Pushing the docs to dev/ for branch: main, commit 893d5accaf9d16f447645e704f85a216187564f7

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -115,8 +115,10 @@
     n_labeled_points += len(uncertainty_index)
 
 f.suptitle(
-    "Active learning with Label Propagation.\nRows show 5 most "
-    "uncertain labels to learn with the next model.",
+    (
+        "Active learning with Label Propagation.\nRows show 5 most "
+        "uncertain labels to learn with the next model."
+    ),
     y=1.15,
 )
 plt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)

dev/_downloads/0dd5e0a7942f1446accf7dffd47aed28/plot_label_propagation_digits_active_learning.py

@@ -174,7 +174,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\n\nlinear_regression = LinearRegression()\nquantile_regression = QuantileRegressor(quantile=0.5, alpha=0, solver=solver)\n\ny_pred_lr = linear_regression.fit(X, y_pareto).predict(X)\ny_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)\n\nprint(\n    f\"\"\"Training error (in-sample performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_lr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_lr):.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_qr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_qr):.3f}\n    \"\"\"\n)"
+        "from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\n\nlinear_regression = LinearRegression()\nquantile_regression = QuantileRegressor(quantile=0.5, alpha=0, solver=solver)\n\ny_pred_lr = linear_regression.fit(X, y_pareto).predict(X)\ny_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)\n\nprint(f\"\"\"Training error (in-sample performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_lr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_lr):.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_qr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_qr):.3f}\n    \"\"\")"
       ]
     },
     {
@@ -192,7 +192,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import cross_validate\n\ncv_results_lr = cross_validate(\n    linear_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\ncv_results_qr = cross_validate(\n    quantile_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\nprint(\n    f\"\"\"Test error (cross-validated performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {-cv_results_lr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_lr[\"test_neg_mean_squared_error\"].mean():.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {-cv_results_qr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_qr[\"test_neg_mean_squared_error\"].mean():.3f}\n    \"\"\"\n)"
+        "from sklearn.model_selection import cross_validate\n\ncv_results_lr = cross_validate(\n    linear_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\ncv_results_qr = cross_validate(\n    quantile_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\nprint(f\"\"\"Test error (cross-validated performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {-cv_results_lr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_lr[\"test_neg_mean_squared_error\"].mean():.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {-cv_results_qr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_qr[\"test_neg_mean_squared_error\"].mean():.3f}\n    \"\"\")"
       ]
     },
     {

dev/_downloads/10754505339af88c10b8a48127535a4a/plot_quantile_regression.ipynb

@@ -231,7 +231,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import PartialDependenceDisplay\n\ncommon_params = {\n    \"subsample\": 50,\n    \"n_jobs\": 2,\n    \"grid_resolution\": 20,\n    \"random_state\": 0,\n}\n\nprint(\"Computing partial dependence plots...\")\nfeatures_info = {\n    # features of interest\n    \"features\": [\"temp\", \"humidity\", \"windspeed\", \"season\", \"weather\", \"hour\"],\n    # type of partial dependence plot\n    \"kind\": \"average\",\n    # information regarding categorical features\n    \"categorical_features\": categorical_features,\n}\ntic = time()\n_, ax = plt.subplots(ncols=3, nrows=2, figsize=(9, 8), constrained_layout=True)\ndisplay = PartialDependenceDisplay.from_estimator(\n    mlp_model,\n    X_train,\n    **features_info,\n    ax=ax,\n    **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\n_ = display.figure_.suptitle(\n    \"Partial dependence of the number of bike rentals\\n\"\n    \"for the bike rental dataset with an MLPRegressor\",\n    fontsize=16,\n)"
+        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import PartialDependenceDisplay\n\ncommon_params = {\n    \"subsample\": 50,\n    \"n_jobs\": 2,\n    \"grid_resolution\": 20,\n    \"random_state\": 0,\n}\n\nprint(\"Computing partial dependence plots...\")\nfeatures_info = {\n    # features of interest\n    \"features\": [\"temp\", \"humidity\", \"windspeed\", \"season\", \"weather\", \"hour\"],\n    # type of partial dependence plot\n    \"kind\": \"average\",\n    # information regarding categorical features\n    \"categorical_features\": categorical_features,\n}\ntic = time()\n_, ax = plt.subplots(ncols=3, nrows=2, figsize=(9, 8), constrained_layout=True)\ndisplay = PartialDependenceDisplay.from_estimator(\n    mlp_model,\n    X_train,\n    **features_info,\n    ax=ax,\n    **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\n_ = display.figure_.suptitle(\n    (\n        \"Partial dependence of the number of bike rentals\\n\"\n        \"for the bike rental dataset with an MLPRegressor\"\n    ),\n    fontsize=16,\n)"
       ]
     },
     {
@@ -267,7 +267,7 @@
       },
       "outputs": [],
       "source": [
-        "print(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, nrows=2, figsize=(9, 8), constrained_layout=True)\ndisplay = PartialDependenceDisplay.from_estimator(\n    hgbdt_model,\n    X_train,\n    **features_info,\n    ax=ax,\n    **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\n_ = display.figure_.suptitle(\n    \"Partial dependence of the number of bike rentals\\n\"\n    \"for the bike rental dataset with a gradient boosting\",\n    fontsize=16,\n)"
+        "print(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, nrows=2, figsize=(9, 8), constrained_layout=True)\ndisplay = PartialDependenceDisplay.from_estimator(\n    hgbdt_model,\n    X_train,\n    **features_info,\n    ax=ax,\n    **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\n_ = display.figure_.suptitle(\n    (\n        \"Partial dependence of the number of bike rentals\\n\"\n        \"for the bike rental dataset with a gradient boosting\"\n    ),\n    fontsize=16,\n)"
       ]
     },
     {

dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb

@@ -70,7 +70,6 @@
 
 # Loop for each trial
 for i in range(NUM_TRIALS):
-
     # Choose cross-validation techniques for the inner and outer loops,
     # independently of the dataset.
     # E.g "GroupKFold", "LeaveOneOut", "LeaveOneGroupOut", etc.

dev/_downloads/23614d75e8327ef369659da7d2ed62db/plot_nested_cross_validation_iris.py

@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = np.where(non_zero)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * embedding[0].ptp(),\n    embedding[0].max() + 0.10 * embedding[0].ptp(),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * embedding[1].ptp(),\n    embedding[1].max() + 0.03 * embedding[1].ptp(),\n)\n\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = np.where(non_zero)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * embedding[0].ptp(),\n    embedding[0].max() + 0.10 * embedding[0].ptp(),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * embedding[1].ptp(),\n    embedding[1].max() + 0.03 * embedding[1].ptp(),\n)\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/2840d928d4f93cd381486b35c2031752/plot_stock_market.ipynb

@@ -135,8 +135,10 @@ def plot_digits(X, title):
 )
 plot_digits(
     X_reconstructed_kernel_pca,
-    "Kernel PCA reconstruction\n"
-    f"MSE: {np.mean((X_test - X_reconstructed_kernel_pca) ** 2):.2f}",
+    (
+        "Kernel PCA reconstruction\n"
+        f"MSE: {np.mean((X_test - X_reconstructed_kernel_pca) ** 2):.2f}"
+    ),
 )
 
 # %%

dev/_downloads/32173eb704d697c23dffbbf3fd74942a/plot_digits_denoising.py

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Code source: Andreas Mueller, Adrin Jalali\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import make_blobs\n\nX, y = make_blobs(random_state=27)\n\nfig, sub = plt.subplots(2, 1, figsize=(5, 8))\ntitles = (\"break_ties = False\", \"break_ties = True\")\n\nfor break_ties, title, ax in zip((False, True), titles, sub.flatten()):\n\n    svm = SVC(\n        kernel=\"linear\", C=1, break_ties=break_ties, decision_function_shape=\"ovr\"\n    ).fit(X, y)\n\n    xlim = [X[:, 0].min(), X[:, 0].max()]\n    ylim = [X[:, 1].min(), X[:, 1].max()]\n\n    xs = np.linspace(xlim[0], xlim[1], 1000)\n    ys = np.linspace(ylim[0], ylim[1], 1000)\n    xx, yy = np.meshgrid(xs, ys)\n\n    pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    colors = [plt.cm.Accent(i) for i in [0, 4, 7]]\n\n    points = ax.scatter(X[:, 0], X[:, 1], c=y, cmap=\"Accent\")\n    classes = [(0, 1), (0, 2), (1, 2)]\n    line = np.linspace(X[:, 1].min() - 5, X[:, 1].max() + 5)\n    ax.imshow(\n        -pred.reshape(xx.shape),\n        cmap=\"Accent\",\n        alpha=0.2,\n        extent=(xlim[0], xlim[1], ylim[1], ylim[0]),\n    )\n\n    for coef, intercept, col in zip(svm.coef_, svm.intercept_, classes):\n        line2 = -(line * coef[1] + intercept) / coef[0]\n        ax.plot(line2, line, \"-\", c=colors[col[0]])\n        ax.plot(line2, line, \"--\", c=colors[col[1]])\n    ax.set_xlim(xlim)\n    ax.set_ylim(ylim)\n    ax.set_title(title)\n    ax.set_aspect(\"equal\")\n\nplt.show()"
+        "# Code source: Andreas Mueller, Adrin Jalali\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import make_blobs\n\nX, y = make_blobs(random_state=27)\n\nfig, sub = plt.subplots(2, 1, figsize=(5, 8))\ntitles = (\"break_ties = False\", \"break_ties = True\")\n\nfor break_ties, title, ax in zip((False, True), titles, sub.flatten()):\n    svm = SVC(\n        kernel=\"linear\", C=1, break_ties=break_ties, decision_function_shape=\"ovr\"\n    ).fit(X, y)\n\n    xlim = [X[:, 0].min(), X[:, 0].max()]\n    ylim = [X[:, 1].min(), X[:, 1].max()]\n\n    xs = np.linspace(xlim[0], xlim[1], 1000)\n    ys = np.linspace(ylim[0], ylim[1], 1000)\n    xx, yy = np.meshgrid(xs, ys)\n\n    pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    colors = [plt.cm.Accent(i) for i in [0, 4, 7]]\n\n    points = ax.scatter(X[:, 0], X[:, 1], c=y, cmap=\"Accent\")\n    classes = [(0, 1), (0, 2), (1, 2)]\n    line = np.linspace(X[:, 1].min() - 5, X[:, 1].max() + 5)\n    ax.imshow(\n        -pred.reshape(xx.shape),\n        cmap=\"Accent\",\n        alpha=0.2,\n        extent=(xlim[0], xlim[1], ylim[1], ylim[0]),\n    )\n\n    for coef, intercept, col in zip(svm.coef_, svm.intercept_, classes):\n        line2 = -(line * coef[1] + intercept) / coef[0]\n        ax.plot(line2, line, \"-\", c=colors[col[0]])\n        ax.plot(line2, line, \"--\", c=colors[col[1]])\n    ax.set_xlim(xlim)\n    ax.set_ylim(ylim)\n    ax.set_title(title)\n    ax.set_aspect(\"equal\")\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/358cfc6f7881b63fd4067f3b13ced9ab/plot_svm_tie_breaking.ipynb

@@ -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        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}\")"
+        "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    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}\")"
       ]
     },
     {

dev/_downloads/40f4aad91af595a370d7582e3a23bed7/plot_roc.ipynb

@@ -58,7 +58,6 @@
 figure = plt.figure(figsize=(14, 9))
 i = 1
 for ds_cnt, X in enumerate(X_list):
-
     ax = plt.subplot(len(X_list), len(strategies) + 1, i)
     ax.scatter(X[:, 0], X[:, 1], edgecolors="k")
     if ds_cnt == 0: