Pushing the docs to dev/ for branch: master, commit 92ed38598f543c94bdbb9103862bc696cae01b13

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nfrom scipy import interp\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import plot_roc_curve\nfrom sklearn.model_selection import StratifiedKFold\n\n# #############################################################################\n# Data IO and generation\n\n# Import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nX, y = X[y != 2], y[y != 2]\nn_samples, n_features = X.shape\n\n# Add noisy features\nrandom_state = np.random.RandomState(0)\nX = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n\n# #############################################################################\n# Classification and ROC analysis\n\n# Run classifier with cross-validation and plot ROC curves\ncv = StratifiedKFold(n_splits=6)\nclassifier = svm.SVC(kernel='linear', probability=True,\n                     random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\nfig, ax = plt.subplots()\nfor i, (train, test) in enumerate(cv.split(X, y)):\n    classifier.fit(X[train], y[train])\n    viz = plot_roc_curve(classifier, X[test], y[test],\n                         name='ROC fold {}'.format(i),\n                         alpha=0.3, lw=1, ax=ax)\n    interp_tpr = interp(mean_fpr, viz.fpr, viz.tpr)\n    interp_tpr[0] = 0.0\n    tprs.append(interp_tpr)\n    aucs.append(viz.roc_auc)\n\nax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n        label='Chance', alpha=.8)\n\nmean_tpr = np.mean(tprs, axis=0)\nmean_tpr[-1] = 1.0\nmean_auc = auc(mean_fpr, mean_tpr)\nstd_auc = np.std(aucs)\nax.plot(mean_fpr, mean_tpr, color='b',\n        label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n        lw=2, alpha=.8)\n\nstd_tpr = np.std(tprs, axis=0)\ntprs_upper = np.minimum(mean_tpr + std_tpr, 1)\ntprs_lower = np.maximum(mean_tpr - std_tpr, 0)\nax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n                label=r'$\\pm$ 1 std. dev.')\n\nax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],\n       title=\"Receiver operating characteristic example\")\nax.legend(loc=\"lower right\")\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import plot_roc_curve\nfrom sklearn.model_selection import StratifiedKFold\n\n# #############################################################################\n# Data IO and generation\n\n# Import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nX, y = X[y != 2], y[y != 2]\nn_samples, n_features = X.shape\n\n# Add noisy features\nrandom_state = np.random.RandomState(0)\nX = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n\n# #############################################################################\n# Classification and ROC analysis\n\n# Run classifier with cross-validation and plot ROC curves\ncv = StratifiedKFold(n_splits=6)\nclassifier = svm.SVC(kernel='linear', probability=True,\n                     random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\nfig, ax = plt.subplots()\nfor i, (train, test) in enumerate(cv.split(X, y)):\n    classifier.fit(X[train], y[train])\n    viz = plot_roc_curve(classifier, X[test], y[test],\n                         name='ROC fold {}'.format(i),\n                         alpha=0.3, lw=1, ax=ax)\n    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n    interp_tpr[0] = 0.0\n    tprs.append(interp_tpr)\n    aucs.append(viz.roc_auc)\n\nax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n        label='Chance', alpha=.8)\n\nmean_tpr = np.mean(tprs, axis=0)\nmean_tpr[-1] = 1.0\nmean_auc = auc(mean_fpr, mean_tpr)\nstd_auc = np.std(aucs)\nax.plot(mean_fpr, mean_tpr, color='b',\n        label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n        lw=2, alpha=.8)\n\nstd_tpr = np.std(tprs, axis=0)\ntprs_upper = np.minimum(mean_tpr + std_tpr, 1)\ntprs_lower = np.maximum(mean_tpr - std_tpr, 0)\nax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n                label=r'$\\pm$ 1 std. dev.')\n\nax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],\n       title=\"Receiver operating characteristic example\")\nax.legend(loc=\"lower right\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/d33f7865941f1e2c2c62fcc641599cc5/plot_roc_crossval.ipynb

@@ -32,7 +32,6 @@
 print(__doc__)
 
 import numpy as np
-from scipy import interp
 import matplotlib.pyplot as plt
 
 from sklearn import svm, datasets
@@ -72,7 +71,7 @@
     viz = plot_roc_curve(classifier, X[test], y[test],
                          name='ROC fold {}'.format(i),
                          alpha=0.3, lw=1, ax=ax)
-    interp_tpr = interp(mean_fpr, viz.fpr, viz.tpr)
+    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
     interp_tpr[0] = 0.0
     tprs.append(interp_tpr)
     aucs.append(viz.roc_auc)