Pushing the docs to dev/ for branch: main, commit efe2b766b6be66a81b69df1e6273a75c21eed088

Author: sklearn-ci

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -666,6 +666,17 @@
       "source": [
         "We observe that we obtained the exact same results but the fitting process\nwas much faster since we did not perform any hyper-parameter search.\n\nFinally, the estimate of the (average) business metric itself can be unreliable, in\nparticular when the number of data points in the minority class is very small.\nAny business impact estimated by cross-validation of a business metric on\nhistorical data (offline evaluation) should ideally be confirmed by A/B testing\non live data (online evaluation). Note however that A/B testing models is\nbeyond the scope of the scikit-learn library itself.\n\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# At the end, we disable the configuration flag for metadata routing::\nsklearn.set_config(enable_metadata_routing=False)"
+      ]
     }
   ],
   "metadata": {

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -36,7 +36,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Transforming data other than X in a Pipeline\n\nThe :class:`~pipeline.Pipeline` now supports transforming passed data other than `X`\nif necessary. This can be done by setting the new `transform_input` parameter. This\nis particularly useful when passing a validation set through the pipeline.\n\nAs an example, imagine `EstimatorWithValidationSet` is an estimator which accepts\na validation set. We can now have a pipeline which will transform the validation set\nand pass it to the estimator::\n\n    sklearn.set_config(enable_metadata_routing=True)\n    est_gs = GridSearchCV(\n        Pipeline(\n            (\n                StandardScaler(),\n                EstimatorWithValidationSet(...).set_fit_request(X_val=True, y_val=True),\n            ),\n            # telling pipeline to transform these inputs up to the step which is\n            # requesting them.\n            transform_input=[\"X_val\"],\n        ),\n        param_grid={\"estimatorwithvalidationset__param_to_optimize\": list(range(5))},\n        cv=5,\n    ).fit(X, y, X_val=X_val, y_val=y_val)\n\nIn the above code, the key parts are the call to `set_fit_request` to specify that\n`X_val` and `y_val` are required by the `EstimatorWithValidationSet.fit` method, and\nthe `transform_input` parameter to tell the pipeline to transform `X_val` before\npassing it to `EstimatorWithValidationSet.fit`.\n\nNote that at this time scikit-learn estimators have not yet been extended to accept\nuser specified validation sets. This feature is released early to collect feedback\nfrom third-party libraries who might benefit from it.\n\n"
+        "## Transforming data other than X in a Pipeline\n\nThe :class:`~pipeline.Pipeline` now supports transforming passed data other than `X`\nif necessary. This can be done by setting the new `transform_input` parameter. This\nis particularly useful when passing a validation set through the pipeline.\n\nAs an example, imagine `EstimatorWithValidationSet` is an estimator which accepts\na validation set. We can now have a pipeline which will transform the validation set\nand pass it to the estimator::\n\n    with sklearn.config_context(enable_metadata_routing=True):\n        est_gs = GridSearchCV(\n            Pipeline(\n                (\n                    StandardScaler(),\n                    EstimatorWithValidationSet(...).set_fit_request(X_val=True, y_val=True),\n                ),\n                # telling pipeline to transform these inputs up to the step which is\n                # requesting them.\n                transform_input=[\"X_val\"],\n            ),\n            param_grid={\"estimatorwithvalidationset__param_to_optimize\": list(range(5))},\n            cv=5,\n        ).fit(X, y, X_val=X_val, y_val=y_val)\n\nIn the above code, the key parts are the call to `set_fit_request` to specify that\n`X_val` and `y_val` are required by the `EstimatorWithValidationSet.fit` method, and\nthe `transform_input` parameter to tell the pipeline to transform `X_val` before\npassing it to `EstimatorWithValidationSet.fit`.\n\nNote that at this time scikit-learn estimators have not yet been extended to accept\nuser specified validation sets. This feature is released early to collect feedback\nfrom third-party libraries who might benefit from it.\n\n"
       ]
     },
     {

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -689,3 +689,6 @@ def business_metric(y_true, y_pred, amount):
 # historical data (offline evaluation) should ideally be confirmed by A/B testing
 # on live data (online evaluation). Note however that A/B testing models is
 # beyond the scope of the scikit-learn library itself.
+
+# At the end, we disable the configuration flag for metadata routing::
+sklearn.set_config(enable_metadata_routing=False)

dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip

@@ -69,20 +69,20 @@
 # a validation set. We can now have a pipeline which will transform the validation set
 # and pass it to the estimator::
 #
-#     sklearn.set_config(enable_metadata_routing=True)
-#     est_gs = GridSearchCV(
-#         Pipeline(
-#             (
-#                 StandardScaler(),
-#                 EstimatorWithValidationSet(...).set_fit_request(X_val=True, y_val=True),
+#     with sklearn.config_context(enable_metadata_routing=True):
+#         est_gs = GridSearchCV(
+#             Pipeline(
+#                 (
+#                     StandardScaler(),
+#                     EstimatorWithValidationSet(...).set_fit_request(X_val=True, y_val=True),
+#                 ),
+#                 # telling pipeline to transform these inputs up to the step which is
+#                 # requesting them.
+#                 transform_input=["X_val"],
 #             ),
-#             # telling pipeline to transform these inputs up to the step which is
-#             # requesting them.
-#             transform_input=["X_val"],
-#         ),
-#         param_grid={"estimatorwithvalidationset__param_to_optimize": list(range(5))},
-#         cv=5,
-#     ).fit(X, y, X_val=X_val, y_val=y_val)
+#             param_grid={"estimatorwithvalidationset__param_to_optimize": list(range(5))},
+#             cv=5,
+#         ).fit(X, y, X_val=X_val, y_val=y_val)
 #
 # In the above code, the key parts are the call to `set_fit_request` to specify that
 # `X_val` and `y_val` are required by the `EstimatorWithValidationSet.fit` method, and