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test · 980 rows

instance_id stringlengths 14 33	version stringclasses 1 value	gold_patches dict	test_patch null	pre_patches dict	pre_scripts null	repo stringclasses 11 values	base_commit stringclasses 11 values	base_commit_timestamp stringdate 2024-07-22 07:32:48-0400 2024-10-09 00:56:33-0400	hints_text null	created_at null	problem_statement dict	environment_setup_commit stringclasses 11 values	evaluation dict
scikit-learn__scikit-learn-22	1.0	{ "code": "diff --git b/sklearn/compose/_column_transformer.py a/sklearn/compose/_column_transformer.py\nindex 1f7041765..73a0f5e2b 100644\n--- b/sklearn/compose/_column_transformer.py\n+++ a/sklearn/compose/_column_transformer.py\n@@ -951,6 +951,10 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n self.fit_transform(X, y=y, params)\n return self\n \n+ @_fit_context(\n+ # estimators in ColumnTransformer.transformers are not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit all transformers, transform the data and concatenate results.\n \n@@ -981,6 +985,54 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n any result is a sparse matrix, everything will be converted to\n sparse matrices.\n \"\"\"\n+ _raise_for_params(params, self, \"fit_transform\")\n+ _check_feature_names(self, X, reset=True)\n+\n+ X = _check_X(X)\n+ # set n_features_in_ attribute\n+ _check_n_features(self, X, reset=True)\n+ self._validate_transformers()\n+ n_samples = _num_samples(X)\n+\n+ self._validate_column_callables(X)\n+ self._validate_remainder(X)\n+\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"fit_transform\", *params)\n+ else:\n+ routed_params = self._get_empty_routing()\n+\n+ result = self._call_func_on_transformers(\n+ X,\n+ y,\n+ _fit_transform_one,\n+ column_as_labels=False,\n+ routed_params=routed_params,\n+ )\n+\n+ if not result:\n+ self._update_fitted_transformers([])\n+ # All transformers are None\n+ return np.zeros((n_samples, 0))\n+\n+ Xs, transformers = zip(result)\n+\n+ # determine if concatenated output will be sparse or not\n+ if any(sparse.issparse(X) for X in Xs):\n+ nnz = sum(X.nnz if sparse.issparse(X) else X.size for X in Xs)\n+ total = sum(\n+ X.shape[0] * X.shape[1] if sparse.issparse(X) else X.size for X in Xs\n+ )\n+ density = nnz / total\n+ self.sparse_output_ = density < self.sparse_threshold\n+ else:\n+ self.sparse_output_ = False\n+\n+ self._update_fitted_transformers(transformers)\n+ self._validate_output(Xs)\n+ self._record_output_indices(Xs)\n+\n+ return self._hstack(list(Xs), n_samples=n_samples)\n \n def transform(self, X, **params):\n \"\"\"Transform X separately by each transformer, concatenate results.\n", "test": null }	null	{ "code": "diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py\nindex 73a0f5e2b..1f7041765 100644\n--- a/sklearn/compose/_column_transformer.py\n+++ b/sklearn/compose/_column_transformer.py\n@@ -951,10 +951,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n self.fit_transform(X, y=y, params)\n return self\n \n- @_fit_context(\n- # estimators in ColumnTransformer.transformers are not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit all transformers, transform the data and concatenate results.\n \n@@ -985,54 +981,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n any result is a sparse matrix, everything will be converted to\n sparse matrices.\n \"\"\"\n- _raise_for_params(params, self, \"fit_transform\")\n- _check_feature_names(self, X, reset=True)\n-\n- X = _check_X(X)\n- # set n_features_in_ attribute\n- _check_n_features(self, X, reset=True)\n- self._validate_transformers()\n- n_samples = _num_samples(X)\n-\n- self._validate_column_callables(X)\n- self._validate_remainder(X)\n-\n- if _routing_enabled():\n- routed_params = process_routing(self, \"fit_transform\", *params)\n- else:\n- routed_params = self._get_empty_routing()\n-\n- result = self._call_func_on_transformers(\n- X,\n- y,\n- _fit_transform_one,\n- column_as_labels=False,\n- routed_params=routed_params,\n- )\n-\n- if not result:\n- self._update_fitted_transformers([])\n- # All transformers are None\n- return np.zeros((n_samples, 0))\n-\n- Xs, transformers = zip(result)\n-\n- # determine if concatenated output will be sparse or not\n- if any(sparse.issparse(X) for X in Xs):\n- nnz = sum(X.nnz if sparse.issparse(X) else X.size for X in Xs)\n- total = sum(\n- X.shape[0] * X.shape[1] if sparse.issparse(X) else X.size for X in Xs\n- )\n- density = nnz / total\n- self.sparse_output_ = density < self.sparse_threshold\n- else:\n- self.sparse_output_ = False\n-\n- self._update_fitted_transformers(transformers)\n- self._validate_output(Xs)\n- self._record_output_indices(Xs)\n-\n- return self._hstack(list(Xs), n_samples=n_samples)\n \n def transform(self, X, **params):\n \"\"\"Transform X separately by each transformer, concatenate results.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/compose/_column_transformer.py.\nHere is the description for the function:\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit all transformers, transform the data and concatenate results.\n\n Parameters\n ----------\n X : {array-like, dataframe} of shape (n_samples, n_features)\n Input data, of which specified subsets are used to fit the\n transformers.\n\n y : array-like of shape (n_samples,), default=None\n Targets for supervised learning.\n\n params : dict, default=None\n Parameters to be passed to the underlying transformers' ``fit`` and\n ``transform`` methods.\n\n You can only pass this if metadata routing is enabled, which you\n can enable using ``sklearn.set_config(enable_metadata_routing=True)``.\n\n .. versionadded:: 1.4\n\n Returns\n -------\n X_t : {array-like, sparse matrix} of \\\n shape (n_samples, sum_n_components)\n Horizontally stacked results of transformers. sum_n_components is the\n sum of n_components (output dimension) over transformers. If\n any result is a sparse matrix, everything will be converted to\n sparse matrices.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/compose/tests/test_column_transformer.py::test_column_transformer", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_tuple_transformers_parameter", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_dataframe[dataframe]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-list-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-list-numpy]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-bool-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-bool-numpy]", 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"sklearn/tests/test_common.py::test_transformers_get_feature_names_out[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_check_param_validation[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-23	1.0	{ "code": "diff --git b/sklearn/compose/_column_transformer.py a/sklearn/compose/_column_transformer.py\nindex 0abedee19..73a0f5e2b 100644\n--- b/sklearn/compose/_column_transformer.py\n+++ a/sklearn/compose/_column_transformer.py\n@@ -631,6 +631,31 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n+ check_is_fitted(self)\n+ input_features = _check_feature_names_in(self, input_features)\n+\n+ # List of tuples (name, feature_names_out)\n+ transformer_with_feature_names_out = []\n+ for name, trans, *_ in self._iter(\n+ fitted=True,\n+ column_as_labels=False,\n+ skip_empty_columns=True,\n+ skip_drop=True,\n+ ):\n+ feature_names_out = self._get_feature_name_out_for_transformer(\n+ name, trans, input_features\n+ )\n+ if feature_names_out is None:\n+ continue\n+ transformer_with_feature_names_out.append((name, feature_names_out))\n+\n+ if not transformer_with_feature_names_out:\n+ # No feature names\n+ return np.array([], dtype=object)\n+\n+ return self._add_prefix_for_feature_names_out(\n+ transformer_with_feature_names_out\n+ )\n \n def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out):\n \"\"\"Add prefix for feature names out that includes the transformer names.\n", "test": null }	null	{ "code": "diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py\nindex 73a0f5e2b..0abedee19 100644\n--- a/sklearn/compose/_column_transformer.py\n+++ b/sklearn/compose/_column_transformer.py\n@@ -631,31 +631,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n- check_is_fitted(self)\n- input_features = _check_feature_names_in(self, input_features)\n-\n- # List of tuples (name, feature_names_out)\n- transformer_with_feature_names_out = []\n- for name, trans, *_ in self._iter(\n- fitted=True,\n- column_as_labels=False,\n- skip_empty_columns=True,\n- skip_drop=True,\n- ):\n- feature_names_out = self._get_feature_name_out_for_transformer(\n- name, trans, input_features\n- )\n- if feature_names_out is None:\n- continue\n- transformer_with_feature_names_out.append((name, feature_names_out))\n-\n- if not transformer_with_feature_names_out:\n- # No feature names\n- return np.array([], dtype=object)\n-\n- return self._add_prefix_for_feature_names_out(\n- transformer_with_feature_names_out\n- )\n \n def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out):\n \"\"\"Add prefix for feature names out that includes the transformer names.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/compose/_column_transformer.py.\nHere is the description for the function:\n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\n\n Parameters\n ----------\n input_features : array-like of str or None, default=None\n Input features.\n\n - If `input_features` is `None`, then `feature_names_in_` is\n used as feature names in. If `feature_names_in_` is not defined,\n then the following input feature names are generated:\n `[\"x0\", \"x1\", ..., \"x(n_features_in_ - 1)\"]`.\n - If `input_features` is an array-like, then `input_features` must\n match `feature_names_in_` if `feature_names_in_` is defined.\n\n Returns\n -------\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_get_feature_names", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_empty_columns[list]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_empty_columns[array]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_empty_columns[callable]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector0]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[<lambda>0]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector2]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[<lambda>1]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector4]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[<lambda>2]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[selector0]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[<lambda>0]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[selector2]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[<lambda>1]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers0-passthrough-expected_names0]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers1-drop-expected_names1]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers2-passthrough-expected_names2]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers3-passthrough-expected_names3]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers4-drop-expected_names4]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers5-passthrough-expected_names5]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers6-drop-expected_names6]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers7-drop-expected_names7]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers8-passthrough-expected_names8]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers9-passthrough-expected_names9]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers10-drop-expected_names10]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers11-passthrough-expected_names11]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_true[transformers12-passthrough-expected_names12]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_callable_or_str[transformers0-passthrough-_feature_names_out_callable_name_clash-expected_names0]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_callable_or_str[transformers1-drop-{feature_name}-{transformer_name}-expected_names1]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_callable_or_str[transformers2-passthrough-_feature_names_out_callable_upper-expected_names2]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers0-passthrough-expected_names0]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers1-drop-expected_names1]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers2-passthrough-expected_names2]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers3-passthrough-expected_names3]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers4-drop-expected_names4]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers5-passthrough-expected_names5]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers6-drop-expected_names6]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers7-passthrough-expected_names7]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers8-passthrough-expected_names8]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers9-drop-expected_names9]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers10-passthrough-expected_names10]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers11-passthrough-expected_names11]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers12-drop-expected_names12]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false[transformers13-drop-expected_names13]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers0-drop-['b']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers1-drop-['c']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers2-passthrough-['a']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers3-passthrough-['a']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers4-drop-['b', 'c']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers5-passthrough-['a']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers6-passthrough-['a', 'b']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers7-passthrough-['pca0', 'pca1', 'pca2', 'pca3', 'pca4', ...]]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers8-passthrough-['a', 'b']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers9-passthrough-['a', 'b']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers10-passthrough-['a', 'b']]", "sklearn/compose/tests/test_column_transformer.py::test_verbose_feature_names_out_false_errors[transformers11-passthrough-['a', 'b']]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[drop-True]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[drop-False]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[passthrough-True]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[passthrough-False]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[True-drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[True-passthrough]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[False-drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[False-passthrough]", "sklearn/compose/tests/test_column_transformer.py::test_transformers_with_pandas_out_but_not_feature_names_out[trans_10-expected_verbose_names0-expected_non_verbose_names0]", "sklearn/compose/tests/test_column_transformer.py::test_transformers_with_pandas_out_but_not_feature_names_out[drop-expected_verbose_names1-expected_non_verbose_names1]", "sklearn/compose/tests/test_column_transformer.py::test_transformers_with_pandas_out_but_not_feature_names_out[passthrough-expected_verbose_names2-expected_non_verbose_names2]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_passthrough_naming_consistency[default]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_passthrough_naming_consistency[pandas]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_estimators_get_feature_names_out_error[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-24	1.0	{ "code": "diff --git b/sklearn/compose/_column_transformer.py a/sklearn/compose/_column_transformer.py\nindex 1ac6ded26..73a0f5e2b 100644\n--- b/sklearn/compose/_column_transformer.py\n+++ a/sklearn/compose/_column_transformer.py\n@@ -1294,6 +1294,31 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating\n routing information.\n \"\"\"\n+ router = MetadataRouter(owner=self.__class__.__name__)\n+ # Here we don't care about which columns are used for which\n+ # transformers, and whether or not a transformer is used at all, which\n+ # might happen if no columns are selected for that transformer. We\n+ # request all metadata requested by all transformers.\n+ transformers = chain(self.transformers, [(\"remainder\", self.remainder, None)])\n+ for name, step, _ in transformers:\n+ method_mapping = MethodMapping()\n+ if hasattr(step, \"fit_transform\"):\n+ (\n+ method_mapping.add(caller=\"fit\", callee=\"fit_transform\").add(\n+ caller=\"fit_transform\", callee=\"fit_transform\"\n+ )\n+ )\n+ else:\n+ (\n+ method_mapping.add(caller=\"fit\", callee=\"fit\")\n+ .add(caller=\"fit\", callee=\"transform\")\n+ .add(caller=\"fit_transform\", callee=\"fit\")\n+ .add(caller=\"fit_transform\", callee=\"transform\")\n+ )\n+ method_mapping.add(caller=\"transform\", callee=\"transform\")\n+ router.add(method_mapping=method_mapping, **{name: step})\n+\n+ return router\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py\nindex 73a0f5e2b..1ac6ded26 100644\n--- a/sklearn/compose/_column_transformer.py\n+++ b/sklearn/compose/_column_transformer.py\n@@ -1294,31 +1294,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating\n routing information.\n \"\"\"\n- router = MetadataRouter(owner=self.__class__.__name__)\n- # Here we don't care about which columns are used for which\n- # transformers, and whether or not a transformer is used at all, which\n- # might happen if no columns are selected for that transformer. We\n- # request all metadata requested by all transformers.\n- transformers = chain(self.transformers, [(\"remainder\", self.remainder, None)])\n- for name, step, _ in transformers:\n- method_mapping = MethodMapping()\n- if hasattr(step, \"fit_transform\"):\n- (\n- method_mapping.add(caller=\"fit\", callee=\"fit_transform\").add(\n- caller=\"fit_transform\", callee=\"fit_transform\"\n- )\n- )\n- else:\n- (\n- method_mapping.add(caller=\"fit\", callee=\"fit\")\n- .add(caller=\"fit\", callee=\"transform\")\n- .add(caller=\"fit_transform\", callee=\"fit\")\n- .add(caller=\"fit_transform\", callee=\"transform\")\n- )\n- method_mapping.add(caller=\"transform\", callee=\"transform\")\n- router.add(method_mapping=method_mapping, **{name: step})\n-\n- return router\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/compose/_column_transformer.py.\nHere is the description for the function:\n def get_metadata_routing(self):\n \"\"\"Get metadata routing of this object.\n\n Please check :ref:`User Guide <metadata_routing>` on how the routing\n mechanism works.\n\n .. versionadded:: 1.4\n\n Returns\n -------\n routing : MetadataRouter\n A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating\n routing information.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_for_column_transformer[transform]", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_for_column_transformer[fit_transform]", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_for_column_transformer[fit]", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_no_fit_transform", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_error_for_column_transformer[transform]", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_error_for_column_transformer[fit_transform]", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_error_for_column_transformer[fit]", "sklearn/compose/tests/test_column_transformer.py::test_get_metadata_routing_works_without_fit", "sklearn/compose/tests/test_column_transformer.py::test_remainder_request_always_present", "sklearn/compose/tests/test_column_transformer.py::test_unused_transformer_request_present" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-25	1.0	{ "code": "diff --git b/sklearn/compose/_column_transformer.py a/sklearn/compose/_column_transformer.py\nindex 4f62c21f4..73a0f5e2b 100644\n--- b/sklearn/compose/_column_transformer.py\n+++ a/sklearn/compose/_column_transformer.py\n@@ -376,6 +376,22 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n self : estimator instance\n Estimator instance.\n \"\"\"\n+ super().set_output(transform=transform)\n+\n+ transformers = (\n+ trans\n+ for _, trans, _ in chain(\n+ self.transformers, getattr(self, \"transformers_\", [])\n+ )\n+ if trans not in {\"passthrough\", \"drop\"}\n+ )\n+ for trans in transformers:\n+ _safe_set_output(trans, transform=transform)\n+\n+ if self.remainder not in {\"passthrough\", \"drop\"}:\n+ _safe_set_output(self.remainder, transform=transform)\n+\n+ return self\n \n def get_params(self, deep=True):\n \"\"\"Get parameters for this estimator.\n", "test": null }	null	{ "code": "diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py\nindex 73a0f5e2b..4f62c21f4 100644\n--- a/sklearn/compose/_column_transformer.py\n+++ b/sklearn/compose/_column_transformer.py\n@@ -376,22 +376,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n self : estimator instance\n Estimator instance.\n \"\"\"\n- super().set_output(transform=transform)\n-\n- transformers = (\n- trans\n- for _, trans, _ in chain(\n- self.transformers, getattr(self, \"transformers_\", [])\n- )\n- if trans not in {\"passthrough\", \"drop\"}\n- )\n- for trans in transformers:\n- _safe_set_output(trans, transform=transform)\n-\n- if self.remainder not in {\"passthrough\", \"drop\"}:\n- _safe_set_output(self.remainder, transform=transform)\n-\n- return self\n \n def get_params(self, deep=True):\n \"\"\"Get parameters for this estimator.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/compose/_column_transformer.py.\nHere is the description for the function:\n def set_output(self, *, transform=None):\n \"\"\"Set the output container when `\"transform\"` and `\"fit_transform\"` are called.\n\n Calling `set_output` will set the output of all estimators in `transformers`\n and `transformers_`.\n\n Parameters\n ----------\n transform : {\"default\", \"pandas\", \"polars\"}, default=None\n Configure output of `transform` and `fit_transform`.\n\n - `\"default\"`: Default output format of a transformer\n - `\"pandas\"`: DataFrame output\n - `\"polars\"`: Polars output\n - `None`: Transform configuration is unchanged\n\n .. versionadded:: 1.4\n `\"polars\"` option was added.\n\n Returns\n -------\n self : estimator instance\n Estimator instance.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[drop-True]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[drop-False]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[passthrough-True]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[passthrough-False]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[True-drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[True-passthrough]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[False-drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[False-passthrough]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_after_fitting[drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_after_fitting[passthrough]", "sklearn/compose/tests/test_column_transformer.py::test_transformers_with_pandas_out_but_not_feature_names_out[trans_10-expected_verbose_names0-expected_non_verbose_names0]", "sklearn/compose/tests/test_column_transformer.py::test_transformers_with_pandas_out_but_not_feature_names_out[drop-expected_verbose_names1-expected_non_verbose_names1]", "sklearn/compose/tests/test_column_transformer.py::test_transformers_with_pandas_out_but_not_feature_names_out[passthrough-expected_verbose_names2-expected_non_verbose_names2]", "sklearn/compose/tests/test_column_transformer.py::test_empty_selection_pandas_output[list]", "sklearn/compose/tests/test_column_transformer.py::test_empty_selection_pandas_output[bool]", "sklearn/compose/tests/test_column_transformer.py::test_empty_selection_pandas_output[bool_int]", "sklearn/compose/tests/test_column_transformer.py::test_raise_error_if_index_not_aligned", "sklearn/compose/tests/test_column_transformer.py::test_remainder_set_output", "sklearn/compose/tests/test_column_transformer.py::test_transform_pd_na", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_passthrough_naming_consistency[default]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_passthrough_naming_consistency[pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_column_renaming[pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_error_with_duplicated_columns[pandas]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingRegressor-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingClassifier-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingClassifier-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingRegressor-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingClassifier-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingClassifier-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingRegressor-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingClassifier-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingClassifier-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingRegressor-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingClassifier-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingClassifier-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_encoding_strategies", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_spec_errors[categorical_features4-monotonic_cst4-Categorical features cannot have monotonic constraints-HistGradientBoostingClassifier]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_spec_errors[categorical_features4-monotonic_cst4-Categorical features cannot have monotonic constraints-HistGradientBoostingRegressor]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_bad_encoding_errors[False-at index 0-HistGradientBoostingClassifier]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_bad_encoding_errors[False-at index 0-HistGradientBoostingRegressor]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_bad_encoding_errors[True-'f0'-HistGradientBoostingClassifier]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_bad_encoding_errors[True-'f0'-HistGradientBoostingRegressor]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_category_that_are_negative", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_dataframe_categorical_results_same_as_ndarray[HistGradientBoostingClassifier-pandas]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_dataframe_categorical_results_same_as_ndarray[HistGradientBoostingRegressor-pandas]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_dataframe_categorical_errors[HistGradientBoostingClassifier-pandas]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_dataframe_categorical_errors[HistGradientBoostingRegressor-pandas]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_different_order_same_model[pandas]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_constraints.py::test_predictions[42-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_constraints.py::test_predictions[42-False]", "sklearn/tests/test_common.py::test_set_output_transform[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-26	1.0	{ "code": "diff --git b/sklearn/compose/_column_transformer.py a/sklearn/compose/_column_transformer.py\nindex 6616c14e8..73a0f5e2b 100644\n--- b/sklearn/compose/_column_transformer.py\n+++ a/sklearn/compose/_column_transformer.py\n@@ -1060,6 +1060,62 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n any result is a sparse matrix, everything will be converted to\n sparse matrices.\n \"\"\"\n+ _raise_for_params(params, self, \"transform\")\n+ check_is_fitted(self)\n+ X = _check_X(X)\n+\n+ # If ColumnTransformer is fit using a dataframe, and now a dataframe is\n+ # passed to be transformed, we select columns by name instead. This\n+ # enables the user to pass X at transform time with extra columns which\n+ # were not present in fit time, and the order of the columns doesn't\n+ # matter.\n+ fit_dataframe_and_transform_dataframe = hasattr(self, \"feature_names_in_\") and (\n+ _is_pandas_df(X) or hasattr(X, \"__dataframe__\")\n+ )\n+\n+ n_samples = _num_samples(X)\n+ column_names = _get_feature_names(X)\n+\n+ if fit_dataframe_and_transform_dataframe:\n+ named_transformers = self.named_transformers_\n+ # check that all names seen in fit are in transform, unless\n+ # they were dropped\n+ non_dropped_indices = [\n+ ind\n+ for name, ind in self._transformer_to_input_indices.items()\n+ if name in named_transformers and named_transformers[name] != \"drop\"\n+ ]\n+\n+ all_indices = set(chain(non_dropped_indices))\n+ all_names = set(self.feature_names_in_[ind] for ind in all_indices)\n+\n+ diff = all_names - set(column_names)\n+ if diff:\n+ raise ValueError(f\"columns are missing: {diff}\")\n+ else:\n+ # ndarray was used for fitting or transforming, thus we only\n+ # check that n_features_in_ is consistent\n+ _check_n_features(self, X, reset=False)\n+\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"transform\", params)\n+ else:\n+ routed_params = self._get_empty_routing()\n+\n+ Xs = self._call_func_on_transformers(\n+ X,\n+ None,\n+ _transform_one,\n+ column_as_labels=fit_dataframe_and_transform_dataframe,\n+ routed_params=routed_params,\n+ )\n+ self._validate_output(Xs)\n+\n+ if not Xs:\n+ # All transformers are None\n+ return np.zeros((n_samples, 0))\n+\n+ return self._hstack(list(Xs), n_samples=n_samples)\n \n def _hstack(self, Xs, , n_samples):\n \"\"\"Stacks Xs horizontally.\n", "test": null }	null	{ "code": "diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py\nindex 73a0f5e2b..6616c14e8 100644\n--- a/sklearn/compose/_column_transformer.py\n+++ b/sklearn/compose/_column_transformer.py\n@@ -1060,62 +1060,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):\n any result is a sparse matrix, everything will be converted to\n sparse matrices.\n \"\"\"\n- _raise_for_params(params, self, \"transform\")\n- check_is_fitted(self)\n- X = _check_X(X)\n-\n- # If ColumnTransformer is fit using a dataframe, and now a dataframe is\n- # passed to be transformed, we select columns by name instead. This\n- # enables the user to pass X at transform time with extra columns which\n- # were not present in fit time, and the order of the columns doesn't\n- # matter.\n- fit_dataframe_and_transform_dataframe = hasattr(self, \"feature_names_in_\") and (\n- _is_pandas_df(X) or hasattr(X, \"__dataframe__\")\n- )\n-\n- n_samples = _num_samples(X)\n- column_names = _get_feature_names(X)\n-\n- if fit_dataframe_and_transform_dataframe:\n- named_transformers = self.named_transformers_\n- # check that all names seen in fit are in transform, unless\n- # they were dropped\n- non_dropped_indices = [\n- ind\n- for name, ind in self._transformer_to_input_indices.items()\n- if name in named_transformers and named_transformers[name] != \"drop\"\n- ]\n-\n- all_indices = set(chain(non_dropped_indices))\n- all_names = set(self.feature_names_in_[ind] for ind in all_indices)\n-\n- diff = all_names - set(column_names)\n- if diff:\n- raise ValueError(f\"columns are missing: {diff}\")\n- else:\n- # ndarray was used for fitting or transforming, thus we only\n- # check that n_features_in_ is consistent\n- _check_n_features(self, X, reset=False)\n-\n- if _routing_enabled():\n- routed_params = process_routing(self, \"transform\", params)\n- else:\n- routed_params = self._get_empty_routing()\n-\n- Xs = self._call_func_on_transformers(\n- X,\n- None,\n- _transform_one,\n- column_as_labels=fit_dataframe_and_transform_dataframe,\n- routed_params=routed_params,\n- )\n- self._validate_output(Xs)\n-\n- if not Xs:\n- # All transformers are None\n- return np.zeros((n_samples, 0))\n-\n- return self._hstack(list(Xs), n_samples=n_samples)\n \n def _hstack(self, Xs, , n_samples):\n \"\"\"Stacks Xs horizontally.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/compose/_column_transformer.py.\nHere is the description for the function:\n def transform(self, X, params):\n \"\"\"Transform X separately by each transformer, concatenate results.\n\n Parameters\n ----------\n X : {array-like, dataframe} of shape (n_samples, n_features)\n The data to be transformed by subset.\n\n params : dict, default=None\n Parameters to be passed to the underlying transformers' ``transform``\n method.\n\n You can only pass this if metadata routing is enabled, which you\n can enable using ``sklearn.set_config(enable_metadata_routing=True)``.\n\n .. versionadded:: 1.4\n\n Returns\n -------\n X_t : {array-like, sparse matrix} of \\\n shape (n_samples, sum_n_components)\n Horizontally stacked results of transformers. sum_n_components is the\n sum of n_components (output dimension) over transformers. If\n any result is a sparse matrix, everything will be converted to\n sparse matrices.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/compose/tests/test_column_transformer.py::test_column_transformer", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_tuple_transformers_parameter", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_dataframe[dataframe]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-list-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-list-numpy]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-bool-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-bool-numpy]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-bool_int-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-bool_int-numpy]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[True-list-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[True-list-numpy]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[True-bool-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[True-bool-numpy]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[True-bool_int-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[True-bool_int-numpy]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_sparse_array[csr_matrix]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_sparse_array[csr_array]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_list", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_sparse_stacking[csr_matrix]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_sparse_stacking[csr_array]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_invalid_columns[drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_invalid_columns[passthrough]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_special_strings", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_numpy[key0-expected_cols0]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_numpy[key1-expected_cols1]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_numpy[key2-expected_cols2]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_numpy[key3-expected_cols3]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key0-expected_cols0]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key1-expected_cols1]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key2-expected_cols2]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key3-expected_cols3]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[pd-index-expected_cols4]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key5-expected_cols5]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key6-expected_cols6]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key7-expected_cols7]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_pandas[key8-expected_cols8]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_transformer[key0-expected_cols0]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_transformer[key1-expected_cols1]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_transformer[key2-expected_cols2]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_remainder_transformer[key3-expected_cols3]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_no_remaining_remainder_transformer", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_drops_all_remainder_transformer", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_callable_specifier", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_callable_specifier_dataframe", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[remainder0-first]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[remainder0-second]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[remainder0-0]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[remainder0-1]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[passthrough-first]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[passthrough-second]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[passthrough-0]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[passthrough-1]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[drop-first]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[drop-second]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[drop-0]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_reordered_column_names_remainder[drop-1]", "sklearn/compose/tests/test_column_transformer.py::test_feature_name_validation_missing_columns_drop_passthough", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[drop-True]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[drop-False]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[passthrough-True]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_set_output[passthrough-False]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[False-drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_mixed[False-passthrough]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_after_fitting[drop]", "sklearn/compose/tests/test_column_transformer.py::test_column_transform_set_output_after_fitting[passthrough]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-column-transformer-estimator-brute]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-column-transformer-estimator-recursion]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-column-transformer-passthrough-estimator-brute]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-column-transformer-passthrough-estimator-recursion]", "sklearn/compose/tests/test_column_transformer.py::test_routing_passed_metadata_not_supported[transform]", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_for_column_transformer[transform]", "sklearn/compose/tests/test_column_transformer.py::test_metadata_routing_error_for_column_transformer[transform]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-column-transformer-estimator-brute]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-column-transformer-estimator-recursion]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-column-transformer-passthrough-estimator-brute]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-column-transformer-passthrough-estimator-recursion]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_feature_type[scalar-int]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_feature_type[scalar-str]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_feature_type[list-int]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_feature_type[list-str]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_feature_type[mask]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[0-estimator0]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[0-estimator1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[0-estimator2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[0-estimator3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[1-estimator0]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[1-estimator1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[1-estimator2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[1-estimator3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[-1-estimator0]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[-1-estimator1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[-1-estimator2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[-1-estimator3]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_fit_idempotent]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingRegressor-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingClassifier-False]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_with_categorical[categorical_features0-dataframe]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingClassifier-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingRegressor-True]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_with_categorical[categorical_features1-array]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_with_categorical[categorical_features2-array]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingClassifier-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-False-HistGradientBoostingClassifier-True]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_legend", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingRegressor-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingClassifier-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-True-HistGradientBoostingClassifier-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingRegressor-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingRegressor-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingClassifier-False]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[-1-False-HistGradientBoostingClassifier-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_categorical_encoding_strategies", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_category_that_are_negative", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_display_curve_not_fitted_errors[DetCurveDisplay-clf2]", "sklearn/metrics/_plot/tests/test_roc_curve_display.py::test_roc_curve_display_complex_pipeline[from_estimator-clf2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_dataframe_categorical_results_same_as_ndarray[HistGradientBoostingClassifier-pandas]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_dataframe_categorical_results_same_as_ndarray[HistGradientBoostingRegressor-pandas]", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_display_curve_not_fitted_errors[PrecisionRecallDisplay-clf2]", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_display_curve_not_fitted_errors[RocCurveDisplay-clf2]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_pipeline[clf1]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_mixed_types_pandas", "sklearn/metrics/_plot/tests/test_confusion_matrix_display.py::test_confusion_matrix_pipeline[pipeline-column_transformer-clf]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_constraints.py::test_predictions[42-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_constraints.py::test_predictions[42-False]", "sklearn/utils/tests/test_parallel.py::test_dispatch_config_parallel[1]", "sklearn/utils/tests/test_parallel.py::test_dispatch_config_parallel[2]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-27	1.0	{ "code": "diff --git b/sklearn/cluster/_dbscan.py a/sklearn/cluster/_dbscan.py\nindex c9f545be4..12f9b74d0 100644\n--- b/sklearn/cluster/_dbscan.py\n+++ a/sklearn/cluster/_dbscan.py\n@@ -360,6 +360,10 @@ class DBSCAN(ClusterMixin, BaseEstimator):\n self.p = p\n self.n_jobs = n_jobs\n \n+ @_fit_context(\n+ # DBSCAN.metric is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Perform DBSCAN clustering from features, or distance matrix.\n \n@@ -385,6 +389,59 @@ class DBSCAN(ClusterMixin, BaseEstimator):\n self : object\n Returns a fitted instance of self.\n \"\"\"\n+ X = validate_data(self, X, accept_sparse=\"csr\")\n+\n+ if sample_weight is not None:\n+ sample_weight = _check_sample_weight(sample_weight, X)\n+\n+ # Calculate neighborhood for all samples. This leaves the original\n+ # point in, which needs to be considered later (i.e. point i is in the\n+ # neighborhood of point i. While True, its useless information)\n+ if self.metric == \"precomputed\" and sparse.issparse(X):\n+ # set the diagonal to explicit values, as a point is its own\n+ # neighbor\n+ X = X.copy() # copy to avoid in-place modification\n+ with warnings.catch_warnings():\n+ warnings.simplefilter(\"ignore\", sparse.SparseEfficiencyWarning)\n+ X.setdiag(X.diagonal())\n+\n+ neighbors_model = NearestNeighbors(\n+ radius=self.eps,\n+ algorithm=self.algorithm,\n+ leaf_size=self.leaf_size,\n+ metric=self.metric,\n+ metric_params=self.metric_params,\n+ p=self.p,\n+ n_jobs=self.n_jobs,\n+ )\n+ neighbors_model.fit(X)\n+ # This has worst case O(n^2) memory complexity\n+ neighborhoods = neighbors_model.radius_neighbors(X, return_distance=False)\n+\n+ if sample_weight is None:\n+ n_neighbors = np.array([len(neighbors) for neighbors in neighborhoods])\n+ else:\n+ n_neighbors = np.array(\n+ [np.sum(sample_weight[neighbors]) for neighbors in neighborhoods]\n+ )\n+\n+ # Initially, all samples are noise.\n+ labels = np.full(X.shape[0], -1, dtype=np.intp)\n+\n+ # A list of all core samples found.\n+ core_samples = np.asarray(n_neighbors >= self.min_samples, dtype=np.uint8)\n+ dbscan_inner(core_samples, neighborhoods, labels)\n+\n+ self.core_sample_indices_ = np.where(core_samples)[0]\n+ self.labels_ = labels\n+\n+ if len(self.core_sample_indices_):\n+ # fix for scipy sparse indexing issue\n+ self.components_ = X[self.core_sample_indices_].copy()\n+ else:\n+ # no core samples\n+ self.components_ = np.empty((0, X.shape[1]))\n+ return self\n \n def fit_predict(self, X, y=None, sample_weight=None):\n \"\"\"Compute clusters from a data or distance matrix and predict labels.\n", "test": null }	null	{ "code": "diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py\nindex 12f9b74d0..c9f545be4 100644\n--- a/sklearn/cluster/_dbscan.py\n+++ b/sklearn/cluster/_dbscan.py\n@@ -360,10 +360,6 @@ class DBSCAN(ClusterMixin, BaseEstimator):\n self.p = p\n self.n_jobs = n_jobs\n \n- @_fit_context(\n- # DBSCAN.metric is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Perform DBSCAN clustering from features, or distance matrix.\n \n@@ -389,59 +385,6 @@ class DBSCAN(ClusterMixin, BaseEstimator):\n self : object\n Returns a fitted instance of self.\n \"\"\"\n- X = validate_data(self, X, accept_sparse=\"csr\")\n-\n- if sample_weight is not None:\n- sample_weight = _check_sample_weight(sample_weight, X)\n-\n- # Calculate neighborhood for all samples. This leaves the original\n- # point in, which needs to be considered later (i.e. point i is in the\n- # neighborhood of point i. While True, its useless information)\n- if self.metric == \"precomputed\" and sparse.issparse(X):\n- # set the diagonal to explicit values, as a point is its own\n- # neighbor\n- X = X.copy() # copy to avoid in-place modification\n- with warnings.catch_warnings():\n- warnings.simplefilter(\"ignore\", sparse.SparseEfficiencyWarning)\n- X.setdiag(X.diagonal())\n-\n- neighbors_model = NearestNeighbors(\n- radius=self.eps,\n- algorithm=self.algorithm,\n- leaf_size=self.leaf_size,\n- metric=self.metric,\n- metric_params=self.metric_params,\n- p=self.p,\n- n_jobs=self.n_jobs,\n- )\n- neighbors_model.fit(X)\n- # This has worst case O(n^2) memory complexity\n- neighborhoods = neighbors_model.radius_neighbors(X, return_distance=False)\n-\n- if sample_weight is None:\n- n_neighbors = np.array([len(neighbors) for neighbors in neighborhoods])\n- else:\n- n_neighbors = np.array(\n- [np.sum(sample_weight[neighbors]) for neighbors in neighborhoods]\n- )\n-\n- # Initially, all samples are noise.\n- labels = np.full(X.shape[0], -1, dtype=np.intp)\n-\n- # A list of all core samples found.\n- core_samples = np.asarray(n_neighbors >= self.min_samples, dtype=np.uint8)\n- dbscan_inner(core_samples, neighborhoods, labels)\n-\n- self.core_sample_indices_ = np.where(core_samples)[0]\n- self.labels_ = labels\n-\n- if len(self.core_sample_indices_):\n- # fix for scipy sparse indexing issue\n- self.components_ = X[self.core_sample_indices_].copy()\n- else:\n- # no core samples\n- self.components_ = np.empty((0, X.shape[1]))\n- return self\n \n def fit_predict(self, X, y=None, sample_weight=None):\n \"\"\"Compute clusters from a data or distance matrix and predict labels.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/cluster/_dbscan.py.\nHere is the description for the function:\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Perform DBSCAN clustering from features, or distance matrix.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features), or \\\n (n_samples, n_samples)\n Training instances to cluster, or distances between instances if\n ``metric='precomputed'``. If a sparse matrix is provided, it will\n be converted into a sparse ``csr_matrix``.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Weight of each sample, such that a sample with a weight of at least\n ``min_samples`` is by itself a core sample; a sample with a\n negative weight may inhibit its eps-neighbor from being core.\n Note that weights are absolute, and default to 1.\n\n Returns\n -------\n self : object\n Returns a fitted instance of self.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_sample_weights_list]", "sklearn/tests/test_public_functions.py::test_class_wrapper_param_validation[sklearn.cluster.dbscan-sklearn.cluster.DBSCAN]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_clustering]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_clustering(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[DBSCAN()-check_fit2d_predict1d]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-3-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-3-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-3-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-10-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-10-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-10-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-20-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-20-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-minkowski-20-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-3-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-3-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-3-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-10-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-10-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-10-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-20-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-20-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-None-euclidean-20-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-3-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-3-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-3-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-10-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-10-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-10-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-20-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-20-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_matrix-euclidean-20-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-3-0.1]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_similarity", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_feature", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-3-0.3]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_sparse[lil_matrix]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_sparse[lil_array]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_sparse_precomputed[False]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_sparse_precomputed[True]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_sparse_precomputed_different_eps", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified[csr_matrix-precomputed]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified[csr_matrix-minkowski]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified[csr_array-precomputed]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified[csr_array-minkowski]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-3-0.5]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified[None-precomputed]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified[None-minkowski]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified_precomputed_sparse_nodiag[csr_matrix]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified_precomputed_sparse_nodiag[csr_array]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_no_core_samples[csr_matrix]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_no_core_samples[csr_array]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_callable", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_metric_params", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_balltree", "sklearn/cluster/tests/test_dbscan.py::test_input_validation", "sklearn/cluster/tests/test_dbscan.py::test_boundaries", "sklearn/cluster/tests/test_dbscan.py::test_weighted_dbscan[42]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-10-0.1]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_core_samples_toy[brute]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_core_samples_toy[kd_tree]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_core_samples_toy[ball_tree]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_precomputed_metric_with_degenerate_input_arrays", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_precomputed_metric_with_initial_rows_zero[csr_matrix]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-10-0.3]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_precomputed_metric_with_initial_rows_zero[csr_array]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-10-0.5]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-20-0.1]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-20-0.3]", "sklearn/cluster/tests/test_optics.py::test_dbscan_optics_parity[float64-csr_array-euclidean-20-0.5]", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_dbscan", "sklearn/neighbors/_graph.py::sklearn.neighbors._graph.RadiusNeighborsTransformer", "sklearn/cluster/_dbscan.py::sklearn.cluster._dbscan.DBSCAN", "sklearn/cluster/_dbscan.py::sklearn.cluster._dbscan.dbscan", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[DBSCAN()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[DBSCAN()]", "sklearn/tests/test_common.py::test_check_param_validation[DBSCAN()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-28	1.0	{ "code": "diff --git b/sklearn/tree/_classes.py a/sklearn/tree/_classes.py\nindex 826eb75fd..7366d5384 100644\n--- b/sklearn/tree/_classes.py\n+++ a/sklearn/tree/_classes.py\n@@ -972,6 +972,7 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):\n ccp_alpha=ccp_alpha,\n )\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Build a decision tree classifier from the training set (X, y).\n \n@@ -1002,6 +1003,14 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):\n Fitted estimator.\n \"\"\"\n \n+ super()._fit(\n+ X,\n+ y,\n+ sample_weight=sample_weight,\n+ check_input=check_input,\n+ )\n+ return self\n+\n def predict_proba(self, X, check_input=True):\n \"\"\"Predict class probabilities of the input samples X.\n \n", "test": null }	null	{ "code": "diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py\nindex 7366d5384..826eb75fd 100644\n--- a/sklearn/tree/_classes.py\n+++ b/sklearn/tree/_classes.py\n@@ -972,7 +972,6 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):\n ccp_alpha=ccp_alpha,\n )\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Build a decision tree classifier from the training set (X, y).\n \n@@ -1003,14 +1002,6 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):\n Fitted estimator.\n \"\"\"\n \n- super()._fit(\n- X,\n- y,\n- sample_weight=sample_weight,\n- check_input=check_input,\n- )\n- return self\n-\n def predict_proba(self, X, check_input=True):\n \"\"\"Predict class probabilities of the input samples X.\n \n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/tree/_classes.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Build a decision tree classifier from the training set (X, y).\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The training input samples. Internally, it will be converted to\n ``dtype=np.float32`` and if a sparse matrix is provided\n to a sparse ``csc_matrix``.\n\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n The target values (class labels) as integers or strings.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights. If None, then samples are equally weighted. Splits\n that would create child nodes with net zero or negative weight are\n ignored while searching for a split in each node. Splits are also\n ignored if they would result in any single class carrying a\n negative weight in either child node.\n\n check_input : bool, default=True\n Allow to bypass several input checking.\n Don't use this parameter unless you know what you're doing.\n\n Returns\n -------\n self : DecisionTreeClassifier\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tree/tests/test_tree.py::test_classification_toy", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tree/tests/test_tree.py::test_weighted_classification_toy", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_dtype_object]", "sklearn/tree/tests/test_tree.py::test_xor", "sklearn/tree/tests/test_tree.py::test_iris", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tree/tests/test_tree.py::test_probability", "sklearn/tree/tests/test_tree.py::test_pure_set", "sklearn/tree/tests/test_tree.py::test_importances", "sklearn/tree/tests/test_tree.py::test_importances_gini_equal_squared_error", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tree/tests/test_tree.py::test_max_features", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[accuracy-accuracy_score]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifier_data_not_an_array]", "sklearn/tree/tests/test_tree.py::test_error", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[balanced_accuracy-balanced_accuracy_score]", "sklearn/tree/tests/test_tree.py::test_min_samples_split", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_weighted-metric2]", "sklearn/tree/tests/test_tree.py::test_min_samples_leaf", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_macro-metric3]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_one_label]", "sklearn/tree/tests/test_tree.py::test_min_weight_fraction_leaf_on_dense_input[DecisionTreeClassifier]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_micro-metric4]", "sklearn/tree/tests/test_tree.py::test_min_weight_fraction_leaf_on_dense_input[ExtraTreeClassifier]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[precision_weighted-metric5]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[precision_macro-metric6]", 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"sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[balanced_accuracy]", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[completeness_score]", "sklearn/tree/tests/test_tree.py::test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input[csc_matrix-ExtraTreeClassifier]", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[d2_absolute_error_score]", "sklearn/tree/tests/test_tree.py::test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input[csc_array-DecisionTreeClassifier]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_regression_target]", "sklearn/tree/tests/test_tree.py::test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input[csc_array-ExtraTreeClassifier]", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[explained_variance]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_supervised_y_2d]", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[f1]", "sklearn/tree/tests/test_tree.py::test_min_impurity_decrease[42]", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[f1_macro]", "sklearn/tree/tests/test_tree.py::test_pickle", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[f1_micro]", "sklearn/tree/tests/test_tree.py::test_multioutput", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_decision_proba_consistency]", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[f1_samples]", "sklearn/tree/tests/test_tree.py::test_classes_shape", "sklearn/metrics/tests/test_score_objects.py::test_scorer_memmap_input[f1_weighted]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_methods_sample_order_invariance]", 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"sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[AdaBoostClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[BaggingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[DecisionTreeClassifier()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[ExtraTreeClassifier()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[AdaBoostClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[BaggingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[DecisionTreeClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ExtraTreeClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[BaggingClassifier(n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_check_param_validation[DecisionTreeClassifier()]", "sklearn/tests/test_common.py::test_check_param_validation[ExtraTreeClassifier()]", "sklearn/tests/test_common.py::test_set_output_transform[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-29	1.0	{ "code": "diff --git b/sklearn/tree/_classes.py a/sklearn/tree/_classes.py\nindex ba8e04d46..7366d5384 100644\n--- b/sklearn/tree/_classes.py\n+++ a/sklearn/tree/_classes.py\n@@ -1035,6 +1035,18 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):\n The class probabilities of the input samples. The order of the\n classes corresponds to that in the attribute :term:`classes_`.\n \"\"\"\n+ check_is_fitted(self)\n+ X = self._validate_X_predict(X, check_input)\n+ proba = self.tree_.predict(X)\n+\n+ if self.n_outputs_ == 1:\n+ return proba[:, : self.n_classes_]\n+ else:\n+ all_proba = []\n+ for k in range(self.n_outputs_):\n+ proba_k = proba[:, k, : self.n_classes_[k]]\n+ all_proba.append(proba_k)\n+ return all_proba\n \n def predict_log_proba(self, X):\n \"\"\"Predict class log-probabilities of the input samples X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py\nindex 7366d5384..ba8e04d46 100644\n--- a/sklearn/tree/_classes.py\n+++ b/sklearn/tree/_classes.py\n@@ -1035,18 +1035,6 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):\n The class probabilities of the input samples. The order of the\n classes corresponds to that in the attribute :term:`classes_`.\n \"\"\"\n- check_is_fitted(self)\n- X = self._validate_X_predict(X, check_input)\n- proba = self.tree_.predict(X)\n-\n- if self.n_outputs_ == 1:\n- return proba[:, : self.n_classes_]\n- else:\n- all_proba = []\n- for k in range(self.n_outputs_):\n- proba_k = proba[:, k, : self.n_classes_[k]]\n- all_proba.append(proba_k)\n- return all_proba\n \n def predict_log_proba(self, X):\n \"\"\"Predict class log-probabilities of the input samples X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/tree/_classes.py.\nHere is the description for the function:\n def predict_proba(self, X, check_input=True):\n \"\"\"Predict class probabilities of the input samples X.\n\n The predicted class probability is the fraction of samples of the same\n class in a leaf.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input samples. Internally, it will be converted to\n ``dtype=np.float32`` and if a sparse matrix is provided\n to a sparse ``csr_matrix``.\n\n check_input : bool, default=True\n Allow to bypass several input checking.\n Don't use this parameter unless you know what you're doing.\n\n Returns\n -------\n proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \\\n such arrays if n_outputs > 1\n The class probabilities of the input samples. The order of the\n classes corresponds to that in the attribute :term:`classes_`.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/ensemble/tests/test_forest.py::test_classification_toy[ExtraTreesClassifier]", "sklearn/ensemble/tests/test_forest.py::test_classification_toy[RandomForestClassifier]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_dtype_object]", "sklearn/ensemble/tests/test_forest.py::test_iris_criterion[gini-ExtraTreesClassifier]", "sklearn/ensemble/tests/test_forest.py::test_iris_criterion[gini-RandomForestClassifier]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_nan_inf]", "sklearn/ensemble/tests/test_forest.py::test_iris_criterion[log_loss-ExtraTreesClassifier]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimator_sparse_array]", "sklearn/ensemble/tests/test_forest.py::test_iris_criterion[log_loss-RandomForestClassifier]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_one_label]", "sklearn/ensemble/tests/test_forest.py::test_probability[ExtraTreesClassifier]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_one_label_sample_weights]", "sklearn/ensemble/tests/test_forest.py::test_probability[RandomForestClassifier]", "sklearn/tree/tests/test_tree.py::test_probability", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_train]", "sklearn/tree/tests/test_tree.py::test_error", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_supervised_y_2d]", "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_decision_proba_consistency]", "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers_multilabel_indicator_data", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_methods_sample_order_invariance]", 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"sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_array]", 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"sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[AdaBoostClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[DecisionTreeClassifier()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[ExtraTreeClassifier()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[AdaBoostClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[BaggingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[DecisionTreeClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ExtraTreeClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ExtraTreesClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[BaggingClassifier(n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ExtraTreesClassifier(bootstrap=True,n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestClassifier(n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-30	1.0	{ "code": "diff --git b/sklearn/tree/_classes.py a/sklearn/tree/_classes.py\nindex 145976a24..7366d5384 100644\n--- b/sklearn/tree/_classes.py\n+++ a/sklearn/tree/_classes.py\n@@ -1344,6 +1344,7 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):\n monotonic_cst=monotonic_cst,\n )\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Build a decision tree regressor from the training set (X, y).\n \n@@ -1373,6 +1374,14 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):\n Fitted estimator.\n \"\"\"\n \n+ super()._fit(\n+ X,\n+ y,\n+ sample_weight=sample_weight,\n+ check_input=check_input,\n+ )\n+ return self\n+\n def _compute_partial_dependence_recursion(self, grid, target_features):\n \"\"\"Fast partial dependence computation.\n \n", "test": null }	null	{ "code": "diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py\nindex 7366d5384..145976a24 100644\n--- a/sklearn/tree/_classes.py\n+++ b/sklearn/tree/_classes.py\n@@ -1344,7 +1344,6 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):\n monotonic_cst=monotonic_cst,\n )\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Build a decision tree regressor from the training set (X, y).\n \n@@ -1374,14 +1373,6 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):\n Fitted estimator.\n \"\"\"\n \n- super()._fit(\n- X,\n- y,\n- sample_weight=sample_weight,\n- check_input=check_input,\n- )\n- return self\n-\n def _compute_partial_dependence_recursion(self, grid, target_features):\n \"\"\"Fast partial dependence computation.\n \n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/tree/_classes.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Build a decision tree regressor from the training set (X, y).\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The training input samples. Internally, it will be converted to\n ``dtype=np.float32`` and if a sparse matrix is provided\n to a sparse ``csc_matrix``.\n\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n The target values (real numbers). Use ``dtype=np.float64`` and\n ``order='C'`` for maximum efficiency.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights. If None, then samples are equally weighted. Splits\n that would create child nodes with net zero or negative weight are\n ignored while searching for a split in each node.\n\n check_input : bool, default=True\n Allow to bypass several input checking.\n Don't use this parameter unless you know what you're doing.\n\n Returns\n -------\n self : DecisionTreeRegressor\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_sample_weights_shape]", "sklearn/tree/tests/test_tree.py::test_regression_toy[squared_error-DecisionTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-auto-data0]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tree/tests/test_tree.py::test_regression_toy[squared_error-ExtraTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-auto-data1]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tree/tests/test_tree.py::test_regression_toy[absolute_error-DecisionTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data2]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tree/tests/test_tree.py::test_regression_toy[absolute_error-ExtraTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data3]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tree/tests/test_tree.py::test_regression_toy[friedman_mse-DecisionTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingRegressor-auto-data4]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tree/tests/test_tree.py::test_regression_toy[friedman_mse-ExtraTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingRegressor-brute-data5]", "sklearn/tree/tests/test_tree.py::test_regression_toy[poisson-DecisionTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-DecisionTreeRegressor-brute-data6]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_dtype_object]", "sklearn/tree/tests/test_tree.py::test_regression_toy[poisson-ExtraTreeRegressor]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_estimators_nan_inf]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[squared_error-DecisionTreeRegressor-DecisionTreeRegressor]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[squared_error-ExtraTreeRegressor-ExtraTreeRegressor]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[absolute_error-DecisionTreeRegressor-DecisionTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-auto-data0]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[absolute_error-ExtraTreeRegressor-ExtraTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-auto-data1]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[friedman_mse-DecisionTreeRegressor-DecisionTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data2]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[friedman_mse-ExtraTreeRegressor-ExtraTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data3]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[poisson-DecisionTreeRegressor-DecisionTreeRegressor]", "sklearn/tree/tests/test_tree.py::test_diabetes_overfit[poisson-ExtraTreeRegressor-ExtraTreeRegressor]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingRegressor-auto-data4]", 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"sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[AdaBoostRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[BaggingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[DecisionTreeRegressor()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[ExtraTreeRegressor()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GradientBoostingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GradientBoostingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IsolationForest(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[AdaBoostRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[BaggingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[DecisionTreeRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ExtraTreeRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IsolationForest(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[BaggingRegressor(n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingRegressor(n_estimators=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_check_param_validation[DecisionTreeRegressor()]", "sklearn/tests/test_common.py::test_check_param_validation[ExtraTreeRegressor()]", "sklearn/tests/test_common.py::test_set_output_transform[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-31	1.0	{ "code": "diff --git b/sklearn/feature_extraction/_dict_vectorizer.py a/sklearn/feature_extraction/_dict_vectorizer.py\nindex 13ada2f8e..64c9a5704 100644\n--- b/sklearn/feature_extraction/_dict_vectorizer.py\n+++ a/sklearn/feature_extraction/_dict_vectorizer.py\n@@ -135,6 +135,7 @@ class DictVectorizer(TransformerMixin, BaseEstimator):\n indices.append(vocab[feature_name])\n values.append(self.dtype(vv))\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Learn a list of feature name -> indices mappings.\n \n@@ -155,6 +156,38 @@ class DictVectorizer(TransformerMixin, BaseEstimator):\n self : object\n DictVectorizer class instance.\n \"\"\"\n+ feature_names = []\n+ vocab = {}\n+\n+ for x in X:\n+ for f, v in x.items():\n+ if isinstance(v, str):\n+ feature_name = \"%s%s%s\" % (f, self.separator, v)\n+ elif isinstance(v, Number) or (v is None):\n+ feature_name = f\n+ elif isinstance(v, Mapping):\n+ raise TypeError(\n+ f\"Unsupported value type {type(v)} \"\n+ f\"for {f}: {v}.\\n\"\n+ \"Mapping objects are not supported.\"\n+ )\n+ elif isinstance(v, Iterable):\n+ feature_name = None\n+ self._add_iterable_element(f, v, feature_names, vocab)\n+\n+ if feature_name is not None:\n+ if feature_name not in vocab:\n+ vocab[feature_name] = len(feature_names)\n+ feature_names.append(feature_name)\n+\n+ if self.sort:\n+ feature_names.sort()\n+ vocab = {f: i for i, f in enumerate(feature_names)}\n+\n+ self.feature_names_ = feature_names\n+ self.vocabulary_ = vocab\n+\n+ return self\n \n def _transform(self, X, fitting):\n # Sanity check: Python's array has no way of explicitly requesting the\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_extraction/_dict_vectorizer.py b/sklearn/feature_extraction/_dict_vectorizer.py\nindex 64c9a5704..13ada2f8e 100644\n--- a/sklearn/feature_extraction/_dict_vectorizer.py\n+++ b/sklearn/feature_extraction/_dict_vectorizer.py\n@@ -135,7 +135,6 @@ class DictVectorizer(TransformerMixin, BaseEstimator):\n indices.append(vocab[feature_name])\n values.append(self.dtype(vv))\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Learn a list of feature name -> indices mappings.\n \n@@ -156,38 +155,6 @@ class DictVectorizer(TransformerMixin, BaseEstimator):\n self : object\n DictVectorizer class instance.\n \"\"\"\n- feature_names = []\n- vocab = {}\n-\n- for x in X:\n- for f, v in x.items():\n- if isinstance(v, str):\n- feature_name = \"%s%s%s\" % (f, self.separator, v)\n- elif isinstance(v, Number) or (v is None):\n- feature_name = f\n- elif isinstance(v, Mapping):\n- raise TypeError(\n- f\"Unsupported value type {type(v)} \"\n- f\"for {f}: {v}.\\n\"\n- \"Mapping objects are not supported.\"\n- )\n- elif isinstance(v, Iterable):\n- feature_name = None\n- self._add_iterable_element(f, v, feature_names, vocab)\n-\n- if feature_name is not None:\n- if feature_name not in vocab:\n- vocab[feature_name] = len(feature_names)\n- feature_names.append(feature_name)\n-\n- if self.sort:\n- feature_names.sort()\n- vocab = {f: i for i, f in enumerate(feature_names)}\n-\n- self.feature_names_ = feature_names\n- self.vocabulary_ = vocab\n-\n- return self\n \n def _transform(self, X, fitting):\n # Sanity check: Python's array has no way of explicitly requesting the\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/feature_extraction/_dict_vectorizer.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Learn a list of feature name -> indices mappings.\n\n Parameters\n ----------\n X : Mapping or iterable over Mappings\n Dict(s) or Mapping(s) from feature names (arbitrary Python\n objects) to feature values (strings or convertible to dtype).\n\n .. versionchanged:: 0.24\n Accepts multiple string values for one categorical feature.\n\n y : (ignored)\n Ignored parameter.\n\n Returns\n -------\n self : object\n DictVectorizer class instance.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_feature_selection", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_iterable_not_string_error", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_mapping_error", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_unseen_or_no_features", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_deterministic_vocabulary[42]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_n_features_in", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dict_vectorizer_get_feature_names_out", "sklearn/tests/test_common.py::test_check_param_validation[DictVectorizer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-32	1.0	{ "code": "diff --git b/sklearn/feature_extraction/_dict_vectorizer.py a/sklearn/feature_extraction/_dict_vectorizer.py\nindex 2f0160b95..64c9a5704 100644\n--- b/sklearn/feature_extraction/_dict_vectorizer.py\n+++ a/sklearn/feature_extraction/_dict_vectorizer.py\n@@ -337,6 +337,25 @@ class DictVectorizer(TransformerMixin, BaseEstimator):\n D : list of dict_type objects of shape (n_samples,)\n Feature mappings for the samples in X.\n \"\"\"\n+ check_is_fitted(self, \"feature_names_\")\n+\n+ # COO matrix is not subscriptable\n+ X = check_array(X, accept_sparse=[\"csr\", \"csc\"])\n+ n_samples = X.shape[0]\n+\n+ names = self.feature_names_\n+ dicts = [dict_type() for _ in range(n_samples)]\n+\n+ if sp.issparse(X):\n+ for i, j in zip(*X.nonzero()):\n+ dicts[i][names[j]] = X[i, j]\n+ else:\n+ for i, d in enumerate(dicts):\n+ for j, v in enumerate(X[i, :]):\n+ if v != 0:\n+ d[names[j]] = X[i, j]\n+\n+ return dicts\n \n def transform(self, X):\n \"\"\"Transform feature->value dicts to array or sparse matrix.\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_extraction/_dict_vectorizer.py b/sklearn/feature_extraction/_dict_vectorizer.py\nindex 64c9a5704..2f0160b95 100644\n--- a/sklearn/feature_extraction/_dict_vectorizer.py\n+++ b/sklearn/feature_extraction/_dict_vectorizer.py\n@@ -337,25 +337,6 @@ class DictVectorizer(TransformerMixin, BaseEstimator):\n D : list of dict_type objects of shape (n_samples,)\n Feature mappings for the samples in X.\n \"\"\"\n- check_is_fitted(self, \"feature_names_\")\n-\n- # COO matrix is not subscriptable\n- X = check_array(X, accept_sparse=[\"csr\", \"csc\"])\n- n_samples = X.shape[0]\n-\n- names = self.feature_names_\n- dicts = [dict_type() for _ in range(n_samples)]\n-\n- if sp.issparse(X):\n- for i, j in zip(*X.nonzero()):\n- dicts[i][names[j]] = X[i, j]\n- else:\n- for i, d in enumerate(dicts):\n- for j, v in enumerate(X[i, :]):\n- if v != 0:\n- d[names[j]] = X[i, j]\n-\n- return dicts\n \n def transform(self, X):\n \"\"\"Transform feature->value dicts to array or sparse matrix.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/feature_extraction/_dict_vectorizer.py.\nHere is the description for the function:\n def inverse_transform(self, X, dict_type=dict):\n \"\"\"Transform array or sparse matrix X back to feature mappings.\n\n X must have been produced by this DictVectorizer's transform or\n fit_transform method; it may only have passed through transformers\n that preserve the number of features and their order.\n\n In the case of one-hot/one-of-K coding, the constructed feature\n names and values are returned rather than the original ones.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Sample matrix.\n dict_type : type, default=dict\n Constructor for feature mappings. Must conform to the\n collections.Mapping API.\n\n Returns\n -------\n D : list of dict_type objects of shape (n_samples,)\n Feature mappings for the samples in X.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-True-int-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-True-int-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-True-float32-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-True-float32-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-True-int16-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-True-int16-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-False-int-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-False-int-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-False-float32-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-False-float32-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-False-int16-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[True-False-int16-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-True-int-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-True-int-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-True-float32-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-True-float32-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-True-int16-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-True-int16-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-False-int-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-False-int-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-False-float32-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-False-float32-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-False-int16-True]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer[False-False-int16-False]", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_one_of_k", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_iterable_value", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dictvectorizer_dense_sparse_equivalence", "sklearn/feature_extraction/tests/test_dict_vectorizer.py::test_dict_vectorizer_not_fitted_error[inverse_transform-input1]", "sklearn/feature_extraction/_dict_vectorizer.py::sklearn.feature_extraction._dict_vectorizer.DictVectorizer" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-33	1.0	{ "code": "diff --git b/sklearn/dummy.py a/sklearn/dummy.py\nindex 0774a27ba..6332ff43c 100644\n--- b/sklearn/dummy.py\n+++ a/sklearn/dummy.py\n@@ -158,6 +158,7 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n self.random_state = random_state\n self.constant = constant\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the baseline classifier.\n \n@@ -177,6 +178,76 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n+ validate_data(self, X, skip_check_array=True)\n+\n+ self._strategy = self.strategy\n+\n+ if self._strategy == \"uniform\" and sp.issparse(y):\n+ y = y.toarray()\n+ warnings.warn(\n+ (\n+ \"A local copy of the target data has been converted \"\n+ \"to a numpy array. Predicting on sparse target data \"\n+ \"with the uniform strategy would not save memory \"\n+ \"and would be slower.\"\n+ ),\n+ UserWarning,\n+ )\n+\n+ self.sparse_output_ = sp.issparse(y)\n+\n+ if not self.sparse_output_:\n+ y = np.asarray(y)\n+ y = np.atleast_1d(y)\n+\n+ if y.ndim == 1:\n+ y = np.reshape(y, (-1, 1))\n+\n+ self.n_outputs_ = y.shape[1]\n+\n+ check_consistent_length(X, y)\n+\n+ if sample_weight is not None:\n+ sample_weight = _check_sample_weight(sample_weight, X)\n+\n+ if self._strategy == \"constant\":\n+ if self.constant is None:\n+ raise ValueError(\n+ \"Constant target value has to be specified \"\n+ \"when the constant strategy is used.\"\n+ )\n+ else:\n+ constant = np.reshape(np.atleast_1d(self.constant), (-1, 1))\n+ if constant.shape[0] != self.n_outputs_:\n+ raise ValueError(\n+ \"Constant target value should have shape (%d, 1).\"\n+ % self.n_outputs_\n+ )\n+\n+ (self.classes_, self.n_classes_, self.class_prior_) = class_distribution(\n+ y, sample_weight\n+ )\n+\n+ if self._strategy == \"constant\":\n+ for k in range(self.n_outputs_):\n+ if not any(constant[k][0] == c for c in self.classes_[k]):\n+ # Checking in case of constant strategy if the constant\n+ # provided by the user is in y.\n+ err_msg = (\n+ \"The constant target value must be present in \"\n+ \"the training data. You provided constant={}. \"\n+ \"Possible values are: {}.\".format(\n+ self.constant, self.classes_[k].tolist()\n+ )\n+ )\n+ raise ValueError(err_msg)\n+\n+ if self.n_outputs_ == 1:\n+ self.n_classes_ = self.n_classes_[0]\n+ self.classes_ = self.classes_[0]\n+ self.class_prior_ = self.class_prior_[0]\n+\n+ return self\n \n def predict(self, X):\n \"\"\"Perform classification on test vectors X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/dummy.py b/sklearn/dummy.py\nindex 6332ff43c..0774a27ba 100644\n--- a/sklearn/dummy.py\n+++ b/sklearn/dummy.py\n@@ -158,7 +158,6 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n self.random_state = random_state\n self.constant = constant\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the baseline classifier.\n \n@@ -178,76 +177,6 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n- validate_data(self, X, skip_check_array=True)\n-\n- self._strategy = self.strategy\n-\n- if self._strategy == \"uniform\" and sp.issparse(y):\n- y = y.toarray()\n- warnings.warn(\n- (\n- \"A local copy of the target data has been converted \"\n- \"to a numpy array. Predicting on sparse target data \"\n- \"with the uniform strategy would not save memory \"\n- \"and would be slower.\"\n- ),\n- UserWarning,\n- )\n-\n- self.sparse_output_ = sp.issparse(y)\n-\n- if not self.sparse_output_:\n- y = np.asarray(y)\n- y = np.atleast_1d(y)\n-\n- if y.ndim == 1:\n- y = np.reshape(y, (-1, 1))\n-\n- self.n_outputs_ = y.shape[1]\n-\n- check_consistent_length(X, y)\n-\n- if sample_weight is not None:\n- sample_weight = _check_sample_weight(sample_weight, X)\n-\n- if self._strategy == \"constant\":\n- if self.constant is None:\n- raise ValueError(\n- \"Constant target value has to be specified \"\n- \"when the constant strategy is used.\"\n- )\n- else:\n- constant = np.reshape(np.atleast_1d(self.constant), (-1, 1))\n- if constant.shape[0] != self.n_outputs_:\n- raise ValueError(\n- \"Constant target value should have shape (%d, 1).\"\n- % self.n_outputs_\n- )\n-\n- (self.classes_, self.n_classes_, self.class_prior_) = class_distribution(\n- y, sample_weight\n- )\n-\n- if self._strategy == \"constant\":\n- for k in range(self.n_outputs_):\n- if not any(constant[k][0] == c for c in self.classes_[k]):\n- # Checking in case of constant strategy if the constant\n- # provided by the user is in y.\n- err_msg = (\n- \"The constant target value must be present in \"\n- \"the training data. You provided constant={}. \"\n- \"Possible values are: {}.\".format(\n- self.constant, self.classes_[k].tolist()\n- )\n- )\n- raise ValueError(err_msg)\n-\n- if self.n_outputs_ == 1:\n- self.n_classes_ = self.n_classes_[0]\n- self.classes_ = self.classes_[0]\n- self.class_prior_ = self.class_prior_[0]\n-\n- return self\n \n def predict(self, X):\n \"\"\"Perform classification on test vectors X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/dummy.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the baseline classifier.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data.\n\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Target values.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-auto-data1]", "sklearn/ensemble/tests/test_bagging.py::test_classification", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[fit-HalvingGridSearchCV]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_toy[42-log_loss]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_toy[42-exponential]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_synthetic[42-log_loss]", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[fit-HalvingRandomSearchCV]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_synthetic[42-exponential]", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[predict-HalvingGridSearchCV]", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[predict-HalvingRandomSearchCV]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[True-limited-4-4-3-1-expected_n_candidates0-expected_n_resources0-HalvingGridSearchCV]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data2]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[True-limited-4-4-3-1-expected_n_candidates0-expected_n_resources0-HalvingRandomSearchCV]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data3]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[False-limited-3-4-3-3-expected_n_candidates1-expected_n_resources1-HalvingGridSearchCV]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-None-1.0]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[False-limited-3-4-3-3-expected_n_candidates1-expected_n_resources1-HalvingRandomSearchCV]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-None-0.5]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-1-1.0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-auto-data1]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[True-unlimited-4-4-4-1-expected_n_candidates2-expected_n_resources2-HalvingGridSearchCV]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-brute-data3]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[True-unlimited-4-4-4-1-expected_n_candidates2-expected_n_resources2-HalvingRandomSearchCV]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-1-0.5]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[False-unlimited-4-4-4-1-expected_n_candidates3-expected_n_resources3-HalvingGridSearchCV]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-auto-data0]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_feature_importances[GradientBoostingClassifier-X1-y1]", "sklearn/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[False-unlimited-4-4-4-1-expected_n_candidates3-expected_n_resources3-HalvingRandomSearchCV]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-auto-data1]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_probability_log[42]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-brute-data2]", "sklearn/model_selection/tests/test_successive_halving.py::test_min_max_resources[smallest-auto-2-4-expected_n_resources0-HalvingGridSearchCV]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-brute-data3]", "sklearn/model_selection/tests/test_successive_halving.py::test_min_max_resources[smallest-auto-2-4-expected_n_resources0-HalvingRandomSearchCV]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_check_inputs_predict_stages[csc_matrix]", 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"sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_sample_weights_not_overwritten]", "sklearn/tests/test_dummy.py::test_constant_strategy", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[FixedThresholdClassifier-predict_log_proba-estimator2]", "sklearn/tests/test_dummy.py::test_constant_strategy_multioutput", "sklearn/tests/test_dummy.py::test_constant_strategy_exceptions[no-constant]", "sklearn/tests/test_dummy.py::test_constant_strategy_exceptions[too-many-constant]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[FixedThresholdClassifier-decision_function-estimator2]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_dummy.py::test_constant_strategy_exceptions[not-enough-output]", "sklearn/tests/test_dummy.py::test_constant_strategy_exceptions[single-output]", "sklearn/tests/test_dummy.py::test_constant_strategy_exceptions[multi-output]", "sklearn/tests/test_dummy.py::test_classification_sample_weight", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_fit_returns_self]", "sklearn/tests/test_dummy.py::test_constant_strategy_sparse_target[csc_matrix]", "sklearn/tests/test_dummy.py::test_constant_strategy_sparse_target[csc_array]", "sklearn/tests/test_dummy.py::test_uniform_strategy_sparse_target_warning[42-csc_matrix]", "sklearn/tests/test_dummy.py::test_uniform_strategy_sparse_target_warning[42-csc_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_dummy.py::test_stratified_strategy_sparse_target[42-csc_matrix]", "sklearn/tests/test_dummy.py::test_stratified_strategy_sparse_target[42-csc_array]", "sklearn/tests/test_dummy.py::test_most_frequent_and_prior_strategy_sparse_target[csc_matrix]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_pipeline_consistency]", "sklearn/tests/test_dummy.py::test_most_frequent_and_prior_strategy_sparse_target[csc_array]", "sklearn/tests/test_dummy.py::test_dummy_classifier_on_3D_array", "sklearn/tests/test_dummy.py::test_dtype_of_classifier_probas[stratified]", "sklearn/tests/test_dummy.py::test_dtype_of_classifier_probas[most_frequent]", "sklearn/tests/test_dummy.py::test_dtype_of_classifier_probas[prior]", "sklearn/tests/test_dummy.py::test_dtype_of_classifier_probas[uniform]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[TunedThresholdClassifierCV-predict_proba-estimator2]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_f_contiguous_array_estimator]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[TunedThresholdClassifierCV-predict_log_proba-estimator2]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_classes]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[TunedThresholdClassifierCV-decision_function-estimator2]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifier_multioutput]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_train]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_prefit[StackingClassifier-DummyClassifier-predict_proba-final_estimator0-X0-y0]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_list]", "sklearn/ensemble/tests/test_weight_boosting.py::test_multidimensional_X", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_fit_returns_self]", "sklearn/tests/test_multioutput.py::test_multioutput_estimator_with_fit_params[estimator0-dataset0]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_one_label]", "sklearn/tests/test_dummy.py::test_dtype_of_classifier_probas[constant]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifier_multioutput]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_fit_check_is_fitted]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_large_memmaped_data[array]", "sklearn/tree/tests/test_export.py::test_friedman_mse_in_graphviz", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_large_memmaped_data[dataframe]", "sklearn/tests/test_calibration.py::test_calibration_zero_probability", "sklearn/metrics/_scorer.py::sklearn.metrics._scorer.get_scorer", "sklearn/dummy.py::sklearn.dummy.DummyClassifier", "sklearn/ensemble/_gb.py::sklearn.ensemble._gb.GradientBoostingClassifier", "sklearn/inspection/_partial_dependence.py::sklearn.inspection._partial_dependence.partial_dependence", "sklearn/tests/test_calibration.py::test_float32_predict_proba", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_error_constant_predictor", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GradientBoostingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_check_param_validation[DummyClassifier(strategy='stratified')]", "sklearn/tests/test_common.py::test_check_param_validation[DummyClassifier(strategy='most_frequent')]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-34	1.0	{ "code": "diff --git b/sklearn/dummy.py a/sklearn/dummy.py\nindex 053ca524f..6332ff43c 100644\n--- b/sklearn/dummy.py\n+++ a/sklearn/dummy.py\n@@ -262,6 +262,79 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Predicted target values for X.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ # numpy random_state expects Python int and not long as size argument\n+ # under Windows\n+ n_samples = _num_samples(X)\n+ rs = check_random_state(self.random_state)\n+\n+ n_classes_ = self.n_classes_\n+ classes_ = self.classes_\n+ class_prior_ = self.class_prior_\n+ constant = self.constant\n+ if self.n_outputs_ == 1:\n+ # Get same type even for self.n_outputs_ == 1\n+ n_classes_ = [n_classes_]\n+ classes_ = [classes_]\n+ class_prior_ = [class_prior_]\n+ constant = [constant]\n+ # Compute probability only once\n+ if self._strategy == \"stratified\":\n+ proba = self.predict_proba(X)\n+ if self.n_outputs_ == 1:\n+ proba = [proba]\n+\n+ if self.sparse_output_:\n+ class_prob = None\n+ if self._strategy in (\"most_frequent\", \"prior\"):\n+ classes_ = [np.array([cp.argmax()]) for cp in class_prior_]\n+\n+ elif self._strategy == \"stratified\":\n+ class_prob = class_prior_\n+\n+ elif self._strategy == \"uniform\":\n+ raise ValueError(\n+ \"Sparse target prediction is not \"\n+ \"supported with the uniform strategy\"\n+ )\n+\n+ elif self._strategy == \"constant\":\n+ classes_ = [np.array([c]) for c in constant]\n+\n+ y = _random_choice_csc(n_samples, classes_, class_prob, self.random_state)\n+ else:\n+ if self._strategy in (\"most_frequent\", \"prior\"):\n+ y = np.tile(\n+ [\n+ classes_[k][class_prior_[k].argmax()]\n+ for k in range(self.n_outputs_)\n+ ],\n+ [n_samples, 1],\n+ )\n+\n+ elif self._strategy == \"stratified\":\n+ y = np.vstack(\n+ [\n+ classes_[k][proba[k].argmax(axis=1)]\n+ for k in range(self.n_outputs_)\n+ ]\n+ ).T\n+\n+ elif self._strategy == \"uniform\":\n+ ret = [\n+ classes_[k][rs.randint(n_classes_[k], size=n_samples)]\n+ for k in range(self.n_outputs_)\n+ ]\n+ y = np.vstack(ret).T\n+\n+ elif self._strategy == \"constant\":\n+ y = np.tile(self.constant, (n_samples, 1))\n+\n+ if self.n_outputs_ == 1:\n+ y = np.ravel(y)\n+\n+ return y\n \n def predict_proba(self, X):\n \"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/dummy.py b/sklearn/dummy.py\nindex 6332ff43c..053ca524f 100644\n--- a/sklearn/dummy.py\n+++ b/sklearn/dummy.py\n@@ -262,79 +262,6 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Predicted target values for X.\n \"\"\"\n- check_is_fitted(self)\n-\n- # numpy random_state expects Python int and not long as size argument\n- # under Windows\n- n_samples = _num_samples(X)\n- rs = check_random_state(self.random_state)\n-\n- n_classes_ = self.n_classes_\n- classes_ = self.classes_\n- class_prior_ = self.class_prior_\n- constant = self.constant\n- if self.n_outputs_ == 1:\n- # Get same type even for self.n_outputs_ == 1\n- n_classes_ = [n_classes_]\n- classes_ = [classes_]\n- class_prior_ = [class_prior_]\n- constant = [constant]\n- # Compute probability only once\n- if self._strategy == \"stratified\":\n- proba = self.predict_proba(X)\n- if self.n_outputs_ == 1:\n- proba = [proba]\n-\n- if self.sparse_output_:\n- class_prob = None\n- if self._strategy in (\"most_frequent\", \"prior\"):\n- classes_ = [np.array([cp.argmax()]) for cp in class_prior_]\n-\n- elif self._strategy == \"stratified\":\n- class_prob = class_prior_\n-\n- elif self._strategy == \"uniform\":\n- raise ValueError(\n- \"Sparse target prediction is not \"\n- \"supported with the uniform strategy\"\n- )\n-\n- elif self._strategy == \"constant\":\n- classes_ = [np.array([c]) for c in constant]\n-\n- y = _random_choice_csc(n_samples, classes_, class_prob, self.random_state)\n- else:\n- if self._strategy in (\"most_frequent\", \"prior\"):\n- y = np.tile(\n- [\n- classes_[k][class_prior_[k].argmax()]\n- for k in range(self.n_outputs_)\n- ],\n- [n_samples, 1],\n- )\n-\n- elif self._strategy == \"stratified\":\n- y = np.vstack(\n- [\n- classes_[k][proba[k].argmax(axis=1)]\n- for k in range(self.n_outputs_)\n- ]\n- ).T\n-\n- elif self._strategy == \"uniform\":\n- ret = [\n- classes_[k][rs.randint(n_classes_[k], size=n_samples)]\n- for k in range(self.n_outputs_)\n- ]\n- y = np.vstack(ret).T\n-\n- elif self._strategy == \"constant\":\n- y = np.tile(self.constant, (n_samples, 1))\n-\n- if self.n_outputs_ == 1:\n- y = np.ravel(y)\n-\n- return y\n \n def predict_proba(self, X):\n \"\"\"\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/dummy.py.\nHere is the description for the function:\n def predict(self, X):\n \"\"\"Perform classification on test vectors X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Test data.\n\n Returns\n -------\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Predicted target values for X.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_split.py::test_nested_cv", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_pipeline_consistency]", "sklearn/tests/test_dummy.py::test_most_frequent_and_prior_strategy", "sklearn/tests/test_dummy.py::test_most_frequent_and_prior_strategy_with_2d_column_y", "sklearn/tests/test_dummy.py::test_most_frequent_and_prior_strategy_multioutput", "sklearn/tests/test_dummy.py::test_stratified_strategy[42]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimator_sparse_array]", "sklearn/tests/test_dummy.py::test_stratified_strategy_multioutput[42]", "sklearn/tests/test_dummy.py::test_uniform_strategy[42]", "sklearn/tests/test_dummy.py::test_uniform_strategy_multioutput[42]", "sklearn/tests/test_dummy.py::test_string_labels", "sklearn/tests/test_dummy.py::test_classifier_score_with_None[y0-y_test0]", "sklearn/tests/test_dummy.py::test_classifier_score_with_None[y1-y_test1]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimator_sparse_matrix]", "sklearn/tests/test_dummy.py::test_classifier_prediction_independent_of_X[42-stratified]", "sklearn/tests/test_dummy.py::test_classifier_prediction_independent_of_X[42-most_frequent]", "sklearn/tests/test_dummy.py::test_classifier_prediction_independent_of_X[42-prior]", "sklearn/tests/test_dummy.py::test_classifier_prediction_independent_of_X[42-uniform]", "sklearn/tests/test_dummy.py::test_classifier_prediction_independent_of_X[42-constant]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_pickle]", "sklearn/tests/test_dummy.py::test_constant_strategy", "sklearn/tests/test_dummy.py::test_constant_strategy_multioutput", "sklearn/tests/test_dummy.py::test_constant_strategy_sparse_target[csc_matrix]", "sklearn/tests/test_dummy.py::test_constant_strategy_sparse_target[csc_array]", "sklearn/tests/test_dummy.py::test_uniform_strategy_sparse_target_warning[42-csc_matrix]", "sklearn/tests/test_dummy.py::test_uniform_strategy_sparse_target_warning[42-csc_array]", "sklearn/tests/test_dummy.py::test_stratified_strategy_sparse_target[42-csc_matrix]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifier_data_not_an_array]", "sklearn/tests/test_dummy.py::test_stratified_strategy_sparse_target[42-csc_array]", "sklearn/tests/test_dummy.py::test_most_frequent_and_prior_strategy_sparse_target[csc_matrix]", "sklearn/tests/test_dummy.py::test_most_frequent_and_prior_strategy_sparse_target[csc_array]", "sklearn/tests/test_dummy.py::test_dummy_classifier_on_3D_array", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifier_multioutput]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='stratified')-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifier_multioutput]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_train(readonly_memmap=True)]", "sklearn/ensemble/tests/test_weight_boosting.py::test_multidimensional_X", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[DummyClassifier(strategy='most_frequent')-check_fit_idempotent]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_large_memmaped_data[array]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_large_memmaped_data[dataframe]", "sklearn/metrics/_scorer.py::sklearn.metrics._scorer.get_scorer", "sklearn/dummy.py::sklearn.dummy.DummyClassifier" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-35	1.0	{ "code": "diff --git b/sklearn/dummy.py a/sklearn/dummy.py\nindex 8c6383b9d..6332ff43c 100644\n--- b/sklearn/dummy.py\n+++ a/sklearn/dummy.py\n@@ -352,6 +352,52 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n the model, where classes are ordered arithmetically, for each\n output.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ # numpy random_state expects Python int and not long as size argument\n+ # under Windows\n+ n_samples = _num_samples(X)\n+ rs = check_random_state(self.random_state)\n+\n+ n_classes_ = self.n_classes_\n+ classes_ = self.classes_\n+ class_prior_ = self.class_prior_\n+ constant = self.constant\n+ if self.n_outputs_ == 1:\n+ # Get same type even for self.n_outputs_ == 1\n+ n_classes_ = [n_classes_]\n+ classes_ = [classes_]\n+ class_prior_ = [class_prior_]\n+ constant = [constant]\n+\n+ P = []\n+ for k in range(self.n_outputs_):\n+ if self._strategy == \"most_frequent\":\n+ ind = class_prior_[k].argmax()\n+ out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)\n+ out[:, ind] = 1.0\n+ elif self._strategy == \"prior\":\n+ out = np.ones((n_samples, 1)) * class_prior_[k]\n+\n+ elif self._strategy == \"stratified\":\n+ out = rs.multinomial(1, class_prior_[k], size=n_samples)\n+ out = out.astype(np.float64)\n+\n+ elif self._strategy == \"uniform\":\n+ out = np.ones((n_samples, n_classes_[k]), dtype=np.float64)\n+ out /= n_classes_[k]\n+\n+ elif self._strategy == \"constant\":\n+ ind = np.where(classes_[k] == constant[k])\n+ out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)\n+ out[:, ind] = 1.0\n+\n+ P.append(out)\n+\n+ if self.n_outputs_ == 1:\n+ P = P[0]\n+\n+ return P\n \n def predict_log_proba(self, X):\n \"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/dummy.py b/sklearn/dummy.py\nindex 6332ff43c..8c6383b9d 100644\n--- a/sklearn/dummy.py\n+++ b/sklearn/dummy.py\n@@ -352,52 +352,6 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):\n the model, where classes are ordered arithmetically, for each\n output.\n \"\"\"\n- check_is_fitted(self)\n-\n- # numpy random_state expects Python int and not long as size argument\n- # under Windows\n- n_samples = _num_samples(X)\n- rs = check_random_state(self.random_state)\n-\n- n_classes_ = self.n_classes_\n- classes_ = self.classes_\n- class_prior_ = self.class_prior_\n- constant = self.constant\n- if self.n_outputs_ == 1:\n- # Get same type even for self.n_outputs_ == 1\n- n_classes_ = [n_classes_]\n- classes_ = [classes_]\n- class_prior_ = [class_prior_]\n- constant = [constant]\n-\n- P = []\n- for k in range(self.n_outputs_):\n- if self._strategy == \"most_frequent\":\n- ind = class_prior_[k].argmax()\n- out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)\n- out[:, ind] = 1.0\n- elif self._strategy == \"prior\":\n- out = np.ones((n_samples, 1)) * class_prior_[k]\n-\n- elif self._strategy == \"stratified\":\n- out = rs.multinomial(1, class_prior_[k], size=n_samples)\n- out = out.astype(np.float64)\n-\n- elif self._strategy == \"uniform\":\n- out = np.ones((n_samples, n_classes_[k]), dtype=np.float64)\n- out /= n_classes_[k]\n-\n- elif self._strategy == \"constant\":\n- ind = np.where(classes_[k] == constant[k])\n- out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)\n- out[:, ind] = 1.0\n-\n- P.append(out)\n-\n- if self.n_outputs_ == 1:\n- P = P[0]\n-\n- return P\n \n def predict_log_proba(self, X):\n \"\"\"\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/dummy.py.\nHere is the description for the function:\n def predict_proba(self, X):\n \"\"\"\n Return probability estimates for the test vectors X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Test data.\n\n Returns\n -------\n P : ndarray of shape (n_samples, n_classes) or list of such arrays\n Returns the probability of the sample for each class in\n the model, where classes are ordered arithmetically, for each\n output.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-auto-data1]", "sklearn/ensemble/tests/test_bagging.py::test_classification", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data2]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_toy[42-log_loss]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_toy[42-exponential]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_synthetic[42-log_loss]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_synthetic[42-exponential]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-None-1.0]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-None-0.5]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-1-1.0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-auto-data1]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_iris[42-1-0.5]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_feature_importances[GradientBoostingClassifier-X1-y1]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_probability_log[42]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_check_inputs_predict_stages[csc_matrix]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-auto-data0]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_check_inputs_predict_stages[csc_array]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-auto-data1]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_max_feature_regression[42]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-brute-data2]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_max_features", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_predict_proba", "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_functions_defensive[42-GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_serialization", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_symbol_labels", "sklearn/ensemble/tests/test_gradient_boosting.py::test_float_class_labels", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_multiclass", "sklearn/ensemble/tests/test_gradient_boosting.py::test_shape_y", "sklearn/ensemble/tests/test_gradient_boosting.py::test_mem_layout", "sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_improvement[GradientBoostingClassifier]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-auto-data0]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_scores[GradientBoostingClassifier]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_attributes_error[GradientBoostingClassifier-oob_improvement_]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_attributes_error[GradientBoostingClassifier-oob_scores_]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_attributes_error[GradientBoostingClassifier-oob_score_]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_multilcass_iris", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-auto-data0]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_verbose_output", "sklearn/ensemble/tests/test_gradient_boosting.py::test_more_verbose_output", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start[42-GradientBoostingClassifier]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_multiclass_error[params0-target not in est.classes_, got 4]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_n_estimators[42-GradientBoostingClassifier]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_multiclass_error[params1-target must be specified for multi-class]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_multiclass_error[params2-Each entry in features must be either an int,]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_max_depth[GradientBoostingClassifier]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-auto-data1]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_clear[GradientBoostingClassifier]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-brute-data3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_state_oob_scores[GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_smaller_n_estimators[GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_equal_n_estimators[GradientBoostingClassifier]", 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"sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-36	1.0	{ "code": "diff --git b/sklearn/dummy.py a/sklearn/dummy.py\nindex 296584836..6332ff43c 100644\n--- b/sklearn/dummy.py\n+++ a/sklearn/dummy.py\n@@ -542,6 +542,7 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self.constant = constant\n self.quantile = quantile\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the random regressor.\n \n@@ -561,6 +562,69 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n+ validate_data(self, X, skip_check_array=True)\n+\n+ y = check_array(y, ensure_2d=False, input_name=\"y\")\n+ if len(y) == 0:\n+ raise ValueError(\"y must not be empty.\")\n+\n+ if y.ndim == 1:\n+ y = np.reshape(y, (-1, 1))\n+ self.n_outputs_ = y.shape[1]\n+\n+ check_consistent_length(X, y, sample_weight)\n+\n+ if sample_weight is not None:\n+ sample_weight = _check_sample_weight(sample_weight, X)\n+\n+ if self.strategy == \"mean\":\n+ self.constant_ = np.average(y, axis=0, weights=sample_weight)\n+\n+ elif self.strategy == \"median\":\n+ if sample_weight is None:\n+ self.constant_ = np.median(y, axis=0)\n+ else:\n+ self.constant_ = [\n+ _weighted_percentile(y[:, k], sample_weight, percentile=50.0)\n+ for k in range(self.n_outputs_)\n+ ]\n+\n+ elif self.strategy == \"quantile\":\n+ if self.quantile is None:\n+ raise ValueError(\n+ \"When using `strategy='quantile', you have to specify the desired \"\n+ \"quantile in the range [0, 1].\"\n+ )\n+ percentile = self.quantile * 100.0\n+ if sample_weight is None:\n+ self.constant_ = np.percentile(y, axis=0, q=percentile)\n+ else:\n+ self.constant_ = [\n+ _weighted_percentile(y[:, k], sample_weight, percentile=percentile)\n+ for k in range(self.n_outputs_)\n+ ]\n+\n+ elif self.strategy == \"constant\":\n+ if self.constant is None:\n+ raise TypeError(\n+ \"Constant target value has to be specified \"\n+ \"when the constant strategy is used.\"\n+ )\n+\n+ self.constant_ = check_array(\n+ self.constant,\n+ accept_sparse=[\"csr\", \"csc\", \"coo\"],\n+ ensure_2d=False,\n+ ensure_min_samples=0,\n+ )\n+\n+ if self.n_outputs_ != 1 and self.constant_.shape[0] != y.shape[1]:\n+ raise ValueError(\n+ \"Constant target value should have shape (%d, 1).\" % y.shape[1]\n+ )\n+\n+ self.constant_ = np.reshape(self.constant_, (1, -1))\n+ return self\n \n def predict(self, X, return_std=False):\n \"\"\"Perform classification on test vectors X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/dummy.py b/sklearn/dummy.py\nindex 6332ff43c..296584836 100644\n--- a/sklearn/dummy.py\n+++ b/sklearn/dummy.py\n@@ -542,7 +542,6 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self.constant = constant\n self.quantile = quantile\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the random regressor.\n \n@@ -562,69 +561,6 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n- validate_data(self, X, skip_check_array=True)\n-\n- y = check_array(y, ensure_2d=False, input_name=\"y\")\n- if len(y) == 0:\n- raise ValueError(\"y must not be empty.\")\n-\n- if y.ndim == 1:\n- y = np.reshape(y, (-1, 1))\n- self.n_outputs_ = y.shape[1]\n-\n- check_consistent_length(X, y, sample_weight)\n-\n- if sample_weight is not None:\n- sample_weight = _check_sample_weight(sample_weight, X)\n-\n- if self.strategy == \"mean\":\n- self.constant_ = np.average(y, axis=0, weights=sample_weight)\n-\n- elif self.strategy == \"median\":\n- if sample_weight is None:\n- self.constant_ = np.median(y, axis=0)\n- else:\n- self.constant_ = [\n- _weighted_percentile(y[:, k], sample_weight, percentile=50.0)\n- for k in range(self.n_outputs_)\n- ]\n-\n- elif self.strategy == \"quantile\":\n- if self.quantile is None:\n- raise ValueError(\n- \"When using `strategy='quantile', you have to specify the desired \"\n- \"quantile in the range [0, 1].\"\n- )\n- percentile = self.quantile * 100.0\n- if sample_weight is None:\n- self.constant_ = np.percentile(y, axis=0, q=percentile)\n- else:\n- self.constant_ = [\n- _weighted_percentile(y[:, k], sample_weight, percentile=percentile)\n- for k in range(self.n_outputs_)\n- ]\n-\n- elif self.strategy == \"constant\":\n- if self.constant is None:\n- raise TypeError(\n- \"Constant target value has to be specified \"\n- \"when the constant strategy is used.\"\n- )\n-\n- self.constant_ = check_array(\n- self.constant,\n- accept_sparse=[\"csr\", \"csc\", \"coo\"],\n- ensure_2d=False,\n- ensure_min_samples=0,\n- )\n-\n- if self.n_outputs_ != 1 and self.constant_.shape[0] != y.shape[1]:\n- raise ValueError(\n- \"Constant target value should have shape (%d, 1).\" % y.shape[1]\n- )\n-\n- self.constant_ = np.reshape(self.constant_, (1, -1))\n- return self\n \n def predict(self, X, return_std=False):\n \"\"\"Perform classification on test vectors X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/dummy.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the random regressor.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data.\n\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Target values.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence[10]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence[20]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[average-False-None-shape0]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[individual-False-None-shape1]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[both-False-None-shape2]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[individual-False-20-shape3]", "sklearn/ensemble/tests/test_forest.py::test_poisson_vs_mse", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[both-False-20-shape4]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[individual-False-0.5-shape5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[both-False-0.5-shape6]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[average-True-None-shape7]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[individual-True-None-shape8]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[both-True-None-shape9]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[individual-True-20-shape10]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_kind[both-True-20-shape11]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[dataframe-None]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_regression_dataset[42-1.0-squared_error]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[dataframe-list]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[list-list]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[array-list]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_regression_dataset[42-1.0-absolute_error]", 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"sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[dataframe-series]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[list-series]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[array-series]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[dataframe-index]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[list-index]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_str_features[array-index]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_custom_axes", 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"sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_feature_name_reuse", "sklearn/ensemble/tests/test_gradient_boosting.py::test_regression_synthetic[42]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_gamma", "sklearn/ensemble/tests/test_gradient_boosting.py::test_feature_importances[GradientBoostingRegressor-X0-y0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingRegressor-auto-data4]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_poisson", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_dataframe", "sklearn/ensemble/tests/test_gradient_boosting.py::test_max_features", "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_predict", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingRegressor-brute-data5]", "sklearn/ensemble/tests/test_bagging.py::test_regression", "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_functions_defensive[42-GradientBoostingRegressor]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_degenerate_targets", "sklearn/ensemble/tests/test_gradient_boosting.py::test_quantile_loss[42]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_does_not_override_ylabel", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingRegressor-auto-data4]", 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"sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_kind_centered_interaction[average]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_kind_centered_interaction[kind3]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_kind_centered_interaction[kind4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-10-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_with_constant_sample_weight", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_clear[GradientBoostingRegressor]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_state_oob_scores[GradientBoostingRegressor]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_smaller_n_estimators[GradientBoostingRegressor]", 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"sklearn/ensemble/tests/test_gradient_boosting.py::test_squared_error_exact_backward_compat", "sklearn/ensemble/tests/test_gradient_boosting.py::test_huber_exact_backward_compat", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_subclass_named_constructors_return_type_is_subclass", "sklearn/tests/test_multioutput.py::test_multi_target_regression", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimators_overwrite_params]", "sklearn/tests/test_multioutput.py::test_multi_target_sample_weights_api", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimators_dtypes]", "sklearn/tests/test_multioutput.py::test_multi_target_sample_weights", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-final_estimator2-predict_params2-3]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_sample_weights_list]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-final_estimator2-predict_params2-cv1]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_sample_weights_not_overwritten]", "sklearn/tests/test_dummy.py::test_feature_names_in_and_n_features_in_[42]", "sklearn/tests/test_dummy.py::test_mean_strategy_regressor[42]", "sklearn/tests/test_dummy.py::test_mean_strategy_multioutput_regressor[42]", "sklearn/tests/test_dummy.py::test_median_strategy_regressor[42]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_dummy.py::test_median_strategy_multioutput_regressor[42]", "sklearn/tests/test_dummy.py::test_quantile_strategy_regressor[42]", "sklearn/tests/test_dummy.py::test_quantile_strategy_multioutput_regressor[42]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_dummy.py::test_quantile_invalid", "sklearn/tests/test_dummy.py::test_quantile_strategy_empty_train", "sklearn/tests/test_dummy.py::test_constant_strategy_regressor[42]", "sklearn/tests/test_dummy.py::test_constant_strategy_multioutput_regressor[42]", "sklearn/tests/test_dummy.py::test_y_mean_attribute_regressor", "sklearn/tests/test_dummy.py::test_constants_not_specified_regressor", "sklearn/tests/test_dummy.py::test_constant_size_multioutput_regressor[42]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_dummy.py::test_dummy_regressor_sample_weight[42]", "sklearn/tests/test_dummy.py::test_dummy_regressor_on_3D_array", "sklearn/tests/test_dummy.py::test_dummy_regressor_return_std", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_pipeline_consistency]", "sklearn/tests/test_dummy.py::test_regressor_score_with_None[y0-y_test0]", "sklearn/tests/test_dummy.py::test_regressor_score_with_None[y1-y_test1]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimator_sparse_array]", "sklearn/tests/test_dummy.py::test_regressor_prediction_independent_of_X[mean]", "sklearn/tests/test_dummy.py::test_regressor_prediction_independent_of_X[median]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[True-final_estimator2-predict_params2-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[True-final_estimator2-predict_params2-cv1]", "sklearn/tests/test_dummy.py::test_regressor_prediction_independent_of_X[quantile]", "sklearn/tests/test_dummy.py::test_regressor_prediction_independent_of_X[constant]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_dont_overwrite_parameters]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_prefit[StackingRegressor-DummyRegressor-predict-final_estimator1-X1-y1]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[DummyRegressor()-check_fit_check_is_fitted]", "sklearn/tests/test_multioutput.py::test_multioutput_estimator_with_fit_params[estimator1-dataset1]", "sklearn/ensemble/tests/test_weight_boosting.py::test_multidimensional_X", "sklearn/ensemble/tests/test_voting.py::test_weights_regressor", "sklearn/metrics/tests/test_regression.py::test_dummy_quantile_parameter_tuning", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_ensure_y_array", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_pass_fit_parameters", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_route_pipeline", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_pass_extra_predict_parameters", "sklearn/inspection/_plot/partial_dependence.py::sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay", "sklearn/dummy.py::sklearn.dummy.DummyRegressor", "sklearn/ensemble/_gb.py::sklearn.ensemble._gb.GradientBoostingRegressor", "sklearn/inspection/_plot/partial_dependence.py::sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay.from_estimator", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GradientBoostingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingRegressor(n_estimators=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_check_param_validation[DummyRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-37	1.0	{ "code": "diff --git b/sklearn/dummy.py a/sklearn/dummy.py\nindex 2da3e5d2d..6332ff43c 100644\n--- b/sklearn/dummy.py\n+++ a/sklearn/dummy.py\n@@ -648,6 +648,21 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n y_std : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Standard deviation of predictive distribution of query points.\n \"\"\"\n+ check_is_fitted(self)\n+ n_samples = _num_samples(X)\n+\n+ y = np.full(\n+ (n_samples, self.n_outputs_),\n+ self.constant_,\n+ dtype=np.array(self.constant_).dtype,\n+ )\n+ y_std = np.zeros((n_samples, self.n_outputs_))\n+\n+ if self.n_outputs_ == 1:\n+ y = np.ravel(y)\n+ y_std = np.ravel(y_std)\n+\n+ return (y, y_std) if return_std else y\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/dummy.py b/sklearn/dummy.py\nindex 6332ff43c..2da3e5d2d 100644\n--- a/sklearn/dummy.py\n+++ b/sklearn/dummy.py\n@@ -648,21 +648,6 @@ class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n y_std : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Standard deviation of predictive distribution of query points.\n \"\"\"\n- check_is_fitted(self)\n- n_samples = _num_samples(X)\n-\n- y = np.full(\n- (n_samples, self.n_outputs_),\n- self.constant_,\n- dtype=np.array(self.constant_).dtype,\n- )\n- y_std = np.zeros((n_samples, self.n_outputs_))\n-\n- if self.n_outputs_ == 1:\n- y = np.ravel(y)\n- y_std = np.ravel(y_std)\n-\n- return (y, y_std) if return_std else y\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/dummy.py.\nHere is the description for the function:\n def predict(self, X, return_std=False):\n \"\"\"Perform classification on test vectors X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Test data.\n\n return_std : bool, default=False\n Whether to return the standard deviation of posterior prediction.\n All zeros in this case.\n\n .. versionadded:: 0.20\n\n Returns\n -------\n y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Predicted target values for X.\n\n y_std : array-like of shape (n_samples,) or (n_samples, n_outputs)\n Standard deviation of predictive distribution of query points.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingRegressor-brute-data5]", "sklearn/ensemble/tests/test_forest.py::test_poisson_vs_mse", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingRegressor-brute-data5]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_estimators[estimator1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingRegressor-auto-data4]", 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"sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_gradient_boosting_with_init[42-regression]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_dtype_object]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_gbr_degenerate_feature_importances", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_pipeline_consistency]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_huber_vs_mean_and_median", "sklearn/ensemble/tests/test_gradient_boosting.py::test_squared_error_exact_backward_compat", "sklearn/ensemble/tests/test_gradient_boosting.py::test_huber_exact_backward_compat", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingRegressor(n_estimators=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GradientBoostingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingRegressor(n_estimators=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-38	1.0	{ "code": "diff --git b/sklearn/linear_model/_coordinate_descent.py a/sklearn/linear_model/_coordinate_descent.py\nindex 7b6f855e3..61aaf49a1 100644\n--- b/sklearn/linear_model/_coordinate_descent.py\n+++ a/sklearn/linear_model/_coordinate_descent.py\n@@ -918,6 +918,7 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):\n self.random_state = random_state\n self.selection = selection\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Fit model with coordinate descent.\n \n@@ -956,6 +957,168 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):\n To avoid memory re-allocation it is advised to allocate the\n initial data in memory directly using that format.\n \"\"\"\n+ if self.alpha == 0:\n+ warnings.warn(\n+ (\n+ \"With alpha=0, this algorithm does not converge \"\n+ \"well. You are advised to use the LinearRegression \"\n+ \"estimator\"\n+ ),\n+ stacklevel=2,\n+ )\n+\n+ # Remember if X is copied\n+ X_copied = False\n+ # We expect X and y to be float64 or float32 Fortran ordered arrays\n+ # when bypassing checks\n+ if check_input:\n+ X_copied = self.copy_X and self.fit_intercept\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=\"csc\",\n+ order=\"F\",\n+ dtype=[np.float64, np.float32],\n+ force_writeable=True,\n+ accept_large_sparse=False,\n+ copy=X_copied,\n+ multi_output=True,\n+ y_numeric=True,\n+ )\n+ y = check_array(\n+ y, order=\"F\", copy=False, dtype=X.dtype.type, ensure_2d=False\n+ )\n+\n+ n_samples, n_features = X.shape\n+ alpha = self.alpha\n+\n+ if isinstance(sample_weight, numbers.Number):\n+ sample_weight = None\n+ if sample_weight is not None:\n+ if check_input:\n+ sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n+ # TLDR: Rescale sw to sum up to n_samples.\n+ # Long: The objective function of Enet\n+ #\n+ # 1/2 * np.average(squared error, weights=sw)\n+ # + alpha * penalty (1)\n+ #\n+ # is invariant under rescaling of sw.\n+ # But enet_path coordinate descent minimizes\n+ #\n+ # 1/2 * sum(squared error) + alpha' * penalty (2)\n+ #\n+ # and therefore sets\n+ #\n+ # alpha' = n_samples * alpha (3)\n+ #\n+ # inside its function body, which results in objective (2) being\n+ # equivalent to (1) in case of no sw.\n+ # With sw, however, enet_path should set\n+ #\n+ # alpha' = sum(sw) * alpha (4)\n+ #\n+ # Therefore, we use the freedom of Eq. (1) to rescale sw before\n+ # calling enet_path, i.e.\n+ #\n+ # sw = n_samples / sum(sw)\n+ #\n+ # such that sum(sw) = n_samples. This way, (3) and (4) are the same.\n+ sample_weight = sample_weight (n_samples / np.sum(sample_weight))\n+ # Note: Alternatively, we could also have rescaled alpha instead\n+ # of sample_weight:\n+ #\n+ # alpha = np.sum(sample_weight) / n_samples\n+\n+ # Ensure copying happens only once, don't do it again if done above.\n+ # X and y will be rescaled if sample_weight is not None, order='F'\n+ # ensures that the returned X and y are still F-contiguous.\n+ should_copy = self.copy_X and not X_copied\n+ X, y, X_offset, y_offset, X_scale, precompute, Xy = _pre_fit(\n+ X,\n+ y,\n+ None,\n+ self.precompute,\n+ fit_intercept=self.fit_intercept,\n+ copy=should_copy,\n+ check_input=check_input,\n+ sample_weight=sample_weight,\n+ )\n+ # coordinate descent needs F-ordered arrays and _pre_fit might have\n+ # called _rescale_data\n+ if check_input or sample_weight is not None:\n+ X, y = _set_order(X, y, order=\"F\")\n+ if y.ndim == 1:\n+ y = y[:, np.newaxis]\n+ if Xy is not None and Xy.ndim == 1:\n+ Xy = Xy[:, np.newaxis]\n+\n+ n_targets = y.shape[1]\n+\n+ if not self.warm_start or not hasattr(self, \"coef_\"):\n+ coef_ = np.zeros((n_targets, n_features), dtype=X.dtype, order=\"F\")\n+ else:\n+ coef_ = self.coef_\n+ if coef_.ndim == 1:\n+ coef_ = coef_[np.newaxis, :]\n+\n+ dual_gaps_ = np.zeros(n_targets, dtype=X.dtype)\n+ self.n_iter_ = []\n+\n+ for k in range(n_targets):\n+ if Xy is not None:\n+ this_Xy = Xy[:, k]\n+ else:\n+ this_Xy = None\n+ _, this_coef, this_dual_gap, this_iter = self.path(\n+ X,\n+ y[:, k],\n+ l1_ratio=self.l1_ratio,\n+ eps=None,\n+ n_alphas=None,\n+ alphas=[alpha],\n+ precompute=precompute,\n+ Xy=this_Xy,\n+ copy_X=True,\n+ coef_init=coef_[k],\n+ verbose=False,\n+ return_n_iter=True,\n+ positive=self.positive,\n+ check_input=False,\n+ # from here on *params\n+ tol=self.tol,\n+ X_offset=X_offset,\n+ X_scale=X_scale,\n+ max_iter=self.max_iter,\n+ random_state=self.random_state,\n+ selection=self.selection,\n+ sample_weight=sample_weight,\n+ )\n+ coef_[k] = this_coef[:, 0]\n+ dual_gaps_[k] = this_dual_gap[0]\n+ self.n_iter_.append(this_iter[0])\n+\n+ if n_targets == 1:\n+ self.n_iter_ = self.n_iter_[0]\n+ self.coef_ = coef_[0]\n+ self.dual_gap_ = dual_gaps_[0]\n+ else:\n+ self.coef_ = coef_\n+ self.dual_gap_ = dual_gaps_\n+\n+ self._set_intercept(X_offset, y_offset, X_scale)\n+\n+ # check for finiteness of coefficients\n+ if not all(np.isfinite(w).all() for w in [self.coef_, self.intercept_]):\n+ raise ValueError(\n+ \"Coordinate descent iterations resulted in non-finite parameter\"\n+ \" values. The input data may contain large values and need to\"\n+ \" be preprocessed.\"\n+ )\n+\n+ # return self for chaining fit and predict calls\n+ return self\n \n @property\n def sparse_coef_(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py\nindex 61aaf49a1..7b6f855e3 100644\n--- a/sklearn/linear_model/_coordinate_descent.py\n+++ b/sklearn/linear_model/_coordinate_descent.py\n@@ -918,7 +918,6 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):\n self.random_state = random_state\n self.selection = selection\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Fit model with coordinate descent.\n \n@@ -957,168 +956,6 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):\n To avoid memory re-allocation it is advised to allocate the\n initial data in memory directly using that format.\n \"\"\"\n- if self.alpha == 0:\n- warnings.warn(\n- (\n- \"With alpha=0, this algorithm does not converge \"\n- \"well. You are advised to use the LinearRegression \"\n- \"estimator\"\n- ),\n- stacklevel=2,\n- )\n-\n- # Remember if X is copied\n- X_copied = False\n- # We expect X and y to be float64 or float32 Fortran ordered arrays\n- # when bypassing checks\n- if check_input:\n- X_copied = self.copy_X and self.fit_intercept\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=\"csc\",\n- order=\"F\",\n- dtype=[np.float64, np.float32],\n- force_writeable=True,\n- accept_large_sparse=False,\n- copy=X_copied,\n- multi_output=True,\n- y_numeric=True,\n- )\n- y = check_array(\n- y, order=\"F\", copy=False, dtype=X.dtype.type, ensure_2d=False\n- )\n-\n- n_samples, n_features = X.shape\n- alpha = self.alpha\n-\n- if isinstance(sample_weight, numbers.Number):\n- sample_weight = None\n- if sample_weight is not None:\n- if check_input:\n- sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n- # TLDR: Rescale sw to sum up to n_samples.\n- # Long: The objective function of Enet\n- #\n- # 1/2 * np.average(squared error, weights=sw)\n- # + alpha * penalty (1)\n- #\n- # is invariant under rescaling of sw.\n- # But enet_path coordinate descent minimizes\n- #\n- # 1/2 * sum(squared error) + alpha' * penalty (2)\n- #\n- # and therefore sets\n- #\n- # alpha' = n_samples * alpha (3)\n- #\n- # inside its function body, which results in objective (2) being\n- # equivalent to (1) in case of no sw.\n- # With sw, however, enet_path should set\n- #\n- # alpha' = sum(sw) * alpha (4)\n- #\n- # Therefore, we use the freedom of Eq. (1) to rescale sw before\n- # calling enet_path, i.e.\n- #\n- # sw = n_samples / sum(sw)\n- #\n- # such that sum(sw) = n_samples. This way, (3) and (4) are the same.\n- sample_weight = sample_weight (n_samples / np.sum(sample_weight))\n- # Note: Alternatively, we could also have rescaled alpha instead\n- # of sample_weight:\n- #\n- # alpha = np.sum(sample_weight) / n_samples\n-\n- # Ensure copying happens only once, don't do it again if done above.\n- # X and y will be rescaled if sample_weight is not None, order='F'\n- # ensures that the returned X and y are still F-contiguous.\n- should_copy = self.copy_X and not X_copied\n- X, y, X_offset, y_offset, X_scale, precompute, Xy = _pre_fit(\n- X,\n- y,\n- None,\n- self.precompute,\n- fit_intercept=self.fit_intercept,\n- copy=should_copy,\n- check_input=check_input,\n- sample_weight=sample_weight,\n- )\n- # coordinate descent needs F-ordered arrays and _pre_fit might have\n- # called _rescale_data\n- if check_input or sample_weight is not None:\n- X, y = _set_order(X, y, order=\"F\")\n- if y.ndim == 1:\n- y = y[:, np.newaxis]\n- if Xy is not None and Xy.ndim == 1:\n- Xy = Xy[:, np.newaxis]\n-\n- n_targets = y.shape[1]\n-\n- if not self.warm_start or not hasattr(self, \"coef_\"):\n- coef_ = np.zeros((n_targets, n_features), dtype=X.dtype, order=\"F\")\n- else:\n- coef_ = self.coef_\n- if coef_.ndim == 1:\n- coef_ = coef_[np.newaxis, :]\n-\n- dual_gaps_ = np.zeros(n_targets, dtype=X.dtype)\n- self.n_iter_ = []\n-\n- for k in range(n_targets):\n- if Xy is not None:\n- this_Xy = Xy[:, k]\n- else:\n- this_Xy = None\n- _, this_coef, this_dual_gap, this_iter = self.path(\n- X,\n- y[:, k],\n- l1_ratio=self.l1_ratio,\n- eps=None,\n- n_alphas=None,\n- alphas=[alpha],\n- precompute=precompute,\n- Xy=this_Xy,\n- copy_X=True,\n- coef_init=coef_[k],\n- verbose=False,\n- return_n_iter=True,\n- positive=self.positive,\n- check_input=False,\n- # from here on *params\n- tol=self.tol,\n- X_offset=X_offset,\n- X_scale=X_scale,\n- max_iter=self.max_iter,\n- random_state=self.random_state,\n- selection=self.selection,\n- sample_weight=sample_weight,\n- )\n- coef_[k] = this_coef[:, 0]\n- dual_gaps_[k] = this_dual_gap[0]\n- self.n_iter_.append(this_iter[0])\n-\n- if n_targets == 1:\n- self.n_iter_ = self.n_iter_[0]\n- self.coef_ = coef_[0]\n- self.dual_gap_ = dual_gaps_[0]\n- else:\n- self.coef_ = coef_\n- self.dual_gap_ = dual_gaps_\n-\n- self._set_intercept(X_offset, y_offset, X_scale)\n-\n- # check for finiteness of coefficients\n- if not all(np.isfinite(w).all() for w in [self.coef_, self.intercept_]):\n- raise ValueError(\n- \"Coordinate descent iterations resulted in non-finite parameter\"\n- \" values. The input data may contain large values and need to\"\n- \" be preprocessed.\"\n- )\n-\n- # return self for chaining fit and predict calls\n- return self\n \n @property\n def sparse_coef_(self):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_coordinate_descent.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None, check_input=True):\n \"\"\"Fit model with coordinate descent.\n\n Parameters\n ----------\n X : {ndarray, sparse matrix, sparse array} of (n_samples, n_features)\n Data.\n\n Note that large sparse matrices and arrays requiring `int64`\n indices are not accepted.\n\n y : ndarray of shape (n_samples,) or (n_samples, n_targets)\n Target. Will be cast to X's dtype if necessary.\n\n sample_weight : float or array-like of shape (n_samples,), default=None\n Sample weights. Internally, the `sample_weight` vector will be\n rescaled to sum to `n_samples`.\n\n .. versionadded:: 0.23\n\n check_input : bool, default=True\n Allow to bypass several input checking.\n Don't use this parameter unless you know what you do.\n\n Returns\n -------\n self : object\n Fitted estimator.\n\n Notes\n -----\n Coordinate descent is an algorithm that considers each column of\n data at a time hence it will automatically convert the X input\n as a Fortran-contiguous numpy array if necessary.\n\n To avoid memory re-allocation it is advised to allocate the\n initial data in memory directly using that format.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_sgd.py::test_elasticnet_convergence[SGDRegressor]", "sklearn/linear_model/tests/test_sgd.py::test_elasticnet_convergence[SparseSGDRegressor]", "sklearn/decomposition/tests/test_dict_learning.py::test_sparse_encode_shapes_omp", "sklearn/linear_model/tests/test_coordinate_descent.py::test_lasso_zero", "sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_nonfinite_params", "sklearn/linear_model/tests/test_coordinate_descent.py::test_lasso_toy", "sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_toy", "sklearn/linear_model/tests/test_coordinate_descent.py::test_lasso_dual_gap", "sklearn/linear_model/tests/test_coordinate_descent.py::test_lasso_cv", "sklearn/linear_model/tests/test_coordinate_descent.py::test_lasso_cv_with_some_model_selection", "sklearn/linear_model/tests/test_coordinate_descent.py::test_lasso_cv_positive_constraint", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-Lasso-params0]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-LassoCV-params1]", 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scikit-learn__scikit-learn-39	1.0	{ "code": "diff --git b/sklearn/covariance/_empirical_covariance.py a/sklearn/covariance/_empirical_covariance.py\nindex c97aaab01..fc3d1dc07 100644\n--- b/sklearn/covariance/_empirical_covariance.py\n+++ a/sklearn/covariance/_empirical_covariance.py\n@@ -313,6 +313,27 @@ class EmpiricalCovariance(BaseEstimator):\n The Mean Squared Error (in the sense of the Frobenius norm) between\n `self` and `comp_cov` covariance estimators.\n \"\"\"\n+ # compute the error\n+ error = comp_cov - self.covariance_\n+ # compute the error norm\n+ if norm == \"frobenius\":\n+ squared_norm = np.sum(error**2)\n+ elif norm == \"spectral\":\n+ squared_norm = np.amax(linalg.svdvals(np.dot(error.T, error)))\n+ else:\n+ raise NotImplementedError(\n+ \"Only spectral and frobenius norms are implemented\"\n+ )\n+ # optionally scale the error norm\n+ if scaling:\n+ squared_norm = squared_norm / error.shape[0]\n+ # finally get either the squared norm or the norm\n+ if squared:\n+ result = squared_norm\n+ else:\n+ result = np.sqrt(squared_norm)\n+\n+ return result\n \n def mahalanobis(self, X):\n \"\"\"Compute the squared Mahalanobis distances of given observations.\n", "test": null }	null	{ "code": "diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py\nindex fc3d1dc07..c97aaab01 100644\n--- a/sklearn/covariance/_empirical_covariance.py\n+++ b/sklearn/covariance/_empirical_covariance.py\n@@ -313,27 +313,6 @@ class EmpiricalCovariance(BaseEstimator):\n The Mean Squared Error (in the sense of the Frobenius norm) between\n `self` and `comp_cov` covariance estimators.\n \"\"\"\n- # compute the error\n- error = comp_cov - self.covariance_\n- # compute the error norm\n- if norm == \"frobenius\":\n- squared_norm = np.sum(error**2)\n- elif norm == \"spectral\":\n- squared_norm = np.amax(linalg.svdvals(np.dot(error.T, error)))\n- else:\n- raise NotImplementedError(\n- \"Only spectral and frobenius norms are implemented\"\n- )\n- # optionally scale the error norm\n- if scaling:\n- squared_norm = squared_norm / error.shape[0]\n- # finally get either the squared norm or the norm\n- if squared:\n- result = squared_norm\n- else:\n- result = np.sqrt(squared_norm)\n-\n- return result\n \n def mahalanobis(self, X):\n \"\"\"Compute the squared Mahalanobis distances of given observations.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/covariance/_empirical_covariance.py.\nHere is the description for the function:\n def error_norm(self, comp_cov, norm=\"frobenius\", scaling=True, squared=True):\n \"\"\"Compute the Mean Squared Error between two covariance estimators.\n\n Parameters\n ----------\n comp_cov : array-like of shape (n_features, n_features)\n The covariance to compare with.\n\n norm : {\"frobenius\", \"spectral\"}, default=\"frobenius\"\n The type of norm used to compute the error. Available error types:\n - 'frobenius' (default): sqrt(tr(A^t.A))\n - 'spectral': sqrt(max(eigenvalues(A^t.A))\n where A is the error ``(comp_cov - self.covariance_)``.\n\n scaling : bool, default=True\n If True (default), the squared error norm is divided by n_features.\n If False, the squared error norm is not rescaled.\n\n squared : bool, default=True\n Whether to compute the squared error norm or the error norm.\n If True (default), the squared error norm is returned.\n If False, the error norm is returned.\n\n Returns\n -------\n result : float\n The Mean Squared Error (in the sense of the Frobenius norm) between\n `self` and `comp_cov` covariance estimators.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/mixture/tests/test_gaussian_mixture.py::test_suffstat_sk_full", "sklearn/mixture/tests/test_gaussian_mixture.py::test_suffstat_sk_tied", "sklearn/mixture/tests/test_gaussian_mixture.py::test_suffstat_sk_diag", "sklearn/mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_fit", "sklearn/covariance/tests/test_covariance.py::test_covariance" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-40	1.0	{ "code": "diff --git b/sklearn/covariance/_empirical_covariance.py a/sklearn/covariance/_empirical_covariance.py\nindex 380cc72f0..fc3d1dc07 100644\n--- b/sklearn/covariance/_empirical_covariance.py\n+++ a/sklearn/covariance/_empirical_covariance.py\n@@ -225,6 +225,7 @@ class EmpiricalCovariance(BaseEstimator):\n precision = linalg.pinvh(self.covariance_, check_finite=False)\n return precision\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the maximum likelihood covariance estimator to X.\n \n@@ -242,6 +243,15 @@ class EmpiricalCovariance(BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X)\n+ if self.assume_centered:\n+ self.location_ = np.zeros(X.shape[1])\n+ else:\n+ self.location_ = X.mean(0)\n+ covariance = empirical_covariance(X, assume_centered=self.assume_centered)\n+ self._set_covariance(covariance)\n+\n+ return self\n \n def score(self, X_test, y=None):\n \"\"\"Compute the log-likelihood of `X_test` under the estimated Gaussian model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py\nindex fc3d1dc07..380cc72f0 100644\n--- a/sklearn/covariance/_empirical_covariance.py\n+++ b/sklearn/covariance/_empirical_covariance.py\n@@ -225,7 +225,6 @@ class EmpiricalCovariance(BaseEstimator):\n precision = linalg.pinvh(self.covariance_, check_finite=False)\n return precision\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the maximum likelihood covariance estimator to X.\n \n@@ -243,15 +242,6 @@ class EmpiricalCovariance(BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X)\n- if self.assume_centered:\n- self.location_ = np.zeros(X.shape[1])\n- else:\n- self.location_ = X.mean(0)\n- covariance = empirical_covariance(X, assume_centered=self.assume_centered)\n- self._set_covariance(covariance)\n-\n- return self\n \n def score(self, X_test, y=None):\n \"\"\"Compute the log-likelihood of `X_test` under the estimated Gaussian model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/covariance/_empirical_covariance.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the maximum likelihood covariance estimator to X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/mixture/tests/test_gaussian_mixture.py::test_suffstat_sk_full", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[EmpiricalCovariance()-check_fit2d_predict1d]", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_alphas_iterable[list]", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_alphas_iterable[tuple]", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_alphas_iterable[array]", "sklearn/covariance/tests/test_covariance.py::test_covariance", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_scores", "sklearn/covariance/tests/test_covariance.py::test_EmpiricalCovariance_validates_mahalanobis", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_scores_with_routing[42]", "sklearn/covariance/_graph_lasso.py::sklearn.covariance._graph_lasso.GraphicalLassoCV", "sklearn/covariance/_empirical_covariance.py::sklearn.covariance._empirical_covariance.EmpiricalCovariance", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[GraphicalLassoCV]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[EmpiricalCovariance()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GraphicalLassoCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[EmpiricalCovariance()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GraphicalLassoCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[EmpiricalCovariance()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-41	1.0	{ "code": "diff --git b/sklearn/covariance/_empirical_covariance.py a/sklearn/covariance/_empirical_covariance.py\nindex 69643a032..fc3d1dc07 100644\n--- b/sklearn/covariance/_empirical_covariance.py\n+++ a/sklearn/covariance/_empirical_covariance.py\n@@ -350,3 +350,13 @@ class EmpiricalCovariance(BaseEstimator):\n dist : ndarray of shape (n_samples,)\n Squared Mahalanobis distances of the observations.\n \"\"\"\n+ X = validate_data(self, X, reset=False)\n+\n+ precision = self.get_precision()\n+ with config_context(assume_finite=True):\n+ # compute mahalanobis distances\n+ dist = pairwise_distances(\n+ X, self.location_[np.newaxis, :], metric=\"mahalanobis\", VI=precision\n+ )\n+\n+ return np.reshape(dist, (len(X),)) ** 2\n", "test": null }	null	{ "code": "diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py\nindex fc3d1dc07..69643a032 100644\n--- a/sklearn/covariance/_empirical_covariance.py\n+++ b/sklearn/covariance/_empirical_covariance.py\n@@ -350,13 +350,3 @@ class EmpiricalCovariance(BaseEstimator):\n dist : ndarray of shape (n_samples,)\n Squared Mahalanobis distances of the observations.\n \"\"\"\n- X = validate_data(self, X, reset=False)\n-\n- precision = self.get_precision()\n- with config_context(assume_finite=True):\n- # compute mahalanobis distances\n- dist = pairwise_distances(\n- X, self.location_[np.newaxis, :], metric=\"mahalanobis\", VI=precision\n- )\n-\n- return np.reshape(dist, (len(X),)) ** 2\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/covariance/_empirical_covariance.py.\nHere is the description for the function:\n def mahalanobis(self, X):\n \"\"\"Compute the squared Mahalanobis distances of given observations.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The observations, the Mahalanobis distances of the which we\n compute. Observations are assumed to be drawn from the same\n distribution than the data used in fit.\n\n Returns\n -------\n dist : ndarray of shape (n_samples,)\n Squared Mahalanobis distances of the observations.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_outliers_fit_predict]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_outliers_train]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_outliers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit2d_predict1d]", "sklearn/covariance/tests/test_covariance.py::test_covariance", "sklearn/covariance/tests/test_robust_covariance.py::test_mcd[42]", "sklearn/covariance/tests/test_covariance.py::test_EmpiricalCovariance_validates_mahalanobis", "sklearn/covariance/tests/test_elliptic_envelope.py::test_elliptic_envelope[42]", "sklearn/covariance/tests/test_elliptic_envelope.py::test_score_samples", "sklearn/covariance/_elliptic_envelope.py::sklearn.covariance._elliptic_envelope.EllipticEnvelope", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[EllipticEnvelope()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[EllipticEnvelope()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-42	1.0	{ "code": "diff --git b/sklearn/decomposition/_factor_analysis.py a/sklearn/decomposition/_factor_analysis.py\nindex cf48d17e1..8f30fe0d0 100644\n--- b/sklearn/decomposition/_factor_analysis.py\n+++ a/sklearn/decomposition/_factor_analysis.py\n@@ -199,6 +199,7 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n self.random_state = random_state\n self.rotation = rotation\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the FactorAnalysis model to X using SVD based approach.\n \n@@ -215,6 +216,97 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n self : object\n FactorAnalysis class instance.\n \"\"\"\n+ X = validate_data(\n+ self, X, copy=self.copy, dtype=np.float64, force_writeable=True\n+ )\n+\n+ n_samples, n_features = X.shape\n+ n_components = self.n_components\n+ if n_components is None:\n+ n_components = n_features\n+\n+ self.mean_ = np.mean(X, axis=0)\n+ X -= self.mean_\n+\n+ # some constant terms\n+ nsqrt = sqrt(n_samples)\n+ llconst = n_features * log(2.0 * np.pi) + n_components\n+ var = np.var(X, axis=0)\n+\n+ if self.noise_variance_init is None:\n+ psi = np.ones(n_features, dtype=X.dtype)\n+ else:\n+ if len(self.noise_variance_init) != n_features:\n+ raise ValueError(\n+ \"noise_variance_init dimension does not \"\n+ \"with number of features : %d != %d\"\n+ % (len(self.noise_variance_init), n_features)\n+ )\n+ psi = np.array(self.noise_variance_init)\n+\n+ loglike = []\n+ old_ll = -np.inf\n+ SMALL = 1e-12\n+\n+ # we'll modify svd outputs to return unexplained variance\n+ # to allow for unified computation of loglikelihood\n+ if self.svd_method == \"lapack\":\n+\n+ def my_svd(X):\n+ _, s, Vt = linalg.svd(X, full_matrices=False, check_finite=False)\n+ return (\n+ s[:n_components],\n+ Vt[:n_components],\n+ squared_norm(s[n_components:]),\n+ )\n+\n+ else: # svd_method == \"randomized\"\n+ random_state = check_random_state(self.random_state)\n+\n+ def my_svd(X):\n+ _, s, Vt = randomized_svd(\n+ X,\n+ n_components,\n+ random_state=random_state,\n+ n_iter=self.iterated_power,\n+ )\n+ return s, Vt, squared_norm(X) - squared_norm(s)\n+\n+ for i in range(self.max_iter):\n+ # SMALL helps numerics\n+ sqrt_psi = np.sqrt(psi) + SMALL\n+ s, Vt, unexp_var = my_svd(X / (sqrt_psi * nsqrt))\n+ s *= 2\n+ # Use 'maximum' here to avoid sqrt problems.\n+ W = np.sqrt(np.maximum(s - 1.0, 0.0))[:, np.newaxis] Vt\n+ del Vt\n+ W = sqrt_psi\n+\n+ # loglikelihood\n+ ll = llconst + np.sum(np.log(s))\n+ ll += unexp_var + np.sum(np.log(psi))\n+ ll = -n_samples / 2.0\n+ loglike.append(ll)\n+ if (ll - old_ll) < self.tol:\n+ break\n+ old_ll = ll\n+\n+ psi = np.maximum(var - np.sum(W**2, axis=0), SMALL)\n+ else:\n+ warnings.warn(\n+ \"FactorAnalysis did not converge.\"\n+ + \" You might want\"\n+ + \" to increase the number of iterations.\",\n+ ConvergenceWarning,\n+ )\n+\n+ self.components_ = W\n+ if self.rotation is not None:\n+ self.components_ = self._rotate(W)\n+ self.noise_variance_ = psi\n+ self.loglike_ = loglike\n+ self.n_iter_ = i + 1\n+ return self\n \n def transform(self, X):\n \"\"\"Apply dimensionality reduction to X using the model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_factor_analysis.py b/sklearn/decomposition/_factor_analysis.py\nindex 8f30fe0d0..cf48d17e1 100644\n--- a/sklearn/decomposition/_factor_analysis.py\n+++ b/sklearn/decomposition/_factor_analysis.py\n@@ -199,7 +199,6 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n self.random_state = random_state\n self.rotation = rotation\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the FactorAnalysis model to X using SVD based approach.\n \n@@ -216,97 +215,6 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n self : object\n FactorAnalysis class instance.\n \"\"\"\n- X = validate_data(\n- self, X, copy=self.copy, dtype=np.float64, force_writeable=True\n- )\n-\n- n_samples, n_features = X.shape\n- n_components = self.n_components\n- if n_components is None:\n- n_components = n_features\n-\n- self.mean_ = np.mean(X, axis=0)\n- X -= self.mean_\n-\n- # some constant terms\n- nsqrt = sqrt(n_samples)\n- llconst = n_features * log(2.0 * np.pi) + n_components\n- var = np.var(X, axis=0)\n-\n- if self.noise_variance_init is None:\n- psi = np.ones(n_features, dtype=X.dtype)\n- else:\n- if len(self.noise_variance_init) != n_features:\n- raise ValueError(\n- \"noise_variance_init dimension does not \"\n- \"with number of features : %d != %d\"\n- % (len(self.noise_variance_init), n_features)\n- )\n- psi = np.array(self.noise_variance_init)\n-\n- loglike = []\n- old_ll = -np.inf\n- SMALL = 1e-12\n-\n- # we'll modify svd outputs to return unexplained variance\n- # to allow for unified computation of loglikelihood\n- if self.svd_method == \"lapack\":\n-\n- def my_svd(X):\n- _, s, Vt = linalg.svd(X, full_matrices=False, check_finite=False)\n- return (\n- s[:n_components],\n- Vt[:n_components],\n- squared_norm(s[n_components:]),\n- )\n-\n- else: # svd_method == \"randomized\"\n- random_state = check_random_state(self.random_state)\n-\n- def my_svd(X):\n- _, s, Vt = randomized_svd(\n- X,\n- n_components,\n- random_state=random_state,\n- n_iter=self.iterated_power,\n- )\n- return s, Vt, squared_norm(X) - squared_norm(s)\n-\n- for i in range(self.max_iter):\n- # SMALL helps numerics\n- sqrt_psi = np.sqrt(psi) + SMALL\n- s, Vt, unexp_var = my_svd(X / (sqrt_psi * nsqrt))\n- s *= 2\n- # Use 'maximum' here to avoid sqrt problems.\n- W = np.sqrt(np.maximum(s - 1.0, 0.0))[:, np.newaxis] Vt\n- del Vt\n- W = sqrt_psi\n-\n- # loglikelihood\n- ll = llconst + np.sum(np.log(s))\n- ll += unexp_var + np.sum(np.log(psi))\n- ll = -n_samples / 2.0\n- loglike.append(ll)\n- if (ll - old_ll) < self.tol:\n- break\n- old_ll = ll\n-\n- psi = np.maximum(var - np.sum(W**2, axis=0), SMALL)\n- else:\n- warnings.warn(\n- \"FactorAnalysis did not converge.\"\n- + \" You might want\"\n- + \" to increase the number of iterations.\",\n- ConvergenceWarning,\n- )\n-\n- self.components_ = W\n- if self.rotation is not None:\n- self.components_ = self._rotate(W)\n- self.noise_variance_ = psi\n- self.loglike_ = loglike\n- self.n_iter_ = i + 1\n- return self\n \n def transform(self, X):\n \"\"\"Apply dimensionality reduction to X using the model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_factor_analysis.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the FactorAnalysis model to X using SVD based approach.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data.\n\n y : Ignored\n Ignored parameter.\n\n Returns\n -------\n self : object\n FactorAnalysis class instance.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_n_iter]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit2d_predict1d]", "sklearn/decomposition/tests/test_factor_analysis.py::test_factor_analysis[42]", "sklearn/decomposition/_factor_analysis.py::sklearn.decomposition._factor_analysis.FactorAnalysis", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[FactorAnalysis(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-43	1.0	{ "code": "diff --git b/sklearn/decomposition/_factor_analysis.py a/sklearn/decomposition/_factor_analysis.py\nindex c747a8c2a..8f30fe0d0 100644\n--- b/sklearn/decomposition/_factor_analysis.py\n+++ a/sklearn/decomposition/_factor_analysis.py\n@@ -395,6 +395,14 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n ll : ndarray of shape (n_samples,)\n Log-likelihood of each sample under the current model.\n \"\"\"\n+ check_is_fitted(self)\n+ X = validate_data(self, X, reset=False)\n+ Xr = X - self.mean_\n+ precision = self.get_precision()\n+ n_features = X.shape[1]\n+ log_like = -0.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)\n+ log_like -= 0.5 * (n_features * log(2.0 * np.pi) - fast_logdet(precision))\n+ return log_like\n \n def score(self, X, y=None):\n \"\"\"Compute the average log-likelihood of the samples.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_factor_analysis.py b/sklearn/decomposition/_factor_analysis.py\nindex 8f30fe0d0..c747a8c2a 100644\n--- a/sklearn/decomposition/_factor_analysis.py\n+++ b/sklearn/decomposition/_factor_analysis.py\n@@ -395,14 +395,6 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n ll : ndarray of shape (n_samples,)\n Log-likelihood of each sample under the current model.\n \"\"\"\n- check_is_fitted(self)\n- X = validate_data(self, X, reset=False)\n- Xr = X - self.mean_\n- precision = self.get_precision()\n- n_features = X.shape[1]\n- log_like = -0.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)\n- log_like -= 0.5 * (n_features * log(2.0 * np.pi) - fast_logdet(precision))\n- return log_like\n \n def score(self, X, y=None):\n \"\"\"Compute the average log-likelihood of the samples.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_factor_analysis.py.\nHere is the description for the function:\n def score_samples(self, X):\n \"\"\"Compute the log-likelihood of each sample.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, n_features)\n The data.\n\n Returns\n -------\n ll : ndarray of shape (n_samples,)\n Log-likelihood of each sample under the current model.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_methods_subset_invariance]", "sklearn/decomposition/tests/test_factor_analysis.py::test_factor_analysis[42]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FactorAnalysis(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-44	1.0	{ "code": "diff --git b/sklearn/decomposition/_factor_analysis.py a/sklearn/decomposition/_factor_analysis.py\nindex 14eeaf854..8f30fe0d0 100644\n--- b/sklearn/decomposition/_factor_analysis.py\n+++ a/sklearn/decomposition/_factor_analysis.py\n@@ -324,6 +324,19 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n X_new : ndarray of shape (n_samples, n_components)\n The latent variables of X.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ X = validate_data(self, X, reset=False)\n+ Ih = np.eye(len(self.components_))\n+\n+ X_transformed = X - self.mean_\n+\n+ Wpsi = self.components_ / self.noise_variance_\n+ cov_z = linalg.inv(Ih + np.dot(Wpsi, self.components_.T))\n+ tmp = np.dot(X_transformed, Wpsi.T)\n+ X_transformed = np.dot(tmp, cov_z)\n+\n+ return X_transformed\n \n def get_covariance(self):\n \"\"\"Compute data covariance with the FactorAnalysis model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_factor_analysis.py b/sklearn/decomposition/_factor_analysis.py\nindex 8f30fe0d0..14eeaf854 100644\n--- a/sklearn/decomposition/_factor_analysis.py\n+++ b/sklearn/decomposition/_factor_analysis.py\n@@ -324,19 +324,6 @@ class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti\n X_new : ndarray of shape (n_samples, n_components)\n The latent variables of X.\n \"\"\"\n- check_is_fitted(self)\n-\n- X = validate_data(self, X, reset=False)\n- Ih = np.eye(len(self.components_))\n-\n- X_transformed = X - self.mean_\n-\n- Wpsi = self.components_ / self.noise_variance_\n- cov_z = linalg.inv(Ih + np.dot(Wpsi, self.components_.T))\n- tmp = np.dot(X_transformed, Wpsi.T)\n- X_transformed = np.dot(tmp, cov_z)\n-\n- return X_transformed\n \n def get_covariance(self):\n \"\"\"Compute data covariance with the FactorAnalysis model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_factor_analysis.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Apply dimensionality reduction to X using the model.\n\n Compute the expected mean of the latent variables.\n See Barber, 21.2.33 (or Bishop, 12.66).\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data.\n\n Returns\n -------\n X_new : ndarray of shape (n_samples, n_components)\n The latent variables of X.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_fit2d_predict1d]", "sklearn/decomposition/_factor_analysis.py::sklearn.decomposition._factor_analysis.FactorAnalysis", "sklearn/decomposition/tests/test_factor_analysis.py::test_factor_analysis[42]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-FactorAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[FactorAnalysis(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-45	1.0	{ "code": "diff --git b/sklearn/decomposition/_fastica.py a/sklearn/decomposition/_fastica.py\nindex 1e43c8105..2ef616294 100644\n--- b/sklearn/decomposition/_fastica.py\n+++ a/sklearn/decomposition/_fastica.py\n@@ -754,6 +754,19 @@ class FastICA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n Estimated sources obtained by transforming the data with the\n estimated unmixing matrix.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ X = validate_data(\n+ self,\n+ X,\n+ copy=(copy and self.whiten),\n+ dtype=[np.float64, np.float32],\n+ reset=False,\n+ )\n+ if self.whiten:\n+ X -= self.mean_\n+\n+ return np.dot(X, self.components_.T)\n \n def inverse_transform(self, X, copy=True):\n \"\"\"Transform the sources back to the mixed data (apply mixing matrix).\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_fastica.py b/sklearn/decomposition/_fastica.py\nindex 2ef616294..1e43c8105 100644\n--- a/sklearn/decomposition/_fastica.py\n+++ b/sklearn/decomposition/_fastica.py\n@@ -754,19 +754,6 @@ class FastICA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n Estimated sources obtained by transforming the data with the\n estimated unmixing matrix.\n \"\"\"\n- check_is_fitted(self)\n-\n- X = validate_data(\n- self,\n- X,\n- copy=(copy and self.whiten),\n- dtype=[np.float64, np.float32],\n- reset=False,\n- )\n- if self.whiten:\n- X -= self.mean_\n-\n- return np.dot(X, self.components_.T)\n \n def inverse_transform(self, X, copy=True):\n \"\"\"Transform the sources back to the mixed data (apply mixing matrix).\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_fastica.py.\nHere is the description for the function:\n def transform(self, X, copy=True):\n \"\"\"Recover the sources from X (apply the unmixing matrix).\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Data to transform, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n copy : bool, default=True\n If False, data passed to fit can be overwritten. Defaults to True.\n\n Returns\n -------\n X_new : ndarray of shape (n_samples, n_components)\n Estimated sources obtained by transforming the data with the\n estimated unmixing matrix.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_estimators_pickle]", "sklearn/decomposition/tests/test_fastica.py::test_fastica_simple[float64-42-True]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_transformer_data_not_an_array]", "sklearn/decomposition/tests/test_fastica.py::test_fastica_simple[float64-42-False]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_transformer_general]", "sklearn/decomposition/tests/test_fastica.py::test_fit_transform[float64-42]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FastICA(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FastICA(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[FastICA(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-FastICA(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-FastICA(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-46	1.0	{ "code": "diff --git b/sklearn/feature_extraction/_hash.py a/sklearn/feature_extraction/_hash.py\nindex 7f566e2ae..45570a523 100644\n--- b/sklearn/feature_extraction/_hash.py\n+++ a/sklearn/feature_extraction/_hash.py\n@@ -163,6 +163,35 @@ class FeatureHasher(TransformerMixin, BaseEstimator):\n X : sparse matrix of shape (n_samples, n_features)\n Feature matrix, for use with estimators or further transformers.\n \"\"\"\n+ raw_X = iter(raw_X)\n+ if self.input_type == \"dict\":\n+ raw_X = (_iteritems(d) for d in raw_X)\n+ elif self.input_type == \"string\":\n+ first_raw_X = next(raw_X)\n+ if isinstance(first_raw_X, str):\n+ raise ValueError(\n+ \"Samples can not be a single string. The input must be an iterable\"\n+ \" over iterables of strings.\"\n+ )\n+ raw_X_ = chain([first_raw_X], raw_X)\n+ raw_X = (((f, 1) for f in x) for x in raw_X_)\n+\n+ indices, indptr, values = _hashing_transform(\n+ raw_X, self.n_features, self.dtype, self.alternate_sign, seed=0\n+ )\n+ n_samples = indptr.shape[0] - 1\n+\n+ if n_samples == 0:\n+ raise ValueError(\"Cannot vectorize empty sequence.\")\n+\n+ X = sp.csr_matrix(\n+ (values, indices, indptr),\n+ dtype=self.dtype,\n+ shape=(n_samples, self.n_features),\n+ )\n+ X.sum_duplicates() # also sorts the indices\n+\n+ return X\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_extraction/_hash.py b/sklearn/feature_extraction/_hash.py\nindex 45570a523..7f566e2ae 100644\n--- a/sklearn/feature_extraction/_hash.py\n+++ b/sklearn/feature_extraction/_hash.py\n@@ -163,35 +163,6 @@ class FeatureHasher(TransformerMixin, BaseEstimator):\n X : sparse matrix of shape (n_samples, n_features)\n Feature matrix, for use with estimators or further transformers.\n \"\"\"\n- raw_X = iter(raw_X)\n- if self.input_type == \"dict\":\n- raw_X = (_iteritems(d) for d in raw_X)\n- elif self.input_type == \"string\":\n- first_raw_X = next(raw_X)\n- if isinstance(first_raw_X, str):\n- raise ValueError(\n- \"Samples can not be a single string. The input must be an iterable\"\n- \" over iterables of strings.\"\n- )\n- raw_X_ = chain([first_raw_X], raw_X)\n- raw_X = (((f, 1) for f in x) for x in raw_X_)\n-\n- indices, indptr, values = _hashing_transform(\n- raw_X, self.n_features, self.dtype, self.alternate_sign, seed=0\n- )\n- n_samples = indptr.shape[0] - 1\n-\n- if n_samples == 0:\n- raise ValueError(\"Cannot vectorize empty sequence.\")\n-\n- X = sp.csr_matrix(\n- (values, indices, indptr),\n- dtype=self.dtype,\n- shape=(n_samples, self.n_features),\n- )\n- X.sum_duplicates() # also sorts the indices\n-\n- return X\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/feature_extraction/_hash.py.\nHere is the description for the function:\n def transform(self, raw_X):\n \"\"\"Transform a sequence of instances to a scipy.sparse matrix.\n\n Parameters\n ----------\n raw_X : iterable over iterable over raw features, length = n_samples\n Samples. Each sample must be iterable an (e.g., a list or tuple)\n containing/generating feature names (and optionally values, see\n the input_type constructor argument) which will be hashed.\n raw_X need not support the len function, so it can be the result\n of a generator; n_samples is determined on the fly.\n\n Returns\n -------\n X : sparse matrix of shape (n_samples, n_features)\n Feature matrix, for use with estimators or further transformers.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/feature_extraction/tests/test_text.py::test_hashing_vectorizer", "sklearn/feature_extraction/tests/test_text.py::test_hashed_binary_occurrences", "sklearn/feature_extraction/tests/test_text.py::test_vectorizer_unicode", "sklearn/feature_extraction/tests/test_text.py::test_pickling_vectorizer", "sklearn/feature_extraction/tests/test_text.py::test_hashingvectorizer_nan_in_docs", "sklearn/feature_extraction/tests/test_text.py::test_vectorizer_stop_words_inconsistent", "sklearn/feature_extraction/tests/test_text.py::test_stop_word_validation_custom_preprocessor[HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_error[filename-FileNotFoundError--HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_error[file-AttributeError-'str' object has no attribute 'read'-HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_change_behavior[file-<lambda>0-HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_change_behavior[file-<lambda>1-HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_change_behavior[filename-<lambda>0-HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_change_behavior[filename-<lambda>1-HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_reraise_error[HashingVectorizer]", "sklearn/feature_extraction/tests/test_text.py::test_nonnegative_hashing_vectorizer_result_indices", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_feature_hasher_dicts", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_feature_hasher_strings", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_feature_hasher_single_string[list]", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_feature_hasher_single_string[generator]", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_feature_hasher_pairs", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_feature_hasher_pairs_with_string_values", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_hash_empty_input", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_hasher_zeros", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_hasher_alternate_sign", "sklearn/feature_extraction/tests/test_feature_hasher.py::test_hash_collisions", "sklearn/feature_extraction/text.py::sklearn.feature_extraction.text.HashingVectorizer", "sklearn/feature_extraction/_hash.py::sklearn.feature_extraction._hash.FeatureHasher" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-47	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 76f891d70..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -1639,6 +1639,23 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n self : object\n FeatureUnion class instance.\n \"\"\"\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"fit\", fit_params)\n+ else:\n+ # TODO(SLEP6): remove when metadata routing cannot be disabled.\n+ routed_params = Bunch()\n+ for name, _ in self.transformer_list:\n+ routed_params[name] = Bunch(fit={})\n+ routed_params[name].fit = fit_params\n+\n+ transformers = self._parallel_func(X, y, _fit_one, routed_params)\n+\n+ if not transformers:\n+ # All transformers are None\n+ return self\n+\n+ self._update_transformer_list(transformers)\n+ return self\n \n def fit_transform(self, X, y=None, params):\n \"\"\"Fit all transformers, transform the data and concatenate results.\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..76f891d70 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -1639,23 +1639,6 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n self : object\n FeatureUnion class instance.\n \"\"\"\n- if _routing_enabled():\n- routed_params = process_routing(self, \"fit\", fit_params)\n- else:\n- # TODO(SLEP6): remove when metadata routing cannot be disabled.\n- routed_params = Bunch()\n- for name, _ in self.transformer_list:\n- routed_params[name] = Bunch(fit={})\n- routed_params[name].fit = fit_params\n-\n- transformers = self._parallel_func(X, y, _fit_one, routed_params)\n-\n- if not transformers:\n- # All transformers are None\n- return self\n-\n- self._update_transformer_list(transformers)\n- return self\n \n def fit_transform(self, X, y=None, params):\n \"\"\"Fit all transformers, transform the data and concatenate results.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/pipeline.py.\nHere is the description for the function:\n def fit(self, X, y=None, fit_params):\n \"\"\"Fit all transformers using X.\n\n Parameters\n ----------\n X : iterable or array-like, depending on transformers\n Input data, used to fit transformers.\n\n y : array-like of shape (n_samples, n_outputs), default=None\n Targets for supervised learning.\n\n fit_params : dict, default=None\n - If `enable_metadata_routing=False` (default):\n Parameters directly passed to the `fit` methods of the\n sub-transformers.\n\n - If `enable_metadata_routing=True`:\n Parameters safely routed to the `fit` methods of the\n sub-transformers. See :ref:`Metadata Routing User Guide\n <metadata_routing>` for more details.\n\n .. versionchanged:: 1.5\n `**fit_params` can be routed via metadata routing API.\n\n Returns\n -------\n self : object\n FeatureUnion class instance.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_feature_union[csr_matrix]", "sklearn/tests/test_pipeline.py::test_feature_union[csr_array]", "sklearn/tests/test_pipeline.py::test_feature_union_weights", "sklearn/tests/test_pipeline.py::test_feature_union_parallel", "sklearn/tests/test_pipeline.py::test_feature_union_feature_names", "sklearn/tests/test_pipeline.py::test_set_feature_union_step_drop", "sklearn/tests/test_pipeline.py::test_set_feature_union_passthrough", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_true", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_false", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_false_errors", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_false_errors_overlap_over_5", "sklearn/tests/test_pipeline.py::test_step_name_validation", "sklearn/tests/test_pipeline.py::test_verbose[est12-\\\\[FeatureUnion\\\\].\\\$step 1 of 2\\\$ Processing mult1. total=.\\\\n\\\\[FeatureUnion\\\\].\\\$step 2 of 2\\\$ Processing mult2.* total=.\\\\n$-fit]", "sklearn/tests/test_pipeline.py::test_verbose[est14-\\\\[FeatureUnion\\\\].\\\$step 1 of 1\\\$ Processing mult2.* total=.*\\\\n$-fit]", "sklearn/tests/test_pipeline.py::test_n_features_in_feature_union", "sklearn/tests/test_pipeline.py::test_feature_union_fit_params", "sklearn/tests/test_pipeline.py::test_feature_union_warns_unknown_transformer_weight", "sklearn/tests/test_pipeline.py::test_feature_union_check_if_fitted", "sklearn/tests/test_pipeline.py::test_feature_union_set_output", "sklearn/tests/test_pipeline.py::test_feature_union_feature_names_in_", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing_error", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingTransformer]", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingNoFitTransformTransformer]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit1d]", "sklearn/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[FeatureUnion]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-48	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex cecaab231..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -1688,6 +1688,29 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n The `hstack` of results of transformers. `sum_n_components` is the\n sum of `n_components` (output dimension) over transformers.\n \"\"\"\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"fit_transform\", *params)\n+ else:\n+ # TODO(SLEP6): remove when metadata routing cannot be disabled.\n+ routed_params = Bunch()\n+ for name, obj in self.transformer_list:\n+ if hasattr(obj, \"fit_transform\"):\n+ routed_params[name] = Bunch(fit_transform={})\n+ routed_params[name].fit_transform = params\n+ else:\n+ routed_params[name] = Bunch(fit={})\n+ routed_params[name] = Bunch(transform={})\n+ routed_params[name].fit = params\n+\n+ results = self._parallel_func(X, y, _fit_transform_one, routed_params)\n+ if not results:\n+ # All transformers are None\n+ return np.zeros((X.shape[0], 0))\n+\n+ Xs, transformers = zip(results)\n+ self._update_transformer_list(transformers)\n+\n+ return self._hstack(Xs)\n \n def _log_message(self, name, idx, total):\n if not self.verbose:\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..cecaab231 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -1688,29 +1688,6 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n The `hstack` of results of transformers. `sum_n_components` is the\n sum of `n_components` (output dimension) over transformers.\n \"\"\"\n- if _routing_enabled():\n- routed_params = process_routing(self, \"fit_transform\", *params)\n- else:\n- # TODO(SLEP6): remove when metadata routing cannot be disabled.\n- routed_params = Bunch()\n- for name, obj in self.transformer_list:\n- if hasattr(obj, \"fit_transform\"):\n- routed_params[name] = Bunch(fit_transform={})\n- routed_params[name].fit_transform = params\n- else:\n- routed_params[name] = Bunch(fit={})\n- routed_params[name] = Bunch(transform={})\n- routed_params[name].fit = params\n-\n- results = self._parallel_func(X, y, _fit_transform_one, routed_params)\n- if not results:\n- # All transformers are None\n- return np.zeros((X.shape[0], 0))\n-\n- Xs, transformers = zip(results)\n- self._update_transformer_list(transformers)\n-\n- return self._hstack(Xs)\n \n def _log_message(self, name, idx, total):\n if not self.verbose:\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/pipeline.py.\nHere is the description for the function:\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit all transformers, transform the data and concatenate results.\n\n Parameters\n ----------\n X : iterable or array-like, depending on transformers\n Input data to be transformed.\n\n y : array-like of shape (n_samples, n_outputs), default=None\n Targets for supervised learning.\n\n params : dict, default=None\n - If `enable_metadata_routing=False` (default):\n Parameters directly passed to the `fit` methods of the\n sub-transformers.\n\n - If `enable_metadata_routing=True`:\n Parameters safely routed to the `fit` methods of the\n sub-transformers. See :ref:`Metadata Routing User Guide\n <metadata_routing>` for more details.\n\n .. versionchanged:: 1.5\n `**params` can now be routed via metadata routing API.\n\n Returns\n -------\n X_t : array-like or sparse matrix of \\\n shape (n_samples, sum_n_components)\n The `hstack` of results of transformers. `sum_n_components` is the\n sum of `n_components` (output dimension) over transformers.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/impute/tests/test_impute.py::test_missing_indicator_with_imputer[X0-a-X_trans_exp0]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_with_imputer[X1-nan-X_trans_exp1]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_with_imputer[X2-nan-X_trans_exp2]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_with_imputer[X3-None-X_trans_exp3]", "sklearn/tests/test_pipeline.py::test_feature_union[csr_matrix]", "sklearn/tests/test_pipeline.py::test_feature_union[csr_array]", "sklearn/tests/test_pipeline.py::test_feature_union_weights", "sklearn/tests/test_pipeline.py::test_feature_union_parallel", "sklearn/tests/test_pipeline.py::test_set_feature_union_step_drop", "sklearn/tests/test_pipeline.py::test_set_feature_union_passthrough", "sklearn/tests/test_pipeline.py::test_step_name_validation", "sklearn/tests/test_pipeline.py::test_verbose[est13-\\\\[FeatureUnion\\\\].\\\$step 1 of 2\\\$ Processing mult1. total=.\\\\n\\\\[FeatureUnion\\\\].\\\$step 2 of 2\\\$ Processing mult2.* total=.\\\\n$-fit_transform]", "sklearn/tests/test_pipeline.py::test_verbose[est15-\\\\[FeatureUnion\\\\].\\\$step 1 of 1\\\$ Processing mult2.* total=.*\\\\n$-fit_transform]", "sklearn/tests/test_pipeline.py::test_feature_union_fit_params", "sklearn/tests/test_pipeline.py::test_feature_union_fit_params_without_fit_transform", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingTransformer]", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingNoFitTransformTransformer]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_general(readonly_memmap=True)]", "sklearn/pipeline.py::sklearn.pipeline.FeatureUnion", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-49	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex f6e6a1740..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -1547,6 +1547,20 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n+ # List of tuples (name, feature_names_out)\n+ transformer_with_feature_names_out = []\n+ for name, trans, _ in self._iter():\n+ if not hasattr(trans, \"get_feature_names_out\"):\n+ raise AttributeError(\n+ \"Transformer %s (type %s) does not provide get_feature_names_out.\"\n+ % (str(name), type(trans).__name__)\n+ )\n+ feature_names_out = trans.get_feature_names_out(input_features)\n+ transformer_with_feature_names_out.append((name, feature_names_out))\n+\n+ return self._add_prefix_for_feature_names_out(\n+ transformer_with_feature_names_out\n+ )\n \n def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out):\n \"\"\"Add prefix for feature names out that includes the transformer names.\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..f6e6a1740 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -1547,20 +1547,6 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n- # List of tuples (name, feature_names_out)\n- transformer_with_feature_names_out = []\n- for name, trans, _ in self._iter():\n- if not hasattr(trans, \"get_feature_names_out\"):\n- raise AttributeError(\n- \"Transformer %s (type %s) does not provide get_feature_names_out.\"\n- % (str(name), type(trans).__name__)\n- )\n- feature_names_out = trans.get_feature_names_out(input_features)\n- transformer_with_feature_names_out.append((name, feature_names_out))\n-\n- return self._add_prefix_for_feature_names_out(\n- transformer_with_feature_names_out\n- )\n \n def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out):\n \"\"\"Add prefix for feature names out that includes the transformer names.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/pipeline.py.\nHere is the description for the function:\n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\n\n Parameters\n ----------\n input_features : array-like of str or None, default=None\n Input features.\n\n Returns\n -------\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_feature_union_feature_names", "sklearn/tests/test_pipeline.py::test_set_feature_union_steps", "sklearn/tests/test_pipeline.py::test_set_feature_union_step_drop", "sklearn/tests/test_pipeline.py::test_set_feature_union_passthrough", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_true", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_false", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_false_errors", "sklearn/tests/test_pipeline.py::test_feature_union_passthrough_get_feature_names_out_false_errors_overlap_over_5", "sklearn/tests/test_pipeline.py::test_feature_union_set_output", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_estimators_get_feature_names_out_error[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-50	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex c31b9e539..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -1840,6 +1840,20 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating\n routing information.\n \"\"\"\n+ router = MetadataRouter(owner=self.__class__.__name__)\n+\n+ for name, transformer in self.transformer_list:\n+ router.add(\n+ **{name: transformer},\n+ method_mapping=MethodMapping()\n+ .add(caller=\"fit\", callee=\"fit\")\n+ .add(caller=\"fit_transform\", callee=\"fit_transform\")\n+ .add(caller=\"fit_transform\", callee=\"fit\")\n+ .add(caller=\"fit_transform\", callee=\"transform\")\n+ .add(caller=\"transform\", callee=\"transform\"),\n+ )\n+\n+ return router\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..c31b9e539 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -1840,20 +1840,6 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating\n routing information.\n \"\"\"\n- router = MetadataRouter(owner=self.__class__.__name__)\n-\n- for name, transformer in self.transformer_list:\n- router.add(\n- **{name: transformer},\n- method_mapping=MethodMapping()\n- .add(caller=\"fit\", callee=\"fit\")\n- .add(caller=\"fit_transform\", callee=\"fit_transform\")\n- .add(caller=\"fit_transform\", callee=\"fit\")\n- .add(caller=\"fit_transform\", callee=\"transform\")\n- .add(caller=\"transform\", callee=\"transform\"),\n- )\n-\n- return router\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/pipeline.py.\nHere is the description for the function:\n def get_metadata_routing(self):\n \"\"\"Get metadata routing of this object.\n\n Please check :ref:`User Guide <metadata_routing>` on how the routing\n mechanism works.\n\n .. versionadded:: 1.5\n\n Returns\n -------\n routing : MetadataRouter\n A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating\n routing information.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing_error", "sklearn/tests/test_pipeline.py::test_feature_union_get_metadata_routing_without_fit", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingTransformer]", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingNoFitTransformTransformer]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-51	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex e83860e29..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -1759,6 +1759,25 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n The `hstack` of results of transformers. `sum_n_components` is the\n sum of `n_components` (output dimension) over transformers.\n \"\"\"\n+ _raise_for_params(params, self, \"transform\")\n+\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"transform\", **params)\n+ else:\n+ # TODO(SLEP6): remove when metadata routing cannot be disabled.\n+ routed_params = Bunch()\n+ for name, _ in self.transformer_list:\n+ routed_params[name] = Bunch(transform={})\n+\n+ Xs = Parallel(n_jobs=self.n_jobs)(\n+ delayed(_transform_one)(trans, X, None, weight, params=routed_params[name])\n+ for name, trans, weight in self._iter()\n+ )\n+ if not Xs:\n+ # All transformers are None\n+ return np.zeros((X.shape[0], 0))\n+\n+ return self._hstack(Xs)\n \n def _hstack(self, Xs):\n adapter = _get_container_adapter(\"transform\", self)\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..e83860e29 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -1759,25 +1759,6 @@ class FeatureUnion(TransformerMixin, _BaseComposition):\n The `hstack` of results of transformers. `sum_n_components` is the\n sum of `n_components` (output dimension) over transformers.\n \"\"\"\n- _raise_for_params(params, self, \"transform\")\n-\n- if _routing_enabled():\n- routed_params = process_routing(self, \"transform\", **params)\n- else:\n- # TODO(SLEP6): remove when metadata routing cannot be disabled.\n- routed_params = Bunch()\n- for name, _ in self.transformer_list:\n- routed_params[name] = Bunch(transform={})\n-\n- Xs = Parallel(n_jobs=self.n_jobs)(\n- delayed(_transform_one)(trans, X, None, weight, params=routed_params[name])\n- for name, trans, weight in self._iter()\n- )\n- if not Xs:\n- # All transformers are None\n- return np.zeros((X.shape[0], 0))\n-\n- return self._hstack(Xs)\n \n def _hstack(self, Xs):\n adapter = _get_container_adapter(\"transform\", self)\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/pipeline.py.\nHere is the description for the function:\n def transform(self, X, params):\n \"\"\"Transform X separately by each transformer, concatenate results.\n\n Parameters\n ----------\n X : iterable or array-like, depending on transformers\n Input data to be transformed.\n\n params : dict, default=None\n\n Parameters routed to the `transform` method of the sub-transformers via the\n metadata routing API. See :ref:`Metadata Routing User Guide\n <metadata_routing>` for more details.\n\n .. versionadded:: 1.5\n\n Returns\n -------\n X_t : array-like or sparse matrix of shape (n_samples, sum_n_components)\n The `hstack` of results of transformers. `sum_n_components` is the\n sum of `n_components` (output dimension) over transformers.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_feature_union[csr_matrix]", "sklearn/tests/test_pipeline.py::test_feature_union[csr_array]", "sklearn/tests/test_pipeline.py::test_feature_union_weights", "sklearn/tests/test_pipeline.py::test_feature_union_parallel", "sklearn/tests/test_pipeline.py::test_set_feature_union_steps", "sklearn/tests/test_pipeline.py::test_set_feature_union_step_drop", "sklearn/tests/test_pipeline.py::test_set_feature_union_passthrough", "sklearn/tests/test_pipeline.py::test_feature_union_set_output", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing_error", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingTransformer]", "sklearn/tests/test_pipeline.py::test_feature_union_metadata_routing[ConsumingNoFitTransformTransformer]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform[FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-FeatureUnion(transformer_list=[('trans1',StandardScaler())])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-52	1.0	{ "code": "diff --git b/sklearn/model_selection/_classification_threshold.py a/sklearn/model_selection/_classification_threshold.py\nindex f554f899a..3505d89e1 100644\n--- b/sklearn/model_selection/_classification_threshold.py\n+++ a/sklearn/model_selection/_classification_threshold.py\n@@ -382,6 +382,23 @@ class FixedThresholdClassifier(BaseThresholdClassifier):\n class_labels : ndarray of shape (n_samples,)\n The predicted class.\n \"\"\"\n+ check_is_fitted(self, \"estimator_\")\n+ y_score, _, response_method_used = _get_response_values_binary(\n+ self.estimator_,\n+ X,\n+ self._get_response_method(),\n+ pos_label=self.pos_label,\n+ return_response_method_used=True,\n+ )\n+\n+ if self.threshold == \"auto\":\n+ decision_threshold = 0.5 if response_method_used == \"predict_proba\" else 0.0\n+ else:\n+ decision_threshold = self.threshold\n+\n+ return _threshold_scores_to_class_labels(\n+ y_score, decision_threshold, self.classes_, self.pos_label\n+ )\n \n def get_metadata_routing(self):\n \"\"\"Get metadata routing of this object.\n", "test": null }	null	{ "code": "diff --git a/sklearn/model_selection/_classification_threshold.py b/sklearn/model_selection/_classification_threshold.py\nindex 3505d89e1..f554f899a 100644\n--- a/sklearn/model_selection/_classification_threshold.py\n+++ b/sklearn/model_selection/_classification_threshold.py\n@@ -382,23 +382,6 @@ class FixedThresholdClassifier(BaseThresholdClassifier):\n class_labels : ndarray of shape (n_samples,)\n The predicted class.\n \"\"\"\n- check_is_fitted(self, \"estimator_\")\n- y_score, _, response_method_used = _get_response_values_binary(\n- self.estimator_,\n- X,\n- self._get_response_method(),\n- pos_label=self.pos_label,\n- return_response_method_used=True,\n- )\n-\n- if self.threshold == \"auto\":\n- decision_threshold = 0.5 if response_method_used == \"predict_proba\" else 0.0\n- else:\n- decision_threshold = self.threshold\n-\n- return _threshold_scores_to_class_labels(\n- y_score, decision_threshold, self.classes_, self.pos_label\n- )\n \n def get_metadata_routing(self):\n \"\"\"Get metadata routing of this object.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/model_selection/_classification_threshold.py.\nHere is the description for the function:\n def predict(self, X):\n \"\"\"Predict the target of new samples.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The samples, as accepted by `estimator.predict`.\n\n Returns\n -------\n class_labels : ndarray of shape (n_samples,)\n The predicted class.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[FixedThresholdClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/model_selection/_classification_threshold.py::sklearn.model_selection._classification_threshold.FixedThresholdClassifier", "sklearn/model_selection/tests/test_classification_threshold.py::test_fixed_threshold_classifier_equivalence_default[auto]", "sklearn/model_selection/tests/test_classification_threshold.py::test_fixed_threshold_classifier_equivalence_default[predict_proba]", "sklearn/model_selection/tests/test_classification_threshold.py::test_fixed_threshold_classifier_equivalence_default[decision_function]", "sklearn/model_selection/tests/test_classification_threshold.py::test_fixed_threshold_classifier[0-predict_proba-0.7]", "sklearn/model_selection/tests/test_classification_threshold.py::test_fixed_threshold_classifier[0-decision_function-2.0]", "sklearn/model_selection/tests/test_classification_threshold.py::test_fixed_threshold_classifier[1-predict_proba-0.7]", "sklearn/model_selection/tests/test_classification_threshold.py::test_fixed_threshold_classifier[1-decision_function-2.0]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FixedThresholdClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FixedThresholdClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-53	1.0	{ "code": "diff --git b/sklearn/preprocessing/_function_transformer.py a/sklearn/preprocessing/_function_transformer.py\nindex 6398823bb..02379273e 100644\n--- b/sklearn/preprocessing/_function_transformer.py\n+++ a/sklearn/preprocessing/_function_transformer.py\n@@ -256,6 +256,63 @@ class FunctionTransformer(TransformerMixin, BaseEstimator):\n X_out : array-like, shape (n_samples, n_features)\n Transformed input.\n \"\"\"\n+ X = self._check_input(X, reset=False)\n+ out = self._transform(X, func=self.func, kw_args=self.kw_args)\n+ output_config = _get_output_config(\"transform\", self)[\"dense\"]\n+\n+ if hasattr(out, \"columns\") and self.feature_names_out is not None:\n+ # check the consistency between the column provided by `transform` and\n+ # the the column names provided by `get_feature_names_out`.\n+ feature_names_out = self.get_feature_names_out()\n+ if list(out.columns) != list(feature_names_out):\n+ # we can override the column names of the output if it is inconsistent\n+ # with the column names provided by `get_feature_names_out` in the\n+ # following cases:\n+ # * `func` preserved the column names between the input and the output\n+ # * the input column names are all numbers\n+ # * the output is requested to be a DataFrame (pandas or polars)\n+ feature_names_in = getattr(\n+ X, \"feature_names_in_\", _get_feature_names(X)\n+ )\n+ same_feature_names_in_out = feature_names_in is not None and list(\n+ feature_names_in\n+ ) == list(out.columns)\n+ not_all_str_columns = not all(\n+ isinstance(col, str) for col in out.columns\n+ )\n+ if same_feature_names_in_out or not_all_str_columns:\n+ adapter = _get_adapter_from_container(out)\n+ out = adapter.create_container(\n+ X_output=out,\n+ X_original=out,\n+ columns=feature_names_out,\n+ inplace=False,\n+ )\n+ else:\n+ raise ValueError(\n+ \"The output generated by `func` have different column names \"\n+ \"than the ones provided by `get_feature_names_out`. \"\n+ f\"Got output with columns names: {list(out.columns)} and \"\n+ \"`get_feature_names_out` returned: \"\n+ f\"{list(self.get_feature_names_out())}. \"\n+ \"The column names can be overridden by setting \"\n+ \"`set_output(transform='pandas')` or \"\n+ \"`set_output(transform='polars')` such that the column names \"\n+ \"are set to the names provided by `get_feature_names_out`.\"\n+ )\n+\n+ if self.feature_names_out is None:\n+ warn_msg = (\n+ \"When `set_output` is configured to be '{0}', `func` should return \"\n+ \"a {0} DataFrame to follow the `set_output` API or `feature_names_out`\"\n+ \" should be defined.\"\n+ )\n+ if output_config == \"pandas\" and not _is_pandas_df(out):\n+ warnings.warn(warn_msg.format(\"pandas\"))\n+ elif output_config == \"polars\" and not _is_polars_df(out):\n+ warnings.warn(warn_msg.format(\"polars\"))\n+\n+ return out\n \n def inverse_transform(self, X):\n \"\"\"Transform X using the inverse function.\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_function_transformer.py b/sklearn/preprocessing/_function_transformer.py\nindex 02379273e..6398823bb 100644\n--- a/sklearn/preprocessing/_function_transformer.py\n+++ b/sklearn/preprocessing/_function_transformer.py\n@@ -256,63 +256,6 @@ class FunctionTransformer(TransformerMixin, BaseEstimator):\n X_out : array-like, shape (n_samples, n_features)\n Transformed input.\n \"\"\"\n- X = self._check_input(X, reset=False)\n- out = self._transform(X, func=self.func, kw_args=self.kw_args)\n- output_config = _get_output_config(\"transform\", self)[\"dense\"]\n-\n- if hasattr(out, \"columns\") and self.feature_names_out is not None:\n- # check the consistency between the column provided by `transform` and\n- # the the column names provided by `get_feature_names_out`.\n- feature_names_out = self.get_feature_names_out()\n- if list(out.columns) != list(feature_names_out):\n- # we can override the column names of the output if it is inconsistent\n- # with the column names provided by `get_feature_names_out` in the\n- # following cases:\n- # * `func` preserved the column names between the input and the output\n- # * the input column names are all numbers\n- # * the output is requested to be a DataFrame (pandas or polars)\n- feature_names_in = getattr(\n- X, \"feature_names_in_\", _get_feature_names(X)\n- )\n- same_feature_names_in_out = feature_names_in is not None and list(\n- feature_names_in\n- ) == list(out.columns)\n- not_all_str_columns = not all(\n- isinstance(col, str) for col in out.columns\n- )\n- if same_feature_names_in_out or not_all_str_columns:\n- adapter = _get_adapter_from_container(out)\n- out = adapter.create_container(\n- X_output=out,\n- X_original=out,\n- columns=feature_names_out,\n- inplace=False,\n- )\n- else:\n- raise ValueError(\n- \"The output generated by `func` have different column names \"\n- \"than the ones provided by `get_feature_names_out`. \"\n- f\"Got output with columns names: {list(out.columns)} and \"\n- \"`get_feature_names_out` returned: \"\n- f\"{list(self.get_feature_names_out())}. \"\n- \"The column names can be overridden by setting \"\n- \"`set_output(transform='pandas')` or \"\n- \"`set_output(transform='polars')` such that the column names \"\n- \"are set to the names provided by `get_feature_names_out`.\"\n- )\n-\n- if self.feature_names_out is None:\n- warn_msg = (\n- \"When `set_output` is configured to be '{0}', `func` should return \"\n- \"a {0} DataFrame to follow the `set_output` API or `feature_names_out`\"\n- \" should be defined.\"\n- )\n- if output_config == \"pandas\" and not _is_pandas_df(out):\n- warnings.warn(warn_msg.format(\"pandas\"))\n- elif output_config == \"polars\" and not _is_polars_df(out):\n- warnings.warn(warn_msg.format(\"polars\"))\n-\n- return out\n \n def inverse_transform(self, X):\n \"\"\"Transform X using the inverse function.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_function_transformer.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Transform X using the forward function.\n\n Parameters\n ----------\n X : {array-like, sparse-matrix} of shape (n_samples, n_features) \\\n if `validate=True` else any object that `func` can handle\n Input array.\n\n Returns\n -------\n X_out : array-like, shape (n_samples, n_features)\n Transformed input.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-list-pandas]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_empty_columns[False-list-numpy]", 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"sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_with_dataframe_and_check_inverse_True", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_get_feature_names_out_without_validation", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_feature_names_out_is_None", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_feature_names_out_uses_estimator", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_validate_inverse", "sklearn/preprocessing/tests/test_function_transformer.py::test_get_feature_names_out_dataframe_with_string_data[True-one-to-one-expected0]", "sklearn/preprocessing/tests/test_function_transformer.py::test_get_feature_names_out_dataframe_with_string_data[True-<lambda>-expected1]", "sklearn/preprocessing/tests/test_function_transformer.py::test_get_feature_names_out_dataframe_with_string_data[False-one-to-one-expected0]", "sklearn/preprocessing/tests/test_function_transformer.py::test_get_feature_names_out_dataframe_with_string_data[False-<lambda>-expected1]", "sklearn/preprocessing/tests/test_function_transformer.py::test_set_output_func", "sklearn/preprocessing/tests/test_function_transformer.py::test_consistence_column_name_between_steps", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_overwrite_column_names[pandas-pandas]", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_overwrite_column_names_numerical[one-to-one]", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_overwrite_column_names_numerical[<lambda>]", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_error_column_inconsistent[one-to-one-pandas]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_fit_score_takes_y]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFE-5-importance_getter0]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_estimators_dtypes]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFE-5-regressor_.coef_]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_pipeline_consistency]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFECV-4-importance_getter0]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFECV-4-regressor_.coef_]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_importance_getter_validation[RFE-auto-ValueError]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_importance_getter_validation[RFE-random-AttributeError]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_importance_getter_validation[RFE-<lambda>-AttributeError]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_importance_getter_validation[RFECV-auto-ValueError]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_transformer_general]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_importance_getter_validation[RFECV-random-AttributeError]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_importance_getter_validation[RFECV-<lambda>-AttributeError]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_transformer_general(readonly_memmap=True)]", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_error_column_inconsistent[<lambda>-pandas]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_transformers_unfitted_stateless]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_methods_subset_invariance]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_invertible", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_dict_unchanged]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions_multioutput", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_fit_idempotent]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_1d_transformer[X0-y0]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_1d_transformer[X1-y1]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_3d_target", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_multi_to_single", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_not_warns_with_global_output_set[pandas]", "sklearn/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[TransformedTargetRegressor]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_constraints.py::test_predictions[42-True]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_constraints.py::test_predictions[42-False]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_not_warns_with_global_output_set[polars]", "sklearn/utils/tests/test_parallel.py::test_dispatch_config_parallel[1]", "sklearn/compose/_target.py::sklearn.compose._target.TransformedTargetRegressor", "sklearn/preprocessing/_function_transformer.py::sklearn.preprocessing._function_transformer.FunctionTransformer", "sklearn/utils/tests/test_parallel.py::test_dispatch_config_parallel[2]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[TransformedTargetRegressor]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[TransformedTargetRegressor]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[TransformedTargetRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[TransformedTargetRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-54	1.0	{ "code": "diff --git b/sklearn/gaussian_process/_gpc.py a/sklearn/gaussian_process/_gpc.py\nindex 32a826352..90266625f 100644\n--- b/sklearn/gaussian_process/_gpc.py\n+++ a/sklearn/gaussian_process/_gpc.py\n@@ -678,6 +678,7 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n self.multi_class = multi_class\n self.n_jobs = n_jobs\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit Gaussian process classification model.\n \n@@ -694,6 +695,63 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n self : object\n Returns an instance of self.\n \"\"\"\n+ if isinstance(self.kernel, CompoundKernel):\n+ raise ValueError(\"kernel cannot be a CompoundKernel\")\n+\n+ if self.kernel is None or self.kernel.requires_vector_input:\n+ X, y = validate_data(\n+ self, X, y, multi_output=False, ensure_2d=True, dtype=\"numeric\"\n+ )\n+ else:\n+ X, y = validate_data(\n+ self, X, y, multi_output=False, ensure_2d=False, dtype=None\n+ )\n+\n+ self.base_estimator_ = _BinaryGaussianProcessClassifierLaplace(\n+ kernel=self.kernel,\n+ optimizer=self.optimizer,\n+ n_restarts_optimizer=self.n_restarts_optimizer,\n+ max_iter_predict=self.max_iter_predict,\n+ warm_start=self.warm_start,\n+ copy_X_train=self.copy_X_train,\n+ random_state=self.random_state,\n+ )\n+\n+ self.classes_ = np.unique(y)\n+ self.n_classes_ = self.classes_.size\n+ if self.n_classes_ == 1:\n+ raise ValueError(\n+ \"GaussianProcessClassifier requires 2 or more \"\n+ \"distinct classes; got %d class (only class %s \"\n+ \"is present)\" % (self.n_classes_, self.classes_[0])\n+ )\n+ if self.n_classes_ > 2:\n+ if self.multi_class == \"one_vs_rest\":\n+ self.base_estimator_ = OneVsRestClassifier(\n+ self.base_estimator_, n_jobs=self.n_jobs\n+ )\n+ elif self.multi_class == \"one_vs_one\":\n+ self.base_estimator_ = OneVsOneClassifier(\n+ self.base_estimator_, n_jobs=self.n_jobs\n+ )\n+ else:\n+ raise ValueError(\"Unknown multi-class mode %s\" % self.multi_class)\n+\n+ self.base_estimator_.fit(X, y)\n+\n+ if self.n_classes_ > 2:\n+ self.log_marginal_likelihood_value_ = np.mean(\n+ [\n+ estimator.log_marginal_likelihood()\n+ for estimator in self.base_estimator_.estimators_\n+ ]\n+ )\n+ else:\n+ self.log_marginal_likelihood_value_ = (\n+ self.base_estimator_.log_marginal_likelihood()\n+ )\n+\n+ return self\n \n def predict(self, X):\n \"\"\"Perform classification on an array of test vectors X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/gaussian_process/_gpc.py b/sklearn/gaussian_process/_gpc.py\nindex 90266625f..32a826352 100644\n--- a/sklearn/gaussian_process/_gpc.py\n+++ b/sklearn/gaussian_process/_gpc.py\n@@ -678,7 +678,6 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n self.multi_class = multi_class\n self.n_jobs = n_jobs\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit Gaussian process classification model.\n \n@@ -695,63 +694,6 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n self : object\n Returns an instance of self.\n \"\"\"\n- if isinstance(self.kernel, CompoundKernel):\n- raise ValueError(\"kernel cannot be a CompoundKernel\")\n-\n- if self.kernel is None or self.kernel.requires_vector_input:\n- X, y = validate_data(\n- self, X, y, multi_output=False, ensure_2d=True, dtype=\"numeric\"\n- )\n- else:\n- X, y = validate_data(\n- self, X, y, multi_output=False, ensure_2d=False, dtype=None\n- )\n-\n- self.base_estimator_ = _BinaryGaussianProcessClassifierLaplace(\n- kernel=self.kernel,\n- optimizer=self.optimizer,\n- n_restarts_optimizer=self.n_restarts_optimizer,\n- max_iter_predict=self.max_iter_predict,\n- warm_start=self.warm_start,\n- copy_X_train=self.copy_X_train,\n- random_state=self.random_state,\n- )\n-\n- self.classes_ = np.unique(y)\n- self.n_classes_ = self.classes_.size\n- if self.n_classes_ == 1:\n- raise ValueError(\n- \"GaussianProcessClassifier requires 2 or more \"\n- \"distinct classes; got %d class (only class %s \"\n- \"is present)\" % (self.n_classes_, self.classes_[0])\n- )\n- if self.n_classes_ > 2:\n- if self.multi_class == \"one_vs_rest\":\n- self.base_estimator_ = OneVsRestClassifier(\n- self.base_estimator_, n_jobs=self.n_jobs\n- )\n- elif self.multi_class == \"one_vs_one\":\n- self.base_estimator_ = OneVsOneClassifier(\n- self.base_estimator_, n_jobs=self.n_jobs\n- )\n- else:\n- raise ValueError(\"Unknown multi-class mode %s\" % self.multi_class)\n-\n- self.base_estimator_.fit(X, y)\n-\n- if self.n_classes_ > 2:\n- self.log_marginal_likelihood_value_ = np.mean(\n- [\n- estimator.log_marginal_likelihood()\n- for estimator in self.base_estimator_.estimators_\n- ]\n- )\n- else:\n- self.log_marginal_likelihood_value_ = (\n- self.base_estimator_.log_marginal_likelihood()\n- )\n-\n- return self\n \n def predict(self, X):\n \"\"\"Perform classification on an array of test vectors X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/gaussian_process/_gpc.py.\nHere is the description for the function:\n def fit(self, X, y):\n \"\"\"Fit Gaussian process classification model.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features) or list of object\n Feature vectors or other representations of training data.\n\n y : array-like of shape (n_samples,)\n Target values, must be binary.\n\n Returns\n -------\n self : object\n Returns an instance of self.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent_structured", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_improving[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_improving[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_improving[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_converged_to_local_maximum[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_converged_to_local_maximum[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_converged_to_local_maximum[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_random_starts[42]", "sklearn/gaussian_process/tests/test_gpc.py::test_custom_optimizer[42-kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_custom_optimizer[42-kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_custom_optimizer[42-kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_warning_bounds", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_empty_data_messages]", "sklearn/gaussian_process/tests/test_gpc.py::test_gpc_fit_error[params0-ValueError-kernel cannot be a CompoundKernel]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True)]", 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"sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_n_features_in]", "sklearn/gaussian_process/_gpc.py::sklearn.gaussian_process._gpc.GaussianProcessClassifier", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GaussianProcessClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GaussianProcessClassifier()]", "sklearn/tests/test_common.py::test_check_param_validation[GaussianProcessClassifier()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-55	1.0	{ "code": "diff --git b/sklearn/gaussian_process/_gpc.py a/sklearn/gaussian_process/_gpc.py\nindex 7875c1aa7..90266625f 100644\n--- b/sklearn/gaussian_process/_gpc.py\n+++ a/sklearn/gaussian_process/_gpc.py\n@@ -853,3 +853,49 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n hyperparameters at position theta.\n Only returned when `eval_gradient` is True.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ if theta is None:\n+ if eval_gradient:\n+ raise ValueError(\"Gradient can only be evaluated for theta!=None\")\n+ return self.log_marginal_likelihood_value_\n+\n+ theta = np.asarray(theta)\n+ if self.n_classes_ == 2:\n+ return self.base_estimator_.log_marginal_likelihood(\n+ theta, eval_gradient, clone_kernel=clone_kernel\n+ )\n+ else:\n+ if eval_gradient:\n+ raise NotImplementedError(\n+ \"Gradient of log-marginal-likelihood not implemented for \"\n+ \"multi-class GPC.\"\n+ )\n+ estimators = self.base_estimator_.estimators_\n+ n_dims = estimators[0].kernel_.n_dims\n+ if theta.shape[0] == n_dims: # use same theta for all sub-kernels\n+ return np.mean(\n+ [\n+ estimator.log_marginal_likelihood(\n+ theta, clone_kernel=clone_kernel\n+ )\n+ for i, estimator in enumerate(estimators)\n+ ]\n+ )\n+ elif theta.shape[0] == n_dims * self.classes_.shape[0]:\n+ # theta for compound kernel\n+ return np.mean(\n+ [\n+ estimator.log_marginal_likelihood(\n+ theta[n_dims * i : n_dims * (i + 1)],\n+ clone_kernel=clone_kernel,\n+ )\n+ for i, estimator in enumerate(estimators)\n+ ]\n+ )\n+ else:\n+ raise ValueError(\n+ \"Shape of theta must be either %d or %d. \"\n+ \"Obtained theta with shape %d.\"\n+ % (n_dims, n_dims * self.classes_.shape[0], theta.shape[0])\n+ )\n", "test": null }	null	{ "code": "diff --git a/sklearn/gaussian_process/_gpc.py b/sklearn/gaussian_process/_gpc.py\nindex 90266625f..7875c1aa7 100644\n--- a/sklearn/gaussian_process/_gpc.py\n+++ b/sklearn/gaussian_process/_gpc.py\n@@ -853,49 +853,3 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n hyperparameters at position theta.\n Only returned when `eval_gradient` is True.\n \"\"\"\n- check_is_fitted(self)\n-\n- if theta is None:\n- if eval_gradient:\n- raise ValueError(\"Gradient can only be evaluated for theta!=None\")\n- return self.log_marginal_likelihood_value_\n-\n- theta = np.asarray(theta)\n- if self.n_classes_ == 2:\n- return self.base_estimator_.log_marginal_likelihood(\n- theta, eval_gradient, clone_kernel=clone_kernel\n- )\n- else:\n- if eval_gradient:\n- raise NotImplementedError(\n- \"Gradient of log-marginal-likelihood not implemented for \"\n- \"multi-class GPC.\"\n- )\n- estimators = self.base_estimator_.estimators_\n- n_dims = estimators[0].kernel_.n_dims\n- if theta.shape[0] == n_dims: # use same theta for all sub-kernels\n- return np.mean(\n- [\n- estimator.log_marginal_likelihood(\n- theta, clone_kernel=clone_kernel\n- )\n- for i, estimator in enumerate(estimators)\n- ]\n- )\n- elif theta.shape[0] == n_dims * self.classes_.shape[0]:\n- # theta for compound kernel\n- return np.mean(\n- [\n- estimator.log_marginal_likelihood(\n- theta[n_dims * i : n_dims * (i + 1)],\n- clone_kernel=clone_kernel,\n- )\n- for i, estimator in enumerate(estimators)\n- ]\n- )\n- else:\n- raise ValueError(\n- \"Shape of theta must be either %d or %d. \"\n- \"Obtained theta with shape %d.\"\n- % (n_dims, n_dims * self.classes_.shape[0], theta.shape[0])\n- )\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/gaussian_process/_gpc.py.\nHere is the description for the function:\n def log_marginal_likelihood(\n self, theta=None, eval_gradient=False, clone_kernel=True\n ):\n \"\"\"Return log-marginal likelihood of theta for training data.\n\n In the case of multi-class classification, the mean log-marginal\n likelihood of the one-versus-rest classifiers are returned.\n\n Parameters\n ----------\n theta : array-like of shape (n_kernel_params,), default=None\n Kernel hyperparameters for which the log-marginal likelihood is\n evaluated. In the case of multi-class classification, theta may\n be the hyperparameters of the compound kernel or of an individual\n kernel. In the latter case, all individual kernel get assigned the\n same theta values. If None, the precomputed log_marginal_likelihood\n of ``self.kernel_.theta`` is returned.\n\n eval_gradient : bool, default=False\n If True, the gradient of the log-marginal likelihood with respect\n to the kernel hyperparameters at position theta is returned\n additionally. Note that gradient computation is not supported\n for non-binary classification. If True, theta must not be None.\n\n clone_kernel : bool, default=True\n If True, the kernel attribute is copied. If False, the kernel\n attribute is modified, but may result in a performance improvement.\n\n Returns\n -------\n log_likelihood : float\n Log-marginal likelihood of theta for training data.\n\n log_likelihood_gradient : ndarray of shape (n_kernel_params,), optional\n Gradient of the log-marginal likelihood with respect to the kernel\n hyperparameters at position theta.\n Only returned when `eval_gradient` is True.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/gaussian_process/tests/test_gpc.py::test_lml_improving[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_improving[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_improving[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_precomputed[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_without_cloning_kernel[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_converged_to_local_maximum[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_converged_to_local_maximum[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_converged_to_local_maximum[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_lml_gradient[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_random_starts[42]", "sklearn/gaussian_process/tests/test_gpc.py::test_custom_optimizer[42-kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_custom_optimizer[42-kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_custom_optimizer[42-kernel2]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-56	1.0	{ "code": "diff --git b/sklearn/gaussian_process/_gpc.py a/sklearn/gaussian_process/_gpc.py\nindex aa4fd013d..90266625f 100644\n--- b/sklearn/gaussian_process/_gpc.py\n+++ a/sklearn/gaussian_process/_gpc.py\n@@ -790,6 +790,20 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n the model. The columns correspond to the classes in sorted\n order, as they appear in the attribute :term:`classes_`.\n \"\"\"\n+ check_is_fitted(self)\n+ if self.n_classes_ > 2 and self.multi_class == \"one_vs_one\":\n+ raise ValueError(\n+ \"one_vs_one multi-class mode does not support \"\n+ \"predicting probability estimates. Use \"\n+ \"one_vs_rest mode instead.\"\n+ )\n+\n+ if self.kernel is None or self.kernel.requires_vector_input:\n+ X = validate_data(self, X, ensure_2d=True, dtype=\"numeric\", reset=False)\n+ else:\n+ X = validate_data(self, X, ensure_2d=False, dtype=None, reset=False)\n+\n+ return self.base_estimator_.predict_proba(X)\n \n @property\n def kernel_(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/gaussian_process/_gpc.py b/sklearn/gaussian_process/_gpc.py\nindex 90266625f..aa4fd013d 100644\n--- a/sklearn/gaussian_process/_gpc.py\n+++ b/sklearn/gaussian_process/_gpc.py\n@@ -790,20 +790,6 @@ class GaussianProcessClassifier(ClassifierMixin, BaseEstimator):\n the model. The columns correspond to the classes in sorted\n order, as they appear in the attribute :term:`classes_`.\n \"\"\"\n- check_is_fitted(self)\n- if self.n_classes_ > 2 and self.multi_class == \"one_vs_one\":\n- raise ValueError(\n- \"one_vs_one multi-class mode does not support \"\n- \"predicting probability estimates. Use \"\n- \"one_vs_rest mode instead.\"\n- )\n-\n- if self.kernel is None or self.kernel.requires_vector_input:\n- X = validate_data(self, X, ensure_2d=True, dtype=\"numeric\", reset=False)\n- else:\n- X = validate_data(self, X, ensure_2d=False, dtype=None, reset=False)\n-\n- return self.base_estimator_.predict_proba(X)\n \n @property\n def kernel_(self):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/gaussian_process/_gpc.py.\nHere is the description for the function:\n def predict_proba(self, X):\n \"\"\"Return probability estimates for the test vector X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features) or list of object\n Query points where the GP is evaluated for classification.\n\n Returns\n -------\n C : array-like of shape (n_samples, n_classes)\n Returns the probability of the samples for each class in\n the model. The columns correspond to the classes in sorted\n order, as they appear in the attribute :term:`classes_`.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_predict_consistent_structured", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel0]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel2]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel3]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel0]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True)]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel1]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel2]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel3]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit2d_predict1d]", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Matern", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.PairwiseKernel", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.RBF", "sklearn/gaussian_process/_gpc.py::sklearn.gaussian_process._gpc.GaussianProcessClassifier", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.RationalQuadratic", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GaussianProcessClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GaussianProcessClassifier()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-57	1.0	{ "code": "diff --git b/sklearn/gaussian_process/_gpr.py a/sklearn/gaussian_process/_gpr.py\nindex 844e8cf10..d30854eae 100644\n--- b/sklearn/gaussian_process/_gpr.py\n+++ a/sklearn/gaussian_process/_gpr.py\n@@ -218,6 +218,7 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self.n_targets = n_targets\n self.random_state = random_state\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit Gaussian process regression model.\n \n@@ -234,6 +235,131 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self : object\n GaussianProcessRegressor class instance.\n \"\"\"\n+ if self.kernel is None: # Use an RBF kernel as default\n+ self.kernel_ = C(1.0, constant_value_bounds=\"fixed\") * RBF(\n+ 1.0, length_scale_bounds=\"fixed\"\n+ )\n+ else:\n+ self.kernel_ = clone(self.kernel)\n+\n+ self._rng = check_random_state(self.random_state)\n+\n+ if self.kernel_.requires_vector_input:\n+ dtype, ensure_2d = \"numeric\", True\n+ else:\n+ dtype, ensure_2d = None, False\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ multi_output=True,\n+ y_numeric=True,\n+ ensure_2d=ensure_2d,\n+ dtype=dtype,\n+ )\n+\n+ n_targets_seen = y.shape[1] if y.ndim > 1 else 1\n+ if self.n_targets is not None and n_targets_seen != self.n_targets:\n+ raise ValueError(\n+ \"The number of targets seen in `y` is different from the parameter \"\n+ f\"`n_targets`. Got {n_targets_seen} != {self.n_targets}.\"\n+ )\n+\n+ # Normalize target value\n+ if self.normalize_y:\n+ self._y_train_mean = np.mean(y, axis=0)\n+ self._y_train_std = _handle_zeros_in_scale(np.std(y, axis=0), copy=False)\n+\n+ # Remove mean and make unit variance\n+ y = (y - self._y_train_mean) / self._y_train_std\n+\n+ else:\n+ shape_y_stats = (y.shape[1],) if y.ndim == 2 else 1\n+ self._y_train_mean = np.zeros(shape=shape_y_stats)\n+ self._y_train_std = np.ones(shape=shape_y_stats)\n+\n+ if np.iterable(self.alpha) and self.alpha.shape[0] != y.shape[0]:\n+ if self.alpha.shape[0] == 1:\n+ self.alpha = self.alpha[0]\n+ else:\n+ raise ValueError(\n+ \"alpha must be a scalar or an array with same number of \"\n+ f\"entries as y. ({self.alpha.shape[0]} != {y.shape[0]})\"\n+ )\n+\n+ self.X_train_ = np.copy(X) if self.copy_X_train else X\n+ self.y_train_ = np.copy(y) if self.copy_X_train else y\n+\n+ if self.optimizer is not None and self.kernel_.n_dims > 0:\n+ # Choose hyperparameters based on maximizing the log-marginal\n+ # likelihood (potentially starting from several initial values)\n+ def obj_func(theta, eval_gradient=True):\n+ if eval_gradient:\n+ lml, grad = self.log_marginal_likelihood(\n+ theta, eval_gradient=True, clone_kernel=False\n+ )\n+ return -lml, -grad\n+ else:\n+ return -self.log_marginal_likelihood(theta, clone_kernel=False)\n+\n+ # First optimize starting from theta specified in kernel\n+ optima = [\n+ (\n+ self._constrained_optimization(\n+ obj_func, self.kernel_.theta, self.kernel_.bounds\n+ )\n+ )\n+ ]\n+\n+ # Additional runs are performed from log-uniform chosen initial\n+ # theta\n+ if self.n_restarts_optimizer > 0:\n+ if not np.isfinite(self.kernel_.bounds).all():\n+ raise ValueError(\n+ \"Multiple optimizer restarts (n_restarts_optimizer>0) \"\n+ \"requires that all bounds are finite.\"\n+ )\n+ bounds = self.kernel_.bounds\n+ for iteration in range(self.n_restarts_optimizer):\n+ theta_initial = self._rng.uniform(bounds[:, 0], bounds[:, 1])\n+ optima.append(\n+ self._constrained_optimization(obj_func, theta_initial, bounds)\n+ )\n+ # Select result from run with minimal (negative) log-marginal\n+ # likelihood\n+ lml_values = list(map(itemgetter(1), optima))\n+ self.kernel_.theta = optima[np.argmin(lml_values)][0]\n+ self.kernel_._check_bounds_params()\n+\n+ self.log_marginal_likelihood_value_ = -np.min(lml_values)\n+ else:\n+ self.log_marginal_likelihood_value_ = self.log_marginal_likelihood(\n+ self.kernel_.theta, clone_kernel=False\n+ )\n+\n+ # Precompute quantities required for predictions which are independent\n+ # of actual query points\n+ # Alg. 2.1, page 19, line 2 -> L = cholesky(K + sigma^2 I)\n+ K = self.kernel_(self.X_train_)\n+ K[np.diag_indices_from(K)] += self.alpha\n+ try:\n+ self.L_ = cholesky(K, lower=GPR_CHOLESKY_LOWER, check_finite=False)\n+ except np.linalg.LinAlgError as exc:\n+ exc.args = (\n+ (\n+ f\"The kernel, {self.kernel_}, is not returning a positive \"\n+ \"definite matrix. Try gradually increasing the 'alpha' \"\n+ \"parameter of your GaussianProcessRegressor estimator.\"\n+ ),\n+ ) + exc.args\n+ raise\n+ # Alg 2.1, page 19, line 3 -> alpha = L^T \\ (L \\ y)\n+ self.alpha_ = cho_solve(\n+ (self.L_, GPR_CHOLESKY_LOWER),\n+ self.y_train_,\n+ check_finite=False,\n+ )\n+ return self\n \n def predict(self, X, return_std=False, return_cov=False):\n \"\"\"Predict using the Gaussian process regression model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py\nindex d30854eae..844e8cf10 100644\n--- a/sklearn/gaussian_process/_gpr.py\n+++ b/sklearn/gaussian_process/_gpr.py\n@@ -218,7 +218,6 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self.n_targets = n_targets\n self.random_state = random_state\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit Gaussian process regression model.\n \n@@ -235,131 +234,6 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n self : object\n GaussianProcessRegressor class instance.\n \"\"\"\n- if self.kernel is None: # Use an RBF kernel as default\n- self.kernel_ = C(1.0, constant_value_bounds=\"fixed\") * RBF(\n- 1.0, length_scale_bounds=\"fixed\"\n- )\n- else:\n- self.kernel_ = clone(self.kernel)\n-\n- self._rng = check_random_state(self.random_state)\n-\n- if self.kernel_.requires_vector_input:\n- dtype, ensure_2d = \"numeric\", True\n- else:\n- dtype, ensure_2d = None, False\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- multi_output=True,\n- y_numeric=True,\n- ensure_2d=ensure_2d,\n- dtype=dtype,\n- )\n-\n- n_targets_seen = y.shape[1] if y.ndim > 1 else 1\n- if self.n_targets is not None and n_targets_seen != self.n_targets:\n- raise ValueError(\n- \"The number of targets seen in `y` is different from the parameter \"\n- f\"`n_targets`. Got {n_targets_seen} != {self.n_targets}.\"\n- )\n-\n- # Normalize target value\n- if self.normalize_y:\n- self._y_train_mean = np.mean(y, axis=0)\n- self._y_train_std = _handle_zeros_in_scale(np.std(y, axis=0), copy=False)\n-\n- # Remove mean and make unit variance\n- y = (y - self._y_train_mean) / self._y_train_std\n-\n- else:\n- shape_y_stats = (y.shape[1],) if y.ndim == 2 else 1\n- self._y_train_mean = np.zeros(shape=shape_y_stats)\n- self._y_train_std = np.ones(shape=shape_y_stats)\n-\n- if np.iterable(self.alpha) and self.alpha.shape[0] != y.shape[0]:\n- if self.alpha.shape[0] == 1:\n- self.alpha = self.alpha[0]\n- else:\n- raise ValueError(\n- \"alpha must be a scalar or an array with same number of \"\n- f\"entries as y. ({self.alpha.shape[0]} != {y.shape[0]})\"\n- )\n-\n- self.X_train_ = np.copy(X) if self.copy_X_train else X\n- self.y_train_ = np.copy(y) if self.copy_X_train else y\n-\n- if self.optimizer is not None and self.kernel_.n_dims > 0:\n- # Choose hyperparameters based on maximizing the log-marginal\n- # likelihood (potentially starting from several initial values)\n- def obj_func(theta, eval_gradient=True):\n- if eval_gradient:\n- lml, grad = self.log_marginal_likelihood(\n- theta, eval_gradient=True, clone_kernel=False\n- )\n- return -lml, -grad\n- else:\n- return -self.log_marginal_likelihood(theta, clone_kernel=False)\n-\n- # First optimize starting from theta specified in kernel\n- optima = [\n- (\n- self._constrained_optimization(\n- obj_func, self.kernel_.theta, self.kernel_.bounds\n- )\n- )\n- ]\n-\n- # Additional runs are performed from log-uniform chosen initial\n- # theta\n- if self.n_restarts_optimizer > 0:\n- if not np.isfinite(self.kernel_.bounds).all():\n- raise ValueError(\n- \"Multiple optimizer restarts (n_restarts_optimizer>0) \"\n- \"requires that all bounds are finite.\"\n- )\n- bounds = self.kernel_.bounds\n- for iteration in range(self.n_restarts_optimizer):\n- theta_initial = self._rng.uniform(bounds[:, 0], bounds[:, 1])\n- optima.append(\n- self._constrained_optimization(obj_func, theta_initial, bounds)\n- )\n- # Select result from run with minimal (negative) log-marginal\n- # likelihood\n- lml_values = list(map(itemgetter(1), optima))\n- self.kernel_.theta = optima[np.argmin(lml_values)][0]\n- self.kernel_._check_bounds_params()\n-\n- self.log_marginal_likelihood_value_ = -np.min(lml_values)\n- else:\n- self.log_marginal_likelihood_value_ = self.log_marginal_likelihood(\n- self.kernel_.theta, clone_kernel=False\n- )\n-\n- # Precompute quantities required for predictions which are independent\n- # of actual query points\n- # Alg. 2.1, page 19, line 2 -> L = cholesky(K + sigma^2 I)\n- K = self.kernel_(self.X_train_)\n- K[np.diag_indices_from(K)] += self.alpha\n- try:\n- self.L_ = cholesky(K, lower=GPR_CHOLESKY_LOWER, check_finite=False)\n- except np.linalg.LinAlgError as exc:\n- exc.args = (\n- (\n- f\"The kernel, {self.kernel_}, is not returning a positive \"\n- \"definite matrix. Try gradually increasing the 'alpha' \"\n- \"parameter of your GaussianProcessRegressor estimator.\"\n- ),\n- ) + exc.args\n- raise\n- # Alg 2.1, page 19, line 3 -> alpha = L^T \\ (L \\ y)\n- self.alpha_ = cho_solve(\n- (self.L_, GPR_CHOLESKY_LOWER),\n- self.y_train_,\n- check_finite=False,\n- )\n- return self\n \n def predict(self, X, return_std=False, return_cov=False):\n \"\"\"Predict using the Gaussian process regression model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/gaussian_process/_gpr.py.\nHere is the description for the function:\n def fit(self, X, y):\n \"\"\"Fit Gaussian process regression model.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features) or list of object\n Feature vectors or other representations of training data.\n\n y : array-like of shape (n_samples,) or (n_samples, n_targets)\n Target values.\n\n Returns\n -------\n self : object\n GaussianProcessRegressor class instance.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation_structured", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_improving[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_improving[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_improving[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_improving[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_improving[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_precomputed[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_precomputed[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_precomputed[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_precomputed[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_precomputed[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_precomputed[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_without_cloning_kernel[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_without_cloning_kernel[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_without_cloning_kernel[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_without_cloning_kernel[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_without_cloning_kernel[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_without_cloning_kernel[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_converged_to_local_maximum[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_converged_to_local_maximum[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_converged_to_local_maximum[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_converged_to_local_maximum[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_converged_to_local_maximum[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_solution_inside_bounds[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_solution_inside_bounds[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_solution_inside_bounds[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_solution_inside_bounds[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_solution_inside_bounds[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_gradient[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_gradient[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_gradient[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_gradient[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_gradient[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_lml_gradient[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_no_optimizer", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target1-kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target1-kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target1-kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target1-kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target1-kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target1-kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_anisotropic_kernel", "sklearn/gaussian_process/tests/test_gpr.py::test_random_starts", "sklearn/gaussian_process/tests/test_gpr.py::test_y_normalization[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_y_normalization[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_y_normalization[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_y_normalization[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_y_normalization[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_y_normalization[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_large_variance_y", "sklearn/gaussian_process/tests/test_gpr.py::test_y_multioutput", "sklearn/gaussian_process/tests/test_gpr.py::test_custom_optimizer[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_custom_optimizer[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_custom_optimizer[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_custom_optimizer[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_custom_optimizer[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_correct_error_message", "sklearn/gaussian_process/tests/test_gpr.py::test_duplicate_input[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_duplicate_input[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_duplicate_input[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_duplicate_input[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_duplicate_input[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_duplicate_input[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_warning_bounds", "sklearn/gaussian_process/tests/test_gpr.py::test_bound_check_fixed_hyperparameter", "sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_consistency_std_cov_non_invertible_kernel", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_fit_error[params0-ValueError-alpha must be a scalar or an array with same number of entries as y]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_fit_error[params1-ValueError-requires that all bounds are finite]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_lml_error", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_predict_error", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shapes[None-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shapes[None-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shapes[1-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shapes[1-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shapes[10-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shapes[10-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[None-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[None-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[1-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[1-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[10-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[10-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-None]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-3]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-None]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-3]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[None]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[1]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[2]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[3]", "sklearn/gaussian_process/tests/test_gpr.py::test_n_targets_error", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_predict_input_not_modified", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_pickle]", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.ConstantKernel", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.DotProduct", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.ExpSineSquared", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Exponentiation", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Product", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_supervised_y_no_nan]", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Sum", "sklearn/gaussian_process/_gpr.py::sklearn.gaussian_process._gpr.GaussianProcessRegressor", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.WhiteKernel", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GaussianProcessRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GaussianProcessRegressor()]", "sklearn/tests/test_common.py::test_check_param_validation[GaussianProcessRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-58	1.0	{ "code": "diff --git b/sklearn/gaussian_process/_gpr.py a/sklearn/gaussian_process/_gpr.py\nindex cf68e678a..d30854eae 100644\n--- b/sklearn/gaussian_process/_gpr.py\n+++ a/sklearn/gaussian_process/_gpr.py\n@@ -562,6 +562,91 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n hyperparameters at position theta.\n Only returned when eval_gradient is True.\n \"\"\"\n+ if theta is None:\n+ if eval_gradient:\n+ raise ValueError(\"Gradient can only be evaluated for theta!=None\")\n+ return self.log_marginal_likelihood_value_\n+\n+ if clone_kernel:\n+ kernel = self.kernel_.clone_with_theta(theta)\n+ else:\n+ kernel = self.kernel_\n+ kernel.theta = theta\n+\n+ if eval_gradient:\n+ K, K_gradient = kernel(self.X_train_, eval_gradient=True)\n+ else:\n+ K = kernel(self.X_train_)\n+\n+ # Alg. 2.1, page 19, line 2 -> L = cholesky(K + sigma^2 I)\n+ K[np.diag_indices_from(K)] += self.alpha\n+ try:\n+ L = cholesky(K, lower=GPR_CHOLESKY_LOWER, check_finite=False)\n+ except np.linalg.LinAlgError:\n+ return (-np.inf, np.zeros_like(theta)) if eval_gradient else -np.inf\n+\n+ # Support multi-dimensional output of self.y_train_\n+ y_train = self.y_train_\n+ if y_train.ndim == 1:\n+ y_train = y_train[:, np.newaxis]\n+\n+ # Alg 2.1, page 19, line 3 -> alpha = L^T \\ (L \\ y)\n+ alpha = cho_solve((L, GPR_CHOLESKY_LOWER), y_train, check_finite=False)\n+\n+ # Alg 2.1, page 19, line 7\n+ # -0.5 . y^T . alpha - sum(log(diag(L))) - n_samples / 2 log(2pi)\n+ # y is originally thought to be a (1, n_samples) row vector. However,\n+ # in multioutputs, y is of shape (n_samples, 2) and we need to compute\n+ # y^T . alpha for each output, independently using einsum. Thus, it\n+ # is equivalent to:\n+ # for output_idx in range(n_outputs):\n+ # log_likelihood_dims[output_idx] = (\n+ # y_train[:, [output_idx]] @ alpha[:, [output_idx]]\n+ # )\n+ log_likelihood_dims = -0.5 np.einsum(\"ik,ik->k\", y_train, alpha)\n+ log_likelihood_dims -= np.log(np.diag(L)).sum()\n+ log_likelihood_dims -= K.shape[0] / 2 * np.log(2 * np.pi)\n+ # the log likehood is sum-up across the outputs\n+ log_likelihood = log_likelihood_dims.sum(axis=-1)\n+\n+ if eval_gradient:\n+ # Eq. 5.9, p. 114, and footnote 5 in p. 114\n+ # 0.5 * trace((alpha . alpha^T - K^-1) . K_gradient)\n+ # alpha is supposed to be a vector of (n_samples,) elements. With\n+ # multioutputs, alpha is a matrix of size (n_samples, n_outputs).\n+ # Therefore, we want to construct a matrix of\n+ # (n_samples, n_samples, n_outputs) equivalent to\n+ # for output_idx in range(n_outputs):\n+ # output_alpha = alpha[:, [output_idx]]\n+ # inner_term[..., output_idx] = output_alpha @ output_alpha.T\n+ inner_term = np.einsum(\"ik,jk->ijk\", alpha, alpha)\n+ # compute K^-1 of shape (n_samples, n_samples)\n+ K_inv = cho_solve(\n+ (L, GPR_CHOLESKY_LOWER), np.eye(K.shape[0]), check_finite=False\n+ )\n+ # create a new axis to use broadcasting between inner_term and\n+ # K_inv\n+ inner_term -= K_inv[..., np.newaxis]\n+ # Since we are interested about the trace of\n+ # inner_term @ K_gradient, we don't explicitly compute the\n+ # matrix-by-matrix operation and instead use an einsum. Therefore\n+ # it is equivalent to:\n+ # for param_idx in range(n_kernel_params):\n+ # for output_idx in range(n_output):\n+ # log_likehood_gradient_dims[param_idx, output_idx] = (\n+ # inner_term[..., output_idx] @\n+ # K_gradient[..., param_idx]\n+ # )\n+ log_likelihood_gradient_dims = 0.5 * np.einsum(\n+ \"ijl,jik->kl\", inner_term, K_gradient\n+ )\n+ # the log likehood gradient is the sum-up across the outputs\n+ log_likelihood_gradient = log_likelihood_gradient_dims.sum(axis=-1)\n+\n+ if eval_gradient:\n+ return log_likelihood, log_likelihood_gradient\n+ else:\n+ return log_likelihood\n \n def _constrained_optimization(self, obj_func, initial_theta, bounds):\n if self.optimizer == \"fmin_l_bfgs_b\":\n", "test": null }	null	{ "code": "diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py\nindex d30854eae..cf68e678a 100644\n--- a/sklearn/gaussian_process/_gpr.py\n+++ b/sklearn/gaussian_process/_gpr.py\n@@ -562,91 +562,6 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n hyperparameters at position theta.\n Only returned when eval_gradient is True.\n \"\"\"\n- if theta is None:\n- if eval_gradient:\n- raise ValueError(\"Gradient can only be evaluated for theta!=None\")\n- return self.log_marginal_likelihood_value_\n-\n- if clone_kernel:\n- kernel = self.kernel_.clone_with_theta(theta)\n- else:\n- kernel = self.kernel_\n- kernel.theta = theta\n-\n- if eval_gradient:\n- K, K_gradient = kernel(self.X_train_, eval_gradient=True)\n- else:\n- K = kernel(self.X_train_)\n-\n- # Alg. 2.1, page 19, line 2 -> L = cholesky(K + sigma^2 I)\n- K[np.diag_indices_from(K)] += self.alpha\n- try:\n- L = cholesky(K, lower=GPR_CHOLESKY_LOWER, check_finite=False)\n- except np.linalg.LinAlgError:\n- return (-np.inf, np.zeros_like(theta)) if eval_gradient else -np.inf\n-\n- # Support multi-dimensional output of self.y_train_\n- y_train = self.y_train_\n- if y_train.ndim == 1:\n- y_train = y_train[:, np.newaxis]\n-\n- # Alg 2.1, page 19, line 3 -> alpha = L^T \\ (L \\ y)\n- alpha = cho_solve((L, GPR_CHOLESKY_LOWER), y_train, check_finite=False)\n-\n- # Alg 2.1, page 19, line 7\n- # -0.5 . y^T . alpha - sum(log(diag(L))) - n_samples / 2 log(2pi)\n- # y is originally thought to be a (1, n_samples) row vector. However,\n- # in multioutputs, y is of shape (n_samples, 2) and we need to compute\n- # y^T . alpha for each output, independently using einsum. Thus, it\n- # is equivalent to:\n- # for output_idx in range(n_outputs):\n- # log_likelihood_dims[output_idx] = (\n- # y_train[:, [output_idx]] @ alpha[:, [output_idx]]\n- # )\n- log_likelihood_dims = -0.5 np.einsum(\"ik,ik->k\", y_train, alpha)\n- log_likelihood_dims -= np.log(np.diag(L)).sum()\n- log_likelihood_dims -= K.shape[0] / 2 * np.log(2 * np.pi)\n- # the log likehood is sum-up across the outputs\n- log_likelihood = log_likelihood_dims.sum(axis=-1)\n-\n- if eval_gradient:\n- # Eq. 5.9, p. 114, and footnote 5 in p. 114\n- # 0.5 * trace((alpha . alpha^T - K^-1) . K_gradient)\n- # alpha is supposed to be a vector of (n_samples,) elements. With\n- # multioutputs, alpha is a matrix of size (n_samples, n_outputs).\n- # Therefore, we want to construct a matrix of\n- # (n_samples, n_samples, n_outputs) equivalent to\n- # for output_idx in range(n_outputs):\n- # output_alpha = alpha[:, [output_idx]]\n- # inner_term[..., output_idx] = output_alpha @ output_alpha.T\n- inner_term = np.einsum(\"ik,jk->ijk\", alpha, alpha)\n- # compute K^-1 of shape (n_samples, n_samples)\n- K_inv = cho_solve(\n- (L, GPR_CHOLESKY_LOWER), np.eye(K.shape[0]), check_finite=False\n- )\n- # create a new axis to use broadcasting between inner_term and\n- # K_inv\n- inner_term -= K_inv[..., np.newaxis]\n- # Since we are interested about the trace of\n- # inner_term @ K_gradient, we don't explicitly compute the\n- # matrix-by-matrix operation and instead use an einsum. Therefore\n- # it is equivalent to:\n- # for param_idx in range(n_kernel_params):\n- # for output_idx in range(n_output):\n- # log_likehood_gradient_dims[param_idx, output_idx] = (\n- # inner_term[..., output_idx] @\n- # K_gradient[..., param_idx]\n- # )\n- log_likelihood_gradient_dims = 0.5 * np.einsum(\n- \"ijl,jik->kl\", inner_term, K_gradient\n- )\n- # the log likehood gradient is the sum-up across the outputs\n- log_likelihood_gradient = log_likelihood_gradient_dims.sum(axis=-1)\n-\n- if eval_gradient:\n- return log_likelihood, log_likelihood_gradient\n- else:\n- return log_likelihood\n \n def _constrained_optimization(self, obj_func, initial_theta, bounds):\n if self.optimizer == \"fmin_l_bfgs_b\":\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/gaussian_process/_gpr.py.\nHere is the description for the function:\n def log_marginal_likelihood(\n self, theta=None, eval_gradient=False, clone_kernel=True\n ):\n \"\"\"Return log-marginal likelihood of theta for training data.\n\n Parameters\n ----------\n theta : array-like of shape (n_kernel_params,) default=None\n Kernel hyperparameters for which the log-marginal likelihood is\n evaluated. If None, the precomputed log_marginal_likelihood\n of ``self.kernel_.theta`` is returned.\n\n eval_gradient : bool, default=False\n If True, the gradient of the log-marginal likelihood with respect\n to the kernel hyperparameters at position theta is returned\n additionally. If True, theta must not be None.\n\n clone_kernel : bool, default=True\n If True, the kernel attribute is copied. If False, the kernel\n attribute is modified, but may result in a performance improvement.\n\n Returns\n -------\n log_likelihood : float\n Log-marginal likelihood of theta for training data.\n\n log_likelihood_gradient : ndarray of shape (n_kernel_params,), optional\n Gradient of the log-marginal likelihood with respect to the kernel\n hyperparameters at position theta.\n Only returned when eval_gradient is True.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel5]", 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"sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[10-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-None]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-3]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-None]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-3]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[None]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[1]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[2]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_shape_with_prior[3]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_predict_input_not_modified", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GaussianProcessRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GaussianProcessRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-59	1.0	{ "code": "diff --git b/sklearn/gaussian_process/_gpr.py a/sklearn/gaussian_process/_gpr.py\nindex bd088d4d8..d30854eae 100644\n--- b/sklearn/gaussian_process/_gpr.py\n+++ a/sklearn/gaussian_process/_gpr.py\n@@ -396,6 +396,101 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n Covariance of joint predictive distribution at query points.\n Only returned when `return_cov` is True.\n \"\"\"\n+ if return_std and return_cov:\n+ raise RuntimeError(\n+ \"At most one of return_std or return_cov can be requested.\"\n+ )\n+\n+ if self.kernel is None or self.kernel.requires_vector_input:\n+ dtype, ensure_2d = \"numeric\", True\n+ else:\n+ dtype, ensure_2d = None, False\n+\n+ X = validate_data(self, X, ensure_2d=ensure_2d, dtype=dtype, reset=False)\n+\n+ if not hasattr(self, \"X_train_\"): # Unfitted;predict based on GP prior\n+ if self.kernel is None:\n+ kernel = C(1.0, constant_value_bounds=\"fixed\") * RBF(\n+ 1.0, length_scale_bounds=\"fixed\"\n+ )\n+ else:\n+ kernel = self.kernel\n+\n+ n_targets = self.n_targets if self.n_targets is not None else 1\n+ y_mean = np.zeros(shape=(X.shape[0], n_targets)).squeeze()\n+\n+ if return_cov:\n+ y_cov = kernel(X)\n+ if n_targets > 1:\n+ y_cov = np.repeat(\n+ np.expand_dims(y_cov, -1), repeats=n_targets, axis=-1\n+ )\n+ return y_mean, y_cov\n+ elif return_std:\n+ y_var = kernel.diag(X)\n+ if n_targets > 1:\n+ y_var = np.repeat(\n+ np.expand_dims(y_var, -1), repeats=n_targets, axis=-1\n+ )\n+ return y_mean, np.sqrt(y_var)\n+ else:\n+ return y_mean\n+ else: # Predict based on GP posterior\n+ # Alg 2.1, page 19, line 4 -> f_bar = K(X_test, X_train) . alpha\n+ K_trans = self.kernel_(X, self.X_train_)\n+ y_mean = K_trans @ self.alpha_\n+\n+ # undo normalisation\n+ y_mean = self._y_train_std y_mean + self._y_train_mean\n+\n+ # if y_mean has shape (n_samples, 1), reshape to (n_samples,)\n+ if y_mean.ndim > 1 and y_mean.shape[1] == 1:\n+ y_mean = np.squeeze(y_mean, axis=1)\n+\n+ # Alg 2.1, page 19, line 5 -> v = L \\ K(X_test, X_train)^T\n+ V = solve_triangular(\n+ self.L_, K_trans.T, lower=GPR_CHOLESKY_LOWER, check_finite=False\n+ )\n+\n+ if return_cov:\n+ # Alg 2.1, page 19, line 6 -> K(X_test, X_test) - v^T. v\n+ y_cov = self.kernel_(X) - V.T @ V\n+\n+ # undo normalisation\n+ y_cov = np.outer(y_cov, self._y_train_std*2).reshape(y_cov.shape, -1)\n+ # if y_cov has shape (n_samples, n_samples, 1), reshape to\n+ # (n_samples, n_samples)\n+ if y_cov.shape[2] == 1:\n+ y_cov = np.squeeze(y_cov, axis=2)\n+\n+ return y_mean, y_cov\n+ elif return_std:\n+ # Compute variance of predictive distribution\n+ # Use einsum to avoid explicitly forming the large matrix\n+ # V^T @ V just to extract its diagonal afterward.\n+ y_var = self.kernel_.diag(X).copy()\n+ y_var -= np.einsum(\"ij,ji->i\", V.T, V)\n+\n+ # Check if any of the variances is negative because of\n+ # numerical issues. If yes: set the variance to 0.\n+ y_var_negative = y_var < 0\n+ if np.any(y_var_negative):\n+ warnings.warn(\n+ \"Predicted variances smaller than 0. \"\n+ \"Setting those variances to 0.\"\n+ )\n+ y_var[y_var_negative] = 0.0\n+\n+ # undo normalisation\n+ y_var = np.outer(y_var, self._y_train_std*2).reshape(y_var.shape, -1)\n+\n+ # if y_var has shape (n_samples, 1), reshape to (n_samples,)\n+ if y_var.shape[1] == 1:\n+ y_var = np.squeeze(y_var, axis=1)\n+\n+ return y_mean, np.sqrt(y_var)\n+ else:\n+ return y_mean\n \n def sample_y(self, X, n_samples=1, random_state=0):\n \"\"\"Draw samples from Gaussian process and evaluate at X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py\nindex d30854eae..bd088d4d8 100644\n--- a/sklearn/gaussian_process/_gpr.py\n+++ b/sklearn/gaussian_process/_gpr.py\n@@ -396,101 +396,6 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n Covariance of joint predictive distribution at query points.\n Only returned when `return_cov` is True.\n \"\"\"\n- if return_std and return_cov:\n- raise RuntimeError(\n- \"At most one of return_std or return_cov can be requested.\"\n- )\n-\n- if self.kernel is None or self.kernel.requires_vector_input:\n- dtype, ensure_2d = \"numeric\", True\n- else:\n- dtype, ensure_2d = None, False\n-\n- X = validate_data(self, X, ensure_2d=ensure_2d, dtype=dtype, reset=False)\n-\n- if not hasattr(self, \"X_train_\"): # Unfitted;predict based on GP prior\n- if self.kernel is None:\n- kernel = C(1.0, constant_value_bounds=\"fixed\") * RBF(\n- 1.0, length_scale_bounds=\"fixed\"\n- )\n- else:\n- kernel = self.kernel\n-\n- n_targets = self.n_targets if self.n_targets is not None else 1\n- y_mean = np.zeros(shape=(X.shape[0], n_targets)).squeeze()\n-\n- if return_cov:\n- y_cov = kernel(X)\n- if n_targets > 1:\n- y_cov = np.repeat(\n- np.expand_dims(y_cov, -1), repeats=n_targets, axis=-1\n- )\n- return y_mean, y_cov\n- elif return_std:\n- y_var = kernel.diag(X)\n- if n_targets > 1:\n- y_var = np.repeat(\n- np.expand_dims(y_var, -1), repeats=n_targets, axis=-1\n- )\n- return y_mean, np.sqrt(y_var)\n- else:\n- return y_mean\n- else: # Predict based on GP posterior\n- # Alg 2.1, page 19, line 4 -> f_bar = K(X_test, X_train) . alpha\n- K_trans = self.kernel_(X, self.X_train_)\n- y_mean = K_trans @ self.alpha_\n-\n- # undo normalisation\n- y_mean = self._y_train_std y_mean + self._y_train_mean\n-\n- # if y_mean has shape (n_samples, 1), reshape to (n_samples,)\n- if y_mean.ndim > 1 and y_mean.shape[1] == 1:\n- y_mean = np.squeeze(y_mean, axis=1)\n-\n- # Alg 2.1, page 19, line 5 -> v = L \\ K(X_test, X_train)^T\n- V = solve_triangular(\n- self.L_, K_trans.T, lower=GPR_CHOLESKY_LOWER, check_finite=False\n- )\n-\n- if return_cov:\n- # Alg 2.1, page 19, line 6 -> K(X_test, X_test) - v^T. v\n- y_cov = self.kernel_(X) - V.T @ V\n-\n- # undo normalisation\n- y_cov = np.outer(y_cov, self._y_train_std*2).reshape(y_cov.shape, -1)\n- # if y_cov has shape (n_samples, n_samples, 1), reshape to\n- # (n_samples, n_samples)\n- if y_cov.shape[2] == 1:\n- y_cov = np.squeeze(y_cov, axis=2)\n-\n- return y_mean, y_cov\n- elif return_std:\n- # Compute variance of predictive distribution\n- # Use einsum to avoid explicitly forming the large matrix\n- # V^T @ V just to extract its diagonal afterward.\n- y_var = self.kernel_.diag(X).copy()\n- y_var -= np.einsum(\"ij,ji->i\", V.T, V)\n-\n- # Check if any of the variances is negative because of\n- # numerical issues. If yes: set the variance to 0.\n- y_var_negative = y_var < 0\n- if np.any(y_var_negative):\n- warnings.warn(\n- \"Predicted variances smaller than 0. \"\n- \"Setting those variances to 0.\"\n- )\n- y_var[y_var_negative] = 0.0\n-\n- # undo normalisation\n- y_var = np.outer(y_var, self._y_train_std*2).reshape(y_var.shape, -1)\n-\n- # if y_var has shape (n_samples, 1), reshape to (n_samples,)\n- if y_var.shape[1] == 1:\n- y_var = np.squeeze(y_var, axis=1)\n-\n- return y_mean, np.sqrt(y_var)\n- else:\n- return y_mean\n \n def sample_y(self, X, n_samples=1, random_state=0):\n \"\"\"Draw samples from Gaussian process and evaluate at X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/gaussian_process/_gpr.py.\nHere is the description for the function:\n def predict(self, X, return_std=False, return_cov=False):\n \"\"\"Predict using the Gaussian process regression model.\n\n We can also predict based on an unfitted model by using the GP prior.\n In addition to the mean of the predictive distribution, optionally also\n returns its standard deviation (`return_std=True`) or covariance\n (`return_cov=True`). Note that at most one of the two can be requested.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features) or list of object\n Query points where the GP is evaluated.\n\n return_std : bool, default=False\n If True, the standard-deviation of the predictive distribution at\n the query points is returned along with the mean.\n\n return_cov : bool, default=False\n If True, the covariance of the joint predictive distribution at\n the query points is returned along with the mean.\n\n Returns\n -------\n y_mean : ndarray of shape (n_samples,) or (n_samples, n_targets)\n Mean of predictive distribution at query points.\n\n y_std : ndarray of shape (n_samples,) or (n_samples, n_targets), optional\n Standard deviation of predictive distribution at query points.\n Only returned when `return_std` is True.\n\n y_cov : ndarray of shape (n_samples, n_samples) or \\\n (n_samples, n_samples, n_targets), optional\n Covariance of joint predictive distribution at query points.\n Only returned when `return_cov` is True.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_gpr_interpolation_structured", "sklearn/gaussian_process/tests/test_gpr.py::test_prior[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_prior[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_prior[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_prior[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_prior[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_prior[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_predict_cov_vs_std[target0-kernel3]", 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"sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessRegressor()-check_fit2d_predict1d]", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.ConstantKernel", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.DotProduct", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.ExpSineSquared", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Exponentiation", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Product", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Sum", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.WhiteKernel", "sklearn/gaussian_process/_gpr.py::sklearn.gaussian_process._gpr.GaussianProcessRegressor", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GaussianProcessRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GaussianProcessRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-60	1.0	{ "code": "diff --git b/sklearn/gaussian_process/_gpr.py a/sklearn/gaussian_process/_gpr.py\nindex 9d133253f..d30854eae 100644\n--- b/sklearn/gaussian_process/_gpr.py\n+++ a/sklearn/gaussian_process/_gpr.py\n@@ -516,6 +516,20 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n Values of n_samples samples drawn from Gaussian process and\n evaluated at query points.\n \"\"\"\n+ rng = check_random_state(random_state)\n+\n+ y_mean, y_cov = self.predict(X, return_cov=True)\n+ if y_mean.ndim == 1:\n+ y_samples = rng.multivariate_normal(y_mean, y_cov, n_samples).T\n+ else:\n+ y_samples = [\n+ rng.multivariate_normal(\n+ y_mean[:, target], y_cov[..., target], n_samples\n+ ).T[:, np.newaxis]\n+ for target in range(y_mean.shape[1])\n+ ]\n+ y_samples = np.hstack(y_samples)\n+ return y_samples\n \n def log_marginal_likelihood(\n self, theta=None, eval_gradient=False, clone_kernel=True\n", "test": null }	null	{ "code": "diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py\nindex d30854eae..9d133253f 100644\n--- a/sklearn/gaussian_process/_gpr.py\n+++ b/sklearn/gaussian_process/_gpr.py\n@@ -516,20 +516,6 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):\n Values of n_samples samples drawn from Gaussian process and\n evaluated at query points.\n \"\"\"\n- rng = check_random_state(random_state)\n-\n- y_mean, y_cov = self.predict(X, return_cov=True)\n- if y_mean.ndim == 1:\n- y_samples = rng.multivariate_normal(y_mean, y_cov, n_samples).T\n- else:\n- y_samples = [\n- rng.multivariate_normal(\n- y_mean[:, target], y_cov[..., target], n_samples\n- ).T[:, np.newaxis]\n- for target in range(y_mean.shape[1])\n- ]\n- y_samples = np.hstack(y_samples)\n- return y_samples\n \n def log_marginal_likelihood(\n self, theta=None, eval_gradient=False, clone_kernel=True\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/gaussian_process/_gpr.py.\nHere is the description for the function:\n def sample_y(self, X, n_samples=1, random_state=0):\n \"\"\"Draw samples from Gaussian process and evaluate at X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples_X, n_features) or list of object\n Query points where the GP is evaluated.\n\n n_samples : int, default=1\n Number of samples drawn from the Gaussian process per query point.\n\n random_state : int, RandomState instance or None, default=0\n Determines random number generation to randomly draw samples.\n Pass an int for reproducible results across multiple function\n calls.\n See :term:`Glossary <random_state>`.\n\n Returns\n -------\n y_samples : ndarray of shape (n_samples_X, n_samples), or \\\n (n_samples_X, n_targets, n_samples)\n Values of n_samples samples drawn from Gaussian process and\n evaluated at query points.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel0]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel3]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel4]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_statistics[kernel5]", "sklearn/gaussian_process/tests/test_gpr.py::test_y_multioutput", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[None-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[None-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[1-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[1-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[10-True]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shapes[10-False]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-None]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[1-3]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-None]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-1]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-2]", "sklearn/gaussian_process/tests/test_gpr.py::test_sample_y_shape_with_prior[5-3]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-61	1.0	{ "code": "diff --git b/sklearn/ensemble/_gb.py a/sklearn/ensemble/_gb.py\nindex 54d516d34..15b76e04f 100644\n--- b/sklearn/ensemble/_gb.py\n+++ a/sklearn/ensemble/_gb.py\n@@ -1566,6 +1566,13 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):\n :term:`classes_`. Regression and binary classification produce an\n array of shape (n_samples,).\n \"\"\"\n+ X = validate_data(\n+ self, X, dtype=DTYPE, order=\"C\", accept_sparse=\"csr\", reset=False\n+ )\n+ raw_predictions = self._raw_predict(X)\n+ if raw_predictions.shape[1] == 1:\n+ return raw_predictions.ravel()\n+ return raw_predictions\n \n def staged_decision_function(self, X):\n \"\"\"Compute decision function of ``X`` for each iteration.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py\nindex 15b76e04f..54d516d34 100644\n--- a/sklearn/ensemble/_gb.py\n+++ b/sklearn/ensemble/_gb.py\n@@ -1566,13 +1566,6 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):\n :term:`classes_`. Regression and binary classification produce an\n array of shape (n_samples,).\n \"\"\"\n- X = validate_data(\n- self, X, dtype=DTYPE, order=\"C\", accept_sparse=\"csr\", reset=False\n- )\n- raw_predictions = self._raw_predict(X)\n- if raw_predictions.shape[1] == 1:\n- return raw_predictions.ravel()\n- return raw_predictions\n \n def staged_decision_function(self, X):\n \"\"\"Compute decision function of ``X`` for each iteration.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/ensemble/_gb.py.\nHere is the description for the function:\n def decision_function(self, X):\n \"\"\"Compute the decision function of ``X``.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input samples. Internally, it will be converted to\n ``dtype=np.float32`` and if a sparse matrix is provided\n to a sparse ``csr_matrix``.\n\n Returns\n -------\n score : ndarray of shape (n_samples, n_classes) or (n_samples,)\n The decision function of the input samples, which corresponds to\n the raw values predicted from the trees of the ensemble . The\n order of the classes corresponds to that in the attribute\n :term:`classes_`. Regression and binary classification produce an\n array of shape (n_samples,).\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-5-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-5-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-10-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-10-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-10-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-5-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-5-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-5-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-5-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-10-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-10-GradientBoostingClassifier-auto-data1]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-10-GradientBoostingClassifier-brute-data2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-10-GradientBoostingClassifier-brute-data3]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[0-est0]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[1-est0]", 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"sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-column-transformer-estimator-recursion]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-column-transformer-passthrough-estimator-recursion]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[0-estimator3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[1-estimator3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[-1-estimator3]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_toy[42-log_loss]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_toy[42-exponential]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_classification_synthetic[42-log_loss]", 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"sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_multilcass_iris", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start[42-GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_n_estimators[42-GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_clear[GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_equal_n_estimators[GradientBoostingClassifier]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[FixedThresholdClassifier-predict_proba-estimator2]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[FixedThresholdClassifier-predict_log_proba-estimator2]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[FixedThresholdClassifier-decision_function-estimator2]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_sparse[coo_matrix-GradientBoostingClassifier]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[TunedThresholdClassifierCV-predict_proba-estimator2]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_sparse[coo_array-GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_sparse[csc_matrix-GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_warm_start_sparse[csc_array-GradientBoostingClassifier]", "sklearn/model_selection/tests/test_classification_threshold.py::test_threshold_classifier_estimator_response_methods[TunedThresholdClassifierCV-predict_log_proba-estimator2]", 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"sklearn/ensemble/tests/test_gradient_boosting.py::test_sparse_input[coo_array-GradientBoostingClassifier]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_pickle]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_sparse_input[csc_matrix-GradientBoostingClassifier]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_sparse_input[csc_array-GradientBoostingClassifier]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifier_data_not_an_array]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_sparse_input[csr_matrix-GradientBoostingClassifier]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_train]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_sparse_input[csr_array-GradientBoostingClassifier]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True)]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_gradient_boosting_early_stopping[GradientBoostingClassifier]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_fit_idempotent]", "sklearn/ensemble/_gb.py::sklearn.ensemble._gb.GradientBoostingClassifier", "sklearn/tests/test_common.py::test_estimators[GradientBoostingClassifier(n_estimators=5)-check_fit2d_predict1d]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_binomial_error_exact_backward_compat", "sklearn/ensemble/tests/test_gradient_boosting.py::test_multinomial_error_exact_backward_compat", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GradientBoostingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-62	1.0	{ "code": "diff --git b/sklearn/ensemble/_gb.py a/sklearn/ensemble/_gb.py\nindex 45cae8924..15b76e04f 100644\n--- b/sklearn/ensemble/_gb.py\n+++ a/sklearn/ensemble/_gb.py\n@@ -1638,6 +1638,14 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):\n y : generator of ndarray of shape (n_samples,)\n The predicted value of the input samples.\n \"\"\"\n+ if self.n_classes_ == 2: # n_trees_per_iteration_ = 1\n+ for raw_predictions in self._staged_raw_predict(X):\n+ encoded_classes = (raw_predictions.squeeze() >= 0).astype(int)\n+ yield self.classes_.take(encoded_classes, axis=0)\n+ else:\n+ for raw_predictions in self._staged_raw_predict(X):\n+ encoded_classes = np.argmax(raw_predictions, axis=1)\n+ yield self.classes_.take(encoded_classes, axis=0)\n \n def predict_proba(self, X):\n \"\"\"Predict class probabilities for X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py\nindex 15b76e04f..45cae8924 100644\n--- a/sklearn/ensemble/_gb.py\n+++ b/sklearn/ensemble/_gb.py\n@@ -1638,14 +1638,6 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):\n y : generator of ndarray of shape (n_samples,)\n The predicted value of the input samples.\n \"\"\"\n- if self.n_classes_ == 2: # n_trees_per_iteration_ = 1\n- for raw_predictions in self._staged_raw_predict(X):\n- encoded_classes = (raw_predictions.squeeze() >= 0).astype(int)\n- yield self.classes_.take(encoded_classes, axis=0)\n- else:\n- for raw_predictions in self._staged_raw_predict(X):\n- encoded_classes = np.argmax(raw_predictions, axis=1)\n- yield self.classes_.take(encoded_classes, axis=0)\n \n def predict_proba(self, X):\n \"\"\"Predict class probabilities for X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/ensemble/_gb.py.\nHere is the description for the function:\n def staged_predict(self, X):\n \"\"\"Predict class at each stage for X.\n\n This method allows monitoring (i.e. determine error on testing set)\n after each stage.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input samples. Internally, it will be converted to\n ``dtype=np.float32`` and if a sparse matrix is provided\n to a sparse ``csr_matrix``.\n\n Yields\n ------\n y : generator of ndarray of shape (n_samples,)\n The predicted value of the input samples.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_predict_proba", "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_functions_defensive[42-GradientBoostingClassifier]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-63	1.0	{ "code": "diff --git b/sklearn/ensemble/_gb.py a/sklearn/ensemble/_gb.py\nindex 6520f9f62..15b76e04f 100644\n--- b/sklearn/ensemble/_gb.py\n+++ a/sklearn/ensemble/_gb.py\n@@ -1713,6 +1713,15 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):\n y : generator of ndarray of shape (n_samples,)\n The predicted value of the input samples.\n \"\"\"\n+ try:\n+ for raw_predictions in self._staged_raw_predict(X):\n+ yield self._loss.predict_proba(raw_predictions)\n+ except NotFittedError:\n+ raise\n+ except AttributeError as e:\n+ raise AttributeError(\n+ \"loss=%r does not support predict_proba\" % self.loss\n+ ) from e\n \n \n class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py\nindex 15b76e04f..6520f9f62 100644\n--- a/sklearn/ensemble/_gb.py\n+++ b/sklearn/ensemble/_gb.py\n@@ -1713,15 +1713,6 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):\n y : generator of ndarray of shape (n_samples,)\n The predicted value of the input samples.\n \"\"\"\n- try:\n- for raw_predictions in self._staged_raw_predict(X):\n- yield self._loss.predict_proba(raw_predictions)\n- except NotFittedError:\n- raise\n- except AttributeError as e:\n- raise AttributeError(\n- \"loss=%r does not support predict_proba\" % self.loss\n- ) from e\n \n \n class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/ensemble/_gb.py.\nHere is the description for the function:\n def staged_predict_proba(self, X):\n \"\"\"Predict class probabilities at each stage for X.\n\n This method allows monitoring (i.e. determine error on testing set)\n after each stage.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input samples. Internally, it will be converted to\n ``dtype=np.float32`` and if a sparse matrix is provided\n to a sparse ``csr_matrix``.\n\n Yields\n ------\n y : generator of ndarray of shape (n_samples,)\n The predicted value of the input samples.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_predict_proba", "sklearn/ensemble/tests/test_gradient_boosting.py::test_staged_functions_defensive[42-GradientBoostingClassifier]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-64	1.0	{ "code": "diff --git b/sklearn/linear_model/_huber.py a/sklearn/linear_model/_huber.py\nindex 14015c769..9e41cc4ea 100644\n--- b/sklearn/linear_model/_huber.py\n+++ a/sklearn/linear_model/_huber.py\n@@ -273,6 +273,7 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self.fit_intercept = fit_intercept\n self.tol = tol\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model according to the given training data.\n \n@@ -293,3 +294,60 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self : object\n Fitted `HuberRegressor` estimator.\n \"\"\"\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ copy=False,\n+ accept_sparse=[\"csr\"],\n+ y_numeric=True,\n+ dtype=[np.float64, np.float32],\n+ )\n+\n+ sample_weight = _check_sample_weight(sample_weight, X)\n+\n+ if self.warm_start and hasattr(self, \"coef_\"):\n+ parameters = np.concatenate((self.coef_, [self.intercept_, self.scale_]))\n+ else:\n+ if self.fit_intercept:\n+ parameters = np.zeros(X.shape[1] + 2)\n+ else:\n+ parameters = np.zeros(X.shape[1] + 1)\n+ # Make sure to initialize the scale parameter to a strictly\n+ # positive value:\n+ parameters[-1] = 1\n+\n+ # Sigma or the scale factor should be non-negative.\n+ # Setting it to be zero might cause undefined bounds hence we set it\n+ # to a value close to zero.\n+ bounds = np.tile([-np.inf, np.inf], (parameters.shape[0], 1))\n+ bounds[-1][0] = np.finfo(np.float64).eps * 10\n+\n+ opt_res = optimize.minimize(\n+ _huber_loss_and_gradient,\n+ parameters,\n+ method=\"L-BFGS-B\",\n+ jac=True,\n+ args=(X, y, self.epsilon, self.alpha, sample_weight),\n+ options={\"maxiter\": self.max_iter, \"gtol\": self.tol, \"iprint\": -1},\n+ bounds=bounds,\n+ )\n+\n+ parameters = opt_res.x\n+\n+ if opt_res.status == 2:\n+ raise ValueError(\n+ \"HuberRegressor convergence failed: l-BFGS-b solver terminated with %s\"\n+ % opt_res.message\n+ )\n+ self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n+ self.scale_ = parameters[-1]\n+ if self.fit_intercept:\n+ self.intercept_ = parameters[-2]\n+ else:\n+ self.intercept_ = 0.0\n+ self.coef_ = parameters[: X.shape[1]]\n+\n+ residual = np.abs(y - safe_sparse_dot(X, self.coef_) - self.intercept_)\n+ self.outliers_ = residual > self.scale_ * self.epsilon\n+ return self\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_huber.py b/sklearn/linear_model/_huber.py\nindex 9e41cc4ea..14015c769 100644\n--- a/sklearn/linear_model/_huber.py\n+++ b/sklearn/linear_model/_huber.py\n@@ -273,7 +273,6 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self.fit_intercept = fit_intercept\n self.tol = tol\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model according to the given training data.\n \n@@ -294,60 +293,3 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self : object\n Fitted `HuberRegressor` estimator.\n \"\"\"\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- copy=False,\n- accept_sparse=[\"csr\"],\n- y_numeric=True,\n- dtype=[np.float64, np.float32],\n- )\n-\n- sample_weight = _check_sample_weight(sample_weight, X)\n-\n- if self.warm_start and hasattr(self, \"coef_\"):\n- parameters = np.concatenate((self.coef_, [self.intercept_, self.scale_]))\n- else:\n- if self.fit_intercept:\n- parameters = np.zeros(X.shape[1] + 2)\n- else:\n- parameters = np.zeros(X.shape[1] + 1)\n- # Make sure to initialize the scale parameter to a strictly\n- # positive value:\n- parameters[-1] = 1\n-\n- # Sigma or the scale factor should be non-negative.\n- # Setting it to be zero might cause undefined bounds hence we set it\n- # to a value close to zero.\n- bounds = np.tile([-np.inf, np.inf], (parameters.shape[0], 1))\n- bounds[-1][0] = np.finfo(np.float64).eps * 10\n-\n- opt_res = optimize.minimize(\n- _huber_loss_and_gradient,\n- parameters,\n- method=\"L-BFGS-B\",\n- jac=True,\n- args=(X, y, self.epsilon, self.alpha, sample_weight),\n- options={\"maxiter\": self.max_iter, \"gtol\": self.tol, \"iprint\": -1},\n- bounds=bounds,\n- )\n-\n- parameters = opt_res.x\n-\n- if opt_res.status == 2:\n- raise ValueError(\n- \"HuberRegressor convergence failed: l-BFGS-b solver terminated with %s\"\n- % opt_res.message\n- )\n- self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n- self.scale_ = parameters[-1]\n- if self.fit_intercept:\n- self.intercept_ = parameters[-2]\n- else:\n- self.intercept_ = 0.0\n- self.coef_ = parameters[: X.shape[1]]\n-\n- residual = np.abs(y - safe_sparse_dot(X, self.coef_) - self.intercept_)\n- self.outliers_ = residual > self.scale_ * self.epsilon\n- return self\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_huber.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model according to the given training data.\n\n Parameters\n ----------\n X : array-like, shape (n_samples, n_features)\n Training vector, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : array-like, shape (n_samples,)\n Target vector relative to X.\n\n sample_weight : array-like, shape (n_samples,)\n Weight given to each sample.\n\n Returns\n -------\n self : object\n Fitted `HuberRegressor` estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_quantile.py::test_quantile_equals_huber_for_low_epsilon[True]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_equals_huber_for_low_epsilon[False]", "sklearn/linear_model/tests/test_huber.py::test_huber_equals_lr_for_high_epsilon", "sklearn/linear_model/tests/test_huber.py::test_huber_max_iter", "sklearn/linear_model/tests/test_huber.py::test_huber_sample_weights[csr_matrix]", "sklearn/linear_model/tests/test_huber.py::test_huber_sample_weights[csr_array]", "sklearn/linear_model/tests/test_huber.py::test_huber_sparse[csr_matrix]", "sklearn/linear_model/tests/test_huber.py::test_huber_sparse[csr_array]", "sklearn/linear_model/tests/test_huber.py::test_huber_scaling_invariant", "sklearn/linear_model/tests/test_huber.py::test_huber_and_sgd_same_results", "sklearn/linear_model/tests/test_huber.py::test_huber_warm_start", "sklearn/linear_model/tests/test_huber.py::test_huber_better_r2_score", "sklearn/linear_model/tests/test_huber.py::test_huber_bool", "sklearn/linear_model/_huber.py::sklearn.linear_model._huber.HuberRegressor", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[HuberRegressor(max_iter=5)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HuberRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HuberRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[HuberRegressor(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-65	1.0	{ "code": "diff --git b/sklearn/decomposition/_incremental_pca.py a/sklearn/decomposition/_incremental_pca.py\nindex 474e2437c..fa4421018 100644\n--- b/sklearn/decomposition/_incremental_pca.py\n+++ a/sklearn/decomposition/_incremental_pca.py\n@@ -197,6 +197,7 @@ class IncrementalPCA(_BasePCA):\n self.copy = copy\n self.batch_size = batch_size\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model with X, using minibatches of size batch_size.\n \n@@ -214,6 +215,39 @@ class IncrementalPCA(_BasePCA):\n self : object\n Returns the instance itself.\n \"\"\"\n+ self.components_ = None\n+ self.n_samples_seen_ = 0\n+ self.mean_ = 0.0\n+ self.var_ = 0.0\n+ self.singular_values_ = None\n+ self.explained_variance_ = None\n+ self.explained_variance_ratio_ = None\n+ self.noise_variance_ = None\n+\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=[\"csr\", \"csc\", \"lil\"],\n+ copy=self.copy,\n+ dtype=[np.float64, np.float32],\n+ force_writeable=True,\n+ )\n+ n_samples, n_features = X.shape\n+\n+ if self.batch_size is None:\n+ self.batch_size_ = 5 * n_features\n+ else:\n+ self.batch_size_ = self.batch_size\n+\n+ for batch in gen_batches(\n+ n_samples, self.batch_size_, min_batch_size=self.n_components or 0\n+ ):\n+ X_batch = X[batch]\n+ if sparse.issparse(X_batch):\n+ X_batch = X_batch.toarray()\n+ self.partial_fit(X_batch, check_input=False)\n+\n+ return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, check_input=True):\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_incremental_pca.py b/sklearn/decomposition/_incremental_pca.py\nindex fa4421018..474e2437c 100644\n--- a/sklearn/decomposition/_incremental_pca.py\n+++ b/sklearn/decomposition/_incremental_pca.py\n@@ -197,7 +197,6 @@ class IncrementalPCA(_BasePCA):\n self.copy = copy\n self.batch_size = batch_size\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model with X, using minibatches of size batch_size.\n \n@@ -215,39 +214,6 @@ class IncrementalPCA(_BasePCA):\n self : object\n Returns the instance itself.\n \"\"\"\n- self.components_ = None\n- self.n_samples_seen_ = 0\n- self.mean_ = 0.0\n- self.var_ = 0.0\n- self.singular_values_ = None\n- self.explained_variance_ = None\n- self.explained_variance_ratio_ = None\n- self.noise_variance_ = None\n-\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=[\"csr\", \"csc\", \"lil\"],\n- copy=self.copy,\n- dtype=[np.float64, np.float32],\n- force_writeable=True,\n- )\n- n_samples, n_features = X.shape\n-\n- if self.batch_size is None:\n- self.batch_size_ = 5 * n_features\n- else:\n- self.batch_size_ = self.batch_size\n-\n- for batch in gen_batches(\n- n_samples, self.batch_size_, min_batch_size=self.n_components or 0\n- ):\n- X_batch = X[batch]\n- if sparse.issparse(X_batch):\n- X_batch = X_batch.toarray()\n- self.partial_fit(X_batch, check_input=False)\n-\n- return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, check_input=True):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_incremental_pca.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the model with X, using minibatches of size batch_size.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csc_matrix]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csc_array]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csr_matrix]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csr_array]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[lil_matrix]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[lil_array]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_check_projection", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_inverse", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_validation", "sklearn/decomposition/tests/test_incremental_pca.py::test_n_samples_equal_n_components", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_set_params", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_num_features_change", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_batch_signs", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_batch_values", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_batch_rank", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_partial_fit", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_against_pca_iris", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_against_pca_random_data", "sklearn/decomposition/tests/test_incremental_pca.py::test_explained_variances", "sklearn/decomposition/tests/test_incremental_pca.py::test_singular_values", "sklearn/decomposition/tests/test_incremental_pca.py::test_whitening[42]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_fit_overflow_error", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_feature_names_out", "sklearn/decomposition/_incremental_pca.py::sklearn.decomposition._incremental_pca.IncrementalPCA", "sklearn/decomposition/_incremental_pca.py::sklearn.decomposition._incremental_pca.IncrementalPCA.transform", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_check_param_validation[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_set_output_transform[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[IncrementalPCA(batch_size=10)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-66	1.0	{ "code": "diff --git b/sklearn/decomposition/_incremental_pca.py a/sklearn/decomposition/_incremental_pca.py\nindex 72bd81d46..fa4421018 100644\n--- b/sklearn/decomposition/_incremental_pca.py\n+++ a/sklearn/decomposition/_incremental_pca.py\n@@ -249,6 +249,7 @@ class IncrementalPCA(_BasePCA):\n \n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, check_input=True):\n \"\"\"Incremental fit with X. All of X is processed as a single batch.\n \n@@ -269,6 +270,109 @@ class IncrementalPCA(_BasePCA):\n self : object\n Returns the instance itself.\n \"\"\"\n+ first_pass = not hasattr(self, \"components_\")\n+\n+ if check_input:\n+ if sparse.issparse(X):\n+ raise TypeError(\n+ \"IncrementalPCA.partial_fit does not support \"\n+ \"sparse input. Either convert data to dense \"\n+ \"or use IncrementalPCA.fit to do so in batches.\"\n+ )\n+ X = validate_data(\n+ self,\n+ X,\n+ copy=self.copy,\n+ dtype=[np.float64, np.float32],\n+ force_writeable=True,\n+ reset=first_pass,\n+ )\n+ n_samples, n_features = X.shape\n+ if first_pass:\n+ self.components_ = None\n+\n+ if self.n_components is None:\n+ if self.components_ is None:\n+ self.n_components_ = min(n_samples, n_features)\n+ else:\n+ self.n_components_ = self.components_.shape[0]\n+ elif not self.n_components <= n_features:\n+ raise ValueError(\n+ \"n_components=%r invalid for n_features=%d, need \"\n+ \"more rows than columns for IncrementalPCA \"\n+ \"processing\" % (self.n_components, n_features)\n+ )\n+ elif not self.n_components <= n_samples:\n+ raise ValueError(\n+ \"n_components=%r must be less or equal to \"\n+ \"the batch number of samples \"\n+ \"%d.\" % (self.n_components, n_samples)\n+ )\n+ else:\n+ self.n_components_ = self.n_components\n+\n+ if (self.components_ is not None) and (\n+ self.components_.shape[0] != self.n_components_\n+ ):\n+ raise ValueError(\n+ \"Number of input features has changed from %i \"\n+ \"to %i between calls to partial_fit! Try \"\n+ \"setting n_components to a fixed value.\"\n+ % (self.components_.shape[0], self.n_components_)\n+ )\n+\n+ # This is the first partial_fit\n+ if not hasattr(self, \"n_samples_seen_\"):\n+ self.n_samples_seen_ = 0\n+ self.mean_ = 0.0\n+ self.var_ = 0.0\n+\n+ # Update stats - they are 0 if this is the first step\n+ col_mean, col_var, n_total_samples = _incremental_mean_and_var(\n+ X,\n+ last_mean=self.mean_,\n+ last_variance=self.var_,\n+ last_sample_count=np.repeat(self.n_samples_seen_, X.shape[1]),\n+ )\n+ n_total_samples = n_total_samples[0]\n+\n+ # Whitening\n+ if self.n_samples_seen_ == 0:\n+ # If it is the first step, simply whiten X\n+ X -= col_mean\n+ else:\n+ col_batch_mean = np.mean(X, axis=0)\n+ X -= col_batch_mean\n+ # Build matrix of combined previous basis and new data\n+ mean_correction = np.sqrt(\n+ (self.n_samples_seen_ / n_total_samples) * n_samples\n+ ) * (self.mean_ - col_batch_mean)\n+ X = np.vstack(\n+ (\n+ self.singular_values_.reshape((-1, 1)) * self.components_,\n+ X,\n+ mean_correction,\n+ )\n+ )\n+\n+ U, S, Vt = linalg.svd(X, full_matrices=False, check_finite=False)\n+ U, Vt = svd_flip(U, Vt, u_based_decision=False)\n+ explained_variance = S2 / (n_total_samples - 1)\n+ explained_variance_ratio = S2 / np.sum(col_var * n_total_samples)\n+\n+ self.n_samples_seen_ = n_total_samples\n+ self.components_ = Vt[: self.n_components_]\n+ self.singular_values_ = S[: self.n_components_]\n+ self.mean_ = col_mean\n+ self.var_ = col_var\n+ self.explained_variance_ = explained_variance[: self.n_components_]\n+ self.explained_variance_ratio_ = explained_variance_ratio[: self.n_components_]\n+ # we already checked `self.n_components <= n_samples` above\n+ if self.n_components_ not in (n_samples, n_features):\n+ self.noise_variance_ = explained_variance[self.n_components_ :].mean()\n+ else:\n+ self.noise_variance_ = 0.0\n+ return self\n \n def transform(self, X):\n \"\"\"Apply dimensionality reduction to X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_incremental_pca.py b/sklearn/decomposition/_incremental_pca.py\nindex fa4421018..72bd81d46 100644\n--- a/sklearn/decomposition/_incremental_pca.py\n+++ b/sklearn/decomposition/_incremental_pca.py\n@@ -249,7 +249,6 @@ class IncrementalPCA(_BasePCA):\n \n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, check_input=True):\n \"\"\"Incremental fit with X. All of X is processed as a single batch.\n \n@@ -270,109 +269,6 @@ class IncrementalPCA(_BasePCA):\n self : object\n Returns the instance itself.\n \"\"\"\n- first_pass = not hasattr(self, \"components_\")\n-\n- if check_input:\n- if sparse.issparse(X):\n- raise TypeError(\n- \"IncrementalPCA.partial_fit does not support \"\n- \"sparse input. Either convert data to dense \"\n- \"or use IncrementalPCA.fit to do so in batches.\"\n- )\n- X = validate_data(\n- self,\n- X,\n- copy=self.copy,\n- dtype=[np.float64, np.float32],\n- force_writeable=True,\n- reset=first_pass,\n- )\n- n_samples, n_features = X.shape\n- if first_pass:\n- self.components_ = None\n-\n- if self.n_components is None:\n- if self.components_ is None:\n- self.n_components_ = min(n_samples, n_features)\n- else:\n- self.n_components_ = self.components_.shape[0]\n- elif not self.n_components <= n_features:\n- raise ValueError(\n- \"n_components=%r invalid for n_features=%d, need \"\n- \"more rows than columns for IncrementalPCA \"\n- \"processing\" % (self.n_components, n_features)\n- )\n- elif not self.n_components <= n_samples:\n- raise ValueError(\n- \"n_components=%r must be less or equal to \"\n- \"the batch number of samples \"\n- \"%d.\" % (self.n_components, n_samples)\n- )\n- else:\n- self.n_components_ = self.n_components\n-\n- if (self.components_ is not None) and (\n- self.components_.shape[0] != self.n_components_\n- ):\n- raise ValueError(\n- \"Number of input features has changed from %i \"\n- \"to %i between calls to partial_fit! Try \"\n- \"setting n_components to a fixed value.\"\n- % (self.components_.shape[0], self.n_components_)\n- )\n-\n- # This is the first partial_fit\n- if not hasattr(self, \"n_samples_seen_\"):\n- self.n_samples_seen_ = 0\n- self.mean_ = 0.0\n- self.var_ = 0.0\n-\n- # Update stats - they are 0 if this is the first step\n- col_mean, col_var, n_total_samples = _incremental_mean_and_var(\n- X,\n- last_mean=self.mean_,\n- last_variance=self.var_,\n- last_sample_count=np.repeat(self.n_samples_seen_, X.shape[1]),\n- )\n- n_total_samples = n_total_samples[0]\n-\n- # Whitening\n- if self.n_samples_seen_ == 0:\n- # If it is the first step, simply whiten X\n- X -= col_mean\n- else:\n- col_batch_mean = np.mean(X, axis=0)\n- X -= col_batch_mean\n- # Build matrix of combined previous basis and new data\n- mean_correction = np.sqrt(\n- (self.n_samples_seen_ / n_total_samples) * n_samples\n- ) * (self.mean_ - col_batch_mean)\n- X = np.vstack(\n- (\n- self.singular_values_.reshape((-1, 1)) * self.components_,\n- X,\n- mean_correction,\n- )\n- )\n-\n- U, S, Vt = linalg.svd(X, full_matrices=False, check_finite=False)\n- U, Vt = svd_flip(U, Vt, u_based_decision=False)\n- explained_variance = S2 / (n_total_samples - 1)\n- explained_variance_ratio = S2 / np.sum(col_var * n_total_samples)\n-\n- self.n_samples_seen_ = n_total_samples\n- self.components_ = Vt[: self.n_components_]\n- self.singular_values_ = S[: self.n_components_]\n- self.mean_ = col_mean\n- self.var_ = col_var\n- self.explained_variance_ = explained_variance[: self.n_components_]\n- self.explained_variance_ratio_ = explained_variance_ratio[: self.n_components_]\n- # we already checked `self.n_components <= n_samples` above\n- if self.n_components_ not in (n_samples, n_features):\n- self.noise_variance_ = explained_variance[self.n_components_ :].mean()\n- else:\n- self.noise_variance_ = 0.0\n- return self\n \n def transform(self, X):\n \"\"\"Apply dimensionality reduction to X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_incremental_pca.py.\nHere is the description for the function:\n def partial_fit(self, X, y=None, check_input=True):\n \"\"\"Incremental fit with X. All of X is processed as a single batch.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n check_input : bool, default=True\n Run check_array on X.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csc_matrix]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csc_array]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csr_matrix]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[csr_array]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[lil_matrix]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_sparse[lil_array]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_check_projection", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_inverse", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_validation", "sklearn/decomposition/tests/test_incremental_pca.py::test_n_samples_equal_n_components", "sklearn/decomposition/tests/test_incremental_pca.py::test_n_components_none", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_set_params", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_num_features_change", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_batch_signs", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_batch_values", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_batch_rank", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_partial_fit", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_against_pca_iris", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_against_pca_random_data", "sklearn/decomposition/tests/test_incremental_pca.py::test_explained_variances", "sklearn/decomposition/tests/test_incremental_pca.py::test_singular_values", "sklearn/decomposition/tests/test_incremental_pca.py::test_whitening[42]", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_partial_fit_float_division", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_fit_overflow_error", "sklearn/decomposition/tests/test_incremental_pca.py::test_incremental_pca_feature_names_out", "sklearn/decomposition/_incremental_pca.py::sklearn.decomposition._incremental_pca.IncrementalPCA", "sklearn/decomposition/_incremental_pca.py::sklearn.decomposition._incremental_pca.IncrementalPCA.transform", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_check_param_validation[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_set_output_transform[IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-IncrementalPCA(batch_size=10)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[IncrementalPCA(batch_size=10)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-67	1.0	{ "code": "diff --git b/sklearn/ensemble/_iforest.py a/sklearn/ensemble/_iforest.py\nindex 94601eebc..d7d9a06eb 100644\n--- b/sklearn/ensemble/_iforest.py\n+++ a/sklearn/ensemble/_iforest.py\n@@ -293,6 +293,7 @@ class IsolationForest(OutlierMixin, BaseBagging):\n # copies. This is only used in the fit method.\n return {\"prefer\": \"threads\"}\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit estimator.\n@@ -315,6 +316,73 @@ class IsolationForest(OutlierMixin, BaseBagging):\n self : object\n Fitted estimator.\n \"\"\"\n+ X = validate_data(\n+ self, X, accept_sparse=[\"csc\"], dtype=tree_dtype, ensure_all_finite=False\n+ )\n+ if issparse(X):\n+ # Pre-sort indices to avoid that each individual tree of the\n+ # ensemble sorts the indices.\n+ X.sort_indices()\n+\n+ rnd = check_random_state(self.random_state)\n+ y = rnd.uniform(size=X.shape[0])\n+\n+ # ensure that max_sample is in [1, n_samples]:\n+ n_samples = X.shape[0]\n+\n+ if isinstance(self.max_samples, str) and self.max_samples == \"auto\":\n+ max_samples = min(256, n_samples)\n+\n+ elif isinstance(self.max_samples, numbers.Integral):\n+ if self.max_samples > n_samples:\n+ warn(\n+ \"max_samples (%s) is greater than the \"\n+ \"total number of samples (%s). max_samples \"\n+ \"will be set to n_samples for estimation.\"\n+ % (self.max_samples, n_samples)\n+ )\n+ max_samples = n_samples\n+ else:\n+ max_samples = self.max_samples\n+ else: # max_samples is float\n+ max_samples = int(self.max_samples * X.shape[0])\n+\n+ self.max_samples_ = max_samples\n+ max_depth = int(np.ceil(np.log2(max(max_samples, 2))))\n+ super()._fit(\n+ X,\n+ y,\n+ max_samples,\n+ max_depth=max_depth,\n+ sample_weight=sample_weight,\n+ check_input=False,\n+ )\n+\n+ self._average_path_length_per_tree, self._decision_path_lengths = zip(\n+ [\n+ (\n+ _average_path_length(tree.tree_.n_node_samples),\n+ tree.tree_.compute_node_depths(),\n+ )\n+ for tree in self.estimators_\n+ ]\n+ )\n+\n+ if self.contamination == \"auto\":\n+ # 0.5 plays a special role as described in the original paper.\n+ # we take the opposite as we consider the opposite of their score.\n+ self.offset_ = -0.5\n+ return self\n+\n+ # Else, define offset_ wrt contamination parameter\n+ # To avoid performing input validation a second time we call\n+ # _score_samples rather than score_samples.\n+ # _score_samples expects a CSR matrix, so we convert if necessary.\n+ if issparse(X):\n+ X = X.tocsr()\n+ self.offset_ = np.percentile(self._score_samples(X), 100.0 self.contamination)\n+\n+ return self\n \n def predict(self, X):\n \"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_iforest.py b/sklearn/ensemble/_iforest.py\nindex d7d9a06eb..94601eebc 100644\n--- a/sklearn/ensemble/_iforest.py\n+++ b/sklearn/ensemble/_iforest.py\n@@ -293,7 +293,6 @@ class IsolationForest(OutlierMixin, BaseBagging):\n # copies. This is only used in the fit method.\n return {\"prefer\": \"threads\"}\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit estimator.\n@@ -316,73 +315,6 @@ class IsolationForest(OutlierMixin, BaseBagging):\n self : object\n Fitted estimator.\n \"\"\"\n- X = validate_data(\n- self, X, accept_sparse=[\"csc\"], dtype=tree_dtype, ensure_all_finite=False\n- )\n- if issparse(X):\n- # Pre-sort indices to avoid that each individual tree of the\n- # ensemble sorts the indices.\n- X.sort_indices()\n-\n- rnd = check_random_state(self.random_state)\n- y = rnd.uniform(size=X.shape[0])\n-\n- # ensure that max_sample is in [1, n_samples]:\n- n_samples = X.shape[0]\n-\n- if isinstance(self.max_samples, str) and self.max_samples == \"auto\":\n- max_samples = min(256, n_samples)\n-\n- elif isinstance(self.max_samples, numbers.Integral):\n- if self.max_samples > n_samples:\n- warn(\n- \"max_samples (%s) is greater than the \"\n- \"total number of samples (%s). max_samples \"\n- \"will be set to n_samples for estimation.\"\n- % (self.max_samples, n_samples)\n- )\n- max_samples = n_samples\n- else:\n- max_samples = self.max_samples\n- else: # max_samples is float\n- max_samples = int(self.max_samples * X.shape[0])\n-\n- self.max_samples_ = max_samples\n- max_depth = int(np.ceil(np.log2(max(max_samples, 2))))\n- super()._fit(\n- X,\n- y,\n- max_samples,\n- max_depth=max_depth,\n- sample_weight=sample_weight,\n- check_input=False,\n- )\n-\n- self._average_path_length_per_tree, self._decision_path_lengths = zip(\n- [\n- (\n- _average_path_length(tree.tree_.n_node_samples),\n- tree.tree_.compute_node_depths(),\n- )\n- for tree in self.estimators_\n- ]\n- )\n-\n- if self.contamination == \"auto\":\n- # 0.5 plays a special role as described in the original paper.\n- # we take the opposite as we consider the opposite of their score.\n- self.offset_ = -0.5\n- return self\n-\n- # Else, define offset_ wrt contamination parameter\n- # To avoid performing input validation a second time we call\n- # _score_samples rather than score_samples.\n- # _score_samples expects a CSR matrix, so we convert if necessary.\n- if issparse(X):\n- X = X.tocsr()\n- self.offset_ = np.percentile(self._score_samples(X), 100.0 self.contamination)\n-\n- return self\n \n def predict(self, X):\n \"\"\"\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/ensemble/_iforest.py.\nHere is the description for the function:\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit estimator.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input samples. Use ``dtype=np.float32`` for maximum\n efficiency. Sparse matrices are also supported, use sparse\n ``csc_matrix`` for maximum efficiency.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights. If None, then samples are equally weighted.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_outlier_detector[contourf-auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_outlier_detector[contourf-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_outlier_detector[contourf-decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_outlier_detector[contour-auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_outlier_detector[contour-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_outlier_detector[contour-decision_function]", "sklearn/utils/tests/test_response.py::test_get_response_values_outlier_detection[True-predict]", "sklearn/utils/tests/test_response.py::test_get_response_values_outlier_detection[True-decision_function]", "sklearn/utils/tests/test_response.py::test_get_response_values_outlier_detection[True-response_method2]", "sklearn/utils/tests/test_response.py::test_get_response_values_outlier_detection[False-predict]", "sklearn/utils/tests/test_response.py::test_get_response_values_outlier_detection[False-decision_function]", "sklearn/utils/tests/test_response.py::test_get_response_values_outlier_detection[False-response_method2]", "sklearn/ensemble/tests/test_iforest.py::test_iforest[42]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse[42-csc_matrix]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse[42-csc_array]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse[42-csr_matrix]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse[42-csr_array]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_error", "sklearn/ensemble/tests/test_iforest.py::test_recalculate_max_depth", "sklearn/ensemble/tests/test_iforest.py::test_max_samples_attribute", "sklearn/ensemble/tests/test_iforest.py::test_iforest_parallel_regression[42]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_performance[42]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_works[42-0.25]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_works[42-auto]", "sklearn/ensemble/tests/test_iforest.py::test_max_samples_consistency", "sklearn/ensemble/tests/test_iforest.py::test_iforest_subsampled_features", "sklearn/ensemble/tests/test_iforest.py::test_score_samples", "sklearn/ensemble/tests/test_iforest.py::test_iforest_warm_start", "sklearn/ensemble/tests/test_iforest.py::test_iforest_chunks_works1[42-0.25-3]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_chunks_works1[42-auto-2]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_chunks_works2[42-0.25-3]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_chunks_works2[42-auto-2]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_with_uniform_data", "sklearn/ensemble/tests/test_iforest.py::test_iforest_with_n_jobs_does_not_segfault[csc_matrix]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_with_n_jobs_does_not_segfault[csc_array]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_preserve_feature_names", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse_input_float_contamination[csc_matrix]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse_input_float_contamination[csc_array]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse_input_float_contamination[csr_matrix]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_sparse_input_float_contamination[csr_array]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_predict_parallel[42-0.25-1]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_predict_parallel[42-0.25-2]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_predict_parallel[42-auto-1]", "sklearn/ensemble/tests/test_iforest.py::test_iforest_predict_parallel[42-auto-2]", "sklearn/ensemble/_iforest.py::sklearn.ensemble._iforest.IsolationForest", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_outliers_fit_predict]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_outliers_train]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_outliers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[IsolationForest(n_estimators=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IsolationForest(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IsolationForest(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_param_validation[IsolationForest(n_estimators=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-68	1.0	{ "code": "diff --git b/sklearn/manifold/_isomap.py a/sklearn/manifold/_isomap.py\nindex 2a2b9490d..ee302bc07 100644\n--- b/sklearn/manifold/_isomap.py\n+++ a/sklearn/manifold/_isomap.py\n@@ -407,6 +407,33 @@ class Isomap(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n X_new : array-like, shape (n_queries, n_components)\n X transformed in the new space.\n \"\"\"\n+ check_is_fitted(self)\n+ if self.n_neighbors is not None:\n+ distances, indices = self.nbrs_.kneighbors(X, return_distance=True)\n+ else:\n+ distances, indices = self.nbrs_.radius_neighbors(X, return_distance=True)\n+\n+ # Create the graph of shortest distances from X to\n+ # training data via the nearest neighbors of X.\n+ # This can be done as a single array operation, but it potentially\n+ # takes a lot of memory. To avoid that, use a loop:\n+\n+ n_samples_fit = self.nbrs_.n_samples_fit_\n+ n_queries = distances.shape[0]\n+\n+ if hasattr(X, \"dtype\") and X.dtype == np.float32:\n+ dtype = np.float32\n+ else:\n+ dtype = np.float64\n+\n+ G_X = np.zeros((n_queries, n_samples_fit), dtype)\n+ for i in range(n_queries):\n+ G_X[i] = np.min(self.dist_matrix_[indices[i]] + distances[i][:, None], 0)\n+\n+ G_X *= 2\n+ G_X = -0.5\n+\n+ return self.kernel_pca_.transform(G_X)\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/manifold/_isomap.py b/sklearn/manifold/_isomap.py\nindex ee302bc07..2a2b9490d 100644\n--- a/sklearn/manifold/_isomap.py\n+++ b/sklearn/manifold/_isomap.py\n@@ -407,33 +407,6 @@ class Isomap(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n X_new : array-like, shape (n_queries, n_components)\n X transformed in the new space.\n \"\"\"\n- check_is_fitted(self)\n- if self.n_neighbors is not None:\n- distances, indices = self.nbrs_.kneighbors(X, return_distance=True)\n- else:\n- distances, indices = self.nbrs_.radius_neighbors(X, return_distance=True)\n-\n- # Create the graph of shortest distances from X to\n- # training data via the nearest neighbors of X.\n- # This can be done as a single array operation, but it potentially\n- # takes a lot of memory. To avoid that, use a loop:\n-\n- n_samples_fit = self.nbrs_.n_samples_fit_\n- n_queries = distances.shape[0]\n-\n- if hasattr(X, \"dtype\") and X.dtype == np.float32:\n- dtype = np.float32\n- else:\n- dtype = np.float64\n-\n- G_X = np.zeros((n_queries, n_samples_fit), dtype)\n- for i in range(n_queries):\n- G_X[i] = np.min(self.dist_matrix_[indices[i]] + distances[i][:, None], 0)\n-\n- G_X *= 2\n- G_X = -0.5\n-\n- return self.kernel_pca_.transform(G_X)\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/manifold/_isomap.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Transform X.\n\n This is implemented by linking the points X into the graph of geodesic\n distances of the training data. First the `n_neighbors` nearest\n neighbors of X are found in the training data, and from these the\n shortest geodesic distances from each point in X to each point in\n the training data are computed in order to construct the kernel.\n The embedding of X is the projection of this kernel onto the\n embedding vectors of the training set.\n\n Parameters\n ----------\n X : {array-like, sparse matrix}, shape (n_queries, n_features)\n If neighbors_algorithm='precomputed', X is assumed to be a\n distance matrix or a sparse graph of shape\n (n_queries, n_samples_fit).\n\n Returns\n -------\n X_new : array-like, shape (n_queries, n_components)\n X transformed in the new space.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/manifold/tests/test_isomap.py::test_transform[float64-2-None]", "sklearn/manifold/tests/test_isomap.py::test_transform[float64-None-0.5]", "sklearn/manifold/tests/test_isomap.py::test_pipeline[float64-2-None]", "sklearn/manifold/tests/test_isomap.py::test_pipeline[float64-None-10.0]", "sklearn/manifold/tests/test_isomap.py::test_pipeline_with_nearest_neighbors_transformer[float64]", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_isomap", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[Isomap(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Isomap()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Isomap()]", "sklearn/tests/test_common.py::test_set_output_transform[Isomap()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-Isomap()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-Isomap()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-69	1.0	{ "code": "diff --git b/sklearn/isotonic.py a/sklearn/isotonic.py\nindex 1f535018d..7312fdba7 100644\n--- b/sklearn/isotonic.py\n+++ a/sklearn/isotonic.py\n@@ -354,6 +354,7 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):\n # prediction speed).\n return X, y\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -382,6 +383,26 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):\n X is stored for future use, as :meth:`transform` needs X to interpolate\n new input data.\n \"\"\"\n+ check_params = dict(accept_sparse=False, ensure_2d=False)\n+ X = check_array(\n+ X, input_name=\"X\", dtype=[np.float64, np.float32], check_params\n+ )\n+ y = check_array(y, input_name=\"y\", dtype=X.dtype, check_params)\n+ check_consistent_length(X, y, sample_weight)\n+\n+ # Transform y by running the isotonic regression algorithm and\n+ # transform X accordingly.\n+ X, y = self._build_y(X, y, sample_weight)\n+\n+ # It is necessary to store the non-redundant part of the training set\n+ # on the model to make it possible to support model persistence via\n+ # the pickle module as the object built by scipy.interp1d is not\n+ # picklable directly.\n+ self.X_thresholds_, self.y_thresholds_ = X, y\n+\n+ # Build the interpolation function\n+ self._build_f(X, y)\n+ return self\n \n def _transform(self, T):\n \"\"\"`_transform` is called by both `transform` and `predict` methods.\n", "test": null }	null	{ "code": "diff --git a/sklearn/isotonic.py b/sklearn/isotonic.py\nindex 7312fdba7..1f535018d 100644\n--- a/sklearn/isotonic.py\n+++ b/sklearn/isotonic.py\n@@ -354,7 +354,6 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):\n # prediction speed).\n return X, y\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -383,26 +382,6 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):\n X is stored for future use, as :meth:`transform` needs X to interpolate\n new input data.\n \"\"\"\n- check_params = dict(accept_sparse=False, ensure_2d=False)\n- X = check_array(\n- X, input_name=\"X\", dtype=[np.float64, np.float32], check_params\n- )\n- y = check_array(y, input_name=\"y\", dtype=X.dtype, check_params)\n- check_consistent_length(X, y, sample_weight)\n-\n- # Transform y by running the isotonic regression algorithm and\n- # transform X accordingly.\n- X, y = self._build_y(X, y, sample_weight)\n-\n- # It is necessary to store the non-redundant part of the training set\n- # on the model to make it possible to support model persistence via\n- # the pickle module as the object built by scipy.interp1d is not\n- # picklable directly.\n- self.X_thresholds_, self.y_thresholds_ = X, y\n-\n- # Build the interpolation function\n- self._build_f(X, y)\n- return self\n \n def _transform(self, T):\n \"\"\"`_transform` is called by both `transform` and `predict` methods.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/isotonic.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model using X, y as training data.\n\n Parameters\n ----------\n X : array-like of shape (n_samples,) or (n_samples, 1)\n Training data.\n\n .. versionchanged:: 0.24\n Also accepts 2d array with 1 feature.\n\n y : array-like of shape (n_samples,)\n Training target.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Weights. If set to None, all weights will be set to 1 (equal\n weights).\n\n Returns\n -------\n self : object\n Returns an instance of self.\n\n Notes\n -----\n X is stored for future use, as :meth:`transform` needs X to interpolate\n new input data.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_calibration.py::test_calibration[True-isotonic-csr_matrix]", "sklearn/tests/test_calibration.py::test_calibration[True-isotonic-csr_array]", "sklearn/tests/test_calibration.py::test_calibration[False-isotonic-csr_matrix]", "sklearn/tests/test_calibration.py::test_calibration[False-isotonic-csr_array]", "sklearn/tests/test_calibration.py::test_sample_weight[True-isotonic]", "sklearn/tests/test_calibration.py::test_sample_weight[False-isotonic]", "sklearn/tests/test_calibration.py::test_parallel_execution[True-isotonic]", "sklearn/tests/test_calibration.py::test_parallel_execution[False-isotonic]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[0-True-isotonic]", "sklearn/tests/test_isotonic.py::test_permutation_invariance", "sklearn/tests/test_calibration.py::test_calibration_multiclass[0-False-isotonic]", "sklearn/tests/test_isotonic.py::test_isotonic_regression", "sklearn/tests/test_isotonic.py::test_isotonic_regression_ties_min", "sklearn/tests/test_isotonic.py::test_isotonic_regression_ties_max", "sklearn/tests/test_isotonic.py::test_isotonic_regression_ties_secondary_", "sklearn/tests/test_isotonic.py::test_isotonic_regression_with_ties_in_differently_sized_groups", "sklearn/tests/test_isotonic.py::test_isotonic_regression_reversed", "sklearn/tests/test_isotonic.py::test_isotonic_regression_auto_decreasing", "sklearn/tests/test_isotonic.py::test_isotonic_regression_auto_increasing", "sklearn/tests/test_isotonic.py::test_assert_raises_exceptions", "sklearn/tests/test_isotonic.py::test_isotonic_sample_weight_parameter_default_value", "sklearn/tests/test_isotonic.py::test_isotonic_min_max_boundaries", "sklearn/tests/test_isotonic.py::test_isotonic_sample_weight", "sklearn/tests/test_isotonic.py::test_isotonic_regression_oob_raise", "sklearn/tests/test_isotonic.py::test_isotonic_regression_oob_clip", "sklearn/tests/test_isotonic.py::test_isotonic_regression_oob_nan", "sklearn/tests/test_isotonic.py::test_isotonic_regression_pickle", "sklearn/tests/test_isotonic.py::test_isotonic_duplicate_min_entry", "sklearn/tests/test_isotonic.py::test_isotonic_zero_weight_loop", "sklearn/tests/test_isotonic.py::test_fast_predict", "sklearn/tests/test_isotonic.py::test_isotonic_dtype[int32]", "sklearn/tests/test_isotonic.py::test_isotonic_dtype[int64]", "sklearn/tests/test_isotonic.py::test_isotonic_dtype[float32]", "sklearn/tests/test_isotonic.py::test_isotonic_dtype[float64]", "sklearn/tests/test_isotonic.py::test_isotonic_mismatched_dtype[int32]", "sklearn/tests/test_isotonic.py::test_isotonic_mismatched_dtype[int64]", "sklearn/tests/test_isotonic.py::test_isotonic_mismatched_dtype[float32]", "sklearn/tests/test_isotonic.py::test_isotonic_mismatched_dtype[float64]", "sklearn/tests/test_isotonic.py::test_isotonic_make_unique_tolerance", "sklearn/tests/test_isotonic.py::test_isotonic_non_regression_inf_slope", "sklearn/tests/test_isotonic.py::test_isotonic_thresholds[True]", "sklearn/tests/test_isotonic.py::test_isotonic_thresholds[False]", "sklearn/tests/test_isotonic.py::test_input_shape_validation", "sklearn/tests/test_isotonic.py::test_isotonic_2darray_more_than_1_feature", "sklearn/tests/test_isotonic.py::test_isotonic_regression_sample_weight_not_overwritten", "sklearn/tests/test_isotonic.py::test_get_feature_names_out[1d]", "sklearn/tests/test_isotonic.py::test_get_feature_names_out[2d]", "sklearn/tests/test_isotonic.py::test_isotonic_regression_output_predict", "sklearn/tests/test_calibration.py::test_calibration_multiclass[1-True-isotonic]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[1-False-isotonic]", "sklearn/tests/test_calibration.py::test_calibration_prefit[csr_matrix]", "sklearn/tests/test_calibration.py::test_calibration_prefit[csr_array]", "sklearn/tests/test_calibration.py::test_calibration_ensemble_false[isotonic]", "sklearn/tests/test_calibration.py::test_calibration_nan_imputer[True]", "sklearn/tests/test_calibration.py::test_calibration_nan_imputer[False]", "sklearn/manifold/tests/test_mds.py::test_MDS", "sklearn/isotonic.py::sklearn.isotonic.IsotonicRegression", "sklearn/tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence[True-isotonic]", "sklearn/tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence[False-isotonic]", "sklearn/tests/test_calibration.py::test_calibrated_classifier_cv_works_with_large_confidence_scores[42]", "sklearn/manifold/tests/test_mds.py::test_normed_stress[0.5]", "sklearn/manifold/tests/test_mds.py::test_normed_stress[1.5]", "sklearn/manifold/tests/test_mds.py::test_normed_stress[2]", "sklearn/manifold/tests/test_mds.py::test_normalized_stress_auto[False]", "sklearn/tests/test_common.py::test_check_param_validation[IsotonicRegression()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-70	1.0	{ "code": "diff --git b/sklearn/impute/_iterative.py a/sklearn/impute/_iterative.py\nindex 2802aa13c..b1f722441 100644\n--- b/sklearn/impute/_iterative.py\n+++ a/sklearn/impute/_iterative.py\n@@ -695,6 +695,10 @@ class IterativeImputer(_BaseImputer):\n )\n return limit\n \n+ @_fit_context(\n+ # IterativeImputer.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit the imputer on `X` and return the transformed `X`.\n \n@@ -722,6 +726,118 @@ class IterativeImputer(_BaseImputer):\n Xt : array-like, shape (n_samples, n_features)\n The imputed input data.\n \"\"\"\n+ _raise_for_params(params, self, \"fit\")\n+\n+ routed_params = process_routing(\n+ self,\n+ \"fit\",\n+ params,\n+ )\n+\n+ self.random_state_ = getattr(\n+ self, \"random_state_\", check_random_state(self.random_state)\n+ )\n+\n+ if self.estimator is None:\n+ from ..linear_model import BayesianRidge\n+\n+ self._estimator = BayesianRidge()\n+ else:\n+ self._estimator = clone(self.estimator)\n+\n+ self.imputation_sequence_ = []\n+\n+ self.initial_imputer_ = None\n+\n+ X, Xt, mask_missing_values, complete_mask = self._initial_imputation(\n+ X, in_fit=True\n+ )\n+\n+ super()._fit_indicator(complete_mask)\n+ X_indicator = super()._transform_indicator(complete_mask)\n+\n+ if self.max_iter == 0 or np.all(mask_missing_values):\n+ self.n_iter_ = 0\n+ return super()._concatenate_indicator(Xt, X_indicator)\n+\n+ # Edge case: a single feature, we return the initial imputation.\n+ if Xt.shape[1] == 1:\n+ self.n_iter_ = 0\n+ return super()._concatenate_indicator(Xt, X_indicator)\n+\n+ self._min_value = self._validate_limit(self.min_value, \"min\", X.shape[1])\n+ self._max_value = self._validate_limit(self.max_value, \"max\", X.shape[1])\n+\n+ if not np.all(np.greater(self._max_value, self._min_value)):\n+ raise ValueError(\"One (or more) features have min_value >= max_value.\")\n+\n+ # order in which to impute\n+ # note this is probably too slow for large feature data (d > 100000)\n+ # and a better way would be good.\n+ # see: https://goo.gl/KyCNwj and subsequent comments\n+ ordered_idx = self._get_ordered_idx(mask_missing_values)\n+ self.n_features_with_missing_ = len(ordered_idx)\n+\n+ abs_corr_mat = self._get_abs_corr_mat(Xt)\n+\n+ n_samples, n_features = Xt.shape\n+ if self.verbose > 0:\n+ print(\"[IterativeImputer] Completing matrix with shape %s\" % (X.shape,))\n+ start_t = time()\n+ if not self.sample_posterior:\n+ Xt_previous = Xt.copy()\n+ normalized_tol = self.tol * np.max(np.abs(X[~mask_missing_values]))\n+ for self.n_iter_ in range(1, self.max_iter + 1):\n+ if self.imputation_order == \"random\":\n+ ordered_idx = self._get_ordered_idx(mask_missing_values)\n+\n+ for feat_idx in ordered_idx:\n+ neighbor_feat_idx = self._get_neighbor_feat_idx(\n+ n_features, feat_idx, abs_corr_mat\n+ )\n+ Xt, estimator = self._impute_one_feature(\n+ Xt,\n+ mask_missing_values,\n+ feat_idx,\n+ neighbor_feat_idx,\n+ estimator=None,\n+ fit_mode=True,\n+ params=routed_params.estimator.fit,\n+ )\n+ estimator_triplet = _ImputerTriplet(\n+ feat_idx, neighbor_feat_idx, estimator\n+ )\n+ self.imputation_sequence_.append(estimator_triplet)\n+\n+ if self.verbose > 1:\n+ print(\n+ \"[IterativeImputer] Ending imputation round \"\n+ \"%d/%d, elapsed time %0.2f\"\n+ % (self.n_iter_, self.max_iter, time() - start_t)\n+ )\n+\n+ if not self.sample_posterior:\n+ inf_norm = np.linalg.norm(Xt - Xt_previous, ord=np.inf, axis=None)\n+ if self.verbose > 0:\n+ print(\n+ \"[IterativeImputer] Change: {}, scaled tolerance: {} \".format(\n+ inf_norm, normalized_tol\n+ )\n+ )\n+ if inf_norm < normalized_tol:\n+ if self.verbose > 0:\n+ print(\"[IterativeImputer] Early stopping criterion reached.\")\n+ break\n+ Xt_previous = Xt.copy()\n+ else:\n+ if not self.sample_posterior:\n+ warnings.warn(\n+ \"[IterativeImputer] Early stopping criterion not reached.\",\n+ ConvergenceWarning,\n+ )\n+ _assign_where(Xt, X, cond=~mask_missing_values)\n+\n+ return super()._concatenate_indicator(Xt, X_indicator)\n \n def transform(self, X):\n \"\"\"Impute all missing values in `X`.\n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_iterative.py b/sklearn/impute/_iterative.py\nindex b1f722441..2802aa13c 100644\n--- a/sklearn/impute/_iterative.py\n+++ b/sklearn/impute/_iterative.py\n@@ -695,10 +695,6 @@ class IterativeImputer(_BaseImputer):\n )\n return limit\n \n- @_fit_context(\n- # IterativeImputer.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit the imputer on `X` and return the transformed `X`.\n \n@@ -726,118 +722,6 @@ class IterativeImputer(_BaseImputer):\n Xt : array-like, shape (n_samples, n_features)\n The imputed input data.\n \"\"\"\n- _raise_for_params(params, self, \"fit\")\n-\n- routed_params = process_routing(\n- self,\n- \"fit\",\n- params,\n- )\n-\n- self.random_state_ = getattr(\n- self, \"random_state_\", check_random_state(self.random_state)\n- )\n-\n- if self.estimator is None:\n- from ..linear_model import BayesianRidge\n-\n- self._estimator = BayesianRidge()\n- else:\n- self._estimator = clone(self.estimator)\n-\n- self.imputation_sequence_ = []\n-\n- self.initial_imputer_ = None\n-\n- X, Xt, mask_missing_values, complete_mask = self._initial_imputation(\n- X, in_fit=True\n- )\n-\n- super()._fit_indicator(complete_mask)\n- X_indicator = super()._transform_indicator(complete_mask)\n-\n- if self.max_iter == 0 or np.all(mask_missing_values):\n- self.n_iter_ = 0\n- return super()._concatenate_indicator(Xt, X_indicator)\n-\n- # Edge case: a single feature, we return the initial imputation.\n- if Xt.shape[1] == 1:\n- self.n_iter_ = 0\n- return super()._concatenate_indicator(Xt, X_indicator)\n-\n- self._min_value = self._validate_limit(self.min_value, \"min\", X.shape[1])\n- self._max_value = self._validate_limit(self.max_value, \"max\", X.shape[1])\n-\n- if not np.all(np.greater(self._max_value, self._min_value)):\n- raise ValueError(\"One (or more) features have min_value >= max_value.\")\n-\n- # order in which to impute\n- # note this is probably too slow for large feature data (d > 100000)\n- # and a better way would be good.\n- # see: https://goo.gl/KyCNwj and subsequent comments\n- ordered_idx = self._get_ordered_idx(mask_missing_values)\n- self.n_features_with_missing_ = len(ordered_idx)\n-\n- abs_corr_mat = self._get_abs_corr_mat(Xt)\n-\n- n_samples, n_features = Xt.shape\n- if self.verbose > 0:\n- print(\"[IterativeImputer] Completing matrix with shape %s\" % (X.shape,))\n- start_t = time()\n- if not self.sample_posterior:\n- Xt_previous = Xt.copy()\n- normalized_tol = self.tol * np.max(np.abs(X[~mask_missing_values]))\n- for self.n_iter_ in range(1, self.max_iter + 1):\n- if self.imputation_order == \"random\":\n- ordered_idx = self._get_ordered_idx(mask_missing_values)\n-\n- for feat_idx in ordered_idx:\n- neighbor_feat_idx = self._get_neighbor_feat_idx(\n- n_features, feat_idx, abs_corr_mat\n- )\n- Xt, estimator = self._impute_one_feature(\n- Xt,\n- mask_missing_values,\n- feat_idx,\n- neighbor_feat_idx,\n- estimator=None,\n- fit_mode=True,\n- params=routed_params.estimator.fit,\n- )\n- estimator_triplet = _ImputerTriplet(\n- feat_idx, neighbor_feat_idx, estimator\n- )\n- self.imputation_sequence_.append(estimator_triplet)\n-\n- if self.verbose > 1:\n- print(\n- \"[IterativeImputer] Ending imputation round \"\n- \"%d/%d, elapsed time %0.2f\"\n- % (self.n_iter_, self.max_iter, time() - start_t)\n- )\n-\n- if not self.sample_posterior:\n- inf_norm = np.linalg.norm(Xt - Xt_previous, ord=np.inf, axis=None)\n- if self.verbose > 0:\n- print(\n- \"[IterativeImputer] Change: {}, scaled tolerance: {} \".format(\n- inf_norm, normalized_tol\n- )\n- )\n- if inf_norm < normalized_tol:\n- if self.verbose > 0:\n- print(\"[IterativeImputer] Early stopping criterion reached.\")\n- break\n- Xt_previous = Xt.copy()\n- else:\n- if not self.sample_posterior:\n- warnings.warn(\n- \"[IterativeImputer] Early stopping criterion not reached.\",\n- ConvergenceWarning,\n- )\n- _assign_where(Xt, X, cond=~mask_missing_values)\n-\n- return super()._concatenate_indicator(Xt, X_indicator)\n \n def transform(self, X):\n \"\"\"Impute all missing values in `X`.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/impute/_iterative.py.\nHere is the description for the function:\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit the imputer on `X` and return the transformed `X`.\n\n Parameters\n ----------\n X : array-like, shape (n_samples, n_features)\n Input data, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n params : dict\n Parameters routed to the `fit` method of the sub-estimator via the\n metadata routing API.\n\n .. versionadded:: 1.5\n Only available if\n `sklearn.set_config(enable_metadata_routing=True)` is set. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n Xt : array-like, shape (n_samples, n_features)\n The imputed input data.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_matrix-mean]", "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_matrix-median]", "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_matrix-most_frequent]", "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_matrix-constant]", "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_array-mean]", "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_array-median]", "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_array-most_frequent]", "sklearn/impute/tests/test_impute.py::test_imputation_shape[csr_array-constant]", 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"sklearn/impute/tests/test_impute.py::test_iterative_imputer_estimators[estimator2]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_estimators[estimator3]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_estimators[estimator4]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_clip", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_clip_truncnorm", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_truncated_normal_posterior", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_missing_at_transform[mean]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_missing_at_transform[median]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_missing_at_transform[most_frequent]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_transform_stochasticity", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_no_missing", 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"sklearn/impute/tests/test_impute.py::test_iterative_imputer_catch_min_max_error[100-0-min_value >= max_value.]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_catch_min_max_error[inf--inf-min_value >= max_value.]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_catch_min_max_error[min_value2-max_value2-_value' should be of shape]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_min_max_array_like_imputation[None-vs-inf]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_min_max_array_like_imputation[Scalar-vs-vector]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_skip_non_missing[True]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_skip_non_missing[False]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[None-None]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[None-1]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[None-rs_imputer2]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[1-None]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[1-1]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[1-rs_imputer2]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[rs_estimator2-None]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[rs_estimator2-1]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_dont_set_random_state[rs_estimator2-rs_imputer2]", "sklearn/impute/tests/test_impute.py::test_inconsistent_dtype_X_missing_values[NaN-nan-Input X contains NaN-IterativeImputer]", "sklearn/impute/tests/test_impute.py::test_inconsistent_dtype_X_missing_values[-1--1-types are expected to be both numerical.-IterativeImputer]", "sklearn/impute/tests/test_impute.py::test_imputer_without_indicator[IterativeImputer]", "sklearn/impute/tests/test_impute.py::test_imputation_order[ascending-idx_order0]", "sklearn/impute/tests/test_impute.py::test_imputation_order[descending-idx_order1]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[mean]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[median]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[most_frequent]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[constant]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_constant_fill_value", "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputation_missing_value_in_test_array[IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[IterativeImputer-nan]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[IterativeImputer--1]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[IterativeImputer-0]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[True-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[False-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputers_feature_names_out_pandas[True-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputers_feature_names_out_pandas[False-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[IterativeImputer-True]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[IterativeImputer-False]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[nan-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-IterativeImputer]", "sklearn/impute/_iterative.py::sklearn.impute._iterative.IterativeImputer", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[IterativeImputer]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_empty_data_messages]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[IterativeImputer]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_n_iter]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IterativeImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IterativeImputer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[IterativeImputer()]", "sklearn/tests/test_common.py::test_check_param_validation[IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-IterativeImputer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-71	1.0	{ "code": "diff --git b/sklearn/impute/_iterative.py a/sklearn/impute/_iterative.py\nindex ed699373a..b1f722441 100644\n--- b/sklearn/impute/_iterative.py\n+++ a/sklearn/impute/_iterative.py\n@@ -855,6 +855,43 @@ class IterativeImputer(_BaseImputer):\n Xt : array-like, shape (n_samples, n_features)\n The imputed input data.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ X, Xt, mask_missing_values, complete_mask = self._initial_imputation(\n+ X, in_fit=False\n+ )\n+\n+ X_indicator = super()._transform_indicator(complete_mask)\n+\n+ if self.n_iter_ == 0 or np.all(mask_missing_values):\n+ return super()._concatenate_indicator(Xt, X_indicator)\n+\n+ imputations_per_round = len(self.imputation_sequence_) // self.n_iter_\n+ i_rnd = 0\n+ if self.verbose > 0:\n+ print(\"[IterativeImputer] Completing matrix with shape %s\" % (X.shape,))\n+ start_t = time()\n+ for it, estimator_triplet in enumerate(self.imputation_sequence_):\n+ Xt, _ = self._impute_one_feature(\n+ Xt,\n+ mask_missing_values,\n+ estimator_triplet.feat_idx,\n+ estimator_triplet.neighbor_feat_idx,\n+ estimator=estimator_triplet.estimator,\n+ fit_mode=False,\n+ )\n+ if not (it + 1) % imputations_per_round:\n+ if self.verbose > 1:\n+ print(\n+ \"[IterativeImputer] Ending imputation round \"\n+ \"%d/%d, elapsed time %0.2f\"\n+ % (i_rnd + 1, self.n_iter_, time() - start_t)\n+ )\n+ i_rnd += 1\n+\n+ _assign_where(Xt, X, cond=~mask_missing_values)\n+\n+ return super()._concatenate_indicator(Xt, X_indicator)\n \n def fit(self, X, y=None, **fit_params):\n \"\"\"Fit the imputer on `X` and return self.\n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_iterative.py b/sklearn/impute/_iterative.py\nindex b1f722441..ed699373a 100644\n--- a/sklearn/impute/_iterative.py\n+++ b/sklearn/impute/_iterative.py\n@@ -855,43 +855,6 @@ class IterativeImputer(_BaseImputer):\n Xt : array-like, shape (n_samples, n_features)\n The imputed input data.\n \"\"\"\n- check_is_fitted(self)\n-\n- X, Xt, mask_missing_values, complete_mask = self._initial_imputation(\n- X, in_fit=False\n- )\n-\n- X_indicator = super()._transform_indicator(complete_mask)\n-\n- if self.n_iter_ == 0 or np.all(mask_missing_values):\n- return super()._concatenate_indicator(Xt, X_indicator)\n-\n- imputations_per_round = len(self.imputation_sequence_) // self.n_iter_\n- i_rnd = 0\n- if self.verbose > 0:\n- print(\"[IterativeImputer] Completing matrix with shape %s\" % (X.shape,))\n- start_t = time()\n- for it, estimator_triplet in enumerate(self.imputation_sequence_):\n- Xt, _ = self._impute_one_feature(\n- Xt,\n- mask_missing_values,\n- estimator_triplet.feat_idx,\n- estimator_triplet.neighbor_feat_idx,\n- estimator=estimator_triplet.estimator,\n- fit_mode=False,\n- )\n- if not (it + 1) % imputations_per_round:\n- if self.verbose > 1:\n- print(\n- \"[IterativeImputer] Ending imputation round \"\n- \"%d/%d, elapsed time %0.2f\"\n- % (i_rnd + 1, self.n_iter_, time() - start_t)\n- )\n- i_rnd += 1\n-\n- _assign_where(Xt, X, cond=~mask_missing_values)\n-\n- return super()._concatenate_indicator(Xt, X_indicator)\n \n def fit(self, X, y=None, **fit_params):\n \"\"\"Fit the imputer on `X` and return self.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/impute/_iterative.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Impute all missing values in `X`.\n\n Note that this is stochastic, and that if `random_state` is not fixed,\n repeated calls, or permuted input, results will differ.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The input data to complete.\n\n Returns\n -------\n Xt : array-like, shape (n_samples, n_features)\n The imputed input data.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/impute/tests/test_impute.py::test_iterative_imputer_zero_iters", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_verbose", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_truncated_normal_posterior", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_missing_at_transform[mean]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_missing_at_transform[median]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_missing_at_transform[most_frequent]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_transform_stochasticity", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_no_missing", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_transform_recovery[3]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_transform_recovery[5]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_additive_matrix", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_min_max_array_like_imputation[None-vs-inf]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_min_max_array_like_imputation[Scalar-vs-vector]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_skip_non_missing[True]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_skip_non_missing[False]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[mean]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[median]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[most_frequent]", "sklearn/impute/tests/test_impute.py::test_iterative_imputer_keep_empty_features[constant]", "sklearn/impute/tests/test_common.py::test_imputation_missing_value_in_test_array[IterativeImputer]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[IterativeImputer-True]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[IterativeImputer-False]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[nan-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-IterativeImputer]", "sklearn/impute/_iterative.py::sklearn.impute._iterative.IterativeImputer", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IterativeImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-IterativeImputer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-72	1.0	{ "code": "diff --git b/sklearn/preprocessing/_discretization.py a/sklearn/preprocessing/_discretization.py\nindex 9a8fc964b..6a6a739c4 100644\n--- b/sklearn/preprocessing/_discretization.py\n+++ a/sklearn/preprocessing/_discretization.py\n@@ -197,6 +197,7 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n self.subsample = subsample\n self.random_state = random_state\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit the estimator.\n@@ -221,6 +222,108 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X, dtype=\"numeric\")\n+\n+ if self.dtype in (np.float64, np.float32):\n+ output_dtype = self.dtype\n+ else: # self.dtype is None\n+ output_dtype = X.dtype\n+\n+ n_samples, n_features = X.shape\n+\n+ if sample_weight is not None and self.strategy == \"uniform\":\n+ raise ValueError(\n+ \"`sample_weight` was provided but it cannot be \"\n+ \"used with strategy='uniform'. Got strategy=\"\n+ f\"{self.strategy!r} instead.\"\n+ )\n+\n+ if self.subsample is not None and n_samples > self.subsample:\n+ # Take a subsample of `X`\n+ X = resample(\n+ X,\n+ replace=False,\n+ n_samples=self.subsample,\n+ random_state=self.random_state,\n+ )\n+\n+ n_features = X.shape[1]\n+ n_bins = self._validate_n_bins(n_features)\n+\n+ if sample_weight is not None:\n+ sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n+\n+ bin_edges = np.zeros(n_features, dtype=object)\n+ for jj in range(n_features):\n+ column = X[:, jj]\n+ col_min, col_max = column.min(), column.max()\n+\n+ if col_min == col_max:\n+ warnings.warn(\n+ \"Feature %d is constant and will be replaced with 0.\" % jj\n+ )\n+ n_bins[jj] = 1\n+ bin_edges[jj] = np.array([-np.inf, np.inf])\n+ continue\n+\n+ if self.strategy == \"uniform\":\n+ bin_edges[jj] = np.linspace(col_min, col_max, n_bins[jj] + 1)\n+\n+ elif self.strategy == \"quantile\":\n+ quantiles = np.linspace(0, 100, n_bins[jj] + 1)\n+ if sample_weight is None:\n+ bin_edges[jj] = np.asarray(np.percentile(column, quantiles))\n+ else:\n+ bin_edges[jj] = np.asarray(\n+ [\n+ _weighted_percentile(column, sample_weight, q)\n+ for q in quantiles\n+ ],\n+ dtype=np.float64,\n+ )\n+ elif self.strategy == \"kmeans\":\n+ from ..cluster import KMeans # fixes import loops\n+\n+ # Deterministic initialization with uniform spacing\n+ uniform_edges = np.linspace(col_min, col_max, n_bins[jj] + 1)\n+ init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5\n+\n+ # 1D k-means procedure\n+ km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1)\n+ centers = km.fit(\n+ column[:, None], sample_weight=sample_weight\n+ ).cluster_centers_[:, 0]\n+ # Must sort, centers may be unsorted even with sorted init\n+ centers.sort()\n+ bin_edges[jj] = (centers[1:] + centers[:-1]) * 0.5\n+ bin_edges[jj] = np.r_[col_min, bin_edges[jj], col_max]\n+\n+ # Remove bins whose width are too small (i.e., <= 1e-8)\n+ if self.strategy in (\"quantile\", \"kmeans\"):\n+ mask = np.ediff1d(bin_edges[jj], to_begin=np.inf) > 1e-8\n+ bin_edges[jj] = bin_edges[jj][mask]\n+ if len(bin_edges[jj]) - 1 != n_bins[jj]:\n+ warnings.warn(\n+ \"Bins whose width are too small (i.e., <= \"\n+ \"1e-8) in feature %d are removed. Consider \"\n+ \"decreasing the number of bins.\" % jj\n+ )\n+ n_bins[jj] = len(bin_edges[jj]) - 1\n+\n+ self.bin_edges_ = bin_edges\n+ self.n_bins_ = n_bins\n+\n+ if \"onehot\" in self.encode:\n+ self._encoder = OneHotEncoder(\n+ categories=[np.arange(i) for i in self.n_bins_],\n+ sparse_output=self.encode == \"onehot\",\n+ dtype=output_dtype,\n+ )\n+ # Fit the OneHotEncoder with toy datasets\n+ # so that it's ready for use after the KBinsDiscretizer is fitted\n+ self._encoder.fit(np.zeros((1, len(self.n_bins_))))\n+\n+ return self\n \n def _validate_n_bins(self, n_features):\n \"\"\"Returns n_bins_, the number of bins per feature.\"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_discretization.py b/sklearn/preprocessing/_discretization.py\nindex 6a6a739c4..9a8fc964b 100644\n--- a/sklearn/preprocessing/_discretization.py\n+++ b/sklearn/preprocessing/_discretization.py\n@@ -197,7 +197,6 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n self.subsample = subsample\n self.random_state = random_state\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit the estimator.\n@@ -222,108 +221,6 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X, dtype=\"numeric\")\n-\n- if self.dtype in (np.float64, np.float32):\n- output_dtype = self.dtype\n- else: # self.dtype is None\n- output_dtype = X.dtype\n-\n- n_samples, n_features = X.shape\n-\n- if sample_weight is not None and self.strategy == \"uniform\":\n- raise ValueError(\n- \"`sample_weight` was provided but it cannot be \"\n- \"used with strategy='uniform'. Got strategy=\"\n- f\"{self.strategy!r} instead.\"\n- )\n-\n- if self.subsample is not None and n_samples > self.subsample:\n- # Take a subsample of `X`\n- X = resample(\n- X,\n- replace=False,\n- n_samples=self.subsample,\n- random_state=self.random_state,\n- )\n-\n- n_features = X.shape[1]\n- n_bins = self._validate_n_bins(n_features)\n-\n- if sample_weight is not None:\n- sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n-\n- bin_edges = np.zeros(n_features, dtype=object)\n- for jj in range(n_features):\n- column = X[:, jj]\n- col_min, col_max = column.min(), column.max()\n-\n- if col_min == col_max:\n- warnings.warn(\n- \"Feature %d is constant and will be replaced with 0.\" % jj\n- )\n- n_bins[jj] = 1\n- bin_edges[jj] = np.array([-np.inf, np.inf])\n- continue\n-\n- if self.strategy == \"uniform\":\n- bin_edges[jj] = np.linspace(col_min, col_max, n_bins[jj] + 1)\n-\n- elif self.strategy == \"quantile\":\n- quantiles = np.linspace(0, 100, n_bins[jj] + 1)\n- if sample_weight is None:\n- bin_edges[jj] = np.asarray(np.percentile(column, quantiles))\n- else:\n- bin_edges[jj] = np.asarray(\n- [\n- _weighted_percentile(column, sample_weight, q)\n- for q in quantiles\n- ],\n- dtype=np.float64,\n- )\n- elif self.strategy == \"kmeans\":\n- from ..cluster import KMeans # fixes import loops\n-\n- # Deterministic initialization with uniform spacing\n- uniform_edges = np.linspace(col_min, col_max, n_bins[jj] + 1)\n- init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5\n-\n- # 1D k-means procedure\n- km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1)\n- centers = km.fit(\n- column[:, None], sample_weight=sample_weight\n- ).cluster_centers_[:, 0]\n- # Must sort, centers may be unsorted even with sorted init\n- centers.sort()\n- bin_edges[jj] = (centers[1:] + centers[:-1]) * 0.5\n- bin_edges[jj] = np.r_[col_min, bin_edges[jj], col_max]\n-\n- # Remove bins whose width are too small (i.e., <= 1e-8)\n- if self.strategy in (\"quantile\", \"kmeans\"):\n- mask = np.ediff1d(bin_edges[jj], to_begin=np.inf) > 1e-8\n- bin_edges[jj] = bin_edges[jj][mask]\n- if len(bin_edges[jj]) - 1 != n_bins[jj]:\n- warnings.warn(\n- \"Bins whose width are too small (i.e., <= \"\n- \"1e-8) in feature %d are removed. Consider \"\n- \"decreasing the number of bins.\" % jj\n- )\n- n_bins[jj] = len(bin_edges[jj]) - 1\n-\n- self.bin_edges_ = bin_edges\n- self.n_bins_ = n_bins\n-\n- if \"onehot\" in self.encode:\n- self._encoder = OneHotEncoder(\n- categories=[np.arange(i) for i in self.n_bins_],\n- sparse_output=self.encode == \"onehot\",\n- dtype=output_dtype,\n- )\n- # Fit the OneHotEncoder with toy datasets\n- # so that it's ready for use after the KBinsDiscretizer is fitted\n- self._encoder.fit(np.zeros((1, len(self.n_bins_))))\n-\n- return self\n \n def _validate_n_bins(self, n_features):\n \"\"\"Returns n_bins_, the number of bins per feature.\"\"\"\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_discretization.py.\nHere is the description for the function:\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit the estimator.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Data to be discretized.\n\n y : None\n Ignored. This parameter exists only for compatibility with\n :class:`~sklearn.pipeline.Pipeline`.\n\n sample_weight : ndarray of shape (n_samples,)\n Contains weight values to be associated with each sample.\n Cannot be used when `strategy` is set to `\"uniform\"`.\n\n .. versionadded:: 1.3\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_kbindiscretizer", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_minmax_imputation", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[uniform-expected0-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[kmeans-expected1-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected2-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected3-sample_weight3]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected4-sample_weight4]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected5-sample_weight5]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[kmeans-expected6-sample_weight6]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[kmeans-expected7-sample_weight7]", "sklearn/preprocessing/tests/test_discretization.py::test_valid_n_bins", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_wrong_strategy_with_weights[uniform]", "sklearn/preprocessing/tests/test_discretization.py::test_invalid_n_bins_array", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[uniform-expected0-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[kmeans-expected1-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-expected2-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-expected3-sample_weight3]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-expected4-sample_weight4]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[kmeans-expected5-sample_weight5]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_effect_sample_weight", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_no_mutating_sample_weight[kmeans]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_no_mutating_sample_weight[quantile]", "sklearn/preprocessing/tests/test_discretization.py::test_same_min_max[uniform]", "sklearn/preprocessing/tests/test_discretization.py::test_same_min_max[kmeans]", "sklearn/preprocessing/tests/test_discretization.py::test_same_min_max[quantile]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_1d_behavior", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[1]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[2]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[3]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[4]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[5]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[6]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[7]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[8]", "sklearn/preprocessing/tests/test_discretization.py::test_encode_options", "sklearn/preprocessing/tests/test_discretization.py::test_nonuniform_strategies[uniform-expected_2bins0-expected_3bins0-expected_5bins0]", "sklearn/preprocessing/tests/test_discretization.py::test_nonuniform_strategies[kmeans-expected_2bins1-expected_3bins1-expected_5bins1]", "sklearn/preprocessing/tests/test_discretization.py::test_nonuniform_strategies[quantile-expected_2bins2-expected_3bins2-expected_5bins2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_outside_fit_range[uniform]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_outside_fit_range[kmeans]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_outside_fit_range[quantile]", "sklearn/preprocessing/tests/test_discretization.py::test_overwrite", "sklearn/preprocessing/tests/test_discretization.py::test_redundant_bins[quantile-expected_bin_edges0]", "sklearn/preprocessing/tests/test_discretization.py::test_redundant_bins[kmeans-expected_bin_edges1]", "sklearn/preprocessing/tests/test_discretization.py::test_percentile_numeric_stability", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-None-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-None-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-None-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float32-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float32-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float32-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float64-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float64-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float64-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-None-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-None-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-None-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float32-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float32-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float32-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float64-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float64-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float64-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-None-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-None-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-None-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float32-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float32-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float32-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float64-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float64-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float64-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[ordinal-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[ordinal-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[ordinal-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-dense-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-dense-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-dense-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_subsample_default", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscrtizer_get_feature_names_out[onehot-expected_names0]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscrtizer_get_feature_names_out[onehot-dense-expected_names1]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscrtizer_get_feature_names_out[ordinal-expected_names2]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_subsample[42-uniform]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_subsample[42-kmeans]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_subsample[42-quantile]", "sklearn/preprocessing/tests/test_discretization.py::test_KBD_inverse_transform_Xt_deprecation", "sklearn/preprocessing/tests/test_target_encoder.py::test_invariance_of_encoding_under_label_permutation[42-0.0]", "sklearn/preprocessing/tests/test_target_encoder.py::test_invariance_of_encoding_under_label_permutation[42-1000.0]", "sklearn/preprocessing/tests/test_target_encoder.py::test_invariance_of_encoding_under_label_permutation[42-auto]", "sklearn/preprocessing/tests/test_target_encoder.py::test_target_encoding_for_linear_regression[42-0.0]", "sklearn/preprocessing/tests/test_target_encoder.py::test_target_encoding_for_linear_regression[42-auto]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[0.5-None]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[0.5-1]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[0.5-2]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[1.0-None]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[1.0-1]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[1.0-2]", "sklearn/preprocessing/_discretization.py::sklearn.preprocessing._discretization.KBinsDiscretizer", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_check_param_validation[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KBinsDiscretizer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-73	1.0	{ "code": "diff --git b/sklearn/preprocessing/_discretization.py a/sklearn/preprocessing/_discretization.py\nindex 7694bad00..6a6a739c4 100644\n--- b/sklearn/preprocessing/_discretization.py\n+++ a/sklearn/preprocessing/_discretization.py\n@@ -412,6 +412,28 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n Xinv : ndarray, dtype={np.float32, np.float64}\n Data in the original feature space.\n \"\"\"\n+ X = _deprecate_Xt_in_inverse_transform(X, Xt)\n+\n+ check_is_fitted(self)\n+\n+ if \"onehot\" in self.encode:\n+ X = self._encoder.inverse_transform(X)\n+\n+ Xinv = check_array(X, copy=True, dtype=(np.float64, np.float32))\n+ n_features = self.n_bins_.shape[0]\n+ if Xinv.shape[1] != n_features:\n+ raise ValueError(\n+ \"Incorrect number of features. Expecting {}, received {}.\".format(\n+ n_features, Xinv.shape[1]\n+ )\n+ )\n+\n+ for jj in range(n_features):\n+ bin_edges = self.bin_edges_[jj]\n+ bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5\n+ Xinv[:, jj] = bin_centers[(Xinv[:, jj]).astype(np.int64)]\n+\n+ return Xinv\n \n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names.\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_discretization.py b/sklearn/preprocessing/_discretization.py\nindex 6a6a739c4..7694bad00 100644\n--- a/sklearn/preprocessing/_discretization.py\n+++ b/sklearn/preprocessing/_discretization.py\n@@ -412,28 +412,6 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n Xinv : ndarray, dtype={np.float32, np.float64}\n Data in the original feature space.\n \"\"\"\n- X = _deprecate_Xt_in_inverse_transform(X, Xt)\n-\n- check_is_fitted(self)\n-\n- if \"onehot\" in self.encode:\n- X = self._encoder.inverse_transform(X)\n-\n- Xinv = check_array(X, copy=True, dtype=(np.float64, np.float32))\n- n_features = self.n_bins_.shape[0]\n- if Xinv.shape[1] != n_features:\n- raise ValueError(\n- \"Incorrect number of features. Expecting {}, received {}.\".format(\n- n_features, Xinv.shape[1]\n- )\n- )\n-\n- for jj in range(n_features):\n- bin_edges = self.bin_edges_[jj]\n- bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5\n- Xinv[:, jj] = bin_centers[(Xinv[:, jj]).astype(np.int64)]\n-\n- return Xinv\n \n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_discretization.py.\nHere is the description for the function:\n def inverse_transform(self, X=None, *, Xt=None):\n \"\"\"\n Transform discretized data back to original feature space.\n\n Note that this function does not regenerate the original data\n due to discretization rounding.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Transformed data in the binned space.\n\n Xt : array-like of shape (n_samples, n_features)\n Transformed data in the binned space.\n\n .. deprecated:: 1.5\n `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.\n\n Returns\n -------\n Xinv : ndarray, dtype={np.float32, np.float64}\n Data in the original feature space.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_overwrite", "sklearn/preprocessing/tests/test_discretization.py::test_KBD_inverse_transform_Xt_deprecation", "sklearn/preprocessing/_discretization.py::sklearn.preprocessing._discretization.KBinsDiscretizer" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-74	1.0	{ "code": "diff --git b/sklearn/preprocessing/_discretization.py a/sklearn/preprocessing/_discretization.py\nindex d280df781..6a6a739c4 100644\n--- b/sklearn/preprocessing/_discretization.py\n+++ a/sklearn/preprocessing/_discretization.py\n@@ -365,6 +365,29 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n Data in the binned space. Will be a sparse matrix if\n `self.encode='onehot'` and ndarray otherwise.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ # check input and attribute dtypes\n+ dtype = (np.float64, np.float32) if self.dtype is None else self.dtype\n+ Xt = validate_data(self, X, copy=True, dtype=dtype, reset=False)\n+\n+ bin_edges = self.bin_edges_\n+ for jj in range(Xt.shape[1]):\n+ Xt[:, jj] = np.searchsorted(bin_edges[jj][1:-1], Xt[:, jj], side=\"right\")\n+\n+ if self.encode == \"ordinal\":\n+ return Xt\n+\n+ dtype_init = None\n+ if \"onehot\" in self.encode:\n+ dtype_init = self._encoder.dtype\n+ self._encoder.dtype = Xt.dtype\n+ try:\n+ Xt_enc = self._encoder.transform(Xt)\n+ finally:\n+ # revert the initial dtype to avoid modifying self.\n+ self._encoder.dtype = dtype_init\n+ return Xt_enc\n \n def inverse_transform(self, X=None, *, Xt=None):\n \"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_discretization.py b/sklearn/preprocessing/_discretization.py\nindex 6a6a739c4..d280df781 100644\n--- a/sklearn/preprocessing/_discretization.py\n+++ b/sklearn/preprocessing/_discretization.py\n@@ -365,29 +365,6 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):\n Data in the binned space. Will be a sparse matrix if\n `self.encode='onehot'` and ndarray otherwise.\n \"\"\"\n- check_is_fitted(self)\n-\n- # check input and attribute dtypes\n- dtype = (np.float64, np.float32) if self.dtype is None else self.dtype\n- Xt = validate_data(self, X, copy=True, dtype=dtype, reset=False)\n-\n- bin_edges = self.bin_edges_\n- for jj in range(Xt.shape[1]):\n- Xt[:, jj] = np.searchsorted(bin_edges[jj][1:-1], Xt[:, jj], side=\"right\")\n-\n- if self.encode == \"ordinal\":\n- return Xt\n-\n- dtype_init = None\n- if \"onehot\" in self.encode:\n- dtype_init = self._encoder.dtype\n- self._encoder.dtype = Xt.dtype\n- try:\n- Xt_enc = self._encoder.transform(Xt)\n- finally:\n- # revert the initial dtype to avoid modifying self.\n- self._encoder.dtype = dtype_init\n- return Xt_enc\n \n def inverse_transform(self, X=None, *, Xt=None):\n \"\"\"\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_discretization.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"\n Discretize the data.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Data to be discretized.\n\n Returns\n -------\n Xt : {ndarray, sparse matrix}, dtype={np.float32, np.float64}\n Data in the binned space. Will be a sparse matrix if\n `self.encode='onehot'` and ndarray otherwise.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_kbindiscretizer", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_minmax_imputation", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[uniform-expected0-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[kmeans-expected1-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected2-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected3-sample_weight3]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected4-sample_weight4]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[quantile-expected5-sample_weight5]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[kmeans-expected6-sample_weight6]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform[kmeans-expected7-sample_weight7]", "sklearn/preprocessing/tests/test_discretization.py::test_valid_n_bins", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[uniform-expected0-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[kmeans-expected1-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-expected2-None]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-expected3-sample_weight3]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-expected4-sample_weight4]", "sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[kmeans-expected5-sample_weight5]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_effect_sample_weight", "sklearn/preprocessing/tests/test_discretization.py::test_same_min_max[uniform]", "sklearn/preprocessing/tests/test_discretization.py::test_same_min_max[kmeans]", "sklearn/preprocessing/tests/test_discretization.py::test_same_min_max[quantile]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_1d_behavior", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[1]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[2]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[3]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[4]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[5]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[6]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[7]", "sklearn/preprocessing/tests/test_discretization.py::test_numeric_stability[8]", "sklearn/preprocessing/tests/test_discretization.py::test_encode_options", "sklearn/preprocessing/tests/test_discretization.py::test_nonuniform_strategies[uniform-expected_2bins0-expected_3bins0-expected_5bins0]", "sklearn/preprocessing/tests/test_discretization.py::test_nonuniform_strategies[kmeans-expected_2bins1-expected_3bins1-expected_5bins1]", "sklearn/preprocessing/tests/test_discretization.py::test_nonuniform_strategies[quantile-expected_2bins2-expected_3bins2-expected_5bins2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[ordinal-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-uniform-expected_inv0]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-kmeans-expected_inv1]", "sklearn/preprocessing/tests/test_discretization.py::test_inverse_transform[onehot-dense-quantile-expected_inv2]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_outside_fit_range[uniform]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_outside_fit_range[kmeans]", "sklearn/preprocessing/tests/test_discretization.py::test_transform_outside_fit_range[quantile]", "sklearn/preprocessing/tests/test_discretization.py::test_overwrite", "sklearn/preprocessing/tests/test_discretization.py::test_percentile_numeric_stability", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-None-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-None-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-None-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float32-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float32-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float32-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float64-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float64-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[ordinal-float64-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-None-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-None-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-None-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float32-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float32-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float32-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float64-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float64-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-float64-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-None-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-None-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-None-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float32-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float32-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float32-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float64-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float64-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_consistent_dtype[onehot-dense-float64-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[ordinal-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[ordinal-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[ordinal-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-dense-float16]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-dense-float32]", "sklearn/preprocessing/tests/test_discretization.py::test_32_equal_64[onehot-dense-float64]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscrtizer_get_feature_names_out[onehot-expected_names0]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscrtizer_get_feature_names_out[onehot-dense-expected_names1]", "sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscrtizer_get_feature_names_out[ordinal-expected_names2]", "sklearn/preprocessing/tests/test_target_encoder.py::test_invariance_of_encoding_under_label_permutation[42-0.0]", "sklearn/preprocessing/tests/test_target_encoder.py::test_invariance_of_encoding_under_label_permutation[42-1000.0]", "sklearn/preprocessing/tests/test_target_encoder.py::test_invariance_of_encoding_under_label_permutation[42-auto]", "sklearn/preprocessing/tests/test_target_encoder.py::test_target_encoding_for_linear_regression[42-0.0]", "sklearn/preprocessing/tests/test_target_encoder.py::test_target_encoding_for_linear_regression[42-auto]", "sklearn/preprocessing/tests/test_discretization.py::test_KBD_inverse_transform_Xt_deprecation", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[0.5-None]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[0.5-1]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[0.5-2]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[1.0-None]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[1.0-1]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[1.0-2]", "sklearn/preprocessing/_discretization.py::sklearn.preprocessing._discretization.KBinsDiscretizer", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KBinsDiscretizer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-75	1.0	{ "code": "diff --git b/sklearn/cluster/_kmeans.py a/sklearn/cluster/_kmeans.py\nindex ac2d2e7c7..ef7b910e1 100644\n--- b/sklearn/cluster/_kmeans.py\n+++ a/sklearn/cluster/_kmeans.py\n@@ -1427,6 +1427,7 @@ class KMeans(_BaseKMeans):\n f\" variable OMP_NUM_THREADS={n_active_threads}.\"\n )\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute k-means clustering.\n \n@@ -1454,6 +1455,107 @@ class KMeans(_BaseKMeans):\n self : object\n Fitted estimator.\n \"\"\"\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=\"csr\",\n+ dtype=[np.float64, np.float32],\n+ order=\"C\",\n+ copy=self.copy_x,\n+ accept_large_sparse=False,\n+ )\n+\n+ self._check_params_vs_input(X)\n+\n+ random_state = check_random_state(self.random_state)\n+ sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n+ self._n_threads = _openmp_effective_n_threads()\n+\n+ # Validate init array\n+ init = self.init\n+ init_is_array_like = _is_arraylike_not_scalar(init)\n+ if init_is_array_like:\n+ init = check_array(init, dtype=X.dtype, copy=True, order=\"C\")\n+ self._validate_center_shape(X, init)\n+\n+ # subtract of mean of x for more accurate distance computations\n+ if not sp.issparse(X):\n+ X_mean = X.mean(axis=0)\n+ # The copy was already done above\n+ X -= X_mean\n+\n+ if init_is_array_like:\n+ init -= X_mean\n+\n+ # precompute squared norms of data points\n+ x_squared_norms = row_norms(X, squared=True)\n+\n+ if self._algorithm == \"elkan\":\n+ kmeans_single = _kmeans_single_elkan\n+ else:\n+ kmeans_single = _kmeans_single_lloyd\n+ self._check_mkl_vcomp(X, X.shape[0])\n+\n+ best_inertia, best_labels = None, None\n+\n+ for i in range(self._n_init):\n+ # Initialize centers\n+ centers_init = self._init_centroids(\n+ X,\n+ x_squared_norms=x_squared_norms,\n+ init=init,\n+ random_state=random_state,\n+ sample_weight=sample_weight,\n+ )\n+ if self.verbose:\n+ print(\"Initialization complete\")\n+\n+ # run a k-means once\n+ labels, inertia, centers, n_iter_ = kmeans_single(\n+ X,\n+ sample_weight,\n+ centers_init,\n+ max_iter=self.max_iter,\n+ verbose=self.verbose,\n+ tol=self._tol,\n+ n_threads=self._n_threads,\n+ )\n+\n+ # determine if these results are the best so far\n+ # we chose a new run if it has a better inertia and the clustering is\n+ # different from the best so far (it's possible that the inertia is\n+ # slightly better even if the clustering is the same with potentially\n+ # permuted labels, due to rounding errors)\n+ if best_inertia is None or (\n+ inertia < best_inertia\n+ and not _is_same_clustering(labels, best_labels, self.n_clusters)\n+ ):\n+ best_labels = labels\n+ best_centers = centers\n+ best_inertia = inertia\n+ best_n_iter = n_iter_\n+\n+ if not sp.issparse(X):\n+ if not self.copy_x:\n+ X += X_mean\n+ best_centers += X_mean\n+\n+ distinct_clusters = len(set(best_labels))\n+ if distinct_clusters < self.n_clusters:\n+ warnings.warn(\n+ \"Number of distinct clusters ({}) found smaller than \"\n+ \"n_clusters ({}). Possibly due to duplicate points \"\n+ \"in X.\".format(distinct_clusters, self.n_clusters),\n+ ConvergenceWarning,\n+ stacklevel=2,\n+ )\n+\n+ self.cluster_centers_ = best_centers\n+ self._n_features_out = self.cluster_centers_.shape[0]\n+ self.labels_ = best_labels\n+ self.inertia_ = best_inertia\n+ self.n_iter_ = best_n_iter\n+ return self\n \n \n def _mini_batch_step(\n", "test": null }	null	{ "code": "diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py\nindex ef7b910e1..ac2d2e7c7 100644\n--- a/sklearn/cluster/_kmeans.py\n+++ b/sklearn/cluster/_kmeans.py\n@@ -1427,7 +1427,6 @@ class KMeans(_BaseKMeans):\n f\" variable OMP_NUM_THREADS={n_active_threads}.\"\n )\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute k-means clustering.\n \n@@ -1455,107 +1454,6 @@ class KMeans(_BaseKMeans):\n self : object\n Fitted estimator.\n \"\"\"\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=\"csr\",\n- dtype=[np.float64, np.float32],\n- order=\"C\",\n- copy=self.copy_x,\n- accept_large_sparse=False,\n- )\n-\n- self._check_params_vs_input(X)\n-\n- random_state = check_random_state(self.random_state)\n- sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n- self._n_threads = _openmp_effective_n_threads()\n-\n- # Validate init array\n- init = self.init\n- init_is_array_like = _is_arraylike_not_scalar(init)\n- if init_is_array_like:\n- init = check_array(init, dtype=X.dtype, copy=True, order=\"C\")\n- self._validate_center_shape(X, init)\n-\n- # subtract of mean of x for more accurate distance computations\n- if not sp.issparse(X):\n- X_mean = X.mean(axis=0)\n- # The copy was already done above\n- X -= X_mean\n-\n- if init_is_array_like:\n- init -= X_mean\n-\n- # precompute squared norms of data points\n- x_squared_norms = row_norms(X, squared=True)\n-\n- if self._algorithm == \"elkan\":\n- kmeans_single = _kmeans_single_elkan\n- else:\n- kmeans_single = _kmeans_single_lloyd\n- self._check_mkl_vcomp(X, X.shape[0])\n-\n- best_inertia, best_labels = None, None\n-\n- for i in range(self._n_init):\n- # Initialize centers\n- centers_init = self._init_centroids(\n- X,\n- x_squared_norms=x_squared_norms,\n- init=init,\n- random_state=random_state,\n- sample_weight=sample_weight,\n- )\n- if self.verbose:\n- print(\"Initialization complete\")\n-\n- # run a k-means once\n- labels, inertia, centers, n_iter_ = kmeans_single(\n- X,\n- sample_weight,\n- centers_init,\n- max_iter=self.max_iter,\n- verbose=self.verbose,\n- tol=self._tol,\n- n_threads=self._n_threads,\n- )\n-\n- # determine if these results are the best so far\n- # we chose a new run if it has a better inertia and the clustering is\n- # different from the best so far (it's possible that the inertia is\n- # slightly better even if the clustering is the same with potentially\n- # permuted labels, due to rounding errors)\n- if best_inertia is None or (\n- inertia < best_inertia\n- and not _is_same_clustering(labels, best_labels, self.n_clusters)\n- ):\n- best_labels = labels\n- best_centers = centers\n- best_inertia = inertia\n- best_n_iter = n_iter_\n-\n- if not sp.issparse(X):\n- if not self.copy_x:\n- X += X_mean\n- best_centers += X_mean\n-\n- distinct_clusters = len(set(best_labels))\n- if distinct_clusters < self.n_clusters:\n- warnings.warn(\n- \"Number of distinct clusters ({}) found smaller than \"\n- \"n_clusters ({}). Possibly due to duplicate points \"\n- \"in X.\".format(distinct_clusters, self.n_clusters),\n- ConvergenceWarning,\n- stacklevel=2,\n- )\n-\n- self.cluster_centers_ = best_centers\n- self._n_features_out = self.cluster_centers_.shape[0]\n- self.labels_ = best_labels\n- self.inertia_ = best_inertia\n- self.n_iter_ = best_n_iter\n- return self\n \n \n def _mini_batch_step(\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/cluster/_kmeans.py.\nHere is the description for the function:\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute k-means clustering.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training instances to cluster. It must be noted that the data\n will be converted to C ordering, which will cause a memory\n copy if the given data is not C-contiguous.\n If a sparse matrix is passed, a copy will be made if it's not in\n CSR format.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n sample_weight : array-like of shape (n_samples,), default=None\n The weights for each observation in X. If None, all observations\n are assigned equal weight. `sample_weight` is not used during\n initialization if `init` is a callable or a user provided array.\n\n .. versionadded:: 0.20\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/cluster/tests/test_k_means.py::test_kmeans_results[float32-lloyd-dense]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_results[float32-lloyd-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_results[float32-lloyd-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_results[float32-elkan-dense]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_results[float32-elkan-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_results[float32-elkan-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_results[float64-lloyd-dense]", 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"sklearn/tests/test_common.py::test_estimators[SpectralCoclustering(n_clusters=2,n_init=2)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SpectralCoclustering(n_clusters=2,n_init=2)-check_n_features_in]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[BayesianGaussianMixture(max_iter=5,n_init=2)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GaussianMixture(max_iter=5,n_init=2)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SpectralBiclustering(n_best=1,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SpectralClustering(n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SpectralCoclustering(n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[BayesianGaussianMixture(max_iter=5,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GaussianMixture(max_iter=5,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SpectralBiclustering(n_best=1,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SpectralClustering(n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SpectralCoclustering(n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_param_validation[KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_set_output_transform[KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KMeans(max_iter=5,n_clusters=2,n_init=2)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-76	1.0	{ "code": "diff --git b/sklearn/impute/_knn.py a/sklearn/impute/_knn.py\nindex a09b21b42..1b7ef06ed 100644\n--- b/sklearn/impute/_knn.py\n+++ a/sklearn/impute/_knn.py\n@@ -210,6 +210,7 @@ class KNNImputer(_BaseImputer):\n \n return np.ma.average(donors, axis=1, weights=weight_matrix).data\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the imputer on X.\n \n@@ -227,6 +228,28 @@ class KNNImputer(_BaseImputer):\n self : object\n The fitted `KNNImputer` class instance.\n \"\"\"\n+ # Check data integrity and calling arguments\n+ if not is_scalar_nan(self.missing_values):\n+ ensure_all_finite = True\n+ else:\n+ ensure_all_finite = \"allow-nan\"\n+\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=False,\n+ dtype=FLOAT_DTYPES,\n+ ensure_all_finite=ensure_all_finite,\n+ copy=self.copy,\n+ )\n+\n+ self._fit_X = X\n+ self._mask_fit_X = _get_mask(self._fit_X, self.missing_values)\n+ self._valid_mask = ~np.all(self._mask_fit_X, axis=0)\n+\n+ super()._fit_indicator(self._mask_fit_X)\n+\n+ return self\n \n def transform(self, X):\n \"\"\"Impute all missing values in X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py\nindex 1b7ef06ed..a09b21b42 100644\n--- a/sklearn/impute/_knn.py\n+++ b/sklearn/impute/_knn.py\n@@ -210,7 +210,6 @@ class KNNImputer(_BaseImputer):\n \n return np.ma.average(donors, axis=1, weights=weight_matrix).data\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the imputer on X.\n \n@@ -228,28 +227,6 @@ class KNNImputer(_BaseImputer):\n self : object\n The fitted `KNNImputer` class instance.\n \"\"\"\n- # Check data integrity and calling arguments\n- if not is_scalar_nan(self.missing_values):\n- ensure_all_finite = True\n- else:\n- ensure_all_finite = \"allow-nan\"\n-\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=False,\n- dtype=FLOAT_DTYPES,\n- ensure_all_finite=ensure_all_finite,\n- copy=self.copy,\n- )\n-\n- self._fit_X = X\n- self._mask_fit_X = _get_mask(self._fit_X, self.missing_values)\n- self._valid_mask = ~np.all(self._mask_fit_X, axis=0)\n-\n- super()._fit_indicator(self._mask_fit_X)\n-\n- return self\n \n def transform(self, X):\n \"\"\"Impute all missing values in X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/impute/_knn.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the imputer on X.\n\n Parameters\n ----------\n X : array-like shape of (n_samples, n_features)\n Input data, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n self : object\n The fitted `KNNImputer` class instance.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/impute/tests/test_impute.py::test_knn_imputer_keep_empty_features[True]", "sklearn/impute/tests/test_impute.py::test_knn_imputer_keep_empty_features[False]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[1-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[1-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[2-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[2-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[3-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[3-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[4-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[4-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[5-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[5-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_default_with_invalid_input[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_default_with_invalid_input[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_removes_all_na_features[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_removes_all_na_features[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_zero_nan_imputes_the_same[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_zero_nan_imputes_the_same[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_verify[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_verify[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_one_n_neighbors[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_one_n_neighbors[-1]", "sklearn/impute/tests/test_common.py::test_imputation_missing_value_in_test_array[KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_all_samples_are_neighbors[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_all_samples_are_neighbors[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_uniform[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_uniform[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_distance[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_distance[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_callable_metric", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[-1-None]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[-1-0]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[nan-None]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer--1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[nan-0]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[uniform--1]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer-0]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[uniform-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[distance--1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[distance-nan]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[True-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[uniform--1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[uniform-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[distance--1]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[False-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[distance-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_drops_all_nan_features[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_drops_all_nan_features[nan]", "sklearn/impute/tests/test_common.py::test_imputers_feature_names_out_pandas[True-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[-1-None]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[-1-0]", "sklearn/impute/tests/test_common.py::test_imputers_feature_names_out_pandas[False-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[nan-None]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[KNNImputer-True]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[KNNImputer-False]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[nan-KNNImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[nan-0]", "sklearn/impute/_knn.py::sklearn.impute._knn.KNNImputer", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KNNImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KNNImputer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KNNImputer()]", "sklearn/tests/test_common.py::test_check_param_validation[KNNImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[KNNImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-KNNImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KNNImputer()]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[KNNImputer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-77	1.0	{ "code": "diff --git b/sklearn/impute/_knn.py a/sklearn/impute/_knn.py\nindex ef720d12a..1b7ef06ed 100644\n--- b/sklearn/impute/_knn.py\n+++ a/sklearn/impute/_knn.py\n@@ -266,6 +266,125 @@ class KNNImputer(_BaseImputer):\n that is not always missing during `fit`.\n \"\"\"\n \n+ check_is_fitted(self)\n+ if not is_scalar_nan(self.missing_values):\n+ ensure_all_finite = True\n+ else:\n+ ensure_all_finite = \"allow-nan\"\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=False,\n+ dtype=FLOAT_DTYPES,\n+ force_writeable=True,\n+ ensure_all_finite=ensure_all_finite,\n+ copy=self.copy,\n+ reset=False,\n+ )\n+\n+ mask = _get_mask(X, self.missing_values)\n+ mask_fit_X = self._mask_fit_X\n+ valid_mask = self._valid_mask\n+\n+ X_indicator = super()._transform_indicator(mask)\n+\n+ # Removes columns where the training data is all nan\n+ if not np.any(mask[:, valid_mask]):\n+ # No missing values in X\n+ if self.keep_empty_features:\n+ Xc = X\n+ Xc[:, ~valid_mask] = 0\n+ else:\n+ Xc = X[:, valid_mask]\n+\n+ # Even if there are no missing values in X, we still concatenate Xc\n+ # with the missing value indicator matrix, X_indicator.\n+ # This is to ensure that the output maintains consistency in terms\n+ # of columns, regardless of whether missing values exist in X or not.\n+ return super()._concatenate_indicator(Xc, X_indicator)\n+\n+ row_missing_idx = np.flatnonzero(mask[:, valid_mask].any(axis=1))\n+\n+ non_missing_fix_X = np.logical_not(mask_fit_X)\n+\n+ # Maps from indices from X to indices in dist matrix\n+ dist_idx_map = np.zeros(X.shape[0], dtype=int)\n+ dist_idx_map[row_missing_idx] = np.arange(row_missing_idx.shape[0])\n+\n+ def process_chunk(dist_chunk, start):\n+ row_missing_chunk = row_missing_idx[start : start + len(dist_chunk)]\n+\n+ # Find and impute missing by column\n+ for col in range(X.shape[1]):\n+ if not valid_mask[col]:\n+ # column was all missing during training\n+ continue\n+\n+ col_mask = mask[row_missing_chunk, col]\n+ if not np.any(col_mask):\n+ # column has no missing values\n+ continue\n+\n+ (potential_donors_idx,) = np.nonzero(non_missing_fix_X[:, col])\n+\n+ # receivers_idx are indices in X\n+ receivers_idx = row_missing_chunk[np.flatnonzero(col_mask)]\n+\n+ # distances for samples that needed imputation for column\n+ dist_subset = dist_chunk[dist_idx_map[receivers_idx] - start][\n+ :, potential_donors_idx\n+ ]\n+\n+ # receivers with all nan distances impute with mean\n+ all_nan_dist_mask = np.isnan(dist_subset).all(axis=1)\n+ all_nan_receivers_idx = receivers_idx[all_nan_dist_mask]\n+\n+ if all_nan_receivers_idx.size:\n+ col_mean = np.ma.array(\n+ self._fit_X[:, col], mask=mask_fit_X[:, col]\n+ ).mean()\n+ X[all_nan_receivers_idx, col] = col_mean\n+\n+ if len(all_nan_receivers_idx) == len(receivers_idx):\n+ # all receivers imputed with mean\n+ continue\n+\n+ # receivers with at least one defined distance\n+ receivers_idx = receivers_idx[~all_nan_dist_mask]\n+ dist_subset = dist_chunk[dist_idx_map[receivers_idx] - start][\n+ :, potential_donors_idx\n+ ]\n+\n+ n_neighbors = min(self.n_neighbors, len(potential_donors_idx))\n+ value = self._calc_impute(\n+ dist_subset,\n+ n_neighbors,\n+ self._fit_X[potential_donors_idx, col],\n+ mask_fit_X[potential_donors_idx, col],\n+ )\n+ X[receivers_idx, col] = value\n+\n+ # process in fixed-memory chunks\n+ gen = pairwise_distances_chunked(\n+ X[row_missing_idx, :],\n+ self._fit_X,\n+ metric=self.metric,\n+ missing_values=self.missing_values,\n+ ensure_all_finite=ensure_all_finite,\n+ reduce_func=process_chunk,\n+ )\n+ for chunk in gen:\n+ # process_chunk modifies X in place. No return value.\n+ pass\n+\n+ if self.keep_empty_features:\n+ Xc = X\n+ Xc[:, ~valid_mask] = 0\n+ else:\n+ Xc = X[:, valid_mask]\n+\n+ return super()._concatenate_indicator(Xc, X_indicator)\n+\n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\n \n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py\nindex 1b7ef06ed..ef720d12a 100644\n--- a/sklearn/impute/_knn.py\n+++ b/sklearn/impute/_knn.py\n@@ -266,125 +266,6 @@ class KNNImputer(_BaseImputer):\n that is not always missing during `fit`.\n \"\"\"\n \n- check_is_fitted(self)\n- if not is_scalar_nan(self.missing_values):\n- ensure_all_finite = True\n- else:\n- ensure_all_finite = \"allow-nan\"\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=False,\n- dtype=FLOAT_DTYPES,\n- force_writeable=True,\n- ensure_all_finite=ensure_all_finite,\n- copy=self.copy,\n- reset=False,\n- )\n-\n- mask = _get_mask(X, self.missing_values)\n- mask_fit_X = self._mask_fit_X\n- valid_mask = self._valid_mask\n-\n- X_indicator = super()._transform_indicator(mask)\n-\n- # Removes columns where the training data is all nan\n- if not np.any(mask[:, valid_mask]):\n- # No missing values in X\n- if self.keep_empty_features:\n- Xc = X\n- Xc[:, ~valid_mask] = 0\n- else:\n- Xc = X[:, valid_mask]\n-\n- # Even if there are no missing values in X, we still concatenate Xc\n- # with the missing value indicator matrix, X_indicator.\n- # This is to ensure that the output maintains consistency in terms\n- # of columns, regardless of whether missing values exist in X or not.\n- return super()._concatenate_indicator(Xc, X_indicator)\n-\n- row_missing_idx = np.flatnonzero(mask[:, valid_mask].any(axis=1))\n-\n- non_missing_fix_X = np.logical_not(mask_fit_X)\n-\n- # Maps from indices from X to indices in dist matrix\n- dist_idx_map = np.zeros(X.shape[0], dtype=int)\n- dist_idx_map[row_missing_idx] = np.arange(row_missing_idx.shape[0])\n-\n- def process_chunk(dist_chunk, start):\n- row_missing_chunk = row_missing_idx[start : start + len(dist_chunk)]\n-\n- # Find and impute missing by column\n- for col in range(X.shape[1]):\n- if not valid_mask[col]:\n- # column was all missing during training\n- continue\n-\n- col_mask = mask[row_missing_chunk, col]\n- if not np.any(col_mask):\n- # column has no missing values\n- continue\n-\n- (potential_donors_idx,) = np.nonzero(non_missing_fix_X[:, col])\n-\n- # receivers_idx are indices in X\n- receivers_idx = row_missing_chunk[np.flatnonzero(col_mask)]\n-\n- # distances for samples that needed imputation for column\n- dist_subset = dist_chunk[dist_idx_map[receivers_idx] - start][\n- :, potential_donors_idx\n- ]\n-\n- # receivers with all nan distances impute with mean\n- all_nan_dist_mask = np.isnan(dist_subset).all(axis=1)\n- all_nan_receivers_idx = receivers_idx[all_nan_dist_mask]\n-\n- if all_nan_receivers_idx.size:\n- col_mean = np.ma.array(\n- self._fit_X[:, col], mask=mask_fit_X[:, col]\n- ).mean()\n- X[all_nan_receivers_idx, col] = col_mean\n-\n- if len(all_nan_receivers_idx) == len(receivers_idx):\n- # all receivers imputed with mean\n- continue\n-\n- # receivers with at least one defined distance\n- receivers_idx = receivers_idx[~all_nan_dist_mask]\n- dist_subset = dist_chunk[dist_idx_map[receivers_idx] - start][\n- :, potential_donors_idx\n- ]\n-\n- n_neighbors = min(self.n_neighbors, len(potential_donors_idx))\n- value = self._calc_impute(\n- dist_subset,\n- n_neighbors,\n- self._fit_X[potential_donors_idx, col],\n- mask_fit_X[potential_donors_idx, col],\n- )\n- X[receivers_idx, col] = value\n-\n- # process in fixed-memory chunks\n- gen = pairwise_distances_chunked(\n- X[row_missing_idx, :],\n- self._fit_X,\n- metric=self.metric,\n- missing_values=self.missing_values,\n- ensure_all_finite=ensure_all_finite,\n- reduce_func=process_chunk,\n- )\n- for chunk in gen:\n- # process_chunk modifies X in place. No return value.\n- pass\n-\n- if self.keep_empty_features:\n- Xc = X\n- Xc[:, ~valid_mask] = 0\n- else:\n- Xc = X[:, valid_mask]\n-\n- return super()._concatenate_indicator(Xc, X_indicator)\n-\n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\n \n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/impute/_knn.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Impute all missing values in X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The input data to complete.\n\n Returns\n -------\n X : array-like of shape (n_samples, n_output_features)\n The imputed dataset. `n_output_features` is the number of features\n that is not always missing during `fit`.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/impute/tests/test_impute.py::test_knn_imputer_keep_empty_features[True]", "sklearn/impute/tests/test_impute.py::test_knn_imputer_keep_empty_features[False]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[1-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[1-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[2-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[2-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[3-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[3-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[4-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[4-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[5-uniform]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_shape[5-distance]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_default_with_invalid_input[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_default_with_invalid_input[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_removes_all_na_features[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_removes_all_na_features[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_zero_nan_imputes_the_same[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_zero_nan_imputes_the_same[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_verify[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_verify[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_one_n_neighbors[nan]", "sklearn/impute/tests/test_common.py::test_imputation_missing_value_in_test_array[KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_one_n_neighbors[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_all_samples_are_neighbors[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_all_samples_are_neighbors[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_uniform[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_uniform[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_distance[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_distance[-1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_callable_metric", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[-1-None]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[-1-0]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer--1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[nan-None]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[nan-0]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer-0]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[uniform--1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[uniform-nan]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[True-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[distance--1]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[False-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_not_enough_valid_distances[distance-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[uniform--1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[uniform-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[distance--1]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_nan_distance[distance-nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_drops_all_nan_features[-1]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[KNNImputer-True]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_drops_all_nan_features[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[-1-None]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[KNNImputer-False]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[nan-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[-1-0]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-KNNImputer]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[nan-None]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[nan-0]", "sklearn/impute/_knn.py::sklearn.impute._knn.KNNImputer", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KNNImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KNNImputer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KNNImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[KNNImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-KNNImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KNNImputer()]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[KNNImputer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-78	1.0	{ "code": "diff --git b/sklearn/neighbors/_classification.py a/sklearn/neighbors/_classification.py\nindex 642f4ae05..43004a213 100644\n--- b/sklearn/neighbors/_classification.py\n+++ a/sklearn/neighbors/_classification.py\n@@ -252,6 +252,58 @@ class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase):\n y : ndarray of shape (n_queries,) or (n_queries, n_outputs)\n Class labels for each data sample.\n \"\"\"\n+ check_is_fitted(self, \"_fit_method\")\n+ if self.weights == \"uniform\":\n+ if self._fit_method == \"brute\" and ArgKminClassMode.is_usable_for(\n+ X, self._fit_X, self.metric\n+ ):\n+ probabilities = self.predict_proba(X)\n+ if self.outputs_2d_:\n+ return np.stack(\n+ [\n+ self.classes_[idx][np.argmax(probas, axis=1)]\n+ for idx, probas in enumerate(probabilities)\n+ ],\n+ axis=1,\n+ )\n+ return self.classes_[np.argmax(probabilities, axis=1)]\n+ # In that case, we do not need the distances to perform\n+ # the weighting so we do not compute them.\n+ neigh_ind = self.kneighbors(X, return_distance=False)\n+ neigh_dist = None\n+ else:\n+ neigh_dist, neigh_ind = self.kneighbors(X)\n+\n+ classes_ = self.classes_\n+ _y = self._y\n+ if not self.outputs_2d_:\n+ _y = self._y.reshape((-1, 1))\n+ classes_ = [self.classes_]\n+\n+ n_outputs = len(classes_)\n+ n_queries = _num_samples(X)\n+ weights = _get_weights(neigh_dist, self.weights)\n+ if weights is not None and _all_with_any_reduction_axis_1(weights, value=0):\n+ raise ValueError(\n+ \"All neighbors of some sample is getting zero weights. \"\n+ \"Please modify 'weights' to avoid this case if you are \"\n+ \"using a user-defined function.\"\n+ )\n+\n+ y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)\n+ for k, classes_k in enumerate(classes_):\n+ if weights is None:\n+ mode, _ = _mode(_y[neigh_ind, k], axis=1)\n+ else:\n+ mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)\n+\n+ mode = np.asarray(mode.ravel(), dtype=np.intp)\n+ y_pred[:, k] = classes_k.take(mode)\n+\n+ if not self.outputs_2d_:\n+ y_pred = y_pred.ravel()\n+\n+ return y_pred\n \n def predict_proba(self, X):\n \"\"\"Return probability estimates for the test data X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_classification.py b/sklearn/neighbors/_classification.py\nindex 43004a213..642f4ae05 100644\n--- a/sklearn/neighbors/_classification.py\n+++ b/sklearn/neighbors/_classification.py\n@@ -252,58 +252,6 @@ class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase):\n y : ndarray of shape (n_queries,) or (n_queries, n_outputs)\n Class labels for each data sample.\n \"\"\"\n- check_is_fitted(self, \"_fit_method\")\n- if self.weights == \"uniform\":\n- if self._fit_method == \"brute\" and ArgKminClassMode.is_usable_for(\n- X, self._fit_X, self.metric\n- ):\n- probabilities = self.predict_proba(X)\n- if self.outputs_2d_:\n- return np.stack(\n- [\n- self.classes_[idx][np.argmax(probas, axis=1)]\n- for idx, probas in enumerate(probabilities)\n- ],\n- axis=1,\n- )\n- return self.classes_[np.argmax(probabilities, axis=1)]\n- # In that case, we do not need the distances to perform\n- # the weighting so we do not compute them.\n- neigh_ind = self.kneighbors(X, return_distance=False)\n- neigh_dist = None\n- else:\n- neigh_dist, neigh_ind = self.kneighbors(X)\n-\n- classes_ = self.classes_\n- _y = self._y\n- if not self.outputs_2d_:\n- _y = self._y.reshape((-1, 1))\n- classes_ = [self.classes_]\n-\n- n_outputs = len(classes_)\n- n_queries = _num_samples(X)\n- weights = _get_weights(neigh_dist, self.weights)\n- if weights is not None and _all_with_any_reduction_axis_1(weights, value=0):\n- raise ValueError(\n- \"All neighbors of some sample is getting zero weights. \"\n- \"Please modify 'weights' to avoid this case if you are \"\n- \"using a user-defined function.\"\n- )\n-\n- y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)\n- for k, classes_k in enumerate(classes_):\n- if weights is None:\n- mode, _ = _mode(_y[neigh_ind, k], axis=1)\n- else:\n- mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)\n-\n- mode = np.asarray(mode.ravel(), dtype=np.intp)\n- y_pred[:, k] = classes_k.take(mode)\n-\n- if not self.outputs_2d_:\n- y_pred = y_pred.ravel()\n-\n- return y_pred\n \n def predict_proba(self, X):\n \"\"\"Return probability estimates for the test data X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_classification.py.\nHere is the description for the function:\n def predict(self, X):\n \"\"\"Predict the class labels for the provided data.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_queries, n_features), \\\n or (n_queries, n_indexed) if metric == 'precomputed'\n Test samples.\n\n Returns\n -------\n y : ndarray of shape (n_queries,) or (n_queries, n_outputs)\n Class labels for each data sample.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-chebyshev-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-chebyshev-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-cityblock-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-cityblock-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-l1-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-l1-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-l2-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-l2-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-manhattan-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-manhattan-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-minkowski-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-minkowski-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-chebyshev-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-chebyshev-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-cityblock-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-cityblock-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-l1-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-l1-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-l2-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-l2-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-manhattan-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-manhattan-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-minkowski-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-minkowski-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-chebyshev-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-chebyshev-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-cityblock-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-cityblock-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-l1-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-l1-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-l2-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-l2-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-manhattan-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-manhattan-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-minkowski-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-minkowski-1000-5-100]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_score_multilabel", "sklearn/model_selection/tests/test_search.py::test_search_cv_pairwise_property_equivalence_of_precomputed", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_dense", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_sparse_knn[csr]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_sparse_knn[lil]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-uniform-ball_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-uniform-brute]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-uniform-kd_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-uniform-auto]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-distance-ball_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-distance-brute]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-distance-kd_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-distance-auto]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-_weight_func-ball_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-_weight_func-brute]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-_weight_func-kd_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier[float64-_weight_func-auto]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier_float_labels[float64]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_classifier_sparse", "sklearn/neighbors/tests/test_neighbors.py::test_KNeighborsClassifier_multioutput", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_iris", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_digits", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_matrix-KNeighborsClassifier]", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_array-KNeighborsClassifier]", "sklearn/neighbors/tests/test_neighbors.py::test_predict_sparse_ball_kd_tree[csr_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_predict_sparse_ball_kd_tree[csr_array]", "sklearn/neighbors/tests/test_neighbors.py::test_same_knn_parallel[ball_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_same_knn_parallel[brute]", "sklearn/neighbors/tests/test_neighbors.py::test_same_knn_parallel[kd_tree]", "sklearn/neighbors/tests/test_neighbors.py::test_same_knn_parallel[auto]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[ball_tree-threading]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[ball_tree-loky]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[brute-threading]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[brute-loky]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[kd_tree-threading]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[kd_tree-loky]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[auto-threading]", "sklearn/neighbors/tests/test_neighbors.py::test_knn_forcing_backend[auto-loky]", "sklearn/neighbors/tests/test_neighbors.py::test_dtype_convert", "sklearn/manifold/tests/test_isomap.py::test_pipeline[float64-2-None]", "sklearn/manifold/tests/test_isomap.py::test_pipeline[float64-None-10.0]", "sklearn/neighbors/tests/test_neighbors.py::test_predict_dataframe", "sklearn/tests/test_multiclass.py::test_ovo_consistent_binary_classification", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_predict_proba[MLPClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_predict_proba[RandomForestClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_decision_function", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[False-auto]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[False-predict]", "sklearn/neighbors/tests/test_neighbors.py::test_KNeighborsClassifier_raise_on_all_zero_weights", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-auto]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[threshold-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[k_best-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y0-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y1-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_no_unlabeled", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-predict]", "sklearn/manifold/tests/test_locally_linear.py::test_pipeline", "sklearn/neighbors/_classification.py::sklearn.neighbors._classification.KNeighborsClassifier", "sklearn/neighbors/_nca.py::sklearn.neighbors._nca.NeighborhoodComponentsAnalysis", "sklearn/feature_selection/_sequential.py::sklearn.feature_selection._sequential.SequentialFeatureSelector", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifier_multioutput]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifiers_multilabel_representation_invariance]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_classifiers_multilabel_output_format_predict]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[KNeighborsClassifier()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KNeighborsClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KNeighborsClassifier()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-79	1.0	{ "code": "diff --git b/sklearn/neighbors/_regression.py a/sklearn/neighbors/_regression.py\nindex e8b255b5f..545e6b875 100644\n--- b/sklearn/neighbors/_regression.py\n+++ a/sklearn/neighbors/_regression.py\n@@ -235,6 +235,34 @@ class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):\n y : ndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=int\n Target values.\n \"\"\"\n+ if self.weights == \"uniform\":\n+ # In that case, we do not need the distances to perform\n+ # the weighting so we do not compute them.\n+ neigh_ind = self.kneighbors(X, return_distance=False)\n+ neigh_dist = None\n+ else:\n+ neigh_dist, neigh_ind = self.kneighbors(X)\n+\n+ weights = _get_weights(neigh_dist, self.weights)\n+\n+ _y = self._y\n+ if _y.ndim == 1:\n+ _y = _y.reshape((-1, 1))\n+\n+ if weights is None:\n+ y_pred = np.mean(_y[neigh_ind], axis=1)\n+ else:\n+ y_pred = np.empty((neigh_dist.shape[0], _y.shape[1]), dtype=np.float64)\n+ denom = np.sum(weights, axis=1)\n+\n+ for j in range(_y.shape[1]):\n+ num = np.sum(_y[neigh_ind, j] * weights, axis=1)\n+ y_pred[:, j] = num / denom\n+\n+ if self._y.ndim == 1:\n+ y_pred = y_pred.ravel()\n+\n+ return y_pred\n \n \n class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBase):\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_regression.py b/sklearn/neighbors/_regression.py\nindex 545e6b875..e8b255b5f 100644\n--- a/sklearn/neighbors/_regression.py\n+++ b/sklearn/neighbors/_regression.py\n@@ -235,34 +235,6 @@ class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):\n y : ndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=int\n Target values.\n \"\"\"\n- if self.weights == \"uniform\":\n- # In that case, we do not need the distances to perform\n- # the weighting so we do not compute them.\n- neigh_ind = self.kneighbors(X, return_distance=False)\n- neigh_dist = None\n- else:\n- neigh_dist, neigh_ind = self.kneighbors(X)\n-\n- weights = _get_weights(neigh_dist, self.weights)\n-\n- _y = self._y\n- if _y.ndim == 1:\n- _y = _y.reshape((-1, 1))\n-\n- if weights is None:\n- y_pred = np.mean(_y[neigh_ind], axis=1)\n- else:\n- y_pred = np.empty((neigh_dist.shape[0], _y.shape[1]), dtype=np.float64)\n- denom = np.sum(weights, axis=1)\n-\n- for j in range(_y.shape[1]):\n- num = np.sum(_y[neigh_ind, j] * weights, axis=1)\n- y_pred[:, j] = num / denom\n-\n- if self._y.ndim == 1:\n- y_pred = y_pred.ravel()\n-\n- return y_pred\n \n \n class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBase):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_regression.py.\nHere is the description for the function:\n def predict(self, X):\n \"\"\"Predict the target for the provided data.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_queries, n_features), \\\n or (n_queries, n_indexed) if metric == 'precomputed'\n Test samples.\n\n Returns\n -------\n y : ndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=int\n Target values.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-chebyshev-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-chebyshev-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-cityblock-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-cityblock-1000-5-100]", "sklearn/ensemble/tests/test_bagging.py::test_regression", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-l1-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-l1-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-l2-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-l2-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-manhattan-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-manhattan-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-minkowski-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-minkowski-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-DM_euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-DM_euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-chebyshev-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-chebyshev-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-cityblock-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-cityblock-1000-5-100]", "sklearn/ensemble/tests/test_bagging.py::test_single_estimator", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-l1-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-l1-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-l2-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-l2-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-manhattan-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-manhattan-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-minkowski-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-minkowski-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-DM_euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-DM_euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-chebyshev-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-chebyshev-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-cityblock-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-cityblock-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-l1-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-l1-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-l2-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-l2-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-manhattan-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-manhattan-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-minkowski-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-minkowski-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-DM_euclidean-100-100-10]", "sklearn/neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-DM_euclidean-1000-5-100]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_dense", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_sparse_knn[csr]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_sparse_knn[lil]", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_regressors_zero_distance", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_regressor", "sklearn/neighbors/tests/test_neighbors.py::test_KNeighborsRegressor_multioutput_uniform_weight", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_regressor_multioutput", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_regressor_sparse", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_iris", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_matrix-KNeighborsRegressor]", "sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_array-KNeighborsRegressor]", "sklearn/neighbors/tests/test_neighbors.py::test_predict_sparse_ball_kd_tree[csr_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_predict_sparse_ball_kd_tree[csr_array]", "sklearn/neighbors/tests/test_neighbors.py::test_pipeline_with_nearest_neighbors_transformer", "sklearn/neighbors/tests/test_neighbors.py::test_regressor_predict_on_arraylikes", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_distance[nan]", "sklearn/impute/tests/test_knn.py::test_knn_imputer_weight_distance[-1]", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_kneighbors_regressor", "sklearn/neighbors/_regression.py::sklearn.neighbors._regression.KNeighborsRegressor", "sklearn/ensemble/_voting.py::sklearn.ensemble._voting.VotingRegressor", "sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_pairwise", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[KNeighborsRegressor()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KNeighborsRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KNeighborsRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-80	1.0	{ "code": "diff --git b/sklearn/neighbors/_kde.py a/sklearn/neighbors/_kde.py\nindex 1b6f9c8bf..ae82ea636 100644\n--- b/sklearn/neighbors/_kde.py\n+++ a/sklearn/neighbors/_kde.py\n@@ -187,6 +187,10 @@ class KernelDensity(BaseEstimator):\n )\n return algorithm\n \n+ @_fit_context(\n+ # KernelDensity.metric is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Fit the Kernel Density model on the data.\n \n@@ -210,6 +214,36 @@ class KernelDensity(BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n+ algorithm = self._choose_algorithm(self.algorithm, self.metric)\n+\n+ if isinstance(self.bandwidth, str):\n+ if self.bandwidth == \"scott\":\n+ self.bandwidth_ = X.shape[0] ** (-1 / (X.shape[1] + 4))\n+ elif self.bandwidth == \"silverman\":\n+ self.bandwidth_ = (X.shape[0] * (X.shape[1] + 2) / 4) (\n+ -1 / (X.shape[1] + 4)\n+ )\n+ else:\n+ self.bandwidth_ = self.bandwidth\n+\n+ X = validate_data(self, X, order=\"C\", dtype=np.float64)\n+\n+ if sample_weight is not None:\n+ sample_weight = _check_sample_weight(\n+ sample_weight, X, dtype=np.float64, ensure_non_negative=True\n+ )\n+\n+ kwargs = self.metric_params\n+ if kwargs is None:\n+ kwargs = {}\n+ self.tree_ = TREE_DICT[algorithm](\n+ X,\n+ metric=self.metric,\n+ leaf_size=self.leaf_size,\n+ sample_weight=sample_weight,\n+ kwargs,\n+ )\n+ return self\n \n def score_samples(self, X):\n \"\"\"Compute the log-likelihood of each sample under the model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_kde.py b/sklearn/neighbors/_kde.py\nindex ae82ea636..1b6f9c8bf 100644\n--- a/sklearn/neighbors/_kde.py\n+++ b/sklearn/neighbors/_kde.py\n@@ -187,10 +187,6 @@ class KernelDensity(BaseEstimator):\n )\n return algorithm\n \n- @_fit_context(\n- # KernelDensity.metric is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Fit the Kernel Density model on the data.\n \n@@ -214,36 +210,6 @@ class KernelDensity(BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n- algorithm = self._choose_algorithm(self.algorithm, self.metric)\n-\n- if isinstance(self.bandwidth, str):\n- if self.bandwidth == \"scott\":\n- self.bandwidth_ = X.shape[0] ** (-1 / (X.shape[1] + 4))\n- elif self.bandwidth == \"silverman\":\n- self.bandwidth_ = (X.shape[0] * (X.shape[1] + 2) / 4) (\n- -1 / (X.shape[1] + 4)\n- )\n- else:\n- self.bandwidth_ = self.bandwidth\n-\n- X = validate_data(self, X, order=\"C\", dtype=np.float64)\n-\n- if sample_weight is not None:\n- sample_weight = _check_sample_weight(\n- sample_weight, X, dtype=np.float64, ensure_non_negative=True\n- )\n-\n- kwargs = self.metric_params\n- if kwargs is None:\n- kwargs = {}\n- self.tree_ = TREE_DICT[algorithm](\n- X,\n- metric=self.metric,\n- leaf_size=self.leaf_size,\n- sample_weight=sample_weight,\n- kwargs,\n- )\n- return self\n \n def score_samples(self, X):\n \"\"\"Compute the log-likelihood of each sample under the model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_kde.py.\nHere is the description for the function:\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Fit the Kernel Density model on the data.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n List of n_features-dimensional data points. Each row\n corresponds to a single data point.\n\n y : None\n Ignored. This parameter exists only for compatibility with\n :class:`~sklearn.pipeline.Pipeline`.\n\n sample_weight : array-like of shape (n_samples,), default=None\n List of sample weights attached to the data X.\n\n .. versionadded:: 0.20\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_search.py::test_gridsearch_no_predict", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density_sampling", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[euclidean-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[euclidean-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[euclidean-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[minkowski-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[minkowski-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[minkowski-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[manhattan-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[manhattan-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[manhattan-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[chebyshev-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[chebyshev-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[chebyshev-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[haversine-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[haversine-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[haversine-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_sample_weights_error", "sklearn/neighbors/tests/test_kde.py::test_kde_pipeline_gridsearch", "sklearn/neighbors/tests/test_kde.py::test_kde_sample_weights", "sklearn/neighbors/tests/test_kde.py::test_pickling[None]", "sklearn/neighbors/tests/test_kde.py::test_pickling[sample_weight1]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[scott]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[silverman]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[0.1]", "sklearn/neighbors/_kde.py::sklearn.neighbors._kde.KernelDensity", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KernelDensity()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KernelDensity()]", "sklearn/tests/test_common.py::test_check_param_validation[KernelDensity()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-81	1.0	{ "code": "diff --git b/sklearn/neighbors/_kde.py a/sklearn/neighbors/_kde.py\nindex a55ff31b6..ae82ea636 100644\n--- b/sklearn/neighbors/_kde.py\n+++ a/sklearn/neighbors/_kde.py\n@@ -326,6 +326,37 @@ class KernelDensity(BaseEstimator):\n X : array-like of shape (n_samples, n_features)\n List of samples.\n \"\"\"\n+ check_is_fitted(self)\n+ # TODO: implement sampling for other valid kernel shapes\n+ if self.kernel not in [\"gaussian\", \"tophat\"]:\n+ raise NotImplementedError()\n+\n+ data = np.asarray(self.tree_.data)\n+\n+ rng = check_random_state(random_state)\n+ u = rng.uniform(0, 1, size=n_samples)\n+ if self.tree_.sample_weight is None:\n+ i = (u * data.shape[0]).astype(np.int64)\n+ else:\n+ cumsum_weight = np.cumsum(np.asarray(self.tree_.sample_weight))\n+ sum_weight = cumsum_weight[-1]\n+ i = np.searchsorted(cumsum_weight, u * sum_weight)\n+ if self.kernel == \"gaussian\":\n+ return np.atleast_2d(rng.normal(data[i], self.bandwidth_))\n+\n+ elif self.kernel == \"tophat\":\n+ # we first draw points from a d-dimensional normal distribution,\n+ # then use an incomplete gamma function to map them to a uniform\n+ # d-dimensional tophat distribution.\n+ dim = data.shape[1]\n+ X = rng.normal(size=(n_samples, dim))\n+ s_sq = row_norms(X, squared=True)\n+ correction = (\n+ gammainc(0.5 * dim, 0.5 * s_sq) ** (1.0 / dim)\n+ * self.bandwidth_\n+ / np.sqrt(s_sq)\n+ )\n+ return data[i] + X * correction[:, np.newaxis]\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_kde.py b/sklearn/neighbors/_kde.py\nindex ae82ea636..a55ff31b6 100644\n--- a/sklearn/neighbors/_kde.py\n+++ b/sklearn/neighbors/_kde.py\n@@ -326,37 +326,6 @@ class KernelDensity(BaseEstimator):\n X : array-like of shape (n_samples, n_features)\n List of samples.\n \"\"\"\n- check_is_fitted(self)\n- # TODO: implement sampling for other valid kernel shapes\n- if self.kernel not in [\"gaussian\", \"tophat\"]:\n- raise NotImplementedError()\n-\n- data = np.asarray(self.tree_.data)\n-\n- rng = check_random_state(random_state)\n- u = rng.uniform(0, 1, size=n_samples)\n- if self.tree_.sample_weight is None:\n- i = (u * data.shape[0]).astype(np.int64)\n- else:\n- cumsum_weight = np.cumsum(np.asarray(self.tree_.sample_weight))\n- sum_weight = cumsum_weight[-1]\n- i = np.searchsorted(cumsum_weight, u * sum_weight)\n- if self.kernel == \"gaussian\":\n- return np.atleast_2d(rng.normal(data[i], self.bandwidth_))\n-\n- elif self.kernel == \"tophat\":\n- # we first draw points from a d-dimensional normal distribution,\n- # then use an incomplete gamma function to map them to a uniform\n- # d-dimensional tophat distribution.\n- dim = data.shape[1]\n- X = rng.normal(size=(n_samples, dim))\n- s_sq = row_norms(X, squared=True)\n- correction = (\n- gammainc(0.5 * dim, 0.5 * s_sq) ** (1.0 / dim)\n- * self.bandwidth_\n- / np.sqrt(s_sq)\n- )\n- return data[i] + X * correction[:, np.newaxis]\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_kde.py.\nHere is the description for the function:\n def sample(self, n_samples=1, random_state=None):\n \"\"\"Generate random samples from the model.\n\n Currently, this is implemented only for gaussian and tophat kernels.\n\n Parameters\n ----------\n n_samples : int, default=1\n Number of samples to generate.\n\n random_state : int, RandomState instance or None, default=None\n Determines random number generation used to generate\n random samples. Pass an int for reproducible results\n across multiple function calls.\n See :term:`Glossary <random_state>`.\n\n Returns\n -------\n X : array-like of shape (n_samples, n_features)\n List of samples.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neighbors/tests/test_kde.py::test_kernel_density_sampling", "sklearn/neighbors/tests/test_kde.py::test_kde_sample_weights", "sklearn/neighbors/tests/test_kde.py::test_check_is_fitted[sample]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[scott]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[silverman]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[0.1]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-82	1.0	{ "code": "diff --git b/sklearn/neighbors/_kde.py a/sklearn/neighbors/_kde.py\nindex 90446c066..ae82ea636 100644\n--- b/sklearn/neighbors/_kde.py\n+++ a/sklearn/neighbors/_kde.py\n@@ -261,6 +261,27 @@ class KernelDensity(BaseEstimator):\n probability densities, so values will be low for high-dimensional\n data.\n \"\"\"\n+ check_is_fitted(self)\n+ # The returned density is normalized to the number of points.\n+ # For it to be a probability, we must scale it. For this reason\n+ # we'll also scale atol.\n+ X = validate_data(self, X, order=\"C\", dtype=np.float64, reset=False)\n+ if self.tree_.sample_weight is None:\n+ N = self.tree_.data.shape[0]\n+ else:\n+ N = self.tree_.sum_weight\n+ atol_N = self.atol * N\n+ log_density = self.tree_.kernel_density(\n+ X,\n+ h=self.bandwidth_,\n+ kernel=self.kernel,\n+ atol=atol_N,\n+ rtol=self.rtol,\n+ breadth_first=self.breadth_first,\n+ return_log=True,\n+ )\n+ log_density -= np.log(N)\n+ return log_density\n \n def score(self, X, y=None):\n \"\"\"Compute the total log-likelihood under the model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_kde.py b/sklearn/neighbors/_kde.py\nindex ae82ea636..90446c066 100644\n--- a/sklearn/neighbors/_kde.py\n+++ b/sklearn/neighbors/_kde.py\n@@ -261,27 +261,6 @@ class KernelDensity(BaseEstimator):\n probability densities, so values will be low for high-dimensional\n data.\n \"\"\"\n- check_is_fitted(self)\n- # The returned density is normalized to the number of points.\n- # For it to be a probability, we must scale it. For this reason\n- # we'll also scale atol.\n- X = validate_data(self, X, order=\"C\", dtype=np.float64, reset=False)\n- if self.tree_.sample_weight is None:\n- N = self.tree_.data.shape[0]\n- else:\n- N = self.tree_.sum_weight\n- atol_N = self.atol * N\n- log_density = self.tree_.kernel_density(\n- X,\n- h=self.bandwidth_,\n- kernel=self.kernel,\n- atol=atol_N,\n- rtol=self.rtol,\n- breadth_first=self.breadth_first,\n- return_log=True,\n- )\n- log_density -= np.log(N)\n- return log_density\n \n def score(self, X, y=None):\n \"\"\"Compute the total log-likelihood under the model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_kde.py.\nHere is the description for the function:\n def score_samples(self, X):\n \"\"\"Compute the log-likelihood of each sample under the model.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n An array of points to query. Last dimension should match dimension\n of training data (n_features).\n\n Returns\n -------\n density : ndarray of shape (n_samples,)\n Log-likelihood of each sample in `X`. These are normalized to be\n probability densities, so values will be low for high-dimensional\n data.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.01-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[0.1-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[1-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[scott-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-gaussian]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-tophat]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-epanechnikov]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-exponential]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-linear]", "sklearn/neighbors/tests/test_kde.py::test_kernel_density[silverman-cosine]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[euclidean-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[euclidean-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[euclidean-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[minkowski-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[minkowski-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[minkowski-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[manhattan-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[manhattan-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[manhattan-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[chebyshev-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[chebyshev-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[chebyshev-kd_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[haversine-auto]", "sklearn/neighbors/tests/test_kde.py::test_kde_algorithm_metric_choice[haversine-ball_tree]", "sklearn/neighbors/tests/test_kde.py::test_kde_pipeline_gridsearch", "sklearn/neighbors/tests/test_kde.py::test_kde_sample_weights", "sklearn/neighbors/tests/test_kde.py::test_pickling[None]", "sklearn/neighbors/tests/test_kde.py::test_pickling[sample_weight1]", "sklearn/neighbors/tests/test_kde.py::test_check_is_fitted[score_samples]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[scott]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[silverman]", "sklearn/neighbors/tests/test_kde.py::test_bandwidth[0.1]", "sklearn/neighbors/_kde.py::sklearn.neighbors._kde.KernelDensity", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[KernelDensity()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KernelDensity()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KernelDensity()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-83	1.0	{ "code": "diff --git b/sklearn/preprocessing/_label.py a/sklearn/preprocessing/_label.py\nindex 7e6cf4d9d..345d55556 100644\n--- b/sklearn/preprocessing/_label.py\n+++ a/sklearn/preprocessing/_label.py\n@@ -271,6 +271,7 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n self.pos_label = pos_label\n self.sparse_output = sparse_output\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, y):\n \"\"\"Fit label binarizer.\n \n@@ -285,6 +286,31 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n self : object\n Returns the instance itself.\n \"\"\"\n+ if self.neg_label >= self.pos_label:\n+ raise ValueError(\n+ f\"neg_label={self.neg_label} must be strictly less than \"\n+ f\"pos_label={self.pos_label}.\"\n+ )\n+\n+ if self.sparse_output and (self.pos_label == 0 or self.neg_label != 0):\n+ raise ValueError(\n+ \"Sparse binarization is only supported with non \"\n+ \"zero pos_label and zero neg_label, got \"\n+ f\"pos_label={self.pos_label} and neg_label={self.neg_label}\"\n+ )\n+\n+ self.y_type_ = type_of_target(y, input_name=\"y\")\n+\n+ if \"multioutput\" in self.y_type_:\n+ raise ValueError(\n+ \"Multioutput target data is not supported with label binarization\"\n+ )\n+ if _num_samples(y) == 0:\n+ raise ValueError(\"y has 0 samples: %r\" % y)\n+\n+ self.sparse_input_ = sp.issparse(y)\n+ self.classes_ = unique_labels(y)\n+ return self\n \n def fit_transform(self, y):\n \"\"\"Fit label binarizer/transform multi-class labels to binary labels.\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py\nindex 345d55556..7e6cf4d9d 100644\n--- a/sklearn/preprocessing/_label.py\n+++ b/sklearn/preprocessing/_label.py\n@@ -271,7 +271,6 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n self.pos_label = pos_label\n self.sparse_output = sparse_output\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, y):\n \"\"\"Fit label binarizer.\n \n@@ -286,31 +285,6 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n self : object\n Returns the instance itself.\n \"\"\"\n- if self.neg_label >= self.pos_label:\n- raise ValueError(\n- f\"neg_label={self.neg_label} must be strictly less than \"\n- f\"pos_label={self.pos_label}.\"\n- )\n-\n- if self.sparse_output and (self.pos_label == 0 or self.neg_label != 0):\n- raise ValueError(\n- \"Sparse binarization is only supported with non \"\n- \"zero pos_label and zero neg_label, got \"\n- f\"pos_label={self.pos_label} and neg_label={self.neg_label}\"\n- )\n-\n- self.y_type_ = type_of_target(y, input_name=\"y\")\n-\n- if \"multioutput\" in self.y_type_:\n- raise ValueError(\n- \"Multioutput target data is not supported with label binarization\"\n- )\n- if _num_samples(y) == 0:\n- raise ValueError(\"y has 0 samples: %r\" % y)\n-\n- self.sparse_input_ = sp.issparse(y)\n- self.classes_ = unique_labels(y)\n- return self\n \n def fit_transform(self, y):\n \"\"\"Fit label binarizer/transform multi-class labels to binary labels.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_label.py.\nHere is the description for the function:\n def fit(self, y):\n \"\"\"Fit label binarizer.\n\n Parameters\n ----------\n y : ndarray of shape (n_samples,) or (n_samples, n_classes)\n Target values. The 2-d matrix should only contain 0 and 1,\n represents multilabel classification.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[hinge_loss]", "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[log_loss]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-RidgeClassifier-params3]", "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[unnormalized_log_loss]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_multinomial_score[neg_log_loss-multiclass_agg_list3]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-RidgeClassifierCV-params9]", 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], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-84	1.0	{ "code": "diff --git b/sklearn/preprocessing/_label.py a/sklearn/preprocessing/_label.py\nindex 17965fca2..345d55556 100644\n--- b/sklearn/preprocessing/_label.py\n+++ a/sklearn/preprocessing/_label.py\n@@ -400,6 +400,24 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n linear model's :term:`decision_function` method directly as the input\n of :meth:`inverse_transform`.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ if threshold is None:\n+ threshold = (self.pos_label + self.neg_label) / 2.0\n+\n+ if self.y_type_ == \"multiclass\":\n+ y_inv = _inverse_binarize_multiclass(Y, self.classes_)\n+ else:\n+ y_inv = _inverse_binarize_thresholding(\n+ Y, self.y_type_, self.classes_, threshold\n+ )\n+\n+ if self.sparse_input_:\n+ y_inv = sp.csr_matrix(y_inv)\n+ elif sp.issparse(y_inv):\n+ y_inv = y_inv.toarray()\n+\n+ return y_inv\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py\nindex 345d55556..17965fca2 100644\n--- a/sklearn/preprocessing/_label.py\n+++ b/sklearn/preprocessing/_label.py\n@@ -400,24 +400,6 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n linear model's :term:`decision_function` method directly as the input\n of :meth:`inverse_transform`.\n \"\"\"\n- check_is_fitted(self)\n-\n- if threshold is None:\n- threshold = (self.pos_label + self.neg_label) / 2.0\n-\n- if self.y_type_ == \"multiclass\":\n- y_inv = _inverse_binarize_multiclass(Y, self.classes_)\n- else:\n- y_inv = _inverse_binarize_thresholding(\n- Y, self.y_type_, self.classes_, threshold\n- )\n-\n- if self.sparse_input_:\n- y_inv = sp.csr_matrix(y_inv)\n- elif sp.issparse(y_inv):\n- y_inv = y_inv.toarray()\n-\n- return y_inv\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_label.py.\nHere is the description for the function:\n def inverse_transform(self, Y, threshold=None):\n \"\"\"Transform binary labels back to multi-class labels.\n\n Parameters\n ----------\n Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)\n Target values. All sparse matrices are converted to CSR before\n inverse transformation.\n\n threshold : float, default=None\n Threshold used in the binary and multi-label cases.\n\n Use 0 when ``Y`` contains the output of :term:`decision_function`\n (classifier).\n Use 0.5 when ``Y`` contains the output of :term:`predict_proba`.\n\n If None, the threshold is assumed to be half way between\n neg_label and pos_label.\n\n Returns\n -------\n y : {ndarray, sparse matrix} of shape (n_samples,)\n Target values. Sparse matrix will be of CSR format.\n\n Notes\n -----\n In the case when the binary labels are fractional\n (probabilistic), :meth:`inverse_transform` chooses the class with the\n greatest value. Typically, this allows to use the output of a\n linear model's :term:`decision_function` method directly as the input\n of :meth:`inverse_transform`.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/svm/tests/test_svm.py::test_decision_function_shape_two_class", "sklearn/linear_model/tests/test_ridge.py::test_ridgeclassifier_multilabel[RidgeClassifier-params0]", "sklearn/linear_model/tests/test_ridge.py::test_ridgeclassifier_multilabel[RidgeClassifierCV-params1]", "sklearn/linear_model/tests/test_ridge.py::test_ridgeclassifier_multilabel[RidgeClassifierCV-params2]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csr_matrix]", "sklearn/preprocessing/tests/test_label.py::test_label_binarizer", "sklearn/preprocessing/tests/test_label.py::test_label_binarizer_set_label_encoding", "sklearn/preprocessing/tests/test_label.py::test_label_binarizer_errors", 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"sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[array]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[coo_matrix]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[coo_array]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[csc_matrix]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction[csr_array]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[csc_array]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[csr_matrix]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[csr_array]", "sklearn/tests/test_multiclass.py::test_ovr_always_present", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[dok_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_multiclass", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[dok_array]", "sklearn/tests/test_multiclass.py::test_ovr_binary", "sklearn/neural_network/tests/test_mlp.py::test_lbfgs_classification[X0-y0]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[lil_matrix]", "sklearn/preprocessing/tests/test_label.py::test_label_binarize_multilabel[lil_array]", "sklearn/tests/test_multiclass.py::test_ovr_multilabel", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_dataset", "sklearn/neural_network/tests/test_mlp.py::test_lbfgs_classification[X1-y1]", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_predict_proba", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_decision_function", "sklearn/tests/test_multiclass.py::test_ovr_single_label_decision_function", "sklearn/neural_network/tests/test_mlp.py::test_multilabel_classification", "sklearn/tests/test_multioutput.py::test_classifier_chain_vs_independent_models", "sklearn/neural_network/tests/test_mlp.py::test_partial_fit_classification", "sklearn/neural_network/tests/test_mlp.py::test_partial_fit_unseen_classes", "sklearn/neural_network/tests/test_mlp.py::test_sparse_matrices[csr_matrix]", "sklearn/neural_network/tests/test_mlp.py::test_sparse_matrices[csr_array]", "sklearn/neural_network/tests/test_mlp.py::test_early_stopping[MLPClassifier]", "sklearn/neural_network/tests/test_mlp.py::test_mlp_classifier_dtypes_casting", "sklearn/neural_network/tests/test_mlp.py::test_mlp_param_dtypes[MLPClassifier-float32]", "sklearn/neural_network/tests/test_mlp.py::test_mlp_param_dtypes[MLPClassifier-float64]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[False-predict]", "sklearn/neural_network/tests/test_mlp.py::test_preserve_feature_names[MLPClassifier]", "sklearn/neural_network/tests/test_mlp.py::test_mlp_warm_start_with_early_stopping[MLPClassifier]", "sklearn/neural_network/tests/test_mlp.py::test_mlp_partial_fit_after_fit[MLPClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-predict]", "sklearn/neural_network/_multilayer_perceptron.py::sklearn.neural_network._multilayer_perceptron.MLPClassifier", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_multilabel_representation_invariance]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_multilabel_output_format_predict]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_classifier_multioutput]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifier()-check_classifiers_multilabel_representation_invariance]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifier()-check_classifiers_multilabel_output_format_predict]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifierCV()-check_classifiers_multilabel_representation_invariance]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifierCV()-check_classifiers_multilabel_output_format_predict]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MLPClassifier(max_iter=100)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsRestClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-85	1.0	{ "code": "diff --git b/sklearn/preprocessing/_label.py a/sklearn/preprocessing/_label.py\nindex f2782dc1f..345d55556 100644\n--- b/sklearn/preprocessing/_label.py\n+++ a/sklearn/preprocessing/_label.py\n@@ -354,6 +354,19 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n Shape will be (n_samples, 1) for binary problems. Sparse matrix\n will be of CSR format.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ y_is_multilabel = type_of_target(y).startswith(\"multilabel\")\n+ if y_is_multilabel and not self.y_type_.startswith(\"multilabel\"):\n+ raise ValueError(\"The object was not fitted with multilabel input.\")\n+\n+ return label_binarize(\n+ y,\n+ classes=self.classes_,\n+ pos_label=self.pos_label,\n+ neg_label=self.neg_label,\n+ sparse_output=self.sparse_output,\n+ )\n \n def inverse_transform(self, Y, threshold=None):\n \"\"\"Transform binary labels back to multi-class labels.\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py\nindex 345d55556..f2782dc1f 100644\n--- a/sklearn/preprocessing/_label.py\n+++ b/sklearn/preprocessing/_label.py\n@@ -354,19 +354,6 @@ class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None\n Shape will be (n_samples, 1) for binary problems. Sparse matrix\n will be of CSR format.\n \"\"\"\n- check_is_fitted(self)\n-\n- y_is_multilabel = type_of_target(y).startswith(\"multilabel\")\n- if y_is_multilabel and not self.y_type_.startswith(\"multilabel\"):\n- raise ValueError(\"The object was not fitted with multilabel input.\")\n-\n- return label_binarize(\n- y,\n- classes=self.classes_,\n- pos_label=self.pos_label,\n- neg_label=self.neg_label,\n- sparse_output=self.sparse_output,\n- )\n \n def inverse_transform(self, Y, threshold=None):\n \"\"\"Transform binary labels back to multi-class labels.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_label.py.\nHere is the description for the function:\n def transform(self, y):\n \"\"\"Transform multi-class labels to binary labels.\n\n The output of transform is sometimes referred to by some authors as\n the 1-of-K coding scheme.\n\n Parameters\n ----------\n y : {array, sparse matrix} of shape (n_samples,) or \\\n (n_samples, n_classes)\n Target values. The 2-d matrix should only contain 0 and 1,\n represents multilabel classification. Sparse matrix can be\n CSR, CSC, COO, DOK, or LIL.\n\n Returns\n -------\n Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)\n Shape will be (n_samples, 1) for binary problems. Sparse matrix\n will be of CSR format.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[hinge_loss]", "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[log_loss]", "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[unnormalized_log_loss]", "sklearn/metrics/tests/test_common.py::test_sample_order_invariance_multilabel_and_multioutput", "sklearn/metrics/tests/test_common.py::test_format_invariance_with_1d_vectors[hinge_loss]", "sklearn/metrics/tests/test_common.py::test_format_invariance_with_1d_vectors[log_loss]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_multinomial_score[neg_log_loss-multiclass_agg_list3]", "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers", "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers_multilabel_indicator_data", 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scikit-learn__scikit-learn-86	1.0	{ "code": "diff --git b/sklearn/preprocessing/_label.py a/sklearn/preprocessing/_label.py\nindex 84c9ead16..345d55556 100644\n--- b/sklearn/preprocessing/_label.py\n+++ a/sklearn/preprocessing/_label.py\n@@ -146,6 +146,22 @@ class LabelEncoder(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):\n y : ndarray of shape (n_samples,)\n Original encoding.\n \"\"\"\n+ check_is_fitted(self)\n+ xp, _ = get_namespace(y)\n+ y = column_or_1d(y, warn=True)\n+ # inverse transform of empty array is empty array\n+ if _num_samples(y) == 0:\n+ return xp.asarray([])\n+\n+ diff = _setdiff1d(\n+ ar1=y,\n+ ar2=xp.arange(self.classes_.shape[0], device=device(y)),\n+ xp=xp,\n+ )\n+ if diff.shape[0]:\n+ raise ValueError(\"y contains previously unseen labels: %s\" % str(diff))\n+ y = xp.asarray(y)\n+ return xp.take(self.classes_, y, axis=0)\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py\nindex 345d55556..84c9ead16 100644\n--- a/sklearn/preprocessing/_label.py\n+++ b/sklearn/preprocessing/_label.py\n@@ -146,22 +146,6 @@ class LabelEncoder(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):\n y : ndarray of shape (n_samples,)\n Original encoding.\n \"\"\"\n- check_is_fitted(self)\n- xp, _ = get_namespace(y)\n- y = column_or_1d(y, warn=True)\n- # inverse transform of empty array is empty array\n- if _num_samples(y) == 0:\n- return xp.asarray([])\n-\n- diff = _setdiff1d(\n- ar1=y,\n- ar2=xp.arange(self.classes_.shape[0], device=device(y)),\n- xp=xp,\n- )\n- if diff.shape[0]:\n- raise ValueError(\"y contains previously unseen labels: %s\" % str(diff))\n- y = xp.asarray(y)\n- return xp.take(self.classes_, y, axis=0)\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_label.py.\nHere is the description for the function:\n def inverse_transform(self, y):\n \"\"\"Transform labels back to original encoding.\n\n Parameters\n ----------\n y : array-like of shape (n_samples,)\n Target values.\n\n Returns\n -------\n y : ndarray of shape (n_samples,)\n Original encoding.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_logistic.py::test_multinomial_logistic_regression_string_inputs", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-None-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-None-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-final_estimator1-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-final_estimator1-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-None-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-None-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-final_estimator1-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-final_estimator1-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_drop_estimator", "sklearn/preprocessing/tests/test_label.py::test_label_encoder[int64]", "sklearn/preprocessing/tests/test_label.py::test_label_encoder[object]", "sklearn/preprocessing/tests/test_label.py::test_label_encoder[str]", "sklearn/preprocessing/tests/test_label.py::test_label_encoder_negative_ints", "sklearn/preprocessing/tests/test_label.py::test_label_encoder_errors", "sklearn/preprocessing/tests/test_label.py::test_label_encoder_empty_array[int64]", "sklearn/preprocessing/tests/test_label.py::test_label_encoder_empty_array[object]", "sklearn/preprocessing/tests/test_label.py::test_label_encoder_empty_array[str]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_with_sample_weight[StackingClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_predict_proba[MLPClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_predict_proba[RandomForestClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_decision_function", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[False-auto]", "sklearn/ensemble/tests/test_voting.py::test_majority_label_iris[42]", "sklearn/ensemble/tests/test_voting.py::test_tie_situation", "sklearn/ensemble/tests/test_voting.py::test_weights_iris[42]", "sklearn/ensemble/tests/test_voting.py::test_predict_on_toy_problem[42]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[False-predict]", "sklearn/ensemble/tests/test_voting.py::test_parallel_fit[42]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-auto]", "sklearn/ensemble/tests/test_voting.py::test_sample_weight[42]", "sklearn/ensemble/tests/test_voting.py::test_voting_classifier_set_params[42]", "sklearn/ensemble/tests/test_voting.py::test_set_estimator_drop", "sklearn/ensemble/tests/test_voting.py::test_none_estimator_with_weights[X0-y0-voter0]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-predict]", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[StackingClassifier-LogisticRegression-X0-y0]", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[VotingClassifier-LogisticRegression-X2-y2]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_base_regressor", "sklearn/ensemble/tests/test_stacking.py::test_metadata_routing_for_stacking_estimators[sample_weight-prop_value0-StackingClassifier-ConsumingClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_metadata_routing_for_stacking_estimators[metadata-a-StackingClassifier-ConsumingClassifier]", "sklearn/preprocessing/_label.py::sklearn.preprocessing._label.LabelEncoder", "sklearn/ensemble/_voting.py::sklearn.ensemble._voting.VotingClassifier", "sklearn/ensemble/_stacking.py::sklearn.ensemble._stacking.StackingClassifier", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-87	1.0	{ "code": "diff --git b/sklearn/linear_model/_least_angle.py a/sklearn/linear_model/_least_angle.py\nindex 1d7347217..403090f85 100644\n--- b/sklearn/linear_model/_least_angle.py\n+++ a/sklearn/linear_model/_least_angle.py\n@@ -2218,6 +2218,7 @@ class LassoLarsIC(LassoLars):\n tags.target_tags.multi_output = False\n return tags\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, copy_X=None):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -2239,6 +2240,74 @@ class LassoLarsIC(LassoLars):\n self : object\n Returns an instance of self.\n \"\"\"\n+ if copy_X is None:\n+ copy_X = self.copy_X\n+ X, y = validate_data(self, X, y, force_writeable=True, y_numeric=True)\n+\n+ X, y, Xmean, ymean, Xstd = _preprocess_data(\n+ X, y, fit_intercept=self.fit_intercept, copy=copy_X\n+ )\n+\n+ Gram = self.precompute\n+\n+ alphas_, _, coef_path_, self.n_iter_ = lars_path(\n+ X,\n+ y,\n+ Gram=Gram,\n+ copy_X=copy_X,\n+ copy_Gram=True,\n+ alpha_min=0.0,\n+ method=\"lasso\",\n+ verbose=self.verbose,\n+ max_iter=self.max_iter,\n+ eps=self.eps,\n+ return_n_iter=True,\n+ positive=self.positive,\n+ )\n+\n+ n_samples = X.shape[0]\n+\n+ if self.criterion == \"aic\":\n+ criterion_factor = 2\n+ elif self.criterion == \"bic\":\n+ criterion_factor = log(n_samples)\n+ else:\n+ raise ValueError(\n+ f\"criterion should be either bic or aic, got {self.criterion!r}\"\n+ )\n+\n+ residuals = y[:, np.newaxis] - np.dot(X, coef_path_)\n+ residuals_sum_squares = np.sum(residuals*2, axis=0)\n+ degrees_of_freedom = np.zeros(coef_path_.shape[1], dtype=int)\n+ for k, coef in enumerate(coef_path_.T):\n+ mask = np.abs(coef) > np.finfo(coef.dtype).eps\n+ if not np.any(mask):\n+ continue\n+ # get the number of degrees of freedom equal to:\n+ # Xc = X[:, mask]\n+ # Trace(Xc inv(Xc.T, Xc) * Xc.T) ie the number of non-zero coefs\n+ degrees_of_freedom[k] = np.sum(mask)\n+\n+ self.alphas_ = alphas_\n+\n+ if self.noise_variance is None:\n+ self.noise_variance_ = self._estimate_noise_variance(\n+ X, y, positive=self.positive\n+ )\n+ else:\n+ self.noise_variance_ = self.noise_variance\n+\n+ self.criterion_ = (\n+ n_samples * np.log(2 * np.pi * self.noise_variance_)\n+ + residuals_sum_squares / self.noise_variance_\n+ + criterion_factor * degrees_of_freedom\n+ )\n+ n_best = np.argmin(self.criterion_)\n+\n+ self.alpha_ = alphas_[n_best]\n+ self.coef_ = coef_path_[:, n_best]\n+ self._set_intercept(Xmean, ymean, Xstd)\n+ return self\n \n def _estimate_noise_variance(self, X, y, positive):\n \"\"\"Compute an estimate of the variance with an OLS model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py\nindex 403090f85..1d7347217 100644\n--- a/sklearn/linear_model/_least_angle.py\n+++ b/sklearn/linear_model/_least_angle.py\n@@ -2218,7 +2218,6 @@ class LassoLarsIC(LassoLars):\n tags.target_tags.multi_output = False\n return tags\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, copy_X=None):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -2240,74 +2239,6 @@ class LassoLarsIC(LassoLars):\n self : object\n Returns an instance of self.\n \"\"\"\n- if copy_X is None:\n- copy_X = self.copy_X\n- X, y = validate_data(self, X, y, force_writeable=True, y_numeric=True)\n-\n- X, y, Xmean, ymean, Xstd = _preprocess_data(\n- X, y, fit_intercept=self.fit_intercept, copy=copy_X\n- )\n-\n- Gram = self.precompute\n-\n- alphas_, _, coef_path_, self.n_iter_ = lars_path(\n- X,\n- y,\n- Gram=Gram,\n- copy_X=copy_X,\n- copy_Gram=True,\n- alpha_min=0.0,\n- method=\"lasso\",\n- verbose=self.verbose,\n- max_iter=self.max_iter,\n- eps=self.eps,\n- return_n_iter=True,\n- positive=self.positive,\n- )\n-\n- n_samples = X.shape[0]\n-\n- if self.criterion == \"aic\":\n- criterion_factor = 2\n- elif self.criterion == \"bic\":\n- criterion_factor = log(n_samples)\n- else:\n- raise ValueError(\n- f\"criterion should be either bic or aic, got {self.criterion!r}\"\n- )\n-\n- residuals = y[:, np.newaxis] - np.dot(X, coef_path_)\n- residuals_sum_squares = np.sum(residuals*2, axis=0)\n- degrees_of_freedom = np.zeros(coef_path_.shape[1], dtype=int)\n- for k, coef in enumerate(coef_path_.T):\n- mask = np.abs(coef) > np.finfo(coef.dtype).eps\n- if not np.any(mask):\n- continue\n- # get the number of degrees of freedom equal to:\n- # Xc = X[:, mask]\n- # Trace(Xc inv(Xc.T, Xc) * Xc.T) ie the number of non-zero coefs\n- degrees_of_freedom[k] = np.sum(mask)\n-\n- self.alphas_ = alphas_\n-\n- if self.noise_variance is None:\n- self.noise_variance_ = self._estimate_noise_variance(\n- X, y, positive=self.positive\n- )\n- else:\n- self.noise_variance_ = self.noise_variance\n-\n- self.criterion_ = (\n- n_samples * np.log(2 * np.pi * self.noise_variance_)\n- + residuals_sum_squares / self.noise_variance_\n- + criterion_factor * degrees_of_freedom\n- )\n- n_best = np.argmin(self.criterion_)\n-\n- self.alpha_ = alphas_[n_best]\n- self.coef_ = coef_path_[:, n_best]\n- self._set_intercept(Xmean, ymean, Xstd)\n- return self\n \n def _estimate_noise_variance(self, X, y, positive):\n \"\"\"Compute an estimate of the variance with an OLS model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_least_angle.py.\nHere is the description for the function:\n def fit(self, X, y, copy_X=None):\n \"\"\"Fit the model using X, y as training data.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data.\n\n y : array-like of shape (n_samples,)\n Target values. Will be cast to X's dtype if necessary.\n\n copy_X : bool, default=None\n If provided, this parameter will override the choice\n of copy_X made at instance creation.\n If ``True``, X will be copied; else, it may be overwritten.\n\n Returns\n -------\n self : object\n Returns an instance of self.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_least_angle.py::test_lars_precompute[LassoLarsIC]", "sklearn/linear_model/tests/test_least_angle.py::test_lasso_lars_ic", "sklearn/linear_model/tests/test_least_angle.py::test_estimatorclasses_positive_constraint", "sklearn/linear_model/tests/test_least_angle.py::test_lasso_lars_copyX_behaviour[True]", "sklearn/linear_model/tests/test_least_angle.py::test_lasso_lars_copyX_behaviour[False]", "sklearn/linear_model/tests/test_least_angle.py::test_lasso_lars_fit_copyX_behaviour[True]", "sklearn/linear_model/tests/test_least_angle.py::test_lasso_lars_fit_copyX_behaviour[False]", "sklearn/linear_model/tests/test_least_angle.py::test_lars_dtype_match[float32-LassoLarsIC-False-args2]", "sklearn/linear_model/tests/test_least_angle.py::test_lars_dtype_match[float64-LassoLarsIC-False-args2]", "sklearn/linear_model/tests/test_least_angle.py::test_lars_numeric_consistency[LassoLarsIC-False-args2]", "sklearn/linear_model/tests/test_least_angle.py::test_lassolarsic_alpha_selection[aic]", "sklearn/linear_model/tests/test_least_angle.py::test_lassolarsic_alpha_selection[bic]", "sklearn/linear_model/tests/test_least_angle.py::test_lassolarsic_noise_variance[True]", "sklearn/linear_model/tests/test_least_angle.py::test_lassolarsic_noise_variance[False]", "sklearn/linear_model/tests/test_common.py::test_balance_property[42-False-LassoLarsIC]", "sklearn/linear_model/_least_angle.py::sklearn.linear_model._least_angle.LassoLarsIC", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LassoLarsIC(max_iter=5,noise_variance=1.0)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LassoLarsIC(max_iter=5,noise_variance=1.0)]", "sklearn/tests/test_common.py::test_check_param_validation[LassoLarsIC(max_iter=5,noise_variance=1.0)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoLarsIC(max_iter=5,noise_variance=1.0)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-88	1.0	{ "code": "diff --git b/sklearn/decomposition/_lda.py a/sklearn/decomposition/_lda.py\nindex ac966a105..0082272a1 100644\n--- b/sklearn/decomposition/_lda.py\n+++ a/sklearn/decomposition/_lda.py\n@@ -872,6 +872,28 @@ class LatentDirichletAllocation(\n score : float\n Perplexity score.\n \"\"\"\n+ if doc_topic_distr is None:\n+ doc_topic_distr = self._unnormalized_transform(X)\n+ else:\n+ n_samples, n_components = doc_topic_distr.shape\n+ if n_samples != X.shape[0]:\n+ raise ValueError(\n+ \"Number of samples in X and doc_topic_distr do not match.\"\n+ )\n+\n+ if n_components != self.n_components:\n+ raise ValueError(\"Number of topics does not match.\")\n+\n+ current_samples = X.shape[0]\n+ bound = self._approx_bound(X, doc_topic_distr, sub_sampling)\n+\n+ if sub_sampling:\n+ word_cnt = X.sum() * (float(self.total_samples) / current_samples)\n+ else:\n+ word_cnt = X.sum()\n+ perword_bound = bound / word_cnt\n+\n+ return np.exp(-1.0 * perword_bound)\n \n def perplexity(self, X, sub_sampling=False):\n \"\"\"Calculate approximate perplexity for data X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_lda.py b/sklearn/decomposition/_lda.py\nindex 0082272a1..ac966a105 100644\n--- a/sklearn/decomposition/_lda.py\n+++ b/sklearn/decomposition/_lda.py\n@@ -872,28 +872,6 @@ class LatentDirichletAllocation(\n score : float\n Perplexity score.\n \"\"\"\n- if doc_topic_distr is None:\n- doc_topic_distr = self._unnormalized_transform(X)\n- else:\n- n_samples, n_components = doc_topic_distr.shape\n- if n_samples != X.shape[0]:\n- raise ValueError(\n- \"Number of samples in X and doc_topic_distr do not match.\"\n- )\n-\n- if n_components != self.n_components:\n- raise ValueError(\"Number of topics does not match.\")\n-\n- current_samples = X.shape[0]\n- bound = self._approx_bound(X, doc_topic_distr, sub_sampling)\n-\n- if sub_sampling:\n- word_cnt = X.sum() * (float(self.total_samples) / current_samples)\n- else:\n- word_cnt = X.sum()\n- perword_bound = bound / word_cnt\n-\n- return np.exp(-1.0 * perword_bound)\n \n def perplexity(self, X, sub_sampling=False):\n \"\"\"Calculate approximate perplexity for data X.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_lda.py.\nHere is the description for the function:\n def _perplexity_precomp_distr(self, X, doc_topic_distr=None, sub_sampling=False):\n \"\"\"Calculate approximate perplexity for data X with ability to accept\n precomputed doc_topic_distr\n\n Perplexity is defined as exp(-1. * log-likelihood per word)\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Document word matrix.\n\n doc_topic_distr : ndarray of shape (n_samples, n_components), \\\n default=None\n Document topic distribution.\n If it is None, it will be generated by applying transform on X.\n\n Returns\n -------\n score : float\n Perplexity score.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_online_lda.py::test_lda_default_prior_params[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_default_prior_params[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_batch[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_batch[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_online[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_online[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dense_input[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dense_input[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_transform", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_transform[online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_transform[batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[online-csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[online-csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[batch-csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[batch-csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_preplexity_mismatch", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_matrix-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_matrix-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_array-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_array-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_perplexity_input_format[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_perplexity_input_format[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score_perplexity[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score_perplexity[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_perplexity[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_perplexity[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-False-1-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-False-0-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-True-0-3-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-True-1-3-3]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-True-2-3-1]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-False-1-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-False-0-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-True-0-3-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-True-1-3-3]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-True-2-3-1]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_feature_names_out[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_feature_names_out[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dtype_match[float64-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dtype_match[float64-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-online]", "sklearn/decomposition/_lda.py::sklearn.decomposition._lda.LatentDirichletAllocation", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_n_iter]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-LatentDirichletAllocation(batch_size=10,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-89	1.0	{ "code": "diff --git b/sklearn/decomposition/_lda.py a/sklearn/decomposition/_lda.py\nindex bb840765d..0082272a1 100644\n--- b/sklearn/decomposition/_lda.py\n+++ a/sklearn/decomposition/_lda.py\n@@ -623,6 +623,7 @@ class LatentDirichletAllocation(\n \n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Learn model for the data X with variational Bayes method.\n \n@@ -642,6 +643,68 @@ class LatentDirichletAllocation(\n self\n Fitted estimator.\n \"\"\"\n+ X = self._check_non_neg_array(\n+ X, reset_n_features=True, whom=\"LatentDirichletAllocation.fit\"\n+ )\n+ n_samples, n_features = X.shape\n+ max_iter = self.max_iter\n+ evaluate_every = self.evaluate_every\n+ learning_method = self.learning_method\n+\n+ batch_size = self.batch_size\n+\n+ # initialize parameters\n+ self._init_latent_vars(n_features, dtype=X.dtype)\n+ # change to perplexity later\n+ last_bound = None\n+ n_jobs = effective_n_jobs(self.n_jobs)\n+ with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:\n+ for i in range(max_iter):\n+ if learning_method == \"online\":\n+ for idx_slice in gen_batches(n_samples, batch_size):\n+ self._em_step(\n+ X[idx_slice, :],\n+ total_samples=n_samples,\n+ batch_update=False,\n+ parallel=parallel,\n+ )\n+ else:\n+ # batch update\n+ self._em_step(\n+ X, total_samples=n_samples, batch_update=True, parallel=parallel\n+ )\n+\n+ # check perplexity\n+ if evaluate_every > 0 and (i + 1) % evaluate_every == 0:\n+ doc_topics_distr, _ = self._e_step(\n+ X, cal_sstats=False, random_init=False, parallel=parallel\n+ )\n+ bound = self._perplexity_precomp_distr(\n+ X, doc_topics_distr, sub_sampling=False\n+ )\n+ if self.verbose:\n+ print(\n+ \"iteration: %d of max_iter: %d, perplexity: %.4f\"\n+ % (i + 1, max_iter, bound)\n+ )\n+\n+ if last_bound and abs(last_bound - bound) < self.perp_tol:\n+ break\n+ last_bound = bound\n+\n+ elif self.verbose:\n+ print(\"iteration: %d of max_iter: %d\" % (i + 1, max_iter))\n+ self.n_iter_ += 1\n+\n+ # calculate final perplexity value on train set\n+ doc_topics_distr, _ = self._e_step(\n+ X, cal_sstats=False, random_init=False, parallel=parallel\n+ )\n+ self.bound_ = self._perplexity_precomp_distr(\n+ X, doc_topics_distr, sub_sampling=False\n+ )\n+\n+ return self\n \n def _unnormalized_transform(self, X):\n \"\"\"Transform data X according to fitted model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_lda.py b/sklearn/decomposition/_lda.py\nindex 0082272a1..bb840765d 100644\n--- a/sklearn/decomposition/_lda.py\n+++ b/sklearn/decomposition/_lda.py\n@@ -623,7 +623,6 @@ class LatentDirichletAllocation(\n \n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Learn model for the data X with variational Bayes method.\n \n@@ -643,68 +642,6 @@ class LatentDirichletAllocation(\n self\n Fitted estimator.\n \"\"\"\n- X = self._check_non_neg_array(\n- X, reset_n_features=True, whom=\"LatentDirichletAllocation.fit\"\n- )\n- n_samples, n_features = X.shape\n- max_iter = self.max_iter\n- evaluate_every = self.evaluate_every\n- learning_method = self.learning_method\n-\n- batch_size = self.batch_size\n-\n- # initialize parameters\n- self._init_latent_vars(n_features, dtype=X.dtype)\n- # change to perplexity later\n- last_bound = None\n- n_jobs = effective_n_jobs(self.n_jobs)\n- with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:\n- for i in range(max_iter):\n- if learning_method == \"online\":\n- for idx_slice in gen_batches(n_samples, batch_size):\n- self._em_step(\n- X[idx_slice, :],\n- total_samples=n_samples,\n- batch_update=False,\n- parallel=parallel,\n- )\n- else:\n- # batch update\n- self._em_step(\n- X, total_samples=n_samples, batch_update=True, parallel=parallel\n- )\n-\n- # check perplexity\n- if evaluate_every > 0 and (i + 1) % evaluate_every == 0:\n- doc_topics_distr, _ = self._e_step(\n- X, cal_sstats=False, random_init=False, parallel=parallel\n- )\n- bound = self._perplexity_precomp_distr(\n- X, doc_topics_distr, sub_sampling=False\n- )\n- if self.verbose:\n- print(\n- \"iteration: %d of max_iter: %d, perplexity: %.4f\"\n- % (i + 1, max_iter, bound)\n- )\n-\n- if last_bound and abs(last_bound - bound) < self.perp_tol:\n- break\n- last_bound = bound\n-\n- elif self.verbose:\n- print(\"iteration: %d of max_iter: %d\" % (i + 1, max_iter))\n- self.n_iter_ += 1\n-\n- # calculate final perplexity value on train set\n- doc_topics_distr, _ = self._e_step(\n- X, cal_sstats=False, random_init=False, parallel=parallel\n- )\n- self.bound_ = self._perplexity_precomp_distr(\n- X, doc_topics_distr, sub_sampling=False\n- )\n-\n- return self\n \n def _unnormalized_transform(self, X):\n \"\"\"Transform data X according to fitted model.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_lda.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Learn model for the data X with variational Bayes method.\n\n When `learning_method` is 'online', use mini-batch update.\n Otherwise, use batch update.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Document word matrix.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n self\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_online_lda.py::test_lda_default_prior_params[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_default_prior_params[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_batch[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_batch[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_online[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_online[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dense_input[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dense_input[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_transform", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_transform[online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_transform[batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_negative_input", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[online-csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[online-csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[batch-csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_multi_jobs[batch-csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_preplexity_mismatch", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_matrix-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_matrix-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_array-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_perplexity[csr_array-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_perplexity_input_format[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_perplexity_input_format[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score_perplexity[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score_perplexity[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_perplexity[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_perplexity[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-False-1-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-False-0-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-True-0-3-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-True-1-3-3]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_matrix-True-2-3-1]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-False-1-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-False-0-0-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-True-0-3-0]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-True-1-3-3]", "sklearn/decomposition/tests/test_online_lda.py::test_verbosity[csr_array-True-2-3-1]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_feature_names_out[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_feature_names_out[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dtype_match[float64-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_dtype_match[float64-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-online]", 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"sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_non_negative]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-LatentDirichletAllocation(batch_size=10,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-90	1.0	{ "code": "diff --git b/sklearn/decomposition/_lda.py a/sklearn/decomposition/_lda.py\nindex beae442da..0082272a1 100644\n--- b/sklearn/decomposition/_lda.py\n+++ a/sklearn/decomposition/_lda.py\n@@ -575,6 +575,7 @@ class LatentDirichletAllocation(\n \n return X\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None):\n \"\"\"Online VB with Mini-Batch update.\n \n@@ -591,6 +592,36 @@ class LatentDirichletAllocation(\n self\n Partially fitted estimator.\n \"\"\"\n+ first_time = not hasattr(self, \"components_\")\n+\n+ X = self._check_non_neg_array(\n+ X, reset_n_features=first_time, whom=\"LatentDirichletAllocation.partial_fit\"\n+ )\n+ n_samples, n_features = X.shape\n+ batch_size = self.batch_size\n+\n+ # initialize parameters or check\n+ if first_time:\n+ self._init_latent_vars(n_features, dtype=X.dtype)\n+\n+ if n_features != self.components_.shape[1]:\n+ raise ValueError(\n+ \"The provided data has %d dimensions while \"\n+ \"the model was trained with feature size %d.\"\n+ % (n_features, self.components_.shape[1])\n+ )\n+\n+ n_jobs = effective_n_jobs(self.n_jobs)\n+ with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:\n+ for idx_slice in gen_batches(n_samples, batch_size):\n+ self._em_step(\n+ X[idx_slice, :],\n+ total_samples=self.total_samples,\n+ batch_update=False,\n+ parallel=parallel,\n+ )\n+\n+ return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_lda.py b/sklearn/decomposition/_lda.py\nindex 0082272a1..beae442da 100644\n--- a/sklearn/decomposition/_lda.py\n+++ b/sklearn/decomposition/_lda.py\n@@ -575,7 +575,6 @@ class LatentDirichletAllocation(\n \n return X\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None):\n \"\"\"Online VB with Mini-Batch update.\n \n@@ -592,36 +591,6 @@ class LatentDirichletAllocation(\n self\n Partially fitted estimator.\n \"\"\"\n- first_time = not hasattr(self, \"components_\")\n-\n- X = self._check_non_neg_array(\n- X, reset_n_features=first_time, whom=\"LatentDirichletAllocation.partial_fit\"\n- )\n- n_samples, n_features = X.shape\n- batch_size = self.batch_size\n-\n- # initialize parameters or check\n- if first_time:\n- self._init_latent_vars(n_features, dtype=X.dtype)\n-\n- if n_features != self.components_.shape[1]:\n- raise ValueError(\n- \"The provided data has %d dimensions while \"\n- \"the model was trained with feature size %d.\"\n- % (n_features, self.components_.shape[1])\n- )\n-\n- n_jobs = effective_n_jobs(self.n_jobs)\n- with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:\n- for idx_slice in gen_batches(n_samples, batch_size):\n- self._em_step(\n- X[idx_slice, :],\n- total_samples=self.total_samples,\n- batch_update=False,\n- parallel=parallel,\n- )\n-\n- return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_lda.py.\nHere is the description for the function:\n def partial_fit(self, X, y=None):\n \"\"\"Online VB with Mini-Batch update.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Document word matrix.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n self\n Partially fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_online_lda.py::test_lda_partial_fit[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_partial_fit[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_partial_fit_multi_jobs[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_partial_fit_multi_jobs[csr_array]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[LatentDirichletAllocation(batch_size=10,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-91	1.0	{ "code": "diff --git b/sklearn/decomposition/_lda.py a/sklearn/decomposition/_lda.py\nindex a0ebedc22..0082272a1 100644\n--- b/sklearn/decomposition/_lda.py\n+++ a/sklearn/decomposition/_lda.py\n@@ -842,6 +842,14 @@ class LatentDirichletAllocation(\n score : float\n Use approximate bound as score.\n \"\"\"\n+ check_is_fitted(self)\n+ X = self._check_non_neg_array(\n+ X, reset_n_features=False, whom=\"LatentDirichletAllocation.score\"\n+ )\n+\n+ doc_topic_distr = self._unnormalized_transform(X)\n+ score = self._approx_bound(X, doc_topic_distr, sub_sampling=False)\n+ return score\n \n def _perplexity_precomp_distr(self, X, doc_topic_distr=None, sub_sampling=False):\n \"\"\"Calculate approximate perplexity for data X with ability to accept\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_lda.py b/sklearn/decomposition/_lda.py\nindex 0082272a1..a0ebedc22 100644\n--- a/sklearn/decomposition/_lda.py\n+++ b/sklearn/decomposition/_lda.py\n@@ -842,14 +842,6 @@ class LatentDirichletAllocation(\n score : float\n Use approximate bound as score.\n \"\"\"\n- check_is_fitted(self)\n- X = self._check_non_neg_array(\n- X, reset_n_features=False, whom=\"LatentDirichletAllocation.score\"\n- )\n-\n- doc_topic_distr = self._unnormalized_transform(X)\n- score = self._approx_bound(X, doc_topic_distr, sub_sampling=False)\n- return score\n \n def _perplexity_precomp_distr(self, X, doc_topic_distr=None, sub_sampling=False):\n \"\"\"Calculate approximate perplexity for data X with ability to accept\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_lda.py.\nHere is the description for the function:\n def score(self, X, y=None):\n \"\"\"Calculate approximate log-likelihood as score.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Document word matrix.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n score : float\n Use approximate bound as score.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score_perplexity[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score_perplexity[csr_array]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LatentDirichletAllocation(batch_size=10,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-92	1.0	{ "code": "diff --git b/sklearn/decomposition/_lda.py a/sklearn/decomposition/_lda.py\nindex 547548808..0082272a1 100644\n--- b/sklearn/decomposition/_lda.py\n+++ a/sklearn/decomposition/_lda.py\n@@ -739,6 +739,13 @@ class LatentDirichletAllocation(\n doc_topic_distr : ndarray of shape (n_samples, n_components)\n Document topic distribution for X.\n \"\"\"\n+ check_is_fitted(self)\n+ X = self._check_non_neg_array(\n+ X, reset_n_features=False, whom=\"LatentDirichletAllocation.transform\"\n+ )\n+ doc_topic_distr = self._unnormalized_transform(X)\n+ doc_topic_distr /= doc_topic_distr.sum(axis=1)[:, np.newaxis]\n+ return doc_topic_distr\n \n def _approx_bound(self, X, doc_topic_distr, sub_sampling):\n \"\"\"Estimate the variational bound.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_lda.py b/sklearn/decomposition/_lda.py\nindex 0082272a1..547548808 100644\n--- a/sklearn/decomposition/_lda.py\n+++ b/sklearn/decomposition/_lda.py\n@@ -739,13 +739,6 @@ class LatentDirichletAllocation(\n doc_topic_distr : ndarray of shape (n_samples, n_components)\n Document topic distribution for X.\n \"\"\"\n- check_is_fitted(self)\n- X = self._check_non_neg_array(\n- X, reset_n_features=False, whom=\"LatentDirichletAllocation.transform\"\n- )\n- doc_topic_distr = self._unnormalized_transform(X)\n- doc_topic_distr /= doc_topic_distr.sum(axis=1)[:, np.newaxis]\n- return doc_topic_distr\n \n def _approx_bound(self, X, doc_topic_distr, sub_sampling):\n \"\"\"Estimate the variational bound.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_lda.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Transform data X according to the fitted model.\n\n .. versionchanged:: 0.18\n `doc_topic_distr` is now normalized.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Document word matrix.\n\n Returns\n -------\n doc_topic_distr : ndarray of shape (n_samples, n_components)\n Document topic distribution for X.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_online_lda.py::test_lda_default_prior_params[csr_matrix]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_default_prior_params[csr_array]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_transform", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_transform[online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_fit_transform[batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_matrix-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-online]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_score[csr_array-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-batch]", "sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-online]", "sklearn/decomposition/_lda.py::sklearn.decomposition._lda.LatentDirichletAllocation", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-LatentDirichletAllocation(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-LatentDirichletAllocation(batch_size=10,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-93	1.0	{ "code": "diff --git b/sklearn/covariance/_shrunk_covariance.py a/sklearn/covariance/_shrunk_covariance.py\nindex a5329de87..2a5e09f2c 100644\n--- b/sklearn/covariance/_shrunk_covariance.py\n+++ a/sklearn/covariance/_shrunk_covariance.py\n@@ -576,6 +576,7 @@ class LedoitWolf(EmpiricalCovariance):\n )\n self.block_size = block_size\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the Ledoit-Wolf shrunk covariance model to X.\n \n@@ -592,6 +593,20 @@ class LedoitWolf(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n+ # Not calling the parent object to fit, to avoid computing the\n+ # covariance matrix (and potentially the precision)\n+ X = validate_data(self, X)\n+ if self.assume_centered:\n+ self.location_ = np.zeros(X.shape[1])\n+ else:\n+ self.location_ = X.mean(0)\n+ covariance, shrinkage = _ledoit_wolf(\n+ X - self.location_, assume_centered=True, block_size=self.block_size\n+ )\n+ self.shrinkage_ = shrinkage\n+ self._set_covariance(covariance)\n+\n+ return self\n \n \n # OAS estimator\n", "test": null }	null	{ "code": "diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py\nindex 2a5e09f2c..a5329de87 100644\n--- a/sklearn/covariance/_shrunk_covariance.py\n+++ b/sklearn/covariance/_shrunk_covariance.py\n@@ -576,7 +576,6 @@ class LedoitWolf(EmpiricalCovariance):\n )\n self.block_size = block_size\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the Ledoit-Wolf shrunk covariance model to X.\n \n@@ -593,20 +592,6 @@ class LedoitWolf(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n- # Not calling the parent object to fit, to avoid computing the\n- # covariance matrix (and potentially the precision)\n- X = validate_data(self, X)\n- if self.assume_centered:\n- self.location_ = np.zeros(X.shape[1])\n- else:\n- self.location_ = X.mean(0)\n- covariance, shrinkage = _ledoit_wolf(\n- X - self.location_, assume_centered=True, block_size=self.block_size\n- )\n- self.shrinkage_ = shrinkage\n- self._set_covariance(covariance)\n-\n- return self\n \n \n # OAS estimator\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/covariance/_shrunk_covariance.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the Ledoit-Wolf shrunk covariance model to X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_public_functions.py::test_class_wrapper_param_validation[sklearn.covariance.ledoit_wolf-sklearn.covariance.LedoitWolf]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict", "sklearn/tests/test_discriminant_analysis.py::test_lda_ledoitwolf", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[float32-float32]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[float64-float64]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[int32-float64]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[int64-float64]", "sklearn/tests/test_discriminant_analysis.py::test_lda_numeric_consistency_float32_float64", "sklearn/tests/test_discriminant_analysis.py::test_covariance", "sklearn/covariance/tests/test_covariance.py::test_ledoit_wolf", "sklearn/covariance/tests/test_covariance.py::test_ledoit_wolf_small", "sklearn/covariance/tests/test_covariance.py::test_ledoit_wolf_large", "sklearn/covariance/tests/test_covariance.py::test_ledoit_wolf_empty_array[fit]", "sklearn/covariance/_shrunk_covariance.py::sklearn.covariance._shrunk_covariance.LedoitWolf", "sklearn/covariance/_shrunk_covariance.py::sklearn.covariance._shrunk_covariance.ledoit_wolf", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[LedoitWolf()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LedoitWolf()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LedoitWolf()]", "sklearn/tests/test_common.py::test_check_param_validation[LedoitWolf()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-94	1.0	{ "code": "diff --git b/sklearn/discriminant_analysis.py a/sklearn/discriminant_analysis.py\nindex 846e1c4ff..69339491d 100644\n--- b/sklearn/discriminant_analysis.py\n+++ a/sklearn/discriminant_analysis.py\n@@ -554,6 +554,10 @@ class LinearDiscriminantAnalysis(\n self.coef_ = coef @ self.scalings_.T\n self.intercept_ -= self.xbar_ @ self.coef_.T\n \n+ @_fit_context(\n+ # LinearDiscriminantAnalysis.covariance_estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y):\n \"\"\"Fit the Linear Discriminant Analysis model.\n \n@@ -573,6 +577,79 @@ class LinearDiscriminantAnalysis(\n self : object\n Fitted estimator.\n \"\"\"\n+ xp, _ = get_namespace(X)\n+\n+ X, y = validate_data(\n+ self, X, y, ensure_min_samples=2, dtype=[xp.float64, xp.float32]\n+ )\n+ self.classes_ = unique_labels(y)\n+ n_samples, _ = X.shape\n+ n_classes = self.classes_.shape[0]\n+\n+ if n_samples == n_classes:\n+ raise ValueError(\n+ \"The number of samples must be more than the number of classes.\"\n+ )\n+\n+ if self.priors is None: # estimate priors from sample\n+ _, cnts = xp.unique_counts(y) # non-negative ints\n+ self.priors_ = xp.astype(cnts, X.dtype) / float(y.shape[0])\n+ else:\n+ self.priors_ = xp.asarray(self.priors, dtype=X.dtype)\n+\n+ if xp.any(self.priors_ < 0):\n+ raise ValueError(\"priors must be non-negative\")\n+\n+ if xp.abs(xp.sum(self.priors_) - 1.0) > 1e-5:\n+ warnings.warn(\"The priors do not sum to 1. Renormalizing\", UserWarning)\n+ self.priors_ = self.priors_ / self.priors_.sum()\n+\n+ # Maximum number of components no matter what n_components is\n+ # specified:\n+ max_components = min(n_classes - 1, X.shape[1])\n+\n+ if self.n_components is None:\n+ self._max_components = max_components\n+ else:\n+ if self.n_components > max_components:\n+ raise ValueError(\n+ \"n_components cannot be larger than min(n_features, n_classes - 1).\"\n+ )\n+ self._max_components = self.n_components\n+\n+ if self.solver == \"svd\":\n+ if self.shrinkage is not None:\n+ raise NotImplementedError(\"shrinkage not supported with 'svd' solver.\")\n+ if self.covariance_estimator is not None:\n+ raise ValueError(\n+ \"covariance estimator \"\n+ \"is not supported \"\n+ \"with svd solver. Try another solver\"\n+ )\n+ self._solve_svd(X, y)\n+ elif self.solver == \"lsqr\":\n+ self._solve_lstsq(\n+ X,\n+ y,\n+ shrinkage=self.shrinkage,\n+ covariance_estimator=self.covariance_estimator,\n+ )\n+ elif self.solver == \"eigen\":\n+ self._solve_eigen(\n+ X,\n+ y,\n+ shrinkage=self.shrinkage,\n+ covariance_estimator=self.covariance_estimator,\n+ )\n+ if size(self.classes_) == 2: # treat binary case as a special case\n+ coef_ = xp.asarray(self.coef_[1, :] - self.coef_[0, :], dtype=X.dtype)\n+ self.coef_ = xp.reshape(coef_, (1, -1))\n+ intercept_ = xp.asarray(\n+ self.intercept_[1] - self.intercept_[0], dtype=X.dtype\n+ )\n+ self.intercept_ = xp.reshape(intercept_, (1,))\n+ self._n_features_out = self._max_components\n+ return self\n \n def transform(self, X):\n \"\"\"Project data to maximize class separation.\n", "test": null }	null	{ "code": "diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py\nindex 69339491d..846e1c4ff 100644\n--- a/sklearn/discriminant_analysis.py\n+++ b/sklearn/discriminant_analysis.py\n@@ -554,10 +554,6 @@ class LinearDiscriminantAnalysis(\n self.coef_ = coef @ self.scalings_.T\n self.intercept_ -= self.xbar_ @ self.coef_.T\n \n- @_fit_context(\n- # LinearDiscriminantAnalysis.covariance_estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y):\n \"\"\"Fit the Linear Discriminant Analysis model.\n \n@@ -577,79 +573,6 @@ class LinearDiscriminantAnalysis(\n self : object\n Fitted estimator.\n \"\"\"\n- xp, _ = get_namespace(X)\n-\n- X, y = validate_data(\n- self, X, y, ensure_min_samples=2, dtype=[xp.float64, xp.float32]\n- )\n- self.classes_ = unique_labels(y)\n- n_samples, _ = X.shape\n- n_classes = self.classes_.shape[0]\n-\n- if n_samples == n_classes:\n- raise ValueError(\n- \"The number of samples must be more than the number of classes.\"\n- )\n-\n- if self.priors is None: # estimate priors from sample\n- _, cnts = xp.unique_counts(y) # non-negative ints\n- self.priors_ = xp.astype(cnts, X.dtype) / float(y.shape[0])\n- else:\n- self.priors_ = xp.asarray(self.priors, dtype=X.dtype)\n-\n- if xp.any(self.priors_ < 0):\n- raise ValueError(\"priors must be non-negative\")\n-\n- if xp.abs(xp.sum(self.priors_) - 1.0) > 1e-5:\n- warnings.warn(\"The priors do not sum to 1. Renormalizing\", UserWarning)\n- self.priors_ = self.priors_ / self.priors_.sum()\n-\n- # Maximum number of components no matter what n_components is\n- # specified:\n- max_components = min(n_classes - 1, X.shape[1])\n-\n- if self.n_components is None:\n- self._max_components = max_components\n- else:\n- if self.n_components > max_components:\n- raise ValueError(\n- \"n_components cannot be larger than min(n_features, n_classes - 1).\"\n- )\n- self._max_components = self.n_components\n-\n- if self.solver == \"svd\":\n- if self.shrinkage is not None:\n- raise NotImplementedError(\"shrinkage not supported with 'svd' solver.\")\n- if self.covariance_estimator is not None:\n- raise ValueError(\n- \"covariance estimator \"\n- \"is not supported \"\n- \"with svd solver. Try another solver\"\n- )\n- self._solve_svd(X, y)\n- elif self.solver == \"lsqr\":\n- self._solve_lstsq(\n- X,\n- y,\n- shrinkage=self.shrinkage,\n- covariance_estimator=self.covariance_estimator,\n- )\n- elif self.solver == \"eigen\":\n- self._solve_eigen(\n- X,\n- y,\n- shrinkage=self.shrinkage,\n- covariance_estimator=self.covariance_estimator,\n- )\n- if size(self.classes_) == 2: # treat binary case as a special case\n- coef_ = xp.asarray(self.coef_[1, :] - self.coef_[0, :], dtype=X.dtype)\n- self.coef_ = xp.reshape(coef_, (1, -1))\n- intercept_ = xp.asarray(\n- self.intercept_[1] - self.intercept_[0], dtype=X.dtype\n- )\n- self.intercept_ = xp.reshape(intercept_, (1,))\n- self._n_features_out = self._max_components\n- return self\n \n def transform(self, X):\n \"\"\"Project data to maximize class separation.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/discriminant_analysis.py.\nHere is the description for the function:\n def fit(self, X, y):\n \"\"\"Fit the Linear Discriminant Analysis model.\n\n .. versionchanged:: 0.19\n `store_covariance` and `tol` has been moved to main constructor.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data.\n\n y : array-like of shape (n_samples,)\n Target values.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neighbors/tests/test_nca.py::test_init_transformation", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-3-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-3-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-5-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-5-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-7-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-11-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-5-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-7-3-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-7-5-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-7-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[3-7-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-7-5-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-7-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-7-11-11]", "sklearn/neighbors/tests/test_nca.py::test_verbose[lda]", "sklearn/neighbors/tests/test_nca.py::test_nca_feature_names_out[2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[svd-2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[svd-3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[lsqr-2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[lsqr-3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[eigen-2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[eigen-3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_priors", "sklearn/tests/test_discriminant_analysis.py::test_lda_coefs", "sklearn/tests/test_discriminant_analysis.py::test_lda_transform", "sklearn/tests/test_discriminant_analysis.py::test_lda_explained_variance_ratio", "sklearn/tests/test_discriminant_analysis.py::test_lda_orthogonality", "sklearn/tests/test_discriminant_analysis.py::test_lda_scaling", "sklearn/tests/test_discriminant_analysis.py::test_lda_store_covariance", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[0]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[1]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[4]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[5]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[6]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[7]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[8]", "sklearn/tests/test_discriminant_analysis.py::test_lda_shrinkage[9]", "sklearn/tests/test_discriminant_analysis.py::test_lda_ledoitwolf", "sklearn/tests/test_discriminant_analysis.py::test_lda_dimension_warning[5-3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dimension_warning[5-5]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dimension_warning[3-3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dimension_warning[3-5]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[float32-float32]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[float64-float64]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[int32-float64]", "sklearn/tests/test_discriminant_analysis.py::test_lda_dtype_match[int64-float64]", "sklearn/tests/test_discriminant_analysis.py::test_lda_numeric_consistency_float32_float64", "sklearn/tests/test_discriminant_analysis.py::test_raises_value_error_on_same_number_of_classes_and_samples[svd]", "sklearn/tests/test_discriminant_analysis.py::test_raises_value_error_on_same_number_of_classes_and_samples[lsqr]", "sklearn/tests/test_discriminant_analysis.py::test_raises_value_error_on_same_number_of_classes_and_samples[eigen]", "sklearn/tests/test_discriminant_analysis.py::test_get_feature_names_out", "sklearn/discriminant_analysis.py::sklearn.discriminant_analysis.LinearDiscriminantAnalysis", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_requires_y_none]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5,n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_check_param_validation[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_set_output_transform[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-LinearDiscriminantAnalysis()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-95	1.0	{ "code": "diff --git b/sklearn/discriminant_analysis.py a/sklearn/discriminant_analysis.py\nindex 68c9680b2..69339491d 100644\n--- b/sklearn/discriminant_analysis.py\n+++ a/sklearn/discriminant_analysis.py\n@@ -716,6 +716,18 @@ class LinearDiscriminantAnalysis(\n C : ndarray of shape (n_samples, n_classes)\n Estimated log probabilities.\n \"\"\"\n+ xp, _ = get_namespace(X)\n+ prediction = self.predict_proba(X)\n+\n+ info = xp.finfo(prediction.dtype)\n+ if hasattr(info, \"smallest_normal\"):\n+ smallest_normal = info.smallest_normal\n+ else:\n+ # smallest_normal was introduced in NumPy 1.22\n+ smallest_normal = info.tiny\n+\n+ prediction[prediction == 0.0] += smallest_normal\n+ return xp.log(prediction)\n \n def decision_function(self, X):\n \"\"\"Apply decision function to an array of samples.\n", "test": null }	null	{ "code": "diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py\nindex 69339491d..68c9680b2 100644\n--- a/sklearn/discriminant_analysis.py\n+++ b/sklearn/discriminant_analysis.py\n@@ -716,18 +716,6 @@ class LinearDiscriminantAnalysis(\n C : ndarray of shape (n_samples, n_classes)\n Estimated log probabilities.\n \"\"\"\n- xp, _ = get_namespace(X)\n- prediction = self.predict_proba(X)\n-\n- info = xp.finfo(prediction.dtype)\n- if hasattr(info, \"smallest_normal\"):\n- smallest_normal = info.smallest_normal\n- else:\n- # smallest_normal was introduced in NumPy 1.22\n- smallest_normal = info.tiny\n-\n- prediction[prediction == 0.0] += smallest_normal\n- return xp.log(prediction)\n \n def decision_function(self, X):\n \"\"\"Apply decision function to an array of samples.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/discriminant_analysis.py.\nHere is the description for the function:\n def predict_log_proba(self, X):\n \"\"\"Estimate log probability.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Input data.\n\n Returns\n -------\n C : ndarray of shape (n_samples, n_classes)\n Estimated log probabilities.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_discriminant_analysis.py::test_lda_predict", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearDiscriminantAnalysis()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-96	1.0	{ "code": "diff --git b/sklearn/discriminant_analysis.py a/sklearn/discriminant_analysis.py\nindex adbcc6acb..69339491d 100644\n--- b/sklearn/discriminant_analysis.py\n+++ a/sklearn/discriminant_analysis.py\n@@ -694,6 +694,14 @@ class LinearDiscriminantAnalysis(\n C : ndarray of shape (n_samples, n_classes)\n Estimated probabilities.\n \"\"\"\n+ check_is_fitted(self)\n+ xp, is_array_api_compliant = get_namespace(X)\n+ decision = self.decision_function(X)\n+ if size(self.classes_) == 2:\n+ proba = _expit(decision, xp)\n+ return xp.stack([1 - proba, proba], axis=1)\n+ else:\n+ return softmax(decision)\n \n def predict_log_proba(self, X):\n \"\"\"Estimate log probability.\n", "test": null }	null	{ "code": "diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py\nindex 69339491d..adbcc6acb 100644\n--- a/sklearn/discriminant_analysis.py\n+++ b/sklearn/discriminant_analysis.py\n@@ -694,14 +694,6 @@ class LinearDiscriminantAnalysis(\n C : ndarray of shape (n_samples, n_classes)\n Estimated probabilities.\n \"\"\"\n- check_is_fitted(self)\n- xp, is_array_api_compliant = get_namespace(X)\n- decision = self.decision_function(X)\n- if size(self.classes_) == 2:\n- proba = _expit(decision, xp)\n- return xp.stack([1 - proba, proba], axis=1)\n- else:\n- return softmax(decision)\n \n def predict_log_proba(self, X):\n \"\"\"Estimate log probability.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/discriminant_analysis.py.\nHere is the description for the function:\n def predict_proba(self, X):\n \"\"\"Estimate probability.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Input data.\n\n Returns\n -------\n C : ndarray of shape (n_samples, n_classes)\n Estimated probabilities.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_discriminant_analysis.py::test_lda_predict", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[svd-2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[svd-3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[lsqr-2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[lsqr-3]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[eigen-2]", "sklearn/tests/test_discriminant_analysis.py::test_lda_predict_proba[eigen-3]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearDiscriminantAnalysis()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-97	1.0	{ "code": "diff --git b/sklearn/discriminant_analysis.py a/sklearn/discriminant_analysis.py\nindex 78842789f..69339491d 100644\n--- b/sklearn/discriminant_analysis.py\n+++ a/sklearn/discriminant_analysis.py\n@@ -666,6 +666,20 @@ class LinearDiscriminantAnalysis(\n Transformed data. In the case of the 'svd' solver, the shape\n is (n_samples, min(rank, n_components)).\n \"\"\"\n+ if self.solver == \"lsqr\":\n+ raise NotImplementedError(\n+ \"transform not implemented for 'lsqr' solver (use 'svd' or 'eigen').\"\n+ )\n+ check_is_fitted(self)\n+ xp, _ = get_namespace(X)\n+ X = validate_data(self, X, reset=False)\n+\n+ if self.solver == \"svd\":\n+ X_new = (X - self.xbar_) @ self.scalings_\n+ elif self.solver == \"eigen\":\n+ X_new = X @ self.scalings_\n+\n+ return X_new[:, : self._max_components]\n \n def predict_proba(self, X):\n \"\"\"Estimate probability.\n", "test": null }	null	{ "code": "diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py\nindex 69339491d..78842789f 100644\n--- a/sklearn/discriminant_analysis.py\n+++ b/sklearn/discriminant_analysis.py\n@@ -666,20 +666,6 @@ class LinearDiscriminantAnalysis(\n Transformed data. In the case of the 'svd' solver, the shape\n is (n_samples, min(rank, n_components)).\n \"\"\"\n- if self.solver == \"lsqr\":\n- raise NotImplementedError(\n- \"transform not implemented for 'lsqr' solver (use 'svd' or 'eigen').\"\n- )\n- check_is_fitted(self)\n- xp, _ = get_namespace(X)\n- X = validate_data(self, X, reset=False)\n-\n- if self.solver == \"svd\":\n- X_new = (X - self.xbar_) @ self.scalings_\n- elif self.solver == \"eigen\":\n- X_new = X @ self.scalings_\n-\n- return X_new[:, : self._max_components]\n \n def predict_proba(self, X):\n \"\"\"Estimate probability.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/discriminant_analysis.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Project data to maximize class separation.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Input data.\n\n Returns\n -------\n X_new : ndarray of shape (n_samples, n_components) or \\\n (n_samples, min(rank, n_components))\n Transformed data. In the case of the 'svd' solver, the shape\n is (n_samples, min(rank, n_components)).\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_discriminant_analysis.py::test_lda_transform", "sklearn/tests/test_discriminant_analysis.py::test_lda_orthogonality", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_set_output_transform[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-LinearDiscriminantAnalysis()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-98	1.0	{ "code": "diff --git b/sklearn/linear_model/_linear_loss.py a/sklearn/linear_model/_linear_loss.py\nindex 3085b2d50..cfac0a273 100644\n--- b/sklearn/linear_model/_linear_loss.py\n+++ a/sklearn/linear_model/_linear_loss.py\n@@ -348,6 +348,39 @@ class LinearModelLoss:\n gradient : ndarray of shape coef.shape\n The gradient of the loss.\n \"\"\"\n+ (n_samples, n_features), n_classes = X.shape, self.base_loss.n_classes\n+ n_dof = n_features + int(self.fit_intercept)\n+\n+ if raw_prediction is None:\n+ weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n+ else:\n+ weights, intercept = self.weight_intercept(coef)\n+\n+ grad_pointwise = self.base_loss.gradient(\n+ y_true=y,\n+ raw_prediction=raw_prediction,\n+ sample_weight=sample_weight,\n+ n_threads=n_threads,\n+ )\n+ sw_sum = n_samples if sample_weight is None else np.sum(sample_weight)\n+ grad_pointwise /= sw_sum\n+\n+ if not self.base_loss.is_multiclass:\n+ grad = np.empty_like(coef, dtype=weights.dtype)\n+ grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n+ if self.fit_intercept:\n+ grad[-1] = grad_pointwise.sum()\n+ return grad\n+ else:\n+ grad = np.empty((n_classes, n_dof), dtype=weights.dtype, order=\"F\")\n+ # gradient.shape = (n_samples, n_classes)\n+ grad[:, :n_features] = grad_pointwise.T @ X + l2_reg_strength * weights\n+ if self.fit_intercept:\n+ grad[:, -1] = grad_pointwise.sum(axis=0)\n+ if coef.ndim == 1:\n+ return grad.ravel(order=\"F\")\n+ else:\n+ return grad\n \n def gradient_hessian(\n self,\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_linear_loss.py b/sklearn/linear_model/_linear_loss.py\nindex cfac0a273..3085b2d50 100644\n--- a/sklearn/linear_model/_linear_loss.py\n+++ b/sklearn/linear_model/_linear_loss.py\n@@ -348,39 +348,6 @@ class LinearModelLoss:\n gradient : ndarray of shape coef.shape\n The gradient of the loss.\n \"\"\"\n- (n_samples, n_features), n_classes = X.shape, self.base_loss.n_classes\n- n_dof = n_features + int(self.fit_intercept)\n-\n- if raw_prediction is None:\n- weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n- else:\n- weights, intercept = self.weight_intercept(coef)\n-\n- grad_pointwise = self.base_loss.gradient(\n- y_true=y,\n- raw_prediction=raw_prediction,\n- sample_weight=sample_weight,\n- n_threads=n_threads,\n- )\n- sw_sum = n_samples if sample_weight is None else np.sum(sample_weight)\n- grad_pointwise /= sw_sum\n-\n- if not self.base_loss.is_multiclass:\n- grad = np.empty_like(coef, dtype=weights.dtype)\n- grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n- if self.fit_intercept:\n- grad[-1] = grad_pointwise.sum()\n- return grad\n- else:\n- grad = np.empty((n_classes, n_dof), dtype=weights.dtype, order=\"F\")\n- # gradient.shape = (n_samples, n_classes)\n- grad[:, :n_features] = grad_pointwise.T @ X + l2_reg_strength * weights\n- if self.fit_intercept:\n- grad[:, -1] = grad_pointwise.sum(axis=0)\n- if coef.ndim == 1:\n- return grad.ravel(order=\"F\")\n- else:\n- return grad\n \n def gradient_hessian(\n self,\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_linear_loss.py.\nHere is the description for the function:\n def gradient(\n self,\n coef,\n X,\n y,\n sample_weight=None,\n l2_reg_strength=0.0,\n n_threads=1,\n raw_prediction=None,\n ):\n \"\"\"Computes the gradient w.r.t. coef.\n\n Parameters\n ----------\n coef : ndarray of shape (n_dof,), (n_classes, n_dof) or (n_classes * n_dof,)\n Coefficients of a linear model.\n If shape (n_classes * n_dof,), the classes of one feature are contiguous,\n i.e. one reconstructs the 2d-array via\n coef.reshape((n_classes, -1), order=\"F\").\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n y : contiguous array of shape (n_samples,)\n Observed, true target values.\n sample_weight : None or contiguous array of shape (n_samples,), default=None\n Sample weights.\n l2_reg_strength : float, default=0.0\n L2 regularization strength\n n_threads : int, default=1\n Number of OpenMP threads to use.\n raw_prediction : C-contiguous array of shape (n_samples,) or array of \\\n shape (n_samples, n_classes)\n Raw prediction values (in link space). If provided, these are used. If\n None, then raw_prediction = X @ coef + intercept is calculated.\n\n Returns\n -------\n gradient : ndarray of shape coef.shape\n The gradient of the loss.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-PoissonRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-PoissonRegressor()-42-False-newton-cholesky]", 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"sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-PoissonRegressor()-42-True-lbfgs]", "sklearn/linear_model/tests/test_linear_loss.py::test_multinomial_coef_shape[False]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-PoissonRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-PoissonRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-PoissonRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-GammaRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-GammaRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-GammaRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-GammaRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-TweedieRegressor(power=1.5)-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-TweedieRegressor(power=1.5)-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-TweedieRegressor(power=1.5)-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[long-TweedieRegressor(power=1.5)-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-BinomialRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-PoissonRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-PoissonRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-PoissonRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-PoissonRegressor()-42-False-newton-cholesky]", 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"sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_unpenalized_vstacked_X[wide-TweedieRegressor(power=1.5)-42-False-newton-cholesky]", "sklearn/linear_model/tests/test_linear_loss.py::test_multinomial_coef_shape[True]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-99	1.0	{ "code": "diff --git b/sklearn/linear_model/_linear_loss.py a/sklearn/linear_model/_linear_loss.py\nindex 0d9714309..cfac0a273 100644\n--- b/sklearn/linear_model/_linear_loss.py\n+++ a/sklearn/linear_model/_linear_loss.py\n@@ -435,6 +435,91 @@ class LinearModelLoss:\n hessian_warning : bool\n True if pointwise hessian has more than half of its elements non-positive.\n \"\"\"\n+ n_samples, n_features = X.shape\n+ n_dof = n_features + int(self.fit_intercept)\n+\n+ if raw_prediction is None:\n+ weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n+ else:\n+ weights, intercept = self.weight_intercept(coef)\n+\n+ grad_pointwise, hess_pointwise = self.base_loss.gradient_hessian(\n+ y_true=y,\n+ raw_prediction=raw_prediction,\n+ sample_weight=sample_weight,\n+ n_threads=n_threads,\n+ )\n+ sw_sum = n_samples if sample_weight is None else np.sum(sample_weight)\n+ grad_pointwise /= sw_sum\n+ hess_pointwise /= sw_sum\n+\n+ # For non-canonical link functions and far away from the optimum, the pointwise\n+ # hessian can be negative. We take care that 75% of the hessian entries are\n+ # positive.\n+ hessian_warning = np.mean(hess_pointwise <= 0) > 0.25\n+ hess_pointwise = np.abs(hess_pointwise)\n+\n+ if not self.base_loss.is_multiclass:\n+ # gradient\n+ if gradient_out is None:\n+ grad = np.empty_like(coef, dtype=weights.dtype)\n+ else:\n+ grad = gradient_out\n+ grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n+ if self.fit_intercept:\n+ grad[-1] = grad_pointwise.sum()\n+\n+ # hessian\n+ if hessian_out is None:\n+ hess = np.empty(shape=(n_dof, n_dof), dtype=weights.dtype)\n+ else:\n+ hess = hessian_out\n+\n+ if hessian_warning:\n+ # Exit early without computing the hessian.\n+ return grad, hess, hessian_warning\n+\n+ # TODO: This \"sandwich product\", X' diag(W) X, is the main computational\n+ # bottleneck for solvers. A dedicated Cython routine might improve it\n+ # exploiting the symmetry (as opposed to, e.g., BLAS gemm).\n+ if sparse.issparse(X):\n+ hess[:n_features, :n_features] = (\n+ X.T\n+ @ sparse.dia_matrix(\n+ (hess_pointwise, 0), shape=(n_samples, n_samples)\n+ )\n+ @ X\n+ ).toarray()\n+ else:\n+ # np.einsum may use less memory but the following, using BLAS matrix\n+ # multiplication (gemm), is by far faster.\n+ WX = hess_pointwise[:, None] * X\n+ hess[:n_features, :n_features] = np.dot(X.T, WX)\n+\n+ if l2_reg_strength > 0:\n+ # The L2 penalty enters the Hessian on the diagonal only. To add those\n+ # terms, we use a flattened view on the array.\n+ hess.reshape(-1)[\n+ : (n_features * n_dof) : (n_dof + 1)\n+ ] += l2_reg_strength\n+\n+ if self.fit_intercept:\n+ # With intercept included as added column to X, the hessian becomes\n+ # hess = (X, 1)' @ diag(h) @ (X, 1)\n+ # = (X' @ diag(h) @ X, X' @ h)\n+ # ( h @ X, sum(h))\n+ # The left upper part has already been filled, it remains to compute\n+ # the last row and the last column.\n+ Xh = X.T @ hess_pointwise\n+ hess[:-1, -1] = Xh\n+ hess[-1, :-1] = Xh\n+ hess[-1, -1] = hess_pointwise.sum()\n+ else:\n+ # Here we may safely assume HalfMultinomialLoss aka categorical\n+ # cross-entropy.\n+ raise NotImplementedError\n+\n+ return grad, hess, hessian_warning\n \n def gradient_hessian_product(\n self, coef, X, y, sample_weight=None, l2_reg_strength=0.0, n_threads=1\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_linear_loss.py b/sklearn/linear_model/_linear_loss.py\nindex cfac0a273..0d9714309 100644\n--- a/sklearn/linear_model/_linear_loss.py\n+++ b/sklearn/linear_model/_linear_loss.py\n@@ -435,91 +435,6 @@ class LinearModelLoss:\n hessian_warning : bool\n True if pointwise hessian has more than half of its elements non-positive.\n \"\"\"\n- n_samples, n_features = X.shape\n- n_dof = n_features + int(self.fit_intercept)\n-\n- if raw_prediction is None:\n- weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n- else:\n- weights, intercept = self.weight_intercept(coef)\n-\n- grad_pointwise, hess_pointwise = self.base_loss.gradient_hessian(\n- y_true=y,\n- raw_prediction=raw_prediction,\n- sample_weight=sample_weight,\n- n_threads=n_threads,\n- )\n- sw_sum = n_samples if sample_weight is None else np.sum(sample_weight)\n- grad_pointwise /= sw_sum\n- hess_pointwise /= sw_sum\n-\n- # For non-canonical link functions and far away from the optimum, the pointwise\n- # hessian can be negative. We take care that 75% of the hessian entries are\n- # positive.\n- hessian_warning = np.mean(hess_pointwise <= 0) > 0.25\n- hess_pointwise = np.abs(hess_pointwise)\n-\n- if not self.base_loss.is_multiclass:\n- # gradient\n- if gradient_out is None:\n- grad = np.empty_like(coef, dtype=weights.dtype)\n- else:\n- grad = gradient_out\n- grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n- if self.fit_intercept:\n- grad[-1] = grad_pointwise.sum()\n-\n- # hessian\n- if hessian_out is None:\n- hess = np.empty(shape=(n_dof, n_dof), dtype=weights.dtype)\n- else:\n- hess = hessian_out\n-\n- if hessian_warning:\n- # Exit early without computing the hessian.\n- return grad, hess, hessian_warning\n-\n- # TODO: This \"sandwich product\", X' diag(W) X, is the main computational\n- # bottleneck for solvers. A dedicated Cython routine might improve it\n- # exploiting the symmetry (as opposed to, e.g., BLAS gemm).\n- if sparse.issparse(X):\n- hess[:n_features, :n_features] = (\n- X.T\n- @ sparse.dia_matrix(\n- (hess_pointwise, 0), shape=(n_samples, n_samples)\n- )\n- @ X\n- ).toarray()\n- else:\n- # np.einsum may use less memory but the following, using BLAS matrix\n- # multiplication (gemm), is by far faster.\n- WX = hess_pointwise[:, None] * X\n- hess[:n_features, :n_features] = np.dot(X.T, WX)\n-\n- if l2_reg_strength > 0:\n- # The L2 penalty enters the Hessian on the diagonal only. To add those\n- # terms, we use a flattened view on the array.\n- hess.reshape(-1)[\n- : (n_features * n_dof) : (n_dof + 1)\n- ] += l2_reg_strength\n-\n- if self.fit_intercept:\n- # With intercept included as added column to X, the hessian becomes\n- # hess = (X, 1)' @ diag(h) @ (X, 1)\n- # = (X' @ diag(h) @ X, X' @ h)\n- # ( h @ X, sum(h))\n- # The left upper part has already been filled, it remains to compute\n- # the last row and the last column.\n- Xh = X.T @ hess_pointwise\n- hess[:-1, -1] = Xh\n- hess[-1, :-1] = Xh\n- hess[-1, -1] = hess_pointwise.sum()\n- else:\n- # Here we may safely assume HalfMultinomialLoss aka categorical\n- # cross-entropy.\n- raise NotImplementedError\n-\n- return grad, hess, hessian_warning\n \n def gradient_hessian_product(\n self, coef, X, y, sample_weight=None, l2_reg_strength=0.0, n_threads=1\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_linear_loss.py.\nHere is the description for the function:\n def gradient_hessian(\n self,\n coef,\n X,\n y,\n sample_weight=None,\n l2_reg_strength=0.0,\n n_threads=1,\n gradient_out=None,\n hessian_out=None,\n raw_prediction=None,\n ):\n \"\"\"Computes gradient and hessian w.r.t. coef.\n\n Parameters\n ----------\n coef : ndarray of shape (n_dof,), (n_classes, n_dof) or (n_classes * n_dof,)\n Coefficients of a linear model.\n If shape (n_classes * n_dof,), the classes of one feature are contiguous,\n i.e. one reconstructs the 2d-array via\n coef.reshape((n_classes, -1), order=\"F\").\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n y : contiguous array of shape (n_samples,)\n Observed, true target values.\n sample_weight : None or contiguous array of shape (n_samples,), default=None\n Sample weights.\n l2_reg_strength : float, default=0.0\n L2 regularization strength\n n_threads : int, default=1\n Number of OpenMP threads to use.\n gradient_out : None or ndarray of shape coef.shape\n A location into which the gradient is stored. If None, a new array\n might be created.\n hessian_out : None or ndarray\n A location into which the hessian is stored. If None, a new array\n might be created.\n raw_prediction : C-contiguous array of shape (n_samples,) or array of \\\n shape (n_samples, n_classes)\n Raw prediction values (in link space). If provided, these are used. If\n None, then raw_prediction = X @ coef + intercept is calculated.\n\n Returns\n -------\n gradient : ndarray of shape coef.shape\n The gradient of the loss.\n\n hessian : ndarray\n Hessian matrix.\n\n hessian_warning : bool\n True if pointwise hessian has more than half of its elements non-positive.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-PoissonRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-PoissonRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-GammaRegressor()-42-False-newton-cholesky]", 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scikit-learn__scikit-learn-100	1.0	{ "code": "diff --git b/sklearn/linear_model/_linear_loss.py a/sklearn/linear_model/_linear_loss.py\nindex c53997ce4..cfac0a273 100644\n--- b/sklearn/linear_model/_linear_loss.py\n+++ a/sklearn/linear_model/_linear_loss.py\n@@ -553,3 +553,123 @@ class LinearModelLoss:\n Function that takes in a vector input of shape of gradient and\n and returns matrix-vector product with hessian.\n \"\"\"\n+ (n_samples, n_features), n_classes = X.shape, self.base_loss.n_classes\n+ n_dof = n_features + int(self.fit_intercept)\n+ weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n+ sw_sum = n_samples if sample_weight is None else np.sum(sample_weight)\n+\n+ if not self.base_loss.is_multiclass:\n+ grad_pointwise, hess_pointwise = self.base_loss.gradient_hessian(\n+ y_true=y,\n+ raw_prediction=raw_prediction,\n+ sample_weight=sample_weight,\n+ n_threads=n_threads,\n+ )\n+ grad_pointwise /= sw_sum\n+ hess_pointwise /= sw_sum\n+ grad = np.empty_like(coef, dtype=weights.dtype)\n+ grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n+ if self.fit_intercept:\n+ grad[-1] = grad_pointwise.sum()\n+\n+ # Precompute as much as possible: hX, hX_sum and hessian_sum\n+ hessian_sum = hess_pointwise.sum()\n+ if sparse.issparse(X):\n+ hX = (\n+ sparse.dia_matrix((hess_pointwise, 0), shape=(n_samples, n_samples))\n+ @ X\n+ )\n+ else:\n+ hX = hess_pointwise[:, np.newaxis] * X\n+\n+ if self.fit_intercept:\n+ # Calculate the double derivative with respect to intercept.\n+ # Note: In case hX is sparse, hX.sum is a matrix object.\n+ hX_sum = np.squeeze(np.asarray(hX.sum(axis=0)))\n+ # prevent squeezing to zero-dim array if n_features == 1\n+ hX_sum = np.atleast_1d(hX_sum)\n+\n+ # With intercept included and l2_reg_strength = 0, hessp returns\n+ # res = (X, 1)' @ diag(h) @ (X, 1) @ s\n+ # = (X, 1)' @ (hX @ s[:n_features], sum(h) * s[-1])\n+ # res[:n_features] = X' @ hX @ s[:n_features] + sum(h) * s[-1]\n+ # res[-1] = 1' @ hX @ s[:n_features] + sum(h) * s[-1]\n+ def hessp(s):\n+ ret = np.empty_like(s)\n+ if sparse.issparse(X):\n+ ret[:n_features] = X.T @ (hX @ s[:n_features])\n+ else:\n+ ret[:n_features] = np.linalg.multi_dot([X.T, hX, s[:n_features]])\n+ ret[:n_features] += l2_reg_strength * s[:n_features]\n+\n+ if self.fit_intercept:\n+ ret[:n_features] += s[-1] * hX_sum\n+ ret[-1] = hX_sum @ s[:n_features] + hessian_sum * s[-1]\n+ return ret\n+\n+ else:\n+ # Here we may safely assume HalfMultinomialLoss aka categorical\n+ # cross-entropy.\n+ # HalfMultinomialLoss computes only the diagonal part of the hessian, i.e.\n+ # diagonal in the classes. Here, we want the matrix-vector product of the\n+ # full hessian. Therefore, we call gradient_proba.\n+ grad_pointwise, proba = self.base_loss.gradient_proba(\n+ y_true=y,\n+ raw_prediction=raw_prediction,\n+ sample_weight=sample_weight,\n+ n_threads=n_threads,\n+ )\n+ grad_pointwise /= sw_sum\n+ grad = np.empty((n_classes, n_dof), dtype=weights.dtype, order=\"F\")\n+ grad[:, :n_features] = grad_pointwise.T @ X + l2_reg_strength * weights\n+ if self.fit_intercept:\n+ grad[:, -1] = grad_pointwise.sum(axis=0)\n+\n+ # Full hessian-vector product, i.e. not only the diagonal part of the\n+ # hessian. Derivation with some index battle for input vector s:\n+ # - sample index i\n+ # - feature indices j, m\n+ # - class indices k, l\n+ # - 1_{k=l} is one if k=l else 0\n+ # - p_i_k is the (predicted) probability that sample i belongs to class k\n+ # for all i: sum_k p_i_k = 1\n+ # - s_l_m is input vector for class l and feature m\n+ # - X' = X transposed\n+ #\n+ # Note: Hessian with dropping most indices is just:\n+ # X' @ p_k (1(k=l) - p_l) @ X\n+ #\n+ # result_{k j} = sum_{i, l, m} Hessian_{i, k j, m l} * s_l_m\n+ # = sum_{i, l, m} (X')_{ji} * p_i_k * (1_{k=l} - p_i_l)\n+ # * X_{im} s_l_m\n+ # = sum_{i, m} (X')_{ji} * p_i_k\n+ # * (X_{im} * s_k_m - sum_l p_i_l * X_{im} * s_l_m)\n+ #\n+ # See also https://github.com/scikit-learn/scikit-learn/pull/3646#discussion_r17461411 # noqa\n+ def hessp(s):\n+ s = s.reshape((n_classes, -1), order=\"F\") # shape = (n_classes, n_dof)\n+ if self.fit_intercept:\n+ s_intercept = s[:, -1]\n+ s = s[:, :-1] # shape = (n_classes, n_features)\n+ else:\n+ s_intercept = 0\n+ tmp = X @ s.T + s_intercept # X_{im} * s_k_m\n+ tmp += (-proba * tmp).sum(axis=1)[:, np.newaxis] # - sum_l ..\n+ tmp = proba # p_i_k\n+ if sample_weight is not None:\n+ tmp = sample_weight[:, np.newaxis]\n+ # hess_prod = empty_like(grad), but we ravel grad below and this\n+ # function is run after that.\n+ hess_prod = np.empty((n_classes, n_dof), dtype=weights.dtype, order=\"F\")\n+ hess_prod[:, :n_features] = (tmp.T @ X) / sw_sum + l2_reg_strength s\n+ if self.fit_intercept:\n+ hess_prod[:, -1] = tmp.sum(axis=0) / sw_sum\n+ if coef.ndim == 1:\n+ return hess_prod.ravel(order=\"F\")\n+ else:\n+ return hess_prod\n+\n+ if coef.ndim == 1:\n+ return grad.ravel(order=\"F\"), hessp\n+\n+ return grad, hessp\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_linear_loss.py b/sklearn/linear_model/_linear_loss.py\nindex cfac0a273..c53997ce4 100644\n--- a/sklearn/linear_model/_linear_loss.py\n+++ b/sklearn/linear_model/_linear_loss.py\n@@ -553,123 +553,3 @@ class LinearModelLoss:\n Function that takes in a vector input of shape of gradient and\n and returns matrix-vector product with hessian.\n \"\"\"\n- (n_samples, n_features), n_classes = X.shape, self.base_loss.n_classes\n- n_dof = n_features + int(self.fit_intercept)\n- weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n- sw_sum = n_samples if sample_weight is None else np.sum(sample_weight)\n-\n- if not self.base_loss.is_multiclass:\n- grad_pointwise, hess_pointwise = self.base_loss.gradient_hessian(\n- y_true=y,\n- raw_prediction=raw_prediction,\n- sample_weight=sample_weight,\n- n_threads=n_threads,\n- )\n- grad_pointwise /= sw_sum\n- hess_pointwise /= sw_sum\n- grad = np.empty_like(coef, dtype=weights.dtype)\n- grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n- if self.fit_intercept:\n- grad[-1] = grad_pointwise.sum()\n-\n- # Precompute as much as possible: hX, hX_sum and hessian_sum\n- hessian_sum = hess_pointwise.sum()\n- if sparse.issparse(X):\n- hX = (\n- sparse.dia_matrix((hess_pointwise, 0), shape=(n_samples, n_samples))\n- @ X\n- )\n- else:\n- hX = hess_pointwise[:, np.newaxis] * X\n-\n- if self.fit_intercept:\n- # Calculate the double derivative with respect to intercept.\n- # Note: In case hX is sparse, hX.sum is a matrix object.\n- hX_sum = np.squeeze(np.asarray(hX.sum(axis=0)))\n- # prevent squeezing to zero-dim array if n_features == 1\n- hX_sum = np.atleast_1d(hX_sum)\n-\n- # With intercept included and l2_reg_strength = 0, hessp returns\n- # res = (X, 1)' @ diag(h) @ (X, 1) @ s\n- # = (X, 1)' @ (hX @ s[:n_features], sum(h) * s[-1])\n- # res[:n_features] = X' @ hX @ s[:n_features] + sum(h) * s[-1]\n- # res[-1] = 1' @ hX @ s[:n_features] + sum(h) * s[-1]\n- def hessp(s):\n- ret = np.empty_like(s)\n- if sparse.issparse(X):\n- ret[:n_features] = X.T @ (hX @ s[:n_features])\n- else:\n- ret[:n_features] = np.linalg.multi_dot([X.T, hX, s[:n_features]])\n- ret[:n_features] += l2_reg_strength * s[:n_features]\n-\n- if self.fit_intercept:\n- ret[:n_features] += s[-1] * hX_sum\n- ret[-1] = hX_sum @ s[:n_features] + hessian_sum * s[-1]\n- return ret\n-\n- else:\n- # Here we may safely assume HalfMultinomialLoss aka categorical\n- # cross-entropy.\n- # HalfMultinomialLoss computes only the diagonal part of the hessian, i.e.\n- # diagonal in the classes. Here, we want the matrix-vector product of the\n- # full hessian. Therefore, we call gradient_proba.\n- grad_pointwise, proba = self.base_loss.gradient_proba(\n- y_true=y,\n- raw_prediction=raw_prediction,\n- sample_weight=sample_weight,\n- n_threads=n_threads,\n- )\n- grad_pointwise /= sw_sum\n- grad = np.empty((n_classes, n_dof), dtype=weights.dtype, order=\"F\")\n- grad[:, :n_features] = grad_pointwise.T @ X + l2_reg_strength * weights\n- if self.fit_intercept:\n- grad[:, -1] = grad_pointwise.sum(axis=0)\n-\n- # Full hessian-vector product, i.e. not only the diagonal part of the\n- # hessian. Derivation with some index battle for input vector s:\n- # - sample index i\n- # - feature indices j, m\n- # - class indices k, l\n- # - 1_{k=l} is one if k=l else 0\n- # - p_i_k is the (predicted) probability that sample i belongs to class k\n- # for all i: sum_k p_i_k = 1\n- # - s_l_m is input vector for class l and feature m\n- # - X' = X transposed\n- #\n- # Note: Hessian with dropping most indices is just:\n- # X' @ p_k (1(k=l) - p_l) @ X\n- #\n- # result_{k j} = sum_{i, l, m} Hessian_{i, k j, m l} * s_l_m\n- # = sum_{i, l, m} (X')_{ji} * p_i_k * (1_{k=l} - p_i_l)\n- # * X_{im} s_l_m\n- # = sum_{i, m} (X')_{ji} * p_i_k\n- # * (X_{im} * s_k_m - sum_l p_i_l * X_{im} * s_l_m)\n- #\n- # See also https://github.com/scikit-learn/scikit-learn/pull/3646#discussion_r17461411 # noqa\n- def hessp(s):\n- s = s.reshape((n_classes, -1), order=\"F\") # shape = (n_classes, n_dof)\n- if self.fit_intercept:\n- s_intercept = s[:, -1]\n- s = s[:, :-1] # shape = (n_classes, n_features)\n- else:\n- s_intercept = 0\n- tmp = X @ s.T + s_intercept # X_{im} * s_k_m\n- tmp += (-proba * tmp).sum(axis=1)[:, np.newaxis] # - sum_l ..\n- tmp = proba # p_i_k\n- if sample_weight is not None:\n- tmp = sample_weight[:, np.newaxis]\n- # hess_prod = empty_like(grad), but we ravel grad below and this\n- # function is run after that.\n- hess_prod = np.empty((n_classes, n_dof), dtype=weights.dtype, order=\"F\")\n- hess_prod[:, :n_features] = (tmp.T @ X) / sw_sum + l2_reg_strength s\n- if self.fit_intercept:\n- hess_prod[:, -1] = tmp.sum(axis=0) / sw_sum\n- if coef.ndim == 1:\n- return hess_prod.ravel(order=\"F\")\n- else:\n- return hess_prod\n-\n- if coef.ndim == 1:\n- return grad.ravel(order=\"F\"), hessp\n-\n- return grad, hessp\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_linear_loss.py.\nHere is the description for the function:\n def gradient_hessian_product(\n self, coef, X, y, sample_weight=None, l2_reg_strength=0.0, n_threads=1\n ):\n \"\"\"Computes gradient and hessp (hessian product function) w.r.t. coef.\n\n Parameters\n ----------\n coef : ndarray of shape (n_dof,), (n_classes, n_dof) or (n_classes * n_dof,)\n Coefficients of a linear model.\n If shape (n_classes * n_dof,), the classes of one feature are contiguous,\n i.e. one reconstructs the 2d-array via\n coef.reshape((n_classes, -1), order=\"F\").\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n y : contiguous array of shape (n_samples,)\n Observed, true target values.\n sample_weight : None or contiguous array of shape (n_samples,), default=None\n Sample weights.\n l2_reg_strength : float, default=0.0\n L2 regularization strength\n n_threads : int, default=1\n Number of OpenMP threads to use.\n\n Returns\n -------\n gradient : ndarray of shape coef.shape\n The gradient of the loss.\n\n hessp : callable\n Function that takes in a vector input of shape of gradient and\n and returns matrix-vector product with hessian.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_logistic.py::test_predict_iris[clf2]", "sklearn/linear_model/tests/test_logistic.py::test_multinomial_binary[newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_consistency_path", "sklearn/linear_model/tests/test_logistic.py::test_ovr_multinomial_iris", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_solvers", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_solvers_multiclass", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regressioncv_class_weights[42-weight-weight0]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regressioncv_class_weights[42-weight-weight1]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regressioncv_class_weights[42-balanced-weight0]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regressioncv_class_weights[42-balanced-weight1]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_sample_weights[42-newton-cg-single]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_sample_weights[42-newton-cg-cv]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_solver_class_weights[42-newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_class_weights[<lambda>]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_class_weights[csr_matrix]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_class_weights[csr_array]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multinomial", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-ovr-max_iter0]", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-ovr-max_iter1]", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-ovr-max_iter2]", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-ovr-max_iter3]", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-multinomial-max_iter0]", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-multinomial-max_iter1]", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-multinomial-max_iter2]", "sklearn/linear_model/tests/test_logistic.py::test_max_iter[newton-cg-newton-cg failed to converge.* Increase the number of iterations.-multinomial-max_iter3]", "sklearn/linear_model/tests/test_logistic.py::test_n_iter[newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_warm_start[True-True-newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_warm_start[True-False-newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_warm_start[False-True-newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_warm_start[False-False-newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_matrix-False-newton-cg-ovr]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_matrix-False-newton-cg-multinomial]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_matrix-True-newton-cg-ovr]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_matrix-True-newton-cg-multinomial]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_array-False-newton-cg-ovr]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_array-False-newton-cg-multinomial]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_array-True-newton-cg-ovr]", "sklearn/linear_model/tests/test_logistic.py::test_dtype_match[csr_array-True-newton-cg-multinomial]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[newton-cg-LogisticRegression]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[newton-cg-LogisticRegressionCV]", "sklearn/linear_model/tests/test_logistic.py::test_penalty_none[newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_large_sparse_matrix[42-csr_matrix-newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_large_sparse_matrix[42-csr_array-newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_single_feature_newton_cg", "sklearn/linear_model/tests/test_linear_loss.py::test_loss_grad_hess_are_the_same[csr_matrix-0-None-False-HalfBinomialLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_loss_grad_hess_are_the_same[csr_matrix-0-None-False-HalfMultinomialLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_loss_grad_hess_are_the_same[csr_matrix-0-None-False-HalfPoissonLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_loss_grad_hess_are_the_same[csr_matrix-0-None-True-HalfBinomialLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_loss_grad_hess_are_the_same[csr_matrix-0-None-True-HalfMultinomialLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_loss_grad_hess_are_the_same[csr_matrix-0-None-True-HalfPoissonLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_loss_grad_hess_are_the_same[csr_matrix-0-range-False-HalfBinomialLoss]", 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"sklearn/linear_model/tests/test_linear_loss.py::test_gradients_hessians_numerically[1-range-True-HalfMultinomialLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_gradients_hessians_numerically[1-range-True-HalfPoissonLoss]", "sklearn/linear_model/tests/test_linear_loss.py::test_multinomial_coef_shape[False]", "sklearn/linear_model/tests/test_linear_loss.py::test_multinomial_coef_shape[True]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-101	1.0	{ "code": "diff --git b/sklearn/linear_model/_linear_loss.py a/sklearn/linear_model/_linear_loss.py\nindex 0bd2950f4..cfac0a273 100644\n--- b/sklearn/linear_model/_linear_loss.py\n+++ a/sklearn/linear_model/_linear_loss.py\n@@ -89,6 +89,17 @@ class LinearModelLoss:\n coef : ndarray of shape (n_dof,) or (n_classes, n_dof)\n Coefficients of a linear model.\n \"\"\"\n+ n_features = X.shape[1]\n+ n_classes = self.base_loss.n_classes\n+ if self.fit_intercept:\n+ n_dof = n_features + 1\n+ else:\n+ n_dof = n_features\n+ if self.base_loss.is_multiclass:\n+ coef = np.zeros_like(X, shape=(n_classes, n_dof), dtype=dtype, order=\"F\")\n+ else:\n+ coef = np.zeros_like(X, shape=n_dof, dtype=dtype)\n+ return coef\n \n def weight_intercept(self, coef):\n \"\"\"Helper function to get coefficients and intercept.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_linear_loss.py b/sklearn/linear_model/_linear_loss.py\nindex cfac0a273..0bd2950f4 100644\n--- a/sklearn/linear_model/_linear_loss.py\n+++ b/sklearn/linear_model/_linear_loss.py\n@@ -89,17 +89,6 @@ class LinearModelLoss:\n coef : ndarray of shape (n_dof,) or (n_classes, n_dof)\n Coefficients of a linear model.\n \"\"\"\n- n_features = X.shape[1]\n- n_classes = self.base_loss.n_classes\n- if self.fit_intercept:\n- n_dof = n_features + 1\n- else:\n- n_dof = n_features\n- if self.base_loss.is_multiclass:\n- coef = np.zeros_like(X, shape=(n_classes, n_dof), dtype=dtype, order=\"F\")\n- else:\n- coef = np.zeros_like(X, shape=n_dof, dtype=dtype)\n- return coef\n \n def weight_intercept(self, coef):\n \"\"\"Helper function to get coefficients and intercept.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_linear_loss.py.\nHere is the description for the function:\n def init_zero_coef(self, X, dtype=None):\n \"\"\"Allocate coef of correct shape with zeros.\n\n Parameters:\n -----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n dtype : data-type, default=None\n Overrides the data type of coef. With dtype=None, coef will have the same\n dtype as X.\n\n Returns\n -------\n coef : ndarray of shape (n_dof,) or (n_classes, n_dof)\n Coefficients of a linear model.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-False-lbfgs]", "sklearn/linear_model/tests/test_linear_loss.py::test_init_zero_coef[None-0-False-HalfBinomialLoss]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/tests/test_linear_loss.py::test_init_zero_coef[None-0-False-HalfMultinomialLoss]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/tests/test_linear_loss.py::test_init_zero_coef[None-0-False-HalfPoissonLoss]", 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"sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GammaRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PoissonRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[TweedieRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GammaRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PoissonRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[TweedieRegressor(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-102	1.0	{ "code": "diff --git b/sklearn/linear_model/_linear_loss.py a/sklearn/linear_model/_linear_loss.py\nindex 5787f15ec..cfac0a273 100644\n--- b/sklearn/linear_model/_linear_loss.py\n+++ a/sklearn/linear_model/_linear_loss.py\n@@ -216,6 +216,20 @@ class LinearModelLoss:\n loss : float\n Weighted average of losses per sample, plus penalty.\n \"\"\"\n+ if raw_prediction is None:\n+ weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n+ else:\n+ weights, intercept = self.weight_intercept(coef)\n+\n+ loss = self.base_loss.loss(\n+ y_true=y,\n+ raw_prediction=raw_prediction,\n+ sample_weight=None,\n+ n_threads=n_threads,\n+ )\n+ loss = np.average(loss, weights=sample_weight)\n+\n+ return loss + self.l2_penalty(weights, l2_reg_strength)\n \n def loss_gradient(\n self,\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_linear_loss.py b/sklearn/linear_model/_linear_loss.py\nindex cfac0a273..5787f15ec 100644\n--- a/sklearn/linear_model/_linear_loss.py\n+++ b/sklearn/linear_model/_linear_loss.py\n@@ -216,20 +216,6 @@ class LinearModelLoss:\n loss : float\n Weighted average of losses per sample, plus penalty.\n \"\"\"\n- if raw_prediction is None:\n- weights, intercept, raw_prediction = self.weight_intercept_raw(coef, X)\n- else:\n- weights, intercept = self.weight_intercept(coef)\n-\n- loss = self.base_loss.loss(\n- y_true=y,\n- raw_prediction=raw_prediction,\n- sample_weight=None,\n- n_threads=n_threads,\n- )\n- loss = np.average(loss, weights=sample_weight)\n-\n- return loss + self.l2_penalty(weights, l2_reg_strength)\n \n def loss_gradient(\n self,\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_linear_loss.py.\nHere is the description for the function:\n def loss(\n self,\n coef,\n X,\n y,\n sample_weight=None,\n l2_reg_strength=0.0,\n n_threads=1,\n raw_prediction=None,\n ):\n \"\"\"Compute the loss as weighted average over point-wise losses.\n\n Parameters\n ----------\n coef : ndarray of shape (n_dof,), (n_classes, n_dof) or (n_classes * n_dof,)\n Coefficients of a linear model.\n If shape (n_classes * n_dof,), the classes of one feature are contiguous,\n i.e. one reconstructs the 2d-array via\n coef.reshape((n_classes, -1), order=\"F\").\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n y : contiguous array of shape (n_samples,)\n Observed, true target values.\n sample_weight : None or contiguous array of shape (n_samples,), default=None\n Sample weights.\n l2_reg_strength : float, default=0.0\n L2 regularization strength\n n_threads : int, default=1\n Number of OpenMP threads to use.\n raw_prediction : C-contiguous array of shape (n_samples,) or array of \\\n shape (n_samples, n_classes)\n Raw prediction values (in link space). If provided, these are used. If\n None, then raw_prediction = X @ coef + intercept is calculated.\n\n Returns\n -------\n loss : float\n Weighted average of losses per sample, plus penalty.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-PoissonRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-PoissonRegressor()-42-False-newton-cholesky]", 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"sklearn/linear_model/_glm/tests/test_glm.py::test_newton_solver_verbosity[2]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-103	1.0	{ "code": "diff --git b/sklearn/linear_model/_base.py a/sklearn/linear_model/_base.py\nindex 343fd21a6..02e6e7575 100644\n--- b/sklearn/linear_model/_base.py\n+++ a/sklearn/linear_model/_base.py\n@@ -566,6 +566,7 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):\n self.n_jobs = n_jobs\n self.positive = positive\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"\n Fit linear model.\n@@ -589,6 +590,96 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):\n self : object\n Fitted Estimator.\n \"\"\"\n+ n_jobs_ = self.n_jobs\n+\n+ accept_sparse = False if self.positive else [\"csr\", \"csc\", \"coo\"]\n+\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=accept_sparse,\n+ y_numeric=True,\n+ multi_output=True,\n+ force_writeable=True,\n+ )\n+\n+ has_sw = sample_weight is not None\n+ if has_sw:\n+ sample_weight = _check_sample_weight(\n+ sample_weight, X, dtype=X.dtype, ensure_non_negative=True\n+ )\n+\n+ # Note that neither _rescale_data nor the rest of the fit method of\n+ # LinearRegression can benefit from in-place operations when X is a\n+ # sparse matrix. Therefore, let's not copy X when it is sparse.\n+ copy_X_in_preprocess_data = self.copy_X and not sp.issparse(X)\n+\n+ X, y, X_offset, y_offset, X_scale = _preprocess_data(\n+ X,\n+ y,\n+ fit_intercept=self.fit_intercept,\n+ copy=copy_X_in_preprocess_data,\n+ sample_weight=sample_weight,\n+ )\n+\n+ if has_sw:\n+ # Sample weight can be implemented via a simple rescaling. Note\n+ # that we safely do inplace rescaling when _preprocess_data has\n+ # already made a copy if requested.\n+ X, y, sample_weight_sqrt = _rescale_data(\n+ X, y, sample_weight, inplace=copy_X_in_preprocess_data\n+ )\n+\n+ if self.positive:\n+ if y.ndim < 2:\n+ self.coef_ = optimize.nnls(X, y)[0]\n+ else:\n+ # scipy.optimize.nnls cannot handle y with shape (M, K)\n+ outs = Parallel(n_jobs=n_jobs_)(\n+ delayed(optimize.nnls)(X, y[:, j]) for j in range(y.shape[1])\n+ )\n+ self.coef_ = np.vstack([out[0] for out in outs])\n+ elif sp.issparse(X):\n+ X_offset_scale = X_offset / X_scale\n+\n+ if has_sw:\n+\n+ def matvec(b):\n+ return X.dot(b) - sample_weight_sqrt * b.dot(X_offset_scale)\n+\n+ def rmatvec(b):\n+ return X.T.dot(b) - X_offset_scale * b.dot(sample_weight_sqrt)\n+\n+ else:\n+\n+ def matvec(b):\n+ return X.dot(b) - b.dot(X_offset_scale)\n+\n+ def rmatvec(b):\n+ return X.T.dot(b) - X_offset_scale * b.sum()\n+\n+ X_centered = sparse.linalg.LinearOperator(\n+ shape=X.shape, matvec=matvec, rmatvec=rmatvec\n+ )\n+\n+ if y.ndim < 2:\n+ self.coef_ = lsqr(X_centered, y)[0]\n+ else:\n+ # sparse_lstsq cannot handle y with shape (M, K)\n+ outs = Parallel(n_jobs=n_jobs_)(\n+ delayed(lsqr)(X_centered, y[:, j].ravel())\n+ for j in range(y.shape[1])\n+ )\n+ self.coef_ = np.vstack([out[0] for out in outs])\n+ else:\n+ self.coef_, _, self.rank_, self.singular_ = linalg.lstsq(X, y)\n+ self.coef_ = self.coef_.T\n+\n+ if y.ndim == 1:\n+ self.coef_ = np.ravel(self.coef_)\n+ self._set_intercept(X_offset, y_offset, X_scale)\n+ return self\n \n \n def _check_precomputed_gram_matrix(\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py\nindex 02e6e7575..343fd21a6 100644\n--- a/sklearn/linear_model/_base.py\n+++ b/sklearn/linear_model/_base.py\n@@ -566,7 +566,6 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):\n self.n_jobs = n_jobs\n self.positive = positive\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"\n Fit linear model.\n@@ -590,96 +589,6 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):\n self : object\n Fitted Estimator.\n \"\"\"\n- n_jobs_ = self.n_jobs\n-\n- accept_sparse = False if self.positive else [\"csr\", \"csc\", \"coo\"]\n-\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=accept_sparse,\n- y_numeric=True,\n- multi_output=True,\n- force_writeable=True,\n- )\n-\n- has_sw = sample_weight is not None\n- if has_sw:\n- sample_weight = _check_sample_weight(\n- sample_weight, X, dtype=X.dtype, ensure_non_negative=True\n- )\n-\n- # Note that neither _rescale_data nor the rest of the fit method of\n- # LinearRegression can benefit from in-place operations when X is a\n- # sparse matrix. Therefore, let's not copy X when it is sparse.\n- copy_X_in_preprocess_data = self.copy_X and not sp.issparse(X)\n-\n- X, y, X_offset, y_offset, X_scale = _preprocess_data(\n- X,\n- y,\n- fit_intercept=self.fit_intercept,\n- copy=copy_X_in_preprocess_data,\n- sample_weight=sample_weight,\n- )\n-\n- if has_sw:\n- # Sample weight can be implemented via a simple rescaling. Note\n- # that we safely do inplace rescaling when _preprocess_data has\n- # already made a copy if requested.\n- X, y, sample_weight_sqrt = _rescale_data(\n- X, y, sample_weight, inplace=copy_X_in_preprocess_data\n- )\n-\n- if self.positive:\n- if y.ndim < 2:\n- self.coef_ = optimize.nnls(X, y)[0]\n- else:\n- # scipy.optimize.nnls cannot handle y with shape (M, K)\n- outs = Parallel(n_jobs=n_jobs_)(\n- delayed(optimize.nnls)(X, y[:, j]) for j in range(y.shape[1])\n- )\n- self.coef_ = np.vstack([out[0] for out in outs])\n- elif sp.issparse(X):\n- X_offset_scale = X_offset / X_scale\n-\n- if has_sw:\n-\n- def matvec(b):\n- return X.dot(b) - sample_weight_sqrt * b.dot(X_offset_scale)\n-\n- def rmatvec(b):\n- return X.T.dot(b) - X_offset_scale * b.dot(sample_weight_sqrt)\n-\n- else:\n-\n- def matvec(b):\n- return X.dot(b) - b.dot(X_offset_scale)\n-\n- def rmatvec(b):\n- return X.T.dot(b) - X_offset_scale * b.sum()\n-\n- X_centered = sparse.linalg.LinearOperator(\n- shape=X.shape, matvec=matvec, rmatvec=rmatvec\n- )\n-\n- if y.ndim < 2:\n- self.coef_ = lsqr(X_centered, y)[0]\n- else:\n- # sparse_lstsq cannot handle y with shape (M, K)\n- outs = Parallel(n_jobs=n_jobs_)(\n- delayed(lsqr)(X_centered, y[:, j].ravel())\n- for j in range(y.shape[1])\n- )\n- self.coef_ = np.vstack([out[0] for out in outs])\n- else:\n- self.coef_, _, self.rank_, self.singular_ = linalg.lstsq(X, y)\n- self.coef_ = self.coef_.T\n-\n- if y.ndim == 1:\n- self.coef_ = np.ravel(self.coef_)\n- self._set_intercept(X_offset, y_offset, X_scale)\n- return self\n \n \n def _check_precomputed_gram_matrix(\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_base.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None):\n \"\"\"\n Fit linear model.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n\n y : array-like of shape (n_samples,) or (n_samples, n_targets)\n Target values. Will be cast to X's dtype if necessary.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Individual weights for each sample.\n\n .. versionadded:: 0.17\n parameter sample_weight support to LinearRegression.\n\n Returns\n -------\n self : object\n Fitted Estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-LinearRegression-brute-data8]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-LinearRegression-brute-data8]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-LinearRegression-brute-data8]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-LinearRegression-brute-data8]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_linear_regression[True-False]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_linear_regression[False-True]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_periodic_linear_regression[True-False]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_periodic_linear_regression[False-True]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-LinearRegression-brute-data8]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[1-True-False]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[1-False-True]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[2-True-False]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[2-False-True]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[3-True-False]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[3-False-True]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[4-True-False]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[4-False-True]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[5-True-False]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_extrapolation[5-False-True]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-LinearRegression-brute-data8]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-LinearRegression-brute-data8]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-LinearRegression-brute-data7]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-LinearRegression-brute-data8]", 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"sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LinearRegression()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[TransformedTargetRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearRegression()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[TransformedTargetRegressor()]", "sklearn/tests/test_common.py::test_check_param_validation[LinearRegression()]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LinearRegression()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-104	1.0	{ "code": "diff --git b/sklearn/svm/_classes.py a/sklearn/svm/_classes.py\nindex cada560c8..064aa50ec 100644\n--- b/sklearn/svm/_classes.py\n+++ a/sklearn/svm/_classes.py\n@@ -277,6 +277,7 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):\n self.penalty = penalty\n self.loss = loss\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model according to the given training data.\n \n@@ -301,6 +302,52 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):\n self : object\n An instance of the estimator.\n \"\"\"\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=\"csr\",\n+ dtype=np.float64,\n+ order=\"C\",\n+ accept_large_sparse=False,\n+ )\n+ check_classification_targets(y)\n+ self.classes_ = np.unique(y)\n+\n+ _dual = _validate_dual_parameter(\n+ self.dual, self.loss, self.penalty, self.multi_class, X\n+ )\n+\n+ self.coef_, self.intercept_, n_iter_ = _fit_liblinear(\n+ X,\n+ y,\n+ self.C,\n+ self.fit_intercept,\n+ self.intercept_scaling,\n+ self.class_weight,\n+ self.penalty,\n+ _dual,\n+ self.verbose,\n+ self.max_iter,\n+ self.tol,\n+ self.random_state,\n+ self.multi_class,\n+ self.loss,\n+ sample_weight=sample_weight,\n+ )\n+ # Backward compatibility: _fit_liblinear is used both by LinearSVC/R\n+ # and LogisticRegression but LogisticRegression sets a structured\n+ # `n_iter_` attribute with information about the underlying OvR fits\n+ # while LinearSVC/R only reports the maximum value.\n+ self.n_iter_ = n_iter_.max().item()\n+\n+ if self.multi_class == \"crammer_singer\" and len(self.classes_) == 2:\n+ self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)\n+ if self.fit_intercept:\n+ intercept = self.intercept_[1] - self.intercept_[0]\n+ self.intercept_ = np.array([intercept])\n+\n+ return self\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py\nindex 064aa50ec..cada560c8 100644\n--- a/sklearn/svm/_classes.py\n+++ b/sklearn/svm/_classes.py\n@@ -277,7 +277,6 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):\n self.penalty = penalty\n self.loss = loss\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model according to the given training data.\n \n@@ -302,52 +301,6 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):\n self : object\n An instance of the estimator.\n \"\"\"\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=\"csr\",\n- dtype=np.float64,\n- order=\"C\",\n- accept_large_sparse=False,\n- )\n- check_classification_targets(y)\n- self.classes_ = np.unique(y)\n-\n- _dual = _validate_dual_parameter(\n- self.dual, self.loss, self.penalty, self.multi_class, X\n- )\n-\n- self.coef_, self.intercept_, n_iter_ = _fit_liblinear(\n- X,\n- y,\n- self.C,\n- self.fit_intercept,\n- self.intercept_scaling,\n- self.class_weight,\n- self.penalty,\n- _dual,\n- self.verbose,\n- self.max_iter,\n- self.tol,\n- self.random_state,\n- self.multi_class,\n- self.loss,\n- sample_weight=sample_weight,\n- )\n- # Backward compatibility: _fit_liblinear is used both by LinearSVC/R\n- # and LogisticRegression but LogisticRegression sets a structured\n- # `n_iter_` attribute with information about the underlying OvR fits\n- # while LinearSVC/R only reports the maximum value.\n- self.n_iter_ = n_iter_.max().item()\n-\n- if self.multi_class == \"crammer_singer\" and len(self.classes_) == 2:\n- self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)\n- if self.fit_intercept:\n- intercept = self.intercept_[1] - self.intercept_[0]\n- self.intercept_ = np.array([intercept])\n-\n- return self\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/svm/_classes.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit the model according to the given training data.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vector, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n y : array-like of shape (n_samples,)\n Target vector relative to X.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Array of weights that are assigned to individual\n samples. If not provided,\n then each sample is given unit weight.\n\n .. versionadded:: 0.18\n\n Returns\n -------\n self : object\n An instance of the estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring[single_tuple]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring[single_list]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring[dict_str]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring[multi_tuple]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring[multi_list]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring[dict_callable]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[f1-f1_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[f1_weighted-metric1]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[f1_macro-metric2]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[f1_micro-metric3]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[precision-precision_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[precision_weighted-metric5]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[precision_macro-metric6]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[precision_micro-metric7]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[recall-recall_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[recall_weighted-metric9]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[recall_macro-metric10]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[recall_micro-metric11]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[jaccard-jaccard_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[jaccard_weighted-metric13]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[jaccard_macro-metric14]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[jaccard_micro-metric15]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[top_k_accuracy-top_k_accuracy_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[matthews_corrcoef-matthews_corrcoef]", "sklearn/metrics/tests/test_score_objects.py::test_custom_scorer_pickling", "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers_multilabel_indicator_data", "sklearn/ensemble/tests/test_forest.py::test_random_hasher", "sklearn/feature_extraction/tests/test_text.py::test_count_vectorizer_pipeline_grid_selection", "sklearn/feature_extraction/tests/test_text.py::test_vectorizer_pipeline_grid_selection", "sklearn/feature_extraction/tests/test_text.py::test_vectorizer_pipeline_cross_validation", "sklearn/svm/tests/test_sparse.py::test_linearsvc[lil_matrix-dok_matrix]", "sklearn/svm/tests/test_sparse.py::test_linearsvc[lil_array-dok_array]", "sklearn/svm/tests/test_sparse.py::test_linearsvc_iris[csr_matrix]", "sklearn/svm/tests/test_sparse.py::test_linearsvc_iris[csr_array]", "sklearn/svm/tests/test_sparse.py::test_weight[csr_matrix]", "sklearn/svm/tests/test_sparse.py::test_weight[csr_array]", "sklearn/svm/tests/test_svm.py::test_weight", "sklearn/svm/tests/test_sparse.py::test_sparse_liblinear_intercept_handling", 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"sklearn/svm/tests/test_svm.py::test_linearsvc", "sklearn/svm/tests/test_svm.py::test_linearsvc_crammer_singer", "sklearn/model_selection/tests/test_search.py::test_grid_search_groups", "sklearn/svm/tests/test_svm.py::test_linearsvc_fit_sampleweight", "sklearn/svm/tests/test_svm.py::test_crammer_singer_binary", "sklearn/svm/tests/test_svm.py::test_linearsvc_iris", "sklearn/svm/tests/test_svm.py::test_dense_liblinear_intercept_handling", "sklearn/svm/tests/test_svm.py::test_liblinear_set_coef", "sklearn/model_selection/tests/test_search.py::test_classes__property", "sklearn/svm/tests/test_svm.py::test_linearsvc_verbose", "sklearn/model_selection/tests/test_search.py::test_grid_search_sparse[csr_matrix]", "sklearn/svm/tests/test_svm.py::test_linear_svm_convergence_warnings", "sklearn/svm/tests/test_svm.py::test_lsvc_intercept_scaling_zero", "sklearn/svm/tests/test_svm.py::test_linearsvm_liblinear_sample_weight[LinearSVC-params0]", "sklearn/model_selection/tests/test_search.py::test_grid_search_sparse[csr_array]", "sklearn/svm/tests/test_svm.py::test_linearsvm_liblinear_sample_weight[LinearSVC-params1]", "sklearn/model_selection/tests/test_search.py::test_grid_search_sparse_scoring[csr_matrix]", "sklearn/svm/tests/test_svm.py::test_linearsvm_liblinear_sample_weight[LinearSVC-params2]", "sklearn/svm/tests/test_svm.py::test_linearsvm_liblinear_sample_weight[LinearSVC-params3]", "sklearn/model_selection/tests/test_search.py::test_grid_search_sparse_scoring[csr_array]", "sklearn/model_selection/tests/test_search.py::test_refit_callable", "sklearn/model_selection/tests/test_search.py::test_refit_callable_invalid_type", "sklearn/model_selection/tests/test_search.py::test_refit_callable_out_bound[RandomizedSearchCV--1]", "sklearn/model_selection/tests/test_search.py::test_refit_callable_out_bound[RandomizedSearchCV-2]", "sklearn/model_selection/tests/test_search.py::test_refit_callable_out_bound[GridSearchCV--1]", "sklearn/model_selection/tests/test_search.py::test_refit_callable_out_bound[GridSearchCV-2]", "sklearn/model_selection/tests/test_search.py::test_refit_callable_multi_metric", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-None-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-None-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-final_estimator1-3]", "sklearn/model_selection/tests/test_search.py::test_search_cv_timing", "sklearn/model_selection/tests/test_search.py::test_grid_search_correct_score_results", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[False-final_estimator1-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-None-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-None-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-final_estimator1-3]", "sklearn/model_selection/tests/test_search.py::test_search_train_scores_set_to_false", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_iris[True-final_estimator1-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_drop_column_binary_classification", "sklearn/model_selection/tests/test_search.py::test_grid_search_cv_splits_consistency", "sklearn/tests/test_calibration.py::test_calibration_default_estimator", "sklearn/tests/test_calibration.py::test_calibration_cv_splitter[True]", "sklearn/tests/test_calibration.py::test_calibration_cv_splitter[False]", "sklearn/tests/test_calibration.py::test_sample_weight[True-sigmoid]", "sklearn/tests/test_calibration.py::test_sample_weight[True-isotonic]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_drop_estimator", "sklearn/tests/test_calibration.py::test_sample_weight[False-sigmoid]", "sklearn/tests/test_calibration.py::test_sample_weight[False-isotonic]", "sklearn/tests/test_calibration.py::test_parallel_execution[True-sigmoid]", "sklearn/model_selection/tests/test_search.py::test_callable_multimetric_confusion_matrix", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict", "sklearn/model_selection/tests/test_search.py::test_callable_multimetric_same_as_list_of_strings", "sklearn/model_selection/tests/test_search.py::test_callable_single_metric_same_as_single_string", "sklearn/model_selection/tests/test_search.py::test_callable_multimetric_error_on_invalid_key", "sklearn/tests/test_calibration.py::test_parallel_execution[True-isotonic]", "sklearn/model_selection/tests/test_search.py::test_search_cv_verbose_3[True]", "sklearn/model_selection/tests/test_search.py::test_search_cv_verbose_3[False]", "sklearn/tests/test_multiclass.py::test_ovr_multiclass", "sklearn/tests/test_multiclass.py::test_ovr_binary", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[coo_matrix]", "sklearn/model_selection/tests/test_search.py::test_search_estimator_param[GridSearchCV-param_grid]", "sklearn/tests/test_multiclass.py::test_ovr_multilabel", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[coo_array]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[csc_matrix]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[csc_array]", "sklearn/model_selection/tests/test_search.py::test_search_estimator_param[RandomizedSearchCV-param_distributions]", "sklearn/tests/test_multiclass.py::test_ovr_gridsearch", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[csr_matrix]", "sklearn/model_selection/tests/test_search.py::test_search_estimator_param[HalvingGridSearchCV-param_grid]", "sklearn/tests/test_multiclass.py::test_ovo_fit_on_list", "sklearn/tests/test_calibration.py::test_parallel_execution[False-sigmoid]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[csr_array]", "sklearn/tests/test_multiclass.py::test_ovo_fit_predict", "sklearn/tests/test_multioutput.py::test_multiclass_multioutput_estimator", "sklearn/tests/test_multiclass.py::test_ovo_decision_function", "sklearn/tests/test_multioutput.py::test_multi_output_exceptions", "sklearn/tests/test_multiclass.py::test_ovo_gridsearch", "sklearn/tests/test_multiclass.py::test_ovo_string_y", "sklearn/tests/test_multioutput.py::test_multi_output_delegate_predict_proba", "sklearn/tests/test_multiclass.py::test_ecoc_fit_predict", "sklearn/tests/test_multioutput.py::test_classifier_chain_fit_and_predict_with_linear_svc[predict]", "sklearn/tests/test_multiclass.py::test_ecoc_gridsearch", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_error[y3-params3-TypeError-does not support sample weight]", "sklearn/tests/test_multioutput.py::test_classifier_chain_fit_and_predict_with_linear_svc[decision_function]", "sklearn/tests/test_multiclass.py::test_ecoc_delegate_sparse_base_estimator[csc_matrix]", "sklearn/tests/test_multiclass.py::test_ecoc_delegate_sparse_base_estimator[csc_array]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_randomness[StackingClassifier]", "sklearn/model_selection/tests/test_validation.py::test_callable_multimetric_confusion_matrix_cross_validate", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_stratify_default", "sklearn/tests/test_calibration.py::test_parallel_execution[False-isotonic]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_with_sample_weight[StackingClassifier]", "sklearn/model_selection/tests/test_search.py::test_multi_metric_search_forwards_metadata[GridSearchCV-param_grid]", "sklearn/feature_selection/tests/test_from_model.py::test_calling_fit_reinitializes", "sklearn/model_selection/tests/test_search.py::test_multi_metric_search_forwards_metadata[RandomizedSearchCV-param_distributions]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_cv_influence[StackingClassifier]", "sklearn/model_selection/tests/test_search.py::test_score_rejects_params_with_no_routing_enabled[GridSearchCV-param_grid]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[no-intercept-two-classes-squared_hinge-csr_matrix]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[no-intercept-two-classes-squared_hinge-csr_array]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[no-intercept-two-classes-squared_hinge-array]", "sklearn/model_selection/tests/test_search.py::test_score_rejects_params_with_no_routing_enabled[RandomizedSearchCV-param_distributions]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[no-intercept-multi-class-squared_hinge-csr_matrix]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[no-intercept-multi-class-squared_hinge-csr_array]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[no-intercept-multi-class-squared_hinge-array]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[fit-intercept-two-classes-squared_hinge-csr_matrix]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[fit-intercept-two-classes-squared_hinge-csr_array]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[fit-intercept-two-classes-squared_hinge-array]", "sklearn/model_selection/tests/test_search.py::test_score_rejects_params_with_no_routing_enabled[HalvingGridSearchCV-param_grid]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[fit-intercept-multi-class-squared_hinge-csr_matrix]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[fit-intercept-multi-class-squared_hinge-csr_array]", "sklearn/svm/tests/test_bounds.py::test_l1_min_c[fit-intercept-multi-class-squared_hinge-array]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[0-True-sigmoid]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[0-True-isotonic]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[0-False-sigmoid]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[0-False-isotonic]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[1-True-sigmoid]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[1-True-isotonic]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[1-False-sigmoid]", "sklearn/tests/test_calibration.py::test_calibration_multiclass[1-False-isotonic]", "sklearn/tests/test_calibration.py::test_calibration_ensemble_false[sigmoid]", "sklearn/tests/test_calibration.py::test_calibration_ensemble_false[isotonic]", "sklearn/tests/test_calibration.py::test_calibration_prob_sum[True]", "sklearn/tests/test_calibration.py::test_calibration_prob_sum[False]", "sklearn/ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_behavior[stacking-classifier]", "sklearn/ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_behavior[voting-classifier]", "sklearn/metrics/_classification.py::sklearn.metrics._classification.hinge_loss", "sklearn/tests/test_calibration.py::test_calibration_attributes[clf0-2]", "sklearn/tests/test_calibration.py::test_calibration_attributes[clf1-prefit]", "sklearn/tests/test_calibration.py::test_calibration_inconsistent_prefit_n_features_in", "sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingGridSearchCV]", "sklearn/svm/_classes.py::sklearn.svm._classes.LinearSVC", "sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV]", "sklearn/kernel_approximation.py::sklearn.kernel_approximation.Nystroem", "sklearn/tests/test_calibration.py::test_error_less_class_samples_than_folds", "sklearn/multiclass.py::sklearn.multiclass.OneVsOneClassifier", "sklearn/ensemble/_stacking.py::sklearn.ensemble._stacking.StackingClassifier", "sklearn/ensemble/tests/test_stacking.py::test_get_feature_names_out[True-StackingClassifier_multiclass]", "sklearn/ensemble/tests/test_stacking.py::test_get_feature_names_out[True-StackingClassifier_binary]", "sklearn/ensemble/tests/test_stacking.py::test_get_feature_names_out[False-StackingClassifier_multiclass]", "sklearn/ensemble/tests/test_stacking.py::test_get_feature_names_out[False-StackingClassifier_binary]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_sparsify_coefficients]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_class_weight_classifiers]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_class_weight_balanced_linear_classifier0]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_class_weight_balanced_linear_classifier1]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[LinearSVC(max_iter=20)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LinearSVC(max_iter=20)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearSVC(max_iter=20)]", "sklearn/tests/test_common.py::test_check_param_validation[LinearSVC(max_iter=20)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-105	1.0	{ "code": "diff --git b/sklearn/neighbors/_lof.py a/sklearn/neighbors/_lof.py\nindex 9c0a56aa1..c05a4f607 100644\n--- b/sklearn/neighbors/_lof.py\n+++ a/sklearn/neighbors/_lof.py\n@@ -254,6 +254,10 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n \n return self.fit(X)._predict()\n \n+ @_fit_context(\n+ # LocalOutlierFactor.metric is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y=None):\n \"\"\"Fit the local outlier factor detector from the training dataset.\n \n@@ -271,6 +275,56 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n self : LocalOutlierFactor\n The fitted local outlier factor detector.\n \"\"\"\n+ self._fit(X)\n+\n+ n_samples = self.n_samples_fit_\n+ if self.n_neighbors > n_samples:\n+ warnings.warn(\n+ \"n_neighbors (%s) is greater than the \"\n+ \"total number of samples (%s). n_neighbors \"\n+ \"will be set to (n_samples - 1) for estimation.\"\n+ % (self.n_neighbors, n_samples)\n+ )\n+ self.n_neighbors_ = max(1, min(self.n_neighbors, n_samples - 1))\n+\n+ self._distances_fit_X_, _neighbors_indices_fit_X_ = self.kneighbors(\n+ n_neighbors=self.n_neighbors_\n+ )\n+\n+ if self._fit_X.dtype == np.float32:\n+ self._distances_fit_X_ = self._distances_fit_X_.astype(\n+ self._fit_X.dtype,\n+ copy=False,\n+ )\n+\n+ self._lrd = self._local_reachability_density(\n+ self._distances_fit_X_, _neighbors_indices_fit_X_\n+ )\n+\n+ # Compute lof score over training samples to define offset_:\n+ lrd_ratios_array = (\n+ self._lrd[_neighbors_indices_fit_X_] / self._lrd[:, np.newaxis]\n+ )\n+\n+ self.negative_outlier_factor_ = -np.mean(lrd_ratios_array, axis=1)\n+\n+ if self.contamination == \"auto\":\n+ # inliers score around -1 (the higher, the less abnormal).\n+ self.offset_ = -1.5\n+ else:\n+ self.offset_ = np.percentile(\n+ self.negative_outlier_factor_, 100.0 * self.contamination\n+ )\n+\n+ # Verify if negative_outlier_factor_ values are within acceptable range.\n+ # Novelty must also be false to detect outliers\n+ if np.min(self.negative_outlier_factor_) < -1e7 and not self.novelty:\n+ warnings.warn(\n+ \"Duplicate values are leading to incorrect results. \"\n+ \"Increase the number of neighbors for more accurate results.\"\n+ )\n+\n+ return self\n \n def _check_novelty_predict(self):\n if not self.novelty:\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_lof.py b/sklearn/neighbors/_lof.py\nindex c05a4f607..9c0a56aa1 100644\n--- a/sklearn/neighbors/_lof.py\n+++ b/sklearn/neighbors/_lof.py\n@@ -254,10 +254,6 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n \n return self.fit(X)._predict()\n \n- @_fit_context(\n- # LocalOutlierFactor.metric is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y=None):\n \"\"\"Fit the local outlier factor detector from the training dataset.\n \n@@ -275,56 +271,6 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n self : LocalOutlierFactor\n The fitted local outlier factor detector.\n \"\"\"\n- self._fit(X)\n-\n- n_samples = self.n_samples_fit_\n- if self.n_neighbors > n_samples:\n- warnings.warn(\n- \"n_neighbors (%s) is greater than the \"\n- \"total number of samples (%s). n_neighbors \"\n- \"will be set to (n_samples - 1) for estimation.\"\n- % (self.n_neighbors, n_samples)\n- )\n- self.n_neighbors_ = max(1, min(self.n_neighbors, n_samples - 1))\n-\n- self._distances_fit_X_, _neighbors_indices_fit_X_ = self.kneighbors(\n- n_neighbors=self.n_neighbors_\n- )\n-\n- if self._fit_X.dtype == np.float32:\n- self._distances_fit_X_ = self._distances_fit_X_.astype(\n- self._fit_X.dtype,\n- copy=False,\n- )\n-\n- self._lrd = self._local_reachability_density(\n- self._distances_fit_X_, _neighbors_indices_fit_X_\n- )\n-\n- # Compute lof score over training samples to define offset_:\n- lrd_ratios_array = (\n- self._lrd[_neighbors_indices_fit_X_] / self._lrd[:, np.newaxis]\n- )\n-\n- self.negative_outlier_factor_ = -np.mean(lrd_ratios_array, axis=1)\n-\n- if self.contamination == \"auto\":\n- # inliers score around -1 (the higher, the less abnormal).\n- self.offset_ = -1.5\n- else:\n- self.offset_ = np.percentile(\n- self.negative_outlier_factor_, 100.0 * self.contamination\n- )\n-\n- # Verify if negative_outlier_factor_ values are within acceptable range.\n- # Novelty must also be false to detect outliers\n- if np.min(self.negative_outlier_factor_) < -1e7 and not self.novelty:\n- warnings.warn(\n- \"Duplicate values are leading to incorrect results. \"\n- \"Increase the number of neighbors for more accurate results.\"\n- )\n-\n- return self\n \n def _check_novelty_predict(self):\n if not self.novelty:\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_lof.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the local outlier factor detector from the training dataset.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features) or \\\n (n_samples, n_samples) if metric='precomputed'\n Training data.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : LocalOutlierFactor\n The fitted local outlier factor detector.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_pipeline_score_samples_pca_lof", "sklearn/model_selection/tests/test_search.py::test_search_cv_score_samples_method[search_cv0]", "sklearn/model_selection/tests/test_search.py::test_search_cv_score_samples_method[search_cv1]", "sklearn/neighbors/tests/test_lof.py::test_lof[float64]", "sklearn/neighbors/tests/test_lof.py::test_lof_performance[float64]", "sklearn/neighbors/tests/test_lof.py::test_lof_values[float64]", "sklearn/neighbors/tests/test_lof.py::test_lof_precomputed[float64]", "sklearn/neighbors/tests/test_lof.py::test_n_neighbors_attribute", "sklearn/neighbors/tests/test_lof.py::test_score_samples[float64]", "sklearn/neighbors/tests/test_lof.py::test_novelty_errors", "sklearn/neighbors/tests/test_lof.py::test_novelty_training_scores[float64]", "sklearn/neighbors/tests/test_lof.py::test_hasattr_prediction", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit_score_takes_y]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_overwrite_params]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_dtypes]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_fit_returns_self]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_complex_data]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_dtype_object]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_empty_data_messages]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_pipeline_consistency]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_nan_inf]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimator_sparse_array]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimator_sparse_matrix]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_pickle]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_f_contiguous_array_estimator]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_outliers_train]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_outliers_train(readonly_memmap=True)]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_classifier_data_not_an_array]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_methods_sample_order_invariance]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_methods_subset_invariance]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit2d_1sample]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit2d_1feature]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_dict_unchanged]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_dont_overwrite_parameters]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit_idempotent]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit_check_is_fitted]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_n_features_in]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit1d]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit2d_predict1d]", "sklearn/neighbors/tests/test_lof.py::test_predicted_outlier_number[30]", "sklearn/neighbors/tests/test_lof.py::test_predicted_outlier_number[53]", "sklearn/neighbors/tests/test_lof.py::test_sparse[csr_matrix]", "sklearn/neighbors/tests/test_lof.py::test_sparse[csr_array]", "sklearn/neighbors/tests/test_lof.py::test_lof_error_n_neighbors_too_large", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_duplicate_samples", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_lof_novelty_false", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_lof_novelty_true", "sklearn/neighbors/_lof.py::sklearn.neighbors._lof.LocalOutlierFactor", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_outliers_fit_predict]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LocalOutlierFactor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LocalOutlierFactor()]", "sklearn/tests/test_common.py::test_check_param_validation[LocalOutlierFactor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-106	1.0	{ "code": "diff --git b/sklearn/neighbors/_lof.py a/sklearn/neighbors/_lof.py\nindex f6ec73292..c05a4f607 100644\n--- b/sklearn/neighbors/_lof.py\n+++ a/sklearn/neighbors/_lof.py\n@@ -442,6 +442,7 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n raise AttributeError(msg)\n return True\n \n+ @available_if(_check_novelty_score_samples)\n def score_samples(self, X):\n \"\"\"Opposite of the Local Outlier Factor of X.\n \n@@ -469,6 +470,25 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n The opposite of the Local Outlier Factor of each input samples.\n The lower, the more abnormal.\n \"\"\"\n+ check_is_fitted(self)\n+ X = check_array(X, accept_sparse=\"csr\")\n+\n+ distances_X, neighbors_indices_X = self.kneighbors(\n+ X, n_neighbors=self.n_neighbors_\n+ )\n+\n+ if X.dtype == np.float32:\n+ distances_X = distances_X.astype(X.dtype, copy=False)\n+\n+ X_lrd = self._local_reachability_density(\n+ distances_X,\n+ neighbors_indices_X,\n+ )\n+\n+ lrd_ratios_array = self._lrd[neighbors_indices_X] / X_lrd[:, np.newaxis]\n+\n+ # as bigger is better:\n+ return -np.mean(lrd_ratios_array, axis=1)\n \n def _local_reachability_density(self, distances_X, neighbors_indices):\n \"\"\"The local reachability density (LRD)\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_lof.py b/sklearn/neighbors/_lof.py\nindex c05a4f607..f6ec73292 100644\n--- a/sklearn/neighbors/_lof.py\n+++ b/sklearn/neighbors/_lof.py\n@@ -442,7 +442,6 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n raise AttributeError(msg)\n return True\n \n- @available_if(_check_novelty_score_samples)\n def score_samples(self, X):\n \"\"\"Opposite of the Local Outlier Factor of X.\n \n@@ -470,25 +469,6 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase):\n The opposite of the Local Outlier Factor of each input samples.\n The lower, the more abnormal.\n \"\"\"\n- check_is_fitted(self)\n- X = check_array(X, accept_sparse=\"csr\")\n-\n- distances_X, neighbors_indices_X = self.kneighbors(\n- X, n_neighbors=self.n_neighbors_\n- )\n-\n- if X.dtype == np.float32:\n- distances_X = distances_X.astype(X.dtype, copy=False)\n-\n- X_lrd = self._local_reachability_density(\n- distances_X,\n- neighbors_indices_X,\n- )\n-\n- lrd_ratios_array = self._lrd[neighbors_indices_X] / X_lrd[:, np.newaxis]\n-\n- # as bigger is better:\n- return -np.mean(lrd_ratios_array, axis=1)\n \n def _local_reachability_density(self, distances_X, neighbors_indices):\n \"\"\"The local reachability density (LRD)\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_lof.py.\nHere is the description for the function:\n def score_samples(self, X):\n \"\"\"Opposite of the Local Outlier Factor of X.\n\n It is the opposite as bigger is better, i.e. large values correspond\n to inliers.\n\n Only available for novelty detection (when novelty is set to True).\n The argument X is supposed to contain new data: if X contains a\n point from training, it considers the later in its own neighborhood.\n Also, the samples in X are not considered in the neighborhood of any\n point. Because of this, the scores obtained via ``score_samples`` may\n differ from the standard LOF scores.\n The standard LOF scores for the training data is available via the\n ``negative_outlier_factor_`` attribute.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The query sample or samples to compute the Local Outlier Factor\n w.r.t. the training samples.\n\n Returns\n -------\n opposite_lof_scores : ndarray of shape (n_samples,)\n The opposite of the Local Outlier Factor of each input samples.\n The lower, the more abnormal.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_pipeline_score_samples_pca_lof", "sklearn/neighbors/tests/test_lof.py::test_lof_performance[float64]", "sklearn/neighbors/tests/test_lof.py::test_lof_values[float64]", "sklearn/neighbors/tests/test_lof.py::test_lof_precomputed[float64]", "sklearn/neighbors/tests/test_lof.py::test_score_samples[float64]", "sklearn/neighbors/tests/test_lof.py::test_novelty_errors", "sklearn/neighbors/tests/test_lof.py::test_hasattr_prediction", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_dtypes]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_dtype_object]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_nan_inf]", "sklearn/model_selection/tests/test_search.py::test_search_cv_score_samples_method[search_cv0]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimator_sparse_array]", "sklearn/model_selection/tests/test_search.py::test_search_cv_score_samples_method[search_cv1]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimator_sparse_matrix]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_pickle]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_f_contiguous_array_estimator]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_outliers_train]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_outliers_train(readonly_memmap=True)]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_classifier_data_not_an_array]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_unfitted]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_methods_sample_order_invariance]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_methods_subset_invariance]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_dict_unchanged]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit_idempotent]", "sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_fit2d_predict1d]", "sklearn/neighbors/tests/test_lof.py::test_sparse[csr_matrix]", "sklearn/neighbors/tests/test_lof.py::test_sparse[csr_array]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-0.5-False-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_input_dtype_preservation[float64-auto-False-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-False-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-auto]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-ball_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-kd_tree]", "sklearn/neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-False-brute]", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_lof_novelty_true", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[LocalOutlierFactor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LocalOutlierFactor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-107	1.0	{ "code": "diff --git b/sklearn/linear_model/_logistic.py a/sklearn/linear_model/_logistic.py\nindex ab01b8bd0..b3ef71539 100644\n--- b/sklearn/linear_model/_logistic.py\n+++ a/sklearn/linear_model/_logistic.py\n@@ -1416,6 +1416,26 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):\n Returns the probability of the sample for each class in the model,\n where classes are ordered as they are in ``self.classes_``.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ ovr = self.multi_class in [\"ovr\", \"warn\"] or (\n+ self.multi_class in [\"auto\", \"deprecated\"]\n+ and (\n+ self.classes_.size <= 2\n+ or self.solver in (\"liblinear\", \"newton-cholesky\")\n+ )\n+ )\n+ if ovr:\n+ return super()._predict_proba_lr(X)\n+ else:\n+ decision = self.decision_function(X)\n+ if decision.ndim == 1:\n+ # Workaround for multi_class=\"multinomial\" and binary outcomes\n+ # which requires softmax prediction with only a 1D decision.\n+ decision_2d = np.c_[-decision, decision]\n+ else:\n+ decision_2d = decision\n+ return softmax(decision_2d, copy=False)\n \n def predict_log_proba(self, X):\n \"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py\nindex b3ef71539..ab01b8bd0 100644\n--- a/sklearn/linear_model/_logistic.py\n+++ b/sklearn/linear_model/_logistic.py\n@@ -1416,26 +1416,6 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):\n Returns the probability of the sample for each class in the model,\n where classes are ordered as they are in ``self.classes_``.\n \"\"\"\n- check_is_fitted(self)\n-\n- ovr = self.multi_class in [\"ovr\", \"warn\"] or (\n- self.multi_class in [\"auto\", \"deprecated\"]\n- and (\n- self.classes_.size <= 2\n- or self.solver in (\"liblinear\", \"newton-cholesky\")\n- )\n- )\n- if ovr:\n- return super()._predict_proba_lr(X)\n- else:\n- decision = self.decision_function(X)\n- if decision.ndim == 1:\n- # Workaround for multi_class=\"multinomial\" and binary outcomes\n- # which requires softmax prediction with only a 1D decision.\n- decision_2d = np.c_[-decision, decision]\n- else:\n- decision_2d = decision\n- return softmax(decision_2d, copy=False)\n \n def predict_log_proba(self, X):\n \"\"\"\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_logistic.py.\nHere is the description for the function:\n def predict_proba(self, X):\n \"\"\"\n Probability estimates.\n\n The returned estimates for all classes are ordered by the\n label of classes.\n\n For a multi_class problem, if multi_class is set to be \"multinomial\"\n the softmax function is used to find the predicted probability of\n each class.\n Else use a one-vs-rest approach, i.e. calculate the probability\n of each class assuming it to be positive using the logistic function\n and normalize these values across all the classes.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Vector to be scored, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n Returns\n -------\n T : array-like of shape (n_samples, n_classes)\n Returns the probability of the sample for each class in the model,\n where classes are ordered as they are in ``self.classes_``.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_logistic.py::test_predict_2_classes[csr_matrix]", "sklearn/linear_model/tests/test_logistic.py::test_predict_2_classes[csr_array]", "sklearn/linear_model/tests/test_logistic.py::test_predict_3_classes[csr_matrix]", "sklearn/linear_model/tests/test_logistic.py::test_predict_3_classes[csr_array]", "sklearn/linear_model/tests/test_logistic.py::test_predict_iris[clf0]", "sklearn/linear_model/tests/test_logistic.py::test_predict_iris[clf1]", "sklearn/linear_model/tests/test_logistic.py::test_predict_iris[clf2]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-LogisticRegression-brute-data9]", "sklearn/linear_model/tests/test_logistic.py::test_predict_iris[clf3]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-LogisticRegression-brute-data10]", "sklearn/linear_model/tests/test_logistic.py::test_predict_iris[clf4]", "sklearn/linear_model/tests/test_logistic.py::test_predict_iris[clf5]", "sklearn/linear_model/tests/test_logistic.py::test_multinomial_binary[lbfgs]", "sklearn/linear_model/tests/test_logistic.py::test_multinomial_binary[newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_multinomial_binary[sag]", "sklearn/linear_model/tests/test_logistic.py::test_multinomial_binary[saga]", 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"sklearn/tests/test_common.py::test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[FixedThresholdClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GridSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),param_grid={'C':[0.1,1.0]})]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HalvingGridSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LogisticRegression(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LogisticRegressionCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MultiOutputClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsRestClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomizedSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ClassifierChain(base_estimator=LogisticRegression(C=1),cv=3)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[FixedThresholdClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GridSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),param_grid={'C':[0.1,1.0]})]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HalvingGridSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),min_resources='smallest',param_grid={'logisticregression__C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LogisticRegression(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LogisticRegressionCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultiOutputClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsRestClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RFE(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomizedSearchCV(cv=2,error_score='raise',estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[StackingClassifier(cv=3,estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Pipeline(steps=[('logisticregression',LogisticRegression(C=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-108	1.0	{ "code": "diff --git b/sklearn/neural_network/_multilayer_perceptron.py a/sklearn/neural_network/_multilayer_perceptron.py\nindex 322f89d35..1aa890847 100644\n--- b/sklearn/neural_network/_multilayer_perceptron.py\n+++ a/sklearn/neural_network/_multilayer_perceptron.py\n@@ -1175,6 +1175,8 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):\n # Input validation would remove feature names, so we disable it\n return accuracy_score(y, self._predict(X, check_input=False))\n \n+ @available_if(lambda est: est._check_solver())\n+ @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y, classes=None):\n \"\"\"Update the model with a single iteration over the given data.\n \n@@ -1199,6 +1201,14 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):\n self : object\n Trained MLP model.\n \"\"\"\n+ if _check_partial_fit_first_call(self, classes):\n+ self._label_binarizer = LabelBinarizer()\n+ if type_of_target(y).startswith(\"multilabel\"):\n+ self._label_binarizer.fit(y)\n+ else:\n+ self._label_binarizer.fit(classes)\n+\n+ return self._fit(X, y, incremental=True)\n \n def predict_log_proba(self, X):\n \"\"\"Return the log of probability estimates.\n", "test": null }	null	{ "code": "diff --git a/sklearn/neural_network/_multilayer_perceptron.py b/sklearn/neural_network/_multilayer_perceptron.py\nindex 1aa890847..322f89d35 100644\n--- a/sklearn/neural_network/_multilayer_perceptron.py\n+++ b/sklearn/neural_network/_multilayer_perceptron.py\n@@ -1175,8 +1175,6 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):\n # Input validation would remove feature names, so we disable it\n return accuracy_score(y, self._predict(X, check_input=False))\n \n- @available_if(lambda est: est._check_solver())\n- @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y, classes=None):\n \"\"\"Update the model with a single iteration over the given data.\n \n@@ -1201,14 +1199,6 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):\n self : object\n Trained MLP model.\n \"\"\"\n- if _check_partial_fit_first_call(self, classes):\n- self._label_binarizer = LabelBinarizer()\n- if type_of_target(y).startswith(\"multilabel\"):\n- self._label_binarizer.fit(y)\n- else:\n- self._label_binarizer.fit(classes)\n-\n- return self._fit(X, y, incremental=True)\n \n def predict_log_proba(self, X):\n \"\"\"Return the log of probability estimates.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neural_network/_multilayer_perceptron.py.\nHere is the description for the function:\n def partial_fit(self, X, y, classes=None):\n \"\"\"Update the model with a single iteration over the given data.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input data.\n\n y : array-like of shape (n_samples,)\n The target values.\n\n classes : array of shape (n_classes,), default=None\n Classes across all calls to partial_fit.\n Can be obtained via `np.unique(y_all)`, where y_all is the\n target vector of the entire dataset.\n This argument is required for the first call to partial_fit\n and can be omitted in the subsequent calls.\n Note that y doesn't need to contain all labels in `classes`.\n\n Returns\n -------\n self : object\n Trained MLP model.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neural_network/tests/test_mlp.py::test_fit", "sklearn/neural_network/tests/test_mlp.py::test_multilabel_classification", "sklearn/neural_network/tests/test_mlp.py::test_partial_fit_classes_error", "sklearn/neural_network/tests/test_mlp.py::test_partial_fit_classification", "sklearn/neural_network/tests/test_mlp.py::test_partial_fit_unseen_classes", "sklearn/neural_network/tests/test_mlp.py::test_partial_fit_errors", "sklearn/neural_network/tests/test_mlp.py::test_verbose_sgd", "sklearn/neural_network/tests/test_mlp.py::test_mlp_partial_fit_after_fit[MLPClassifier]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_partial_fit_n_features]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MLPClassifier(max_iter=100)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MLPClassifier(max_iter=100)]", "sklearn/tests/test_common.py::test_check_param_validation[MLPClassifier(max_iter=100)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-109	1.0	{ "code": "diff --git b/sklearn/neural_network/_multilayer_perceptron.py a/sklearn/neural_network/_multilayer_perceptron.py\nindex 3f9336e32..1aa890847 100644\n--- b/sklearn/neural_network/_multilayer_perceptron.py\n+++ a/sklearn/neural_network/_multilayer_perceptron.py\n@@ -1242,6 +1242,16 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):\n The predicted probability of the sample for each class in the\n model, where classes are ordered as they are in `self.classes_`.\n \"\"\"\n+ check_is_fitted(self)\n+ y_pred = self._forward_pass_fast(X)\n+\n+ if self.n_outputs_ == 1:\n+ y_pred = y_pred.ravel()\n+\n+ if y_pred.ndim == 1:\n+ return np.vstack([1 - y_pred, y_pred]).T\n+ else:\n+ return y_pred\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/neural_network/_multilayer_perceptron.py b/sklearn/neural_network/_multilayer_perceptron.py\nindex 1aa890847..3f9336e32 100644\n--- a/sklearn/neural_network/_multilayer_perceptron.py\n+++ b/sklearn/neural_network/_multilayer_perceptron.py\n@@ -1242,16 +1242,6 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):\n The predicted probability of the sample for each class in the\n model, where classes are ordered as they are in `self.classes_`.\n \"\"\"\n- check_is_fitted(self)\n- y_pred = self._forward_pass_fast(X)\n-\n- if self.n_outputs_ == 1:\n- y_pred = y_pred.ravel()\n-\n- if y_pred.ndim == 1:\n- return np.vstack([1 - y_pred, y_pred]).T\n- else:\n- return y_pred\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neural_network/_multilayer_perceptron.py.\nHere is the description for the function:\n def predict_proba(self, X):\n \"\"\"Probability estimates.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input data.\n\n Returns\n -------\n y_prob : ndarray of shape (n_samples, n_classes)\n The predicted probability of the sample for each class in the\n model, where classes are ordered as they are in `self.classes_`.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neural_network/tests/test_mlp.py::test_fit", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_predict_proba[MLPClassifier]", "sklearn/neural_network/tests/test_mlp.py::test_predict_proba_binary", "sklearn/neural_network/tests/test_mlp.py::test_predict_proba_multiclass", "sklearn/neural_network/tests/test_mlp.py::test_predict_proba_multilabel", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[False-auto]", "sklearn/neural_network/tests/test_mlp.py::test_mlp_classifier_dtypes_casting", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-auto]", "sklearn/neural_network/_multilayer_perceptron.py::sklearn.neural_network._multilayer_perceptron.MLPClassifier", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_classifiers_multilabel_output_format_predict_proba]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[MLPClassifier(max_iter=100)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MLPClassifier(max_iter=100)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MLPClassifier(max_iter=100)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-110	1.0	{ "code": "diff --git b/sklearn/cluster/_mean_shift.py a/sklearn/cluster/_mean_shift.py\nindex d8a2c8cd4..5190936e6 100644\n--- b/sklearn/cluster/_mean_shift.py\n+++ a/sklearn/cluster/_mean_shift.py\n@@ -463,6 +463,7 @@ class MeanShift(ClusterMixin, BaseEstimator):\n self.n_jobs = n_jobs\n self.max_iter = max_iter\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Perform clustering.\n \n@@ -479,6 +480,82 @@ class MeanShift(ClusterMixin, BaseEstimator):\n self : object\n Fitted instance.\n \"\"\"\n+ X = validate_data(self, X)\n+ bandwidth = self.bandwidth\n+ if bandwidth is None:\n+ bandwidth = estimate_bandwidth(X, n_jobs=self.n_jobs)\n+\n+ seeds = self.seeds\n+ if seeds is None:\n+ if self.bin_seeding:\n+ seeds = get_bin_seeds(X, bandwidth, self.min_bin_freq)\n+ else:\n+ seeds = X\n+ n_samples, n_features = X.shape\n+ center_intensity_dict = {}\n+\n+ # We use n_jobs=1 because this will be used in nested calls under\n+ # parallel calls to _mean_shift_single_seed so there is no need for\n+ # for further parallelism.\n+ nbrs = NearestNeighbors(radius=bandwidth, n_jobs=1).fit(X)\n+\n+ # execute iterations on all seeds in parallel\n+ all_res = Parallel(n_jobs=self.n_jobs)(\n+ delayed(_mean_shift_single_seed)(seed, X, nbrs, self.max_iter)\n+ for seed in seeds\n+ )\n+ # copy results in a dictionary\n+ for i in range(len(seeds)):\n+ if all_res[i][1]: # i.e. len(points_within) > 0\n+ center_intensity_dict[all_res[i][0]] = all_res[i][1]\n+\n+ self.n_iter_ = max([x[2] for x in all_res])\n+\n+ if not center_intensity_dict:\n+ # nothing near seeds\n+ raise ValueError(\n+ \"No point was within bandwidth=%f of any seed. Try a different seeding\"\n+ \" strategy or increase the bandwidth.\"\n+ % bandwidth\n+ )\n+\n+ # POST PROCESSING: remove near duplicate points\n+ # If the distance between two kernels is less than the bandwidth,\n+ # then we have to remove one because it is a duplicate. Remove the\n+ # one with fewer points.\n+\n+ sorted_by_intensity = sorted(\n+ center_intensity_dict.items(),\n+ key=lambda tup: (tup[1], tup[0]),\n+ reverse=True,\n+ )\n+ sorted_centers = np.array([tup[0] for tup in sorted_by_intensity])\n+ unique = np.ones(len(sorted_centers), dtype=bool)\n+ nbrs = NearestNeighbors(radius=bandwidth, n_jobs=self.n_jobs).fit(\n+ sorted_centers\n+ )\n+ for i, center in enumerate(sorted_centers):\n+ if unique[i]:\n+ neighbor_idxs = nbrs.radius_neighbors([center], return_distance=False)[\n+ 0\n+ ]\n+ unique[neighbor_idxs] = 0\n+ unique[i] = 1 # leave the current point as unique\n+ cluster_centers = sorted_centers[unique]\n+\n+ # ASSIGN LABELS: a point belongs to the cluster that it is closest to\n+ nbrs = NearestNeighbors(n_neighbors=1, n_jobs=self.n_jobs).fit(cluster_centers)\n+ labels = np.zeros(n_samples, dtype=int)\n+ distances, idxs = nbrs.kneighbors(X)\n+ if self.cluster_all:\n+ labels = idxs.flatten()\n+ else:\n+ labels.fill(-1)\n+ bool_selector = distances.flatten() <= bandwidth\n+ labels[bool_selector] = idxs.flatten()[bool_selector]\n+\n+ self.cluster_centers_, self.labels_ = cluster_centers, labels\n+ return self\n \n def predict(self, X):\n \"\"\"Predict the closest cluster each sample in X belongs to.\n", "test": null }	null	{ "code": "diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py\nindex 5190936e6..d8a2c8cd4 100644\n--- a/sklearn/cluster/_mean_shift.py\n+++ b/sklearn/cluster/_mean_shift.py\n@@ -463,7 +463,6 @@ class MeanShift(ClusterMixin, BaseEstimator):\n self.n_jobs = n_jobs\n self.max_iter = max_iter\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Perform clustering.\n \n@@ -480,82 +479,6 @@ class MeanShift(ClusterMixin, BaseEstimator):\n self : object\n Fitted instance.\n \"\"\"\n- X = validate_data(self, X)\n- bandwidth = self.bandwidth\n- if bandwidth is None:\n- bandwidth = estimate_bandwidth(X, n_jobs=self.n_jobs)\n-\n- seeds = self.seeds\n- if seeds is None:\n- if self.bin_seeding:\n- seeds = get_bin_seeds(X, bandwidth, self.min_bin_freq)\n- else:\n- seeds = X\n- n_samples, n_features = X.shape\n- center_intensity_dict = {}\n-\n- # We use n_jobs=1 because this will be used in nested calls under\n- # parallel calls to _mean_shift_single_seed so there is no need for\n- # for further parallelism.\n- nbrs = NearestNeighbors(radius=bandwidth, n_jobs=1).fit(X)\n-\n- # execute iterations on all seeds in parallel\n- all_res = Parallel(n_jobs=self.n_jobs)(\n- delayed(_mean_shift_single_seed)(seed, X, nbrs, self.max_iter)\n- for seed in seeds\n- )\n- # copy results in a dictionary\n- for i in range(len(seeds)):\n- if all_res[i][1]: # i.e. len(points_within) > 0\n- center_intensity_dict[all_res[i][0]] = all_res[i][1]\n-\n- self.n_iter_ = max([x[2] for x in all_res])\n-\n- if not center_intensity_dict:\n- # nothing near seeds\n- raise ValueError(\n- \"No point was within bandwidth=%f of any seed. Try a different seeding\"\n- \" strategy or increase the bandwidth.\"\n- % bandwidth\n- )\n-\n- # POST PROCESSING: remove near duplicate points\n- # If the distance between two kernels is less than the bandwidth,\n- # then we have to remove one because it is a duplicate. Remove the\n- # one with fewer points.\n-\n- sorted_by_intensity = sorted(\n- center_intensity_dict.items(),\n- key=lambda tup: (tup[1], tup[0]),\n- reverse=True,\n- )\n- sorted_centers = np.array([tup[0] for tup in sorted_by_intensity])\n- unique = np.ones(len(sorted_centers), dtype=bool)\n- nbrs = NearestNeighbors(radius=bandwidth, n_jobs=self.n_jobs).fit(\n- sorted_centers\n- )\n- for i, center in enumerate(sorted_centers):\n- if unique[i]:\n- neighbor_idxs = nbrs.radius_neighbors([center], return_distance=False)[\n- 0\n- ]\n- unique[neighbor_idxs] = 0\n- unique[i] = 1 # leave the current point as unique\n- cluster_centers = sorted_centers[unique]\n-\n- # ASSIGN LABELS: a point belongs to the cluster that it is closest to\n- nbrs = NearestNeighbors(n_neighbors=1, n_jobs=self.n_jobs).fit(cluster_centers)\n- labels = np.zeros(n_samples, dtype=int)\n- distances, idxs = nbrs.kneighbors(X)\n- if self.cluster_all:\n- labels = idxs.flatten()\n- else:\n- labels.fill(-1)\n- bool_selector = distances.flatten() <= bandwidth\n- labels[bool_selector] = idxs.flatten()[bool_selector]\n-\n- self.cluster_centers_, self.labels_ = cluster_centers, labels\n- return self\n \n def predict(self, X):\n \"\"\"Predict the closest cluster each sample in X belongs to.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/cluster/_mean_shift.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Perform clustering.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Samples to cluster.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : object\n Fitted instance.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_public_functions.py::test_class_wrapper_param_validation[sklearn.cluster.mean_shift-sklearn.cluster.MeanShift]", "sklearn/cluster/tests/test_mean_shift.py::test_convergence_of_1d_constant_data", "sklearn/cluster/tests/test_mean_shift.py::test_mean_shift[float64-1.2-True-3-0]", "sklearn/cluster/tests/test_mean_shift.py::test_mean_shift[float64-1.2-False-4--1]", "sklearn/cluster/tests/test_mean_shift.py::test_parallel[float64]", "sklearn/cluster/tests/test_mean_shift.py::test_meanshift_predict[float64]", "sklearn/cluster/tests/test_mean_shift.py::test_meanshift_all_orphans", "sklearn/cluster/tests/test_mean_shift.py::test_cluster_intensity_tie[float64]", "sklearn/cluster/tests/test_mean_shift.py::test_max_iter[1]", "sklearn/cluster/tests/test_mean_shift.py::test_max_iter[100]", "sklearn/cluster/tests/test_mean_shift.py::test_mean_shift_zero_bandwidth[float64]", "sklearn/cluster/_mean_shift.py::sklearn.cluster._mean_shift.MeanShift", "sklearn/cluster/_mean_shift.py::sklearn.cluster._mean_shift.mean_shift", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_clustering]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_clustering(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[MeanShift(bandwidth=1.0,max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MeanShift(bandwidth=1.0,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MeanShift(bandwidth=1.0,max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[MeanShift(bandwidth=1.0,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-111	1.0	{ "code": "diff --git b/sklearn/covariance/_robust_covariance.py a/sklearn/covariance/_robust_covariance.py\nindex 650a94aaa..786c4e17b 100644\n--- b/sklearn/covariance/_robust_covariance.py\n+++ a/sklearn/covariance/_robust_covariance.py\n@@ -723,6 +723,7 @@ class MinCovDet(EmpiricalCovariance):\n self.support_fraction = support_fraction\n self.random_state = random_state\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit a Minimum Covariance Determinant with the FastMCD algorithm.\n \n@@ -740,6 +741,41 @@ class MinCovDet(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X, ensure_min_samples=2, estimator=\"MinCovDet\")\n+ random_state = check_random_state(self.random_state)\n+ n_samples, n_features = X.shape\n+ # check that the empirical covariance is full rank\n+ if (linalg.svdvals(np.dot(X.T, X)) > 1e-8).sum() != n_features:\n+ warnings.warn(\n+ \"The covariance matrix associated to your dataset is not full rank\"\n+ )\n+ # compute and store raw estimates\n+ raw_location, raw_covariance, raw_support, raw_dist = fast_mcd(\n+ X,\n+ support_fraction=self.support_fraction,\n+ cov_computation_method=self._nonrobust_covariance,\n+ random_state=random_state,\n+ )\n+ if self.assume_centered:\n+ raw_location = np.zeros(n_features)\n+ raw_covariance = self._nonrobust_covariance(\n+ X[raw_support], assume_centered=True\n+ )\n+ # get precision matrix in an optimized way\n+ precision = linalg.pinvh(raw_covariance)\n+ raw_dist = np.sum(np.dot(X, precision) * X, 1)\n+ self.raw_location_ = raw_location\n+ self.raw_covariance_ = raw_covariance\n+ self.raw_support_ = raw_support\n+ self.location_ = raw_location\n+ self.support_ = raw_support\n+ self.dist_ = raw_dist\n+ # obtain consistency at normal models\n+ self.correct_covariance(X)\n+ # re-weight estimator\n+ self.reweight_covariance(X)\n+\n+ return self\n \n def correct_covariance(self, data):\n \"\"\"Apply a correction to raw Minimum Covariance Determinant estimates.\n", "test": null }	null	{ "code": "diff --git a/sklearn/covariance/_robust_covariance.py b/sklearn/covariance/_robust_covariance.py\nindex 786c4e17b..650a94aaa 100644\n--- a/sklearn/covariance/_robust_covariance.py\n+++ b/sklearn/covariance/_robust_covariance.py\n@@ -723,7 +723,6 @@ class MinCovDet(EmpiricalCovariance):\n self.support_fraction = support_fraction\n self.random_state = random_state\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit a Minimum Covariance Determinant with the FastMCD algorithm.\n \n@@ -741,41 +740,6 @@ class MinCovDet(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X, ensure_min_samples=2, estimator=\"MinCovDet\")\n- random_state = check_random_state(self.random_state)\n- n_samples, n_features = X.shape\n- # check that the empirical covariance is full rank\n- if (linalg.svdvals(np.dot(X.T, X)) > 1e-8).sum() != n_features:\n- warnings.warn(\n- \"The covariance matrix associated to your dataset is not full rank\"\n- )\n- # compute and store raw estimates\n- raw_location, raw_covariance, raw_support, raw_dist = fast_mcd(\n- X,\n- support_fraction=self.support_fraction,\n- cov_computation_method=self._nonrobust_covariance,\n- random_state=random_state,\n- )\n- if self.assume_centered:\n- raw_location = np.zeros(n_features)\n- raw_covariance = self._nonrobust_covariance(\n- X[raw_support], assume_centered=True\n- )\n- # get precision matrix in an optimized way\n- precision = linalg.pinvh(raw_covariance)\n- raw_dist = np.sum(np.dot(X, precision) * X, 1)\n- self.raw_location_ = raw_location\n- self.raw_covariance_ = raw_covariance\n- self.raw_support_ = raw_support\n- self.location_ = raw_location\n- self.support_ = raw_support\n- self.dist_ = raw_dist\n- # obtain consistency at normal models\n- self.correct_covariance(X)\n- # re-weight estimator\n- self.reweight_covariance(X)\n-\n- return self\n \n def correct_covariance(self, data):\n \"\"\"Apply a correction to raw Minimum Covariance Determinant estimates.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/covariance/_robust_covariance.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit a Minimum Covariance Determinant with the FastMCD algorithm.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_outliers_fit_predict]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_outliers_train]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_outliers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[EllipticEnvelope()-check_fit2d_predict1d]", "sklearn/covariance/tests/test_robust_covariance.py::test_mcd[42]", "sklearn/covariance/tests/test_robust_covariance.py::test_mcd_class_on_invalid_input", "sklearn/covariance/tests/test_robust_covariance.py::test_mcd_issue1127", "sklearn/covariance/tests/test_robust_covariance.py::test_mcd_issue3367[42]", "sklearn/covariance/tests/test_robust_covariance.py::test_mcd_support_covariance_is_zero", "sklearn/covariance/tests/test_robust_covariance.py::test_mcd_increasing_det_warning[42]", "sklearn/covariance/tests/test_elliptic_envelope.py::test_elliptic_envelope[42]", "sklearn/covariance/tests/test_elliptic_envelope.py::test_score_samples", "sklearn/covariance/_elliptic_envelope.py::sklearn.covariance._elliptic_envelope.EllipticEnvelope", "sklearn/covariance/_robust_covariance.py::sklearn.covariance._robust_covariance.MinCovDet", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[MinCovDet()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[EllipticEnvelope()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MinCovDet()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[EllipticEnvelope()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MinCovDet()]", "sklearn/tests/test_common.py::test_check_param_validation[MinCovDet()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-112	1.0	{ "code": "diff --git b/sklearn/decomposition/_dict_learning.py a/sklearn/decomposition/_dict_learning.py\nindex cb4b82cce..2d916dac1 100644\n--- b/sklearn/decomposition/_dict_learning.py\n+++ a/sklearn/decomposition/_dict_learning.py\n@@ -2157,6 +2157,7 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n \n return False\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model from data in X.\n \n@@ -2174,6 +2175,82 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(\n+ self, X, dtype=[np.float64, np.float32], order=\"C\", copy=False\n+ )\n+\n+ self._check_params(X)\n+ self._random_state = check_random_state(self.random_state)\n+\n+ dictionary = self._initialize_dict(X, self._random_state)\n+ old_dict = dictionary.copy()\n+\n+ if self.shuffle:\n+ X_train = X.copy()\n+ self._random_state.shuffle(X_train)\n+ else:\n+ X_train = X\n+\n+ n_samples, n_features = X_train.shape\n+\n+ if self.verbose:\n+ print(\"[dict_learning]\")\n+\n+ # Inner stats\n+ self._A = np.zeros(\n+ (self._n_components, self._n_components), dtype=X_train.dtype\n+ )\n+ self._B = np.zeros((n_features, self._n_components), dtype=X_train.dtype)\n+\n+ # TODO(1.6): remove in 1.6\n+ if self.max_iter is None:\n+ warn(\n+ (\n+ \"`max_iter=None` is deprecated in version 1.4 and will be removed\"\n+ \" in version 1.6. Use the default value (i.e. `1_000`) instead.\"\n+ ),\n+ FutureWarning,\n+ )\n+ max_iter = 1_000\n+ else:\n+ max_iter = self.max_iter\n+\n+ # Attributes to monitor the convergence\n+ self._ewa_cost = None\n+ self._ewa_cost_min = None\n+ self._no_improvement = 0\n+\n+ batches = gen_batches(n_samples, self._batch_size)\n+ batches = itertools.cycle(batches)\n+ n_steps_per_iter = int(np.ceil(n_samples / self._batch_size))\n+ n_steps = max_iter * n_steps_per_iter\n+\n+ i = -1 # to allow max_iter = 0\n+\n+ for i, batch in zip(range(n_steps), batches):\n+ X_batch = X_train[batch]\n+\n+ batch_cost = self._minibatch_step(\n+ X_batch, dictionary, self._random_state, i\n+ )\n+\n+ if self._check_convergence(\n+ X_batch, batch_cost, dictionary, old_dict, n_samples, i, n_steps\n+ ):\n+ break\n+\n+ # XXX callback param added for backward compat in #18975 but a common\n+ # unified callback API should be preferred\n+ if self.callback is not None:\n+ self.callback(locals())\n+\n+ old_dict[:] = dictionary\n+\n+ self.n_steps_ = i + 1\n+ self.n_iter_ = np.ceil(self.n_steps_ / n_steps_per_iter)\n+ self.components_ = dictionary\n+\n+ return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py\nindex 2d916dac1..cb4b82cce 100644\n--- a/sklearn/decomposition/_dict_learning.py\n+++ b/sklearn/decomposition/_dict_learning.py\n@@ -2157,7 +2157,6 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n \n return False\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model from data in X.\n \n@@ -2175,82 +2174,6 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(\n- self, X, dtype=[np.float64, np.float32], order=\"C\", copy=False\n- )\n-\n- self._check_params(X)\n- self._random_state = check_random_state(self.random_state)\n-\n- dictionary = self._initialize_dict(X, self._random_state)\n- old_dict = dictionary.copy()\n-\n- if self.shuffle:\n- X_train = X.copy()\n- self._random_state.shuffle(X_train)\n- else:\n- X_train = X\n-\n- n_samples, n_features = X_train.shape\n-\n- if self.verbose:\n- print(\"[dict_learning]\")\n-\n- # Inner stats\n- self._A = np.zeros(\n- (self._n_components, self._n_components), dtype=X_train.dtype\n- )\n- self._B = np.zeros((n_features, self._n_components), dtype=X_train.dtype)\n-\n- # TODO(1.6): remove in 1.6\n- if self.max_iter is None:\n- warn(\n- (\n- \"`max_iter=None` is deprecated in version 1.4 and will be removed\"\n- \" in version 1.6. Use the default value (i.e. `1_000`) instead.\"\n- ),\n- FutureWarning,\n- )\n- max_iter = 1_000\n- else:\n- max_iter = self.max_iter\n-\n- # Attributes to monitor the convergence\n- self._ewa_cost = None\n- self._ewa_cost_min = None\n- self._no_improvement = 0\n-\n- batches = gen_batches(n_samples, self._batch_size)\n- batches = itertools.cycle(batches)\n- n_steps_per_iter = int(np.ceil(n_samples / self._batch_size))\n- n_steps = max_iter * n_steps_per_iter\n-\n- i = -1 # to allow max_iter = 0\n-\n- for i, batch in zip(range(n_steps), batches):\n- X_batch = X_train[batch]\n-\n- batch_cost = self._minibatch_step(\n- X_batch, dictionary, self._random_state, i\n- )\n-\n- if self._check_convergence(\n- X_batch, batch_cost, dictionary, old_dict, n_samples, i, n_steps\n- ):\n- break\n-\n- # XXX callback param added for backward compat in #18975 but a common\n- # unified callback API should be preferred\n- if self.callback is not None:\n- self.callback(locals())\n-\n- old_dict[:] = dictionary\n-\n- self.n_steps_ = i + 1\n- self.n_iter_ = np.ceil(self.n_steps_ / n_steps_per_iter)\n- self.components_ = dictionary\n-\n- return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_dict_learning.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the model from data in X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training vector, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_shapes", "sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_lars_positive_parameter", "sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_positivity[False-False-lasso_lars]", "sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_positivity[False-False-lasso_cd]", "sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_positivity[False-False-threshold]", "sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_positivity[False-True-lasso_lars]", "sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_positivity[False-True-lasso_cd]", "sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_positivity[False-True-threshold]", 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"sklearn/tests/test_common.py::test_set_output_transform[MiniBatchDictionaryLearning(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[MiniBatchSparsePCA(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-MiniBatchDictionaryLearning(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-MiniBatchSparsePCA(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-MiniBatchDictionaryLearning(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-MiniBatchSparsePCA(batch_size=10,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-113	1.0	{ "code": "diff --git b/sklearn/decomposition/_dict_learning.py a/sklearn/decomposition/_dict_learning.py\nindex 3c558cdb4..2d916dac1 100644\n--- b/sklearn/decomposition/_dict_learning.py\n+++ a/sklearn/decomposition/_dict_learning.py\n@@ -2252,6 +2252,7 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n \n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None):\n \"\"\"Update the model using the data in X as a mini-batch.\n \n@@ -2269,6 +2270,32 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n self : object\n Return the instance itself.\n \"\"\"\n+ has_components = hasattr(self, \"components_\")\n+\n+ X = validate_data(\n+ self, X, dtype=[np.float64, np.float32], order=\"C\", reset=not has_components\n+ )\n+\n+ if not has_components:\n+ # This instance has not been fitted yet (fit or partial_fit)\n+ self._check_params(X)\n+ self._random_state = check_random_state(self.random_state)\n+\n+ dictionary = self._initialize_dict(X, self._random_state)\n+\n+ self.n_steps_ = 0\n+\n+ self._A = np.zeros((self._n_components, self._n_components), dtype=X.dtype)\n+ self._B = np.zeros((X.shape[1], self._n_components), dtype=X.dtype)\n+ else:\n+ dictionary = self.components_\n+\n+ self._minibatch_step(X, dictionary, self._random_state, self.n_steps_)\n+\n+ self.components_ = dictionary\n+ self.n_steps_ += 1\n+\n+ return self\n \n @property\n def _n_features_out(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py\nindex 2d916dac1..3c558cdb4 100644\n--- a/sklearn/decomposition/_dict_learning.py\n+++ b/sklearn/decomposition/_dict_learning.py\n@@ -2252,7 +2252,6 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n \n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None):\n \"\"\"Update the model using the data in X as a mini-batch.\n \n@@ -2270,32 +2269,6 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):\n self : object\n Return the instance itself.\n \"\"\"\n- has_components = hasattr(self, \"components_\")\n-\n- X = validate_data(\n- self, X, dtype=[np.float64, np.float32], order=\"C\", reset=not has_components\n- )\n-\n- if not has_components:\n- # This instance has not been fitted yet (fit or partial_fit)\n- self._check_params(X)\n- self._random_state = check_random_state(self.random_state)\n-\n- dictionary = self._initialize_dict(X, self._random_state)\n-\n- self.n_steps_ = 0\n-\n- self._A = np.zeros((self._n_components, self._n_components), dtype=X.dtype)\n- self._B = np.zeros((X.shape[1], self._n_components), dtype=X.dtype)\n- else:\n- dictionary = self.components_\n-\n- self._minibatch_step(X, dictionary, self._random_state, self.n_steps_)\n-\n- self.components_ = dictionary\n- self.n_steps_ += 1\n-\n- return self\n \n @property\n def _n_features_out(self):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/decomposition/_dict_learning.py.\nHere is the description for the function:\n def partial_fit(self, X, y=None):\n \"\"\"Update the model using the data in X as a mini-batch.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training vector, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n self : object\n Return the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_partial_fit", "sklearn/tests/test_common.py::test_estimators[MiniBatchDictionaryLearning(batch_size=10,max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MiniBatchDictionaryLearning(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MiniBatchDictionaryLearning(batch_size=10,max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[MiniBatchDictionaryLearning(batch_size=10,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-114	1.0	{ "code": "diff --git b/sklearn/cluster/_kmeans.py a/sklearn/cluster/_kmeans.py\nindex 82be1e02e..ef7b910e1 100644\n--- b/sklearn/cluster/_kmeans.py\n+++ a/sklearn/cluster/_kmeans.py\n@@ -2037,6 +2037,7 @@ class MiniBatchKMeans(_BaseKMeans):\n return True\n return False\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute the centroids on X by chunking it into mini-batches.\n \n@@ -2064,6 +2065,134 @@ class MiniBatchKMeans(_BaseKMeans):\n self : object\n Fitted estimator.\n \"\"\"\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=\"csr\",\n+ dtype=[np.float64, np.float32],\n+ order=\"C\",\n+ accept_large_sparse=False,\n+ )\n+\n+ self._check_params_vs_input(X)\n+ random_state = check_random_state(self.random_state)\n+ sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n+ self._n_threads = _openmp_effective_n_threads()\n+ n_samples, n_features = X.shape\n+\n+ # Validate init array\n+ init = self.init\n+ if _is_arraylike_not_scalar(init):\n+ init = check_array(init, dtype=X.dtype, copy=True, order=\"C\")\n+ self._validate_center_shape(X, init)\n+\n+ self._check_mkl_vcomp(X, self._batch_size)\n+\n+ # precompute squared norms of data points\n+ x_squared_norms = row_norms(X, squared=True)\n+\n+ # Validation set for the init\n+ validation_indices = random_state.randint(0, n_samples, self._init_size)\n+ X_valid = X[validation_indices]\n+ sample_weight_valid = sample_weight[validation_indices]\n+\n+ # perform several inits with random subsets\n+ best_inertia = None\n+ for init_idx in range(self._n_init):\n+ if self.verbose:\n+ print(f\"Init {init_idx + 1}/{self._n_init} with method {init}\")\n+\n+ # Initialize the centers using only a fraction of the data as we\n+ # expect n_samples to be very large when using MiniBatchKMeans.\n+ cluster_centers = self._init_centroids(\n+ X,\n+ x_squared_norms=x_squared_norms,\n+ init=init,\n+ random_state=random_state,\n+ init_size=self._init_size,\n+ sample_weight=sample_weight,\n+ )\n+\n+ # Compute inertia on a validation set.\n+ _, inertia = _labels_inertia_threadpool_limit(\n+ X_valid,\n+ sample_weight_valid,\n+ cluster_centers,\n+ n_threads=self._n_threads,\n+ )\n+\n+ if self.verbose:\n+ print(f\"Inertia for init {init_idx + 1}/{self._n_init}: {inertia}\")\n+ if best_inertia is None or inertia < best_inertia:\n+ init_centers = cluster_centers\n+ best_inertia = inertia\n+\n+ centers = init_centers\n+ centers_new = np.empty_like(centers)\n+\n+ # Initialize counts\n+ self._counts = np.zeros(self.n_clusters, dtype=X.dtype)\n+\n+ # Attributes to monitor the convergence\n+ self._ewa_inertia = None\n+ self._ewa_inertia_min = None\n+ self._no_improvement = 0\n+\n+ # Initialize number of samples seen since last reassignment\n+ self._n_since_last_reassign = 0\n+\n+ n_steps = (self.max_iter * n_samples) // self._batch_size\n+\n+ with _get_threadpool_controller().limit(limits=1, user_api=\"blas\"):\n+ # Perform the iterative optimization until convergence\n+ for i in range(n_steps):\n+ # Sample a minibatch from the full dataset\n+ minibatch_indices = random_state.randint(0, n_samples, self._batch_size)\n+\n+ # Perform the actual update step on the minibatch data\n+ batch_inertia = _mini_batch_step(\n+ X=X[minibatch_indices],\n+ sample_weight=sample_weight[minibatch_indices],\n+ centers=centers,\n+ centers_new=centers_new,\n+ weight_sums=self._counts,\n+ random_state=random_state,\n+ random_reassign=self._random_reassign(),\n+ reassignment_ratio=self.reassignment_ratio,\n+ verbose=self.verbose,\n+ n_threads=self._n_threads,\n+ )\n+\n+ if self._tol > 0.0:\n+ centers_squared_diff = np.sum((centers_new - centers) ** 2)\n+ else:\n+ centers_squared_diff = 0\n+\n+ centers, centers_new = centers_new, centers\n+\n+ # Monitor convergence and do early stopping if necessary\n+ if self._mini_batch_convergence(\n+ i, n_steps, n_samples, centers_squared_diff, batch_inertia\n+ ):\n+ break\n+\n+ self.cluster_centers_ = centers\n+ self._n_features_out = self.cluster_centers_.shape[0]\n+\n+ self.n_steps_ = i + 1\n+ self.n_iter_ = int(np.ceil(((i + 1) * self._batch_size) / n_samples))\n+\n+ if self.compute_labels:\n+ self.labels_, self.inertia_ = _labels_inertia_threadpool_limit(\n+ X,\n+ sample_weight,\n+ self.cluster_centers_,\n+ n_threads=self._n_threads,\n+ )\n+ else:\n+ self.inertia_ = self._ewa_inertia * n_samples\n+\n+ return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, sample_weight=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py\nindex ef7b910e1..82be1e02e 100644\n--- a/sklearn/cluster/_kmeans.py\n+++ b/sklearn/cluster/_kmeans.py\n@@ -2037,7 +2037,6 @@ class MiniBatchKMeans(_BaseKMeans):\n return True\n return False\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute the centroids on X by chunking it into mini-batches.\n \n@@ -2065,134 +2064,6 @@ class MiniBatchKMeans(_BaseKMeans):\n self : object\n Fitted estimator.\n \"\"\"\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=\"csr\",\n- dtype=[np.float64, np.float32],\n- order=\"C\",\n- accept_large_sparse=False,\n- )\n-\n- self._check_params_vs_input(X)\n- random_state = check_random_state(self.random_state)\n- sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n- self._n_threads = _openmp_effective_n_threads()\n- n_samples, n_features = X.shape\n-\n- # Validate init array\n- init = self.init\n- if _is_arraylike_not_scalar(init):\n- init = check_array(init, dtype=X.dtype, copy=True, order=\"C\")\n- self._validate_center_shape(X, init)\n-\n- self._check_mkl_vcomp(X, self._batch_size)\n-\n- # precompute squared norms of data points\n- x_squared_norms = row_norms(X, squared=True)\n-\n- # Validation set for the init\n- validation_indices = random_state.randint(0, n_samples, self._init_size)\n- X_valid = X[validation_indices]\n- sample_weight_valid = sample_weight[validation_indices]\n-\n- # perform several inits with random subsets\n- best_inertia = None\n- for init_idx in range(self._n_init):\n- if self.verbose:\n- print(f\"Init {init_idx + 1}/{self._n_init} with method {init}\")\n-\n- # Initialize the centers using only a fraction of the data as we\n- # expect n_samples to be very large when using MiniBatchKMeans.\n- cluster_centers = self._init_centroids(\n- X,\n- x_squared_norms=x_squared_norms,\n- init=init,\n- random_state=random_state,\n- init_size=self._init_size,\n- sample_weight=sample_weight,\n- )\n-\n- # Compute inertia on a validation set.\n- _, inertia = _labels_inertia_threadpool_limit(\n- X_valid,\n- sample_weight_valid,\n- cluster_centers,\n- n_threads=self._n_threads,\n- )\n-\n- if self.verbose:\n- print(f\"Inertia for init {init_idx + 1}/{self._n_init}: {inertia}\")\n- if best_inertia is None or inertia < best_inertia:\n- init_centers = cluster_centers\n- best_inertia = inertia\n-\n- centers = init_centers\n- centers_new = np.empty_like(centers)\n-\n- # Initialize counts\n- self._counts = np.zeros(self.n_clusters, dtype=X.dtype)\n-\n- # Attributes to monitor the convergence\n- self._ewa_inertia = None\n- self._ewa_inertia_min = None\n- self._no_improvement = 0\n-\n- # Initialize number of samples seen since last reassignment\n- self._n_since_last_reassign = 0\n-\n- n_steps = (self.max_iter * n_samples) // self._batch_size\n-\n- with _get_threadpool_controller().limit(limits=1, user_api=\"blas\"):\n- # Perform the iterative optimization until convergence\n- for i in range(n_steps):\n- # Sample a minibatch from the full dataset\n- minibatch_indices = random_state.randint(0, n_samples, self._batch_size)\n-\n- # Perform the actual update step on the minibatch data\n- batch_inertia = _mini_batch_step(\n- X=X[minibatch_indices],\n- sample_weight=sample_weight[minibatch_indices],\n- centers=centers,\n- centers_new=centers_new,\n- weight_sums=self._counts,\n- random_state=random_state,\n- random_reassign=self._random_reassign(),\n- reassignment_ratio=self.reassignment_ratio,\n- verbose=self.verbose,\n- n_threads=self._n_threads,\n- )\n-\n- if self._tol > 0.0:\n- centers_squared_diff = np.sum((centers_new - centers) ** 2)\n- else:\n- centers_squared_diff = 0\n-\n- centers, centers_new = centers_new, centers\n-\n- # Monitor convergence and do early stopping if necessary\n- if self._mini_batch_convergence(\n- i, n_steps, n_samples, centers_squared_diff, batch_inertia\n- ):\n- break\n-\n- self.cluster_centers_ = centers\n- self._n_features_out = self.cluster_centers_.shape[0]\n-\n- self.n_steps_ = i + 1\n- self.n_iter_ = int(np.ceil(((i + 1) * self._batch_size) / n_samples))\n-\n- if self.compute_labels:\n- self.labels_, self.inertia_ = _labels_inertia_threadpool_limit(\n- X,\n- sample_weight,\n- self.cluster_centers_,\n- n_threads=self._n_threads,\n- )\n- else:\n- self.inertia_ = self._ewa_inertia * n_samples\n-\n- return self\n \n @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, sample_weight=None):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/cluster/_kmeans.py.\nHere is the description for the function:\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute the centroids on X by chunking it into mini-batches.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training instances to cluster. It must be noted that the data\n will be converted to C ordering, which will cause a memory copy\n if the given data is not C-contiguous.\n If a sparse matrix is passed, a copy will be made if it's not in\n CSR format.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n sample_weight : array-like of shape (n_samples,), default=None\n The weights for each observation in X. If None, all observations\n are assigned equal weight. `sample_weight` is not used during\n initialization if `init` is a callable or a user provided array.\n\n .. versionadded:: 0.20\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-random-dense]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-random-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-random-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-k-means++-dense]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-k-means++-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-k-means++-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-ndarray-dense]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-ndarray-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-ndarray-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-callable-dense]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-callable-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_all_init[MiniBatchKMeans-callable-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_init_auto_with_initial_centroids[MiniBatchKMeans-k-means++-1]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_init_auto_with_initial_centroids[MiniBatchKMeans-random-default]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_init_auto_with_initial_centroids[MiniBatchKMeans-<lambda>-default]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_init_auto_with_initial_centroids[MiniBatchKMeans-array-like-1]", "sklearn/cluster/tests/test_k_means.py::test_fortran_aligned_data[42-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_kmeans_verbose", "sklearn/cluster/tests/test_k_means.py::test_minibatch_kmeans_warning_init_size", "sklearn/cluster/tests/test_k_means.py::test_warning_n_init_precomputed_centers[MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_sensible_reassign[42]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_with_many_reassignments", "sklearn/cluster/tests/test_k_means.py::test_minibatch_kmeans_init_size", "sklearn/cluster/tests/test_k_means.py::test_minibatch_declared_convergence[0.0001-None]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_declared_convergence[0-10]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_iter_steps", "sklearn/cluster/tests/test_k_means.py::test_score_max_iter[42-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_predict[float64-42-2-MiniBatchKMeans-None-dense]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_predict[float64-42-2-MiniBatchKMeans-None-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_predict[float64-42-2-MiniBatchKMeans-None-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_predict[float64-42-100-MiniBatchKMeans-None-dense]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_predict[float64-42-100-MiniBatchKMeans-None-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_kmeans_predict[float64-42-100-MiniBatchKMeans-None-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_dense_sparse[42-MiniBatchKMeans-X_csr0]", "sklearn/cluster/tests/test_k_means.py::test_dense_sparse[42-MiniBatchKMeans-X_csr1]", "sklearn/cluster/tests/test_k_means.py::test_predict_dense_sparse[MiniBatchKMeans-random-X_csr0]", "sklearn/cluster/tests/test_k_means.py::test_predict_dense_sparse[MiniBatchKMeans-random-X_csr1]", "sklearn/cluster/tests/test_k_means.py::test_predict_dense_sparse[MiniBatchKMeans-k-means++-X_csr0]", "sklearn/cluster/tests/test_k_means.py::test_predict_dense_sparse[MiniBatchKMeans-k-means++-X_csr1]", "sklearn/cluster/tests/test_k_means.py::test_predict_dense_sparse[MiniBatchKMeans-ndarray-X_csr0]", "sklearn/cluster/tests/test_k_means.py::test_predict_dense_sparse[MiniBatchKMeans-ndarray-X_csr1]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int32-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int32-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int32-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int64-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int64-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int64-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int32-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int32-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int32-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int64-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int64-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int64-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_transform[42-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_fit_transform[42-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_float_precision[42-MiniBatchKMeans-dense]", "sklearn/cluster/tests/test_k_means.py::test_float_precision[42-MiniBatchKMeans-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_float_precision[42-MiniBatchKMeans-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_centers_not_mutated[MiniBatchKMeans-int32]", "sklearn/cluster/tests/test_k_means.py::test_centers_not_mutated[MiniBatchKMeans-int64]", "sklearn/cluster/tests/test_k_means.py::test_centers_not_mutated[MiniBatchKMeans-float32]", "sklearn/cluster/tests/test_k_means.py::test_centers_not_mutated[MiniBatchKMeans-float64]", "sklearn/cluster/tests/test_k_means.py::test_unit_weights_vs_no_weights[42-MiniBatchKMeans-dense]", "sklearn/cluster/tests/test_k_means.py::test_unit_weights_vs_no_weights[42-MiniBatchKMeans-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_unit_weights_vs_no_weights[42-MiniBatchKMeans-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_scaled_weights[42-MiniBatchKMeans-dense]", "sklearn/cluster/tests/test_k_means.py::test_scaled_weights[42-MiniBatchKMeans-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_scaled_weights[42-MiniBatchKMeans-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_result_equal_in_diff_n_threads[42-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_n_init_auto[MiniBatchKMeans-3]", "sklearn/cluster/tests/test_k_means.py::test_sample_weight_unchanged[MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_wrong_params[param0-n_samples.* should be >= n_clusters-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_wrong_params[param1-The shape of the initial centers .* does not match the number of clusters-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_wrong_params[param2-The shape of the initial centers .* does not match the number of clusters-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_wrong_params[param3-The shape of the initial centers .* does not match the number of features of the data-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_wrong_params[param4-The shape of the initial centers .* does not match the number of features of the data-MiniBatchKMeans]", "sklearn/cluster/tests/test_k_means.py::test_feature_names_out[MiniBatchKMeans-fit]", "sklearn/cluster/tests/test_bicluster.py::test_spectral_coclustering[42-csr_matrix]", "sklearn/cluster/tests/test_bicluster.py::test_spectral_coclustering[42-csr_array]", "sklearn/cluster/tests/test_bicluster.py::test_spectral_biclustering[42-csr_matrix]", "sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones", "sklearn/cluster/tests/test_bicluster.py::test_spectral_biclustering[42-csr_array]", "sklearn/cluster/_kmeans.py::sklearn.cluster._kmeans.MiniBatchKMeans", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_transformer_n_iter]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_clusterer_compute_labels_predict]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_clustering]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_clustering(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=1,n_init=2)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_param_validation[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_set_output_transform[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-115	1.0	{ "code": "diff --git b/sklearn/cluster/_kmeans.py a/sklearn/cluster/_kmeans.py\nindex 925f185f1..ef7b910e1 100644\n--- b/sklearn/cluster/_kmeans.py\n+++ a/sklearn/cluster/_kmeans.py\n@@ -2194,6 +2194,7 @@ class MiniBatchKMeans(_BaseKMeans):\n \n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, sample_weight=None):\n \"\"\"Update k means estimate on a single mini-batch X.\n \n@@ -2219,3 +2220,79 @@ class MiniBatchKMeans(_BaseKMeans):\n self : object\n Return updated estimator.\n \"\"\"\n+ has_centers = hasattr(self, \"cluster_centers_\")\n+\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=\"csr\",\n+ dtype=[np.float64, np.float32],\n+ order=\"C\",\n+ accept_large_sparse=False,\n+ reset=not has_centers,\n+ )\n+\n+ self._random_state = getattr(\n+ self, \"_random_state\", check_random_state(self.random_state)\n+ )\n+ sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n+ self.n_steps_ = getattr(self, \"n_steps_\", 0)\n+\n+ # precompute squared norms of data points\n+ x_squared_norms = row_norms(X, squared=True)\n+\n+ if not has_centers:\n+ # this instance has not been fitted yet (fit or partial_fit)\n+ self._check_params_vs_input(X)\n+ self._n_threads = _openmp_effective_n_threads()\n+\n+ # Validate init array\n+ init = self.init\n+ if _is_arraylike_not_scalar(init):\n+ init = check_array(init, dtype=X.dtype, copy=True, order=\"C\")\n+ self._validate_center_shape(X, init)\n+\n+ self._check_mkl_vcomp(X, X.shape[0])\n+\n+ # initialize the cluster centers\n+ self.cluster_centers_ = self._init_centroids(\n+ X,\n+ x_squared_norms=x_squared_norms,\n+ init=init,\n+ random_state=self._random_state,\n+ init_size=self._init_size,\n+ sample_weight=sample_weight,\n+ )\n+\n+ # Initialize counts\n+ self._counts = np.zeros(self.n_clusters, dtype=X.dtype)\n+\n+ # Initialize number of samples seen since last reassignment\n+ self._n_since_last_reassign = 0\n+\n+ with _get_threadpool_controller().limit(limits=1, user_api=\"blas\"):\n+ _mini_batch_step(\n+ X,\n+ sample_weight=sample_weight,\n+ centers=self.cluster_centers_,\n+ centers_new=self.cluster_centers_,\n+ weight_sums=self._counts,\n+ random_state=self._random_state,\n+ random_reassign=self._random_reassign(),\n+ reassignment_ratio=self.reassignment_ratio,\n+ verbose=self.verbose,\n+ n_threads=self._n_threads,\n+ )\n+\n+ if self.compute_labels:\n+ self.labels_, self.inertia_ = _labels_inertia_threadpool_limit(\n+ X,\n+ sample_weight,\n+ self.cluster_centers_,\n+ n_threads=self._n_threads,\n+ )\n+\n+ self.n_steps_ += 1\n+ self._n_features_out = self.cluster_centers_.shape[0]\n+\n+ return self\n", "test": null }	null	{ "code": "diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py\nindex ef7b910e1..925f185f1 100644\n--- a/sklearn/cluster/_kmeans.py\n+++ b/sklearn/cluster/_kmeans.py\n@@ -2194,7 +2194,6 @@ class MiniBatchKMeans(_BaseKMeans):\n \n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y=None, sample_weight=None):\n \"\"\"Update k means estimate on a single mini-batch X.\n \n@@ -2220,79 +2219,3 @@ class MiniBatchKMeans(_BaseKMeans):\n self : object\n Return updated estimator.\n \"\"\"\n- has_centers = hasattr(self, \"cluster_centers_\")\n-\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=\"csr\",\n- dtype=[np.float64, np.float32],\n- order=\"C\",\n- accept_large_sparse=False,\n- reset=not has_centers,\n- )\n-\n- self._random_state = getattr(\n- self, \"_random_state\", check_random_state(self.random_state)\n- )\n- sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n- self.n_steps_ = getattr(self, \"n_steps_\", 0)\n-\n- # precompute squared norms of data points\n- x_squared_norms = row_norms(X, squared=True)\n-\n- if not has_centers:\n- # this instance has not been fitted yet (fit or partial_fit)\n- self._check_params_vs_input(X)\n- self._n_threads = _openmp_effective_n_threads()\n-\n- # Validate init array\n- init = self.init\n- if _is_arraylike_not_scalar(init):\n- init = check_array(init, dtype=X.dtype, copy=True, order=\"C\")\n- self._validate_center_shape(X, init)\n-\n- self._check_mkl_vcomp(X, X.shape[0])\n-\n- # initialize the cluster centers\n- self.cluster_centers_ = self._init_centroids(\n- X,\n- x_squared_norms=x_squared_norms,\n- init=init,\n- random_state=self._random_state,\n- init_size=self._init_size,\n- sample_weight=sample_weight,\n- )\n-\n- # Initialize counts\n- self._counts = np.zeros(self.n_clusters, dtype=X.dtype)\n-\n- # Initialize number of samples seen since last reassignment\n- self._n_since_last_reassign = 0\n-\n- with _get_threadpool_controller().limit(limits=1, user_api=\"blas\"):\n- _mini_batch_step(\n- X,\n- sample_weight=sample_weight,\n- centers=self.cluster_centers_,\n- centers_new=self.cluster_centers_,\n- weight_sums=self._counts,\n- random_state=self._random_state,\n- random_reassign=self._random_reassign(),\n- reassignment_ratio=self.reassignment_ratio,\n- verbose=self.verbose,\n- n_threads=self._n_threads,\n- )\n-\n- if self.compute_labels:\n- self.labels_, self.inertia_ = _labels_inertia_threadpool_limit(\n- X,\n- sample_weight,\n- self.cluster_centers_,\n- n_threads=self._n_threads,\n- )\n-\n- self.n_steps_ += 1\n- self._n_features_out = self.cluster_centers_.shape[0]\n-\n- return self\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/cluster/_kmeans.py.\nHere is the description for the function:\n def partial_fit(self, X, y=None, sample_weight=None):\n \"\"\"Update k means estimate on a single mini-batch X.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training instances to cluster. It must be noted that the data\n will be converted to C ordering, which will cause a memory copy\n if the given data is not C-contiguous.\n If a sparse matrix is passed, a copy will be made if it's not in\n CSR format.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n sample_weight : array-like of shape (n_samples,), default=None\n The weights for each observation in X. If None, all observations\n are assigned equal weight. `sample_weight` is not used during\n initialization if `init` is a callable or a user provided array.\n\n Returns\n -------\n self : object\n Return updated estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/cluster/tests/test_k_means.py::test_minibatch_kmeans_partial_fit_init[random]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_kmeans_partial_fit_init[k-means++]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_kmeans_partial_fit_init[ndarray]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_kmeans_partial_fit_init[callable]", "sklearn/cluster/tests/test_k_means.py::test_minibatch_sensible_reassign[42]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int32-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int32-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int32-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int64-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int64-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-k-means++-int64-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int32-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int32-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int32-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int64-dense]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int64-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_integer_input[42-MiniBatchKMeans-ndarray-int64-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_float_precision[42-MiniBatchKMeans-dense]", "sklearn/cluster/tests/test_k_means.py::test_float_precision[42-MiniBatchKMeans-sparse_matrix]", "sklearn/cluster/tests/test_k_means.py::test_float_precision[42-MiniBatchKMeans-sparse_array]", "sklearn/cluster/tests/test_k_means.py::test_feature_names_out[MiniBatchKMeans-partial_fit]", "sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones", "sklearn/cluster/_kmeans.py::sklearn.cluster._kmeans.MiniBatchKMeans", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)-check_estimators_partial_fit_n_features]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_param_validation[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-116	1.0	{ "code": "diff --git b/sklearn/impute/_base.py a/sklearn/impute/_base.py\nindex ee2be9125..aecc235ec 100644\n--- b/sklearn/impute/_base.py\n+++ a/sklearn/impute/_base.py\n@@ -1014,6 +1014,31 @@ class MissingIndicator(TransformerMixin, BaseEstimator):\n The missing indicator for input data. The data type of `Xt`\n will be boolean.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ # Need not validate X again as it would have already been validated\n+ # in the Imputer calling MissingIndicator\n+ if not self._precomputed:\n+ X = self._validate_input(X, in_fit=False)\n+ else:\n+ if not (hasattr(X, \"dtype\") and X.dtype.kind == \"b\"):\n+ raise ValueError(\"precomputed is True but the input data is not a mask\")\n+\n+ imputer_mask, features = self._get_missing_features_info(X)\n+\n+ if self.features == \"missing-only\":\n+ features_diff_fit_trans = np.setdiff1d(features, self.features_)\n+ if self.error_on_new and features_diff_fit_trans.size > 0:\n+ raise ValueError(\n+ \"The features {} have missing values \"\n+ \"in transform but have no missing values \"\n+ \"in fit.\".format(features_diff_fit_trans)\n+ )\n+\n+ if self.features_.size < self._n_features:\n+ imputer_mask = imputer_mask[:, self.features_]\n+\n+ return imputer_mask\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py\nindex aecc235ec..ee2be9125 100644\n--- a/sklearn/impute/_base.py\n+++ b/sklearn/impute/_base.py\n@@ -1014,31 +1014,6 @@ class MissingIndicator(TransformerMixin, BaseEstimator):\n The missing indicator for input data. The data type of `Xt`\n will be boolean.\n \"\"\"\n- check_is_fitted(self)\n-\n- # Need not validate X again as it would have already been validated\n- # in the Imputer calling MissingIndicator\n- if not self._precomputed:\n- X = self._validate_input(X, in_fit=False)\n- else:\n- if not (hasattr(X, \"dtype\") and X.dtype.kind == \"b\"):\n- raise ValueError(\"precomputed is True but the input data is not a mask\")\n-\n- imputer_mask, features = self._get_missing_features_info(X)\n-\n- if self.features == \"missing-only\":\n- features_diff_fit_trans = np.setdiff1d(features, self.features_)\n- if self.error_on_new and features_diff_fit_trans.size > 0:\n- raise ValueError(\n- \"The features {} have missing values \"\n- \"in transform but have no missing values \"\n- \"in fit.\".format(features_diff_fit_trans)\n- )\n-\n- if self.features_.size < self._n_features:\n- imputer_mask = imputer_mask[:, self.features_]\n-\n- return imputer_mask\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None):\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/impute/_base.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Generate missing values indicator for `X`.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input data to complete.\n\n Returns\n -------\n Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) \\\n or (n_samples, n_features_with_missing)\n The missing indicator for input data. The data type of `Xt`\n will be boolean.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/impute/tests/test_impute.py::test_missing_indicator_error[X_fit0-X_trans0-params0-have missing values in transform but have no missing values in fit]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-array-0-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csc_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csc_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csc_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csc_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csr_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csr_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csr_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-csr_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-coo_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-coo_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-coo_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-coo_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-lil_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-lil_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-lil_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-lil_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-bsr_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-bsr_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-bsr_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[missing-only-3-features_indices0-bsr_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-array-0-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csc_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csc_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csc_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csc_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csr_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csr_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csr_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-csr_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-coo_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-coo_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-coo_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-coo_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-lil_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-lil_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-lil_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-lil_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-bsr_matrix-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-bsr_matrix--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-bsr_array-nan-float64]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_new[all-3-features_indices1-bsr_array--1-int32]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[csc_matrix]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[csc_array]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[csr_matrix]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[csr_array]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[coo_matrix]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[coo_array]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[lil_matrix]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[lil_array]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[bsr_matrix]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_raise_on_sparse_with_missing_0[bsr_array]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[array-0-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[array-0-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[array-0-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csc_matrix-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csc_matrix-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csc_matrix-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csc_array-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csc_array-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csc_array-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csr_matrix-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csr_matrix-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csr_matrix-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csr_array-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csr_array-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[csr_array-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[coo_matrix-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[coo_matrix-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[coo_matrix-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[coo_array-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[coo_array-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[coo_array-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[lil_matrix-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[lil_matrix-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[lil_matrix-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[lil_array-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[lil_array-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[lil_array-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[bsr_matrix-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[bsr_matrix-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[bsr_matrix-nan-auto]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[bsr_array-nan-True]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[bsr_array-nan-False]", "sklearn/impute/tests/test_impute.py::test_missing_indicator_sparse_param[bsr_array-nan-auto]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[csc_matrix]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[csc_array]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[csr_matrix]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[csr_array]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[coo_matrix]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[coo_array]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[lil_matrix]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[lil_array]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[bsr_matrix]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_add_indicator_sparse_matrix[bsr_array]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_inverse_transform[-1]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_inverse_transform[nan]", "sklearn/impute/tests/test_common.py::test_imputation_missing_value_in_test_array[IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputation_missing_value_in_test_array[KNNImputer]", "sklearn/impute/tests/test_common.py::test_imputation_missing_value_in_test_array[SimpleImputer]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[IterativeImputer-nan]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[IterativeImputer--1]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[IterativeImputer-0]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer-nan]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer--1]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[KNNImputer-0]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[SimpleImputer-nan]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[SimpleImputer--1]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[SimpleImputer-0]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator_sparse[csr_matrix-SimpleImputer-nan]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator_sparse[csr_matrix-SimpleImputer--1]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator_sparse[csr_array-SimpleImputer-nan]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator_sparse[csr_array-SimpleImputer--1]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[True-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[True-KNNImputer]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[True-SimpleImputer]", "sklearn/impute/tests/test_common.py::test_imputers_feature_names_out_pandas[True-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[nan-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[nan-KNNImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[nan-SimpleImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-KNNImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-SimpleImputer]", "sklearn/impute/tests/test_base.py::test_base_no_precomputed_mask_transform", "sklearn/impute/_base.py::sklearn.impute._base.MissingIndicator", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MissingIndicator()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MissingIndicator()]", "sklearn/tests/test_common.py::test_set_output_transform[MissingIndicator()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-MissingIndicator()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-MissingIndicator()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-117	1.0	{ "code": "diff --git b/sklearn/preprocessing/_label.py a/sklearn/preprocessing/_label.py\nindex bdf851cd3..345d55556 100644\n--- b/sklearn/preprocessing/_label.py\n+++ a/sklearn/preprocessing/_label.py\n@@ -799,6 +799,7 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys\n self.classes_[:] = classes\n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, y):\n \"\"\"Fit the label sets binarizer and transform the given label sets.\n \n@@ -816,6 +817,31 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys\n is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR\n format.\n \"\"\"\n+ if self.classes is not None:\n+ return self.fit(y).transform(y)\n+\n+ self._cached_dict = None\n+\n+ # Automatically increment on new class\n+ class_mapping = defaultdict(int)\n+ class_mapping.default_factory = class_mapping.__len__\n+ yt = self._transform(y, class_mapping)\n+\n+ # sort classes and reorder columns\n+ tmp = sorted(class_mapping, key=class_mapping.get)\n+\n+ # (make safe for tuples)\n+ dtype = int if all(isinstance(c, int) for c in tmp) else object\n+ class_mapping = np.empty(len(tmp), dtype=dtype)\n+ class_mapping[:] = tmp\n+ self.classes_, inverse = np.unique(class_mapping, return_inverse=True)\n+ # ensure yt.indices keeps its current dtype\n+ yt.indices = np.asarray(inverse[yt.indices], dtype=yt.indices.dtype)\n+\n+ if not self.sparse_output:\n+ yt = yt.toarray()\n+\n+ return yt\n \n def transform(self, y):\n \"\"\"Transform the given label sets.\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py\nindex 345d55556..bdf851cd3 100644\n--- a/sklearn/preprocessing/_label.py\n+++ b/sklearn/preprocessing/_label.py\n@@ -799,7 +799,6 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys\n self.classes_[:] = classes\n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, y):\n \"\"\"Fit the label sets binarizer and transform the given label sets.\n \n@@ -817,31 +816,6 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys\n is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR\n format.\n \"\"\"\n- if self.classes is not None:\n- return self.fit(y).transform(y)\n-\n- self._cached_dict = None\n-\n- # Automatically increment on new class\n- class_mapping = defaultdict(int)\n- class_mapping.default_factory = class_mapping.__len__\n- yt = self._transform(y, class_mapping)\n-\n- # sort classes and reorder columns\n- tmp = sorted(class_mapping, key=class_mapping.get)\n-\n- # (make safe for tuples)\n- dtype = int if all(isinstance(c, int) for c in tmp) else object\n- class_mapping = np.empty(len(tmp), dtype=dtype)\n- class_mapping[:] = tmp\n- self.classes_, inverse = np.unique(class_mapping, return_inverse=True)\n- # ensure yt.indices keeps its current dtype\n- yt.indices = np.asarray(inverse[yt.indices], dtype=yt.indices.dtype)\n-\n- if not self.sparse_output:\n- yt = yt.toarray()\n-\n- return yt\n \n def transform(self, y):\n \"\"\"Transform the given label sets.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_label.py.\nHere is the description for the function:\n def fit_transform(self, y):\n \"\"\"Fit the label sets binarizer and transform the given label sets.\n\n Parameters\n ----------\n y : iterable of iterables\n A set of labels (any orderable and hashable object) for each\n sample. If the `classes` parameter is set, `y` will not be\n iterated.\n\n Returns\n -------\n y_indicator : {ndarray, sparse matrix} of shape (n_samples, n_classes)\n A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]`\n is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR\n format.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_label.py::test_sparse_output_multilabel_binarizer", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_empty_sample", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_given_classes", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_multiple_calls", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_same_length_sequence", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_non_integer_labels", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_non_unique", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_inverse_validation", "sklearn/preprocessing/tests/test_label.py::test_label_encoders_do_not_have_set_output[encoder2]", "sklearn/preprocessing/_label.py::sklearn.preprocessing._label.MultiLabelBinarizer", "sklearn/tests/test_common.py::test_check_param_validation[MultiLabelBinarizer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-118	1.0	{ "code": "diff --git b/sklearn/preprocessing/_label.py a/sklearn/preprocessing/_label.py\nindex a53aff327..345d55556 100644\n--- b/sklearn/preprocessing/_label.py\n+++ a/sklearn/preprocessing/_label.py\n@@ -929,6 +929,32 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys\n The set of labels for each sample such that `y[i]` consists of\n `classes_[j]` for each `yt[i, j] == 1`.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ if yt.shape[1] != len(self.classes_):\n+ raise ValueError(\n+ \"Expected indicator for {0} classes, but got {1}\".format(\n+ len(self.classes_), yt.shape[1]\n+ )\n+ )\n+\n+ if sp.issparse(yt):\n+ yt = yt.tocsr()\n+ if len(yt.data) != 0 and len(np.setdiff1d(yt.data, [0, 1])) > 0:\n+ raise ValueError(\"Expected only 0s and 1s in label indicator.\")\n+ return [\n+ tuple(self.classes_.take(yt.indices[start:end]))\n+ for start, end in zip(yt.indptr[:-1], yt.indptr[1:])\n+ ]\n+ else:\n+ unexpected = np.setdiff1d(yt, [0, 1])\n+ if len(unexpected) > 0:\n+ raise ValueError(\n+ \"Expected only 0s and 1s in label indicator. Also got {0}\".format(\n+ unexpected\n+ )\n+ )\n+ return [tuple(self.classes_.compress(indicators)) for indicators in yt]\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py\nindex 345d55556..a53aff327 100644\n--- a/sklearn/preprocessing/_label.py\n+++ b/sklearn/preprocessing/_label.py\n@@ -929,32 +929,6 @@ class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys\n The set of labels for each sample such that `y[i]` consists of\n `classes_[j]` for each `yt[i, j] == 1`.\n \"\"\"\n- check_is_fitted(self)\n-\n- if yt.shape[1] != len(self.classes_):\n- raise ValueError(\n- \"Expected indicator for {0} classes, but got {1}\".format(\n- len(self.classes_), yt.shape[1]\n- )\n- )\n-\n- if sp.issparse(yt):\n- yt = yt.tocsr()\n- if len(yt.data) != 0 and len(np.setdiff1d(yt.data, [0, 1])) > 0:\n- raise ValueError(\"Expected only 0s and 1s in label indicator.\")\n- return [\n- tuple(self.classes_.take(yt.indices[start:end]))\n- for start, end in zip(yt.indptr[:-1], yt.indptr[1:])\n- ]\n- else:\n- unexpected = np.setdiff1d(yt, [0, 1])\n- if len(unexpected) > 0:\n- raise ValueError(\n- \"Expected only 0s and 1s in label indicator. Also got {0}\".format(\n- unexpected\n- )\n- )\n- return [tuple(self.classes_.compress(indicators)) for indicators in yt]\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/preprocessing/_label.py.\nHere is the description for the function:\n def inverse_transform(self, yt):\n \"\"\"Transform the given indicator matrix into label sets.\n\n Parameters\n ----------\n yt : {ndarray, sparse matrix} of shape (n_samples, n_classes)\n A matrix containing only 1s ands 0s.\n\n Returns\n -------\n y : list of tuples\n The set of labels for each sample such that `y[i]` consists of\n `classes_[j]` for each `yt[i, j] == 1`.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_label.py::test_sparse_output_multilabel_binarizer", "sklearn/preprocessing/tests/test_label.py::test_sparse_output_multilabel_binarizer_errors[csr_matrix]", "sklearn/preprocessing/tests/test_label.py::test_sparse_output_multilabel_binarizer_errors[csr_array]", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_same_length_sequence", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_non_integer_labels", "sklearn/preprocessing/tests/test_label.py::test_multilabel_binarizer_inverse_validation" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-119	1.0	{ "code": "diff --git b/sklearn/multioutput.py a/sklearn/multioutput.py\nindex 2e6e646ff..ebcd73e95 100644\n--- b/sklearn/multioutput.py\n+++ a/sklearn/multioutput.py\n@@ -598,6 +598,20 @@ class MultiOutputClassifier(ClassifierMixin, _MultiOutputEstimator):\n scores : float\n Mean accuracy of predicted target versus true target.\n \"\"\"\n+ check_is_fitted(self)\n+ n_outputs_ = len(self.estimators_)\n+ if y.ndim == 1:\n+ raise ValueError(\n+ \"y must have at least two dimensions for \"\n+ \"multi target classification but has only one\"\n+ )\n+ if y.shape[1] != n_outputs_:\n+ raise ValueError(\n+ \"The number of outputs of Y for fit {0} and\"\n+ \" score {1} should be same\".format(n_outputs_, y.shape[1])\n+ )\n+ y_pred = self.predict(X)\n+ return np.mean(np.all(y == y_pred, axis=1))\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py\nindex ebcd73e95..2e6e646ff 100644\n--- a/sklearn/multioutput.py\n+++ b/sklearn/multioutput.py\n@@ -598,20 +598,6 @@ class MultiOutputClassifier(ClassifierMixin, _MultiOutputEstimator):\n scores : float\n Mean accuracy of predicted target versus true target.\n \"\"\"\n- check_is_fitted(self)\n- n_outputs_ = len(self.estimators_)\n- if y.ndim == 1:\n- raise ValueError(\n- \"y must have at least two dimensions for \"\n- \"multi target classification but has only one\"\n- )\n- if y.shape[1] != n_outputs_:\n- raise ValueError(\n- \"The number of outputs of Y for fit {0} and\"\n- \" score {1} should be same\".format(n_outputs_, y.shape[1])\n- )\n- y_pred = self.predict(X)\n- return np.mean(np.all(y == y_pred, axis=1))\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/multioutput.py.\nHere is the description for the function:\n def score(self, X, y):\n \"\"\"Return the mean accuracy on the given test data and labels.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Test samples.\n\n y : array-like of shape (n_samples, n_outputs)\n True values for X.\n\n Returns\n -------\n scores : float\n Mean accuracy of predicted target versus true target.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_multioutput.py::test_multi_output_exceptions", "sklearn/tests/test_multioutput.py::test_support_missing_values[MultiOutputClassifier-LogisticRegression]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MultiOutputClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultiOutputClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-120	1.0	{ "code": "diff --git b/sklearn/linear_model/_coordinate_descent.py a/sklearn/linear_model/_coordinate_descent.py\nindex 7369ba5fc..61aaf49a1 100644\n--- b/sklearn/linear_model/_coordinate_descent.py\n+++ a/sklearn/linear_model/_coordinate_descent.py\n@@ -2566,6 +2566,7 @@ class MultiTaskElasticNet(Lasso):\n self.random_state = random_state\n self.selection = selection\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit MultiTaskElasticNet model with coordinate descent.\n \n@@ -2590,6 +2591,71 @@ class MultiTaskElasticNet(Lasso):\n To avoid memory re-allocation it is advised to allocate the\n initial data in memory directly using that format.\n \"\"\"\n+ # Need to validate separately here.\n+ # We can't pass multi_output=True because that would allow y to be csr.\n+ check_X_params = dict(\n+ dtype=[np.float64, np.float32],\n+ order=\"F\",\n+ force_writeable=True,\n+ copy=self.copy_X and self.fit_intercept,\n+ )\n+ check_y_params = dict(ensure_2d=False, order=\"F\")\n+ X, y = validate_data(\n+ self, X, y, validate_separately=(check_X_params, check_y_params)\n+ )\n+ check_consistent_length(X, y)\n+ y = y.astype(X.dtype)\n+\n+ if hasattr(self, \"l1_ratio\"):\n+ model_str = \"ElasticNet\"\n+ else:\n+ model_str = \"Lasso\"\n+ if y.ndim == 1:\n+ raise ValueError(\"For mono-task outputs, use %s\" % model_str)\n+\n+ n_samples, n_features = X.shape\n+ n_targets = y.shape[1]\n+\n+ X, y, X_offset, y_offset, X_scale = _preprocess_data(\n+ X, y, fit_intercept=self.fit_intercept, copy=False\n+ )\n+\n+ if not self.warm_start or not hasattr(self, \"coef_\"):\n+ self.coef_ = np.zeros(\n+ (n_targets, n_features), dtype=X.dtype.type, order=\"F\"\n+ )\n+\n+ l1_reg = self.alpha * self.l1_ratio * n_samples\n+ l2_reg = self.alpha * (1.0 - self.l1_ratio) * n_samples\n+\n+ self.coef_ = np.asfortranarray(self.coef_) # coef contiguous in memory\n+\n+ random = self.selection == \"random\"\n+\n+ (\n+ self.coef_,\n+ self.dual_gap_,\n+ self.eps_,\n+ self.n_iter_,\n+ ) = cd_fast.enet_coordinate_descent_multi_task(\n+ self.coef_,\n+ l1_reg,\n+ l2_reg,\n+ X,\n+ y,\n+ self.max_iter,\n+ self.tol,\n+ check_random_state(self.random_state),\n+ random,\n+ )\n+\n+ # account for different objective scaling here and in cd_fast\n+ self.dual_gap_ /= n_samples\n+\n+ self._set_intercept(X_offset, y_offset, X_scale)\n+\n+ # return self for chaining fit and predict calls\n+ return self\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py\nindex 61aaf49a1..7369ba5fc 100644\n--- a/sklearn/linear_model/_coordinate_descent.py\n+++ b/sklearn/linear_model/_coordinate_descent.py\n@@ -2566,7 +2566,6 @@ class MultiTaskElasticNet(Lasso):\n self.random_state = random_state\n self.selection = selection\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit MultiTaskElasticNet model with coordinate descent.\n \n@@ -2591,71 +2590,6 @@ class MultiTaskElasticNet(Lasso):\n To avoid memory re-allocation it is advised to allocate the\n initial data in memory directly using that format.\n \"\"\"\n- # Need to validate separately here.\n- # We can't pass multi_output=True because that would allow y to be csr.\n- check_X_params = dict(\n- dtype=[np.float64, np.float32],\n- order=\"F\",\n- force_writeable=True,\n- copy=self.copy_X and self.fit_intercept,\n- )\n- check_y_params = dict(ensure_2d=False, order=\"F\")\n- X, y = validate_data(\n- self, X, y, validate_separately=(check_X_params, check_y_params)\n- )\n- check_consistent_length(X, y)\n- y = y.astype(X.dtype)\n-\n- if hasattr(self, \"l1_ratio\"):\n- model_str = \"ElasticNet\"\n- else:\n- model_str = \"Lasso\"\n- if y.ndim == 1:\n- raise ValueError(\"For mono-task outputs, use %s\" % model_str)\n-\n- n_samples, n_features = X.shape\n- n_targets = y.shape[1]\n-\n- X, y, X_offset, y_offset, X_scale = _preprocess_data(\n- X, y, fit_intercept=self.fit_intercept, copy=False\n- )\n-\n- if not self.warm_start or not hasattr(self, \"coef_\"):\n- self.coef_ = np.zeros(\n- (n_targets, n_features), dtype=X.dtype.type, order=\"F\"\n- )\n-\n- l1_reg = self.alpha * self.l1_ratio * n_samples\n- l2_reg = self.alpha * (1.0 - self.l1_ratio) * n_samples\n-\n- self.coef_ = np.asfortranarray(self.coef_) # coef contiguous in memory\n-\n- random = self.selection == \"random\"\n-\n- (\n- self.coef_,\n- self.dual_gap_,\n- self.eps_,\n- self.n_iter_,\n- ) = cd_fast.enet_coordinate_descent_multi_task(\n- self.coef_,\n- l1_reg,\n- l2_reg,\n- X,\n- y,\n- self.max_iter,\n- self.tol,\n- check_random_state(self.random_state),\n- random,\n- )\n-\n- # account for different objective scaling here and in cd_fast\n- self.dual_gap_ /= n_samples\n-\n- self._set_intercept(X_offset, y_offset, X_scale)\n-\n- # return self for chaining fit and predict calls\n- return self\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/linear_model/_coordinate_descent.py.\nHere is the description for the function:\n def fit(self, X, y):\n \"\"\"Fit MultiTaskElasticNet model with coordinate descent.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, n_features)\n Data.\n y : ndarray of shape (n_samples, n_targets)\n Target. Will be cast to X's dtype if necessary.\n\n Returns\n -------\n self : object\n Fitted estimator.\n\n Notes\n -----\n Coordinate descent is an algorithm that considers each column of\n data at a time hence it will automatically convert the X input\n as a Fortran-contiguous numpy array if necessary.\n\n To avoid memory re-allocation it is advised to allocate the\n initial data in memory directly using that format.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-5-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features1-10-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-5-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features0-10-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-5-MultiTaskLasso-brute-data11]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[both-features1-10-MultiTaskLasso-brute-data11]", 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"sklearn/tests/test_common.py::test_estimators[MultiTaskLassoCV(cv=3,max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[MultiTaskLassoCV(cv=3,max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MultiTaskElasticNet(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MultiTaskElasticNetCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MultiTaskLasso(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[MultiTaskLassoCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultiTaskElasticNet(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultiTaskElasticNetCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultiTaskLasso(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultiTaskLassoCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[MultiTaskElasticNet(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[MultiTaskLasso(max_iter=5)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNet(max_iter=5)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLasso(max_iter=5)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-121	1.0	{ "code": "diff --git b/sklearn/neighbors/_nearest_centroid.py a/sklearn/neighbors/_nearest_centroid.py\nindex b16322b9c..f92ae6897 100644\n--- b/sklearn/neighbors/_nearest_centroid.py\n+++ a/sklearn/neighbors/_nearest_centroid.py\n@@ -107,6 +107,7 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):\n self.metric = metric\n self.shrink_threshold = shrink_threshold\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"\n Fit the NearestCentroid model according to the given training data.\n@@ -125,6 +126,73 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n+ # If X is sparse and the metric is \"manhattan\", store it in a csc\n+ # format is easier to calculate the median.\n+ if self.metric == \"manhattan\":\n+ X, y = validate_data(self, X, y, accept_sparse=[\"csc\"])\n+ else:\n+ X, y = validate_data(self, X, y, accept_sparse=[\"csr\", \"csc\"])\n+ is_X_sparse = sp.issparse(X)\n+ if is_X_sparse and self.shrink_threshold:\n+ raise ValueError(\"threshold shrinking not supported for sparse input\")\n+ check_classification_targets(y)\n+\n+ n_samples, n_features = X.shape\n+ le = LabelEncoder()\n+ y_ind = le.fit_transform(y)\n+ self.classes_ = classes = le.classes_\n+ n_classes = classes.size\n+ if n_classes < 2:\n+ raise ValueError(\n+ \"The number of classes has to be greater than one; got %d class\"\n+ % (n_classes)\n+ )\n+\n+ # Mask mapping each class to its members.\n+ self.centroids_ = np.empty((n_classes, n_features), dtype=np.float64)\n+ # Number of clusters in each class.\n+ nk = np.zeros(n_classes)\n+\n+ for cur_class in range(n_classes):\n+ center_mask = y_ind == cur_class\n+ nk[cur_class] = np.sum(center_mask)\n+ if is_X_sparse:\n+ center_mask = np.where(center_mask)[0]\n+\n+ if self.metric == \"manhattan\":\n+ # NumPy does not calculate median of sparse matrices.\n+ if not is_X_sparse:\n+ self.centroids_[cur_class] = np.median(X[center_mask], axis=0)\n+ else:\n+ self.centroids_[cur_class] = csc_median_axis_0(X[center_mask])\n+ else: # metric == \"euclidean\"\n+ self.centroids_[cur_class] = X[center_mask].mean(axis=0)\n+\n+ if self.shrink_threshold:\n+ if np.all(np.ptp(X, axis=0) == 0):\n+ raise ValueError(\"All features have zero variance. Division by zero.\")\n+ dataset_centroid_ = np.mean(X, axis=0)\n+\n+ # m parameter for determining deviation\n+ m = np.sqrt((1.0 / nk) - (1.0 / n_samples))\n+ # Calculate deviation using the standard deviation of centroids.\n+ variance = (X - self.centroids_[y_ind]) ** 2\n+ variance = variance.sum(axis=0)\n+ s = np.sqrt(variance / (n_samples - n_classes))\n+ s += np.median(s) # To deter outliers from affecting the results.\n+ mm = m.reshape(len(m), 1) # Reshape to allow broadcasting.\n+ ms = mm * s\n+ deviation = (self.centroids_ - dataset_centroid_) / ms\n+ # Soft thresholding: if the deviation crosses 0 during shrinking,\n+ # it becomes zero.\n+ signs = np.sign(deviation)\n+ deviation = np.abs(deviation) - self.shrink_threshold\n+ np.clip(deviation, 0, None, out=deviation)\n+ deviation = signs\n+ # Now adjust the centroids using the deviation\n+ msd = ms deviation\n+ self.centroids_ = dataset_centroid_[np.newaxis, :] + msd\n+ return self\n \n def predict(self, X):\n \"\"\"Perform classification on an array of test vectors `X`.\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_nearest_centroid.py b/sklearn/neighbors/_nearest_centroid.py\nindex f92ae6897..b16322b9c 100644\n--- a/sklearn/neighbors/_nearest_centroid.py\n+++ b/sklearn/neighbors/_nearest_centroid.py\n@@ -107,7 +107,6 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):\n self.metric = metric\n self.shrink_threshold = shrink_threshold\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"\n Fit the NearestCentroid model according to the given training data.\n@@ -126,73 +125,6 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n- # If X is sparse and the metric is \"manhattan\", store it in a csc\n- # format is easier to calculate the median.\n- if self.metric == \"manhattan\":\n- X, y = validate_data(self, X, y, accept_sparse=[\"csc\"])\n- else:\n- X, y = validate_data(self, X, y, accept_sparse=[\"csr\", \"csc\"])\n- is_X_sparse = sp.issparse(X)\n- if is_X_sparse and self.shrink_threshold:\n- raise ValueError(\"threshold shrinking not supported for sparse input\")\n- check_classification_targets(y)\n-\n- n_samples, n_features = X.shape\n- le = LabelEncoder()\n- y_ind = le.fit_transform(y)\n- self.classes_ = classes = le.classes_\n- n_classes = classes.size\n- if n_classes < 2:\n- raise ValueError(\n- \"The number of classes has to be greater than one; got %d class\"\n- % (n_classes)\n- )\n-\n- # Mask mapping each class to its members.\n- self.centroids_ = np.empty((n_classes, n_features), dtype=np.float64)\n- # Number of clusters in each class.\n- nk = np.zeros(n_classes)\n-\n- for cur_class in range(n_classes):\n- center_mask = y_ind == cur_class\n- nk[cur_class] = np.sum(center_mask)\n- if is_X_sparse:\n- center_mask = np.where(center_mask)[0]\n-\n- if self.metric == \"manhattan\":\n- # NumPy does not calculate median of sparse matrices.\n- if not is_X_sparse:\n- self.centroids_[cur_class] = np.median(X[center_mask], axis=0)\n- else:\n- self.centroids_[cur_class] = csc_median_axis_0(X[center_mask])\n- else: # metric == \"euclidean\"\n- self.centroids_[cur_class] = X[center_mask].mean(axis=0)\n-\n- if self.shrink_threshold:\n- if np.all(np.ptp(X, axis=0) == 0):\n- raise ValueError(\"All features have zero variance. Division by zero.\")\n- dataset_centroid_ = np.mean(X, axis=0)\n-\n- # m parameter for determining deviation\n- m = np.sqrt((1.0 / nk) - (1.0 / n_samples))\n- # Calculate deviation using the standard deviation of centroids.\n- variance = (X - self.centroids_[y_ind]) ** 2\n- variance = variance.sum(axis=0)\n- s = np.sqrt(variance / (n_samples - n_classes))\n- s += np.median(s) # To deter outliers from affecting the results.\n- mm = m.reshape(len(m), 1) # Reshape to allow broadcasting.\n- ms = mm * s\n- deviation = (self.centroids_ - dataset_centroid_) / ms\n- # Soft thresholding: if the deviation crosses 0 during shrinking,\n- # it becomes zero.\n- signs = np.sign(deviation)\n- deviation = np.abs(deviation) - self.shrink_threshold\n- np.clip(deviation, 0, None, out=deviation)\n- deviation = signs\n- # Now adjust the centroids using the deviation\n- msd = ms deviation\n- self.centroids_ = dataset_centroid_[np.newaxis, :] + msd\n- return self\n \n def predict(self, X):\n \"\"\"Perform classification on an array of test vectors `X`.\n", "test": null }	null	scikit-learn/scikit-learn	c71340fd74280408b84be7ca008e1205e10c7830	2024-09-17T18:25:43+02:00	null	null	{ "code": "I want to add a new function in file in sklearn/neighbors/_nearest_centroid.py.\nHere is the description for the function:\n def fit(self, X, y):\n \"\"\"\n Fit the NearestCentroid model according to the given training data.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vector, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n Note that centroid shrinking cannot be used with sparse matrices.\n y : array-like of shape (n_samples,)\n Target values.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neighbors/tests/test_nearest_centroid.py::test_classification_toy[csr_matrix]", "sklearn/neighbors/tests/test_nearest_centroid.py::test_classification_toy[csr_array]", "sklearn/neighbors/tests/test_nearest_centroid.py::test_iris", "sklearn/neighbors/tests/test_nearest_centroid.py::test_iris_shrinkage", "sklearn/neighbors/tests/test_nearest_centroid.py::test_pickle", "sklearn/neighbors/tests/test_nearest_centroid.py::test_shrinkage_correct", "sklearn/neighbors/tests/test_nearest_centroid.py::test_shrinkage_threshold_decoded_y", "sklearn/neighbors/tests/test_nearest_centroid.py::test_predict_translated_data", "sklearn/neighbors/tests/test_nearest_centroid.py::test_manhattan_metric[csr_matrix]", "sklearn/neighbors/tests/test_nearest_centroid.py::test_manhattan_metric[csr_array]", "sklearn/neighbors/tests/test_nearest_centroid.py::test_features_zero_var", "sklearn/neighbors/_nearest_centroid.py::sklearn.neighbors._nearest_centroid.NearestCentroid", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[NearestCentroid()-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[NearestCentroid()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[NearestCentroid()]", "sklearn/tests/test_common.py::test_check_param_validation[NearestCentroid()]" ], "PASS_TO_PASS": null }

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