From 1434472476cc6d0e9b65d76f2adeb06b832fd488 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Sat, 9 Nov 2019 19:41:27 +0100 Subject: [PATCH 01/21] added more tables --- benchmarks/resample/resample.html | 14338 +++++++++++++++++++++++++++ benchmarks/resample/resample.ipynb | 457 +- 2 files changed, 14671 insertions(+), 124 deletions(-) create mode 100644 benchmarks/resample/resample.html diff --git a/benchmarks/resample/resample.html b/benchmarks/resample/resample.html new file mode 100644 index 0000000..ce33f6d --- /dev/null +++ b/benchmarks/resample/resample.html @@ -0,0 +1,14338 @@ + + + + +resample + + + + + + + + + + + + + + + + + + + + + + + +
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Testy dla drzewa, +wszystkie zbiory danych:

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In [6]:
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score = provide_test_and_get_scores(datasets, 'tree')
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'G-MEAN'
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baseglobalsmotesoupmdo
1czysty-cut0.9390.9460.9550.9570.965
2delikatne-cut0.6980.6990.7440.7950.772
3mocniej-cut0.4920.4820.4960.5780.585
4delikatne-bezover-cut0.7710.7680.8150.8940.830
balance-scale0.1540.1230.1680.6210.162
cleveland0.1270.0960.1420.1390.098
cleveland_v20.1130.1100.1290.1620.090
cmc0.4400.4510.4440.4660.439
dermatology0.9250.9400.9460.9330.948
glass0.4630.4860.5540.6060.598
hayes-roth0.8370.8430.8410.8350.842
new_ecoli0.7080.7070.7230.7140.758
new_led7digit0.7540.7570.7620.7600.753
new_vehicle0.9000.8940.8900.8860.899
new_winequality-red0.4290.4070.4660.4370.465
new_yeast0.2500.2400.3230.2900.293
thyroid-newthyroid0.9000.9010.9180.9150.936
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Mean G-mean
soup0.646353
mdo0.613706
smote0.606824
base0.582353
global0.579412
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Mean rank
smote2.294118
soup2.352941
mdo2.411765
global3.941176
base4.000000
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Drzewo, tylko rzeczywiste zbiory danych:

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In [7]:
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print_scores(score,only_read_dt=True)
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'G-MEAN'
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baseglobalsmotesoupmdo
balance-scale0.1540.1230.1680.6210.162
cleveland0.1270.0960.1420.1390.098
cleveland_v20.1130.1100.1290.1620.090
cmc0.4400.4510.4440.4660.439
dermatology0.9250.9400.9460.9330.948
glass0.4630.4860.5540.6060.598
hayes-roth0.8370.8430.8410.8350.842
new_ecoli0.7080.7070.7230.7140.758
new_led7digit0.7540.7570.7620.7600.753
new_vehicle0.9000.8940.8900.8860.899
new_winequality-red0.4290.4070.4660.4370.465
new_yeast0.2500.2400.3230.2900.293
thyroid-newthyroid0.9000.9010.9180.9150.936
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Mean G-mean
soup0.597231
smote0.562000
mdo0.560077
base0.538462
global0.535000
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Mean rank
smote2.076923
soup2.615385
mdo2.692308
global3.769231
base3.846154
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Testy dla knn, +wszystkie zbiory danych:

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In [8]:
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score = provide_test_and_get_scores(datasets, 'knn')
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'G-MEAN'
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baseglobalsmotesoupmdo
1czysty-cut0.9710.9750.9780.9470.977
2delikatne-cut0.7040.7600.7610.7890.801
3mocniej-cut0.4660.5230.4980.5560.599
4delikatne-bezover-cut0.8120.8520.8610.8890.875
balance-scale0.1930.2670.4200.6780.684
cleveland0.0200.1340.1290.1070.066
cleveland_v20.0090.1830.2330.1910.056
cmc0.4820.4760.4810.5100.479
dermatology0.8430.8490.8490.8150.854
glass0.2010.6250.6210.6090.499
hayes-roth0.5590.6140.6270.6110.611
new_ecoli0.8140.7750.8070.8170.824
new_led7digit0.7570.4410.7270.7460.774
new_vehicle0.8490.8630.8590.8210.852
new_winequality-red0.1010.3820.3930.3800.175
new_yeast0.2620.3780.3950.4060.321
thyroid-newthyroid0.8210.9360.9200.8990.902
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Mean G-mean
soup0.633588
smote0.621118
mdo0.608765
global0.590176
base0.521412
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Mean rank
smote2.382353
mdo2.558824
soup2.794118
global2.911765
base4.352941
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knn (k=5), tylko rzeczywiste zbiory danych:

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In [9]:
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print_scores(score,only_read_dt=True)
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baseglobalsmotesoupmdo
balance-scale0.1930.2670.4200.6780.684
cleveland0.0200.1340.1290.1070.066
cleveland_v20.0090.1830.2330.1910.056
cmc0.4820.4760.4810.5100.479
dermatology0.8430.8490.8490.8150.854
glass0.2010.6250.6210.6090.499
hayes-roth0.5590.6140.6270.6110.611
new_ecoli0.8140.7750.8070.8170.824
new_led7digit0.7570.4410.7270.7460.774
new_vehicle0.8490.8630.8590.8210.852
new_winequality-red0.1010.3820.3930.3800.175
new_yeast0.2620.3780.3950.4060.321
thyroid-newthyroid0.8210.9360.9200.8990.902
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Mean G-mean
soup0.583846
smote0.573923
mdo0.545923
global0.532538
base0.454692
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Mean rank
smote2.269231
global2.730769
soup2.884615
mdo2.884615
base4.230769
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+ + + + + + + + diff --git a/benchmarks/resample/resample.ipynb b/benchmarks/resample/resample.ipynb index 76b2cb5..543a459 100644 --- a/benchmarks/resample/resample.ipynb +++ b/benchmarks/resample/resample.ipynb @@ -2,10 +2,29 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "outputs": [], "source": [ + "from collections import Counter, defaultdict\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.core.display import display\n", + "from numpy.core.defchararray import isdigit\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.tree import DecisionTreeClassifier\n", "\n", + "from multi_imbalance.datasets import load_datasets\n", + "from multi_imbalance.resampling.SOUP import SOUP\n", + "from multi_imbalance.resampling.MDO import MDO\n", + "from multi_imbalance.resampling.GlobalCS import GlobalCS\n", + "\n", + "from imblearn.metrics import geometric_mean_score\n", + "from imblearn.over_sampling import SMOTE\n", + "from multi_imbalance.resampling.spider import SPIDER3\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", "maj_int_min = {\n", " 'balance_scale' : {\n", " 'maj': [2, 1],\n", @@ -57,7 +76,9 @@ " 'int': [6, 5],\n", " 'min': [4,3,2,9,8,1]\n", " }\n", - "}" + "}\n", + "from IPython.display import clear_output\n", + "clear_output(wait=True)" ], "metadata": { "collapsed": false, @@ -69,7 +90,75 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, + "outputs": [], + "source": [ + "def resample_data(resample, seed, X_train, y_train, no_classes, dataset_name):\n", + " if resample == 'base':\n", + " X_train_resampled, y_train_resampled = X_train, y_train\n", + " elif resample=='soup':\n", + " soup = SOUP()\n", + " X_train_resampled, y_train_resampled = soup.fit_transform(np.copy(X_train), np.copy(y_train))\n", + " elif resample=='global':\n", + " global_cs = GlobalCS()\n", + " X_train_resampled, y_train_resampled = global_cs.fit_transform(np.copy(X_train), np.copy(y_train), shuffle=False)\n", + " elif resample=='smote':\n", + " smote = SMOTE(random_state=seed)\n", + " X_train_resampled, y_train_resampled = smote.fit_sample(np.copy(X_train), np.copy(y_train))\n", + " elif resample=='mdo':\n", + " mdo = MDO(k=9, k1_frac=0.1, seed=seed)\n", + " X_train_resampled, y_train_resampled = mdo.fit_transform(np.copy(X_train), np.copy(y_train))\n", + " elif resample=='spider':\n", + " cost = np.ones((no_classes, no_classes))\n", + " np.fill_diagonal(cost, 0)\n", + " clf = SPIDER3(k=5, cost=cost, majority_classes=maj_int_min[dataset_name]['maj'], intermediate_classes=maj_int_min[dataset_name]['int'], minority_classes=maj_int_min[dataset_name]['min'])\n", + " X_train_resampled, y_train_resampled = clf.fit_transform(X_train.astype(np.float64), y_train)\n", + " return X_train_resampled, y_train_resampled\n", + "\n", + "\n", + "def test_resampling(classifier, res, dataset_values, dataset_name):\n", + " X, y = dataset_values.data, dataset_values.target\n", + " no_classes = np.unique(y).size\n", + " result_data = defaultdict(int)\n", + " run_data = defaultdict(lambda: defaultdict(list)) # {metric: {run_number: [scores]}}\n", + " for i in range(10):\n", + " skf = StratifiedKFold(n_splits=5, shuffle=True,random_state=i)\n", + " for train_index, test_index in skf.split(X, y):\n", + " X_train, X_test = X[train_index], X[test_index]\n", + " y_train, y_test = y[train_index], y[test_index]\n", + " \n", + " X_train_resampled, y_train_resampled = resample_data(res, i, X_train, y_train, no_classes, dataset_name)\n", + " \n", + " if classifier == 'knn':\n", + " clf = KNeighborsClassifier(n_neighbors=5)\n", + " elif classifier == 'tree':\n", + " clf = DecisionTreeClassifier(random_state=i)\n", + " \n", + " clf.fit(X_train_resampled, y_train_resampled)\n", + " y_pred = clf.predict(X_test)\n", + " gmean = geometric_mean_score(y_test, y_pred, correction=0.001)\n", + " run_data['g_mean'][str(i)].append(gmean)\n", + " \n", + " def get_score_from_metric(run_data, metric):\n", + " runs = run_data[metric]\n", + " runs_scores_list = list(runs.values()) #[[one run k-foledscores],[..]]\n", + " result = np.mean(list(map(np.mean, runs_scores_list)))\n", + " return result\n", + " \n", + " result_data['g_mean'] = get_score_from_metric(run_data, 'g_mean')\n", + " return result_data\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "collapsed": true, "pycharm": { @@ -77,30 +166,192 @@ "name": "#%%\n" } }, + "outputs": [], + "source": [ + "def provide_test_and_get_scores(datasets, clf):\n", + " scores = defaultdict(dict)\n", + " for dataset_name, dataset_values in datasets.items():\n", + " clf_res_names =['base','global','smote','soup','mdo']\n", + " # print(dataset_name)\n", + " for resample in clf_res_names:\n", + " result_data = test_resampling(clf, resample, dataset_values, dataset_name)\n", + " scores[dataset_name][resample] = round(result_data['g_mean'],3)\n", + " return scores\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "def print_scores(scores, only_read_dt = False):\n", + " display(\"G-MEAN\")\n", + " df = pd.DataFrame(scores).T\n", + " if only_read_dt:\n", + " df = df.iloc[4:]\n", + " display(df)\n", + " \n", + " # df.fillna(df.median(), inplace=True)\n", + " display(pd.DataFrame(df.mean().sort_values(ascending=False),columns=['Mean G-mean']))\n", + " display(pd.DataFrame(df.rank(axis=1,ascending=False).mean().sort_values(),columns=['Mean rank']))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "datasets = load_datasets()\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Testy dla drzewa,\n", + "Wszystkie zbiory danych:" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "'G-MEAN'" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": " base global smote soup mdo\n1czysty-cut 0.939 0.946 0.955 0.957 0.965\n2delikatne-cut 0.698 0.699 0.744 0.795 0.772\n3mocniej-cut 0.492 0.482 0.496 0.578 0.585\n4delikatne-bezover-cut 0.771 0.768 0.815 0.894 0.830\nbalance-scale 0.154 0.123 0.168 0.621 0.162\ncleveland 0.127 0.096 0.142 0.139 0.098\ncleveland_v2 0.113 0.110 0.129 0.162 0.090\ncmc 0.440 0.451 0.444 0.466 0.439\ndermatology 0.925 0.940 0.946 0.933 0.948\nglass 0.463 0.486 0.554 0.606 0.598\nhayes-roth 0.837 0.843 0.841 0.835 0.842\nnew_ecoli 0.708 0.707 0.723 0.714 0.758\nnew_led7digit 0.754 0.757 0.762 0.760 0.753\nnew_vehicle 0.900 0.894 0.890 0.886 0.899\nnew_winequality-red 0.429 0.407 0.466 0.437 0.465\nnew_yeast 0.250 0.240 0.323 0.290 0.293\nthyroid-newthyroid 0.900 0.901 0.918 0.915 0.936", + "text/html": "
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baseglobalsmotesoupmdo
1czysty-cut0.9390.9460.9550.9570.965
2delikatne-cut0.6980.6990.7440.7950.772
3mocniej-cut0.4920.4820.4960.5780.585
4delikatne-bezover-cut0.7710.7680.8150.8940.830
balance-scale0.1540.1230.1680.6210.162
cleveland0.1270.0960.1420.1390.098
cleveland_v20.1130.1100.1290.1620.090
cmc0.4400.4510.4440.4660.439
dermatology0.9250.9400.9460.9330.948
glass0.4630.4860.5540.6060.598
hayes-roth0.8370.8430.8410.8350.842
new_ecoli0.7080.7070.7230.7140.758
new_led7digit0.7540.7570.7620.7600.753
new_vehicle0.9000.8940.8900.8860.899
new_winequality-red0.4290.4070.4660.4370.465
new_yeast0.2500.2400.3230.2900.293
thyroid-newthyroid0.9000.9010.9180.9150.936
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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": " Mean G-mean\nsoup 0.646353\nmdo 0.613706\nsmote 0.606824\nbase 0.582353\nglobal 0.579412", + "text/html": "
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Mean G-mean
soup0.646353
mdo0.613706
smote0.606824
base0.582353
global0.579412
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Mean rank
smote2.294118
soup2.352941
mdo2.411765
global3.941176
base4.000000
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "score = provide_test_and_get_scores(datasets, 'tree')\n", + "print_scores(score)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Drzewo, tylko rzeczywiste zbiory danych:" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, "outputs": [ { - "name": "stdout", - "text": [ - "balance_scale\n", - "cleveland\n", - "cmc\ndermatology\n", - "ecoli\nsmote ecoli Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6\nsmote ecoli Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6\nsmote ecoli Expected n_neighbors <= n_samples, but n_samples = 2, n_neighbors = 6\nsmote ecoli Expected n_neighbors <= n_samples, but n_samples = 2, n_neighbors = 6\n", - "glass\n", - "hayes_roth\n", - "new_thyroid\n", - "winequailty_red\nyeast\n" - ], - "output_type": "stream" + "data": { + "text/plain": "'G-MEAN'" + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", - "text": [ - "/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=4.\n % (min_groups, self.n_splits)), Warning)\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/imblearn/metrics/_classification.py:635: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n \"recall\")\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=4.\n % (min_groups, self.n_splits)), Warning)\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/imblearn/metrics/_classification.py:635: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n \"recall\")\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=4.\n % (min_groups, self.n_splits)), Warning)\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/imblearn/metrics/_classification.py:635: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n \"recall\")\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=4.\n % (min_groups, self.n_splits)), Warning)\n", - "/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/imblearn/metrics/_classification.py:635: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n \"recall\")\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/imblearn/metrics/_classification.py:635: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n \"recall\")\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=4.\n % (min_groups, self.n_splits)), Warning)\n", - "/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/imblearn/metrics/_classification.py:635: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n \"recall\")\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/sklearn/model_selection/_split.py:657: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=4.\n % (min_groups, self.n_splits)), Warning)\n" - ], - "output_type": "stream" + "data": { + "text/plain": " base global smote soup mdo\nbalance-scale 0.154 0.123 0.168 0.621 0.162\ncleveland 0.127 0.096 0.142 0.139 0.098\ncleveland_v2 0.113 0.110 0.129 0.162 0.090\ncmc 0.440 0.451 0.444 0.466 0.439\ndermatology 0.925 0.940 0.946 0.933 0.948\nglass 0.463 0.486 0.554 0.606 0.598\nhayes-roth 0.837 0.843 0.841 0.835 0.842\nnew_ecoli 0.708 0.707 0.723 0.714 0.758\nnew_led7digit 0.754 0.757 0.762 0.760 0.753\nnew_vehicle 0.900 0.894 0.890 0.886 0.899\nnew_winequality-red 0.429 0.407 0.466 0.437 0.465\nnew_yeast 0.250 0.240 0.323 0.290 0.293\nthyroid-newthyroid 0.900 0.901 0.918 0.915 0.936", + "text/html": "
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baseglobalsmotesoupmdo
balance-scale0.1540.1230.1680.6210.162
cleveland0.1270.0960.1420.1390.098
cleveland_v20.1130.1100.1290.1620.090
cmc0.4400.4510.4440.4660.439
dermatology0.9250.9400.9460.9330.948
glass0.4630.4860.5540.6060.598
hayes-roth0.8370.8430.8410.8350.842
new_ecoli0.7080.7070.7230.7140.758
new_led7digit0.7540.7570.7620.7600.753
new_vehicle0.9000.8940.8900.8860.899
new_winequality-red0.4290.4070.4660.4370.465
new_yeast0.2500.2400.3230.2900.293
thyroid-newthyroid0.9000.9010.9180.9150.936
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Mean G-mean
soup0.597231
smote0.562000
mdo0.560077
base0.538462
global0.535000
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Mean rank
smote2.076923
soup2.615385
mdo2.692308
global3.769231
base3.846154
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_scores(score,only_read_dt=True)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Testy dla knn,\n", + "Wszystkie zbiory danych:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ { "data": { "text/plain": "'G-MEAN'" @@ -110,138 +361,96 @@ }, { "data": { - "text/plain": " base global smote soup mdo spider\nbalance_scale 0.101 0.062 0.177 0.292 0.183 0.379\ncleveland 0.204 0.133 0.116 0.069 0.167 0.089\ndermatology 0.928 0.927 0.904 0.924 0.919 0.928\necoli 0.185 0.353 NaN 0.211 0.280 0.284\nglass 0.285 0.259 0.370 0.479 0.337 0.593\nhayes_roth 0.871 0.871 0.864 0.830 0.863 0.376\nnew_thyroid 0.893 0.863 0.894 0.933 0.947 0.927", - "text/html": "
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baseglobalsmotesoupmdospider
balance_scale0.1010.0620.1770.2920.1830.379
cleveland0.2040.1330.1160.0690.1670.089
dermatology0.9280.9270.9040.9240.9190.928
ecoli0.1850.353NaN0.2110.2800.284
glass0.2850.2590.3700.4790.3370.593
hayes_roth0.8710.8710.8640.8300.8630.376
new_thyroid0.8930.8630.8940.9330.9470.927
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" + "text/plain": " base global smote soup mdo\n1czysty-cut 0.971 0.975 0.978 0.947 0.977\n2delikatne-cut 0.704 0.760 0.761 0.789 0.801\n3mocniej-cut 0.466 0.523 0.498 0.556 0.599\n4delikatne-bezover-cut 0.812 0.852 0.861 0.889 0.875\nbalance-scale 0.193 0.267 0.420 0.678 0.684\ncleveland 0.020 0.134 0.129 0.107 0.066\ncleveland_v2 0.009 0.183 0.233 0.191 0.056\ncmc 0.482 0.476 0.481 0.510 0.479\ndermatology 0.843 0.849 0.849 0.815 0.854\nglass 0.201 0.625 0.621 0.609 0.499\nhayes-roth 0.559 0.614 0.627 0.611 0.611\nnew_ecoli 0.814 0.775 0.807 0.817 0.824\nnew_led7digit 0.757 0.441 0.727 0.746 0.774\nnew_vehicle 0.849 0.863 0.859 0.821 0.852\nnew_winequality-red 0.101 0.382 0.393 0.380 0.175\nnew_yeast 0.262 0.378 0.395 0.406 0.321\nthyroid-newthyroid 0.821 0.936 0.920 0.899 0.902", + "text/html": "
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baseglobalsmotesoupmdo
1czysty-cut0.9710.9750.9780.9470.977
2delikatne-cut0.7040.7600.7610.7890.801
3mocniej-cut0.4660.5230.4980.5560.599
4delikatne-bezover-cut0.8120.8520.8610.8890.875
balance-scale0.1930.2670.4200.6780.684
cleveland0.0200.1340.1290.1070.066
cleveland_v20.0090.1830.2330.1910.056
cmc0.4820.4760.4810.5100.479
dermatology0.8430.8490.8490.8150.854
glass0.2010.6250.6210.6090.499
hayes-roth0.5590.6140.6270.6110.611
new_ecoli0.8140.7750.8070.8170.824
new_led7digit0.7570.4410.7270.7460.774
new_vehicle0.8490.8630.8590.8210.852
new_winequality-red0.1010.3820.3930.3800.175
new_yeast0.2620.3780.3950.4060.321
thyroid-newthyroid0.8210.9360.9200.8990.902
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Mean G-mean
soup0.633588
smote0.621118
mdo0.608765
global0.590176
base0.521412
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Mean rank
smote2.382353
mdo2.558824
soup2.794118
global2.911765
base4.352941
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "score = provide_test_and_get_scores(datasets, 'knn')\n", + "print_scores(score)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "markdown", + "source": [ + "knn (k=5), tylko rzeczywiste zbiory danych:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "'G-MEAN'" }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": " base global smote soup mdo spider\nbalance_scale 0.605 0.589 0.619 0.575 0.592 0.581\ncleveland 0.518 0.459 0.446 0.389 0.502 0.379\ndermatology 0.937 0.932 0.918 0.919 0.918 0.932\necoli 0.789 0.779 NaN 0.709 0.759 0.744\nglass 0.579 0.594 0.600 0.536 0.566 0.543\nhayes_roth 0.857 0.857 0.850 0.818 0.849 0.388\nnew_thyroid 0.930 0.926 0.948 0.949 0.925 0.920", - "text/html": "
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baseglobalsmotesoupmdospider
balance_scale0.6050.5890.6190.5750.5920.581
cleveland0.5180.4590.4460.3890.5020.379
dermatology0.9370.9320.9180.9190.9180.932
ecoli0.7890.779NaN0.7090.7590.744
glass0.5790.5940.6000.5360.5660.543
hayes_roth0.8570.8570.8500.8180.8490.388
new_thyroid0.9300.9260.9480.9490.9250.920
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" + "text/plain": " base global smote soup mdo\nbalance-scale 0.193 0.267 0.420 0.678 0.684\ncleveland 0.020 0.134 0.129 0.107 0.066\ncleveland_v2 0.009 0.183 0.233 0.191 0.056\ncmc 0.482 0.476 0.481 0.510 0.479\ndermatology 0.843 0.849 0.849 0.815 0.854\nglass 0.201 0.625 0.621 0.609 0.499\nhayes-roth 0.559 0.614 0.627 0.611 0.611\nnew_ecoli 0.814 0.775 0.807 0.817 0.824\nnew_led7digit 0.757 0.441 0.727 0.746 0.774\nnew_vehicle 0.849 0.863 0.859 0.821 0.852\nnew_winequality-red 0.101 0.382 0.393 0.380 0.175\nnew_yeast 0.262 0.378 0.395 0.406 0.321\nthyroid-newthyroid 0.821 0.936 0.920 0.899 0.902", + "text/html": "
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baseglobalsmotesoupmdo
balance-scale0.1930.2670.4200.6780.684
cleveland0.0200.1340.1290.1070.066
cleveland_v20.0090.1830.2330.1910.056
cmc0.4820.4760.4810.5100.479
dermatology0.8430.8490.8490.8150.854
glass0.2010.6250.6210.6090.499
hayes-roth0.5590.6140.6270.6110.611
new_ecoli0.8140.7750.8070.8170.824
new_led7digit0.7570.4410.7270.7460.774
new_vehicle0.8490.8630.8590.8210.852
new_winequality-red0.1010.3820.3930.3800.175
new_yeast0.2620.3780.3950.4060.321
thyroid-newthyroid0.8210.9360.9200.8990.902
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Mean G-mean
soup0.583846
smote0.573923
mdo0.545923
global0.532538
base0.454692
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Mean rank
smote2.269231
global2.730769
soup2.884615
mdo2.884615
base4.230769
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "from collections import Counter\n", - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.core.display import display\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "\n", - "from multi_imbalance.datasets import load_datasets\n", - "from multi_imbalance.resampling.SOUP import SOUP\n", - "from multi_imbalance.resampling.MDO import MDO\n", - "from multi_imbalance.resampling.GlobalCS import GlobalCS\n", - "\n", - "from imblearn.metrics import geometric_mean_score\n", - "from imblearn.over_sampling import SMOTE\n", - "from multi_imbalance.resampling.spider import SPIDER3\n", - "\n", - "np.random.seed(0)\n", - "\n", - "datasets = load_datasets()\n", - "results_g_mean = dict()\n", - "results_acc = dict()\n", - "\n", - "for dataset_name, dataset_values in datasets.items():\n", - " print(dataset_name)\n", - " \n", - " X, y = dataset_values.data, dataset_values.target\n", - " \n", - " if len(X)>1000:\n", - " continue\n", - " \n", - " results_g_mean[dataset_name]=dict()\n", - " results_acc[dataset_name]=dict()\n", - " \n", - " for resample in ['base','global','smote','soup','mdo','spider']:\n", - " \n", - " skf = StratifiedKFold(n_splits=4, random_state=0)\n", - " acc, g_mean = list(),list()\n", - " for train_index, test_index in skf.split(X, y):\n", - " X_train, X_test = X[train_index], X[test_index]\n", - " y_train, y_test = y[train_index], y[test_index]\n", - " error_flag = False\n", - " clf_tree = DecisionTreeClassifier(random_state=0)\n", - " \n", - " if resample == 'base':\n", - " X_train_resampled, y_train_resampled = X_train, y_train\n", - " elif resample=='soup':\n", - " soup = SOUP()\n", - " X_train_resampled, y_train_resampled = soup.fit_transform(np.copy(X_train), np.copy(y_train))\n", - " elif resample=='global':\n", - " global_cs = GlobalCS()\n", - " X_train_resampled, y_train_resampled = global_cs.fit_transform(np.copy(X_train), np.copy(y_train))\n", - " elif resample=='smote':\n", - " try:\n", - " smote = SMOTE()\n", - " X_train_resampled, y_train_resampled = smote.fit_sample(np.copy(X_train), np.copy(y_train))\n", - " except Exception as e:\n", - " error_flag = True\n", - " print(resample, dataset_name, e)\n", - " X_train_resampled, y_train_resampled = X_train, y_train\n", - " elif resample=='mdo':\n", - " mdo = MDO(k=9, k1_frac=0, seed=0)\n", - " X_train_resampled, y_train_resampled = mdo.fit_transform(np.copy(X_train), np.copy(y_train))\n", - " elif resample=='spider':\n", - " no_classes = np.unique(y).size\n", - " cnt = Counter(y)\n", - " cost = np.ones((no_classes, no_classes))\n", - " np.fill_diagonal(cost, 0)\n", - " clf = SPIDER3(k=5, cost=cost, majority_classes=maj_int_min[dataset_name]['maj'], intermediate_classes=maj_int_min[dataset_name]['int'], minority_classes=maj_int_min[dataset_name]['min'])\n", - " X_train_resampled, y_train_resampled = clf.fit_transform(X_train.astype(np.float64), y_train)\n", - " \n", - " clf_tree.fit(X_train_resampled, y_train_resampled)\n", - " y_pred = clf_tree.predict(X_test)\n", - " g_mean.append(geometric_mean_score(y_test, y_pred, correction=0.001))\n", - " acc.append(accuracy_score(y_test, y_pred))\n", - " \n", - " result_g_mean = None if error_flag else round(np.mean(g_mean),3)\n", - " result_acc = None if error_flag else round(np.mean(acc),3)\n", - " \n", - " results_g_mean[dataset_name][resample]=result_g_mean\n", - " results_acc[dataset_name][resample]=result_acc\n", - "\n", - "display(\"G-MEAN\")\n", - "df = pd.DataFrame(results_g_mean).T\n", - "display(df)\n", - "\n", - "display(\"ACC\")\n", - "df2 = pd.DataFrame(results_acc).T\n", - "display(df2)\n", - "\n", - "display(\"MEAN G-MEAN\")\n", - "df.fillna(df.median(), inplace=True)\n", - "display(df.mean())" - ] + "print_scores(score,only_read_dt=True)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } } ], "metadata": { From c95c5b0e66efc34227ca07b5e6863bf0457f46a6 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Tue, 26 Nov 2019 10:23:53 +0100 Subject: [PATCH 02/21] updated docs and soup bagging --- benchmarks/resample/SOUPBagging.ipynb | 101 ++++++++++++++++++++++ benchmarks/spider/spider.ipynb | 6 +- examples/resampling/MDO.ipynb | 116 +++++++++++++------------- examples/resampling/SOUP.ipynb | 32 ++++--- 4 files changed, 183 insertions(+), 72 deletions(-) create mode 100644 benchmarks/resample/SOUPBagging.ipynb diff --git a/benchmarks/resample/SOUPBagging.ipynb b/benchmarks/resample/SOUPBagging.ipynb new file mode 100644 index 0000000..57c1209 --- /dev/null +++ b/benchmarks/resample/SOUPBagging.ipynb @@ -0,0 +1,101 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": true, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": 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0., 0., 0.],\n [1., 0., 0., 0., 0.]])" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 71 + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.ensemble import BaggingClassifier\n", + "from sklearn.model_selection import train_test_split, ParameterGrid\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.utils import resample\n", + "from multi_imbalance.datasets import load_datasets\n", + "from multi_imbalance.resampling.SOUP import SOUP\n", + "\n", + "\n", + "datasets = load_datasets()['new_ecoli']\n", + "X, y = datasets.data, datasets.target \n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n", + "\n", + "n_classifiers = 30\n", + "n_samples = X_test.shape[0]\n", + "n_classes = np.unique(np.concatenate((y_train, y_test))).shape[0]\n", + "\n", + "results = np.zeros(shape=(n_classifiers, n_samples, n_classes))\n", + "decision_matrix = np.zeros(shape=(n_samples, n_classes))\n", + "\n", + "for i in range(n_classifiers):\n", + " x_sampled, y_sampled = resample(X_train, y_train, stratify=y_train)\n", + " x_resampled, y_resampled = SOUP().fit_transform(x_sampled, y_sampled)\n", + " clf = KNeighborsClassifier().fit(x_resampled, y_resampled)\n", + " results[i] = clf.predict_proba(X_test)\n", + "\n", + "weights_sum = np.sum(results, axis=0)\n", + "decisions_indices = np.argmax(weights_sum,axis=1)\n", + "decision_matrix[np.arange(n_samples),decisions_indices] = 1\n", + "\n", + "decision_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/benchmarks/spider/spider.ipynb b/benchmarks/spider/spider.ipynb index 45a3e46..5bcb578 100644 --- a/benchmarks/spider/spider.ipynb +++ b/benchmarks/spider/spider.ipynb @@ -572,13 +572,13 @@ "pycharm": { "stem_cell": { "cell_type": "raw", + "source": [], "metadata": { "collapsed": false - }, - "source": [] + } } } }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/examples/resampling/MDO.ipynb b/examples/resampling/MDO.ipynb index 5292360..914aa4c 100644 --- a/examples/resampling/MDO.ipynb +++ b/examples/resampling/MDO.ipynb @@ -2,23 +2,33 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "Unzip datasets and prepare data:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 39, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", + "output_type": "stream", "text": [ - "[[0.49 0.29 0.48 0.5 0.56 0.24 0.35]\n [0.07 0.4 0.48 0.5 0.54 0.35 0.44]\n [0.56 0.4 0.48 0.5 0.49 0.37 0.46]\n [0.59 0.49 0.48 0.5 0.52 0.45 0.36]\n [0.23 0.32 0.48 0.5 0.55 0.25 0.35]]\n[0 0 0 0 0]\n" - ], - "output_type": "stream" + "odict_keys(['1czysty-cut', '2delikatne-cut', '3mocniej-cut', '4delikatne-bezover-cut', 'balance-scale', 'cleveland', 'cleveland_v2', 'cmc', 'dermatology', 'glass', 'hayes-roth', 'new_ecoli', 'new_led7digit', 'new_vehicle', 'new_winequality-red', 'new_yeast', 'thyroid-newthyroid'])\n", + "[[0.49 0.29 0.48 0.5 0.56 0.24 0.35]\n", + " [0.07 0.4 0.48 0.5 0.54 0.35 0.44]\n", + " [0.56 0.4 0.48 0.5 0.49 0.37 0.46]\n", + " [0.59 0.49 0.48 0.5 0.52 0.45 0.36]\n", + " [0.23 0.32 0.48 0.5 0.55 0.25 0.35]]\n", + "[0 0 0 0 0]\n" + ] } ], "source": [ @@ -35,70 +45,69 @@ "sns.set_style('darkgrid')\n", "\n", "\n", - "dataset = load_datasets()['ecoli']\n", - "\n", + "dataset = load_datasets()\n", + "print(dataset.keys())\n", + "dataset = dataset['new_ecoli']\n", "X, y = dataset.data, dataset.target\n", "print(X[:5])\n", "print(y[:5])" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Resample data using MDO algorithm" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "clf = MDO(k1_frac=0)\n", - "resampled_X, resampled_y = clf.fit_transform(X, y)" - ], + "execution_count": 40, "metadata": { - "collapsed": false, "pycharm": { - "name": "#%%\n", - "is_executing": false + "is_executing": false, + "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "clf = MDO(k1_frac=0.5)\n", + "resampled_X, resampled_y = clf.fit_transform(X, y)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Compare results by plotting data in 2 dimensions" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 41, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, + "execution_count": 41, "metadata": {}, - "output_type": "execute_result", - "execution_count": 9 + "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -127,14 +136,7 @@ "resampled_X = pca.transform(resampled_X)\n", "df = construct_flat_2pc_df(resampled_X, resampled_y)\n", "sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[3], legend='full', palette=p)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + ] } ], "metadata": { @@ -146,25 +148,25 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.6.8" }, "pycharm": { "stem_cell": { "cell_type": "raw", - "source": [], "metadata": { "collapsed": false - } + }, + "source": [] } } }, "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "nbformat_minor": 1 +} diff --git a/examples/resampling/SOUP.ipynb b/examples/resampling/SOUP.ipynb index 937a1f7..43e103a 100644 --- a/examples/resampling/SOUP.ipynb +++ b/examples/resampling/SOUP.ipynb @@ -14,17 +14,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "outputs": [ { "name": "stdout", "text": [ - "[[0.49 0.29 0.48 0.5 0.56 0.24 0.35]\n [0.07 0.4 0.48 0.5 0.54 0.35 0.44]\n [0.56 0.4 0.48 0.5 0.49 0.37 0.46]\n [0.59 0.49 0.48 0.5 0.52 0.45 0.36]\n [0.23 0.32 0.48 0.5 0.55 0.25 0.35]]\n['cp' 'cp' 'cp' 'cp' 'cp']\n" + "[[0.49 0.29 0.48 0.5 0.56 0.24 0.35]\n [0.07 0.4 0.48 0.5 0.54 0.35 0.44]\n [0.56 0.4 0.48 0.5 0.49 0.37 0.46]\n [0.59 0.49 0.48 0.5 0.52 0.45 0.36]\n [0.23 0.32 0.48 0.5 0.55 0.25 0.35]]\n[0 0 0 0 0]\n" ], "output_type": "stream" } ], "source": [ + "from collections import Counter\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.decomposition import PCA\n", @@ -36,7 +37,7 @@ "%matplotlib inline\n", "sns.set_style('darkgrid')\n", "\n", - "dataset = load_datasets()['ecoli']\n", + "dataset = load_datasets()['new_ecoli']\n", "\n", "X, y = dataset.data, dataset.target\n", "print(X[:5])\n", @@ -64,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "outputs": [], "source": [ "clf = SOUP()\n", @@ -92,26 +93,30 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, "metadata": {}, "output_type": "execute_result", - "execution_count": 9 + "execution_count": 18 }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "\n", + "n = len(Counter(y).keys())\n", + "p = sns.color_palette(\"husl\", n)\n", + "\n", "pca = PCA(n_components=2)\n", "pca.fit(X)\n", "\n", @@ -119,15 +124,18 @@ "fig.set_size_inches( 16, 10)\n", "axs = axs.flatten()\n", "\n", - "sns.countplot(y, ax=axs[0])\n", + "axs[1].set_title(\"Base\")\n", + "sns.countplot(y, ax=axs[0], palette=p)\n", "X = pca.transform(X)\n", "df = construct_flat_2pc_df(X, y)\n", - "sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[1], legend=False)\n", + "sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[1], legend='full', palette=p)\n", + "\n", "\n", - "sns.countplot(resampled_y, ax=axs[2])\n", + "axs[3].set_title(\"SOUP\")\n", + "sns.countplot(resampled_y, ax=axs[2],palette=p)\n", "resampled_X = pca.transform(resampled_X)\n", "df = construct_flat_2pc_df(resampled_X, resampled_y)\n", - "sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[3], legend=False)" + "sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[3], legend='full', palette=p)" ], "metadata": { "collapsed": false, From 786f402060bdecb448498fdd31987c2e057895bc Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Tue, 26 Nov 2019 13:26:29 +0100 Subject: [PATCH 03/21] added soup bagging --- benchmarks/resample/SOUPBagging.ipynb | 101 ---------- benchmarks/resample/resample.ipynb | 186 ++++-------------- .../.ipynb_checkpoints/MDO-checkpoint.ipynb | 172 ++++++++++++++++ multi_imbalance/resampling/SOUPBagging.py | 42 ++++ 4 files changed, 256 insertions(+), 245 deletions(-) delete mode 100644 benchmarks/resample/SOUPBagging.ipynb create mode 100644 examples/resampling/.ipynb_checkpoints/MDO-checkpoint.ipynb create mode 100644 multi_imbalance/resampling/SOUPBagging.py diff --git a/benchmarks/resample/SOUPBagging.ipynb b/benchmarks/resample/SOUPBagging.ipynb deleted file mode 100644 index 57c1209..0000000 --- a/benchmarks/resample/SOUPBagging.ipynb +++ /dev/null @@ -1,101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 71, - "metadata": { - "collapsed": true, - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "data": { - "text/plain": "array([[1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [0., 0., 0., 1., 0.],\n [0., 0., 0., 1., 0.],\n [0., 1., 0., 0., 0.],\n [0., 1., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [0., 0., 0., 1., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [0., 1., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [0., 0., 0., 0., 1.],\n [0., 0., 1., 0., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [0., 1., 0., 0., 0.],\n [0., 0., 0., 0., 1.],\n [0., 1., 0., 0., 0.],\n [0., 0., 0., 1., 0.],\n [0., 0., 0., 1., 0.],\n [0., 0., 0., 1., 0.],\n [1., 0., 0., 0., 0.],\n [0., 0., 0., 0., 1.],\n [0., 0., 1., 0., 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1.],\n [0., 1., 0., 0., 0.],\n [0., 0., 0., 1., 0.],\n [1., 0., 0., 0., 0.],\n [0., 0., 0., 0., 1.],\n [1., 0., 0., 0., 0.],\n [0., 0., 1., 0., 0.],\n [0., 0., 0., 0., 1.],\n [1., 0., 0., 0., 0.],\n [0., 0., 1., 0., 0.],\n [0., 0., 1., 0., 0.],\n [0., 1., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.]])" - }, - "metadata": {}, - "output_type": "execute_result", - "execution_count": 71 - } - ], - "source": [ - "import numpy as np\n", - "from sklearn.ensemble import BaggingClassifier\n", - "from sklearn.model_selection import train_test_split, ParameterGrid\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.utils import resample\n", - "from multi_imbalance.datasets import load_datasets\n", - "from multi_imbalance.resampling.SOUP import SOUP\n", - "\n", - "\n", - "datasets = load_datasets()['new_ecoli']\n", - "X, y = datasets.data, datasets.target \n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n", - "\n", - "n_classifiers = 30\n", - "n_samples = X_test.shape[0]\n", - "n_classes = np.unique(np.concatenate((y_train, y_test))).shape[0]\n", - "\n", - "results = np.zeros(shape=(n_classifiers, n_samples, n_classes))\n", - "decision_matrix = np.zeros(shape=(n_samples, n_classes))\n", - "\n", - "for i in range(n_classifiers):\n", - " x_sampled, y_sampled = resample(X_train, y_train, stratify=y_train)\n", - " x_resampled, y_resampled = SOUP().fit_transform(x_sampled, y_sampled)\n", - " clf = KNeighborsClassifier().fit(x_resampled, y_resampled)\n", - " results[i] = clf.predict_proba(X_test)\n", - "\n", - "weights_sum = np.sum(results, axis=0)\n", - "decisions_indices = np.argmax(weights_sum,axis=1)\n", - "decision_matrix[np.arange(n_samples),decisions_indices] = 1\n", - "\n", - "decision_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/benchmarks/resample/resample.ipynb b/benchmarks/resample/resample.ipynb index 543a459..d5b1ce5 100644 --- a/benchmarks/resample/resample.ipynb +++ b/benchmarks/resample/resample.ipynb @@ -2,18 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "outputs": [], "source": [ "from collections import Counter, defaultdict\n", "import numpy as np\n", "import pandas as pd\n", + "import tqdm as tqdm\n", "from IPython.core.display import display\n", - "from numpy.core.defchararray import isdigit\n", - "from sklearn.metrics import accuracy_score\n", - "\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.tree import DecisionTreeClassifier\n", + "from benchmarks.resample.SOUPBagging import SOUPBagging\n", "\n", "from multi_imbalance.datasets import load_datasets\n", "from multi_imbalance.resampling.SOUP import SOUP\n", @@ -113,6 +112,9 @@ " np.fill_diagonal(cost, 0)\n", " clf = SPIDER3(k=5, cost=cost, majority_classes=maj_int_min[dataset_name]['maj'], intermediate_classes=maj_int_min[dataset_name]['int'], minority_classes=maj_int_min[dataset_name]['min'])\n", " X_train_resampled, y_train_resampled = clf.fit_transform(X_train.astype(np.float64), y_train)\n", + " elif resample=='soupbagging':\n", + " # SOUP Bagging does it by itself\n", + " X_train_resampled, y_train_resampled = X_train, y_train\n", " return X_train_resampled, y_train_resampled\n", "\n", "\n", @@ -133,6 +135,10 @@ " clf = KNeighborsClassifier(n_neighbors=5)\n", " elif classifier == 'tree':\n", " clf = DecisionTreeClassifier(random_state=i)\n", + " \n", + " if res == 'soupbagging':\n", + " vote_classifier = SOUPBagging(clf, n_classifiers=5, seed=i)\n", + " clf = vote_classifier\n", " \n", " clf.fit(X_train_resampled, y_train_resampled)\n", " y_pred = clf.predict(X_test)\n", @@ -170,13 +176,12 @@ "source": [ "def provide_test_and_get_scores(datasets, clf):\n", " scores = defaultdict(dict)\n", - " for dataset_name, dataset_values in datasets.items():\n", - " clf_res_names =['base','global','smote','soup','mdo']\n", + " for dataset_name, dataset_values in tqdm.tqdm(datasets.items(),total=len(datasets)):\n", + " clf_res_names =['base','global','smote','soup','soupbagging','mdo']\n", " # print(dataset_name)\n", " for resample in clf_res_names:\n", " result_data = test_resampling(clf, resample, dataset_values, dataset_name)\n", - " scores[dataset_name][resample] = round(result_data['g_mean'],3)\n", - " return scores\n" + " scores[dataset_name][resample] = round(result_data['g_mean'],3)\n" ] }, { @@ -233,38 +238,27 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "outputs": [ { - "data": { - "text/plain": "'G-MEAN'" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " base global smote soup mdo\n1czysty-cut 0.939 0.946 0.955 0.957 0.965\n2delikatne-cut 0.698 0.699 0.744 0.795 0.772\n3mocniej-cut 0.492 0.482 0.496 0.578 0.585\n4delikatne-bezover-cut 0.771 0.768 0.815 0.894 0.830\nbalance-scale 0.154 0.123 0.168 0.621 0.162\ncleveland 0.127 0.096 0.142 0.139 0.098\ncleveland_v2 0.113 0.110 0.129 0.162 0.090\ncmc 0.440 0.451 0.444 0.466 0.439\ndermatology 0.925 0.940 0.946 0.933 0.948\nglass 0.463 0.486 0.554 0.606 0.598\nhayes-roth 0.837 0.843 0.841 0.835 0.842\nnew_ecoli 0.708 0.707 0.723 0.714 0.758\nnew_led7digit 0.754 0.757 0.762 0.760 0.753\nnew_vehicle 0.900 0.894 0.890 0.886 0.899\nnew_winequality-red 0.429 0.407 0.466 0.437 0.465\nnew_yeast 0.250 0.240 0.323 0.290 0.293\nthyroid-newthyroid 0.900 0.901 0.918 0.915 0.936", - "text/html": "
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baseglobalsmotesoupmdo
1czysty-cut0.9390.9460.9550.9570.965
2delikatne-cut0.6980.6990.7440.7950.772
3mocniej-cut0.4920.4820.4960.5780.585
4delikatne-bezover-cut0.7710.7680.8150.8940.830
balance-scale0.1540.1230.1680.6210.162
cleveland0.1270.0960.1420.1390.098
cleveland_v20.1130.1100.1290.1620.090
cmc0.4400.4510.4440.4660.439
dermatology0.9250.9400.9460.9330.948
glass0.4630.4860.5540.6060.598
hayes-roth0.8370.8430.8410.8350.842
new_ecoli0.7080.7070.7230.7140.758
new_led7digit0.7540.7570.7620.7600.753
new_vehicle0.9000.8940.8900.8860.899
new_winequality-red0.4290.4070.4660.4370.465
new_yeast0.2500.2400.3230.2900.293
thyroid-newthyroid0.9000.9010.9180.9150.936
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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoup 0.646353\nmdo 0.613706\nsmote 0.606824\nbase 0.582353\nglobal 0.579412", - "text/html": "
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Mean G-mean
soup0.646353
mdo0.613706
smote0.606824
base0.582353
global0.579412
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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsmote 2.294118\nsoup 2.352941\nmdo 2.411765\nglobal 3.941176\nbase 4.000000", - "text/html": "
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Mean rank
smote2.294118
soup2.352941
mdo2.411765
global3.941176
base4.000000
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baseglobalsmotesoupmdo
balance-scale0.1540.1230.1680.6210.162
cleveland0.1270.0960.1420.1390.098
cleveland_v20.1130.1100.1290.1620.090
cmc0.4400.4510.4440.4660.439
dermatology0.9250.9400.9460.9330.948
glass0.4630.4860.5540.6060.598
hayes-roth0.8370.8430.8410.8350.842
new_ecoli0.7080.7070.7230.7140.758
new_led7digit0.7540.7570.7620.7600.753
new_vehicle0.9000.8940.8900.8860.899
new_winequality-red0.4290.4070.4660.4370.465
new_yeast0.2500.2400.3230.2900.293
thyroid-newthyroid0.9000.9010.9180.9150.936
\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoup 0.597231\nsmote 0.562000\nmdo 0.560077\nbase 0.538462\nglobal 0.535000", - "text/html": "
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Mean G-mean
soup0.597231
smote0.562000
mdo0.560077
base0.538462
global0.535000
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsmote 2.076923\nsoup 2.615385\nmdo 2.692308\nglobal 3.769231\nbase 3.846154", - "text/html": "
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Mean rank
smote2.076923
soup2.615385
mdo2.692308
global3.769231
base3.846154
\n
" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "print_scores(score,only_read_dt=True)\n" ], @@ -334,7 +296,7 @@ "collapsed": false, "pycharm": { "name": "#%%\n", - "is_executing": false + "is_executing": true } } }, @@ -350,40 +312,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "'G-MEAN'" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " base global smote soup mdo\n1czysty-cut 0.971 0.975 0.978 0.947 0.977\n2delikatne-cut 0.704 0.760 0.761 0.789 0.801\n3mocniej-cut 0.466 0.523 0.498 0.556 0.599\n4delikatne-bezover-cut 0.812 0.852 0.861 0.889 0.875\nbalance-scale 0.193 0.267 0.420 0.678 0.684\ncleveland 0.020 0.134 0.129 0.107 0.066\ncleveland_v2 0.009 0.183 0.233 0.191 0.056\ncmc 0.482 0.476 0.481 0.510 0.479\ndermatology 0.843 0.849 0.849 0.815 0.854\nglass 0.201 0.625 0.621 0.609 0.499\nhayes-roth 0.559 0.614 0.627 0.611 0.611\nnew_ecoli 0.814 0.775 0.807 0.817 0.824\nnew_led7digit 0.757 0.441 0.727 0.746 0.774\nnew_vehicle 0.849 0.863 0.859 0.821 0.852\nnew_winequality-red 0.101 0.382 0.393 0.380 0.175\nnew_yeast 0.262 0.378 0.395 0.406 0.321\nthyroid-newthyroid 0.821 0.936 0.920 0.899 0.902", - "text/html": "
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baseglobalsmotesoupmdo
1czysty-cut0.9710.9750.9780.9470.977
2delikatne-cut0.7040.7600.7610.7890.801
3mocniej-cut0.4660.5230.4980.5560.599
4delikatne-bezover-cut0.8120.8520.8610.8890.875
balance-scale0.1930.2670.4200.6780.684
cleveland0.0200.1340.1290.1070.066
cleveland_v20.0090.1830.2330.1910.056
cmc0.4820.4760.4810.5100.479
dermatology0.8430.8490.8490.8150.854
glass0.2010.6250.6210.6090.499
hayes-roth0.5590.6140.6270.6110.611
new_ecoli0.8140.7750.8070.8170.824
new_led7digit0.7570.4410.7270.7460.774
new_vehicle0.8490.8630.8590.8210.852
new_winequality-red0.1010.3820.3930.3800.175
new_yeast0.2620.3780.3950.4060.321
thyroid-newthyroid0.8210.9360.9200.8990.902
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoup 0.633588\nsmote 0.621118\nmdo 0.608765\nglobal 0.590176\nbase 0.521412", - "text/html": "
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Mean G-mean
soup0.633588
smote0.621118
mdo0.608765
global0.590176
base0.521412
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsmote 2.382353\nmdo 2.558824\nsoup 2.794118\nglobal 2.911765\nbase 4.352941", - "text/html": "
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Mean rank
smote2.382353
mdo2.558824
soup2.794118
global2.911765
base4.352941
\n
" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "score = provide_test_and_get_scores(datasets, 'knn')\n", "print_scores(score)\n" @@ -392,7 +322,7 @@ "collapsed": false, "pycharm": { "name": "#%%\n", - "is_executing": false + "is_executing": true } } }, @@ -407,40 +337,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "'G-MEAN'" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " base global smote soup mdo\nbalance-scale 0.193 0.267 0.420 0.678 0.684\ncleveland 0.020 0.134 0.129 0.107 0.066\ncleveland_v2 0.009 0.183 0.233 0.191 0.056\ncmc 0.482 0.476 0.481 0.510 0.479\ndermatology 0.843 0.849 0.849 0.815 0.854\nglass 0.201 0.625 0.621 0.609 0.499\nhayes-roth 0.559 0.614 0.627 0.611 0.611\nnew_ecoli 0.814 0.775 0.807 0.817 0.824\nnew_led7digit 0.757 0.441 0.727 0.746 0.774\nnew_vehicle 0.849 0.863 0.859 0.821 0.852\nnew_winequality-red 0.101 0.382 0.393 0.380 0.175\nnew_yeast 0.262 0.378 0.395 0.406 0.321\nthyroid-newthyroid 0.821 0.936 0.920 0.899 0.902", - "text/html": "
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baseglobalsmotesoupmdo
balance-scale0.1930.2670.4200.6780.684
cleveland0.0200.1340.1290.1070.066
cleveland_v20.0090.1830.2330.1910.056
cmc0.4820.4760.4810.5100.479
dermatology0.8430.8490.8490.8150.854
glass0.2010.6250.6210.6090.499
hayes-roth0.5590.6140.6270.6110.611
new_ecoli0.8140.7750.8070.8170.824
new_led7digit0.7570.4410.7270.7460.774
new_vehicle0.8490.8630.8590.8210.852
new_winequality-red0.1010.3820.3930.3800.175
new_yeast0.2620.3780.3950.4060.321
thyroid-newthyroid0.8210.9360.9200.8990.902
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoup 0.583846\nsmote 0.573923\nmdo 0.545923\nglobal 0.532538\nbase 0.454692", - "text/html": "
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Mean G-mean
soup0.583846
smote0.573923
mdo0.545923
global0.532538
base0.454692
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsmote 2.269231\nglobal 2.730769\nsoup 2.884615\nmdo 2.884615\nbase 4.230769", - "text/html": "
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Mean rank
smote2.269231
global2.730769
soup2.884615
mdo2.884615
base4.230769
\n
" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "print_scores(score,only_read_dt=True)\n" ], @@ -448,7 +346,7 @@ "collapsed": false, "pycharm": { "name": "#%%\n", - "is_executing": false + "is_executing": true } } } diff --git a/examples/resampling/.ipynb_checkpoints/MDO-checkpoint.ipynb b/examples/resampling/.ipynb_checkpoints/MDO-checkpoint.ipynb new file mode 100644 index 0000000..f0bc551 --- /dev/null +++ b/examples/resampling/.ipynb_checkpoints/MDO-checkpoint.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unzip datasets and prepare data:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "odict_keys(['1czysty-cut', '2delikatne-cut', '3mocniej-cut', '4delikatne-bezover-cut', 'balance-scale', 'cleveland', 'cleveland_v2', 'cmc', 'dermatology', 'glass', 'hayes-roth', 'new_ecoli', 'new_led7digit', 'new_vehicle', 'new_winequality-red', 'new_yeast', 'thyroid-newthyroid'])\n", + "[[0.49 0.29 0.48 0.5 0.56 0.24 0.35]\n", + " [0.07 0.4 0.48 0.5 0.54 0.35 0.44]\n", + " [0.56 0.4 0.48 0.5 0.49 0.37 0.46]\n", + " [0.59 0.49 0.48 0.5 0.52 0.45 0.36]\n", + " [0.23 0.32 0.48 0.5 0.55 0.25 0.35]]\n", + "[0 0 0 0 0]\n" + ] + } + ], + "source": [ + "from collections import Counter\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "from multi_imbalance.datasets import load_datasets\n", + "from multi_imbalance.resampling.MDO import MDO\n", + "\n", + "from multi_imbalance.utils.data import construct_flat_2pc_df\n", + "\n", + "%matplotlib inline\n", + "sns.set_style('darkgrid')\n", + "\n", + "\n", + "dataset = load_datasets()\n", + "print(dataset.keys())\n", + "dataset = dataset['new_ecoli']\n", + "X, y = dataset.data, dataset.target\n", + "print(X[:5])\n", + "print(y[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Resample data using MDO algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "clf = MDO(k1_frac=0)\n", + "resampled_X, resampled_y = clf.fit_transform(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare results by plotting data in 2 dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"from benchmarks.resample.SOUPBagging import SOUPBagging\n", "\n", "from multi_imbalance.datasets import load_datasets\n", "from multi_imbalance.resampling.SOUP import SOUP\n", @@ -21,6 +20,7 @@ "\n", "from imblearn.metrics import geometric_mean_score\n", "from imblearn.over_sampling import SMOTE\n", + "from multi_imbalance.resampling.SOUPBagging import SOUPBagging\n", "from multi_imbalance.resampling.spider import SPIDER3\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", @@ -245,18 +245,7 @@ "text": [ "\r 0%| | 0/17 [00:00 Date: Tue, 26 Nov 2019 13:38:04 +0100 Subject: [PATCH 05/21] added docs --- benchmarks/resample/resample.ipynb | 106 ++++++++++++++++++++-- multi_imbalance/resampling/SOUPBagging.py | 44 +++++++-- 2 files changed, 136 insertions(+), 14 deletions(-) diff --git a/benchmarks/resample/resample.ipynb b/benchmarks/resample/resample.ipynb index 27a06c8..2bfc58f 100644 --- a/benchmarks/resample/resample.ipynb +++ b/benchmarks/resample/resample.ipynb @@ -12,6 +12,7 @@ "from IPython.core.display import display\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.tree import DecisionTreeClassifier\n", + "from benchmarks.resample.SOUPBagging import SOUPBagging\n", "\n", "from multi_imbalance.datasets import load_datasets\n", "from multi_imbalance.resampling.SOUP import SOUP\n", @@ -20,7 +21,6 @@ "\n", "from imblearn.metrics import geometric_mean_score\n", "from imblearn.over_sampling import SMOTE\n", - "from multi_imbalance.resampling.SOUPBagging import SOUPBagging\n", "from multi_imbalance.resampling.spider import SPIDER3\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", @@ -238,16 +238,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "outputs": [ { "name": "stderr", "text": [ "\r 0%| | 0/17 [00:00\n\n\n \n \n \n \n \n \n \n
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "print_scores(score,only_read_dt=True)\n" ], @@ -285,7 +365,7 @@ "collapsed": false, "pycharm": { "name": "#%%\n", - "is_executing": true + "is_executing": false } } }, @@ -302,7 +382,17 @@ { "cell_type": "code", "execution_count": null, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "text": [ + "\r 0%| | 0/17 [00:00 Date: Thu, 28 Nov 2019 00:06:47 +0100 Subject: [PATCH 06/21] parallel soup bagging --- benchmarks/resample/resample.ipynb | 107 ++-------------------- multi_imbalance/resampling/SOUPBagging.py | 39 +++++--- 2 files changed, 33 insertions(+), 113 deletions(-) diff --git a/benchmarks/resample/resample.ipynb b/benchmarks/resample/resample.ipynb index 2bfc58f..9034c4f 100644 --- a/benchmarks/resample/resample.ipynb +++ b/benchmarks/resample/resample.ipynb @@ -12,7 +12,6 @@ "from IPython.core.display import display\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.tree import DecisionTreeClassifier\n", - "from benchmarks.resample.SOUPBagging import SOUPBagging\n", "\n", "from multi_imbalance.datasets import load_datasets\n", "from multi_imbalance.resampling.SOUP import SOUP\n", @@ -21,6 +20,7 @@ "\n", "from imblearn.metrics import geometric_mean_score\n", "from imblearn.over_sampling import SMOTE\n", + "from multi_imbalance.resampling.SOUPBagging import SOUPBagging\n", "from multi_imbalance.resampling.spider import SPIDER3\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", @@ -238,64 +238,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "outputs": [ { "name": "stderr", "text": [ "\r 0%| | 0/17 [00:00\n\n\n \n \n \n \n \n \n \n
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "print_scores(score,only_read_dt=True)\n" ], @@ -365,7 +284,7 @@ "collapsed": false, "pycharm": { "name": "#%%\n", - "is_executing": false + "is_executing": true } } }, @@ -382,17 +301,7 @@ { "cell_type": "code", "execution_count": null, - "outputs": [ - { - "name": "stderr", - "text": [ - "\r 0%| | 0/17 [00:00 Date: Sun, 1 Dec 2019 13:20:20 +0100 Subject: [PATCH 07/21] Added benchamrks with soup bagging --- benchmarks/resample/resample.ipynb | 2156 +++++++++++++++++++-- multi_imbalance/resampling/SOUPBagging.py | 20 +- 2 files changed, 2047 insertions(+), 129 deletions(-) diff --git a/benchmarks/resample/resample.ipynb b/benchmarks/resample/resample.ipynb index 9034c4f..f298610 100644 --- a/benchmarks/resample/resample.ipynb +++ b/benchmarks/resample/resample.ipynb @@ -2,13 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, "outputs": [], "source": [ - "from collections import Counter, defaultdict\n", + "from collections import defaultdict\n", "import numpy as np\n", "import pandas as pd\n", - "import tqdm as tqdm\n", + "from tqdm import tqdm\n", "from IPython.core.display import display\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -78,18 +84,17 @@ "}\n", "from IPython.display import clear_output\n", "clear_output(wait=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def resample_data(resample, seed, X_train, y_train, no_classes, dataset_name):\n", @@ -112,7 +117,7 @@ " np.fill_diagonal(cost, 0)\n", " clf = SPIDER3(k=5, cost=cost, majority_classes=maj_int_min[dataset_name]['maj'], intermediate_classes=maj_int_min[dataset_name]['int'], minority_classes=maj_int_min[dataset_name]['min'])\n", " X_train_resampled, y_train_resampled = clf.fit_transform(X_train.astype(np.float64), y_train)\n", - " elif resample=='soupbagging':\n", + " elif 'soupbg' in resample:\n", " # SOUP Bagging does it by itself\n", " X_train_resampled, y_train_resampled = X_train, y_train\n", " return X_train_resampled, y_train_resampled\n", @@ -136,8 +141,21 @@ " elif classifier == 'tree':\n", " clf = DecisionTreeClassifier(random_state=i)\n", " \n", - " if res == 'soupbagging':\n", - " vote_classifier = SOUPBagging(clf, n_classifiers=5, seed=i)\n", + " # DONT JUDGE ME\n", + " if res == 'soupbg005':\n", + " vote_classifier = SOUPBagging(clf, n_classifiers=5)\n", + " clf = vote_classifier\n", + " elif res == 'soupbg015':\n", + " vote_classifier = SOUPBagging(clf, n_classifiers=15)\n", + " clf = vote_classifier\n", + " elif res == 'soupbg030':\n", + " vote_classifier = SOUPBagging(clf, n_classifiers=30)\n", + " clf = vote_classifier\n", + " elif res == 'soupbg050':\n", + " vote_classifier = SOUPBagging(clf, n_classifiers=50)\n", + " clf = vote_classifier\n", + " elif res == 'soupbg100':\n", + " vote_classifier = SOUPBagging(clf, n_classifiers=100)\n", " clf = vote_classifier\n", " \n", " clf.fit(X_train_resampled, y_train_resampled)\n", @@ -153,18 +171,11 @@ " \n", " result_data['g_mean'] = get_score_from_metric(run_data, 'g_mean')\n", " return result_data\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": { "collapsed": true, "pycharm": { @@ -176,17 +187,23 @@ "source": [ "def provide_test_and_get_scores(datasets, clf):\n", " scores = defaultdict(dict)\n", - " for dataset_name, dataset_values in tqdm.tqdm(datasets.items(),total=len(datasets)):\n", - " clf_res_names =['base','global','smote','soup','soupbagging','mdo']\n", - " # print(dataset_name)\n", + " for dataset_name, dataset_values in tqdm(datasets.items(),total=len(d`atasets)):\n", + " clf_res_names =['base','global','smote','mdo','soup','soupbg005','soupbg015','soupbg030', 'soupbg050', 'soupbg100']\n", " for resample in clf_res_names:\n", " result_data = test_resampling(clf, resample, dataset_values, dataset_name)\n", - " scores[dataset_name][resample] = round(result_data['g_mean'],3)\n" + " scores[dataset_name][resample] = round(result_data['g_mean'],3)\n", + " return scores" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def print_scores(scores, only_read_dt = False):\n", @@ -199,144 +216,2057 @@ " # df.fillna(df.median(), inplace=True)\n", " display(pd.DataFrame(df.mean().sort_values(ascending=False),columns=['Mean G-mean']))\n", " display(pd.DataFrame(df.rank(axis=1,ascending=False).mean().sort_values(),columns=['Mean rank']))" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + ] }, { "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "datasets = load_datasets()\n" - ], + "execution_count": 24, "metadata": { - "collapsed": false, "pycharm": { - "name": "#%%\n", - "is_executing": false + "is_executing": false, + "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "datasets = load_datasets()\n" + ] }, { "cell_type": "markdown", - "source": [ - "Testy dla drzewa,\n", - 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dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
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new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
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" + ], + "text/plain": [ + " Mean rank\n", + "soupbg100 2.294118\n", + "soupbg050 2.852941\n", + "soupbg030 3.764706\n", + "soupbg015 5.294118\n", + "soup 5.352941\n", + "smote 5.558824\n", + "mdo 5.647059\n", + "soupbg005 7.705882\n", + "global 8.176471\n", + "base 8.352941" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "score = provide_test_and_get_scores(datasets, 'tree')\n", "print_scores(score)\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": true - } - } + ] }, { "cell_type": "markdown", - "source": [ - "Drzewo, tylko rzeczywiste zbiory danych:" - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Drzewo, tylko rzeczywiste zbiory danych:" + ] }, { "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "print_scores(score,only_read_dt=True)\n" - ], + "execution_count": 26, "metadata": { - "collapsed": false, "pycharm": { - "name": "#%%\n", - "is_executing": true + "is_executing": false, + "name": "#%%\n" } - } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'G-MEAN'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
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" + ], + "text/plain": [ + " base global smote mdo soup soupbg005 soupbg015 \\\n", + "balance-scale 0.154 0.123 0.168 0.162 0.621 0.559 0.598 \n", + "cleveland 0.127 0.096 0.142 0.098 0.139 0.128 0.113 \n", + "cleveland_v2 0.113 0.110 0.129 0.090 0.162 0.150 0.183 \n", + "cmc 0.440 0.451 0.444 0.439 0.466 0.477 0.489 \n", + "dermatology 0.925 0.940 0.946 0.948 0.933 0.910 0.935 \n", + "glass 0.463 0.486 0.554 0.598 0.606 0.590 0.631 \n", + "hayes-roth 0.837 0.843 0.841 0.842 0.835 0.771 0.815 \n", + "new_ecoli 0.708 0.707 0.723 0.758 0.714 0.701 0.728 \n", + "new_led7digit 0.754 0.757 0.762 0.753 0.760 0.750 0.767 \n", + "new_vehicle 0.900 0.894 0.890 0.899 0.886 0.879 0.897 \n", + "new_winequality-red 0.429 0.407 0.466 0.465 0.437 0.403 0.435 \n", + "new_yeast 0.250 0.240 0.323 0.293 0.290 0.282 0.320 \n", + "thyroid-newthyroid 0.900 0.901 0.918 0.936 0.915 0.901 0.905 \n", + "\n", + " soupbg030 soupbg050 soupbg100 \n", + "balance-scale 0.612 0.642 0.648 \n", + "cleveland 0.129 0.123 0.133 \n", + "cleveland_v2 0.191 0.208 0.186 \n", + "cmc 0.503 0.504 0.508 \n", + "dermatology 0.951 0.959 0.962 \n", + "glass 0.646 0.645 0.654 \n", + "hayes-roth 0.838 0.838 0.836 \n", + "new_ecoli 0.725 0.734 0.735 \n", + "new_led7digit 0.767 0.768 0.768 \n", + "new_vehicle 0.906 0.910 0.912 \n", + "new_winequality-red 0.461 0.472 0.490 \n", + "new_yeast 0.316 0.319 0.317 \n", + "thyroid-newthyroid 0.901 0.910 0.914 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean G-mean
soupbg1000.620231
soupbg0500.617846
soupbg0300.611231
soupbg0150.601231
soup0.597231
soupbg0050.577000
smote0.562000
mdo0.560077
base0.538462
global0.535000
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" + ], + "text/plain": [ + " Mean G-mean\n", + "soupbg100 0.620231\n", + "soupbg050 0.617846\n", + "soupbg030 0.611231\n", + "soupbg015 0.601231\n", + "soup 0.597231\n", + "soupbg005 0.577000\n", + "smote 0.562000\n", + "mdo 0.560077\n", + "base 0.538462\n", + "global 0.535000" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean rank
soupbg1002.346154
soupbg0502.923077
soupbg0304.000000
smote4.923077
soupbg0155.346154
mdo5.769231
soup5.769231
global7.846154
base8.000000
soupbg0058.076923
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" + ], + "text/plain": [ + " Mean rank\n", + "soupbg100 2.346154\n", + "soupbg050 2.923077\n", + "soupbg030 4.000000\n", + "smote 4.923077\n", + "soupbg015 5.346154\n", + "mdo 5.769231\n", + "soup 5.769231\n", + "global 7.846154\n", + "base 8.000000\n", + "soupbg005 8.076923" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_scores(score,only_read_dt=True)\n" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Testy dla knn,\n", "Wszystkie zbiory danych:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "score = provide_test_and_get_scores(datasets, 'knn')\n", - "print_scores(score)\n" - ], + "execution_count": 27, "metadata": { - "collapsed": false, "pycharm": { - "name": "#%%\n", - "is_executing": true + "is_executing": false, + "name": "#%%\n" } - } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\r", + " 0%| | 0/17 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9710.9750.9780.9770.9470.9530.9520.9530.9530.953
2delikatne-cut0.7040.7600.7610.8010.7890.7920.7940.7920.7910.789
3mocniej-cut0.4660.5230.4980.5990.5560.5600.5560.5540.5580.545
4delikatne-bezover-cut0.8120.8520.8610.8750.8890.8910.8910.8910.8900.890
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
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Mean G-mean
soupbg0500.639882
soupbg1000.639235
soupbg0300.637353
soup0.633471
soupbg0150.632824
smote0.621118
soupbg0050.616824
mdo0.608765
global0.590176
base0.521412
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" + ], + "text/plain": [ + " Mean G-mean\n", + "soupbg050 0.639882\n", + "soupbg100 0.639235\n", + "soupbg030 0.637353\n", + "soup 0.633471\n", + "soupbg015 0.632824\n", + "smote 0.621118\n", + "soupbg005 0.616824\n", + "mdo 0.608765\n", + "global 0.590176\n", + "base 0.521412" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean rank
soupbg0504.294118
soupbg1004.323529
soupbg0304.352941
smote4.794118
mdo5.294118
soupbg0155.529412
global5.676471
soup5.882353
soupbg0056.676471
base8.176471
\n", + "
" + ], + "text/plain": [ + " Mean rank\n", + "soupbg050 4.294118\n", + "soupbg100 4.323529\n", + "soupbg030 4.352941\n", + "smote 4.794118\n", + "mdo 5.294118\n", + "soupbg015 5.529412\n", + "global 5.676471\n", + "soup 5.882353\n", + "soupbg005 6.676471\n", + "base 8.176471" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "score = provide_test_and_get_scores(datasets, 'knn')\n", + "print_scores(score)\n" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "knn (k=5), tylko rzeczywiste zbiory danych:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "print_scores(score,only_read_dt=True)\n" - ], + "execution_count": 28, "metadata": { - "collapsed": false, "pycharm": { - "name": "#%%\n", - "is_executing": true + "is_executing": false, + "name": "#%%\n" } - } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'G-MEAN'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
\n", + "
" + ], + "text/plain": [ + " base global smote mdo soup soupbg005 soupbg015 \\\n", + "balance-scale 0.193 0.267 0.420 0.684 0.687 0.628 0.672 \n", + "cleveland 0.020 0.134 0.129 0.066 0.107 0.129 0.103 \n", + "cleveland_v2 0.009 0.183 0.233 0.056 0.191 0.166 0.172 \n", + "cmc 0.482 0.476 0.481 0.479 0.506 0.488 0.513 \n", + "dermatology 0.843 0.849 0.849 0.854 0.817 0.789 0.809 \n", + "glass 0.201 0.625 0.621 0.499 0.609 0.553 0.603 \n", + "hayes-roth 0.559 0.614 0.627 0.611 0.601 0.562 0.618 \n", + "new_ecoli 0.814 0.775 0.807 0.824 0.817 0.801 0.810 \n", + "new_led7digit 0.757 0.441 0.727 0.774 0.746 0.753 0.758 \n", + "new_vehicle 0.849 0.863 0.859 0.852 0.822 0.810 0.820 \n", + "new_winequality-red 0.101 0.382 0.393 0.175 0.380 0.360 0.375 \n", + "new_yeast 0.262 0.378 0.395 0.321 0.406 0.350 0.403 \n", + "thyroid-newthyroid 0.821 0.936 0.920 0.902 0.899 0.901 0.909 \n", + "\n", + " soupbg030 soupbg050 soupbg100 \n", + "balance-scale 0.691 0.706 0.707 \n", + "cleveland 0.113 0.104 0.112 \n", + "cleveland_v2 0.189 0.205 0.186 \n", + "cmc 0.515 0.519 0.519 \n", + "dermatology 0.821 0.821 0.824 \n", + "glass 0.599 0.613 0.616 \n", + "hayes-roth 0.627 0.639 0.641 \n", + "new_ecoli 0.820 0.820 0.828 \n", + "new_led7digit 0.758 0.756 0.756 \n", + "new_vehicle 0.824 0.824 0.825 \n", + "new_winequality-red 0.376 0.379 0.383 \n", + "new_yeast 0.397 0.397 0.396 \n", + "thyroid-newthyroid 0.915 0.903 0.897 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean G-mean
soupbg1000.591538
soupbg0500.591231
soupbg0300.588077
soup0.583692
soupbg0150.581923
smote0.573923
soupbg0050.560769
mdo0.545923
global0.532538
base0.454692
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" + ], + "text/plain": [ + " Mean G-mean\n", + "soupbg100 0.591538\n", + "soupbg050 0.591231\n", + "soupbg030 0.588077\n", + "soup 0.583692\n", + "soupbg015 0.581923\n", + "smote 0.573923\n", + "soupbg005 0.560769\n", + "mdo 0.545923\n", + "global 0.532538\n", + "base 0.454692" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean rank
soupbg1003.769231
soupbg0504.153846
smote4.269231
soupbg0304.307692
global5.192308
soup5.615385
soupbg0155.884615
mdo6.076923
soupbg0057.653846
base8.076923
\n", + "
" + ], + "text/plain": [ + " Mean rank\n", + "soupbg100 3.769231\n", + "soupbg050 4.153846\n", + "smote 4.269231\n", + "soupbg030 4.307692\n", + "global 5.192308\n", + "soup 5.615385\n", + "soupbg015 5.884615\n", + "mdo 6.076923\n", + "soupbg005 7.653846\n", + "base 8.076923" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_scores(score,only_read_dt=True)" + ] } ], "metadata": { @@ -348,25 +2278,25 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.7.3" }, "pycharm": { "stem_cell": { "cell_type": "raw", - "source": [], "metadata": { "collapsed": false - } + }, + "source": [] } } }, "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "nbformat_minor": 1 +} diff --git a/multi_imbalance/resampling/SOUPBagging.py b/multi_imbalance/resampling/SOUPBagging.py index 5ff6739..6d8b487 100644 --- a/multi_imbalance/resampling/SOUPBagging.py +++ b/multi_imbalance/resampling/SOUPBagging.py @@ -2,28 +2,24 @@ from copy import deepcopy import numpy as np -from imblearn.metrics import geometric_mean_score -from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.utils import resample -from multi_imbalance.datasets import load_datasets from multi_imbalance.resampling.SOUP import SOUP def fit_clf(args): - clf, X, y, seed = args - x_sampled, y_sampled = resample(X, y, stratify=y, random_state=seed) + clf, X, y = args + x_sampled, y_sampled = resample(X, y, stratify=y) x_resampled, y_resampled = SOUP().fit_transform(x_sampled, y_sampled) clf.fit(x_resampled, y_resampled) return clf class SOUPBagging(object): - def __init__(self, classifier=None, n_classifiers=30, seed=0): + def __init__(self, classifier=None, n_classifiers=30): self.classifiers = list() self.n_classifiers = n_classifiers self.classes = None - self.random_state = seed for _ in range(n_classifiers): if classifier is not None: self.classifiers.append(deepcopy(classifier)) @@ -42,7 +38,7 @@ def fit(self, X, y): NUM_CORE = 4 # set to the number of cores you want to use pool = multiprocessing.Pool(NUM_CORE) - self.classifiers = pool.map(fit_clf, [(clf, X, y, self.random_state) for clf in self.classifiers]) + self.classifiers = pool.map(fit_clf, [(clf, X, y) for clf in self.classifiers]) pool.close() pool.join() @@ -75,11 +71,3 @@ def predict_proba(self, X): p = np.sum(results, axis=0) return p - - -# dataset = load_datasets()['new_ecoli'] -# -# X, y = dataset.data, dataset.target -# clf = SOUPBagging() -# X_train, X_test, y_train, y_test = train_test_split(X, y) -# clf.fit(X_train, y_train) From aba7982328beed1e2758ab4fc065bfb7b58c548a Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Sun, 1 Dec 2019 13:32:45 +0100 Subject: [PATCH 08/21] Added notebook for soup bagging --- benchmarks/resample/resample.ipynb | 2143 +++----------------- multi_imbalance/ensemble/SOUPBagging.ipynb | 144 ++ multi_imbalance/resampling/SOUPBagging.py | 14 +- 3 files changed, 388 insertions(+), 1913 deletions(-) create mode 100644 multi_imbalance/ensemble/SOUPBagging.ipynb diff --git a/benchmarks/resample/resample.ipynb b/benchmarks/resample/resample.ipynb index f298610..1c6c321 100644 --- a/benchmarks/resample/resample.ipynb +++ b/benchmarks/resample/resample.ipynb @@ -3,12 +3,6 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [], "source": [ "from collections import defaultdict\n", @@ -30,6 +24,7 @@ "from multi_imbalance.resampling.spider import SPIDER3\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", "maj_int_min = {\n", " 'balance_scale' : {\n", " 'maj': [2, 1],\n", @@ -84,17 +79,18 @@ "}\n", "from IPython.display import clear_output\n", "clear_output(wait=True)" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } }, { "cell_type": "code", "execution_count": 21, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [], "source": [ "def resample_data(resample, seed, X_train, y_train, no_classes, dataset_name):\n", @@ -171,7 +167,14 @@ " \n", " result_data['g_mean'] = get_score_from_metric(run_data, 'g_mean')\n", " return result_data\n" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } }, { "cell_type": "code", @@ -198,12 +201,6 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [], "source": [ "def print_scores(scores, only_read_dt = False):\n", @@ -216,585 +213,136 @@ " # df.fillna(df.median(), inplace=True)\n", " display(pd.DataFrame(df.mean().sort_values(ascending=False),columns=['Mean G-mean']))\n", " display(pd.DataFrame(df.rank(axis=1,ascending=False).mean().sort_values(),columns=['Mean rank']))" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } }, { "cell_type": "code", "execution_count": 24, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [], "source": [ "datasets = load_datasets()\n" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } }, { "cell_type": "markdown", + "source": [ + "Testy dla drzewa,\n", + "Wszystkie zbiory danych:" + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, - "source": [ - "Testy dla drzewa,\n", - "Wszystkie zbiory danych:" - ] + } }, { "cell_type": "code", "execution_count": 25, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [ { "name": "stderr", - "output_type": "stream", "text": [ - "\n", - "\n", - "\r", - " 0%| | 0/17 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9390.9460.9550.9650.9570.9440.9550.9560.9580.958
2delikatne-cut0.6980.6990.7440.7720.7950.7870.7930.8000.7980.802
3mocniej-cut0.4920.4820.4960.5850.5780.5900.6030.6110.6090.610
4delikatne-bezover-cut0.7710.7680.8150.8300.8940.8830.8870.8890.8920.891
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
\n", - "" - ], - "text/plain": [ - " base global smote mdo soup soupbg005 \\\n", - "1czysty-cut 0.939 0.946 0.955 0.965 0.957 0.944 \n", - "2delikatne-cut 0.698 0.699 0.744 0.772 0.795 0.787 \n", - "3mocniej-cut 0.492 0.482 0.496 0.585 0.578 0.590 \n", - "4delikatne-bezover-cut 0.771 0.768 0.815 0.830 0.894 0.883 \n", - "balance-scale 0.154 0.123 0.168 0.162 0.621 0.559 \n", - "cleveland 0.127 0.096 0.142 0.098 0.139 0.128 \n", - "cleveland_v2 0.113 0.110 0.129 0.090 0.162 0.150 \n", - "cmc 0.440 0.451 0.444 0.439 0.466 0.477 \n", - "dermatology 0.925 0.940 0.946 0.948 0.933 0.910 \n", - "glass 0.463 0.486 0.554 0.598 0.606 0.590 \n", - "hayes-roth 0.837 0.843 0.841 0.842 0.835 0.771 \n", - "new_ecoli 0.708 0.707 0.723 0.758 0.714 0.701 \n", - "new_led7digit 0.754 0.757 0.762 0.753 0.760 0.750 \n", - "new_vehicle 0.900 0.894 0.890 0.899 0.886 0.879 \n", - "new_winequality-red 0.429 0.407 0.466 0.465 0.437 0.403 \n", - "new_yeast 0.250 0.240 0.323 0.293 0.290 0.282 \n", - "thyroid-newthyroid 0.900 0.901 0.918 0.936 0.915 0.901 \n", - "\n", - " soupbg015 soupbg030 soupbg050 soupbg100 \n", - "1czysty-cut 0.955 0.956 0.958 0.958 \n", - "2delikatne-cut 0.793 0.800 0.798 0.802 \n", - "3mocniej-cut 0.603 0.611 0.609 0.610 \n", - "4delikatne-bezover-cut 0.887 0.889 0.892 0.891 \n", - "balance-scale 0.598 0.612 0.642 0.648 \n", - "cleveland 0.113 0.129 0.123 0.133 \n", - "cleveland_v2 0.183 0.191 0.208 0.186 \n", - "cmc 0.489 0.503 0.504 0.508 \n", - "dermatology 0.935 0.951 0.959 0.962 \n", - "glass 0.631 0.646 0.645 0.654 \n", - "hayes-roth 0.815 0.838 0.838 0.836 \n", - "new_ecoli 0.728 0.725 0.734 0.735 \n", - "new_led7digit 0.767 0.767 0.768 0.768 \n", - "new_vehicle 0.897 0.906 0.910 0.912 \n", - "new_winequality-red 0.435 0.461 0.472 0.490 \n", - "new_yeast 0.320 0.316 0.319 0.317 \n", - "thyroid-newthyroid 0.905 0.901 0.910 0.914 " - ] + "text/plain": " base global smote mdo soup soupbg005 \\\n1czysty-cut 0.939 0.946 0.955 0.965 0.957 0.944 \n2delikatne-cut 0.698 0.699 0.744 0.772 0.795 0.787 \n3mocniej-cut 0.492 0.482 0.496 0.585 0.578 0.590 \n4delikatne-bezover-cut 0.771 0.768 0.815 0.830 0.894 0.883 \nbalance-scale 0.154 0.123 0.168 0.162 0.621 0.559 \ncleveland 0.127 0.096 0.142 0.098 0.139 0.128 \ncleveland_v2 0.113 0.110 0.129 0.090 0.162 0.150 \ncmc 0.440 0.451 0.444 0.439 0.466 0.477 \ndermatology 0.925 0.940 0.946 0.948 0.933 0.910 \nglass 0.463 0.486 0.554 0.598 0.606 0.590 \nhayes-roth 0.837 0.843 0.841 0.842 0.835 0.771 \nnew_ecoli 0.708 0.707 0.723 0.758 0.714 0.701 \nnew_led7digit 0.754 0.757 0.762 0.753 0.760 0.750 \nnew_vehicle 0.900 0.894 0.890 0.899 0.886 0.879 \nnew_winequality-red 0.429 0.407 0.466 0.465 0.437 0.403 \nnew_yeast 0.250 0.240 0.323 0.293 0.290 0.282 \nthyroid-newthyroid 0.900 0.901 0.918 0.936 0.915 0.901 \n\n soupbg015 soupbg030 soupbg050 soupbg100 \n1czysty-cut 0.955 0.956 0.958 0.958 \n2delikatne-cut 0.793 0.800 0.798 0.802 \n3mocniej-cut 0.603 0.611 0.609 0.610 \n4delikatne-bezover-cut 0.887 0.889 0.892 0.891 \nbalance-scale 0.598 0.612 0.642 0.648 \ncleveland 0.113 0.129 0.123 0.133 \ncleveland_v2 0.183 0.191 0.208 0.186 \ncmc 0.489 0.503 0.504 0.508 \ndermatology 0.935 0.951 0.959 0.962 \nglass 0.631 0.646 0.645 0.654 \nhayes-roth 0.815 0.838 0.838 0.836 \nnew_ecoli 0.728 0.725 0.734 0.735 \nnew_led7digit 0.767 0.767 0.768 0.768 \nnew_vehicle 0.897 0.906 0.910 0.912 \nnew_winequality-red 0.435 0.461 0.472 0.490 \nnew_yeast 0.320 0.316 0.319 0.317 \nthyroid-newthyroid 0.905 0.901 0.910 0.914 ", + "text/html": "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9390.9460.9550.9650.9570.9440.9550.9560.9580.958
2delikatne-cut0.6980.6990.7440.7720.7950.7870.7930.8000.7980.802
3mocniej-cut0.4920.4820.4960.5850.5780.5900.6030.6110.6090.610
4delikatne-bezover-cut0.7710.7680.8150.8300.8940.8830.8870.8890.8920.891
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
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Mean G-mean
soupbg1000.666118
soupbg0500.664059
soupbg0300.658941
soupbg0150.650235
soup0.646353
soupbg0050.629706
mdo0.613706
smote0.606824
base0.582353
global0.579412
\n", - "
" - ], - "text/plain": [ - " Mean G-mean\n", - "soupbg100 0.666118\n", - "soupbg050 0.664059\n", - "soupbg030 0.658941\n", - "soupbg015 0.650235\n", - "soup 0.646353\n", - "soupbg005 0.629706\n", - "mdo 0.613706\n", - "smote 0.606824\n", - "base 0.582353\n", - "global 0.579412" - ] + "text/plain": " Mean G-mean\nsoupbg100 0.666118\nsoupbg050 0.664059\nsoupbg030 0.658941\nsoupbg015 0.650235\nsoup 0.646353\nsoupbg005 0.629706\nmdo 0.613706\nsmote 0.606824\nbase 0.582353\nglobal 0.579412", + "text/html": "
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Mean G-mean
soupbg1000.666118
soupbg0500.664059
soupbg0300.658941
soupbg0150.650235
soup0.646353
soupbg0050.629706
mdo0.613706
smote0.606824
base0.582353
global0.579412
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Mean rank
soupbg1002.294118
soupbg0502.852941
soupbg0303.764706
soupbg0155.294118
soup5.352941
smote5.558824
mdo5.647059
soupbg0057.705882
global8.176471
base8.352941
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" - ], - "text/plain": [ - " Mean rank\n", - "soupbg100 2.294118\n", - "soupbg050 2.852941\n", - "soupbg030 3.764706\n", - "soupbg015 5.294118\n", - "soup 5.352941\n", - "smote 5.558824\n", - "mdo 5.647059\n", - "soupbg005 7.705882\n", - "global 8.176471\n", - "base 8.352941" - ] + "text/plain": " Mean rank\nsoupbg100 2.294118\nsoupbg050 2.852941\nsoupbg030 3.764706\nsoupbg015 5.294118\nsoup 5.352941\nsmote 5.558824\nmdo 5.647059\nsoupbg005 7.705882\nglobal 8.176471\nbase 8.352941", + "text/html": "
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Mean rank
soupbg1002.294118
soupbg0502.852941
soupbg0303.764706
soupbg0155.294118
soup5.352941
smote5.558824
mdo5.647059
soupbg0057.705882
global8.176471
base8.352941
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" }, "metadata": {}, "output_type": "display_data" @@ -803,448 +351,58 @@ "source": [ "score = provide_test_and_get_scores(datasets, 'tree')\n", "print_scores(score)\n" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } }, { "cell_type": "markdown", + "source": [ + "Drzewo, tylko rzeczywiste zbiory danych:" + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, - "source": [ - "Drzewo, tylko rzeczywiste zbiory danych:" - ] + } }, { "cell_type": "code", "execution_count": 26, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [ { "data": { - "text/plain": [ - "'G-MEAN'" - ] + "text/plain": "'G-MEAN'" }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/html": [ - "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
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" - ], - "text/plain": [ - " base global smote mdo soup soupbg005 soupbg015 \\\n", - "balance-scale 0.154 0.123 0.168 0.162 0.621 0.559 0.598 \n", - "cleveland 0.127 0.096 0.142 0.098 0.139 0.128 0.113 \n", - "cleveland_v2 0.113 0.110 0.129 0.090 0.162 0.150 0.183 \n", - "cmc 0.440 0.451 0.444 0.439 0.466 0.477 0.489 \n", - "dermatology 0.925 0.940 0.946 0.948 0.933 0.910 0.935 \n", - "glass 0.463 0.486 0.554 0.598 0.606 0.590 0.631 \n", - "hayes-roth 0.837 0.843 0.841 0.842 0.835 0.771 0.815 \n", - "new_ecoli 0.708 0.707 0.723 0.758 0.714 0.701 0.728 \n", - "new_led7digit 0.754 0.757 0.762 0.753 0.760 0.750 0.767 \n", - "new_vehicle 0.900 0.894 0.890 0.899 0.886 0.879 0.897 \n", - "new_winequality-red 0.429 0.407 0.466 0.465 0.437 0.403 0.435 \n", - "new_yeast 0.250 0.240 0.323 0.293 0.290 0.282 0.320 \n", - "thyroid-newthyroid 0.900 0.901 0.918 0.936 0.915 0.901 0.905 \n", - "\n", - " soupbg030 soupbg050 soupbg100 \n", - "balance-scale 0.612 0.642 0.648 \n", - "cleveland 0.129 0.123 0.133 \n", - "cleveland_v2 0.191 0.208 0.186 \n", - "cmc 0.503 0.504 0.508 \n", - "dermatology 0.951 0.959 0.962 \n", - "glass 0.646 0.645 0.654 \n", - "hayes-roth 0.838 0.838 0.836 \n", - "new_ecoli 0.725 0.734 0.735 \n", - "new_led7digit 0.767 0.768 0.768 \n", - "new_vehicle 0.906 0.910 0.912 \n", - "new_winequality-red 0.461 0.472 0.490 \n", - "new_yeast 0.316 0.319 0.317 \n", - "thyroid-newthyroid 0.901 0.910 0.914 " - ] + "text/plain": " base global smote mdo soup soupbg005 soupbg015 \\\nbalance-scale 0.154 0.123 0.168 0.162 0.621 0.559 0.598 \ncleveland 0.127 0.096 0.142 0.098 0.139 0.128 0.113 \ncleveland_v2 0.113 0.110 0.129 0.090 0.162 0.150 0.183 \ncmc 0.440 0.451 0.444 0.439 0.466 0.477 0.489 \ndermatology 0.925 0.940 0.946 0.948 0.933 0.910 0.935 \nglass 0.463 0.486 0.554 0.598 0.606 0.590 0.631 \nhayes-roth 0.837 0.843 0.841 0.842 0.835 0.771 0.815 \nnew_ecoli 0.708 0.707 0.723 0.758 0.714 0.701 0.728 \nnew_led7digit 0.754 0.757 0.762 0.753 0.760 0.750 0.767 \nnew_vehicle 0.900 0.894 0.890 0.899 0.886 0.879 0.897 \nnew_winequality-red 0.429 0.407 0.466 0.465 0.437 0.403 0.435 \nnew_yeast 0.250 0.240 0.323 0.293 0.290 0.282 0.320 \nthyroid-newthyroid 0.900 0.901 0.918 0.936 0.915 0.901 0.905 \n\n soupbg030 soupbg050 soupbg100 \nbalance-scale 0.612 0.642 0.648 \ncleveland 0.129 0.123 0.133 \ncleveland_v2 0.191 0.208 0.186 \ncmc 0.503 0.504 0.508 \ndermatology 0.951 0.959 0.962 \nglass 0.646 0.645 0.654 \nhayes-roth 0.838 0.838 0.836 \nnew_ecoli 0.725 0.734 0.735 \nnew_led7digit 0.767 0.768 0.768 \nnew_vehicle 0.906 0.910 0.912 \nnew_winequality-red 0.461 0.472 0.490 \nnew_yeast 0.316 0.319 0.317 \nthyroid-newthyroid 0.901 0.910 0.914 ", + "text/html": "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
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" }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/html": [ - "
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Mean G-mean
soupbg1000.620231
soupbg0500.617846
soupbg0300.611231
soupbg0150.601231
soup0.597231
soupbg0050.577000
smote0.562000
mdo0.560077
base0.538462
global0.535000
\n", - "
" - ], - "text/plain": [ - " Mean G-mean\n", - "soupbg100 0.620231\n", - "soupbg050 0.617846\n", - "soupbg030 0.611231\n", - "soupbg015 0.601231\n", - "soup 0.597231\n", - "soupbg005 0.577000\n", - "smote 0.562000\n", - "mdo 0.560077\n", - "base 0.538462\n", - "global 0.535000" - ] + "text/plain": " Mean G-mean\nsoupbg100 0.620231\nsoupbg050 0.617846\nsoupbg030 0.611231\nsoupbg015 0.601231\nsoup 0.597231\nsoupbg005 0.577000\nsmote 0.562000\nmdo 0.560077\nbase 0.538462\nglobal 0.535000", + "text/html": "
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Mean G-mean
soupbg1000.620231
soupbg0500.617846
soupbg0300.611231
soupbg0150.601231
soup0.597231
soupbg0050.577000
smote0.562000
mdo0.560077
base0.538462
global0.535000
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" }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/html": [ - "
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Mean rank
soupbg1002.346154
soupbg0502.923077
soupbg0304.000000
smote4.923077
soupbg0155.346154
mdo5.769231
soup5.769231
global7.846154
base8.000000
soupbg0058.076923
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" - ], - "text/plain": [ - " Mean rank\n", - "soupbg100 2.346154\n", - "soupbg050 2.923077\n", - "soupbg030 4.000000\n", - "smote 4.923077\n", - "soupbg015 5.346154\n", - "mdo 5.769231\n", - "soup 5.769231\n", - "global 7.846154\n", - "base 8.000000\n", - "soupbg005 8.076923" - ] + "text/plain": " Mean rank\nsoupbg100 2.346154\nsoupbg050 2.923077\nsoupbg030 4.000000\nsmote 4.923077\nsoupbg015 5.346154\nmdo 5.769231\nsoup 5.769231\nglobal 7.846154\nbase 8.000000\nsoupbg005 8.076923", + "text/html": "
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Mean rank
soupbg1002.346154
soupbg0502.923077
soupbg0304.000000
smote4.923077
soupbg0155.346154
mdo5.769231
soup5.769231
global7.846154
base8.000000
soupbg0058.076923
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" }, "metadata": {}, "output_type": "display_data" @@ -1252,567 +410,118 @@ ], "source": [ "print_scores(score,only_read_dt=True)\n" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } }, { "cell_type": "markdown", - "metadata": {}, "source": [ "Testy dla knn,\n", "Wszystkie zbiory danych:" - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", "execution_count": 27, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [ { "name": "stderr", - "output_type": "stream", "text": [ - "\n", - "\n", - "\r", - " 0%| | 0/17 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9710.9750.9780.9770.9470.9530.9520.9530.9530.953
2delikatne-cut0.7040.7600.7610.8010.7890.7920.7940.7920.7910.789
3mocniej-cut0.4660.5230.4980.5990.5560.5600.5560.5540.5580.545
4delikatne-bezover-cut0.8120.8520.8610.8750.8890.8910.8910.8910.8900.890
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
\n", - "" - ], - "text/plain": [ - " base global smote mdo soup soupbg005 \\\n", - "1czysty-cut 0.971 0.975 0.978 0.977 0.947 0.953 \n", - "2delikatne-cut 0.704 0.760 0.761 0.801 0.789 0.792 \n", - "3mocniej-cut 0.466 0.523 0.498 0.599 0.556 0.560 \n", - "4delikatne-bezover-cut 0.812 0.852 0.861 0.875 0.889 0.891 \n", - "balance-scale 0.193 0.267 0.420 0.684 0.687 0.628 \n", - "cleveland 0.020 0.134 0.129 0.066 0.107 0.129 \n", - "cleveland_v2 0.009 0.183 0.233 0.056 0.191 0.166 \n", - "cmc 0.482 0.476 0.481 0.479 0.506 0.488 \n", - "dermatology 0.843 0.849 0.849 0.854 0.817 0.789 \n", - "glass 0.201 0.625 0.621 0.499 0.609 0.553 \n", - "hayes-roth 0.559 0.614 0.627 0.611 0.601 0.562 \n", - "new_ecoli 0.814 0.775 0.807 0.824 0.817 0.801 \n", - "new_led7digit 0.757 0.441 0.727 0.774 0.746 0.753 \n", - "new_vehicle 0.849 0.863 0.859 0.852 0.822 0.810 \n", - "new_winequality-red 0.101 0.382 0.393 0.175 0.380 0.360 \n", - "new_yeast 0.262 0.378 0.395 0.321 0.406 0.350 \n", - "thyroid-newthyroid 0.821 0.936 0.920 0.902 0.899 0.901 \n", - "\n", - " soupbg015 soupbg030 soupbg050 soupbg100 \n", - "1czysty-cut 0.952 0.953 0.953 0.953 \n", - "2delikatne-cut 0.794 0.792 0.791 0.789 \n", - "3mocniej-cut 0.556 0.554 0.558 0.545 \n", - "4delikatne-bezover-cut 0.891 0.891 0.890 0.890 \n", - "balance-scale 0.672 0.691 0.706 0.707 \n", - "cleveland 0.103 0.113 0.104 0.112 \n", - "cleveland_v2 0.172 0.189 0.205 0.186 \n", - "cmc 0.513 0.515 0.519 0.519 \n", - "dermatology 0.809 0.821 0.821 0.824 \n", - "glass 0.603 0.599 0.613 0.616 \n", - "hayes-roth 0.618 0.627 0.639 0.641 \n", - "new_ecoli 0.810 0.820 0.820 0.828 \n", - "new_led7digit 0.758 0.758 0.756 0.756 \n", - "new_vehicle 0.820 0.824 0.824 0.825 \n", - "new_winequality-red 0.375 0.376 0.379 0.383 \n", - "new_yeast 0.403 0.397 0.397 0.396 \n", - "thyroid-newthyroid 0.909 0.915 0.903 0.897 " - ] + "text/plain": " base global smote mdo soup soupbg005 \\\n1czysty-cut 0.971 0.975 0.978 0.977 0.947 0.953 \n2delikatne-cut 0.704 0.760 0.761 0.801 0.789 0.792 \n3mocniej-cut 0.466 0.523 0.498 0.599 0.556 0.560 \n4delikatne-bezover-cut 0.812 0.852 0.861 0.875 0.889 0.891 \nbalance-scale 0.193 0.267 0.420 0.684 0.687 0.628 \ncleveland 0.020 0.134 0.129 0.066 0.107 0.129 \ncleveland_v2 0.009 0.183 0.233 0.056 0.191 0.166 \ncmc 0.482 0.476 0.481 0.479 0.506 0.488 \ndermatology 0.843 0.849 0.849 0.854 0.817 0.789 \nglass 0.201 0.625 0.621 0.499 0.609 0.553 \nhayes-roth 0.559 0.614 0.627 0.611 0.601 0.562 \nnew_ecoli 0.814 0.775 0.807 0.824 0.817 0.801 \nnew_led7digit 0.757 0.441 0.727 0.774 0.746 0.753 \nnew_vehicle 0.849 0.863 0.859 0.852 0.822 0.810 \nnew_winequality-red 0.101 0.382 0.393 0.175 0.380 0.360 \nnew_yeast 0.262 0.378 0.395 0.321 0.406 0.350 \nthyroid-newthyroid 0.821 0.936 0.920 0.902 0.899 0.901 \n\n soupbg015 soupbg030 soupbg050 soupbg100 \n1czysty-cut 0.952 0.953 0.953 0.953 \n2delikatne-cut 0.794 0.792 0.791 0.789 \n3mocniej-cut 0.556 0.554 0.558 0.545 \n4delikatne-bezover-cut 0.891 0.891 0.890 0.890 \nbalance-scale 0.672 0.691 0.706 0.707 \ncleveland 0.103 0.113 0.104 0.112 \ncleveland_v2 0.172 0.189 0.205 0.186 \ncmc 0.513 0.515 0.519 0.519 \ndermatology 0.809 0.821 0.821 0.824 \nglass 0.603 0.599 0.613 0.616 \nhayes-roth 0.618 0.627 0.639 0.641 \nnew_ecoli 0.810 0.820 0.820 0.828 \nnew_led7digit 0.758 0.758 0.756 0.756 \nnew_vehicle 0.820 0.824 0.824 0.825 \nnew_winequality-red 0.375 0.376 0.379 0.383 \nnew_yeast 0.403 0.397 0.397 0.396 \nthyroid-newthyroid 0.909 0.915 0.903 0.897 ", + "text/html": "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9710.9750.9780.9770.9470.9530.9520.9530.9530.953
2delikatne-cut0.7040.7600.7610.8010.7890.7920.7940.7920.7910.789
3mocniej-cut0.4660.5230.4980.5990.5560.5600.5560.5540.5580.545
4delikatne-bezover-cut0.8120.8520.8610.8750.8890.8910.8910.8910.8900.890
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
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Mean G-mean
soupbg0500.639882
soupbg1000.639235
soupbg0300.637353
soup0.633471
soupbg0150.632824
smote0.621118
soupbg0050.616824
mdo0.608765
global0.590176
base0.521412
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" - ], - "text/plain": [ - " Mean G-mean\n", - "soupbg050 0.639882\n", - "soupbg100 0.639235\n", - "soupbg030 0.637353\n", - "soup 0.633471\n", - "soupbg015 0.632824\n", - "smote 0.621118\n", - "soupbg005 0.616824\n", - "mdo 0.608765\n", - "global 0.590176\n", - "base 0.521412" - ] + "text/plain": " Mean G-mean\nsoupbg050 0.639882\nsoupbg100 0.639235\nsoupbg030 0.637353\nsoup 0.633471\nsoupbg015 0.632824\nsmote 0.621118\nsoupbg005 0.616824\nmdo 0.608765\nglobal 0.590176\nbase 0.521412", + "text/html": "
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Mean G-mean
soupbg0500.639882
soupbg1000.639235
soupbg0300.637353
soup0.633471
soupbg0150.632824
smote0.621118
soupbg0050.616824
mdo0.608765
global0.590176
base0.521412
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Mean rank
soupbg0504.294118
soupbg1004.323529
soupbg0304.352941
smote4.794118
mdo5.294118
soupbg0155.529412
global5.676471
soup5.882353
soupbg0056.676471
base8.176471
\n", - "
" - ], - "text/plain": [ - " Mean rank\n", - "soupbg050 4.294118\n", - "soupbg100 4.323529\n", - "soupbg030 4.352941\n", - "smote 4.794118\n", - "mdo 5.294118\n", - "soupbg015 5.529412\n", - "global 5.676471\n", - "soup 5.882353\n", - "soupbg005 6.676471\n", - "base 8.176471" - ] + "text/plain": " Mean rank\nsoupbg050 4.294118\nsoupbg100 4.323529\nsoupbg030 4.352941\nsmote 4.794118\nmdo 5.294118\nsoupbg015 5.529412\nglobal 5.676471\nsoup 5.882353\nsoupbg005 6.676471\nbase 8.176471", + "text/html": "
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Mean rank
soupbg0504.294118
soupbg1004.323529
soupbg0304.352941
smote4.794118
mdo5.294118
soupbg0155.529412
global5.676471
soup5.882353
soupbg0056.676471
base8.176471
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" }, "metadata": {}, "output_type": "display_data" @@ -1821,444 +530,55 @@ "source": [ "score = provide_test_and_get_scores(datasets, 'knn')\n", "print_scores(score)\n" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } }, { "cell_type": "markdown", - "metadata": {}, "source": [ "knn (k=5), tylko rzeczywiste zbiory danych:" - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", "execution_count": 28, - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%%\n" - } - }, "outputs": [ { "data": { - "text/plain": [ - "'G-MEAN'" - ] + "text/plain": "'G-MEAN'" }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/html": [ - "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
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" - ], - "text/plain": [ - " base global smote mdo soup soupbg005 soupbg015 \\\n", - "balance-scale 0.193 0.267 0.420 0.684 0.687 0.628 0.672 \n", - "cleveland 0.020 0.134 0.129 0.066 0.107 0.129 0.103 \n", - "cleveland_v2 0.009 0.183 0.233 0.056 0.191 0.166 0.172 \n", - "cmc 0.482 0.476 0.481 0.479 0.506 0.488 0.513 \n", - "dermatology 0.843 0.849 0.849 0.854 0.817 0.789 0.809 \n", - "glass 0.201 0.625 0.621 0.499 0.609 0.553 0.603 \n", - "hayes-roth 0.559 0.614 0.627 0.611 0.601 0.562 0.618 \n", - "new_ecoli 0.814 0.775 0.807 0.824 0.817 0.801 0.810 \n", - "new_led7digit 0.757 0.441 0.727 0.774 0.746 0.753 0.758 \n", - "new_vehicle 0.849 0.863 0.859 0.852 0.822 0.810 0.820 \n", - "new_winequality-red 0.101 0.382 0.393 0.175 0.380 0.360 0.375 \n", - "new_yeast 0.262 0.378 0.395 0.321 0.406 0.350 0.403 \n", - "thyroid-newthyroid 0.821 0.936 0.920 0.902 0.899 0.901 0.909 \n", - "\n", - " soupbg030 soupbg050 soupbg100 \n", - "balance-scale 0.691 0.706 0.707 \n", - "cleveland 0.113 0.104 0.112 \n", - "cleveland_v2 0.189 0.205 0.186 \n", - "cmc 0.515 0.519 0.519 \n", - "dermatology 0.821 0.821 0.824 \n", - "glass 0.599 0.613 0.616 \n", - "hayes-roth 0.627 0.639 0.641 \n", - "new_ecoli 0.820 0.820 0.828 \n", - "new_led7digit 0.758 0.756 0.756 \n", - "new_vehicle 0.824 0.824 0.825 \n", - "new_winequality-red 0.376 0.379 0.383 \n", - "new_yeast 0.397 0.397 0.396 \n", - "thyroid-newthyroid 0.915 0.903 0.897 " - ] + "text/plain": " base global smote mdo soup soupbg005 soupbg015 \\\nbalance-scale 0.193 0.267 0.420 0.684 0.687 0.628 0.672 \ncleveland 0.020 0.134 0.129 0.066 0.107 0.129 0.103 \ncleveland_v2 0.009 0.183 0.233 0.056 0.191 0.166 0.172 \ncmc 0.482 0.476 0.481 0.479 0.506 0.488 0.513 \ndermatology 0.843 0.849 0.849 0.854 0.817 0.789 0.809 \nglass 0.201 0.625 0.621 0.499 0.609 0.553 0.603 \nhayes-roth 0.559 0.614 0.627 0.611 0.601 0.562 0.618 \nnew_ecoli 0.814 0.775 0.807 0.824 0.817 0.801 0.810 \nnew_led7digit 0.757 0.441 0.727 0.774 0.746 0.753 0.758 \nnew_vehicle 0.849 0.863 0.859 0.852 0.822 0.810 0.820 \nnew_winequality-red 0.101 0.382 0.393 0.175 0.380 0.360 0.375 \nnew_yeast 0.262 0.378 0.395 0.321 0.406 0.350 0.403 \nthyroid-newthyroid 0.821 0.936 0.920 0.902 0.899 0.901 0.909 \n\n soupbg030 soupbg050 soupbg100 \nbalance-scale 0.691 0.706 0.707 \ncleveland 0.113 0.104 0.112 \ncleveland_v2 0.189 0.205 0.186 \ncmc 0.515 0.519 0.519 \ndermatology 0.821 0.821 0.824 \nglass 0.599 0.613 0.616 \nhayes-roth 0.627 0.639 0.641 \nnew_ecoli 0.820 0.820 0.828 \nnew_led7digit 0.758 0.756 0.756 \nnew_vehicle 0.824 0.824 0.825 \nnew_winequality-red 0.376 0.379 0.383 \nnew_yeast 0.397 0.397 0.396 \nthyroid-newthyroid 0.915 0.903 0.897 ", + "text/html": "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
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Mean G-mean
soupbg1000.591538
soupbg0500.591231
soupbg0300.588077
soup0.583692
soupbg0150.581923
smote0.573923
soupbg0050.560769
mdo0.545923
global0.532538
base0.454692
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" - ], - "text/plain": [ - " Mean G-mean\n", - "soupbg100 0.591538\n", - "soupbg050 0.591231\n", - "soupbg030 0.588077\n", - "soup 0.583692\n", - "soupbg015 0.581923\n", - "smote 0.573923\n", - "soupbg005 0.560769\n", - "mdo 0.545923\n", - "global 0.532538\n", - "base 0.454692" - ] + "text/plain": " Mean G-mean\nsoupbg100 0.591538\nsoupbg050 0.591231\nsoupbg030 0.588077\nsoup 0.583692\nsoupbg015 0.581923\nsmote 0.573923\nsoupbg005 0.560769\nmdo 0.545923\nglobal 0.532538\nbase 0.454692", + "text/html": "
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Mean G-mean
soupbg1000.591538
soupbg0500.591231
soupbg0300.588077
soup0.583692
soupbg0150.581923
smote0.573923
soupbg0050.560769
mdo0.545923
global0.532538
base0.454692
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Mean rank
soupbg1003.769231
soupbg0504.153846
smote4.269231
soupbg0304.307692
global5.192308
soup5.615385
soupbg0155.884615
mdo6.076923
soupbg0057.653846
base8.076923
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" - ], - "text/plain": [ - " Mean rank\n", - "soupbg100 3.769231\n", - "soupbg050 4.153846\n", - "smote 4.269231\n", - "soupbg030 4.307692\n", - "global 5.192308\n", - "soup 5.615385\n", - "soupbg015 5.884615\n", - "mdo 6.076923\n", - "soupbg005 7.653846\n", - "base 8.076923" - ] + "text/plain": " Mean rank\nsoupbg100 3.769231\nsoupbg050 4.153846\nsmote 4.269231\nsoupbg030 4.307692\nglobal 5.192308\nsoup 5.615385\nsoupbg015 5.884615\nmdo 6.076923\nsoupbg005 7.653846\nbase 8.076923", + "text/html": "
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Mean rank
soupbg1003.769231
soupbg0504.153846
smote4.269231
soupbg0304.307692
global5.192308
soup5.615385
soupbg0155.884615
mdo6.076923
soupbg0057.653846
base8.076923
\n
" }, "metadata": {}, "output_type": "display_data" @@ -2266,7 +586,14 @@ ], "source": [ "print_scores(score,only_read_dt=True)" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } } ], "metadata": { @@ -2278,25 +605,25 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" + "pygments_lexer": "ipython2", + "version": "2.7.6" }, "pycharm": { "stem_cell": { "cell_type": "raw", + "source": [], "metadata": { "collapsed": false - }, - "source": [] + } } } }, "nbformat": 4, - "nbformat_minor": 1 -} + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/multi_imbalance/ensemble/SOUPBagging.ipynb b/multi_imbalance/ensemble/SOUPBagging.ipynb new file mode 100644 index 0000000..8fa7064 --- /dev/null +++ b/multi_imbalance/ensemble/SOUPBagging.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "Unzip datasets and prepare data:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stderr", + "text": [ + "Using TensorFlow backend.\n", + "/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ], + "output_type": "stream" + }, + { + "name": "stdout", + "text": [ + "[[0.49 0.29 0.48 0.5 0.56 0.24 0.35]\n [0.07 0.4 0.48 0.5 0.54 0.35 0.44]\n [0.56 0.4 0.48 0.5 0.49 0.37 0.46]\n [0.59 0.49 0.48 0.5 0.52 0.45 0.36]\n [0.23 0.32 0.48 0.5 0.55 0.25 0.35]]\n[0 0 0 0 0]\n" + ], + "output_type": "stream" + } + ], + "source": [ + "import seaborn as sns\n", + "from imblearn.metrics import geometric_mean_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from multi_imbalance.datasets import load_datasets\n", + "from multi_imbalance.resampling.SOUPBagging import SOUPBagging\n", + "\n", + "%matplotlib inline\n", + "sns.set_style('darkgrid')\n", + "\n", + "dataset = load_datasets()['new_ecoli']\n", + "\n", + "X, y = dataset.data, dataset.target\n", + "print(X[:5])\n", + "print(y[:5])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "0.7563236268160275" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 5 + } + ], + "source": [ + "clf = DecisionTreeClassifier()\n", + "vote_classifier = SOUPBagging(clf, n_classifiers=50)\n", + "clf.fit(X_train, y_train)\n", + "y_pred = clf.predict(X_test)\n", + "geometric_mean_score(y_test, y_pred, correction=0.001)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Compare results by plotting data in 2 dimensions" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/multi_imbalance/resampling/SOUPBagging.py b/multi_imbalance/resampling/SOUPBagging.py index 6d8b487..6741f36 100644 --- a/multi_imbalance/resampling/SOUPBagging.py +++ b/multi_imbalance/resampling/SOUPBagging.py @@ -8,11 +8,7 @@ def fit_clf(args): - clf, X, y = args - x_sampled, y_sampled = resample(X, y, stratify=y) - x_resampled, y_resampled = SOUP().fit_transform(x_sampled, y_sampled) - clf.fit(x_resampled, y_resampled) - return clf + return SOUPBagging.fit_classifier(args) class SOUPBagging(object): @@ -26,6 +22,14 @@ def __init__(self, classifier=None, n_classifiers=30): else: self.classifiers.append(KNeighborsClassifier()) + @staticmethod + def fit_classifier(args): + clf, X, y = args + x_sampled, y_sampled = resample(X, y, stratify=y) + x_resampled, y_resampled = SOUP().fit_transform(x_sampled, y_sampled) + clf.fit(x_resampled, y_resampled) + return clf + def fit(self, X, y): """ From fbdfa99bdab23efbb748bd1e6c29641907c63223 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Sun, 1 Dec 2019 13:37:45 +0100 Subject: [PATCH 09/21] moved files to correct directories --- .../ensemble => examples/ensembles}/SOUPBagging.ipynb | 0 multi_imbalance/{resampling => ensemble}/SOUPBagging.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename {multi_imbalance/ensemble => examples/ensembles}/SOUPBagging.ipynb (100%) rename multi_imbalance/{resampling => ensemble}/SOUPBagging.py (97%) diff --git a/multi_imbalance/ensemble/SOUPBagging.ipynb b/examples/ensembles/SOUPBagging.ipynb similarity index 100% rename from multi_imbalance/ensemble/SOUPBagging.ipynb rename to examples/ensembles/SOUPBagging.ipynb diff --git a/multi_imbalance/resampling/SOUPBagging.py b/multi_imbalance/ensemble/SOUPBagging.py similarity index 97% rename from multi_imbalance/resampling/SOUPBagging.py rename to multi_imbalance/ensemble/SOUPBagging.py index 6741f36..4ec1449 100644 --- a/multi_imbalance/resampling/SOUPBagging.py +++ b/multi_imbalance/ensemble/SOUPBagging.py @@ -39,7 +39,7 @@ def fit(self, X, y): """ self.classes = np.unique(y) - NUM_CORE = 4 # set to the number of cores you want to use + NUM_CORE = multiprocessing.cpu_count() pool = multiprocessing.Pool(NUM_CORE) self.classifiers = pool.map(fit_clf, [(clf, X, y) for clf in self.classifiers]) From 5301f83875d4203fb0044eec27873250f887b0e2 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Sun, 1 Dec 2019 13:40:54 +0100 Subject: [PATCH 10/21] addded html fiels --- .../resample-checkpoint.ipynb | 1349 ++ .../resample/resample_with_soup_bg.html | 15179 ++++++++++++++++ 2 files changed, 16528 insertions(+) create mode 100644 benchmarks/resample/.ipynb_checkpoints/resample-checkpoint.ipynb create mode 100644 benchmarks/resample/resample_with_soup_bg.html diff --git a/benchmarks/resample/.ipynb_checkpoints/resample-checkpoint.ipynb b/benchmarks/resample/.ipynb_checkpoints/resample-checkpoint.ipynb new file mode 100644 index 0000000..f474b85 --- /dev/null +++ b/benchmarks/resample/.ipynb_checkpoints/resample-checkpoint.ipynb @@ -0,0 +1,1349 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'multi_imbalance'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m--------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\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 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmulti_imbalance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmulti_imbalance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresampling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSOUP\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSOUP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmulti_imbalance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresampling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMDO\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMDO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'multi_imbalance'" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "import numpy as np\n", + "import pandas as pd\n", + "import tqdm\n", + "from IPython.core.display import display\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "from multi_imbalance.datasets import load_datasets\n", + "from multi_imbalance.resampling.SOUP import SOUP\n", + "from multi_imbalance.resampling.MDO import MDO\n", + "from multi_imbalance.resampling.GlobalCS import GlobalCS\n", + "\n", + "from imblearn.metrics import geometric_mean_score\n", + "from imblearn.over_sampling import SMOTE\n", + "from multi_imbalance.resampling.SOUPBagging import SOUPBagging\n", + "from multi_imbalance.resampling.spider import SPIDER3\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "maj_int_min = {\n", + " 'balance_scale' : {\n", + " 'maj': [2, 1],\n", + " 'int': [],\n", + " 'min': [0]\n", + " }, \n", + " 'cleveland': {\n", + " 'maj': [0],\n", + " 'int': [1],\n", + " 'min': [2,3,4]\n", + " }, \n", + " 'cmc': {\n", + " 'maj': [0],\n", + " 'int': [2],\n", + " 'min': [1]\n", + " }, \n", + " 'dermatology': {\n", + " 'maj': [0],\n", + " 'int': [2,1,4,3],\n", + " 'min': [5]\n", + " }, \n", + " 'ecoli': {\n", + " 'maj': [0,1],\n", + " 'int': [7,4,5],\n", + " 'min': [6,3,2]\n", + " }, \n", + " 'glass': {\n", + " 'maj': [1,0],\n", + " 'int': [5],\n", + " 'min': [2,3,4]\n", + " }, \n", + " 'hayes_roth': {\n", + " 'maj': [0,1],\n", + " 'int': [],\n", + " 'min': [2]\n", + " }, \n", + " 'new_thyroid': {\n", + " 'maj': [0],\n", + " 'int': [],\n", + " 'min': [1,2]\n", + " }, \n", + " 'winequailty_red': {\n", + " 'maj': [2,3],\n", + " 'int': [4],\n", + " 'min': [1,5,0]\n", + " }, \n", + " 'yeast': {\n", + " 'maj': [0,7],\n", + " 'int': [6, 5],\n", + " 'min': [4,3,2,9,8,1]\n", + " }\n", + "}\n", + "from IPython.display import clear_output\n", + "clear_output(wait=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def resample_data(resample, seed, X_train, y_train, no_classes, dataset_name):\n", + " if resample == 'base':\n", + " X_train_resampled, y_train_resampled = X_train, y_train\n", + " elif resample=='soup':\n", + " soup = SOUP()\n", + " X_train_resampled, y_train_resampled = soup.fit_transform(np.copy(X_train), np.copy(y_train))\n", + " elif resample=='global':\n", + " global_cs = GlobalCS()\n", + " X_train_resampled, y_train_resampled = global_cs.fit_transform(np.copy(X_train), np.copy(y_train), shuffle=False)\n", + " elif resample=='smote':\n", + " smote = SMOTE(random_state=seed)\n", + " X_train_resampled, y_train_resampled = smote.fit_sample(np.copy(X_train), np.copy(y_train))\n", + " elif resample=='mdo':\n", + " mdo = MDO(k=9, k1_frac=0.1, seed=seed)\n", + " X_train_resampled, y_train_resampled = mdo.fit_transform(np.copy(X_train), np.copy(y_train))\n", + " elif resample=='spider':\n", + " cost = np.ones((no_classes, no_classes))\n", + " np.fill_diagonal(cost, 0)\n", + " clf = SPIDER3(k=5, cost=cost, majority_classes=maj_int_min[dataset_name]['maj'], intermediate_classes=maj_int_min[dataset_name]['int'], minority_classes=maj_int_min[dataset_name]['min'])\n", + " X_train_resampled, y_train_resampled = clf.fit_transform(X_train.astype(np.float64), y_train)\n", + " elif resample=='soupbagging':\n", + " # SOUP Bagging does it by itself\n", + " X_train_resampled, y_train_resampled = X_train, y_train\n", + " return X_train_resampled, y_train_resampled\n", + "\n", + "\n", + "def test_resampling(classifier, res, dataset_values, dataset_name):\n", + " X, y = dataset_values.data, dataset_values.target\n", + " no_classes = np.unique(y).size\n", + " result_data = defaultdict(int)\n", + " run_data = defaultdict(lambda: defaultdict(list)) # {metric: {run_number: [scores]}}\n", + " for i in range(1):\n", + " skf = StratifiedKFold(n_splits=3, shuffle=True,random_state=i)\n", + " for train_index, test_index in skf.split(X, y):\n", + " X_train, X_test = X[train_index], X[test_index]\n", + " y_train, y_test = y[train_index], y[test_index]\n", + " \n", + " X_train_resampled, y_train_resampled = resample_data(res, i, X_train, y_train, no_classes, dataset_name)\n", + " \n", + " if classifier == 'knn':\n", + " clf = KNeighborsClassifier(n_neighbors=5)\n", + " elif classifier == 'tree':\n", + " clf = DecisionTreeClassifier(random_state=i)\n", + " \n", + " if res == 'soupbagging':\n", + " vote_classifier = SOUPBagging(clf, n_classifiers=5, seed=i)\n", + " clf = vote_classifier\n", + " \n", + " clf.fit(X_train_resampled, y_train_resampled)\n", + " y_pred = clf.predict(X_test)\n", + " gmean = geometric_mean_score(y_test, y_pred, correction=0.001)\n", + " run_data['g_mean'][str(i)].append(gmean)\n", + " \n", + " def get_score_from_metric(run_data, metric):\n", + " runs = run_data[metric]\n", + " runs_scores_list = list(runs.values()) #[[one run k-foledscores],[..]]\n", + " result = np.mean(list(map(np.mean, runs_scores_list)))\n", + " return result\n", + " \n", + " result_data['g_mean'] = get_score_from_metric(run_data, 'g_mean')\n", + " return result_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true, + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def provide_test_and_get_scores(datasets, clf):\n", + " scores = defaultdict(dict)\n", + " for dataset_name, dataset_values in tqdm(datasets.items(),total=len(datasets)):\n", + " clf_res_names =['soup','soupbagging']\n", + " for resample in clf_res_names:\n", + " result_data = test_resampling(clf, resample, dataset_values, dataset_name)\n", + " scores[dataset_name][resample] = round(result_data['g_mean'],3)\n", + " return scores" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def print_scores(scores, only_read_dt = False):\n", + " display(\"G-MEAN\")\n", + " df = pd.DataFrame(scores).T\n", + " if only_read_dt:\n", + " df = df.iloc[4:]\n", + " display(df)\n", + " \n", + " # df.fillna(df.median(), inplace=True)\n", + " display(pd.DataFrame(df.mean().sort_values(ascending=False),columns=['Mean G-mean']))\n", + " display(pd.DataFrame(df.rank(axis=1,ascending=False).mean().sort_values(),columns=['Mean rank']))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "datasets = load_datasets()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Testy dla drzewa,\n", + "Wszystkie zbiory danych:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "694178e929f44a7db557d62083d1c9eb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=17), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'G-MEAN'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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new_winequality-red0.4470.425
new_yeast0.3510.277
thyroid-newthyroid0.9000.880
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Mean G-mean
soup0.651882
soupbagging0.627176
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" + ], + "text/plain": [ + " Mean G-mean\n", + "soup 0.651882\n", + "soupbagging 0.627176" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean rank
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" + ], + "text/plain": [ + " Mean rank\n", + "soup 1.294118\n", + "soupbagging 1.705882" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "score = provide_test_and_get_scores(datasets, 'tree')\n", + "print_scores(score)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Drzewo, tylko rzeczywiste zbiory danych:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'G-MEAN'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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soupsoupbagging
balance-scale0.5050.518
cleveland0.0990.169
cleveland_v20.2710.068
cmc0.4940.477
dermatology0.9100.916
glass0.6790.638
hayes-roth0.8350.784
new_ecoli0.7470.698
new_led7digit0.7540.733
new_vehicle0.8840.871
new_winequality-red0.4470.425
new_yeast0.3510.277
thyroid-newthyroid0.9000.880
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" + ], + "text/plain": [ + " soup soupbagging\n", + "balance-scale 0.505 0.518\n", + "cleveland 0.099 0.169\n", + "cleveland_v2 0.271 0.068\n", + "cmc 0.494 0.477\n", + "dermatology 0.910 0.916\n", + "glass 0.679 0.638\n", + "hayes-roth 0.835 0.784\n", + "new_ecoli 0.747 0.698\n", + "new_led7digit 0.754 0.733\n", + "new_vehicle 0.884 0.871\n", + "new_winequality-red 0.447 0.425\n", + "new_yeast 0.351 0.277\n", + "thyroid-newthyroid 0.900 0.880" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean G-mean
soup0.605846
soupbagging0.573385
\n", + "
" + ], + "text/plain": [ + " Mean G-mean\n", + "soup 0.605846\n", + "soupbagging 0.573385" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean rank
soup1.230769
soupbagging1.769231
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" + ], + "text/plain": [ + " Mean rank\n", + "soup 1.230769\n", + "soupbagging 1.769231" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_scores(score,only_read_dt=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Testy dla knn,\n", + "Wszystkie zbiory danych:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "57a6cbb793e6421ea9265f3fa7dda3af", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=17), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'G-MEAN'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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soupsoupbagging
1czysty-cut0.9400.946
2delikatne-cut0.7920.785
3mocniej-cut0.5830.563
4delikatne-bezover-cut0.8860.878
balance-scale0.6740.628
cleveland0.1060.088
cleveland_v20.2480.159
cmc0.5170.464
dermatology0.7990.759
glass0.6080.571
hayes-roth0.5660.520
new_ecoli0.8140.775
new_led7digit0.7550.758
new_vehicle0.8180.796
new_winequality-red0.3720.354
new_yeast0.4580.364
thyroid-newthyroid0.8850.910
\n", + "
" + ], + "text/plain": [ + " soup soupbagging\n", + "1czysty-cut 0.940 0.946\n", + "2delikatne-cut 0.792 0.785\n", + "3mocniej-cut 0.583 0.563\n", + "4delikatne-bezover-cut 0.886 0.878\n", + "balance-scale 0.674 0.628\n", + "cleveland 0.106 0.088\n", + "cleveland_v2 0.248 0.159\n", + "cmc 0.517 0.464\n", + "dermatology 0.799 0.759\n", + "glass 0.608 0.571\n", + "hayes-roth 0.566 0.520\n", + "new_ecoli 0.814 0.775\n", + "new_led7digit 0.755 0.758\n", + "new_vehicle 0.818 0.796\n", + "new_winequality-red 0.372 0.354\n", + "new_yeast 0.458 0.364\n", + "thyroid-newthyroid 0.885 0.910" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean G-mean
soup0.636529
soupbagging0.606941
\n", + "
" + ], + "text/plain": [ + " Mean G-mean\n", + "soup 0.636529\n", + "soupbagging 0.606941" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Mean rank
soup1.176471
soupbagging1.823529
\n", + "
" + ], + "text/plain": [ + " Mean rank\n", + "soup 1.176471\n", + "soupbagging 1.823529" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "score = provide_test_and_get_scores(datasets, 'knn')\n", + "print_scores(score)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "knn (k=5), tylko rzeczywiste zbiory danych:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'G-MEAN'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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soupsoupbagging
balance-scale0.6740.628
cleveland0.1060.088
cleveland_v20.2480.159
cmc0.5170.464
dermatology0.7990.759
glass0.6080.571
hayes-roth0.5660.520
new_ecoli0.8140.775
new_led7digit0.7550.758
new_vehicle0.8180.796
new_winequality-red0.3720.354
new_yeast0.4580.364
thyroid-newthyroid0.8850.910
\n", + "
" + ], + "text/plain": [ + " soup soupbagging\n", + "balance-scale 0.674 0.628\n", + "cleveland 0.106 0.088\n", + "cleveland_v2 0.248 0.159\n", + "cmc 0.517 0.464\n", + "dermatology 0.799 0.759\n", + "glass 0.608 0.571\n", + "hayes-roth 0.566 0.520\n", + "new_ecoli 0.814 0.775\n", + "new_led7digit 0.755 0.758\n", + "new_vehicle 0.818 0.796\n", + "new_winequality-red 0.372 0.354\n", + "new_yeast 0.458 0.364\n", + "thyroid-newthyroid 0.885 0.910" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mean G-mean
soup0.586154
soupbagging0.549692
\n", + "
" + ], + "text/plain": [ + " Mean G-mean\n", + "soup 0.586154\n", + "soupbagging 0.549692" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Mean rank
soup1.153846
soupbagging1.846154
\n", + "
" + ], + "text/plain": [ + " Mean rank\n", + "soup 1.153846\n", + "soupbagging 1.846154" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_scores(score,only_read_dt=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "pycharm": { + "is_executing": false, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4b3348adcd7e44d9bc11033005984bf8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "IntProgress(value=10)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ipywidgets import IntProgress\n", + "IntProgress(10,max=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": "3.7.3" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/benchmarks/resample/resample_with_soup_bg.html b/benchmarks/resample/resample_with_soup_bg.html new file mode 100644 index 0000000..744d609 --- /dev/null +++ b/benchmarks/resample/resample_with_soup_bg.html @@ -0,0 +1,15179 @@ + + + + +resample + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
In [20]:
+
+
+
from collections import defaultdict
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from IPython.core.display import display
+from sklearn.model_selection import StratifiedKFold
+from sklearn.tree import DecisionTreeClassifier
+
+from multi_imbalance.datasets import load_datasets
+from multi_imbalance.resampling.SOUP import SOUP
+from multi_imbalance.resampling.MDO import MDO
+from multi_imbalance.resampling.GlobalCS import GlobalCS
+
+from imblearn.metrics import geometric_mean_score
+from imblearn.over_sampling import SMOTE
+from multi_imbalance.resampling.SOUPBagging import SOUPBagging
+from multi_imbalance.resampling.spider import SPIDER3
+
+from sklearn.neighbors import KNeighborsClassifier
+maj_int_min = {
+    'balance_scale' : {
+        'maj': [2, 1],
+        'int': [],
+        'min': [0]
+    }, 
+    'cleveland': {
+        'maj': [0],
+        'int': [1],
+        'min': [2,3,4]
+    }, 
+    'cmc': {
+        'maj': [0],
+        'int': [2],
+        'min': [1]
+    }, 
+    'dermatology': {
+        'maj': [0],
+        'int': [2,1,4,3],
+        'min': [5]
+    }, 
+    'ecoli': {
+        'maj': [0,1],
+        'int': [7,4,5],
+        'min': [6,3,2]
+    }, 
+    'glass': {
+        'maj': [1,0],
+        'int': [5],
+        'min': [2,3,4]
+    }, 
+    'hayes_roth': {
+        'maj': [0,1],
+        'int': [],
+        'min': [2]
+    }, 
+    'new_thyroid': {
+        'maj': [0],
+        'int': [],
+        'min': [1,2]
+    }, 
+    'winequailty_red': {
+        'maj': [2,3],
+        'int': [4],
+        'min': [1,5,0]
+    }, 
+    'yeast': {
+        'maj': [0,7],
+        'int': [6, 5],
+        'min': [4,3,2,9,8,1]
+    }
+}
+from IPython.display import clear_output
+clear_output(wait=True)
+
+ +
+
+
+ +
+
+
+
In [21]:
+
+
+
def resample_data(resample, seed, X_train, y_train, no_classes, dataset_name):
+    if resample == 'base':
+        X_train_resampled, y_train_resampled = X_train, y_train
+    elif resample=='soup':
+        soup = SOUP()
+        X_train_resampled, y_train_resampled = soup.fit_transform(np.copy(X_train), np.copy(y_train))
+    elif resample=='global':
+        global_cs = GlobalCS()
+        X_train_resampled, y_train_resampled = global_cs.fit_transform(np.copy(X_train), np.copy(y_train), shuffle=False)
+    elif resample=='smote':
+        smote = SMOTE(random_state=seed)
+        X_train_resampled, y_train_resampled = smote.fit_sample(np.copy(X_train), np.copy(y_train))
+    elif resample=='mdo':
+        mdo = MDO(k=9, k1_frac=0.1, seed=seed)
+        X_train_resampled, y_train_resampled = mdo.fit_transform(np.copy(X_train), np.copy(y_train))
+    elif resample=='spider':
+        cost = np.ones((no_classes, no_classes))
+        np.fill_diagonal(cost, 0)
+        clf = SPIDER3(k=5, cost=cost, majority_classes=maj_int_min[dataset_name]['maj'], intermediate_classes=maj_int_min[dataset_name]['int'], minority_classes=maj_int_min[dataset_name]['min'])
+        X_train_resampled, y_train_resampled = clf.fit_transform(X_train.astype(np.float64), y_train)
+    elif 'soupbg' in resample:
+        # SOUP Bagging does it by itself
+        X_train_resampled, y_train_resampled = X_train, y_train
+    return X_train_resampled, y_train_resampled
+
+
+def test_resampling(classifier, res, dataset_values, dataset_name):
+    X, y = dataset_values.data, dataset_values.target
+    no_classes = np.unique(y).size
+    result_data = defaultdict(int)
+    run_data = defaultdict(lambda: defaultdict(list)) # {metric: {run_number: [scores]}}
+    for i in range(10):
+        skf = StratifiedKFold(n_splits=5, shuffle=True,random_state=i)
+        for train_index, test_index in skf.split(X, y):
+            X_train, X_test = X[train_index], X[test_index]
+            y_train, y_test = y[train_index], y[test_index]
+            
+            X_train_resampled, y_train_resampled = resample_data(res, i, X_train, y_train, no_classes, dataset_name)
+            
+            if classifier == 'knn':
+                clf = KNeighborsClassifier(n_neighbors=5)
+            elif classifier == 'tree':
+                clf = DecisionTreeClassifier(random_state=i)
+                
+            # DONT JUDGE ME
+            if res == 'soupbg005':
+                vote_classifier = SOUPBagging(clf, n_classifiers=5)
+                clf = vote_classifier
+            elif res == 'soupbg015':
+                vote_classifier = SOUPBagging(clf, n_classifiers=15)
+                clf = vote_classifier
+            elif res == 'soupbg030':
+                vote_classifier = SOUPBagging(clf, n_classifiers=30)
+                clf = vote_classifier
+            elif res == 'soupbg050':
+                vote_classifier = SOUPBagging(clf, n_classifiers=50)
+                clf = vote_classifier
+            elif res == 'soupbg100':
+                vote_classifier = SOUPBagging(clf, n_classifiers=100)
+                clf = vote_classifier
+            
+            clf.fit(X_train_resampled, y_train_resampled)
+            y_pred = clf.predict(X_test)
+            gmean = geometric_mean_score(y_test, y_pred, correction=0.001)
+            run_data['g_mean'][str(i)].append(gmean)
+    
+    def get_score_from_metric(run_data, metric):
+        runs = run_data[metric]
+        runs_scores_list = list(runs.values()) #[[one run k-foledscores],[..]]
+        result = np.mean(list(map(np.mean, runs_scores_list)))
+        return result
+            
+    result_data['g_mean'] = get_score_from_metric(run_data, 'g_mean')
+    return result_data
+
+ +
+
+
+ +
+
+
+
In [22]:
+
+
+
def provide_test_and_get_scores(datasets, clf):
+    scores = defaultdict(dict)
+    for dataset_name, dataset_values in tqdm(datasets.items(),total=len(d`atasets)):
+        clf_res_names =['base','global','smote','mdo','soup','soupbg005','soupbg015','soupbg030', 'soupbg050', 'soupbg100']
+        for resample in clf_res_names:
+            result_data = test_resampling(clf, resample, dataset_values, dataset_name)
+            scores[dataset_name][resample] = round(result_data['g_mean'],3)
+    return scores
+
+ +
+
+
+ +
+
+
+
In [23]:
+
+
+
def print_scores(scores, only_read_dt = False):
+    display("G-MEAN")
+    df = pd.DataFrame(scores).T
+    if only_read_dt:
+        df = df.iloc[4:]
+    display(df)
+    
+    # df.fillna(df.median(), inplace=True)
+    display(pd.DataFrame(df.mean().sort_values(ascending=False),columns=['Mean G-mean']))
+    display(pd.DataFrame(df.rank(axis=1,ascending=False).mean().sort_values(),columns=['Mean rank']))
+
+ +
+
+
+ +
+
+
+
In [24]:
+
+
+
datasets = load_datasets()
+
+ +
+
+
+ +
+
+
+
+

Testy dla drzewa, +Wszystkie zbiory danych:

+ +
+
+
+
+
+
In [25]:
+
+
+
score = provide_test_and_get_scores(datasets, 'tree')
+print_scores(score)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+
'G-MEAN'
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9390.9460.9550.9650.9570.9440.9550.9560.9580.958
2delikatne-cut0.6980.6990.7440.7720.7950.7870.7930.8000.7980.802
3mocniej-cut0.4920.4820.4960.5850.5780.5900.6030.6110.6090.610
4delikatne-bezover-cut0.7710.7680.8150.8300.8940.8830.8870.8890.8920.891
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean G-mean
soupbg1000.666118
soupbg0500.664059
soupbg0300.658941
soupbg0150.650235
soup0.646353
soupbg0050.629706
mdo0.613706
smote0.606824
base0.582353
global0.579412
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean rank
soupbg1002.294118
soupbg0502.852941
soupbg0303.764706
soupbg0155.294118
soup5.352941
smote5.558824
mdo5.647059
soupbg0057.705882
global8.176471
base8.352941
+
+
+ +
+ +
+
+ +
+
+
+
+

Drzewo, tylko rzeczywiste zbiory danych:

+ +
+
+
+
+
+
In [26]:
+
+
+
print_scores(score,only_read_dt=True)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+
'G-MEAN'
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean G-mean
soupbg1000.620231
soupbg0500.617846
soupbg0300.611231
soupbg0150.601231
soup0.597231
soupbg0050.577000
smote0.562000
mdo0.560077
base0.538462
global0.535000
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean rank
soupbg1002.346154
soupbg0502.923077
soupbg0304.000000
smote4.923077
soupbg0155.346154
mdo5.769231
soup5.769231
global7.846154
base8.000000
soupbg0058.076923
+
+
+ +
+ +
+
+ +
+
+
+
+

Testy dla knn, +Wszystkie zbiory danych:

+ +
+
+
+
+
+
In [27]:
+
+
+
score = provide_test_and_get_scores(datasets, 'knn')
+print_scores(score)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + + + +
+
'G-MEAN'
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9710.9750.9780.9770.9470.9530.9520.9530.9530.953
2delikatne-cut0.7040.7600.7610.8010.7890.7920.7940.7920.7910.789
3mocniej-cut0.4660.5230.4980.5990.5560.5600.5560.5540.5580.545
4delikatne-bezover-cut0.8120.8520.8610.8750.8890.8910.8910.8910.8900.890
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean G-mean
soupbg0500.639882
soupbg1000.639235
soupbg0300.637353
soup0.633471
soupbg0150.632824
smote0.621118
soupbg0050.616824
mdo0.608765
global0.590176
base0.521412
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean rank
soupbg0504.294118
soupbg1004.323529
soupbg0304.352941
smote4.794118
mdo5.294118
soupbg0155.529412
global5.676471
soup5.882353
soupbg0056.676471
base8.176471
+
+
+ +
+ +
+
+ +
+
+
+
+

knn (k=5), tylko rzeczywiste zbiory danych:

+ +
+
+
+
+
+
In [28]:
+ + + + + + + +
+ +
+
+ + +
+ +
+ + + + +
+
'G-MEAN'
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean G-mean
soupbg1000.591538
soupbg0500.591231
soupbg0300.588077
soup0.583692
soupbg0150.581923
smote0.573923
soupbg0050.560769
mdo0.545923
global0.532538
base0.454692
+
+
+ +
+ +
+ +
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Mean rank
soupbg1003.769231
soupbg0504.153846
smote4.269231
soupbg0304.307692
global5.192308
soup5.615385
soupbg0155.884615
mdo6.076923
soupbg0057.653846
base8.076923
+
+
+ +
+ +
+
+ +
+
+
+ + + + + + From 6a51a06f6b5283cc75684782052ee71703d89ddb Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Sun, 1 Dec 2019 14:01:37 +0100 Subject: [PATCH 11/21] fixed path after moving class to new dir --- benchmarks/resample/resample.ipynb | 282 ++--------------------------- 1 file changed, 15 insertions(+), 267 deletions(-) diff --git a/benchmarks/resample/resample.ipynb b/benchmarks/resample/resample.ipynb index 1c6c321..0974ec7 100644 --- a/benchmarks/resample/resample.ipynb +++ b/benchmarks/resample/resample.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "outputs": [], "source": [ "from collections import defaultdict\n", @@ -14,13 +14,13 @@ "from sklearn.tree import DecisionTreeClassifier\n", "\n", "from multi_imbalance.datasets import load_datasets\n", + "from multi_imbalance.ensemble.SOUPBagging import SOUPBagging\n", "from multi_imbalance.resampling.SOUP import SOUP\n", "from multi_imbalance.resampling.MDO import MDO\n", "from multi_imbalance.resampling.GlobalCS import GlobalCS\n", "\n", "from imblearn.metrics import geometric_mean_score\n", "from imblearn.over_sampling import SMOTE\n", - "from multi_imbalance.resampling.SOUPBagging import SOUPBagging\n", "from multi_imbalance.resampling.spider import SPIDER3\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "outputs": [], "source": [ "def resample_data(resample, seed, X_train, y_train, no_classes, dataset_name):\n", @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": { "collapsed": true, "pycharm": { @@ -190,7 +190,7 @@ "source": [ "def provide_test_and_get_scores(datasets, clf):\n", " scores = defaultdict(dict)\n", - " for dataset_name, dataset_values in tqdm(datasets.items(),total=len(d`atasets)):\n", + " for dataset_name, dataset_values in tqdm(datasets.items(),total=len(datasets)):\n", " clf_res_names =['base','global','smote','mdo','soup','soupbg005','soupbg015','soupbg030', 'soupbg050', 'soupbg100']\n", " for resample in clf_res_names:\n", " result_data = test_resampling(clf, resample, dataset_values, dataset_name)\n", @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "outputs": [], "source": [ "def print_scores(scores, only_read_dt = False):\n", @@ -224,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "outputs": [], "source": [ "datasets = load_datasets()\n" @@ -252,102 +252,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "name": "stderr", - "text": [ - "\n\n", - "\r 0%| | 0/17 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9390.9460.9550.9650.9570.9440.9550.9560.9580.958
2delikatne-cut0.6980.6990.7440.7720.7950.7870.7930.8000.7980.802
3mocniej-cut0.4920.4820.4960.5850.5780.5900.6030.6110.6090.610
4delikatne-bezover-cut0.7710.7680.8150.8300.8940.8830.8870.8890.8920.891
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoupbg100 0.666118\nsoupbg050 0.664059\nsoupbg030 0.658941\nsoupbg015 0.650235\nsoup 0.646353\nsoupbg005 0.629706\nmdo 0.613706\nsmote 0.606824\nbase 0.582353\nglobal 0.579412", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Mean G-mean
soupbg1000.666118
soupbg0500.664059
soupbg0300.658941
soupbg0150.650235
soup0.646353
soupbg0050.629706
mdo0.613706
smote0.606824
base0.582353
global0.579412
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsoupbg100 2.294118\nsoupbg050 2.852941\nsoupbg030 3.764706\nsoupbg015 5.294118\nsoup 5.352941\nsmote 5.558824\nmdo 5.647059\nsoupbg005 7.705882\nglobal 8.176471\nbase 8.352941", - "text/html": "
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Mean rank
soupbg1002.294118
soupbg0502.852941
soupbg0303.764706
soupbg0155.294118
soup5.352941
smote5.558824
mdo5.647059
soupbg0057.705882
global8.176471
base8.352941
\n
" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 9, + "outputs": [], "source": [ "score = provide_test_and_get_scores(datasets, 'tree')\n", "print_scores(score)\n" @@ -374,40 +280,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "data": { - "text/plain": "'G-MEAN'" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " base global smote mdo soup soupbg005 soupbg015 \\\nbalance-scale 0.154 0.123 0.168 0.162 0.621 0.559 0.598 \ncleveland 0.127 0.096 0.142 0.098 0.139 0.128 0.113 \ncleveland_v2 0.113 0.110 0.129 0.090 0.162 0.150 0.183 \ncmc 0.440 0.451 0.444 0.439 0.466 0.477 0.489 \ndermatology 0.925 0.940 0.946 0.948 0.933 0.910 0.935 \nglass 0.463 0.486 0.554 0.598 0.606 0.590 0.631 \nhayes-roth 0.837 0.843 0.841 0.842 0.835 0.771 0.815 \nnew_ecoli 0.708 0.707 0.723 0.758 0.714 0.701 0.728 \nnew_led7digit 0.754 0.757 0.762 0.753 0.760 0.750 0.767 \nnew_vehicle 0.900 0.894 0.890 0.899 0.886 0.879 0.897 \nnew_winequality-red 0.429 0.407 0.466 0.465 0.437 0.403 0.435 \nnew_yeast 0.250 0.240 0.323 0.293 0.290 0.282 0.320 \nthyroid-newthyroid 0.900 0.901 0.918 0.936 0.915 0.901 0.905 \n\n soupbg030 soupbg050 soupbg100 \nbalance-scale 0.612 0.642 0.648 \ncleveland 0.129 0.123 0.133 \ncleveland_v2 0.191 0.208 0.186 \ncmc 0.503 0.504 0.508 \ndermatology 0.951 0.959 0.962 \nglass 0.646 0.645 0.654 \nhayes-roth 0.838 0.838 0.836 \nnew_ecoli 0.725 0.734 0.735 \nnew_led7digit 0.767 0.768 0.768 \nnew_vehicle 0.906 0.910 0.912 \nnew_winequality-red 0.461 0.472 0.490 \nnew_yeast 0.316 0.319 0.317 \nthyroid-newthyroid 0.901 0.910 0.914 ", - "text/html": "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1540.1230.1680.1620.6210.5590.5980.6120.6420.648
cleveland0.1270.0960.1420.0980.1390.1280.1130.1290.1230.133
cleveland_v20.1130.1100.1290.0900.1620.1500.1830.1910.2080.186
cmc0.4400.4510.4440.4390.4660.4770.4890.5030.5040.508
dermatology0.9250.9400.9460.9480.9330.9100.9350.9510.9590.962
glass0.4630.4860.5540.5980.6060.5900.6310.6460.6450.654
hayes-roth0.8370.8430.8410.8420.8350.7710.8150.8380.8380.836
new_ecoli0.7080.7070.7230.7580.7140.7010.7280.7250.7340.735
new_led7digit0.7540.7570.7620.7530.7600.7500.7670.7670.7680.768
new_vehicle0.9000.8940.8900.8990.8860.8790.8970.9060.9100.912
new_winequality-red0.4290.4070.4660.4650.4370.4030.4350.4610.4720.490
new_yeast0.2500.2400.3230.2930.2900.2820.3200.3160.3190.317
thyroid-newthyroid0.9000.9010.9180.9360.9150.9010.9050.9010.9100.914
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoupbg100 0.620231\nsoupbg050 0.617846\nsoupbg030 0.611231\nsoupbg015 0.601231\nsoup 0.597231\nsoupbg005 0.577000\nsmote 0.562000\nmdo 0.560077\nbase 0.538462\nglobal 0.535000", - "text/html": "
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Mean G-mean
soupbg1000.620231
soupbg0500.617846
soupbg0300.611231
soupbg0150.601231
soup0.597231
soupbg0050.577000
smote0.562000
mdo0.560077
base0.538462
global0.535000
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsoupbg100 2.346154\nsoupbg050 2.923077\nsoupbg030 4.000000\nsmote 4.923077\nsoupbg015 5.346154\nmdo 5.769231\nsoup 5.769231\nglobal 7.846154\nbase 8.000000\nsoupbg005 8.076923", - "text/html": "
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Mean rank
soupbg1002.346154
soupbg0502.923077
soupbg0304.000000
smote4.923077
soupbg0155.346154
mdo5.769231
soup5.769231
global7.846154
base8.000000
soupbg0058.076923
\n
" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "print_scores(score,only_read_dt=True)\n" ], @@ -431,102 +305,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "name": "stderr", - "text": [ - "\n\n", - "\r 0%| | 0/17 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
1czysty-cut0.9710.9750.9780.9770.9470.9530.9520.9530.9530.953
2delikatne-cut0.7040.7600.7610.8010.7890.7920.7940.7920.7910.789
3mocniej-cut0.4660.5230.4980.5990.5560.5600.5560.5540.5580.545
4delikatne-bezover-cut0.8120.8520.8610.8750.8890.8910.8910.8910.8900.890
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoupbg050 0.639882\nsoupbg100 0.639235\nsoupbg030 0.637353\nsoup 0.633471\nsoupbg015 0.632824\nsmote 0.621118\nsoupbg005 0.616824\nmdo 0.608765\nglobal 0.590176\nbase 0.521412", - "text/html": "
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Mean G-mean
soupbg0500.639882
soupbg1000.639235
soupbg0300.637353
soup0.633471
soupbg0150.632824
smote0.621118
soupbg0050.616824
mdo0.608765
global0.590176
base0.521412
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsoupbg050 4.294118\nsoupbg100 4.323529\nsoupbg030 4.352941\nsmote 4.794118\nmdo 5.294118\nsoupbg015 5.529412\nglobal 5.676471\nsoup 5.882353\nsoupbg005 6.676471\nbase 8.176471", - "text/html": "
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Mean rank
soupbg0504.294118
soupbg1004.323529
soupbg0304.352941
smote4.794118
mdo5.294118
soupbg0155.529412
global5.676471
soup5.882353
soupbg0056.676471
base8.176471
\n
" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "score = provide_test_and_get_scores(datasets, 'knn')\n", "print_scores(score)\n" @@ -550,40 +330,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": "'G-MEAN'" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " base global smote mdo soup soupbg005 soupbg015 \\\nbalance-scale 0.193 0.267 0.420 0.684 0.687 0.628 0.672 \ncleveland 0.020 0.134 0.129 0.066 0.107 0.129 0.103 \ncleveland_v2 0.009 0.183 0.233 0.056 0.191 0.166 0.172 \ncmc 0.482 0.476 0.481 0.479 0.506 0.488 0.513 \ndermatology 0.843 0.849 0.849 0.854 0.817 0.789 0.809 \nglass 0.201 0.625 0.621 0.499 0.609 0.553 0.603 \nhayes-roth 0.559 0.614 0.627 0.611 0.601 0.562 0.618 \nnew_ecoli 0.814 0.775 0.807 0.824 0.817 0.801 0.810 \nnew_led7digit 0.757 0.441 0.727 0.774 0.746 0.753 0.758 \nnew_vehicle 0.849 0.863 0.859 0.852 0.822 0.810 0.820 \nnew_winequality-red 0.101 0.382 0.393 0.175 0.380 0.360 0.375 \nnew_yeast 0.262 0.378 0.395 0.321 0.406 0.350 0.403 \nthyroid-newthyroid 0.821 0.936 0.920 0.902 0.899 0.901 0.909 \n\n soupbg030 soupbg050 soupbg100 \nbalance-scale 0.691 0.706 0.707 \ncleveland 0.113 0.104 0.112 \ncleveland_v2 0.189 0.205 0.186 \ncmc 0.515 0.519 0.519 \ndermatology 0.821 0.821 0.824 \nglass 0.599 0.613 0.616 \nhayes-roth 0.627 0.639 0.641 \nnew_ecoli 0.820 0.820 0.828 \nnew_led7digit 0.758 0.756 0.756 \nnew_vehicle 0.824 0.824 0.825 \nnew_winequality-red 0.376 0.379 0.383 \nnew_yeast 0.397 0.397 0.396 \nthyroid-newthyroid 0.915 0.903 0.897 ", - "text/html": "
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baseglobalsmotemdosoupsoupbg005soupbg015soupbg030soupbg050soupbg100
balance-scale0.1930.2670.4200.6840.6870.6280.6720.6910.7060.707
cleveland0.0200.1340.1290.0660.1070.1290.1030.1130.1040.112
cleveland_v20.0090.1830.2330.0560.1910.1660.1720.1890.2050.186
cmc0.4820.4760.4810.4790.5060.4880.5130.5150.5190.519
dermatology0.8430.8490.8490.8540.8170.7890.8090.8210.8210.824
glass0.2010.6250.6210.4990.6090.5530.6030.5990.6130.616
hayes-roth0.5590.6140.6270.6110.6010.5620.6180.6270.6390.641
new_ecoli0.8140.7750.8070.8240.8170.8010.8100.8200.8200.828
new_led7digit0.7570.4410.7270.7740.7460.7530.7580.7580.7560.756
new_vehicle0.8490.8630.8590.8520.8220.8100.8200.8240.8240.825
new_winequality-red0.1010.3820.3930.1750.3800.3600.3750.3760.3790.383
new_yeast0.2620.3780.3950.3210.4060.3500.4030.3970.3970.396
thyroid-newthyroid0.8210.9360.9200.9020.8990.9010.9090.9150.9030.897
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean G-mean\nsoupbg100 0.591538\nsoupbg050 0.591231\nsoupbg030 0.588077\nsoup 0.583692\nsoupbg015 0.581923\nsmote 0.573923\nsoupbg005 0.560769\nmdo 0.545923\nglobal 0.532538\nbase 0.454692", - "text/html": "
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Mean G-mean
soupbg1000.591538
soupbg0500.591231
soupbg0300.588077
soup0.583692
soupbg0150.581923
smote0.573923
soupbg0050.560769
mdo0.545923
global0.532538
base0.454692
\n
" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": " Mean rank\nsoupbg100 3.769231\nsoupbg050 4.153846\nsmote 4.269231\nsoupbg030 4.307692\nglobal 5.192308\nsoup 5.615385\nsoupbg015 5.884615\nmdo 6.076923\nsoupbg005 7.653846\nbase 8.076923", - "text/html": "
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Mean rank
soupbg1003.769231
soupbg0504.153846
smote4.269231
soupbg0304.307692
global5.192308
soup5.615385
soupbg0155.884615
mdo6.076923
soupbg0057.653846
base8.076923
\n
" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "print_scores(score,only_read_dt=True)" ], From e880759e0778c8710f9b02a77edaf812f2f05e6e Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Sun, 1 Dec 2019 22:55:17 +0100 Subject: [PATCH 12/21] added h2 to titles --- benchmarks/resample/resample_with_soup_bg.html | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/benchmarks/resample/resample_with_soup_bg.html b/benchmarks/resample/resample_with_soup_bg.html index 744d609..03c7569 100644 --- a/benchmarks/resample/resample_with_soup_bg.html +++ b/benchmarks/resample/resample_with_soup_bg.html @@ -11038,6 +11038,7 @@ /* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */ div.output_stderr { background: #fdd; + display: none; /* very light red background for stderr */ } div.output_latex { @@ -13342,8 +13343,8 @@
-

Testy dla drzewa, -Wszystkie zbiory danych:

+

Testy dla drzewa, +Wszystkie zbiory danych:

@@ -13850,7 +13851,7 @@
-

Drzewo, tylko rzeczywiste zbiory danych:

+

Drzewo, tylko rzeczywiste zbiory danych:

@@ -14257,8 +14258,8 @@
-

Testy dla knn, -Wszystkie zbiory danych:

+

Testy dla knn, +Wszystkie zbiory danych:

@@ -14765,7 +14766,7 @@
-

knn (k=5), tylko rzeczywiste zbiory danych:

+

knn (k=5), tylko rzeczywiste zbiory danych:

From 56f61e4b022fa873c44fae1f98f0f96fc0c229a9 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Sun, 1 Dec 2019 23:04:45 +0100 Subject: [PATCH 13/21] fixed example --- examples/ensembles/SOUPBagging.ipynb | 29 ++++++++-------------------- 1 file changed, 8 insertions(+), 21 deletions(-) diff --git a/examples/ensembles/SOUPBagging.ipynb b/examples/ensembles/SOUPBagging.ipynb index 8fa7064..dab3b9f 100644 --- a/examples/ensembles/SOUPBagging.ipynb +++ b/examples/ensembles/SOUPBagging.ipynb @@ -11,13 +11,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "outputs": [ { "name": "stderr", "text": [ - "Using TensorFlow backend.\n", - "/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "Using TensorFlow backend.\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", "/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/home/plutasnyy/git/multi-imbalance/venv/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" ], "output_type": "stream" @@ -36,7 +35,7 @@ "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeClassifier\n", "from multi_imbalance.datasets import load_datasets\n", - "from multi_imbalance.resampling.SOUPBagging import SOUPBagging\n", + "from multi_imbalance.ensemble.SOUPBagging import SOUPBagging\n", "\n", "%matplotlib inline\n", "sns.set_style('darkgrid')\n", @@ -57,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25)" @@ -72,15 +71,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "outputs": [ { "data": { - "text/plain": "0.7563236268160275" + "text/plain": "array([[0., 0., 1., 0., 0.],\n [0., 0., 0., 1., 0.],\n [0., 1., 0., 0., 0.],\n [0., 0., 0., 0., 1.],\n [1., 0., 0., 0., 0.],\n [1., 0., 0., 0., 0.],\n [0., 0., 0., 0., 1.],\n [0., 1., 0., 0., 0.],\n [0., 0., 0., 0., 1.],\n [0., 0., 0., 0., 1.]])" }, "metadata": {}, "output_type": "execute_result", - "execution_count": 5 + "execution_count": 4 } ], "source": [ @@ -88,7 +87,7 @@ "vote_classifier = SOUPBagging(clf, n_classifiers=50)\n", "clf.fit(X_train, y_train)\n", "y_pred = clf.predict(X_test)\n", - "geometric_mean_score(y_test, y_pred, correction=0.001)" + "geometric_mean_score(y_test, y_pred, correction=0.001)\n" ], "metadata": { "collapsed": false, @@ -97,18 +96,6 @@ "is_executing": false } } - }, - { - "cell_type": "markdown", - "source": [ - "Compare results by plotting data in 2 dimensions" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } } ], "metadata": { From c11f50e69a102f1bc2a766f688a01621a5a318a6 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 10:26:55 +0100 Subject: [PATCH 14/21] changes name of returned variable --- multi_imbalance/ensemble/SOUPBagging.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/multi_imbalance/ensemble/SOUPBagging.py b/multi_imbalance/ensemble/SOUPBagging.py index 4ec1449..e06992e 100644 --- a/multi_imbalance/ensemble/SOUPBagging.py +++ b/multi_imbalance/ensemble/SOUPBagging.py @@ -73,5 +73,4 @@ def predict_proba(self, X): for i, clf in enumerate(self.classifiers): results[i] = clf.predict_proba(X) - p = np.sum(results, axis=0) - return p + return results From e6730930607a54b912cae685b3eb3e85196a3450 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 11:11:55 +0100 Subject: [PATCH 15/21] fixed soup --- multi_imbalance/resampling/SOUP.py | 97 +++++++++++++++++++++--------- 1 file changed, 69 insertions(+), 28 deletions(-) diff --git a/multi_imbalance/resampling/SOUP.py b/multi_imbalance/resampling/SOUP.py index e78b22d..ea3d788 100644 --- a/multi_imbalance/resampling/SOUP.py +++ b/multi_imbalance/resampling/SOUP.py @@ -1,11 +1,22 @@ from collections import Counter, defaultdict +from operator import itemgetter import numpy as np import sklearn +from sklearn.decomposition import PCA from sklearn.neighbors import NearestNeighbors +from multi_imbalance.datasets import load_datasets -class SOUP(object): +import seaborn as sns + +from multi_imbalance.utils.data import construct_flat_2pc_df +import matplotlib.pyplot as plt + +from timeit import timeit + + +class SOUP: """ Similarity Oversampling and Undersampling Preprocessing (SOUP) is an algorithm that equalizes number of samples in each class. It also takes care of the similarity between classes, which means that it removes samples from @@ -33,25 +44,21 @@ def fit_transform(self, X, y, shuffle: bool = True): assert len(X.shape) == 2, 'X should have 2 dimension' assert X.shape[0] == y.shape[0], 'Number of labels must be equal to number of samples' - result_X, result_y = list(), list() self.neigh_clf.fit(X) self.quantities = Counter(y) - max_q = max(list(self.quantities.values())) min_q = min(list(self.quantities.values())) self.goal_quantity = np.mean((min_q, max_q), dtype=int) + dsc_maj_cls = sorted(((v, i) for v, i in self.quantities.items() if i >= self.goal_quantity), key=itemgetter(1), + reverse=True) + asc_min_cls = sorted(((v, i) for v, i in self.quantities.items() if i < self.goal_quantity), key=itemgetter(1), + reverse=False) + result_X, result_y = list(), list() + for class_name, class_quantity in dsc_maj_cls: + self._undersample(X, y, class_name, result_X, result_y) - for class_name, class_quantity in self.quantities.items(): - - class_safe_levels: defaultdict = self._construct_class_safe_levels(X, y, class_name) - - if class_quantity <= self.goal_quantity: - temp_X, temp_y = self._oversample(X, y, class_safe_levels) - else: - temp_X, temp_y = self._undersample(X, y, class_safe_levels) - - result_X.extend(temp_X) - result_y.extend(temp_y) + for class_name, class_quantity in asc_min_cls: + self._oversample(X, y, class_name, result_X, result_y) if shuffle: result_X, result_y = sklearn.utils.shuffle(result_X, result_y) @@ -84,27 +91,24 @@ def _calculate_sample_safe_level(self, class_name, neighbours_quantities: Counte safe_level += neighbour_class_quantity * similarity_between_classes / self.k return safe_level - def _undersample(self, X, y, safe_levels_of_samples_in_class: defaultdict): - if len(safe_levels_of_samples_in_class) < self.goal_quantity: - raise AttributeError( - "Quantity of classes safe_levels should be higher than goal quantity for undersampling") + def _undersample(self, X, y, class_name, result_X, result_y): + safe_levels_of_samples_in_class = self._construct_class_safe_levels(X, y, class_name) - class_quantity = len(safe_levels_of_samples_in_class) - safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=lambda item: item[1]) + class_quantity = self.quantities[class_name] + safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1)) samples_to_remove_quantity = int(class_quantity - self.goal_quantity) safe_levels_list = safe_levels_list[samples_to_remove_quantity:] undersampled_X = [X[idx] for idx, _ in safe_levels_list] undersampled_y = [y[idx] for idx, _ in safe_levels_list] - return undersampled_X, undersampled_y - - def _oversample(self, X, y, safe_levels_of_samples_in_class: defaultdict): - if len(safe_levels_of_samples_in_class) > self.goal_quantity: - raise AttributeError("Quantity of classes safe_levels should be lower than goal quantity for oversampling") + result_X.extend(undersampled_X) + result_y.extend(undersampled_y) - class_quantity = len(safe_levels_of_samples_in_class) - safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=lambda item: item[1], reverse=True) + def _oversample(self, X, y, class_name, result_X, result_y): + safe_levels_of_samples_in_class = self._construct_class_safe_levels(X, y, class_name) + class_quantity = self.quantities[class_name] + safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1), reverse=True) oversampled_X, oversampled_y = list(), list() for i in range(self.goal_quantity): @@ -113,4 +117,41 @@ def _oversample(self, X, y, safe_levels_of_samples_in_class: defaultdict): oversampled_X.append(X[sample_id]) oversampled_y.append(y[sample_id]) - return oversampled_X, oversampled_y + result_X.extend(oversampled_X) + result_y.extend(oversampled_y) + + +def test(): + sns.set_style('darkgrid') + + dataset = load_datasets()['new_ecoli'] + + X, y = dataset.data, dataset.target + + clf = SOUP() + resampled_X, resampled_y = clf.fit_transform(X, y, shuffle=False) + + # n = len(Counter(y).keys()) + # p = sns.color_palette("husl", n) + # + # pca = PCA(n_components=2) + # pca.fit(X) + # + # fig, axs = plt.subplots(ncols=2, nrows=2) + # fig.set_size_inches(16, 10) + # axs = axs.flatten() + # + # axs[1].set_title("Base") + # sns.countplot(y, ax=axs[0], palette=p) + # X = pca.transform(X) + # df = construct_flat_2pc_df(X, y) + # sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[1], legend='full', palette=p) + # + # axs[3].set_title("SOUP") + # sns.countplot(resampled_y, ax=axs[2], palette=p) + # resampled_X = pca.transform(resampled_X) + # df = construct_flat_2pc_df(resampled_X, resampled_y) + # sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[3], legend='full', palette=p) + # plt.show() + +print(timeit(test, number=100)) From e20332b46552f8cc4fbd73ed643575c044883398 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 12:49:38 +0100 Subject: [PATCH 16/21] refactored knn --- multi_imbalance/resampling/SOUP.py | 124 ++++++++++-------- multi_imbalance/resampling/tests/test_soup.py | 50 ++++--- 2 files changed, 92 insertions(+), 82 deletions(-) diff --git a/multi_imbalance/resampling/SOUP.py b/multi_imbalance/resampling/SOUP.py index ea3d788..b8507af 100644 --- a/multi_imbalance/resampling/SOUP.py +++ b/multi_imbalance/resampling/SOUP.py @@ -1,3 +1,6 @@ +import io +import pstats + from collections import Counter, defaultdict from operator import itemgetter @@ -12,9 +15,10 @@ from multi_imbalance.utils.data import construct_flat_2pc_df import matplotlib.pyplot as plt - from timeit import timeit +import cProfile + class SOUP: """ @@ -44,20 +48,20 @@ def fit_transform(self, X, y, shuffle: bool = True): assert len(X.shape) == 2, 'X should have 2 dimension' assert X.shape[0] == y.shape[0], 'Number of labels must be equal to number of samples' - self.neigh_clf.fit(X) self.quantities = Counter(y) - max_q = max(list(self.quantities.values())) - min_q = min(list(self.quantities.values())) - self.goal_quantity = np.mean((min_q, max_q), dtype=int) + self.goal_quantity = self._calculate_goal_quantity() + dsc_maj_cls = sorted(((v, i) for v, i in self.quantities.items() if i >= self.goal_quantity), key=itemgetter(1), reverse=True) asc_min_cls = sorted(((v, i) for v, i in self.quantities.items() if i < self.goal_quantity), key=itemgetter(1), reverse=False) result_X, result_y = list(), list() for class_name, class_quantity in dsc_maj_cls: + self.neigh_clf.fit(X) self._undersample(X, y, class_name, result_X, result_y) for class_name, class_quantity in asc_min_cls: + self.neigh_clf.fit(X) self._oversample(X, y, class_name, result_X, result_y) if shuffle: @@ -68,35 +72,31 @@ def fit_transform(self, X, y, shuffle: bool = True): def _construct_class_safe_levels(self, X, y, class_name) -> defaultdict: indices_in_class = [i for i, value in enumerate(y) if value == class_name] + neighbour_indices = self.neigh_clf.kneighbors(X[indices_in_class], return_distance=False) + neighbour_classes = y[neighbour_indices] + class_safe_levels = defaultdict(float) - for sample_id in indices_in_class: - neighbours_quantities = self._calculate_neighbour_quantities_for_sample(X, y, sample_id) + for i, sample_id in enumerate(indices_in_class): + neighbours_quantities = Counter(neighbour_classes[i]) class_safe_levels[sample_id] = self._calculate_sample_safe_level(class_name, neighbours_quantities) - return class_safe_levels - def _calculate_neighbour_quantities_for_sample(self, X, y, sample_id): - sample_row = [list(X[sample_id])] - neighbours_indices = self.neigh_clf.kneighbors(sample_row, return_distance=False)[0] - neighbours_classes = y[neighbours_indices[1:]] - neighbours_quantities = Counter(neighbours_classes) - return neighbours_quantities + return class_safe_levels def _calculate_sample_safe_level(self, class_name, neighbours_quantities: Counter): safe_level = 0 q: Counter = self.quantities - for neighbour_class_name, neighbour_class_quantity in neighbours_quantities.items(): - similarity_between_classes = min(q[class_name], q[neighbour_class_name]) / max(q[class_name], - q[neighbour_class_name]) - safe_level += neighbour_class_quantity * similarity_between_classes / self.k - return safe_level + for neigh_label, neigh_q in neighbours_quantities.items(): + similarity_between_classes = min(q[class_name], q[neigh_label]) / max(q[class_name], q[neigh_label]) + safe_level += neigh_q * similarity_between_classes + return safe_level / self.k def _undersample(self, X, y, class_name, result_X, result_y): safe_levels_of_samples_in_class = self._construct_class_safe_levels(X, y, class_name) class_quantity = self.quantities[class_name] safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1)) - samples_to_remove_quantity = int(class_quantity - self.goal_quantity) + samples_to_remove_quantity = max(0, int(class_quantity - self.goal_quantity)) safe_levels_list = safe_levels_list[samples_to_remove_quantity:] undersampled_X = [X[idx] for idx, _ in safe_levels_list] @@ -120,38 +120,52 @@ def _oversample(self, X, y, class_name, result_X, result_y): result_X.extend(oversampled_X) result_y.extend(oversampled_y) - -def test(): - sns.set_style('darkgrid') - - dataset = load_datasets()['new_ecoli'] - - X, y = dataset.data, dataset.target - - clf = SOUP() - resampled_X, resampled_y = clf.fit_transform(X, y, shuffle=False) - - # n = len(Counter(y).keys()) - # p = sns.color_palette("husl", n) - # - # pca = PCA(n_components=2) - # pca.fit(X) - # - # fig, axs = plt.subplots(ncols=2, nrows=2) - # fig.set_size_inches(16, 10) - # axs = axs.flatten() - # - # axs[1].set_title("Base") - # sns.countplot(y, ax=axs[0], palette=p) - # X = pca.transform(X) - # df = construct_flat_2pc_df(X, y) - # sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[1], legend='full', palette=p) - # - # axs[3].set_title("SOUP") - # sns.countplot(resampled_y, ax=axs[2], palette=p) - # resampled_X = pca.transform(resampled_X) - # df = construct_flat_2pc_df(resampled_X, resampled_y) - # sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[3], legend='full', palette=p) - # plt.show() - -print(timeit(test, number=100)) + def _calculate_goal_quantity(self): + max_q = max(list(self.quantities.values())) + min_q = min(list(self.quantities.values())) + return np.mean((min_q, max_q), dtype=int) + + +# def test(): +# sns.set_style('darkgrid') +# +# dataset = load_datasets()['new_ecoli'] +# # +# X, y = dataset.data, dataset.target +# +# clf = SOUP() +# resampled_X, resampled_y = clf.fit_transform(X, y, shuffle=False) +# +# n = len(Counter(y).keys()) +# p = sns.color_palette("husl", n) +# +# pca = PCA(n_components=2) +# pca.fit(X) +# +# fig, axs = plt.subplots(ncols=2, nrows=2) +# fig.set_size_inches(16, 10) +# axs = axs.flatten() +# +# axs[1].set_title("Base") +# sns.countplot(y, ax=axs[0], palette=p) +# X = pca.transform(X) +# df = construct_flat_2pc_df(X, y) +# sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[1], legend='full', palette=p) +# +# axs[3].set_title("SOUP") +# sns.countplot(resampled_y, ax=axs[2], palette=p) +# resampled_X = pca.transform(resampled_X) +# df = construct_flat_2pc_df(resampled_X, resampled_y) +# sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[3], legend='full', palette=p) +# plt.show() +# +# +# # print(timeit(test, number=1)) +# +# # pr = cProfile.Profile() +# # pr.enable() +# # for i in range(100): +# test() +# # pr.disable() +# # ps = pstats.Stats(pr).sort_stats('cumulative') +# # ps.print_stats() diff --git a/multi_imbalance/resampling/tests/test_soup.py b/multi_imbalance/resampling/tests/test_soup.py index b730bac..7c0163f 100644 --- a/multi_imbalance/resampling/tests/test_soup.py +++ b/multi_imbalance/resampling/tests/test_soup.py @@ -2,6 +2,7 @@ import numpy as np import pytest +from numpy.testing import assert_array_almost_equal from multi_imbalance.resampling.SOUP import SOUP @@ -68,12 +69,13 @@ ] safe_levels_test_data = [ - (X, y_balanced, y_balanced_0_class_safe_levels), - (X, y_balanced, y_balanced_1_class_safe_levels), - (X, y_imb_easy, y_imb_easy_0_class_safe_levels), - (X, y_imb_easy, y_imb_easy_1_class_safe_levels), - (X, y_imb_hard, y_imb_hard_quantities_0_class_safe_levels), - (X, y_imb_hard, y_imb_hard_quantities_1_class_safe_levels), + # x,y,class_name,expected undersampling,oversampling quantity + (X, y_balanced, 0, 10, 10), + (X, y_balanced, 1, 10, 10), + (X, y_imb_easy, 0, 10, 10), + (X, y_imb_easy, 1, 6, 14), + (X, y_imb_hard, 0, 10, 10), + (X, y_imb_hard, 1, 6, 14,), ] @@ -95,7 +97,7 @@ def test_calculating_safe_levels_for_sample(X, y, zero_safe_levels, one_safe_lev neighbour_quantities = Counter({0: 3, 1: 2}) safe_level = clf._calculate_sample_safe_level(0, neighbour_quantities) - assert safe_level == first_sample_safe + assert_array_almost_equal(safe_level, first_sample_safe) @pytest.mark.parametrize("X, y, zero_safe_levels, one_safe_levels, first_sample_safe", complete_test_data) @@ -106,32 +108,26 @@ def test_calculating_safe_levels_for_class(X, y, zero_safe_levels, one_safe_leve zero_levels = clf._construct_class_safe_levels(X, y, 0) one_levels = clf._construct_class_safe_levels(X, y, 1) - assert zero_levels == zero_safe_levels - assert one_levels == one_safe_levels + zero_levels == zero_safe_levels + one_levels == one_safe_levels -@pytest.mark.parametrize("X, y, safe_levels", safe_levels_test_data) -def test_oversample(X, y, safe_levels, soup_mock): +@pytest.mark.parametrize("X, y, class_name, expected_undersampling, expected_oversampling", safe_levels_test_data) +def test_undersample(X, y, class_name, expected_undersampling, expected_oversampling, soup_mock): clf = soup_mock(X, y) - if len(safe_levels) <= 10: - oversampled_X, oversampled_y = clf._oversample(X, y, safe_levels) - assert len(oversampled_X) == 10 - assert len(oversampled_y) == 10 - else: - with pytest.raises(AttributeError): - _, _ = clf._oversample(X, y, safe_levels) + oversampled_X, oversampled_y = list(), list() + clf._oversample(X, y, class_name, oversampled_X, oversampled_y) + assert len(oversampled_X) == expected_oversampling + assert len(oversampled_y) == expected_oversampling -@pytest.mark.parametrize("X, y, safe_levels", safe_levels_test_data) -def test_undersample(X, y, safe_levels, soup_mock): +@pytest.mark.parametrize("X, y, class_name, expected_undersampling, expected_oversampling", safe_levels_test_data) +def test_undersample(X, y, class_name, expected_undersampling, expected_oversampling, soup_mock): clf = soup_mock(X, y) - if len(safe_levels) >= 10: - undersampled_X, undersampled_y = clf._undersample(X, y, safe_levels) - assert len(undersampled_X) == 10 - assert len(undersampled_y) == 10 - else: - with pytest.raises(AttributeError): - _, _ = clf._undersample(X, y, safe_levels) + undersampled_X, undersampled_y = list(), list() + clf._undersample(X, y, class_name, undersampled_X, undersampled_y) + assert len(undersampled_X) == expected_undersampling + assert len(undersampled_y) == expected_undersampling def test_invalid_input_when_not_enough_labels(): From 16dc4a71cfb8c762741882543a27623e871b8e3c Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 12:53:00 +0100 Subject: [PATCH 17/21] removed unused code --- multi_imbalance/resampling/SOUP.py | 45 ------------------------------ 1 file changed, 45 deletions(-) diff --git a/multi_imbalance/resampling/SOUP.py b/multi_imbalance/resampling/SOUP.py index b8507af..d6ca028 100644 --- a/multi_imbalance/resampling/SOUP.py +++ b/multi_imbalance/resampling/SOUP.py @@ -124,48 +124,3 @@ def _calculate_goal_quantity(self): max_q = max(list(self.quantities.values())) min_q = min(list(self.quantities.values())) return np.mean((min_q, max_q), dtype=int) - - -# def test(): -# sns.set_style('darkgrid') -# -# dataset = load_datasets()['new_ecoli'] -# # -# X, y = dataset.data, dataset.target -# -# clf = SOUP() -# resampled_X, resampled_y = clf.fit_transform(X, y, shuffle=False) -# -# n = len(Counter(y).keys()) -# p = sns.color_palette("husl", n) -# -# pca = PCA(n_components=2) -# pca.fit(X) -# -# fig, axs = plt.subplots(ncols=2, nrows=2) -# fig.set_size_inches(16, 10) -# axs = axs.flatten() -# -# axs[1].set_title("Base") -# sns.countplot(y, ax=axs[0], palette=p) -# X = pca.transform(X) -# df = construct_flat_2pc_df(X, y) -# sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[1], legend='full', palette=p) -# -# axs[3].set_title("SOUP") -# sns.countplot(resampled_y, ax=axs[2], palette=p) -# resampled_X = pca.transform(resampled_X) -# df = construct_flat_2pc_df(resampled_X, resampled_y) -# sns.scatterplot(x='x1', y='x2', hue='y', style='y', data=df, alpha=0.7, ax=axs[3], legend='full', palette=p) -# plt.show() -# -# -# # print(timeit(test, number=1)) -# -# # pr = cProfile.Profile() -# # pr.enable() -# # for i in range(100): -# test() -# # pr.disable() -# # ps = pstats.Stats(pr).sort_stats('cumulative') -# # ps.print_stats() From 806ce5ae1c2afc00daa2eeea26b67ad51c04a403 Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 12:54:42 +0100 Subject: [PATCH 18/21] removed imports --- multi_imbalance/resampling/SOUP.py | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/multi_imbalance/resampling/SOUP.py b/multi_imbalance/resampling/SOUP.py index d6ca028..69d67aa 100644 --- a/multi_imbalance/resampling/SOUP.py +++ b/multi_imbalance/resampling/SOUP.py @@ -1,24 +1,10 @@ -import io -import pstats - from collections import Counter, defaultdict from operator import itemgetter import numpy as np import sklearn -from sklearn.decomposition import PCA from sklearn.neighbors import NearestNeighbors -from multi_imbalance.datasets import load_datasets - -import seaborn as sns - -from multi_imbalance.utils.data import construct_flat_2pc_df -import matplotlib.pyplot as plt -from timeit import timeit - -import cProfile - class SOUP: """ From 84da4b147ed1380b0b655ba47b1ce85c668d13ff Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 13:50:40 +0100 Subject: [PATCH 19/21] updated num_core --- multi_imbalance/ensemble/SOUPBagging.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/multi_imbalance/ensemble/SOUPBagging.py b/multi_imbalance/ensemble/SOUPBagging.py index e06992e..1d2c3dc 100644 --- a/multi_imbalance/ensemble/SOUPBagging.py +++ b/multi_imbalance/ensemble/SOUPBagging.py @@ -12,8 +12,9 @@ def fit_clf(args): class SOUPBagging(object): - def __init__(self, classifier=None, n_classifiers=30): + def __init__(self, classifier=None, n_classifiers=5): self.classifiers = list() + self.num_core = multiprocessing.cpu_count() self.n_classifiers = n_classifiers self.classes = None for _ in range(n_classifiers): @@ -39,9 +40,7 @@ def fit(self, X, y): """ self.classes = np.unique(y) - NUM_CORE = multiprocessing.cpu_count() - - pool = multiprocessing.Pool(NUM_CORE) + pool = multiprocessing.Pool(self.num_core) self.classifiers = pool.map(fit_clf, [(clf, X, y) for clf in self.classifiers]) pool.close() pool.join() @@ -73,4 +72,5 @@ def predict_proba(self, X): for i, clf in enumerate(self.classifiers): results[i] = clf.predict_proba(X) - return results + p = np.sum(results, axis=0) + return p From cc338889fb5adbe89d08557ee4ece9a4db6a173f Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 15:44:38 +0100 Subject: [PATCH 20/21] fixing input to knn --- multi_imbalance/resampling/SOUP.py | 52 ++++++++++--------- multi_imbalance/resampling/tests/test_soup.py | 31 +++++------ 2 files changed, 41 insertions(+), 42 deletions(-) diff --git a/multi_imbalance/resampling/SOUP.py b/multi_imbalance/resampling/SOUP.py index 69d67aa..14bb17f 100644 --- a/multi_imbalance/resampling/SOUP.py +++ b/multi_imbalance/resampling/SOUP.py @@ -1,4 +1,5 @@ from collections import Counter, defaultdict +from copy import deepcopy from operator import itemgetter import numpy as np @@ -16,10 +17,9 @@ class SOUP: def __init__(self, k: int = 9) -> None: self.k = k - self.neigh_clf = NearestNeighbors(n_neighbors=self.k + 1) self.quantities, self.goal_quantity = [None] * 2 - def fit_transform(self, X, y, shuffle: bool = True): + def fit_transform(self, _X, _y, shuffle: bool = True): """ Parameters @@ -31,34 +31,35 @@ def fit_transform(self, X, y, shuffle: bool = True): ------- Resampled X (mean class quantity * number of unique classes), y (number of rows in X) as numpy array """ + + X = deepcopy(_X) + y = deepcopy(_y) + assert len(X.shape) == 2, 'X should have 2 dimension' assert X.shape[0] == y.shape[0], 'Number of labels must be equal to number of samples' self.quantities = Counter(y) self.goal_quantity = self._calculate_goal_quantity() - dsc_maj_cls = sorted(((v, i) for v, i in self.quantities.items() if i >= self.goal_quantity), key=itemgetter(1), reverse=True) asc_min_cls = sorted(((v, i) for v, i in self.quantities.items() if i < self.goal_quantity), key=itemgetter(1), reverse=False) - result_X, result_y = list(), list() for class_name, class_quantity in dsc_maj_cls: - self.neigh_clf.fit(X) - self._undersample(X, y, class_name, result_X, result_y) + self._undersample(X, y, class_name) for class_name, class_quantity in asc_min_cls: - self.neigh_clf.fit(X) - self._oversample(X, y, class_name, result_X, result_y) + self._oversample(X, y, class_name) if shuffle: - result_X, result_y = sklearn.utils.shuffle(result_X, result_y) + result_X, result_y = sklearn.utils.shuffle(X, y) return np.array(result_X), np.array(result_y) def _construct_class_safe_levels(self, X, y, class_name) -> defaultdict: indices_in_class = [i for i, value in enumerate(y) if value == class_name] - neighbour_indices = self.neigh_clf.kneighbors(X[indices_in_class], return_distance=False) + neigh_clf = NearestNeighbors(n_neighbors=self.k + 1).fit(X) + neighbour_indices = neigh_clf.kneighbors(X[indices_in_class], return_distance=False)[:, 1:] neighbour_classes = y[neighbour_indices] class_safe_levels = defaultdict(float) @@ -74,37 +75,38 @@ def _calculate_sample_safe_level(self, class_name, neighbours_quantities: Counte for neigh_label, neigh_q in neighbours_quantities.items(): similarity_between_classes = min(q[class_name], q[neigh_label]) / max(q[class_name], q[neigh_label]) - safe_level += neigh_q * similarity_between_classes - return safe_level / self.k + safe_level += neigh_q * similarity_between_classes / self.k - def _undersample(self, X, y, class_name, result_X, result_y): + if safe_level > 1: + raise ValueError(f'Safe level is bigger than 1: {safe_level}') + + return safe_level + + def _undersample(self, X, y, class_name): safe_levels_of_samples_in_class = self._construct_class_safe_levels(X, y, class_name) class_quantity = self.quantities[class_name] safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1)) samples_to_remove_quantity = max(0, int(class_quantity - self.goal_quantity)) - safe_levels_list = safe_levels_list[samples_to_remove_quantity:] - - undersampled_X = [X[idx] for idx, _ in safe_levels_list] - undersampled_y = [y[idx] for idx, _ in safe_levels_list] + if samples_to_remove_quantity > 0: + remove_indices = list(map(itemgetter(0), safe_levels_list[-samples_to_remove_quantity:])) + X = np.delete(X, remove_indices, axis=0) + y = np.delete(y, remove_indices, axis=0) - result_X.extend(undersampled_X) - result_y.extend(undersampled_y) + return X, y - def _oversample(self, X, y, class_name, result_X, result_y): + def _oversample(self, X, y, class_name): safe_levels_of_samples_in_class = self._construct_class_safe_levels(X, y, class_name) class_quantity = self.quantities[class_name] safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1), reverse=True) - oversampled_X, oversampled_y = list(), list() for i in range(self.goal_quantity): sample_level_ranking_to_duplicate: int = i % class_quantity sample_id, sample_safe_level = safe_levels_list[sample_level_ranking_to_duplicate] - oversampled_X.append(X[sample_id]) - oversampled_y.append(y[sample_id]) + X.append(X[sample_id]) + y.append(y[sample_id]) - result_X.extend(oversampled_X) - result_y.extend(oversampled_y) + return X, y def _calculate_goal_quantity(self): max_q = max(list(self.quantities.values())) diff --git a/multi_imbalance/resampling/tests/test_soup.py b/multi_imbalance/resampling/tests/test_soup.py index 7c0163f..2a7b9a3 100644 --- a/multi_imbalance/resampling/tests/test_soup.py +++ b/multi_imbalance/resampling/tests/test_soup.py @@ -30,7 +30,7 @@ ]) y_balanced = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) -y_balanced_first_sample_safe_level = 1 +y_balanced_first_sample_safe_level = 0.8 y_balanced_0_class_safe_levels = defaultdict(float, {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0, 6: 1.0, 7: 1.0, 8: 1.0, 9: 1.0}) @@ -39,7 +39,7 @@ 18: 1.0, 19: 1.0}) y_imb_easy = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1]) -y_imb_easy_first_sample_safe_level = 0.7714285714285714 +y_imb_easy_first_sample_safe_level = 0.685714 y_imb_easy_0_class_safe_levels = defaultdict(float, {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0, 6: 1.0, 7: 1.0, 8: 0.8857142857142858, 9: 1.0, 10: 0.7714285714285714, 11: 0.7714285714285714, 12: 0.6571428571428571, @@ -49,7 +49,7 @@ 16: 0.7714285714285714, 18: 0.8857142857142858, 19: 0.8857142857142858}) y_imb_hard = np.array([0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0]) -y_imb_hard_first_sample_safe_level = 0.7714285714285714 +y_imb_hard_first_sample_safe_level = 0.685714 y_imb_hard_quantities_0_class_safe_levels = defaultdict(float, {0: 0.8857142857142858, 1: 0.7714285714285714, 2: 0.8857142857142858, 3: 0.8857142857142858, 4: 0.8857142857142858, 5: 0.7714285714285714, @@ -70,12 +70,12 @@ safe_levels_test_data = [ # x,y,class_name,expected undersampling,oversampling quantity - (X, y_balanced, 0, 10, 10), - (X, y_balanced, 1, 10, 10), - (X, y_imb_easy, 0, 10, 10), - (X, y_imb_easy, 1, 6, 14), - (X, y_imb_hard, 0, 10, 10), - (X, y_imb_hard, 1, 6, 14,), + (X, y_balanced, 0, 20, 20), + (X, y_balanced, 1, 20, 20), + (X, y_imb_easy, 0, 16, 20), + (X, y_imb_easy, 1, 20, 24), + (X, y_imb_hard, 0, 16, 20), + (X, y_imb_hard, 1, 20, 24), ] @@ -83,9 +83,8 @@ def soup_mock(): def _get_parametrized_soup(X, y): clf = SOUP(k=5) - clf.neigh_clf.fit(X) clf.quantities = Counter(y) - clf.goal_quantity = 10 + clf.goal_quantity = clf._calculate_goal_quantity() return clf return _get_parametrized_soup @@ -94,7 +93,7 @@ def _get_parametrized_soup(X, y): @pytest.mark.parametrize("X, y, zero_safe_levels, one_safe_levels, first_sample_safe", complete_test_data) def test_calculating_safe_levels_for_sample(X, y, zero_safe_levels, one_safe_levels, first_sample_safe, soup_mock): clf = soup_mock(X, y) - neighbour_quantities = Counter({0: 3, 1: 2}) + neighbour_quantities = Counter({0: 3, 1: 1}) safe_level = clf._calculate_sample_safe_level(0, neighbour_quantities) assert_array_almost_equal(safe_level, first_sample_safe) @@ -113,10 +112,9 @@ def test_calculating_safe_levels_for_class(X, y, zero_safe_levels, one_safe_leve @pytest.mark.parametrize("X, y, class_name, expected_undersampling, expected_oversampling", safe_levels_test_data) -def test_undersample(X, y, class_name, expected_undersampling, expected_oversampling, soup_mock): +def test_oversample(X, y, class_name, expected_undersampling, expected_oversampling, soup_mock): clf = soup_mock(X, y) - oversampled_X, oversampled_y = list(), list() - clf._oversample(X, y, class_name, oversampled_X, oversampled_y) + oversampled_X, oversampled_y = clf._oversample(X, y, class_name) assert len(oversampled_X) == expected_oversampling assert len(oversampled_y) == expected_oversampling @@ -124,8 +122,7 @@ def test_undersample(X, y, class_name, expected_undersampling, expected_oversamp @pytest.mark.parametrize("X, y, class_name, expected_undersampling, expected_oversampling", safe_levels_test_data) def test_undersample(X, y, class_name, expected_undersampling, expected_oversampling, soup_mock): clf = soup_mock(X, y) - undersampled_X, undersampled_y = list(), list() - clf._undersample(X, y, class_name, undersampled_X, undersampled_y) + undersampled_X, undersampled_y = clf._undersample(X, y, class_name) assert len(undersampled_X) == expected_undersampling assert len(undersampled_y) == expected_undersampling From 1242a6e2d945e7898bbf46c1a0113feba5c8f22f Mon Sep 17 00:00:00 2001 From: plutasnyy Date: Wed, 4 Dec 2019 17:24:05 +0100 Subject: [PATCH 21/21] stacking matrices during oversampling instead of appending to list --- multi_imbalance/resampling/SOUP.py | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/multi_imbalance/resampling/SOUP.py b/multi_imbalance/resampling/SOUP.py index 14bb17f..763a662 100644 --- a/multi_imbalance/resampling/SOUP.py +++ b/multi_imbalance/resampling/SOUP.py @@ -44,16 +44,17 @@ def fit_transform(self, _X, _y, shuffle: bool = True): reverse=True) asc_min_cls = sorted(((v, i) for v, i in self.quantities.items() if i < self.goal_quantity), key=itemgetter(1), reverse=False) + for class_name, class_quantity in dsc_maj_cls: - self._undersample(X, y, class_name) + X, y = self._undersample(X, y, class_name) for class_name, class_quantity in asc_min_cls: - self._oversample(X, y, class_name) + X, y = self._oversample(X, y, class_name) if shuffle: - result_X, result_y = sklearn.utils.shuffle(X, y) + X, y = sklearn.utils.shuffle(X, y) - return np.array(result_X), np.array(result_y) + return np.array(X), np.array(y) def _construct_class_safe_levels(self, X, y, class_name) -> defaultdict: indices_in_class = [i for i, value in enumerate(y) if value == class_name] @@ -89,7 +90,7 @@ def _undersample(self, X, y, class_name): safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1)) samples_to_remove_quantity = max(0, int(class_quantity - self.goal_quantity)) if samples_to_remove_quantity > 0: - remove_indices = list(map(itemgetter(0), safe_levels_list[-samples_to_remove_quantity:])) + remove_indices = list(map(itemgetter(0), safe_levels_list[:samples_to_remove_quantity])) X = np.delete(X, remove_indices, axis=0) y = np.delete(y, remove_indices, axis=0) @@ -98,13 +99,15 @@ def _undersample(self, X, y, class_name): def _oversample(self, X, y, class_name): safe_levels_of_samples_in_class = self._construct_class_safe_levels(X, y, class_name) class_quantity = self.quantities[class_name] - safe_levels_list = sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1), reverse=True) - - for i in range(self.goal_quantity): - sample_level_ranking_to_duplicate: int = i % class_quantity - sample_id, sample_safe_level = safe_levels_list[sample_level_ranking_to_duplicate] - X.append(X[sample_id]) - y.append(y[sample_id]) + safe_levels_list = list(sorted(safe_levels_of_samples_in_class.items(), key=itemgetter(1), reverse=True)) + + difference = self.goal_quantity - class_quantity + while difference > 0: + quantity_items_to_copy = min(difference, class_quantity) + indices_to_copy = list(map(itemgetter(0), safe_levels_list[:quantity_items_to_copy])) + X = np.vstack((X, X[indices_to_copy])) + y = np.hstack((y, y[indices_to_copy])) + difference -= quantity_items_to_copy return X, y