Pushing the docs to dev/ for branch: master, commit ec8d9f38aa1a3256cf4355ddbce8259a8bc3fb83

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -60,7 +60,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# 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\nmean_tpr = 0.0\nmean_fpr = np.linspace(0, 1, 100)\n\ncolors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])\nlw = 2\n\ni = 0\nfor (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\nplt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',\n         label='Luck')\n\nmean_tpr /= cv.get_n_splits(X, y)\nmean_tpr[-1] = 1.0\nmean_auc = auc(mean_fpr, mean_tpr)\nplt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',\n         label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)\n\nplt.xlim([-0.05, 1.05])\nplt.ylim([-0.05, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic example')\nplt.legend(loc=\"lower right\")\nplt.show()"
+        "# 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\ni = 0\nfor train, test in cv.split(X, y):\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    tprs.append(interp(mean_fpr, fpr, tpr))\n    tprs[-1][0] = 0.0\n    roc_auc = auc(fpr, tpr)\n    aucs.append(roc_auc)\n    plt.plot(fpr, tpr, lw=1, alpha=0.3,\n             label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n\n    i += 1\nplt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n         label='Luck', 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)\nplt.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)\nplt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n                 label=r'$\\pm$ 1 std. dev.')\n\nplt.xlim([-0.05, 1.05])\nplt.ylim([-0.05, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic example')\nplt.legend(loc=\"lower right\")\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -62,32 +62,39 @@
 classifier = svm.SVC(kernel='linear', probability=True,
                      random_state=random_state)
 
-mean_tpr = 0.0
+tprs = []
+aucs = []
 mean_fpr = np.linspace(0, 1, 100)
 
-colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])
-lw = 2
-
 i = 0
-for (train, test), color in zip(cv.split(X, y), colors):
+for train, test in cv.split(X, y):
     probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
     # Compute ROC curve and area the curve
     fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
-    mean_tpr += interp(mean_fpr, fpr, tpr)
-    mean_tpr[0] = 0.0
+    tprs.append(interp(mean_fpr, fpr, tpr))
+    tprs[-1][0] = 0.0
     roc_auc = auc(fpr, tpr)
-    plt.plot(fpr, tpr, lw=lw, color=color,
-             label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
+    aucs.append(roc_auc)
+    plt.plot(fpr, tpr, lw=1, alpha=0.3,
+             label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))
 
     i += 1
-plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',
-         label='Luck')
+plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
+         label='Luck', alpha=.8)
 
-mean_tpr /= cv.get_n_splits(X, y)
+mean_tpr = np.mean(tprs, axis=0)
 mean_tpr[-1] = 1.0
 mean_auc = auc(mean_fpr, mean_tpr)
-plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',
-         label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)
+std_auc = np.std(aucs)
+plt.plot(mean_fpr, mean_tpr, color='b',
+         label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
+         lw=2, alpha=.8)
+
+std_tpr = np.std(tprs, axis=0)
+tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
+tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
+plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
+                 label=r'$\pm$ 1 std. dev.')
 
 plt.xlim([-0.05, 1.05])
 plt.ylim([-0.05, 1.05])