Pushing the docs to dev/ for branch: main, commit 9aae2adf9198172ec2f583db03c046434709c036

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "# First aggregate all false positive rates\nall_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n\n# Then interpolate all ROC curves at this points\nmean_tpr = np.zeros_like(all_fpr)\nfor i in range(n_classes):\n    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n\n# Finally average it and compute AUC\nmean_tpr /= n_classes\n\nfpr[\"macro\"] = all_fpr\ntpr[\"macro\"] = mean_tpr\nroc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n\n# Plot all ROC curves\nplt.figure()\nplt.plot(\n    fpr[\"micro\"],\n    tpr[\"micro\"],\n    label=\"micro-average ROC curve (area = {0:0.2f})\".format(roc_auc[\"micro\"]),\n    color=\"deeppink\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\nplt.plot(\n    fpr[\"macro\"],\n    tpr[\"macro\"],\n    label=\"macro-average ROC curve (area = {0:0.2f})\".format(roc_auc[\"macro\"]),\n    color=\"navy\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\ncolors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\nfor i, color in zip(range(n_classes), colors):\n    plt.plot(\n        fpr[i],\n        tpr[i],\n        color=color,\n        lw=lw,\n        label=\"ROC curve of class {0} (area = {1:0.2f})\".format(i, roc_auc[i]),\n    )\n\nplt.plot([0, 1], [0, 1], \"k--\", lw=lw)\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Some extension of Receiver operating characteristic to multiclass\")\nplt.legend(loc=\"lower right\")\nplt.show()"
+        "# First aggregate all false positive rates\nall_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n\n# Then interpolate all ROC curves at these points\nmean_tpr = np.zeros_like(all_fpr)\nfor i in range(n_classes):\n    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n\n# Finally average it and compute AUC\nmean_tpr /= n_classes\n\nfpr[\"macro\"] = all_fpr\ntpr[\"macro\"] = mean_tpr\nroc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n\n# Plot all ROC curves\nplt.figure()\nplt.plot(\n    fpr[\"micro\"],\n    tpr[\"micro\"],\n    label=\"micro-average ROC curve (area = {0:0.2f})\".format(roc_auc[\"micro\"]),\n    color=\"deeppink\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\nplt.plot(\n    fpr[\"macro\"],\n    tpr[\"macro\"],\n    label=\"macro-average ROC curve (area = {0:0.2f})\".format(roc_auc[\"macro\"]),\n    color=\"navy\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\ncolors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\nfor i, color in zip(range(n_classes), colors):\n    plt.plot(\n        fpr[i],\n        tpr[i],\n        color=color,\n        lw=lw,\n        label=\"ROC curve of class {0} (area = {1:0.2f})\".format(i, roc_auc[i]),\n    )\n\nplt.plot([0, 1], [0, 1], \"k--\", lw=lw)\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Some extension of Receiver operating characteristic to multiclass\")\nplt.legend(loc=\"lower right\")\nplt.show()"
       ]
     },
     {

dev/_downloads/40f4aad91af595a370d7582e3a23bed7/plot_roc.ipynb

@@ -109,7 +109,7 @@
 # First aggregate all false positive rates
 all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
 
-# Then interpolate all ROC curves at this points
+# Then interpolate all ROC curves at these points
 mean_tpr = np.zeros_like(all_fpr)
 for i in range(n_classes):
     mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])