Pushing the docs to dev/ for branch: main, commit a5620f45614ac3f849c430f53146a66319e4908b

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 61f1dcc056c8a9a5e21505ca3e041e92
+config: 7117e1111de982f9bf7f2c23da1a0d62
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/010337852815f8103ac6cca38a812b3c/plot_roc_crossval.py

@@ -122,12 +122,9 @@
 )
 
 ax.set(
-    xlim=[-0.05, 1.05],
-    ylim=[-0.05, 1.05],
     xlabel="False Positive Rate",
     ylabel="True Positive Rate",
     title=f"Mean ROC curve with variability\n(Positive label '{target_names[1]}')",
 )
-ax.axis("square")
 ax.legend(loc="lower right")
 plt.show()

dev/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb

@@ -58,7 +58,7 @@
       },
       "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    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 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()"
       ]
     }
   ],

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -265,6 +265,8 @@ def make_plot(item_idx):
 make_plot(0)
 
 # %%
+# .. _plot_all_scaling_standard_scaler_section:
+#
 # StandardScaler
 # --------------
 #
@@ -285,6 +287,8 @@ def make_plot(item_idx):
 make_plot(1)
 
 # %%
+# .. _plot_all_scaling_minmax_scaler_section:
+#
 # MinMaxScaler
 # ------------
 #
@@ -301,6 +305,8 @@ def make_plot(item_idx):
 make_plot(2)
 
 # %%
+# .. _plot_all_scaling_max_abs_scaler_section:
+#
 # MaxAbsScaler
 # ------------
 #
@@ -318,6 +324,8 @@ def make_plot(item_idx):
 make_plot(3)
 
 # %%
+# .. _plot_all_scaling_robust_scaler_section:
+#
 # RobustScaler
 # ------------
 #
@@ -335,6 +343,8 @@ def make_plot(item_idx):
 make_plot(4)
 
 # %%
+# .. _plot_all_scaling_power_transformer_section:
+#
 # PowerTransformer
 # ----------------
 #
@@ -353,6 +363,8 @@ def make_plot(item_idx):
 make_plot(6)
 
 # %%
+# .. _plot_all_scaling_quantile_transformer_section:
+#
 # QuantileTransformer (uniform output)
 # ------------------------------------
 #
@@ -384,6 +396,8 @@ def make_plot(item_idx):
 make_plot(8)
 
 # %%
+# .. _plot_all_scaling_normalizer_section:
+#
 # Normalizer
 # ----------
 #