Pushing the docs to dev/ for branch: main, commit 59dd128d4d26fff2ff197b8c1e801647a22e0158

Author: sklearn-ci

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import RocCurveDisplay\n\ndisplay = RocCurveDisplay.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)\n_ = display.ax_.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=\"One-vs-Rest ROC curves:\\nVirginica vs (Setosa & Versicolor)\",\n)"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import RocCurveDisplay\n\ndisplay = RocCurveDisplay.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    despine=True,\n)\n_ = display.ax_.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=\"One-vs-Rest ROC curves:\\nVirginica vs (Setosa & Versicolor)\",\n)"
       ]
     },
     {
@@ -152,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "display = 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)\n_ = display.ax_.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=\"Micro-averaged One-vs-Rest\\nReceiver Operating Characteristic\",\n)"
+        "display = 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    despine=True,\n)\n_ = display.ax_.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=\"Micro-averaged One-vs-Rest\\nReceiver Operating Characteristic\",\n)"
       ]
     },
     {
@@ -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        plot_chance_level=(class_id == 2),\n    )\n\n_ = ax.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=\"Extension of Receiver Operating Characteristic\\nto One-vs-Rest multiclass\",\n)"
+        "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        despine=True,\n    )\n\n_ = ax.set(\n    xlabel=\"False Positive Rate\",\n    ylabel=\"True Positive Rate\",\n    title=\"Extension of Receiver Operating Characteristic\\nto One-vs-Rest multiclass\",\n)"
       ]
     },
     {
@@ -271,7 +271,7 @@
       },
       "outputs": [],
       "source": [
-        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\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    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\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    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        despine=True,\n    )\n    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
       ]
     },
     {

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import PrecisionRecallDisplay\n\ndisplay = PrecisionRecallDisplay.from_estimator(\n    classifier, X_test, y_test, name=\"LinearSVC\", plot_chance_level=True\n)\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
+        "from sklearn.metrics import PrecisionRecallDisplay\n\ndisplay = PrecisionRecallDisplay.from_estimator(\n    classifier, X_test, y_test, name=\"LinearSVC\", plot_chance_level=True, despine=True\n)\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "y_score = classifier.decision_function(X_test)\n\ndisplay = PrecisionRecallDisplay.from_predictions(\n    y_test, y_score, name=\"LinearSVC\", plot_chance_level=True\n)\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
+        "y_score = classifier.decision_function(X_test)\n\ndisplay = PrecisionRecallDisplay.from_predictions(\n    y_test, y_score, name=\"LinearSVC\", plot_chance_level=True, despine=True\n)\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
       ]
     },
     {
@@ -159,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "from collections import Counter\n\ndisplay = PrecisionRecallDisplay(\n    recall=recall[\"micro\"],\n    precision=precision[\"micro\"],\n    average_precision=average_precision[\"micro\"],\n    prevalence_pos_label=Counter(Y_test.ravel())[1] / Y_test.size,\n)\ndisplay.plot(plot_chance_level=True)\n_ = display.ax_.set_title(\"Micro-averaged over all classes\")"
+        "from collections import Counter\n\ndisplay = PrecisionRecallDisplay(\n    recall=recall[\"micro\"],\n    precision=precision[\"micro\"],\n    average_precision=average_precision[\"micro\"],\n    prevalence_pos_label=Counter(Y_test.ravel())[1] / Y_test.size,\n)\ndisplay.plot(plot_chance_level=True, despine=True)\n_ = display.ax_.set_title(\"Micro-averaged over all classes\")"
       ]
     },
     {
@@ -177,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "from itertools import cycle\n\nimport matplotlib.pyplot as plt\n\n# setup plot details\ncolors = cycle([\"navy\", \"turquoise\", \"darkorange\", \"cornflowerblue\", \"teal\"])\n\n_, ax = plt.subplots(figsize=(7, 8))\n\nf_scores = np.linspace(0.2, 0.8, num=4)\nlines, labels = [], []\nfor f_score in f_scores:\n    x = np.linspace(0.01, 1)\n    y = f_score * x / (2 * x - f_score)\n    (l,) = plt.plot(x[y >= 0], y[y >= 0], color=\"gray\", alpha=0.2)\n    plt.annotate(\"f1={0:0.1f}\".format(f_score), xy=(0.9, y[45] + 0.02))\n\ndisplay = PrecisionRecallDisplay(\n    recall=recall[\"micro\"],\n    precision=precision[\"micro\"],\n    average_precision=average_precision[\"micro\"],\n)\ndisplay.plot(ax=ax, name=\"Micro-average precision-recall\", color=\"gold\")\n\nfor i, color in zip(range(n_classes), colors):\n    display = PrecisionRecallDisplay(\n        recall=recall[i],\n        precision=precision[i],\n        average_precision=average_precision[i],\n    )\n    display.plot(ax=ax, name=f\"Precision-recall for class {i}\", color=color)\n\n# add the legend for the iso-f1 curves\nhandles, labels = display.ax_.get_legend_handles_labels()\nhandles.extend([l])\nlabels.extend([\"iso-f1 curves\"])\n# set the legend and the axes\nax.legend(handles=handles, labels=labels, loc=\"best\")\nax.set_title(\"Extension of Precision-Recall curve to multi-class\")\n\nplt.show()"
+        "from itertools import cycle\n\nimport matplotlib.pyplot as plt\n\n# setup plot details\ncolors = cycle([\"navy\", \"turquoise\", \"darkorange\", \"cornflowerblue\", \"teal\"])\n\n_, ax = plt.subplots(figsize=(7, 8))\n\nf_scores = np.linspace(0.2, 0.8, num=4)\nlines, labels = [], []\nfor f_score in f_scores:\n    x = np.linspace(0.01, 1)\n    y = f_score * x / (2 * x - f_score)\n    (l,) = plt.plot(x[y >= 0], y[y >= 0], color=\"gray\", alpha=0.2)\n    plt.annotate(\"f1={0:0.1f}\".format(f_score), xy=(0.9, y[45] + 0.02))\n\ndisplay = PrecisionRecallDisplay(\n    recall=recall[\"micro\"],\n    precision=precision[\"micro\"],\n    average_precision=average_precision[\"micro\"],\n)\ndisplay.plot(ax=ax, name=\"Micro-average precision-recall\", color=\"gold\")\n\nfor i, color in zip(range(n_classes), colors):\n    display = PrecisionRecallDisplay(\n        recall=recall[i],\n        precision=precision[i],\n        average_precision=average_precision[i],\n    )\n    display.plot(\n        ax=ax, name=f\"Precision-recall for class {i}\", color=color, despine=True\n    )\n\n# add the legend for the iso-f1 curves\nhandles, labels = display.ax_.get_legend_handles_labels()\nhandles.extend([l])\nlabels.extend([\"iso-f1 curves\"])\n# set the legend and the axes\nax.legend(handles=handles, labels=labels, loc=\"best\")\nax.set_title(\"Extension of Precision-Recall curve to multi-class\")\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip

@@ -131,6 +131,7 @@
     name=f"{class_of_interest} vs the rest",
     color="darkorange",
     plot_chance_level=True,
+    despine=True,
 )
 _ = display.ax_.set(
     xlabel="False Positive Rate",
@@ -166,6 +167,7 @@
     name="micro-average OvR",
     color="darkorange",
     plot_chance_level=True,
+    despine=True,
 )
 _ = display.ax_.set(
     xlabel="False Positive Rate",
@@ -285,6 +287,7 @@
         color=color,
         ax=ax,
         plot_chance_level=(class_id == 2),
+        despine=True,
     )
 
 _ = ax.set(
@@ -366,6 +369,7 @@
         ax=ax,
         name=f"{label_b} as positive class",
         plot_chance_level=True,
+        despine=True,
     )
     ax.set(
         xlabel="False Positive Rate",

dev/_downloads/03c018a16384c69f3a89e473650d57ee/plot_svm_margin.zip

@@ -147,7 +147,7 @@
 from sklearn.metrics import PrecisionRecallDisplay
 
 display = PrecisionRecallDisplay.from_estimator(
-    classifier, X_test, y_test, name="LinearSVC", plot_chance_level=True
+    classifier, X_test, y_test, name="LinearSVC", plot_chance_level=True, despine=True
 )
 _ = display.ax_.set_title("2-class Precision-Recall curve")
 
@@ -158,7 +158,7 @@
 y_score = classifier.decision_function(X_test)
 
 display = PrecisionRecallDisplay.from_predictions(
-    y_test, y_score, name="LinearSVC", plot_chance_level=True
+    y_test, y_score, name="LinearSVC", plot_chance_level=True, despine=True
 )
 _ = display.ax_.set_title("2-class Precision-Recall curve")
 
@@ -228,7 +228,7 @@
     average_precision=average_precision["micro"],
     prevalence_pos_label=Counter(Y_test.ravel())[1] / Y_test.size,
 )
-display.plot(plot_chance_level=True)
+display.plot(plot_chance_level=True, despine=True)
 _ = display.ax_.set_title("Micro-averaged over all classes")
 
 # %%
@@ -264,7 +264,9 @@
         precision=precision[i],
         average_precision=average_precision[i],
     )
-    display.plot(ax=ax, name=f"Precision-recall for class {i}", color=color)
+    display.plot(
+        ax=ax, name=f"Precision-recall for class {i}", color=color, despine=True
+    )
 
 # add the legend for the iso-f1 curves
 handles, labels = display.ax_.get_legend_handles_labels()