Pushing the docs to 1.6/ for branch: 1.6.X, commit 92c990e275eaf681bfb9e891a889b217632028b2

Author: sklearn-ci

1.6/_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, 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    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"
       ]
     },
     {

1.6/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -82,7 +82,7 @@
 #         ),
 #         param_grid={"estimatorwithvalidationset__param_to_optimize": list(range(5))},
 #         cv=5,
-#     ).fit(X, y, X_val, y_val)
+#     ).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