Pushing the docs to dev/ for branch: main, commit 8cac52f9d1a393f7809801405013b5e0d2785499

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: 525147e589feee6feecd89a2eab08c49
+config: 5cf9cb0e11f67e076f45771c1870a2e7
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -203,6 +203,7 @@
     "scoring": "neg_mean_squared_error",
     "negate_score": True,
     "score_name": "Mean Squared Error",
+    "score_type": "test",
     "std_display_style": None,
     "ax": ax,
 }

dev/_downloads/1dcd684ce26b8c407ec2c2d2101c5c73/plot_kernel_ridge_regression.py

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_digits\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import validation_curve\n\nX, y = load_digits(return_X_y=True)\nsubset_mask = np.isin(y, [1, 2])  # binary classification: 1 vs 2\nX, y = X[subset_mask], y[subset_mask]\n\nparam_range = np.logspace(-6, -1, 5)\ntrain_scores, test_scores = validation_curve(\n    SVC(),\n    X,\n    y,\n    param_name=\"gamma\",\n    param_range=param_range,\n    scoring=\"accuracy\",\n    n_jobs=2,\n)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nplt.title(\"Validation Curve with SVM\")\nplt.xlabel(r\"$\\gamma$\")\nplt.ylabel(\"Score\")\nplt.ylim(0.0, 1.1)\nlw = 2\nplt.semilogx(\n    param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=lw\n)\nplt.fill_between(\n    param_range,\n    train_scores_mean - train_scores_std,\n    train_scores_mean + train_scores_std,\n    alpha=0.2,\n    color=\"darkorange\",\n    lw=lw,\n)\nplt.semilogx(\n    param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=lw\n)\nplt.fill_between(\n    param_range,\n    test_scores_mean - test_scores_std,\n    test_scores_mean + test_scores_std,\n    alpha=0.2,\n    color=\"navy\",\n    lw=lw,\n)\nplt.legend(loc=\"best\")\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_digits\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import ValidationCurveDisplay\n\nX, y = load_digits(return_X_y=True)\nsubset_mask = np.isin(y, [1, 2])  # binary classification: 1 vs 2\nX, y = X[subset_mask], y[subset_mask]\n\ndisp = ValidationCurveDisplay.from_estimator(\n    SVC(),\n    X,\n    y,\n    param_name=\"gamma\",\n    param_range=np.logspace(-6, -1, 5),\n    score_type=\"both\",\n    n_jobs=2,\n    score_name=\"Accuracy\",\n)\ndisp.ax_.set_title(\"Validation Curve for SVM with an RBF kernel\")\ndisp.ax_.set_xlabel(r\"gamma (inverse radius of the RBF kernel)\")\ndisp.ax_.set_ylim(0.0, 1.1)\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -133,7 +133,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import LearningCurveDisplay\n\n_, ax = plt.subplots()\n\nsvr = SVR(kernel=\"rbf\", C=1e1, gamma=0.1)\nkr = KernelRidge(kernel=\"rbf\", alpha=0.1, gamma=0.1)\n\ncommon_params = {\n    \"X\": X[:100],\n    \"y\": y[:100],\n    \"train_sizes\": np.linspace(0.1, 1, 10),\n    \"scoring\": \"neg_mean_squared_error\",\n    \"negate_score\": True,\n    \"score_name\": \"Mean Squared Error\",\n    \"std_display_style\": None,\n    \"ax\": ax,\n}\n\nLearningCurveDisplay.from_estimator(svr, **common_params)\nLearningCurveDisplay.from_estimator(kr, **common_params)\nax.set_title(\"Learning curves\")\nax.legend(handles=ax.get_legend_handles_labels()[0], labels=[\"SVR\", \"KRR\"])\n\nplt.show()"
+        "from sklearn.model_selection import LearningCurveDisplay\n\n_, ax = plt.subplots()\n\nsvr = SVR(kernel=\"rbf\", C=1e1, gamma=0.1)\nkr = KernelRidge(kernel=\"rbf\", alpha=0.1, gamma=0.1)\n\ncommon_params = {\n    \"X\": X[:100],\n    \"y\": y[:100],\n    \"train_sizes\": np.linspace(0.1, 1, 10),\n    \"scoring\": \"neg_mean_squared_error\",\n    \"negate_score\": True,\n    \"score_name\": \"Mean Squared Error\",\n    \"score_type\": \"test\",\n    \"std_display_style\": None,\n    \"ax\": ax,\n}\n\nLearningCurveDisplay.from_estimator(svr, **common_params)\nLearningCurveDisplay.from_estimator(kr, **common_params)\nax.set_title(\"Learning curves\")\nax.legend(handles=ax.get_legend_handles_labels()[0], labels=[\"SVR\", \"KRR\"])\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/7996e584c563a930d174772f44af2089/plot_validation_curve.ipynb

@@ -18,53 +18,23 @@
 
 from sklearn.datasets import load_digits
 from sklearn.svm import SVC
-from sklearn.model_selection import validation_curve
+from sklearn.model_selection import ValidationCurveDisplay
 
 X, y = load_digits(return_X_y=True)
 subset_mask = np.isin(y, [1, 2])  # binary classification: 1 vs 2
 X, y = X[subset_mask], y[subset_mask]
 
-param_range = np.logspace(-6, -1, 5)
-train_scores, test_scores = validation_curve(
+disp = ValidationCurveDisplay.from_estimator(
     SVC(),
     X,
     y,
     param_name="gamma",
-    param_range=param_range,
-    scoring="accuracy",
+    param_range=np.logspace(-6, -1, 5),
+    score_type="both",
     n_jobs=2,
+    score_name="Accuracy",
 )
-train_scores_mean = np.mean(train_scores, axis=1)
-train_scores_std = np.std(train_scores, axis=1)
-test_scores_mean = np.mean(test_scores, axis=1)
-test_scores_std = np.std(test_scores, axis=1)
-
-plt.title("Validation Curve with SVM")
-plt.xlabel(r"$\gamma$")
-plt.ylabel("Score")
-plt.ylim(0.0, 1.1)
-lw = 2
-plt.semilogx(
-    param_range, train_scores_mean, label="Training score", color="darkorange", lw=lw
-)
-plt.fill_between(
-    param_range,
-    train_scores_mean - train_scores_std,
-    train_scores_mean + train_scores_std,
-    alpha=0.2,
-    color="darkorange",
-    lw=lw,
-)
-plt.semilogx(
-    param_range, test_scores_mean, label="Cross-validation score", color="navy", lw=lw
-)
-plt.fill_between(
-    param_range,
-    test_scores_mean - test_scores_std,
-    test_scores_mean + test_scores_std,
-    alpha=0.2,
-    color="navy",
-    lw=lw,
-)
-plt.legend(loc="best")
+disp.ax_.set_title("Validation Curve for SVM with an RBF kernel")
+disp.ax_.set_xlabel(r"gamma (inverse radius of the RBF kernel)")
+disp.ax_.set_ylim(0.0, 1.1)
 plt.show()