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@mulloymorrow
Created October 18, 2016 20:47
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatically created module for IPython interactive environment\n",
"[[ 5.1 3.5 1.4 ..., -1.07709907 -0.4246633\n",
" -0.8299646 ]\n",
" [ 4.9 3. 1.4 ..., 1.4121517 -1.38043075\n",
" -0.53591456]\n",
" [ 4.7 3.2 1.3 ..., 0.65880214 -0.59691711\n",
" -0.22295918]\n",
" ..., \n",
" [ 6.2 2.9 4.3 ..., -0.8281054 -1.43286053\n",
" -0.60855489]\n",
" [ 5.1 2.5 3. ..., 0.06532704 -0.55380986\n",
" -0.88254487]\n",
" [ 5.7 2.8 4.1 ..., -1.04873453 0.37475842\n",
" -0.98731143]]\n"
]
}
],
"source": [
"print(__doc__)\n",
"\n",
"import numpy as np\n",
"from scipy import interp\n",
"import matplotlib.pyplot as plt\n",
"from itertools import cycle\n",
"\n",
"from sklearn import svm, datasets\n",
"from sklearn.metrics import roc_curve, auc\n",
"from sklearn.model_selection import StratifiedKFold\n",
"\n",
"\n",
"# import some data to play with\n",
"iris = datasets.load_iris()\n",
"X = iris.data\n",
"y = iris.target\n",
"X, y = X[y != 2], y[y != 2]\n",
"n_samples, n_features = X.shape\n",
"\n",
"# Add noisy features\n",
"random_state = np.random.RandomState(0)\n",
"X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n",
"print X"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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8li5dSseOHSlVqhSenp6EhobSs2dP1q1bl2WcEydOZO7cuQwcOJB58+bx9NNP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\nz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Pfff/e6Trfz5897ujncrUGpvycnT57M8kJP7jEVmX2yMh2zZ8+eXLx4kWnT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114a53b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import svm, datasets\n",
"\n",
"classifier = svm.SVC(kernel='linear', probability=True,\n",
" random_state=random_state)\n",
"# Run classifier with cross-validation and plot ROC curves\n",
"cv = StratifiedKFold(n_splits=6)\n",
"\n",
"mean_tpr = 0.0\n",
"mean_fpr = np.linspace(0, 1, 100)\n",
"\n",
"colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])\n",
"lw = 2\n",
"\n",
"i = 0\n",
"for (train, test), color in zip(cv.split(X, y), colors):\n",
" probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])\n",
" # Compute ROC curve and area the curve\n",
" fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])\n",
" mean_tpr += interp(mean_fpr, fpr, tpr)\n",
" mean_tpr[0] = 0.0\n",
" roc_auc = auc(fpr, tpr)\n",
" plt.plot(fpr, tpr, lw=lw, color=color,\n",
" label='ROC fold %d (area = %0.2f)' % (i, roc_auc))\n",
"\n",
" i += 1\n",
"plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',\n",
" label='Luck')\n",
"\n",
"mean_tpr /= cv.get_n_splits(X, y)\n",
"mean_tpr[-1] = 1.0\n",
"mean_auc = auc(mean_fpr, mean_tpr)\n",
"plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',\n",
" label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)\n",
"\n",
"plt.xlim([-0.05, 1.05])\n",
"plt.ylim([-0.05, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC curves')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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