Pushing the docs to dev/ for branch: main, commit bb72ec0e8e82e7665e90b523b755ae54cb6d8050

Author: sklearn-ci

dev/_downloads/010337852815f8103ac6cca38a812b3c/plot_roc_crossval.py

@@ -70,7 +70,8 @@
 from sklearn.metrics import RocCurveDisplay
 from sklearn.model_selection import StratifiedKFold
 
-cv = StratifiedKFold(n_splits=6)
+n_splits = 6
+cv = StratifiedKFold(n_splits=n_splits)
 classifier = svm.SVC(kernel="linear", probability=True, random_state=random_state)
 
 tprs = []
@@ -88,12 +89,12 @@
         alpha=0.3,
         lw=1,
         ax=ax,
+        plot_chance_level=(fold == n_splits - 1),
     )
     interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
     interp_tpr[0] = 0.0
     tprs.append(interp_tpr)
     aucs.append(viz.roc_auc)
-ax.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)")
 
 mean_tpr = np.mean(tprs, axis=0)
 mean_tpr[-1] = 1.0

dev/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom sklearn import svm\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import RocCurveDisplay\nfrom sklearn.model_selection import StratifiedKFold\n\ncv = StratifiedKFold(n_splits=6)\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    )\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)\nax.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\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    xlim=[-0.05, 1.05],\n    ylim=[-0.05, 1.05],\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.axis(\"square\")\nax.legend(loc=\"lower right\")\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn import svm\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import RocCurveDisplay\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    xlim=[-0.05, 1.05],\n    ylim=[-0.05, 1.05],\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.axis(\"square\")\nax.legend(loc=\"lower right\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.metrics import RocCurveDisplay\n\nRocCurveDisplay.from_predictions(\n    y_onehot_test[:, class_id],\n    y_score[:, class_id],\n    name=f\"{class_of_interest} vs the rest\",\n    color=\"darkorange\",\n)\nplt.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"One-vs-Rest ROC curves:\\nVirginica vs (Setosa & Versicolor)\")\nplt.legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom sklearn.metrics import RocCurveDisplay\n\nRocCurveDisplay.from_predictions(\n    y_onehot_test[:, class_id],\n    y_score[:, class_id],\n    name=f\"{class_of_interest} vs the rest\",\n    color=\"darkorange\",\n    plot_chance_level=True,\n)\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"One-vs-Rest ROC curves:\\nVirginica vs (Setosa & Versicolor)\")\nplt.legend()\nplt.show()"
       ]
     },
     {
@@ -152,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "RocCurveDisplay.from_predictions(\n    y_onehot_test.ravel(),\n    y_score.ravel(),\n    name=\"micro-average OvR\",\n    color=\"darkorange\",\n)\nplt.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Micro-averaged One-vs-Rest\\nReceiver Operating Characteristic\")\nplt.legend()\nplt.show()"
+        "RocCurveDisplay.from_predictions(\n    y_onehot_test.ravel(),\n    y_score.ravel(),\n    name=\"micro-average OvR\",\n    color=\"darkorange\",\n    plot_chance_level=True,\n)\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Micro-averaged One-vs-Rest\\nReceiver Operating Characteristic\")\nplt.legend()\nplt.show()"
       ]
     },
     {
@@ -242,7 +242,7 @@
       },
       "outputs": [],
       "source": [
-        "from itertools import cycle\n\nfig, ax = plt.subplots(figsize=(6, 6))\n\nplt.plot(\n    fpr[\"micro\"],\n    tpr[\"micro\"],\n    label=f\"micro-average ROC curve (AUC = {roc_auc['micro']:.2f})\",\n    color=\"deeppink\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\nplt.plot(\n    fpr[\"macro\"],\n    tpr[\"macro\"],\n    label=f\"macro-average ROC curve (AUC = {roc_auc['macro']:.2f})\",\n    color=\"navy\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\ncolors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\nfor class_id, color in zip(range(n_classes), colors):\n    RocCurveDisplay.from_predictions(\n        y_onehot_test[:, class_id],\n        y_score[:, class_id],\n        name=f\"ROC curve for {target_names[class_id]}\",\n        color=color,\n        ax=ax,\n    )\n\nplt.plot([0, 1], [0, 1], \"k--\", label=\"ROC curve for chance level (AUC = 0.5)\")\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Extension of Receiver Operating Characteristic\\nto One-vs-Rest multiclass\")\nplt.legend()\nplt.show()"
+        "from itertools import cycle\n\nfig, ax = plt.subplots(figsize=(6, 6))\n\nplt.plot(\n    fpr[\"micro\"],\n    tpr[\"micro\"],\n    label=f\"micro-average ROC curve (AUC = {roc_auc['micro']:.2f})\",\n    color=\"deeppink\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\nplt.plot(\n    fpr[\"macro\"],\n    tpr[\"macro\"],\n    label=f\"macro-average ROC curve (AUC = {roc_auc['macro']:.2f})\",\n    color=\"navy\",\n    linestyle=\":\",\n    linewidth=4,\n)\n\ncolors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\nfor class_id, color in zip(range(n_classes), colors):\n    RocCurveDisplay.from_predictions(\n        y_onehot_test[:, class_id],\n        y_score[:, class_id],\n        name=f\"ROC curve for {target_names[class_id]}\",\n        color=color,\n        ax=ax,\n        plot_chance_level=(class_id == 2),\n    )\n\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Extension of Receiver Operating Characteristic\\nto One-vs-Rest multiclass\")\nplt.legend()\nplt.show()"
       ]
     },
     {
@@ -271,7 +271,7 @@
       },
       "outputs": [],
       "source": [
-        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score :.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n    )\n    plt.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\n    plt.axis(\"square\")\n    plt.xlabel(\"False Positive Rate\")\n    plt.ylabel(\"True Positive Rate\")\n    plt.title(f\"{target_names[idx_a]} vs {label_b} ROC curves\")\n    plt.legend()\n    plt.show()\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
+        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score :.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n        plot_chance_level=True,\n    )\n    plt.axis(\"square\")\n    plt.xlabel(\"False Positive Rate\")\n    plt.ylabel(\"True Positive Rate\")\n    plt.title(f\"{target_names[idx_a]} vs {label_b} ROC curves\")\n    plt.legend()\n    plt.show()\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
       ]
     },
     {
@@ -307,7 +307,7 @@
       },
       "outputs": [],
       "source": [
-        "ovo_tpr = np.zeros_like(fpr_grid)\n\nfig, ax = plt.subplots(figsize=(6, 6))\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    ovo_tpr += mean_tpr[ix]\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {pair_scores[ix]:.2f})\",\n    )\n\novo_tpr /= sum(1 for pair in enumerate(pair_list))\n\nplt.plot(\n    fpr_grid,\n    ovo_tpr,\n    label=f\"One-vs-One macro-average (AUC = {macro_roc_auc_ovo:.2f})\",\n    linestyle=\":\",\n    linewidth=4,\n)\nplt.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Extension of Receiver Operating Characteristic\\nto One-vs-One multiclass\")\nplt.legend()\nplt.show()"
+        "ovo_tpr = np.zeros_like(fpr_grid)\n\nfig, ax = plt.subplots(figsize=(6, 6))\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    ovo_tpr += mean_tpr[ix]\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {pair_scores[ix]:.2f})\",\n    )\n\novo_tpr /= sum(1 for pair in enumerate(pair_list))\n\nplt.plot(\n    fpr_grid,\n    ovo_tpr,\n    label=f\"One-vs-One macro-average (AUC = {macro_roc_auc_ovo:.2f})\",\n    linestyle=\":\",\n    linewidth=4,\n)\nplt.plot([0, 1], [0, 1], \"k--\", label=\"Chance level (AUC = 0.5)\")\nplt.axis(\"square\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Extension of Receiver Operating Characteristic\\nto One-vs-One multiclass\")\nplt.legend()\nplt.show()"
       ]
     },
     {

dev/_downloads/40f4aad91af595a370d7582e3a23bed7/plot_roc.ipynb

@@ -125,8 +125,8 @@
     y_score[:, class_id],
     name=f"{class_of_interest} vs the rest",
     color="darkorange",
+    plot_chance_level=True,
 )
-plt.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)")
 plt.axis("square")
 plt.xlabel("False Positive Rate")
 plt.ylabel("True Positive Rate")
@@ -161,8 +161,8 @@
     y_score.ravel(),
     name="micro-average OvR",
     color="darkorange",
+    plot_chance_level=True,
 )
-plt.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)")
 plt.axis("square")
 plt.xlabel("False Positive Rate")
 plt.ylabel("True Positive Rate")
@@ -281,9 +281,9 @@
         name=f"ROC curve for {target_names[class_id]}",
         color=color,
         ax=ax,
+        plot_chance_level=(class_id == 2),
     )
 
-plt.plot([0, 1], [0, 1], "k--", label="ROC curve for chance level (AUC = 0.5)")
 plt.axis("square")
 plt.xlabel("False Positive Rate")
 plt.ylabel("True Positive Rate")
@@ -364,8 +364,8 @@
         y_score[ab_mask, idx_b],
         ax=ax,
         name=f"{label_b} as positive class",
+        plot_chance_level=True,
     )
-    plt.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)")
     plt.axis("square")
     plt.xlabel("False Positive Rate")
     plt.ylabel("True Positive Rate")
@@ -413,7 +413,7 @@
     linestyle=":",
     linewidth=4,
 )
-plt.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)")
+plt.plot([0, 1], [0, 1], "k--", label="Chance level (AUC = 0.5)")
 plt.axis("square")
 plt.xlabel("False Positive Rate")
 plt.ylabel("True Positive Rate")

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -172,12 +172,12 @@ def compute_prediction(X, model_name):
 pos_label = 0  # mean 0 belongs to positive class
 rows = math.ceil(len(datasets_name) / cols)
 
-fig, axs = plt.subplots(rows, cols, figsize=(10, rows * 3))
+fig, axs = plt.subplots(rows, cols, figsize=(10, rows * 3), sharex=True, sharey=True)
 
 for i, dataset_name in enumerate(datasets_name):
     (X, y) = preprocess_dataset(dataset_name=dataset_name)
 
-    for model_name in models_name:
+    for model_idx, model_name in enumerate(models_name):
         y_pred = compute_prediction(X, model_name=model_name)
         display = RocCurveDisplay.from_predictions(
             y,
@@ -186,10 +186,12 @@ def compute_prediction(X, model_name):
             name=model_name,
             linewidth=linewidth,
             ax=axs[i // cols, i % cols],
+            plot_chance_level=(model_idx == len(models_name) - 1),
+            chance_level_kw={
+                "linewidth": linewidth,
+                "linestyle": ":",
+            },
         )
-    axs[i // cols, i % cols].plot([0, 1], [0, 1], linewidth=linewidth, linestyle=":")
     axs[i // cols, i % cols].set_title(dataset_name)
-    axs[i // cols, i % cols].set_xlabel("False Positive Rate")
-    axs[i // cols, i % cols].set_ylabel("True Positive Rate")
 plt.tight_layout(pad=2.0)  # spacing between subplots
 plt.show()

dev/_downloads/80fef09514fd851560e999a5b7daa303/plot_roc.py

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import math\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import RocCurveDisplay\n\ndatasets_name = [\n    \"http\",\n    \"smtp\",\n    \"SA\",\n    \"SF\",\n    \"forestcover\",\n    \"glass\",\n    \"wdbc\",\n    \"cardiotocography\",\n]\n\nmodels_name = [\n    \"LOF\",\n    \"IForest\",\n]\n\n# plotting parameters\ncols = 2\nlinewidth = 1\npos_label = 0  # mean 0 belongs to positive class\nrows = math.ceil(len(datasets_name) / cols)\n\nfig, axs = plt.subplots(rows, cols, figsize=(10, rows * 3))\n\nfor i, dataset_name in enumerate(datasets_name):\n    (X, y) = preprocess_dataset(dataset_name=dataset_name)\n\n    for model_name in models_name:\n        y_pred = compute_prediction(X, model_name=model_name)\n        display = RocCurveDisplay.from_predictions(\n            y,\n            y_pred,\n            pos_label=pos_label,\n            name=model_name,\n            linewidth=linewidth,\n            ax=axs[i // cols, i % cols],\n        )\n    axs[i // cols, i % cols].plot([0, 1], [0, 1], linewidth=linewidth, linestyle=\":\")\n    axs[i // cols, i % cols].set_title(dataset_name)\n    axs[i // cols, i % cols].set_xlabel(\"False Positive Rate\")\n    axs[i // cols, i % cols].set_ylabel(\"True Positive Rate\")\nplt.tight_layout(pad=2.0)  # spacing between subplots\nplt.show()"
+        "import math\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import RocCurveDisplay\n\ndatasets_name = [\n    \"http\",\n    \"smtp\",\n    \"SA\",\n    \"SF\",\n    \"forestcover\",\n    \"glass\",\n    \"wdbc\",\n    \"cardiotocography\",\n]\n\nmodels_name = [\n    \"LOF\",\n    \"IForest\",\n]\n\n# plotting parameters\ncols = 2\nlinewidth = 1\npos_label = 0  # mean 0 belongs to positive class\nrows = math.ceil(len(datasets_name) / cols)\n\nfig, axs = plt.subplots(rows, cols, figsize=(10, rows * 3), sharex=True, sharey=True)\n\nfor i, dataset_name in enumerate(datasets_name):\n    (X, y) = preprocess_dataset(dataset_name=dataset_name)\n\n    for model_idx, model_name in enumerate(models_name):\n        y_pred = compute_prediction(X, model_name=model_name)\n        display = RocCurveDisplay.from_predictions(\n            y,\n            y_pred,\n            pos_label=pos_label,\n            name=model_name,\n            linewidth=linewidth,\n            ax=axs[i // cols, i % cols],\n            plot_chance_level=(model_idx == len(models_name) - 1),\n            chance_level_kw={\n                \"linewidth\": linewidth,\n                \"linestyle\": \":\",\n            },\n        )\n    axs[i // cols, i % cols].set_title(dataset_name)\nplt.tight_layout(pad=2.0)  # spacing between subplots\nplt.show()"
       ]
     }
   ],