Pushing the docs to dev/ for branch: main, commit 4493f86c463689e4fed76240bfac98f9252d1bbd

Author: sklearn-ci

dev/_downloads/010337852815f8103ac6cca38a812b3c/plot_roc_crossval.py

@@ -89,8 +89,7 @@
         X[test],
         y[test],
         name=f"ROC fold {fold}",
-        alpha=0.3,
-        lw=1,
+        curve_kwargs=dict(alpha=0.3, lw=1),
         ax=ax,
         plot_chance_level=(fold == n_splits - 1),
     )

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom sklearn import svm\nfrom sklearn.metrics import RocCurveDisplay, auc\nfrom sklearn.model_selection import StratifiedKFold\n\nn_splits = 6\ncv = StratifiedKFold(n_splits=n_splits)\nclassifier = svm.SVC(kernel=\"linear\", probability=True, random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\nfig, ax = plt.subplots(figsize=(6, 6))\nfor fold, (train, test) in enumerate(cv.split(X, y)):\n    classifier.fit(X[train], y[train])\n    viz = RocCurveDisplay.from_estimator(\n        classifier,\n        X[test],\n        y[test],\n        name=f\"ROC fold {fold}\",\n        alpha=0.3,\n        lw=1,\n        ax=ax,\n        plot_chance_level=(fold == n_splits - 1),\n    )\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\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(\n    mean_fpr,\n    mean_tpr,\n    color=\"b\",\n    label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n    lw=2,\n    alpha=0.8,\n)\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(\n    mean_fpr,\n    tprs_lower,\n    tprs_upper,\n    color=\"grey\",\n    alpha=0.2,\n    label=r\"$\\pm$ 1 std. dev.\",\n)\n\nax.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=f\"Mean ROC curve with variability\\n(Positive label '{target_names[1]}')\",\n)\nax.legend(loc=\"lower right\")\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn import svm\nfrom sklearn.metrics import RocCurveDisplay, auc\nfrom sklearn.model_selection import StratifiedKFold\n\nn_splits = 6\ncv = StratifiedKFold(n_splits=n_splits)\nclassifier = svm.SVC(kernel=\"linear\", probability=True, random_state=random_state)\n\ntprs = []\naucs = []\nmean_fpr = np.linspace(0, 1, 100)\n\nfig, ax = plt.subplots(figsize=(6, 6))\nfor fold, (train, test) in enumerate(cv.split(X, y)):\n    classifier.fit(X[train], y[train])\n    viz = RocCurveDisplay.from_estimator(\n        classifier,\n        X[test],\n        y[test],\n        name=f\"ROC fold {fold}\",\n        curve_kwargs=dict(alpha=0.3, lw=1),\n        ax=ax,\n        plot_chance_level=(fold == n_splits - 1),\n    )\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\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(\n    mean_fpr,\n    mean_tpr,\n    color=\"b\",\n    label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n    lw=2,\n    alpha=0.8,\n)\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(\n    mean_fpr,\n    tprs_lower,\n    tprs_upper,\n    color=\"grey\",\n    alpha=0.2,\n    label=r\"$\\pm$ 1 std. dev.\",\n)\n\nax.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=f\"Mean ROC curve with variability\\n(Positive label '{target_names[1]}')\",\n)\nax.legend(loc=\"lower right\")\nplt.show()"
       ]
     }
   ],