Merge pull request #151 from BethanyL/HW9

Hw9

BethanyL/HW9.ipynb

@@ -0,0 +1,202 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:245663b949d0c983b529355a6b9cdae018a29591b12622ececb03ebb43996880"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from __future__ import print_function"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from IPython import parallel\n",
+ "clients = parallel.Client()\n",
+ "clients.block = True # use synchronous computations\n",
+ "print(clients.ids)\n",
+ "dview = clients.direct_view()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "[0, 1]\n"
+ ]
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from astroML.datasets import fetch_sdss_specgals\n",
+ "import numpy as np\n",
+ "data = fetch_sdss_specgals()\n",
+ "\n",
+ "# put magnitudes in a matrix\n",
+ "X = np.vstack([data['modelMag_%s' % f] for f in 'ugriz']).T\n",
+ "y = data['z']\n",
+ "\n",
+ "# down-sample the data for speed\n",
+ "X = X[::10]\n",
+ "y = y[::10]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%%px\n",
+ "import numpy as np\n",
+ "\n",
+ "import sklearn.ensemble, sklearn.cross_validation\n",
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "from sklearn.cross_validation import cross_val_score, StratifiedKFold\n",
+ "\n",
+ "def rfreg(x):\n",
+ " # define the model with the given value of C\n",
+ " \n",
+ " [n,m]=x.shape\n",
+ " scores = np.zeros(n)\n",
+ " score = np.zeros(n)\n",
+ " for j in range(n):\n",
+ " model = RandomForestRegressor(n_estimators=x[j][0], max_depth=x[j][1])\n",
+ "\n",
+ " # compute the scores via cross-validation\n",
+ " print('ready to score!!!')\n",
+ " scores = cross_val_score(model, Xd, Yd, scoring='r2', cv=3)\n",
+ "\n",
+ " # print the mean of the cross-validation scores\n",
+ " score[j] = np.mean(scores)\n",
+ "\n",
+ "# print(score)\n",
+ " return score"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "#%px \n",
+ "def parallel_rfreg(dview, n_est, max_d):\n",
+ " dview.push(dict(Xd = X, Yd = y))\n",
+ " #dview.push(y)\n",
+ " \n",
+ " A, B =np.meshgrid(n_est, max_d) #product(n_est, max_d)\n",
+ " A = np.reshape(A,(A.size,1))\n",
+ " B = np.reshape(B,(B.size,1))\n",
+ "\n",
+ " params = np.hstack((A,B))\n",
+ " #print(params)\n",
+ " #A = product(n_est, max_d)\n",
+ " #%px import numpy as np\n",
+ " \n",
+ " dview.scatter('param',params)\n",
+ " #dview.scatter('param',param)\n",
+ " #dview.apply(rfreg,param)\n",
+ " dview.execute('sc=rfreg(param)')\n",
+ " sc = dview.gather('sc')\n",
+ " print(sc)\n",
+ " \n",
+ " return max(sc), params[np.argmax(sc)][:2]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from itertools import product\n",
+ "n_est=np.arange(5, 15)\n",
+ "max_d=np.arange(1,5)\n",
+ "print(n_est)\n",
+ "print(max_d)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "[ 5 6 7 8 9 10 11 12 13 14]\n",
+ "[1 2 3 4]\n"
+ ]
+ }
+ ],
+ "prompt_number": 22
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "parallel_rfreg(dview,n_est,max_d)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "[ 0.33465985 0.33217848 0.32786725 0.33632745 0.33604494 0.33595623\n",
+ " 0.33868149 0.33468641 0.33519086 0.33636052 0.49129664 0.49420653\n",
+ " 0.49188764 0.4891963 0.49128236 0.49017637 0.48764369 0.49241277\n",
+ " 0.49448054 0.49160429 0.55641043 0.55708224 0.55813383 0.55692452\n",
+ " 0.55086971 0.55469108 0.55496256 0.5517019 0.55205004 0.55468357\n",
+ " 0.60631688 0.61041368 0.60785728 0.6139131 0.6120328 0.61333911\n",
+ " 0.61093431 0.61142202 0.61327971 0.61281658]\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 23,
+ "text": [
+ "(0.61391310443364611, array([8, 4]))"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}