386607099998\n", - "\tbest y acquired: 195.657779278\n", - "iteration: 14\n", - "\tacquired COF 19518 with y = 176.468362255\n", - "\tbest y acquired: 195.657779278\n", - "iteration: 15\n", - "\tacquired COF 33330 with y = 195.58268240799998\n", - "\tbest y acquired: 195.657779278\n", - "iteration: 16\n", - "\tacquired COF 33338 with y = 129.689513234\n", - "\tbest y acquired: 195.657779278\n", - "iteration: 17\n", - "\tacquired COF 33332 with y = 205.963467853\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 18\n", - "\tacquired COF 33347 with y = 208.43022665700002\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 19\n", - "\tacquired COF 25951 with y = 196.579974938\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 20\n", - "\tacquired COF 33344 with y = 199.90463220799998\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 21\n", - "\tacquired COF 33349 with y = 206.74476888599997\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 22\n", - "\tacquired COF 29861 with y = 199.72030120099998\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 23\n", - "\tacquired COF 26565 with y = 207.39578187\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 24\n", - "\tacquired COF 16404 with y = 171.299812707\n", - "\tbest y acquired: 208.43022665700002\n", - "\n", - "\n", - "RUN 2\n", - "iteration: 10\n", - "\tacquired COF 15267 with y = 178.83787913\n", - "\tbest y acquired: 178.83787913\n", - "iteration: 11\n", - "\tacquired COF 14751 with y = 181.18376571\n", - "\tbest y acquired: 181.18376571\n", - "iteration: 12\n", - "\tacquired COF 12392 with y = 185.480447434\n", - "\tbest y acquired: 185.480447434\n", - "iteration: 13\n", - "\tacquired COF 66860 with y = 182.910685964\n", - "\tbest y acquired: 185.480447434\n", - "iteration: 14\n", - "\tacquired COF 66075 with y = 199.84356436299998\n", - "\tbest y acquired: 199.84356436299998\n", - "iteration: 15\n", - "\tacquired COF 66117 with y = 202.21921792700002\n", - "\tbest y acquired: 202.21921792700002\n", - "iteration: 16\n", - "\tacquired COF 33366 with y = 204.811726149\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 17\n", - "\tacquired COF 33338 with y = 129.689513234\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 18\n", - "\tacquired COF 66263 with y = 185.76857139\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 19\n", - "\tacquired COF 25951 with y = 196.579974938\n", - "\tbest y acquired: 204.811726149\n", - "iteration: 20\n", - "\tacquired COF 33332 with y = 205.963467853\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 21\n", - "\tacquired COF 33330 with y = 195.58268240799998\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 22\n", - "\tacquired COF 33370 with y = 196.720247142\n", - "\tbest y acquired: 205.963467853\n", - "iteration: 23\n", - "\tacquired COF 33347 with y = 208.43022665700002\n", - "\tbest y acquired: 208.43022665700002\n", - "iteration: 24\n", - "\tacquired COF 33374 with y = 185.76111369\n", - "\tbest y acquired: 208.43022665700002\n" - ] - } - ], - "source": [ - "bo_res = dict()\n", - "bo_res['nb_runs'] = 3\n", - "bo_res['nb_iterations'] = 25\n", - "bo_res['ids_acquired'] = []\n", - "ids_acquired = -np.ones((nb_runs, nb_iterations), dtype=int)\n", - "for r in range(bo_res['nb_runs']):\n", - " print(\"\\n\\nRUN\", r)\n", - " ids_acquired = bo_run(bo_res['nb_iterations'])\n", - " bo_res['ids_acquired'].append(ids_acquired)\n", - "\n", - "with open('bo_results.pkl', 'wb') as file:\n", - " pickle.dump(bo_res, file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "given-chosen", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - 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z^iZ3>e_^9XwrKInU%#kx)86Tq-~W2_pUQv#{;&UKi++pBU;6u6R{s5=!~grAo815J TnzyL@cMs_QD!=}Jf9?MR_3Gqp From 8f96bcb6bca23ba91f8cbc5d7d71d17997f27a45 Mon Sep 17 00:00:00 2001 From: Cory Simon Date: Mon, 5 Jul 2021 11:33:43 -0700 Subject: [PATCH 26/29] Delete evol_search.ipynb --- evol_search.ipynb | 287 ---------------------------------------------- 1 file changed, 287 deletions(-) delete mode 100644 evol_search.ipynb diff --git a/evol_search.ipynb b/evol_search.ipynb deleted file mode 100644 index a364642..0000000 --- a/evol_search.ipynb +++ /dev/null @@ -1,287 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "celtic-hormone", - "metadata": {}, - "outputs": [], - "source": [ - "import cma\n", - "import pickle\n", - "import numpy as np\n", - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "unsigned-three", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "69839" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", - "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", - "nb_data = np.size(y)\n", - "nb_data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "frank-static", - "metadata": {}, - "outputs": [], - "source": [ - "# so evol search is bounded by [0, 1]^12\n", - "assert np.max(X) <= 1.0\n", - "assert np.min(X) >= 0.0" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "residential-google", - "metadata": {}, - "outputs": [], - "source": [ - "# return id of COF closest to x0 in feature space.\n", - "def closest_COF(x0):\n", - " distances_to_xs = np.linalg.norm(X - x0, axis=1)\n", - " assert np.size(distances_to_xs) == nb_data\n", - " return np.argmin(distances_to_xs)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "distant-radio", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5_w,11)-aCMA-ES (mu_w=3.4,w_1=42%) in dimension 12 (seed=257488, Wed Jun 30 21:44:06 2021)\n", - "\t# acquired COFs: 10\n", - "\t# max y value: 171.117194584\n", - "\t# acquired COFs: 20\n", - "\t# max y value: 188.242123191\n", - "\t# acquired COFs: 30\n", - "\t# max y value: 188.242123191\n", - "\t# acquired COFs: 40\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 49\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 58\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 66\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 76\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 87\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 98\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 107\n", - "\t# max y value: 191.507774129\n", - "\t# acquired COFs: 117\n", - "\t# max y value: 192.015638731\n", - "\t# acquired COFs: 128\n", - "\t# max y value: 197.87398978299998\n", - "\t# acquired COFs: 138\n", - "\t# max y value: 202.21921792700002\n", - "\t# acquired COFs: 148\n", - "\t# max y value: 202.21921792700002\n", - "\t# acquired COFs: 158\n", - "\t# max y value: 202.21921792700002\n", - "\t# acquired COFs: 169\n", - "\t# max y value: 202.21921792700002\n", - "\t# acquired COFs: 180\n", - "\t# max y value: 202.21921792700002\n", - "\t# acquired COFs: 191\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 198\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 205\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 211\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 213\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 216\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 220\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 225\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 228\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 232\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 234\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 236\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n", - "\t# acquired COFs: 238\n", - "\t# max y value: 208.120454446\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mids_ask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx_ask\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxs_ask\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mid_ask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosest_COF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_ask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mids_ask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid_ask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# warning: these aren't necessarily unique. and they could have been acquired before. so then they wuldn't count as extra inquires\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mclosest_COF\u001b[0;34m(x0)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# return id of COF closest to x0 in feature space.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclosest_COF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdistances_to_xs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances_to_xs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnb_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances_to_xs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "nb_iterations = 500\n", - "x_init = np.random.rand(np.shape(X)[1])\n", - "\n", - "# initialize evolutionary algo\n", - "ids_acquired = []\n", - "es = cma.CMAEvolutionStrategy(x0=x_init, sigma0=0.5, inopts={'bounds': [0.0, 1.0]})\n", - "\n", - "while len(ids_acquired) < nb_iterations:\n", - " # ask for a set of COFs to evaluate.\n", - " xs_ask = es.ask()\n", - "\n", - " # find IDs of these COFs (the closest COFs in feature space to them.)\n", - " ids_ask = []\n", - " for x_ask in xs_ask:\n", - " id_ask = closest_COF(x_ask)\n", - " ids_ask.append(id_ask)\n", - " # warning: these aren't necessarily unique. and they could have been acquired before. so then they wuldn't count as extra inquires\n", - "\n", - " # tell the ES algo about these COFs.\n", - " es.tell(X[ids_ask, :], -y[ids_ask]) # cuz we wanna minimize\n", - "\n", - " # updated ids acquired\n", - " for id_ask in ids_ask:\n", - " if not id_ask in ids_acquired:\n", - " ids_acquired.append(id_ask)\n", - "\n", - " print(\"\\t# acquired COFs:\", len(ids_acquired))\n", - " print(\"\\t# max y value: \", np.max(y[ids_acquired]))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From aa5b301f455d746c75c13ecf31da4bec85e9eb6f Mon Sep 17 00:00:00 2001 From: Cory Simon Date: Mon, 5 Jul 2021 11:33:48 -0700 Subject: [PATCH 27/29] Delete prepare_Xy.ipynb --- prepare_Xy.ipynb | 759 ----------------------------------------------- 1 file changed, 759 deletions(-) delete mode 100644 prepare_Xy.ipynb diff --git a/prepare_Xy.ipynb b/prepare_Xy.ipynb deleted file mode 100644 index 9728159..0000000 --- a/prepare_Xy.ipynb +++ /dev/null @@ -1,759 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "stylish-avenue", - "metadata": {}, - "source": [ - "# import and prepare data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "federal-consequence", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pickle\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "about-understanding", - "metadata": {}, - "source": [ - "load data from Mercado et al., drop outlier" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "humanitarian-willow", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dimensionsbond typenamevoid fraction [widom]supercell volume [A^3]density [kg/m^3]heat desorption high P [kJ/mol]heat desorption error high P [kJ/mol]absolute methane uptake high P [molec/unit cell]absolute methane uptake error high P [molec/unit cell]...num sulfurnum siliconverticesedgesgenuslargest included sphere diameter [A]largest free sphere diameter [A]largest included sphere along free sphere path diameter [A]absolute methane uptake high P [v STP/v]absolute methane uptake low P [v STP/v]
02amidelinker91_CO_linker87_NH_hcb_relaxed0.90012049204.128057260.21322810.951620.315667233.28922.789059...0011017.1901415.6496117.19004176.16049820.988007
12amidelinker91_CO_linker88_NH_hcb_relaxed0.87923449390.074419297.96338711.817560.478028250.61643.464625...0011017.3491615.7694317.34916188.53207322.643282
22amidelinker91_CO_linker7_NH_hcb_relaxed0.85826950036.985281289.39724911.863780.140491255.15100.921036...0011016.8403215.6190716.84024189.46176422.566022
32amidelinker91_CO_linker8_NH_hcb_relaxed0.85706549135.924517370.06363312.488420.823728257.33682.377728...0011013.9308512.3216713.93085194.58896227.373459
42amidelinker91_CO_linker10_NH_hcb_relaxed0.85801649540.680132367.04015112.259240.191371253.26203.177484...0011016.0692313.4879116.06921189.94309224.774006
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" - ], - "text/plain": [ - " dimensions bond type name \\\n", - "0 2 amide linker91_CO_linker87_NH_hcb_relaxed \n", - "1 2 amide linker91_CO_linker88_NH_hcb_relaxed \n", - "2 2 amide linker91_CO_linker7_NH_hcb_relaxed \n", - "3 2 amide linker91_CO_linker8_NH_hcb_relaxed \n", - "4 2 amide linker91_CO_linker10_NH_hcb_relaxed \n", - "\n", - " void fraction [widom] supercell volume [A^3] density [kg/m^3] \\\n", - "0 0.900120 49204.128057 260.213228 \n", - "1 0.879234 49390.074419 297.963387 \n", - "2 0.858269 50036.985281 289.397249 \n", - "3 0.857065 49135.924517 370.063633 \n", - "4 0.858016 49540.680132 367.040151 \n", - "\n", - " heat desorption high P [kJ/mol] heat desorption error high P [kJ/mol] \\\n", - "0 10.95162 0.315667 \n", - "1 11.81756 0.478028 \n", - "2 11.86378 0.140491 \n", - "3 12.48842 0.823728 \n", - "4 12.25924 0.191371 \n", - "\n", - " absolute methane uptake high P [molec/unit cell] \\\n", - "0 233.2892 \n", - "1 250.6164 \n", - "2 255.1510 \n", - "3 257.3368 \n", - "4 253.2620 \n", - "\n", - " absolute methane uptake error high P [molec/unit cell] ... num sulfur \\\n", - "0 2.789059 ... 0 \n", - "1 3.464625 ... 0 \n", - "2 0.921036 ... 0 \n", - "3 2.377728 ... 0 \n", - "4 3.177484 ... 0 \n", - "\n", - " num silicon vertices edges genus \\\n", - "0 0 1 1 0 \n", - "1 0 1 1 0 \n", - "2 0 1 1 0 \n", - "3 0 1 1 0 \n", - "4 0 1 1 0 \n", - "\n", - " largest included sphere diameter [A] largest free sphere diameter [A] \\\n", - "0 17.19014 15.64961 \n", - "1 17.34916 15.76943 \n", - "2 16.84032 15.61907 \n", - "3 13.93085 12.32167 \n", - "4 16.06923 13.48791 \n", - "\n", - " largest included sphere along free sphere path diameter [A] \\\n", - "0 17.19004 \n", - "1 17.34916 \n", - "2 16.84024 \n", - "3 13.93085 \n", - "4 16.06921 \n", - "\n", - " absolute methane uptake high P [v STP/v] \\\n", - "0 176.160498 \n", - "1 188.532073 \n", - "2 189.461764 \n", - "3 194.588962 \n", - "4 189.943092 \n", - "\n", - " absolute methane uptake low P [v STP/v] \n", - "0 20.988007 \n", - "1 22.643282 \n", - "2 22.566022 \n", - "3 27.373459 \n", - "4 24.774006 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('properties.csv')\n", - "# we remove row 48225 since it is an outlier for void fraction feature # can be seen by df[df[' void fraction [widom]'] > 1]\n", - "df = df.drop(48225)\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "corresponding-substitute", - "metadata": {}, - "source": [ - "define new feature as density of elements" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "threatened-stability", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dimensionsbond typenamevoid fraction [widom]supercell volume [A^3]density [kg/m^3]heat desorption high P [kJ/mol]heat desorption error high P [kJ/mol]absolute methane uptake high P [molec/unit cell]absolute methane uptake error high P [molec/unit cell]...largest included sphere along free sphere path diameter [A]absolute methane uptake high P [v STP/v]absolute methane uptake low P [v STP/v]density of carbondensity of fluorinedensity of hydrogendensity of nitrogendensity of oxygendensity of sulfurdensity of silicon
02amidelinker91_CO_linker87_NH_hcb_relaxed0.90012049204.128057260.21322810.951620.315667233.28922.789059...17.19004176.16049820.9880070.0073160.00.0043900.0029270.0014630.00.0
12amidelinker91_CO_linker88_NH_hcb_relaxed0.87923449390.074419297.96338711.817560.478028250.61643.464625...17.34916188.53207322.6432820.0072890.00.0043730.0029160.0029160.00.0
22amidelinker91_CO_linker7_NH_hcb_relaxed0.85826950036.985281289.39724911.863780.140491255.15100.921036...16.84024189.46176422.5660220.0086340.00.0071950.0028780.0014390.00.0
32amidelinker91_CO_linker8_NH_hcb_relaxed0.85706549135.924517370.06363312.488420.823728257.33682.377728...13.93085194.58896227.3734590.0073270.00.0029310.0043960.0043960.00.0
42amidelinker91_CO_linker10_NH_hcb_relaxed0.85801649540.680132367.04015112.259240.191371253.26203.177484...16.06921189.94309224.7740060.0072670.00.0029070.0043600.0043600.00.0
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5 rows × 60 columns

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" - ], - "text/plain": [ - " dimensions bond type name \\\n", - "0 2 amide linker91_CO_linker87_NH_hcb_relaxed \n", - "1 2 amide linker91_CO_linker88_NH_hcb_relaxed \n", - "2 2 amide linker91_CO_linker7_NH_hcb_relaxed \n", - "3 2 amide linker91_CO_linker8_NH_hcb_relaxed \n", - "4 2 amide linker91_CO_linker10_NH_hcb_relaxed \n", - "\n", - " void fraction [widom] supercell volume [A^3] density [kg/m^3] \\\n", - "0 0.900120 49204.128057 260.213228 \n", - "1 0.879234 49390.074419 297.963387 \n", - "2 0.858269 50036.985281 289.397249 \n", - "3 0.857065 49135.924517 370.063633 \n", - "4 0.858016 49540.680132 367.040151 \n", - "\n", - " heat desorption high P [kJ/mol] heat desorption error high P [kJ/mol] \\\n", - "0 10.95162 0.315667 \n", - "1 11.81756 0.478028 \n", - "2 11.86378 0.140491 \n", - "3 12.48842 0.823728 \n", - "4 12.25924 0.191371 \n", - "\n", - " absolute methane uptake high P [molec/unit cell] \\\n", - "0 233.2892 \n", - "1 250.6164 \n", - "2 255.1510 \n", - "3 257.3368 \n", - "4 253.2620 \n", - "\n", - " absolute methane uptake error high P [molec/unit cell] ... \\\n", - "0 2.789059 ... \n", - "1 3.464625 ... \n", - "2 0.921036 ... \n", - "3 2.377728 ... \n", - "4 3.177484 ... \n", - "\n", - " largest included sphere along free sphere path diameter [A] \\\n", - "0 17.19004 \n", - "1 17.34916 \n", - "2 16.84024 \n", - "3 13.93085 \n", - "4 16.06921 \n", - "\n", - " absolute methane uptake high P [v STP/v] \\\n", - "0 176.160498 \n", - "1 188.532073 \n", - "2 189.461764 \n", - "3 194.588962 \n", - "4 189.943092 \n", - "\n", - " absolute methane uptake low P [v STP/v] density of carbon \\\n", - "0 20.988007 0.007316 \n", - "1 22.643282 0.007289 \n", - "2 22.566022 0.008634 \n", - "3 27.373459 0.007327 \n", - "4 24.774006 0.007267 \n", - "\n", - " density of fluorine density of hydrogen density of nitrogen \\\n", - "0 0.0 0.004390 0.002927 \n", - "1 0.0 0.004373 0.002916 \n", - "2 0.0 0.007195 0.002878 \n", - "3 0.0 0.002931 0.004396 \n", - "4 0.0 0.002907 0.004360 \n", - "\n", - " density of oxygen density of sulfur density of silicon \n", - "0 0.001463 0.0 0.0 \n", - "1 0.002916 0.0 0.0 \n", - "2 0.001439 0.0 0.0 \n", - "3 0.004396 0.0 0.0 \n", - "4 0.004360 0.0 0.0 \n", - "\n", - "[5 rows x 60 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "elements = ['carbon', 'fluorine', 'hydrogen', 'nitrogen', 'oxygen', 'sulfur', 'silicon']\n", - "for el in elements:\n", - " df[\"density of \" + el] = df[' num ' + el] / df[' supercell volume [A^3]']\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "monetary-brick", - "metadata": {}, - "source": [ - "get feature matrix and target vector" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "christian-tradition", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# features: 12\n" - ] - } - ], - "source": [ - "features = [' void fraction [widom]', ' density [kg/m^3]', ' largest included sphere diameter [A]', ' largest free sphere diameter [A]', ' surface area [m^2/g]',\n", - " 'density of carbon', 'density of fluorine', 'density of hydrogen', 'density of nitrogen', 'density of oxygen', 'density of sulfur', 'density of silicon']\n", - "print(\"# features: \", len(features))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "dedicated-activity", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape of Y: (69839,)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([155.17249134, 165.88879162, 166.8957419 , ..., 161.97256899,\n", - " 155.38761768, 155.48070341])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y = df[' deliverable capacity [v STP/v]'].values\n", - "print(\"shape of Y: \", np.shape(y))\n", - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "available-blowing", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape of X: (69839, 12)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[9.00120000e-01, 2.60213228e+02, 1.71901400e+01, ...,\n", - " 1.46329186e-03, 0.00000000e+00, 0.00000000e+00],\n", - " [8.79234000e-01, 2.97963387e+02, 1.73491600e+01, ...,\n", - " 2.91556556e-03, 0.00000000e+00, 0.00000000e+00],\n", - " [8.58269000e-01, 2.89397249e+02, 1.68403200e+01, ...,\n", - " 1.43893561e-03, 0.00000000e+00, 0.00000000e+00],\n", - " ...,\n", - " [8.85007000e-01, 2.59378413e+02, 1.60821800e+01, ...,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", - " [7.37251000e-01, 5.14847059e+02, 1.15594900e+01, ...,\n", - " 1.43384585e-03, 0.00000000e+00, 0.00000000e+00],\n", - " [7.77576000e-01, 5.01030978e+02, 1.15140800e+01, ...,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = df[features].values\n", - "print(\"shape of X: \", np.shape(X))\n", - "X" - ] - }, - { - "cell_type": "markdown", - "id": "immediate-savings", - "metadata": {}, - "source": [ - "Min-Max normalize the features so that they lie in $[0, 1]$. this is ok to do over all data as opposed to just training because in BO we will compute the cheap features of every COF in the database." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "realistic-issue", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "feature 0 in [ 0.0 , 1.0 ]\n", - "feature 1 in [ 0.0 , 1.0 ]\n", - "feature 2 in [ 0.0 , 1.0 ]\n", - "feature 3 in [ 0.0 , 1.0 ]\n", - "feature 4 in [ 0.0 , 1.0 ]\n", - "feature 5 in [ 0.0 , 1.0 ]\n", - "feature 6 in [ 0.0 , 1.0 ]\n", - "feature 7 in [ 0.0 , 1.0 ]\n", - "feature 8 in [ 0.0 , 1.0 ]\n", - "feature 9 in [ 0.0 , 1.0 ]\n", - "feature 10 in [ 0.0 , 1.0 ]\n", - "feature 11 in [ 0.0 , 1.0 ]\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0.90081723, 0.20318731, 0.15909104, ..., 0.15284004, 0. ,\n", - " 0. ],\n", - " [0.8775969 , 0.23561078, 0.16100571, ..., 0.30452924, 0. ,\n", - " 0. ],\n", - " [0.85428875, 0.22825336, 0.15487906, ..., 0.15029605, 0. ,\n", - " 0. ],\n", - " ...,\n", - " [0.88401512, 0.20247029, 0.14575075, ..., 0. , 0. ,\n", - " 0. ],\n", - " [0.71974518, 0.42189136, 0.0912957 , ..., 0.14976442, 0. ,\n", - " 0. ],\n", - " [0.7645771 , 0.41002478, 0.09074894, ..., 0. , 0. ,\n", - " 0. ]])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "for i in range(np.shape(X)[1]):\n", - " X[:, i] = (X[:, i] - np.min(X[:, i])) / (np.max(X[:, i]) - np.min(X[:, i]))\n", - " print(\"feature\", i, \" in [\", np.min(X[:, i]), \",\", np.max(X[:, i]), \"]\")\n", - "X" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "radical-bikini", - "metadata": {}, - "outputs": [], - "source": [ - "with open('inputs_and_outputs.pkl', 'wb') as file:\n", - " pickle.dump({'X': X, 'y': y}, file)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From f7cfce47a979ae6790525bc0259c7462bb917147 Mon Sep 17 00:00:00 2001 From: Cory Simon Date: Mon, 5 Jul 2021 11:33:53 -0700 Subject: [PATCH 28/29] Delete random_forest_run.ipynb --- random_forest_run.ipynb | 272 ---------------------------------------- 1 file changed, 272 deletions(-) delete mode 100644 random_forest_run.ipynb diff --git a/random_forest_run.ipynb b/random_forest_run.ipynb deleted file mode 100644 index 3244221..0000000 --- a/random_forest_run.ipynb +++ /dev/null @@ -1,272 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "indoor-confusion", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pickle\n", - "import torch\n", - "import time\n", - "\n", - "from sklearn.metrics import r2_score, mean_absolute_error, explained_variance_score, mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "import autosklearn.regression\n", - "from sklearn.ensemble import RandomForestRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "tamil-payment", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape of X: (69839, 12)\n" - ] - }, - { - "data": { - "text/plain": [ - "69839" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", - "print(\"shape of X: \", np.shape(X))\n", - "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", - "nb_data = np.size(y)\n", - "nb_data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "stunning-forth", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.17621688, 0.2123145 ])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ids_train = [2, 5]\n", - "np.linalg.norm(X[1, :] - X[ids_train, :], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "universal-seeker", - "metadata": {}, - "outputs": [], - "source": [ - "def diverse_train_test_split(X, train_size):\n", - " ids_train = [np.random.randint(0, nb_data)] # initialize with one random point; pick others in a max diverse fashion\n", - " # select remaining training points\n", - " for j in range(train_size - 1):\n", - " # for each point, compute its min distance to training set\n", - " min_distances_to_train_set = np.zeros((nb_data, ))\n", - " for i in range(nb_data):\n", - " # compute its distance to all points in the training set\n", - " distances_to_train_set = np.linalg.norm(X[i, :] - X[ids_train, :], axis=1)\n", - " min_distances_to_train_set[i] = np.min(distances_to_train_set)\n", - " # select point with max min distance to train set (Furthest from train set)\n", - " ids_train.append(np.argmax(min_distances_to_train_set))\n", - " assert np.size(np.unique(ids_train)) == train_size\n", - " ids_test = [i for i in range(nb_data) if not i in ids_train]\n", - " assert np.size(np.unique(ids_test)) == nb_data - train_size\n", - " return np.array(ids_train), np.array(ids_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "catholic-bulgarian", - "metadata": {}, - "outputs": [], - "source": [ - "ids_train, ids_test = diverse_train_test_split(X, 25)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "wrong-drilling", - "metadata": {}, - "outputs": [], - "source": [ - "diversify_training = False" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "funny-saint", - "metadata": {}, - "outputs": [], - "source": [ - "def rf_run(nb_training_data, nb_acquire):\n", - " if diversify_training:\n", - " print(\"diverse RF run\")\n", - " else:\n", - " print(\"RF run\")\n", - " print(\"\\teval budget\", nb_training_data + nb_acquire, \"=\", nb_training_data, \"training data and\", nb_acquire, \"acquired.\")\n", - " # test/train split\n", - " if diversify_training:\n", - " ids_train, ids_test = diverse_train_test_split(X, nb_training_data)\n", - " else:\n", - " ids_train, ids_test = train_test_split(np.arange(nb_data), train_size=nb_training_data)\n", - " \n", - " X_train = X[ids_train, :]\n", - " X_test = X[ids_test, :]\n", - " \n", - " y_train = y[ids_train]\n", - " y_test = y[ids_test]\n", - " \n", - " # train random forest on training data\n", - " rf = RandomForestRegressor()\n", - " rf.fit(X_train, y_train)\n", - "\n", - " # hv random forest make predictions on test data\n", - " y_pred = rf.predict(X_test)\n", - "\n", - " # rank the test predictions\n", - " ids_test_ranked = np.flip(np.argsort(y_pred))\n", - "\n", - " # acquire the COFs in the test set with highest predicted property\n", - " ids_acquire = ids_test[ids_test_ranked[:nb_acquire]]\n", - "\n", - " # return the acquired COFs but also the trained COFs which count.\n", - " ids_acquire_incld_training = np.concatenate((ids_acquire, ids_train))\n", - " \n", - " assert np.size(np.unique(ids_acquire_incld_training)) == nb_training_data + nb_acquire\n", - " \n", - " print(\"\\tmax y acquired = \", np.max(y[ids_acquire_incld_training]))\n", - " return ids_acquire_incld_training" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "driving-result", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "budget for evals: 200\n", - "\trun 0\n", - "RF run\n", - "\teval budget 50 = 25 training data and 25 acquired.\n", - "\tmax y acquired = 216.894110699\n", - "\trun 1\n", - "RF run\n", - "\teval budget 50 = 25 training data and 25 acquired.\n", - "\tmax y acquired = 181.95215032099998\n", - "budget for evals: 50\n", - "\trun 0\n", - "RF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 193.360391942\n", - "\trun 1\n", - "RF run\n", - "\teval budget 100 = 50 training data and 50 acquired.\n", - "\tmax y acquired = 194.938530808\n", - "budget for evals: 100\n", - "\trun 0\n", - "RF run\n", - "\teval budget 150 = 75 training data and 75 acquired.\n", - "\tmax y acquired = 195.928348822\n", - "\trun 1\n", - "RF run\n", - "\teval budget 150 = 75 training data and 75 acquired.\n", - "\tmax y acquired = 193.25083398700002\n", - "budget for evals: 150\n", - "\trun 0\n", - "RF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 193.620114578\n", - "\trun 1\n", - "RF run\n", - "\teval budget 200 = 100 training data and 100 acquired.\n", - "\tmax y acquired = 208.120454446\n" - ] - } - ], - "source": [ - "rf_res = dict()\n", - "rf_res['nb_runs'] = 2\n", - "rf_res['nb_evals_budgets'] = [50 * i for i in range(1, 5)]\n", - "rf_res['ids_acquired'] = [[] for b in rf_res['nb_evals_budgets']]\n", - "for b in range(len(rf_res['nb_evals_budgets'])):\n", - " print(\"budget for evals:\", nb_evals_budget)\n", - " nb_evals_budget = rf_res['nb_evals_budgets'][b]\n", - " # decide how to spend the evals budget here. say 50/50\n", - " nb_training_data = nb_evals_budget // 2\n", - " nb_acquire = nb_evals_budget // 2\n", - " assert nb_training_data + nb_acquire == nb_evals_budget\n", - " for r in range(rf_res['nb_runs']):\n", - " print(\"\\trun\", r)\n", - " ids_acquired = rf_run(nb_training_data, nb_acquire)\n", - " rf_res['ids_acquired'][b].append(ids_acquired)\n", - "# torch.save({'ids_acquired': ids_acquired}, 'rf_run' + str(r) + '.pkl')\n", - "\n", - "if diversify_training:\n", - " with open('rf_results.pkl', 'wb') as file:\n", - " pickle.dump(rf_res, file)\n", - "else:\n", - " with open('rf_div_results.pkl', 'wb') as file:\n", - " pickle.dump(rf_res, file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "informed-lighting", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From e71bec1a7b73b79c108173546d859acbbb232b73 Mon Sep 17 00:00:00 2001 From: Cory Simon Date: Mon, 5 Jul 2021 11:34:02 -0700 Subject: [PATCH 29/29] Delete viz.ipynb --- viz.ipynb | 476 ------------------------------------------------------ 1 file changed, 476 deletions(-) delete mode 100644 viz.ipynb diff --git a/viz.ipynb b/viz.ipynb deleted file mode 100644 index 56a9174..0000000 --- a/viz.ipynb +++ /dev/null @@ -1,476 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dimensional-influence", - "metadata": {}, - "source": [ - "# viz" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "breeding-piano", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "import matplotlib.pyplot as plt\n", - "import pickle\n", - "import pandas as pd\n", - "import torch\n", - "from sklearn.decomposition import PCA\n", - "\n", - "cool_colors = ['#00BEFF', '#D4CA3A', '#FF6DAE', '#67E1B5', '#EBACFA', '#9E9E9E', '#F1988E', '#5DB15A', '#E28544', '#52B8AA']\n", - "\n", - "search_to_color = {'BO': cool_colors[0], 'random': cool_colors[1], 'evolutionary': cool_colors[2], 'RF': cool_colors[5], 'RF (div)': cool_colors[3]}" - ] - }, - { - "cell_type": "markdown", - "id": "correct-judgment", - "metadata": {}, - "source": [ - "load data" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "wrapped-activity", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape of X: (69839, 12)\n" - ] - }, - { - "data": { - "text/plain": [ - "69839" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['X']\n", - "print(\"shape of X:\", np.shape(X))\n", - "y = pickle.load(open('inputs_and_outputs.pkl', 'rb'))['y']\n", - "y = np.reshape(y, (np.size(y), 1)) # for the GP\n", - "nb_data = np.size(y)\n", - "nb_data" - ] - }, - { - "cell_type": "markdown", - "id": "nonprofit-finding", - "metadata": {}, - "source": [ - "load search results" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "private-harmony", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'nb_runs': 3,\n", - " 'nb_iterations': 25,\n", - " 'ids_acquired': [array([57214, 54824, 63946, 40684, 5488, 37520, 46393, 50134, 67586,\n", - " 53501, 16415, 27798, 27035, 21314, 66263, 33370, 25862, 33366,\n", - " 33330, 33355, 33332, 25951, 12402, 33343, 33347]),\n", - " array([ 1739, 54992, 1072, 7535, 28352, 57664, 2012, 17766, 15085,\n", - " 68802, 68952, 56517, 12392, 34761, 19518, 33330, 33338, 33332,\n", - " 33347, 25951, 33344, 33349, 29861, 26565, 16404]),\n", - " array([53849, 18172, 24262, 11369, 69252, 100, 54, 43031, 11810,\n", - " 44138, 15267, 14751, 12392, 66860, 66075, 66117, 33366, 33338,\n", - " 66263, 25951, 33332, 33330, 33370, 33347, 33374])]}" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bo_res = pickle.load(open('bo_results.pkl', 'rb'))\n", - "bo_res" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "selected-excess", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'nb_runs': 2,\n", - " 'nb_evals_budgets': [50, 100, 150, 200],\n", - " 'ids_acquired': [[array([10053, 10048, 10055, 10050, 10195, 47861, 14579, 2835, 10075,\n", - " 19649, 19498, 5601, 10051, 2691, 9927, 67, 147, 15331,\n", - " 2857, 14755, 10184, 8225, 15461, 25449, 15460, 54113, 1468,\n", - " 16753, 31407, 1904, 51445, 34974, 6790, 31804, 25930, 51295,\n", - " 21591, 41049, 45937, 42528, 66738, 2901, 30411, 14342, 56069,\n", - " 36376, 29915, 33878, 28686, 11132]),\n", - " array([13759, 32661, 65995, 32203, 19230, 19231, 19220, 19221, 16567,\n", - " 17433, 26639, 19241, 19238, 68549, 29862, 32566, 68389, 32636,\n", - " 31975, 25971, 12422, 31130, 31082, 23184, 13763, 57945, 30037,\n", - " 19875, 27850, 34542, 39703, 33117, 19471, 12762, 12151, 34077,\n", - " 12787, 12269, 65975, 1074, 32528, 60687, 15196, 11411, 13206,\n", - " 8192, 6477, 64915, 27400, 23756])],\n", - " [array([59010, 57791, 54590, 37273, 53498, 15480, 58956, 13899, 15227,\n", - " 15482, 39133, 53020, 5281, 15412, 15475, 2359, 15232, 37176,\n", - " 15416, 15418, 15233, 15420, 34794, 58992, 15422, 15235, 34784,\n", - " 35096, 34797, 35165, 15484, 15230, 62271, 34792, 34432, 35094,\n", - " 13894, 61641, 36861, 34816, 34799, 55763, 15481, 34795, 21937,\n", - " 15413, 62223, 66061, 36878, 13890, 49253, 31108, 36137, 19222,\n", - " 49806, 38330, 7189, 54699, 19835, 27116, 34598, 66177, 48803,\n", - " 13580, 38609, 66317, 13358, 69358, 10158, 41782, 13596, 29363,\n", - " 52557, 3304, 54452, 45768, 64038, 54875, 55073, 52207, 54900,\n", - " 69502, 32686, 49984, 54069, 1593, 9130, 22520, 39269, 49899,\n", - " 3274, 41509, 5985, 42153, 51827, 57531, 8497, 56787, 19690,\n", - " 44493]),\n", - " array([22904, 44502, 42094, 21882, 22621, 14296, 1776, 42091, 42085,\n", - " 2070, 42123, 22948, 39079, 42095, 18331, 19589, 19592, 1722,\n", - " 38515, 40585, 40603, 40646, 39104, 32168, 54674, 38542, 54692,\n", - " 5045, 17397, 37172, 31073, 31387, 18327, 22392, 2782, 52538,\n", - " 14290, 32185, 58948, 16739, 17268, 17574, 59660, 5044, 40707,\n", - " 40696, 22976, 36974, 32193, 32194, 30617, 68581, 12206, 5498,\n", - " 62535, 936, 19212, 7223, 8294, 15197, 58302, 36720, 17026,\n", - " 11720, 16145, 58801, 65720, 64383, 61116, 68610, 53398, 43033,\n", - " 43643, 26494, 67438, 23380, 16311, 26291, 68102, 44719, 46406,\n", - " 23512, 38417, 32909, 7099, 6578, 38495, 64016, 20191, 65069,\n", - " 50709, 58833, 32734, 30719, 43865, 442, 16203, 36388, 42145,\n", - " 46489])],\n", - " [array([ 8889, 29365, 35137, 4563, 9962, 25731, 49368, 49371, 46842,\n", - " 5437, 16632, 19769, 35126, 29378, 3029, 10824, 29299, 35196,\n", - " 8910, 10827, 8856, 30460, 5387, 16739, 49435, 37286, 10708,\n", - " 29080, 9706, 8270, 29336, 4031, 8787, 8778, 8855, 69809,\n", - " 3035, 12113, 35149, 31987, 63834, 64045, 17256, 66103, 11945,\n", - " 23338, 16937, 68386, 30119, 11999, 11472, 8876, 21924, 40626,\n", - " 40625, 22831, 5388, 37338, 53203, 55323, 53557, 55348, 14251,\n", - " 5362, 4401, 9959, 56597, 10702, 55381, 11695, 4181, 16726,\n", - " 7298, 7280, 45149, 58841, 2961, 56269, 10736, 16091, 3768,\n", - " 48024, 54275, 41932, 52297, 8445, 20338, 42890, 38565, 5226,\n", - " 67338, 18568, 27821, 41113, 47567, 29561, 24702, 47897, 68630,\n", - " 64028, 240, 55267, 15007, 7245, 15878, 15044, 44191, 1698,\n", - " 67082, 538, 22616, 45125, 38482, 10590, 15250, 69600, 49715,\n", - " 11023, 54014, 9912, 30356, 7522, 63624, 1451, 50277, 61388,\n", - " 25228, 26508, 61491, 68672, 37651, 62291, 28390, 52313, 13608,\n", - " 61959, 2283, 68048, 22118, 32534, 34645, 44751, 3220, 15456,\n", - " 67618, 11154, 48821, 53767, 19108, 37343]),\n", - " array([69698, 5145, 5142, 53139, 442, 26303, 5792, 6034, 6035,\n", - " 15354, 6033, 6036, 45771, 5144, 25981, 26188, 14587, 23338,\n", - " 13757, 25978, 21842, 18382, 2748, 19214, 24047, 32566, 14999,\n", - " 32560, 19927, 46432, 25973, 32371, 21662, 23429, 17269, 24961,\n", - " 14798, 16416, 29862, 16674, 5839, 4272, 17039, 20707, 16419,\n", - " 24636, 23286, 15004, 16823, 5902, 16841, 5127, 2784, 23333,\n", - " 58996, 31992, 14795, 32589, 17402, 28831, 17253, 29865, 17383,\n", - " 23180, 32069, 14592, 16533, 5843, 23203, 31991, 19900, 26165,\n", - " 32037, 25984, 16409, 28327, 4705, 60335, 31414, 42071, 67272,\n", - " 22839, 51316, 30329, 34280, 56609, 62487, 46759, 28329, 64518,\n", - " 34012, 11888, 51902, 39570, 18438, 48608, 29888, 15693, 41726,\n", - " 19705, 83, 4999, 28827, 162, 36306, 34577, 68271, 31896,\n", - " 58350, 48542, 47306, 11148, 6895, 38396, 5562, 9758, 1340,\n", - " 2509, 20776, 58019, 21951, 32497, 66551, 39971, 47800, 55527,\n", - " 10032, 2122, 33113, 15580, 45857, 30699, 55851, 41296, 67206,\n", - " 12461, 29266, 59124, 25100, 20011, 17357, 59567, 28010, 23216,\n", - " 49006, 2820, 33177, 7494, 27821, 23965])],\n", - " [array([17258, 17397, 47865, 17400, 17268, 16417, 17264, 21850, 26514,\n", - " 21662, 21847, 26396, 16411, 26562, 65680, 17293, 17257, 64439,\n", - " 17278, 42091, 42085, 18327, 21859, 58996, 40628, 40633, 17079,\n", - " 40587, 42094, 17514, 29415, 32020, 42113, 4952, 26505, 40629,\n", - " 40598, 32084, 1029, 24636, 31035, 31197, 26507, 19334, 24565,\n", - " 30394, 32159, 50485, 47857, 26553, 16854, 16623, 36977, 21882,\n", - " 17535, 23153, 40579, 40589, 42116, 17295, 32185, 63649, 26387,\n", - " 69756, 32579, 21981, 17455, 40635, 25978, 2070, 29257, 26145,\n", - " 1179, 22904, 31107, 31391, 22222, 26517, 51595, 21852, 36923,\n", - " 15177, 26106, 26565, 53605, 61182, 17039, 32146, 18072, 29822,\n", - " 26399, 21975, 40585, 40603, 30331, 25104, 22521, 22215, 2198,\n", - " 30518, 46487, 20621, 36679, 17402, 1523, 65105, 54341, 68502,\n", - " 42264, 10560, 40538, 26089, 6707, 51585, 63632, 41424, 21195,\n", - " 27520, 31643, 54496, 48149, 62931, 52191, 34472, 16939, 40902,\n", - " 68544, 15108, 39535, 33520, 7810, 12215, 42027, 15672, 17796,\n", - " 16019, 69008, 64405, 54397, 60491, 20318, 34480, 32775, 37214,\n", - " 34376, 32466, 2840, 64444, 56144, 48059, 60611, 22015, 17265,\n", - " 59737, 16389, 53658, 60924, 68348, 64088, 58051, 52859, 34490,\n", - " 65154, 31039, 7872, 32302, 12737, 6320, 12987, 62944, 68066,\n", - " 35592, 34195, 42644, 14411, 66926, 49845, 63447, 29735, 3186,\n", - " 61113, 40478, 181, 56943, 1236, 56873, 62576, 28523, 53538,\n", - " 49985, 14438, 40592, 56040, 64137, 47249, 56634, 61895, 7017,\n", - " 56221, 30155]),\n", - " array([ 5468, 5351, 5469, 5526, 5525, 5470, 5386, 35055, 5471,\n", - " 19475, 35099, 5352, 441, 5283, 5175, 5388, 5193, 6063,\n", - " 19688, 5284, 13912, 5482, 59654, 52774, 5303, 427, 35032,\n", - " 5425, 5330, 5286, 11551, 26726, 5174, 5329, 6371, 5177,\n", - " 5285, 444, 5489, 6370, 5578, 5312, 5483, 5424, 16701,\n", - " 6062, 34802, 55829, 55828, 5263, 59655, 5385, 6392, 5311,\n", - " 5264, 59694, 34817, 5362, 63622, 5173, 5512, 9126, 6247,\n", - " 3035, 35028, 56376, 68492, 49887, 56369, 32131, 64402, 13915,\n", - " 56539, 5361, 34997, 66825, 35116, 35083, 5192, 49773, 6206,\n", - " 19543, 25375, 56370, 50796, 5252, 5338, 5457, 53565, 6090,\n", - " 19743, 35227, 53298, 32155, 34758, 63623, 17294, 35086, 36999,\n", - " 2401, 14919, 52082, 13128, 48863, 60986, 10191, 66853, 22381,\n", - " 8883, 47886, 34619, 62394, 20698, 16483, 56054, 20049, 21469,\n", - " 10706, 45147, 8163, 68339, 29296, 51466, 32386, 338, 43709,\n", - " 69715, 37309, 7023, 37443, 5789, 38126, 51239, 45152, 34496,\n", - " 48409, 41722, 52038, 49245, 9888, 33070, 61028, 882, 13280,\n", - " 62313, 37916, 13317, 57880, 36947, 69561, 41642, 34140, 67109,\n", - " 7733, 16106, 55507, 17031, 25121, 4992, 25493, 44894, 65861,\n", - " 5247, 19995, 4456, 30313, 59683, 16897, 67167, 20685, 42162,\n", - " 4446, 23054, 35658, 47091, 35017, 57598, 47747, 64605, 16122,\n", - " 30903, 8793, 11444, 49962, 64939, 65039, 23894, 48162, 42444,\n", - " 12428, 69757, 6757, 63476, 27997, 67454, 51675, 39709, 44497,\n", - " 17764, 46063])]]}" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rf_res = pickle.load(open('rf_results.pkl', 'rb'))\n", - "rf_res" - ] - }, - { - "cell_type": "markdown", - "id": "purple-nirvana", - "metadata": {}, - "source": [ - "# PCA and viz of acquisition of BO" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "cardiac-russia", - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=2)\n", - "pca.fit(X)\n", - "X_2D = pca.transform(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "weekly-wrestling", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#low dimensional (PCA) visualization of the entire dataset\n", - "plt.hexbin(X_2D[:, 0], X_2D[:, 1], C=y)\n", - "plt.xlabel('PCA dimension 1')\n", - "plt.ylabel('PCA dimension 2')\n", - "cb = plt.colorbar(fraction=0.02, pad=0.04)\n", - "cb.set_label(label=\"deliverable capacity\\n[L STP/L]\")\n", - "plt.xticks()\n", - "plt.yticks()\n", - "plt.gca().set_aspect('equal', 'box')\n", - "plt.tight_layout()\n", - "plt.savefig('feature_space_colored_by_DC.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "electric-myanmar", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "which_BO_run = 0\n", - "\n", - "fig, ax = plt.subplots(1, 4, sharey=True, sharex=True, figsize=[3*6.4, 4.8])\n", - "nb_acquired = [10, 12, 15, 20]\n", - "# gray background\n", - "for a in ax:\n", - " a.set_aspect('equal', 'box')\n", - " a.hexbin(X_2D[:, 0], X_2D[:, 1], C=0.3 * np.ones(nb_data), cmap=\"binary\", vmin=0, vmax=1)\n", - " \n", - "for i in range(4):\n", - " ids_acquired = bo_res['ids_acquired'][which_BO_run][:nb_acquired[i]]\n", - " assert len(ids_acquired) == nb_acquired[i]\n", - " # use above colorbar to assign color!\n", - " ax[i].scatter(X_2D[ids_acquired, 0], X_2D[ids_acquired, 1], \n", - " c=y[ids_acquired], marker=\"+\", s=55, vmin=cb.vmin, vmax=cb.vmax)\n", - " ax[i].set_title('{} acquired COFs'.format(nb_acquired[i]))\n", - " ax[i].tick_params(axis='x', labelsize=10)\n", - "ax[0].set_ylabel('PCA dimension 2', fontsize=14)\n", - "\n", - "ax[2].tick_params(axis='y', labelsize=0)\n", - "\n", - "\n", - "fig.text(0.5, 0.2, 'PCA dimension 1', ha='center', fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig(\"feature_space_acquired_COFs.pdf\")" - ] - }, - { - "cell_type": "markdown", - "id": "visible-scratch", - "metadata": {}, - "source": [ - "# search efficiency\n", - "### max $y$ among acquired set." - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "collect-accommodation", - "metadata": {}, - "outputs": [], - "source": [ - "def y_max(res):\n", - " y_max_mu = np.zeros(res['nb_iterations'])\n", - " y_max_sigma = np.zeros(res['nb_iterations'])\n", - " for i in range(1, res['nb_iterations']):\n", - " y_maxes = [np.max(y[res['ids_acquired'][r]][:i]) for r in range(res['nb_runs'])]# among runs\n", - " assert np.size(y_maxes) == res['nb_runs']\n", - " y_max_mu[i] = np.mean(y_maxes)\n", - " y_max_sigma[i] = np.std(y_maxes)\n", - " return y_max_mu, y_max_sigma\n", - "\n", - "y_max_mu, y_max_sigma = y_max(bo_res)" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "brazilian-theme", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "missing a required argument: 'x'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# RF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m 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\u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'missing a required argument: {arg!r}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2911\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2912\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2913\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2914\u001b[0m \u001b[0;31m# We have a positional argument to process\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: missing a required argument: 'x'" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 2, gridspec_kw={'width_ratios': [4, 1]}, figsize=[1.2 * 6.4, 4.8], sharey=True)\n", - "# BO\n", - "axs[0].plot(np.arange(bo_res['nb_iterations']), y_max_mu, label='BO', color=search_to_color['BO'], lw=4, clip_on=False)\n", - "axs[0].fill_between(np.arange(bo_res['nb_iterations']), y_max_mu - y_max_sigma, \n", - " y_max_mu + y_max_sigma, \n", - " alpha=0.2, ec=\"None\", color=search_to_color['BO'])\n", - "\n", - "axs[0].set_xlabel('# evaluated COFs', fontsize=14)\n", - "axs[0].set_ylabel('maximum deliverable capacity\\namong evaluated COFs\\n[L STP/L]', fontsize=13)\n", - "axs[0].legend(fontsize=14)\n", - "\n", - "# RF\n", - "axs[0].scatter(rf_res['nb_evals_budgets'], ref_res[]\n", - "\n", - "# axs[0].set_xlim([0, ])\n", - "axs[0].set_ylim(ymin=0.0)\n", - "\n", - "axs[1].hist(y, orientation=\"horizontal\", color=cool_colors[7])\n", - "axs[1].set_xlabel(\"# COFs\", fontsize=13)\n", - "axs[1].set_xscale(\"log\")\n", - "plt.tight_layout()\n", - "plt.savefig(\"search_efficiency_max_found.pdf\")" - ] - }, - { - "cell_type": "markdown", - "id": "greenhouse-antibody", - "metadata": {}, - "source": [ - "### max rank among acquired set" - ] - }, - { - "cell_type": "markdown", - "id": "trained-multiple", - "metadata": {}, - "source": [ - "### fraction of top 100 COFs recovered" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}