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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-122	1.0	{ "code": "diff --git b/sklearn/neighbors/_nca.py a/sklearn/neighbors/_nca.py\nindex 46d3fc48d..a4ef3c303 100644\n--- b/sklearn/neighbors/_nca.py\n+++ a/sklearn/neighbors/_nca.py\n@@ -470,6 +470,55 @@ class NeighborhoodComponentsAnalysis(\n The new (flattened) gradient of the loss.\n \"\"\"\n \n+ if self.n_iter_ == 0:\n+ self.n_iter_ += 1\n+ if self.verbose:\n+ header_fields = [\"Iteration\", \"Objective Value\", \"Time(s)\"]\n+ header_fmt = \"{:>10} {:>20} {:>10}\"\n+ header = header_fmt.format(header_fields)\n+ cls_name = self.__class__.__name__\n+ print(\"[{}]\".format(cls_name))\n+ print(\n+ \"[{}] {}\\n[{}] {}\".format(\n+ cls_name, header, cls_name, \"-\" len(header)\n+ )\n+ )\n+\n+ t_funcall = time.time()\n+\n+ transformation = transformation.reshape(-1, X.shape[1])\n+ X_embedded = np.dot(X, transformation.T) # (n_samples, n_components)\n+\n+ # Compute softmax distances\n+ p_ij = pairwise_distances(X_embedded, squared=True)\n+ np.fill_diagonal(p_ij, np.inf)\n+ p_ij = softmax(-p_ij) # (n_samples, n_samples)\n+\n+ # Compute loss\n+ masked_p_ij = p_ij * same_class_mask\n+ p = np.sum(masked_p_ij, axis=1, keepdims=True) # (n_samples, 1)\n+ loss = np.sum(p)\n+\n+ # Compute gradient of loss w.r.t. `transform`\n+ weighted_p_ij = masked_p_ij - p_ij * p\n+ weighted_p_ij_sym = weighted_p_ij + weighted_p_ij.T\n+ np.fill_diagonal(weighted_p_ij_sym, -weighted_p_ij.sum(axis=0))\n+ gradient = 2 * X_embedded.T.dot(weighted_p_ij_sym).dot(X)\n+ # time complexity of the gradient: O(n_components x n_samples x (\n+ # n_samples + n_features))\n+\n+ if self.verbose:\n+ t_funcall = time.time() - t_funcall\n+ values_fmt = \"[{}] {:>10} {:>20.6e} {:>10.2f}\"\n+ print(\n+ values_fmt.format(\n+ self.__class__.__name__, self.n_iter_, loss, t_funcall\n+ )\n+ )\n+ sys.stdout.flush()\n+\n+ return sign * loss, sign * gradient.ravel()\n+\n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n tags.target_tags.required = True\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_nca.py b/sklearn/neighbors/_nca.py\nindex a4ef3c303..46d3fc48d 100644\n--- a/sklearn/neighbors/_nca.py\n+++ b/sklearn/neighbors/_nca.py\n@@ -470,55 +470,6 @@ class NeighborhoodComponentsAnalysis(\n The new (flattened) gradient of the loss.\n \"\"\"\n \n- if self.n_iter_ == 0:\n- self.n_iter_ += 1\n- if self.verbose:\n- header_fields = [\"Iteration\", \"Objective Value\", \"Time(s)\"]\n- header_fmt = \"{:>10} {:>20} {:>10}\"\n- header = header_fmt.format(header_fields)\n- cls_name = self.__class__.__name__\n- print(\"[{}]\".format(cls_name))\n- print(\n- \"[{}] {}\\n[{}] {}\".format(\n- cls_name, header, cls_name, \"-\" len(header)\n- )\n- )\n-\n- t_funcall = time.time()\n-\n- transformation = transformation.reshape(-1, X.shape[1])\n- X_embedded = np.dot(X, transformation.T) # (n_samples, n_components)\n-\n- # Compute softmax distances\n- p_ij = pairwise_distances(X_embedded, squared=True)\n- np.fill_diagonal(p_ij, np.inf)\n- p_ij = softmax(-p_ij) # (n_samples, n_samples)\n-\n- # Compute loss\n- masked_p_ij = p_ij * same_class_mask\n- p = np.sum(masked_p_ij, axis=1, keepdims=True) # (n_samples, 1)\n- loss = np.sum(p)\n-\n- # Compute gradient of loss w.r.t. `transform`\n- weighted_p_ij = masked_p_ij - p_ij * p\n- weighted_p_ij_sym = weighted_p_ij + weighted_p_ij.T\n- np.fill_diagonal(weighted_p_ij_sym, -weighted_p_ij.sum(axis=0))\n- gradient = 2 * X_embedded.T.dot(weighted_p_ij_sym).dot(X)\n- # time complexity of the gradient: O(n_components x n_samples x (\n- # n_samples + n_features))\n-\n- if self.verbose:\n- t_funcall = time.time() - t_funcall\n- values_fmt = \"[{}] {:>10} {:>20.6e} {:>10.2f}\"\n- print(\n- values_fmt.format(\n- self.__class__.__name__, self.n_iter_, loss, t_funcall\n- )\n- )\n- sys.stdout.flush()\n-\n- return sign * loss, sign * gradient.ravel()\n-\n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n tags.target_tags.required = 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/neighbors/_nca.py.\nHere is the description for the function:\n def _loss_grad_lbfgs(self, transformation, X, same_class_mask, sign=1.0):\n \"\"\"Compute the loss and the loss gradient w.r.t. `transformation`.\n\n Parameters\n ----------\n transformation : ndarray of shape (n_components * n_features,)\n The raveled linear transformation on which to compute loss and\n evaluate gradient.\n\n X : ndarray of shape (n_samples, n_features)\n The training samples.\n\n same_class_mask : ndarray of shape (n_samples, n_samples)\n A mask where `mask[i, j] == 1` if `X[i]` and `X[j]` belong\n to the same class, and `0` otherwise.\n\n Returns\n -------\n loss : float\n The loss computed for the given transformation.\n\n gradient : ndarray of shape (n_components * n_features,)\n The new (flattened) gradient of the loss.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/neighbors/tests/test_nca.py::test_simple_example", "sklearn/neighbors/tests/test_nca.py::test_toy_example_collapse_points", "sklearn/neighbors/tests/test_nca.py::test_finite_differences[42]", "sklearn/neighbors/tests/test_nca.py::test_transformation_dimensions", "sklearn/neighbors/tests/test_nca.py::test_n_components", "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[5-5-5-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-5-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-7-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-11-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-11-11]", "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_auto_init[7-5-7-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-5-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-5-11-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-5-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-7-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-7-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[11-5-11-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[11-5-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[11-7-11-11]", "sklearn/neighbors/tests/test_nca.py::test_warm_start_validation", "sklearn/neighbors/tests/test_nca.py::test_warm_start_effectiveness", "sklearn/neighbors/tests/test_nca.py::test_verbose[pca]", "sklearn/neighbors/tests/test_nca.py::test_verbose[lda]", "sklearn/neighbors/tests/test_nca.py::test_verbose[identity]", "sklearn/neighbors/tests/test_nca.py::test_verbose[random]", "sklearn/neighbors/tests/test_nca.py::test_verbose[precomputed]", "sklearn/neighbors/tests/test_nca.py::test_no_verbose", "sklearn/neighbors/tests/test_nca.py::test_singleton_class", "sklearn/neighbors/tests/test_nca.py::test_one_class", "sklearn/neighbors/tests/test_nca.py::test_callback", "sklearn/neighbors/tests/test_nca.py::test_expected_transformation_shape", "sklearn/neighbors/tests/test_nca.py::test_convergence_warning", "sklearn/neighbors/tests/test_nca.py::test_parameters_valid_types[n_components-value0]", "sklearn/neighbors/tests/test_nca.py::test_parameters_valid_types[max_iter-value1]", "sklearn/neighbors/tests/test_nca.py::test_parameters_valid_types[tol-value2]", "sklearn/neighbors/tests/test_nca.py::test_nca_feature_names_out[None]", "sklearn/neighbors/tests/test_nca.py::test_nca_feature_names_out[2]", "sklearn/neighbors/_nca.py::sklearn.neighbors._nca.NeighborhoodComponentsAnalysis", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit_score_takes_y]", "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_dtypes]", "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_dtype_object]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_n_iter]", "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_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_n_features_in]", "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[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-NeighborhoodComponentsAnalysis(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-123	1.0	{ "code": "diff --git b/sklearn/neighbors/_nca.py a/sklearn/neighbors/_nca.py\nindex dfc423107..a4ef3c303 100644\n--- b/sklearn/neighbors/_nca.py\n+++ a/sklearn/neighbors/_nca.py\n@@ -221,6 +221,7 @@ class NeighborhoodComponentsAnalysis(\n self.verbose = verbose\n self.random_state = random_state\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit the model according to the given training data.\n \n@@ -237,6 +238,108 @@ class NeighborhoodComponentsAnalysis(\n self : object\n Fitted estimator.\n \"\"\"\n+ # Validate the inputs X and y, and converts y to numerical classes.\n+ X, y = validate_data(self, X, y, ensure_min_samples=2)\n+ check_classification_targets(y)\n+ y = LabelEncoder().fit_transform(y)\n+\n+ # Check the preferred dimensionality of the projected space\n+ if self.n_components is not None and self.n_components > X.shape[1]:\n+ raise ValueError(\n+ \"The preferred dimensionality of the \"\n+ f\"projected space `n_components` ({self.n_components}) cannot \"\n+ \"be greater than the given data \"\n+ f\"dimensionality ({X.shape[1]})!\"\n+ )\n+ # If warm_start is enabled, check that the inputs are consistent\n+ if (\n+ self.warm_start\n+ and hasattr(self, \"components_\")\n+ and self.components_.shape[1] != X.shape[1]\n+ ):\n+ raise ValueError(\n+ f\"The new inputs dimensionality ({X.shape[1]}) does not \"\n+ \"match the input dimensionality of the \"\n+ f\"previously learned transformation ({self.components_.shape[1]}).\"\n+ )\n+ # Check how the linear transformation should be initialized\n+ init = self.init\n+ if isinstance(init, np.ndarray):\n+ init = check_array(init)\n+ # Assert that init.shape[1] = X.shape[1]\n+ if init.shape[1] != X.shape[1]:\n+ raise ValueError(\n+ f\"The input dimensionality ({init.shape[1]}) of the given \"\n+ \"linear transformation `init` must match the \"\n+ f\"dimensionality of the given inputs `X` ({X.shape[1]}).\"\n+ )\n+ # Assert that init.shape[0] <= init.shape[1]\n+ if init.shape[0] > init.shape[1]:\n+ raise ValueError(\n+ f\"The output dimensionality ({init.shape[0]}) of the given \"\n+ \"linear transformation `init` cannot be \"\n+ f\"greater than its input dimensionality ({init.shape[1]}).\"\n+ )\n+ # Assert that self.n_components = init.shape[0]\n+ if self.n_components is not None and self.n_components != init.shape[0]:\n+ raise ValueError(\n+ \"The preferred dimensionality of the \"\n+ f\"projected space `n_components` ({self.n_components}) does\"\n+ \" not match the output dimensionality of \"\n+ \"the given linear transformation \"\n+ f\"`init` ({init.shape[0]})!\"\n+ )\n+\n+ # Initialize the random generator\n+ self.random_state_ = check_random_state(self.random_state)\n+\n+ # Measure the total training time\n+ t_train = time.time()\n+\n+ # Compute a mask that stays fixed during optimization:\n+ same_class_mask = y[:, np.newaxis] == y[np.newaxis, :]\n+ # (n_samples, n_samples)\n+\n+ # Initialize the transformation\n+ transformation = np.ravel(self._initialize(X, y, init))\n+\n+ # Create a dictionary of parameters to be passed to the optimizer\n+ disp = self.verbose - 2 if self.verbose > 1 else -1\n+ optimizer_params = {\n+ \"method\": \"L-BFGS-B\",\n+ \"fun\": self._loss_grad_lbfgs,\n+ \"args\": (X, same_class_mask, -1.0),\n+ \"jac\": True,\n+ \"x0\": transformation,\n+ \"tol\": self.tol,\n+ \"options\": dict(maxiter=self.max_iter, disp=disp),\n+ \"callback\": self._callback,\n+ }\n+\n+ # Call the optimizer\n+ self.n_iter_ = 0\n+ opt_result = minimize(**optimizer_params)\n+\n+ # Reshape the solution found by the optimizer\n+ self.components_ = opt_result.x.reshape(-1, X.shape[1])\n+\n+ # Stop timer\n+ t_train = time.time() - t_train\n+ if self.verbose:\n+ cls_name = self.__class__.__name__\n+\n+ # Warn the user if the algorithm did not converge\n+ if not opt_result.success:\n+ warn(\n+ \"[{}] NCA did not converge: {}\".format(\n+ cls_name, opt_result.message\n+ ),\n+ ConvergenceWarning,\n+ )\n+\n+ print(\"[{}] Training took {:8.2f}s.\".format(cls_name, t_train))\n+\n+ return self\n \n def transform(self, X):\n \"\"\"Apply the learned transformation to the given data.\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_nca.py b/sklearn/neighbors/_nca.py\nindex a4ef3c303..dfc423107 100644\n--- a/sklearn/neighbors/_nca.py\n+++ b/sklearn/neighbors/_nca.py\n@@ -221,7 +221,6 @@ class NeighborhoodComponentsAnalysis(\n self.verbose = verbose\n self.random_state = random_state\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit the model according to the given training data.\n \n@@ -238,108 +237,6 @@ class NeighborhoodComponentsAnalysis(\n self : object\n Fitted estimator.\n \"\"\"\n- # Validate the inputs X and y, and converts y to numerical classes.\n- X, y = validate_data(self, X, y, ensure_min_samples=2)\n- check_classification_targets(y)\n- y = LabelEncoder().fit_transform(y)\n-\n- # Check the preferred dimensionality of the projected space\n- if self.n_components is not None and self.n_components > X.shape[1]:\n- raise ValueError(\n- \"The preferred dimensionality of the \"\n- f\"projected space `n_components` ({self.n_components}) cannot \"\n- \"be greater than the given data \"\n- f\"dimensionality ({X.shape[1]})!\"\n- )\n- # If warm_start is enabled, check that the inputs are consistent\n- if (\n- self.warm_start\n- and hasattr(self, \"components_\")\n- and self.components_.shape[1] != X.shape[1]\n- ):\n- raise ValueError(\n- f\"The new inputs dimensionality ({X.shape[1]}) does not \"\n- \"match the input dimensionality of the \"\n- f\"previously learned transformation ({self.components_.shape[1]}).\"\n- )\n- # Check how the linear transformation should be initialized\n- init = self.init\n- if isinstance(init, np.ndarray):\n- init = check_array(init)\n- # Assert that init.shape[1] = X.shape[1]\n- if init.shape[1] != X.shape[1]:\n- raise ValueError(\n- f\"The input dimensionality ({init.shape[1]}) of the given \"\n- \"linear transformation `init` must match the \"\n- f\"dimensionality of the given inputs `X` ({X.shape[1]}).\"\n- )\n- # Assert that init.shape[0] <= init.shape[1]\n- if init.shape[0] > init.shape[1]:\n- raise ValueError(\n- f\"The output dimensionality ({init.shape[0]}) of the given \"\n- \"linear transformation `init` cannot be \"\n- f\"greater than its input dimensionality ({init.shape[1]}).\"\n- )\n- # Assert that self.n_components = init.shape[0]\n- if self.n_components is not None and self.n_components != init.shape[0]:\n- raise ValueError(\n- \"The preferred dimensionality of the \"\n- f\"projected space `n_components` ({self.n_components}) does\"\n- \" not match the output dimensionality of \"\n- \"the given linear transformation \"\n- f\"`init` ({init.shape[0]})!\"\n- )\n-\n- # Initialize the random generator\n- self.random_state_ = check_random_state(self.random_state)\n-\n- # Measure the total training time\n- t_train = time.time()\n-\n- # Compute a mask that stays fixed during optimization:\n- same_class_mask = y[:, np.newaxis] == y[np.newaxis, :]\n- # (n_samples, n_samples)\n-\n- # Initialize the transformation\n- transformation = np.ravel(self._initialize(X, y, init))\n-\n- # Create a dictionary of parameters to be passed to the optimizer\n- disp = self.verbose - 2 if self.verbose > 1 else -1\n- optimizer_params = {\n- \"method\": \"L-BFGS-B\",\n- \"fun\": self._loss_grad_lbfgs,\n- \"args\": (X, same_class_mask, -1.0),\n- \"jac\": True,\n- \"x0\": transformation,\n- \"tol\": self.tol,\n- \"options\": dict(maxiter=self.max_iter, disp=disp),\n- \"callback\": self._callback,\n- }\n-\n- # Call the optimizer\n- self.n_iter_ = 0\n- opt_result = minimize(**optimizer_params)\n-\n- # Reshape the solution found by the optimizer\n- self.components_ = opt_result.x.reshape(-1, X.shape[1])\n-\n- # Stop timer\n- t_train = time.time() - t_train\n- if self.verbose:\n- cls_name = self.__class__.__name__\n-\n- # Warn the user if the algorithm did not converge\n- if not opt_result.success:\n- warn(\n- \"[{}] NCA did not converge: {}\".format(\n- cls_name, opt_result.message\n- ),\n- ConvergenceWarning,\n- )\n-\n- print(\"[{}] Training took {:8.2f}s.\".format(cls_name, t_train))\n-\n- return self\n \n def transform(self, X):\n \"\"\"Apply the learned transformation to the given data.\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/_nca.py.\nHere is the description for the function:\n def fit(self, X, y):\n \"\"\"Fit the model according to the given training data.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The training samples.\n\n y : array-like of shape (n_samples,)\n The corresponding training labels.\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_simple_example", "sklearn/neighbors/tests/test_nca.py::test_toy_example_collapse_points", "sklearn/neighbors/tests/test_nca.py::test_params_validation", "sklearn/neighbors/tests/test_nca.py::test_transformation_dimensions", "sklearn/neighbors/tests/test_nca.py::test_n_components", "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[5-5-5-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-5-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-7-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-11-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[5-5-11-11]", "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_auto_init[7-5-7-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-5-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-5-11-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-5-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-7-7-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[7-7-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[11-5-11-7]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[11-5-11-11]", "sklearn/neighbors/tests/test_nca.py::test_auto_init[11-7-11-11]", "sklearn/neighbors/tests/test_nca.py::test_warm_start_validation", "sklearn/neighbors/tests/test_nca.py::test_warm_start_effectiveness", "sklearn/neighbors/tests/test_nca.py::test_verbose[pca]", "sklearn/neighbors/tests/test_nca.py::test_verbose[lda]", "sklearn/neighbors/tests/test_nca.py::test_verbose[identity]", "sklearn/neighbors/tests/test_nca.py::test_verbose[random]", "sklearn/neighbors/tests/test_nca.py::test_verbose[precomputed]", "sklearn/neighbors/tests/test_nca.py::test_no_verbose", "sklearn/neighbors/tests/test_nca.py::test_singleton_class", "sklearn/neighbors/tests/test_nca.py::test_one_class", "sklearn/neighbors/tests/test_nca.py::test_callback", "sklearn/neighbors/tests/test_nca.py::test_expected_transformation_shape", "sklearn/neighbors/tests/test_nca.py::test_convergence_warning", "sklearn/neighbors/tests/test_nca.py::test_parameters_valid_types[n_components-value0]", "sklearn/neighbors/tests/test_nca.py::test_parameters_valid_types[max_iter-value1]", "sklearn/neighbors/tests/test_nca.py::test_parameters_valid_types[tol-value2]", "sklearn/neighbors/tests/test_nca.py::test_nca_feature_names_out[None]", "sklearn/neighbors/tests/test_nca.py::test_nca_feature_names_out[2]", "sklearn/neighbors/_nca.py::sklearn.neighbors._nca.NeighborhoodComponentsAnalysis", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit_score_takes_y]", "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_dtypes]", "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_complex_data]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_transformer_n_iter]", "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_1sample]", "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_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform[NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-NeighborhoodComponentsAnalysis(max_iter=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-NeighborhoodComponentsAnalysis(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-124	1.0	{ "code": "diff --git b/sklearn/kernel_approximation.py a/sklearn/kernel_approximation.py\nindex 0a17da29a..92d906dde 100644\n--- b/sklearn/kernel_approximation.py\n+++ a/sklearn/kernel_approximation.py\n@@ -977,6 +977,7 @@ class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator)\n self.random_state = random_state\n self.n_jobs = n_jobs\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit estimator to data.\n \n@@ -998,6 +999,43 @@ class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator)\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X, accept_sparse=\"csr\")\n+ rnd = check_random_state(self.random_state)\n+ n_samples = X.shape[0]\n+\n+ # get basis vectors\n+ if self.n_components > n_samples:\n+ # XXX should we just bail?\n+ n_components = n_samples\n+ warnings.warn(\n+ \"n_components > n_samples. This is not possible.\\n\"\n+ \"n_components was set to n_samples, which results\"\n+ \" in inefficient evaluation of the full kernel.\"\n+ )\n+\n+ else:\n+ n_components = self.n_components\n+ n_components = min(n_samples, n_components)\n+ inds = rnd.permutation(n_samples)\n+ basis_inds = inds[:n_components]\n+ basis = X[basis_inds]\n+\n+ basis_kernel = pairwise_kernels(\n+ basis,\n+ metric=self.kernel,\n+ filter_params=True,\n+ n_jobs=self.n_jobs,\n+ **self._get_kernel_params(),\n+ )\n+\n+ # sqrt of kernel matrix on basis vectors\n+ U, S, V = svd(basis_kernel)\n+ S = np.maximum(S, 1e-12)\n+ self.normalization_ = np.dot(U / np.sqrt(S), V)\n+ self.components_ = basis\n+ self.component_indices_ = basis_inds\n+ self._n_features_out = n_components\n+ return self\n \n def transform(self, X):\n \"\"\"Apply feature map to X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py\nindex 92d906dde..0a17da29a 100644\n--- a/sklearn/kernel_approximation.py\n+++ b/sklearn/kernel_approximation.py\n@@ -977,7 +977,6 @@ class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator)\n self.random_state = random_state\n self.n_jobs = n_jobs\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit estimator to data.\n \n@@ -999,43 +998,6 @@ class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator)\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X, accept_sparse=\"csr\")\n- rnd = check_random_state(self.random_state)\n- n_samples = X.shape[0]\n-\n- # get basis vectors\n- if self.n_components > n_samples:\n- # XXX should we just bail?\n- n_components = n_samples\n- warnings.warn(\n- \"n_components > n_samples. This is not possible.\\n\"\n- \"n_components was set to n_samples, which results\"\n- \" in inefficient evaluation of the full kernel.\"\n- )\n-\n- else:\n- n_components = self.n_components\n- n_components = min(n_samples, n_components)\n- inds = rnd.permutation(n_samples)\n- basis_inds = inds[:n_components]\n- basis = X[basis_inds]\n-\n- basis_kernel = pairwise_kernels(\n- basis,\n- metric=self.kernel,\n- filter_params=True,\n- n_jobs=self.n_jobs,\n- **self._get_kernel_params(),\n- )\n-\n- # sqrt of kernel matrix on basis vectors\n- U, S, V = svd(basis_kernel)\n- S = np.maximum(S, 1e-12)\n- self.normalization_ = np.dot(U / np.sqrt(S), V)\n- self.components_ = basis\n- self.component_indices_ = basis_inds\n- self._n_features_out = n_components\n- return self\n \n def transform(self, X):\n \"\"\"Apply feature map 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/kernel_approximation.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit estimator to data.\n\n Samples a subset of training points, computes kernel\n on these and computes normalization matrix.\n\n Parameters\n ----------\n X : array-like, 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 : array-like, shape (n_samples,) or (n_samples, n_outputs), \\\n default=None\n Target values (None for unsupervised transformations).\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_sgd.py::test_ocsvm_vs_sgdocsvm", "sklearn/tests/test_kernel_approximation.py::test_nystroem_approximation", "sklearn/tests/test_kernel_approximation.py::test_nystroem_default_parameters", "sklearn/tests/test_kernel_approximation.py::test_nystroem_singular_kernel", "sklearn/tests/test_kernel_approximation.py::test_nystroem_poly_kernel_params", "sklearn/tests/test_kernel_approximation.py::test_nystroem_callable", "sklearn/tests/test_kernel_approximation.py::test_nystroem_precomputed_kernel", "sklearn/tests/test_kernel_approximation.py::test_nystroem_component_indices", "sklearn/tests/test_kernel_approximation.py::test_get_feature_names_out[Nystroem]", "sklearn/kernel_approximation.py::sklearn.kernel_approximation.Nystroem", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[Nystroem(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Nystroem()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Nystroem()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[Nystroem()]", "sklearn/tests/test_common.py::test_check_param_validation[Nystroem()]", "sklearn/tests/test_common.py::test_set_output_transform[Nystroem()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-Nystroem()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-Nystroem()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-125	1.0	{ "code": "diff --git b/sklearn/covariance/_shrunk_covariance.py a/sklearn/covariance/_shrunk_covariance.py\nindex da1cc3e39..2a5e09f2c 100644\n--- b/sklearn/covariance/_shrunk_covariance.py\n+++ a/sklearn/covariance/_shrunk_covariance.py\n@@ -782,6 +782,7 @@ class OAS(EmpiricalCovariance):\n np.float64(0.0195...)\n \"\"\"\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the Oracle Approximating Shrinkage covariance model to X.\n \n@@ -798,3 +799,16 @@ class OAS(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X)\n+ # Not calling the parent object to fit, to avoid computing the\n+ # covariance matrix (and potentially the precision)\n+ if self.assume_centered:\n+ self.location_ = np.zeros(X.shape[1])\n+ else:\n+ self.location_ = X.mean(0)\n+\n+ covariance, shrinkage = _oas(X - self.location_, assume_centered=True)\n+ self.shrinkage_ = shrinkage\n+ self._set_covariance(covariance)\n+\n+ return self\n", "test": null }	null	{ "code": "diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py\nindex 2a5e09f2c..da1cc3e39 100644\n--- a/sklearn/covariance/_shrunk_covariance.py\n+++ b/sklearn/covariance/_shrunk_covariance.py\n@@ -782,7 +782,6 @@ class OAS(EmpiricalCovariance):\n np.float64(0.0195...)\n \"\"\"\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the Oracle Approximating Shrinkage covariance model to X.\n \n@@ -799,16 +798,3 @@ class OAS(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X)\n- # Not calling the parent object to fit, to avoid computing the\n- # covariance matrix (and potentially the precision)\n- if self.assume_centered:\n- self.location_ = np.zeros(X.shape[1])\n- else:\n- self.location_ = X.mean(0)\n-\n- covariance, shrinkage = _oas(X - self.location_, assume_centered=True)\n- self.shrinkage_ = shrinkage\n- self._set_covariance(covariance)\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/covariance/_shrunk_covariance.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the Oracle Approximating Shrinkage 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.oas-sklearn.covariance.OAS]", "sklearn/covariance/tests/test_covariance.py::test_oas", "sklearn/covariance/_shrunk_covariance.py::sklearn.covariance._shrunk_covariance.OAS", "sklearn/covariance/_shrunk_covariance.py::sklearn.covariance._shrunk_covariance.oas", "sklearn/tests/test_common.py::test_estimators[OAS()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[OAS()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OAS()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OAS()]", "sklearn/tests/test_common.py::test_check_param_validation[OAS()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-126	1.0	{ "code": "diff --git b/sklearn/preprocessing/_encoders.py a/sklearn/preprocessing/_encoders.py\nindex 89946be95..0bfae1c47 100644\n--- b/sklearn/preprocessing/_encoders.py\n+++ a/sklearn/preprocessing/_encoders.py\n@@ -961,6 +961,7 @@ class OneHotEncoder(_BaseEncoder):\n \n return output\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"\n Fit OneHotEncoder to X.\n@@ -979,6 +980,14 @@ class OneHotEncoder(_BaseEncoder):\n self\n Fitted encoder.\n \"\"\"\n+ self._fit(\n+ X,\n+ handle_unknown=self.handle_unknown,\n+ ensure_all_finite=\"allow-nan\",\n+ )\n+ self._set_drop_idx()\n+ self._n_features_outs = self._compute_n_features_outs()\n+ return self\n \n def transform(self, X):\n \"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py\nindex 0bfae1c47..89946be95 100644\n--- a/sklearn/preprocessing/_encoders.py\n+++ b/sklearn/preprocessing/_encoders.py\n@@ -961,7 +961,6 @@ class OneHotEncoder(_BaseEncoder):\n \n return output\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"\n Fit OneHotEncoder to X.\n@@ -980,14 +979,6 @@ class OneHotEncoder(_BaseEncoder):\n self\n Fitted encoder.\n \"\"\"\n- self._fit(\n- X,\n- handle_unknown=self.handle_unknown,\n- ensure_all_finite=\"allow-nan\",\n- )\n- self._set_drop_idx()\n- self._n_features_outs = self._compute_n_features_outs()\n- return self\n \n def transform(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/preprocessing/_encoders.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"\n Fit OneHotEncoder to X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data to determine the categories of each feature.\n\n y : None\n Ignored. This parameter exists only for compatibility with\n :class:`~sklearn.pipeline.Pipeline`.\n\n Returns\n -------\n self\n Fitted encoder.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_kbindiscretizer", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_sparse_dense", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown[ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown[infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown_strings[ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown_strings[infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[int32-int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[int32-float32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[int32-float64]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float32-int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float32-float32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float32-float64]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float64-int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float64-float32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float64-float64]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype_pandas[int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype_pandas[float32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype_pandas[float64]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_list", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_unicode", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_custom_feature_name_combiner", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_set_params", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_mixed_cols_sparse", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_sparse_threshold", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[numeric]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[object]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-float-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-None-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-None-float-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-False-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-False-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-True-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-True-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-False-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-False-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-True-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-True-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse_transform_raise_error_with_unknown[X0-X_trans0-False]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse_transform_raise_error_with_unknown[X0-X_trans0-True]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse_transform_raise_error_with_unknown[X1-X_trans1-False]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse_transform_raise_error_with_unknown[X1-X_trans1-True]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse_if_binary", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-None]", "sklearn/preprocessing/tests/test_encoders.py::test_X_is_not_1D[X0-fit]", "sklearn/preprocessing/tests/test_encoders.py::test_X_is_not_1D[X0-fit_transform]", "sklearn/preprocessing/tests/test_encoders.py::test_X_is_not_1D[X1-fit]", "sklearn/preprocessing/tests/test_encoders.py::test_X_is_not_1D[X1-fit_transform]", "sklearn/preprocessing/tests/test_encoders.py::test_X_is_not_1D_pandas[fit]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_with_categorical[categorical_features0-dataframe]", "sklearn/preprocessing/tests/test_encoders.py::test_X_is_not_1D_pandas[fit_transform]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_categories[mixed]", "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/preprocessing/tests/test_encoders.py::test_one_hot_encoder_categories[numeric]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_categories[object]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_categories[string]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_categories[missing-float]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_legend", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_categories[missing-np.nan-object]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_negative_column_indexes", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_categories[missing-float-nan-object]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[numeric-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[numeric-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-infrequent_if_exist]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_grid_resolution_with_categorical[categorical_features0-dataframe]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-none-ignore]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_grid_resolution_with_categorical[categorical_features1-array]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-none-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-nan-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-nan-infrequent_if_exist]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_with_make_column_selector", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-None-and-nan-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-None-and-nan-infrequent_if_exist]", "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/inspection/_plot/tests/test_plot_partial_dependence.py::test_grid_resolution_with_categorical[categorical_features2-array]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_empty_columns[callable]", "sklearn/tests/test_pipeline.py::test_pipeline_with_estimator_with_len", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_unsorted_categories", "sklearn/preprocessing/tests/test_encoders.py::test_encoder_nan_ending_specified_categories[OneHotEncoder]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector0]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories_mixed_columns", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[<lambda>0]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_pandas", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_drop[first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_drop[binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_drop[manual]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector2]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_equals_if_binary", "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/preprocessing/tests/test_encoders.py::test_encoder_dtypes", "sklearn/preprocessing/tests/test_encoders.py::test_encoder_dtypes_pandas", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_warning", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[<lambda>1]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_manual[nan0]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_manual[None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_manual[nan1]", "sklearn/preprocessing/tests/test_encoders.py::test_invalid_drop_length[drop0]", "sklearn/preprocessing/tests/test_encoders.py::test_invalid_drop_length[drop1]", "sklearn/preprocessing/tests/test_encoders.py::test_categories[first-sparse]", "sklearn/preprocessing/tests/test_encoders.py::test_categories[first-dense]", "sklearn/preprocessing/tests/test_encoders.py::test_categories[manual-sparse]", "sklearn/preprocessing/tests/test_encoders.py::test_categories[manual-dense]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[auto-kwargs0]", "sklearn/compose/tests/test_column_transformer.py::test_sk_visual_block_remainder_fitted_pandas[passthrough]", 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"sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_param_validation[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform[OneHotEncoder(sparse_output=False)]", "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_set_output_transform_pandas-OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-OneHotEncoder(sparse_output=False)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-OneHotEncoder(sparse_output=False)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-127	1.0	{ "code": "diff --git b/sklearn/preprocessing/_encoders.py a/sklearn/preprocessing/_encoders.py\nindex e72f83bae..0bfae1c47 100644\n--- b/sklearn/preprocessing/_encoders.py\n+++ a/sklearn/preprocessing/_encoders.py\n@@ -1211,6 +1211,20 @@ class OneHotEncoder(_BaseEncoder):\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+ cats = [\n+ self._compute_transformed_categories(i)\n+ for i, _ in enumerate(self.categories_)\n+ ]\n+\n+ name_combiner = self._check_get_feature_name_combiner()\n+ feature_names = []\n+ for i in range(len(cats)):\n+ names = [name_combiner(input_features[i], t) for t in cats[i]]\n+ feature_names.extend(names)\n+\n+ return np.array(feature_names, dtype=object)\n \n def _check_get_feature_name_combiner(self):\n if self.feature_name_combiner == \"concat\":\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py\nindex 0bfae1c47..e72f83bae 100644\n--- a/sklearn/preprocessing/_encoders.py\n+++ b/sklearn/preprocessing/_encoders.py\n@@ -1211,20 +1211,6 @@ class OneHotEncoder(_BaseEncoder):\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- cats = [\n- self._compute_transformed_categories(i)\n- for i, _ in enumerate(self.categories_)\n- ]\n-\n- name_combiner = self._check_get_feature_name_combiner()\n- feature_names = []\n- for i in range(len(cats)):\n- names = [name_combiner(input_features[i], t) for t in cats[i]]\n- feature_names.extend(names)\n-\n- return np.array(feature_names, dtype=object)\n \n def _check_get_feature_name_combiner(self):\n if self.feature_name_combiner == \"concat\":\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/_encoders.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/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_unicode", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_custom_feature_name_combiner", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_drop[first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_drop[binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_feature_names_drop[manual]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[auto-kwargs0]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[auto-kwargs1]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[auto-kwargs2]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_empty_columns[list]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[auto-kwargs3]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_empty_columns[array]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[auto-kwargs4]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_empty_columns[callable]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[categories1-kwargs0]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector0]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[categories1-kwargs1]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[<lambda>0]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[categories1-kwargs2]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector2]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[categories1-kwargs3]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels[categories1-kwargs4]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[<lambda>1]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels_drop_frequent[if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels_drop_frequent[first]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_pandas[selector4]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_two_levels_drop_frequent[drop2]", "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/preprocessing/tests/test_encoders.py::test_ohe_infrequent_three_levels[kwargs0]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_three_levels[kwargs1]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[<lambda>0]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_three_levels[kwargs2]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_three_levels[kwargs3]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_three_levels[kwargs4]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[selector2]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_three_levels[kwargs5]", "sklearn/compose/tests/test_column_transformer.py::test_feature_names_out_non_pandas[<lambda>1]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_three_levels[kwargs6]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_infrequent_multiple_categories", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_missing_values_get_feature_names[nan]", "sklearn/preprocessing/tests/test_encoders.py::test_ohe_missing_values_get_feature_names[None]", "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/preprocessing/tests/test_encoders.py::test_one_hot_encoder_set_output", "sklearn/preprocessing/tests/test_encoders.py::test_drop_idx_infrequent_categories", "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/_encoders.py::sklearn.preprocessing._encoders.OneHotEncoder", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_estimators_get_feature_names_out_error[OneHotEncoder(handle_unknown='ignore')]", "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_set_output_transform_pandas-OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-OneHotEncoder(sparse_output=False)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-OneHotEncoder(sparse_output=False)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-128	1.0	{ "code": "diff --git b/sklearn/preprocessing/_encoders.py a/sklearn/preprocessing/_encoders.py\nindex 7f77124df..0bfae1c47 100644\n--- b/sklearn/preprocessing/_encoders.py\n+++ a/sklearn/preprocessing/_encoders.py\n@@ -1012,6 +1012,65 @@ class OneHotEncoder(_BaseEncoder):\n Transformed input. If `sparse_output=True`, a sparse matrix will be\n returned.\n \"\"\"\n+ check_is_fitted(self)\n+ transform_output = _get_output_config(\"transform\", estimator=self)[\"dense\"]\n+ if transform_output != \"default\" and self.sparse_output:\n+ capitalize_transform_output = transform_output.capitalize()\n+ raise ValueError(\n+ f\"{capitalize_transform_output} output does not support sparse data.\"\n+ f\" Set sparse_output=False to output {transform_output} dataframes or\"\n+ f\" disable {capitalize_transform_output} output via\"\n+ '` ohe.set_output(transform=\"default\").'\n+ )\n+\n+ # validation of X happens in _check_X called by _transform\n+ warn_on_unknown = self.drop is not None and self.handle_unknown in {\n+ \"ignore\",\n+ \"infrequent_if_exist\",\n+ }\n+ X_int, X_mask = self._transform(\n+ X,\n+ handle_unknown=self.handle_unknown,\n+ ensure_all_finite=\"allow-nan\",\n+ warn_on_unknown=warn_on_unknown,\n+ )\n+\n+ n_samples, n_features = X_int.shape\n+\n+ if self._drop_idx_after_grouping is not None:\n+ to_drop = self._drop_idx_after_grouping.copy()\n+ # We remove all the dropped categories from mask, and decrement all\n+ # categories that occur after them to avoid an empty column.\n+ keep_cells = X_int != to_drop\n+ for i, cats in enumerate(self.categories_):\n+ # drop='if_binary' but feature isn't binary\n+ if to_drop[i] is None:\n+ # set to cardinality to not drop from X_int\n+ to_drop[i] = len(cats)\n+\n+ to_drop = to_drop.reshape(1, -1)\n+ X_int[X_int > to_drop] -= 1\n+ X_mask &= keep_cells\n+\n+ mask = X_mask.ravel()\n+ feature_indices = np.cumsum([0] + self._n_features_outs)\n+ indices = (X_int + feature_indices[:-1]).ravel()[mask]\n+\n+ indptr = np.empty(n_samples + 1, dtype=int)\n+ indptr[0] = 0\n+ np.sum(X_mask, axis=1, out=indptr[1:], dtype=indptr.dtype)\n+ np.cumsum(indptr[1:], out=indptr[1:])\n+ data = np.ones(indptr[-1])\n+\n+ out = sparse.csr_matrix(\n+ (data, indices, indptr),\n+ shape=(n_samples, feature_indices[-1]),\n+ dtype=self.dtype,\n+ )\n+ if not self.sparse_output:\n+ return out.toarray()\n+ else:\n+ return out\n \n def inverse_transform(self, X):\n \"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py\nindex 0bfae1c47..7f77124df 100644\n--- a/sklearn/preprocessing/_encoders.py\n+++ b/sklearn/preprocessing/_encoders.py\n@@ -1012,65 +1012,6 @@ class OneHotEncoder(_BaseEncoder):\n Transformed input. If `sparse_output=True`, a sparse matrix will be\n returned.\n \"\"\"\n- check_is_fitted(self)\n- transform_output = _get_output_config(\"transform\", estimator=self)[\"dense\"]\n- if transform_output != \"default\" and self.sparse_output:\n- capitalize_transform_output = transform_output.capitalize()\n- raise ValueError(\n- f\"{capitalize_transform_output} output does not support sparse data.\"\n- f\" Set sparse_output=False to output {transform_output} dataframes or\"\n- f\" disable {capitalize_transform_output} output via\"\n- '` ohe.set_output(transform=\"default\").'\n- )\n-\n- # validation of X happens in _check_X called by _transform\n- warn_on_unknown = self.drop is not None and self.handle_unknown in {\n- \"ignore\",\n- \"infrequent_if_exist\",\n- }\n- X_int, X_mask = self._transform(\n- X,\n- handle_unknown=self.handle_unknown,\n- ensure_all_finite=\"allow-nan\",\n- warn_on_unknown=warn_on_unknown,\n- )\n-\n- n_samples, n_features = X_int.shape\n-\n- if self._drop_idx_after_grouping is not None:\n- to_drop = self._drop_idx_after_grouping.copy()\n- # We remove all the dropped categories from mask, and decrement all\n- # categories that occur after them to avoid an empty column.\n- keep_cells = X_int != to_drop\n- for i, cats in enumerate(self.categories_):\n- # drop='if_binary' but feature isn't binary\n- if to_drop[i] is None:\n- # set to cardinality to not drop from X_int\n- to_drop[i] = len(cats)\n-\n- to_drop = to_drop.reshape(1, -1)\n- X_int[X_int > to_drop] -= 1\n- X_mask &= keep_cells\n-\n- mask = X_mask.ravel()\n- feature_indices = np.cumsum([0] + self._n_features_outs)\n- indices = (X_int + feature_indices[:-1]).ravel()[mask]\n-\n- indptr = np.empty(n_samples + 1, dtype=int)\n- indptr[0] = 0\n- np.sum(X_mask, axis=1, out=indptr[1:], dtype=indptr.dtype)\n- np.cumsum(indptr[1:], out=indptr[1:])\n- data = np.ones(indptr[-1])\n-\n- out = sparse.csr_matrix(\n- (data, indices, indptr),\n- shape=(n_samples, feature_indices[-1]),\n- dtype=self.dtype,\n- )\n- if not self.sparse_output:\n- return out.toarray()\n- else:\n- return out\n \n def inverse_transform(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/preprocessing/_encoders.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"\n Transform X using one-hot encoding.\n\n If `sparse_output=True` (default), it returns an instance of\n :class:`scipy.sparse._csr.csr_matrix` (CSR format).\n\n If there are infrequent categories for a feature, set by specifying\n `max_categories` or `min_frequency`, the infrequent categories are\n grouped into a single category.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data to encode.\n\n Returns\n -------\n X_out : {ndarray, sparse matrix} of shape \\\n (n_samples, n_encoded_features)\n Transformed input. If `sparse_output=True`, a sparse matrix will be\n returned.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_sparse_dense", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown[ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown[infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown_strings[ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown_strings[infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[int32-int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[int32-float32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[int32-float64]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float32-int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float32-float32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float32-float64]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float64-int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float64-float32]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_list", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype[float64-float64]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_mixed_cols_sparse", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_sparse_threshold", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype_pandas[int32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype_pandas[float32]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_dtype_pandas[float64]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_set_params", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[numeric]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[object]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-float-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-None-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder[mixed-None-float-nan]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-False-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-False-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-True-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-True-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-False-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-False-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-True-ignore]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_kbindiscretizer", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[first-True-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse_if_binary", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[if_binary-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[first-None]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-if_binary]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-first]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_drop_reset[None-None]", "sklearn/compose/tests/test_column_transformer.py::test_column_transformer_negative_column_indexes", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[numeric-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[numeric-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-ignore]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-infrequent_if_exist]", "sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-none-ignore]", 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"sklearn/ensemble/tests/test_forest.py::test_warm_start_clear[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_warm_start_smaller_n_estimators[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_warm_start_equal_n_estimators[RandomTreesEmbedding]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_mixed_types_pandas", "sklearn/preprocessing/_encoders.py::sklearn.preprocessing._encoders.OneHotEncoder", "sklearn/ensemble/tests/test_forest.py::test_random_trees_embedding_feature_names_out", "sklearn/ensemble/_forest.py::sklearn.ensemble._forest.RandomTreesEmbedding", "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_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_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_transformers_unfitted]", 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"sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform[KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform[OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform[OneHotEncoder(sparse_output=False)]", "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_set_output_transform_pandas-OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-OneHotEncoder(sparse_output=False)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-KBinsDiscretizer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-OneHotEncoder(handle_unknown='ignore')]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-OneHotEncoder(sparse_output=False)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-129	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 85bea1642..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -969,6 +969,31 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n output shape changed to ``(n_samples,)`` to conform to\n scikit-learn conventions for binary classification.\n \"\"\"\n+ check_is_fitted(self)\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=True,\n+ ensure_all_finite=False,\n+ reset=False,\n+ )\n+\n+ indices = self.pairwise_indices_\n+ if indices is None:\n+ Xs = [X] * len(self.estimators_)\n+ else:\n+ Xs = [X[:, idx] for idx in indices]\n+\n+ predictions = np.vstack(\n+ [est.predict(Xi) for est, Xi in zip(self.estimators_, Xs)]\n+ ).T\n+ confidences = np.vstack(\n+ [_predict_binary(est, Xi) for est, Xi in zip(self.estimators_, Xs)]\n+ ).T\n+ Y = _ovr_decision_function(predictions, confidences, len(self.classes_))\n+ if self.n_classes_ == 2:\n+ return Y[:, 1]\n+ return Y\n \n @property\n def n_classes_(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..85bea1642 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -969,31 +969,6 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n output shape changed to ``(n_samples,)`` to conform to\n scikit-learn conventions for binary classification.\n \"\"\"\n- check_is_fitted(self)\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=True,\n- ensure_all_finite=False,\n- reset=False,\n- )\n-\n- indices = self.pairwise_indices_\n- if indices is None:\n- Xs = [X] * len(self.estimators_)\n- else:\n- Xs = [X[:, idx] for idx in indices]\n-\n- predictions = np.vstack(\n- [est.predict(Xi) for est, Xi in zip(self.estimators_, Xs)]\n- ).T\n- confidences = np.vstack(\n- [_predict_binary(est, Xi) for est, Xi in zip(self.estimators_, Xs)]\n- ).T\n- Y = _ovr_decision_function(predictions, confidences, len(self.classes_))\n- if self.n_classes_ == 2:\n- return Y[:, 1]\n- return Y\n \n @property\n def n_classes_(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/multiclass.py.\nHere is the description for the function:\n def decision_function(self, X):\n \"\"\"Decision function for the OneVsOneClassifier.\n\n The decision values for the samples are computed by adding the\n normalized sum of pair-wise classification confidence levels to the\n votes in order to disambiguate between the decision values when the\n votes for all the classes are equal leading to a tie.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Input data.\n\n Returns\n -------\n Y : array-like of shape (n_samples, n_classes) or (n_samples,)\n Result of calling `decision_function` on the final estimator.\n\n .. versionchanged:: 0.19\n output shape changed to ``(n_samples,)`` to conform to\n scikit-learn conventions for binary classification.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_multiclass.py::test_ovr_ovo_regressor", "sklearn/tests/test_multiclass.py::test_ovo_exceptions", "sklearn/tests/test_multiclass.py::test_ovo_fit_on_list", "sklearn/tests/test_multiclass.py::test_ovo_fit_predict", "sklearn/tests/test_multiclass.py::test_ovo_partial_fit_predict", "sklearn/tests/test_multiclass.py::test_ovo_decision_function", "sklearn/tests/test_multiclass.py::test_ovo_ties", "sklearn/tests/test_multiclass.py::test_ovo_ties2", "sklearn/tests/test_multiclass.py::test_ovo_string_y", "sklearn/tests/test_multiclass.py::test_pairwise_cross_val_score[OneVsOneClassifier]", "sklearn/tests/test_multiclass.py::test_support_missing_values[OneVsOneClassifier]", "sklearn/tests/test_multiclass.py::test_ovo_consistent_binary_classification", "sklearn/multiclass.py::sklearn.multiclass.OneVsOneClassifier", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsOneClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsOneClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-130	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 71dac2fce..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -757,6 +757,10 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self.estimator = estimator\n self.n_jobs = n_jobs\n \n+ @_fit_context(\n+ # OneVsOneClassifier.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n \n@@ -782,6 +786,51 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self : object\n The fitted underlying estimator.\n \"\"\"\n+ _raise_for_params(fit_params, self, \"fit\")\n+\n+ routed_params = process_routing(\n+ self,\n+ \"fit\",\n+ fit_params,\n+ )\n+\n+ # We need to validate the data because we do a safe_indexing later.\n+ X, y = validate_data(\n+ self, X, y, accept_sparse=[\"csr\", \"csc\"], ensure_all_finite=False\n+ )\n+ check_classification_targets(y)\n+\n+ self.classes_ = np.unique(y)\n+ if len(self.classes_) == 1:\n+ raise ValueError(\n+ \"OneVsOneClassifier can not be fit when only one class is present.\"\n+ )\n+ n_classes = self.classes_.shape[0]\n+ estimators_indices = list(\n+ zip(\n+ *(\n+ Parallel(n_jobs=self.n_jobs)(\n+ delayed(_fit_ovo_binary)(\n+ self.estimator,\n+ X,\n+ y,\n+ self.classes_[i],\n+ self.classes_[j],\n+ fit_params=routed_params.estimator.fit,\n+ )\n+ for i in range(n_classes)\n+ for j in range(i + 1, n_classes)\n+ )\n+ )\n+ )\n+ )\n+\n+ self.estimators_ = estimators_indices[0]\n+\n+ pairwise = self.__sklearn_tags__().input_tags.pairwise\n+ self.pairwise_indices_ = estimators_indices[1] if pairwise else None\n+\n+ return self\n \n @available_if(_estimators_has(\"partial_fit\"))\n @_fit_context(\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..71dac2fce 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -757,10 +757,6 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self.estimator = estimator\n self.n_jobs = n_jobs\n \n- @_fit_context(\n- # OneVsOneClassifier.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n \n@@ -786,51 +782,6 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self : object\n The fitted underlying estimator.\n \"\"\"\n- _raise_for_params(fit_params, self, \"fit\")\n-\n- routed_params = process_routing(\n- self,\n- \"fit\",\n- fit_params,\n- )\n-\n- # We need to validate the data because we do a safe_indexing later.\n- X, y = validate_data(\n- self, X, y, accept_sparse=[\"csr\", \"csc\"], ensure_all_finite=False\n- )\n- check_classification_targets(y)\n-\n- self.classes_ = np.unique(y)\n- if len(self.classes_) == 1:\n- raise ValueError(\n- \"OneVsOneClassifier can not be fit when only one class is present.\"\n- )\n- n_classes = self.classes_.shape[0]\n- estimators_indices = list(\n- zip(\n- *(\n- Parallel(n_jobs=self.n_jobs)(\n- delayed(_fit_ovo_binary)(\n- self.estimator,\n- X,\n- y,\n- self.classes_[i],\n- self.classes_[j],\n- fit_params=routed_params.estimator.fit,\n- )\n- for i in range(n_classes)\n- for j in range(i + 1, n_classes)\n- )\n- )\n- )\n- )\n-\n- self.estimators_ = estimators_indices[0]\n-\n- pairwise = self.__sklearn_tags__().input_tags.pairwise\n- self.pairwise_indices_ = estimators_indices[1] if pairwise else None\n-\n- return self\n \n @available_if(_estimators_has(\"partial_fit\"))\n @_fit_context(\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/multiclass.py.\nHere is the description for the function:\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Data.\n\n y : array-like of shape (n_samples,)\n Multi-class targets.\n\n fit_params : dict\n Parameters passed to the ``estimator.fit`` method of each\n sub-estimator.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n self : object\n The fitted underlying estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[OneVsOneClassifier]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[OneVsOneClassifier]", "sklearn/tests/test_multiclass.py::test_ovr_ovo_regressor", "sklearn/tests/test_multiclass.py::test_ovo_fit_on_list", "sklearn/tests/test_multiclass.py::test_ovo_fit_predict", "sklearn/tests/test_multiclass.py::test_ovo_partial_fit_predict", "sklearn/tests/test_multiclass.py::test_ovo_decision_function", "sklearn/tests/test_multiclass.py::test_ovo_gridsearch", "sklearn/tests/test_multiclass.py::test_ovo_ties", "sklearn/tests/test_multiclass.py::test_ovo_ties2", "sklearn/tests/test_multiclass.py::test_ovo_string_y", "sklearn/tests/test_multiclass.py::test_ovo_one_class", "sklearn/tests/test_multiclass.py::test_ovo_float_y", "sklearn/tests/test_multiclass.py::test_pairwise_indices", "sklearn/tests/test_multiclass.py::test_pairwise_n_features_in", "sklearn/tests/test_multiclass.py::test_pairwise_cross_val_score[OneVsOneClassifier]", "sklearn/tests/test_multiclass.py::test_support_missing_values[OneVsOneClassifier]", "sklearn/tests/test_multiclass.py::test_ovo_consistent_binary_classification", "sklearn/multiclass.py::sklearn.multiclass.OneVsOneClassifier", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[OneVsOneClassifier]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsOneClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsOneClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_param_validation[OneVsOneClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-131	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 5921fa660..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -832,6 +832,11 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n \n return self\n \n+ @available_if(_estimators_has(\"partial_fit\"))\n+ @_fit_context(\n+ # OneVsOneClassifier.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def partial_fit(self, X, y, classes=None, partial_fit_params):\n \"\"\"Partially fit underlying estimators.\n \n@@ -868,6 +873,56 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self : object\n The partially fitted underlying estimator.\n \"\"\"\n+ _raise_for_params(partial_fit_params, self, \"partial_fit\")\n+\n+ routed_params = process_routing(\n+ self,\n+ \"partial_fit\",\n+ partial_fit_params,\n+ )\n+\n+ first_call = _check_partial_fit_first_call(self, classes)\n+ if first_call:\n+ self.estimators_ = [\n+ clone(self.estimator)\n+ for _ in range(self.n_classes_ * (self.n_classes_ - 1) // 2)\n+ ]\n+\n+ if len(np.setdiff1d(y, self.classes_)):\n+ raise ValueError(\n+ \"Mini-batch contains {0} while it must be subset of {1}\".format(\n+ np.unique(y), self.classes_\n+ )\n+ )\n+\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=[\"csr\", \"csc\"],\n+ ensure_all_finite=False,\n+ reset=first_call,\n+ )\n+ check_classification_targets(y)\n+ combinations = itertools.combinations(range(self.n_classes_), 2)\n+ self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n+ delayed(_partial_fit_ovo_binary)(\n+ estimator,\n+ X,\n+ y,\n+ self.classes_[i],\n+ self.classes_[j],\n+ partial_fit_params=routed_params.estimator.partial_fit,\n+ )\n+ for estimator, (i, j) in zip(self.estimators_, (combinations))\n+ )\n+\n+ self.pairwise_indices_ = None\n+\n+ if hasattr(self.estimators_[0], \"n_features_in_\"):\n+ self.n_features_in_ = self.estimators_[0].n_features_in_\n+\n+ return self\n \n def predict(self, X):\n \"\"\"Estimate the best class label for each sample in X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..5921fa660 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -832,11 +832,6 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n \n return self\n \n- @available_if(_estimators_has(\"partial_fit\"))\n- @_fit_context(\n- # OneVsOneClassifier.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def partial_fit(self, X, y, classes=None, partial_fit_params):\n \"\"\"Partially fit underlying estimators.\n \n@@ -873,56 +868,6 @@ class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self : object\n The partially fitted underlying estimator.\n \"\"\"\n- _raise_for_params(partial_fit_params, self, \"partial_fit\")\n-\n- routed_params = process_routing(\n- self,\n- \"partial_fit\",\n- partial_fit_params,\n- )\n-\n- first_call = _check_partial_fit_first_call(self, classes)\n- if first_call:\n- self.estimators_ = [\n- clone(self.estimator)\n- for _ in range(self.n_classes_ * (self.n_classes_ - 1) // 2)\n- ]\n-\n- if len(np.setdiff1d(y, self.classes_)):\n- raise ValueError(\n- \"Mini-batch contains {0} while it must be subset of {1}\".format(\n- np.unique(y), self.classes_\n- )\n- )\n-\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=[\"csr\", \"csc\"],\n- ensure_all_finite=False,\n- reset=first_call,\n- )\n- check_classification_targets(y)\n- combinations = itertools.combinations(range(self.n_classes_), 2)\n- self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n- delayed(_partial_fit_ovo_binary)(\n- estimator,\n- X,\n- y,\n- self.classes_[i],\n- self.classes_[j],\n- partial_fit_params=routed_params.estimator.partial_fit,\n- )\n- for estimator, (i, j) in zip(self.estimators_, (combinations))\n- )\n-\n- self.pairwise_indices_ = None\n-\n- if hasattr(self.estimators_[0], \"n_features_in_\"):\n- self.n_features_in_ = self.estimators_[0].n_features_in_\n-\n- return self\n \n def predict(self, X):\n \"\"\"Estimate the best class label for each sample 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/multiclass.py.\nHere is the description for the function:\n def partial_fit(self, X, y, classes=None, partial_fit_params):\n \"\"\"Partially fit underlying estimators.\n\n Should be used when memory is inefficient to train all data. Chunks\n of data can be passed in several iteration, where the first call\n should have an array of all target variables.\n\n Parameters\n ----------\n X : {array-like, sparse matrix) of shape (n_samples, n_features)\n Data.\n\n y : array-like of shape (n_samples,)\n Multi-class targets.\n\n classes : array, shape (n_classes, )\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 only required in the first call of partial_fit\n and can be omitted in the subsequent calls.\n\n partial_fit_params : dict\n Parameters passed to the ``estimator.partial_fit`` method of each\n sub-estimator.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n self : object\n The partially fitted underlying estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[OneVsOneClassifier]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[OneVsOneClassifier]", "sklearn/tests/test_multiclass.py::test_ovo_partial_fit_predict", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[OneVsOneClassifier]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OneVsOneClassifier(estimator=LogisticRegression(C=1))-check_estimators_partial_fit_n_features]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsOneClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsOneClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_param_validation[OneVsOneClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-132	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 3b1fdb789..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -324,6 +324,10 @@ class OneVsRestClassifier(\n self.n_jobs = n_jobs\n self.verbose = verbose\n \n+ @_fit_context(\n+ # OneVsRestClassifier.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n \n@@ -350,6 +354,45 @@ class OneVsRestClassifier(\n self : object\n Instance of fitted estimator.\n \"\"\"\n+ _raise_for_params(fit_params, self, \"fit\")\n+\n+ routed_params = process_routing(\n+ self,\n+ \"fit\",\n+ fit_params,\n+ )\n+ # A sparse LabelBinarizer, with sparse_output=True, has been shown to\n+ # outperform or match a dense label binarizer in all cases and has also\n+ # resulted in less or equal memory consumption in the fit_ovr function\n+ # overall.\n+ self.label_binarizer_ = LabelBinarizer(sparse_output=True)\n+ Y = self.label_binarizer_.fit_transform(y)\n+ Y = Y.tocsc()\n+ self.classes_ = self.label_binarizer_.classes_\n+ columns = (col.toarray().ravel() for col in Y.T)\n+ # In cases where individual estimators are very fast to train setting\n+ # n_jobs > 1 in can results in slower performance due to the overhead\n+ # of spawning threads. See joblib issue #112.\n+ self.estimators_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(\n+ delayed(_fit_binary)(\n+ self.estimator,\n+ X,\n+ column,\n+ fit_params=routed_params.estimator.fit,\n+ classes=[\n+ \"not %s\" % self.label_binarizer_.classes_[i],\n+ self.label_binarizer_.classes_[i],\n+ ],\n+ )\n+ for i, column in enumerate(columns)\n+ )\n+\n+ if hasattr(self.estimators_[0], \"n_features_in_\"):\n+ self.n_features_in_ = self.estimators_[0].n_features_in_\n+ if hasattr(self.estimators_[0], \"feature_names_in_\"):\n+ self.feature_names_in_ = self.estimators_[0].feature_names_in_\n+\n+ return self\n \n @available_if(_estimators_has(\"partial_fit\"))\n @_fit_context(\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..3b1fdb789 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -324,10 +324,6 @@ class OneVsRestClassifier(\n self.n_jobs = n_jobs\n self.verbose = verbose\n \n- @_fit_context(\n- # OneVsRestClassifier.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n \n@@ -354,45 +350,6 @@ class OneVsRestClassifier(\n self : object\n Instance of fitted estimator.\n \"\"\"\n- _raise_for_params(fit_params, self, \"fit\")\n-\n- routed_params = process_routing(\n- self,\n- \"fit\",\n- fit_params,\n- )\n- # A sparse LabelBinarizer, with sparse_output=True, has been shown to\n- # outperform or match a dense label binarizer in all cases and has also\n- # resulted in less or equal memory consumption in the fit_ovr function\n- # overall.\n- self.label_binarizer_ = LabelBinarizer(sparse_output=True)\n- Y = self.label_binarizer_.fit_transform(y)\n- Y = Y.tocsc()\n- self.classes_ = self.label_binarizer_.classes_\n- columns = (col.toarray().ravel() for col in Y.T)\n- # In cases where individual estimators are very fast to train setting\n- # n_jobs > 1 in can results in slower performance due to the overhead\n- # of spawning threads. See joblib issue #112.\n- self.estimators_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(\n- delayed(_fit_binary)(\n- self.estimator,\n- X,\n- column,\n- fit_params=routed_params.estimator.fit,\n- classes=[\n- \"not %s\" % self.label_binarizer_.classes_[i],\n- self.label_binarizer_.classes_[i],\n- ],\n- )\n- for i, column in enumerate(columns)\n- )\n-\n- if hasattr(self.estimators_[0], \"n_features_in_\"):\n- self.n_features_in_ = self.estimators_[0].n_features_in_\n- if hasattr(self.estimators_[0], \"feature_names_in_\"):\n- self.feature_names_in_ = self.estimators_[0].feature_names_in_\n-\n- return self\n \n @available_if(_estimators_has(\"partial_fit\"))\n @_fit_context(\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/multiclass.py.\nHere is the description for the function:\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Data.\n\n y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)\n Multi-class targets. An indicator matrix turns on multilabel\n classification.\n\n fit_params : dict\n Parameters passed to the ``estimator.fit`` method of each\n sub-estimator.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n self : object\n Instance of fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers_multilabel_indicator_data", "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[OneVsRestClassifier]", "sklearn/linear_model/tests/test_logistic.py::test_logreg_predict_proba_multinomial", "sklearn/svm/tests/test_svm.py::test_decision_function_shape_two_class", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[OneVsRestClassifier]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction[csr_matrix]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction[csr_array]", "sklearn/tests/test_multiclass.py::test_ovr_exceptions", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_with_method_multilabel_ovr", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict", "sklearn/tests/test_multiclass.py::test_ovr_partial_fit", "sklearn/tests/test_multiclass.py::test_ovr_ovo_regressor", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csr_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csr_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csc_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csc_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[coo_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[coo_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[dok_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[dok_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[lil_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[lil_array]", "sklearn/tests/test_multiclass.py::test_ovr_always_present", "sklearn/tests/test_multiclass.py::test_ovr_multiclass", "sklearn/tests/test_multiclass.py::test_ovr_binary", "sklearn/tests/test_multiclass.py::test_ovr_multilabel", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_svc", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_dataset", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_predict_proba", "sklearn/tests/test_multiclass.py::test_ovr_single_label_predict_proba", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_decision_function", "sklearn/tests/test_multiclass.py::test_ovr_single_label_decision_function", "sklearn/tests/test_multiclass.py::test_ovr_gridsearch", "sklearn/tests/test_multiclass.py::test_ovr_pipeline", "sklearn/tests/test_multiclass.py::test_pairwise_n_features_in", "sklearn/tests/test_multiclass.py::test_pairwise_cross_val_score[OneVsRestClassifier]", "sklearn/tests/test_multioutput.py::test_multiclass_multioutput_estimator", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit_score_takes_y]", "sklearn/tests/test_multiclass.py::test_support_missing_values[OneVsRestClassifier]", "sklearn/tests/test_multiclass.py::test_constant_int_target[ones]", "sklearn/tests/test_multiclass.py::test_constant_int_target[zeros]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_estimators_fit_returns_self]", "sklearn/tests/test_multioutput.py::test_classifier_chain_vs_independent_models", "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_dtype_object]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_classes]", "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/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train]", "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/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_regression_target]", "sklearn/linear_model/tests/test_sag.py::test_sag_multiclass_computed_correctly[csr_matrix]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_supervised_y_2d]", "sklearn/linear_model/tests/test_sag.py::test_sag_multiclass_computed_correctly[csr_array]", "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_metaestimators.py::test_meta_estimators_delegate_data_validation[OneVsRestClassifier]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_dont_overwrite_parameters]", "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/kernels.py::sklearn.gaussian_process.kernels.RationalQuadratic", "sklearn/multiclass.py::sklearn.multiclass.OneVsRestClassifier", "sklearn/gaussian_process/_gpc.py::sklearn.gaussian_process._gpc.GaussianProcessClassifier", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[OneVsRestClassifier]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "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_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_empty_data_messages]", "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_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "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_f_contiguous_array_estimator]", "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_one_label_sample_weights]", "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_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsRestClassifier(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_check_param_validation[OneVsRestClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-133	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 941586ebc..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -394,6 +394,11 @@ class OneVsRestClassifier(\n \n return self\n \n+ @available_if(_estimators_has(\"partial_fit\"))\n+ @_fit_context(\n+ # OneVsRestClassifier.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def partial_fit(self, X, y, classes=None, partial_fit_params):\n \"\"\"Partially fit underlying estimators.\n \n@@ -430,6 +435,49 @@ class OneVsRestClassifier(\n self : object\n Instance of partially fitted estimator.\n \"\"\"\n+ _raise_for_params(partial_fit_params, self, \"partial_fit\")\n+\n+ routed_params = process_routing(\n+ self,\n+ \"partial_fit\",\n+ partial_fit_params,\n+ )\n+\n+ if _check_partial_fit_first_call(self, classes):\n+ self.estimators_ = [clone(self.estimator) for _ in range(self.n_classes_)]\n+\n+ # A sparse LabelBinarizer, with sparse_output=True, has been\n+ # shown to outperform or match a dense label binarizer in all\n+ # cases and has also resulted in less or equal memory consumption\n+ # in the fit_ovr function overall.\n+ self.label_binarizer_ = LabelBinarizer(sparse_output=True)\n+ self.label_binarizer_.fit(self.classes_)\n+\n+ if len(np.setdiff1d(y, self.classes_)):\n+ raise ValueError(\n+ (\n+ \"Mini-batch contains {0} while classes \" + \"must be subset of {1}\"\n+ ).format(np.unique(y), self.classes_)\n+ )\n+\n+ Y = self.label_binarizer_.transform(y)\n+ Y = Y.tocsc()\n+ columns = (col.toarray().ravel() for col in Y.T)\n+\n+ self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n+ delayed(_partial_fit_binary)(\n+ estimator,\n+ X,\n+ column,\n+ partial_fit_params=routed_params.estimator.partial_fit,\n+ )\n+ for estimator, column in zip(self.estimators_, columns)\n+ )\n+\n+ if hasattr(self.estimators_[0], \"n_features_in_\"):\n+ self.n_features_in_ = self.estimators_[0].n_features_in_\n+\n+ return self\n \n def predict(self, X):\n \"\"\"Predict multi-class targets using underlying estimators.\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..941586ebc 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -394,11 +394,6 @@ class OneVsRestClassifier(\n \n return self\n \n- @available_if(_estimators_has(\"partial_fit\"))\n- @_fit_context(\n- # OneVsRestClassifier.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def partial_fit(self, X, y, classes=None, partial_fit_params):\n \"\"\"Partially fit underlying estimators.\n \n@@ -435,49 +430,6 @@ class OneVsRestClassifier(\n self : object\n Instance of partially fitted estimator.\n \"\"\"\n- _raise_for_params(partial_fit_params, self, \"partial_fit\")\n-\n- routed_params = process_routing(\n- self,\n- \"partial_fit\",\n- partial_fit_params,\n- )\n-\n- if _check_partial_fit_first_call(self, classes):\n- self.estimators_ = [clone(self.estimator) for _ in range(self.n_classes_)]\n-\n- # A sparse LabelBinarizer, with sparse_output=True, has been\n- # shown to outperform or match a dense label binarizer in all\n- # cases and has also resulted in less or equal memory consumption\n- # in the fit_ovr function overall.\n- self.label_binarizer_ = LabelBinarizer(sparse_output=True)\n- self.label_binarizer_.fit(self.classes_)\n-\n- if len(np.setdiff1d(y, self.classes_)):\n- raise ValueError(\n- (\n- \"Mini-batch contains {0} while classes \" + \"must be subset of {1}\"\n- ).format(np.unique(y), self.classes_)\n- )\n-\n- Y = self.label_binarizer_.transform(y)\n- Y = Y.tocsc()\n- columns = (col.toarray().ravel() for col in Y.T)\n-\n- self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n- delayed(_partial_fit_binary)(\n- estimator,\n- X,\n- column,\n- partial_fit_params=routed_params.estimator.partial_fit,\n- )\n- for estimator, column in zip(self.estimators_, columns)\n- )\n-\n- if hasattr(self.estimators_[0], \"n_features_in_\"):\n- self.n_features_in_ = self.estimators_[0].n_features_in_\n-\n- return self\n \n def predict(self, X):\n \"\"\"Predict multi-class targets using underlying estimators.\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/multiclass.py.\nHere is the description for the function:\n def partial_fit(self, X, y, classes=None, partial_fit_params):\n \"\"\"Partially fit underlying estimators.\n\n Should be used when memory is inefficient to train all data.\n Chunks of data can be passed in several iterations.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Data.\n\n y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)\n Multi-class targets. An indicator matrix turns on multilabel\n classification.\n\n classes : array, shape (n_classes, )\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 only required in the first call of partial_fit\n and can be omitted in the subsequent calls.\n\n partial_fit_params : dict\n Parameters passed to the ``estimator.partial_fit`` method of each\n sub-estimator.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n self : object\n Instance of partially fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[OneVsRestClassifier]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[OneVsRestClassifier]", "sklearn/tests/test_multiclass.py::test_ovr_partial_fit", "sklearn/tests/test_multiclass.py::test_ovr_partial_fit_exceptions", "sklearn/tests/test_multiclass.py::test_multiclass_estimator_attribute_error", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[OneVsRestClassifier]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_partial_fit_n_features]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsRestClassifier(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_check_param_validation[OneVsRestClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-134	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 97fc043fb..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -492,6 +492,30 @@ class OneVsRestClassifier(\n y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)\n Predicted multi-class targets.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ n_samples = _num_samples(X)\n+ if self.label_binarizer_.y_type_ == \"multiclass\":\n+ maxima = np.empty(n_samples, dtype=float)\n+ maxima.fill(-np.inf)\n+ argmaxima = np.zeros(n_samples, dtype=int)\n+ for i, e in enumerate(self.estimators_):\n+ pred = _predict_binary(e, X)\n+ np.maximum(maxima, pred, out=maxima)\n+ argmaxima[maxima == pred] = i\n+ return self.classes_[argmaxima]\n+ else:\n+ thresh = _threshold_for_binary_predict(self.estimators_[0])\n+ indices = array.array(\"i\")\n+ indptr = array.array(\"i\", [0])\n+ for e in self.estimators_:\n+ indices.extend(np.where(_predict_binary(e, X) > thresh)[0])\n+ indptr.append(len(indices))\n+ data = np.ones(len(indices), dtype=int)\n+ indicator = sp.csc_matrix(\n+ (data, indices, indptr), shape=(n_samples, len(self.estimators_))\n+ )\n+ return self.label_binarizer_.inverse_transform(indicator)\n \n @available_if(_estimators_has(\"predict_proba\"))\n def predict_proba(self, X):\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..97fc043fb 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -492,30 +492,6 @@ class OneVsRestClassifier(\n y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)\n Predicted multi-class targets.\n \"\"\"\n- check_is_fitted(self)\n-\n- n_samples = _num_samples(X)\n- if self.label_binarizer_.y_type_ == \"multiclass\":\n- maxima = np.empty(n_samples, dtype=float)\n- maxima.fill(-np.inf)\n- argmaxima = np.zeros(n_samples, dtype=int)\n- for i, e in enumerate(self.estimators_):\n- pred = _predict_binary(e, X)\n- np.maximum(maxima, pred, out=maxima)\n- argmaxima[maxima == pred] = i\n- return self.classes_[argmaxima]\n- else:\n- thresh = _threshold_for_binary_predict(self.estimators_[0])\n- indices = array.array(\"i\")\n- indptr = array.array(\"i\", [0])\n- for e in self.estimators_:\n- indices.extend(np.where(_predict_binary(e, X) > thresh)[0])\n- indptr.append(len(indices))\n- data = np.ones(len(indices), dtype=int)\n- indicator = sp.csc_matrix(\n- (data, indices, indptr), shape=(n_samples, len(self.estimators_))\n- )\n- return self.label_binarizer_.inverse_transform(indicator)\n \n @available_if(_estimators_has(\"predict_proba\"))\n def predict_proba(self, 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/multiclass.py.\nHere is the description for the function:\n def predict(self, X):\n \"\"\"Predict multi-class targets using underlying estimators.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Data.\n\n Returns\n -------\n y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)\n Predicted multi-class targets.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/svm/tests/test_svm.py::test_decision_function_shape_two_class", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction[csr_matrix]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction[csr_array]", "sklearn/tests/test_multiclass.py::test_ovr_exceptions", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict", "sklearn/tests/test_multiclass.py::test_ovr_partial_fit", "sklearn/tests/test_multiclass.py::test_ovr_ovo_regressor", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csr_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csr_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csc_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csc_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[coo_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[coo_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[dok_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[dok_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[lil_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[lil_array]", "sklearn/tests/test_multiclass.py::test_ovr_always_present", "sklearn/tests/test_multiclass.py::test_ovr_multiclass", "sklearn/tests/test_multiclass.py::test_ovr_binary", "sklearn/tests/test_multiclass.py::test_ovr_multilabel", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_svc", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_dataset", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_predict_proba", "sklearn/tests/test_multiclass.py::test_ovr_single_label_predict_proba", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_decision_function", "sklearn/tests/test_multiclass.py::test_ovr_single_label_decision_function", "sklearn/tests/test_multiclass.py::test_ovr_pipeline", "sklearn/tests/test_multiclass.py::test_pairwise_cross_val_score[OneVsRestClassifier]", "sklearn/tests/test_multiclass.py::test_support_missing_values[OneVsRestClassifier]", "sklearn/tests/test_multioutput.py::test_multiclass_multioutput_estimator", "sklearn/tests/test_multioutput.py::test_classifier_chain_vs_independent_models", "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/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_f_contiguous_array_estimator]", "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)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_supervised_y_2d]", "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/multiclass.py::sklearn.multiclass.OneVsRestClassifier", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.Matern", "sklearn/gaussian_process/_gpc.py::sklearn.gaussian_process._gpc.GaussianProcessClassifier", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.PairwiseKernel", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.RBF", "sklearn/gaussian_process/kernels.py::sklearn.gaussian_process.kernels.RationalQuadratic", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "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_dtype_object]", "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_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "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_f_contiguous_array_estimator]", "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_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsRestClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsRestClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-135	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 44f1a8e84..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -517,6 +517,7 @@ class OneVsRestClassifier(\n )\n return self.label_binarizer_.inverse_transform(indicator)\n \n+ @available_if(_estimators_has(\"predict_proba\"))\n def predict_proba(self, X):\n \"\"\"Probability estimates.\n \n@@ -541,6 +542,20 @@ class OneVsRestClassifier(\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+ # Y[i, j] gives the probability that sample i has the label j.\n+ # In the multi-label case, these are not disjoint.\n+ Y = np.array([e.predict_proba(X)[:, 1] for e in self.estimators_]).T\n+\n+ if len(self.estimators_) == 1:\n+ # Only one estimator, but we still want to return probabilities\n+ # for two classes.\n+ Y = np.concatenate(((1 - Y), Y), axis=1)\n+\n+ if not self.multilabel_:\n+ # Then, probabilities should be normalized to 1.\n+ Y /= np.sum(Y, axis=1)[:, np.newaxis]\n+ return Y\n \n @available_if(_estimators_has(\"decision_function\"))\n def decision_function(self, X):\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..44f1a8e84 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -517,7 +517,6 @@ class OneVsRestClassifier(\n )\n return self.label_binarizer_.inverse_transform(indicator)\n \n- @available_if(_estimators_has(\"predict_proba\"))\n def predict_proba(self, X):\n \"\"\"Probability estimates.\n \n@@ -542,20 +541,6 @@ class OneVsRestClassifier(\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- # Y[i, j] gives the probability that sample i has the label j.\n- # In the multi-label case, these are not disjoint.\n- Y = np.array([e.predict_proba(X)[:, 1] for e in self.estimators_]).T\n-\n- if len(self.estimators_) == 1:\n- # Only one estimator, but we still want to return probabilities\n- # for two classes.\n- Y = np.concatenate(((1 - Y), Y), axis=1)\n-\n- if not self.multilabel_:\n- # Then, probabilities should be normalized to 1.\n- Y /= np.sum(Y, axis=1)[:, np.newaxis]\n- return Y\n \n @available_if(_estimators_has(\"decision_function\"))\n def decision_function(self, 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/multiclass.py.\nHere is the description for the function:\n def predict_proba(self, X):\n \"\"\"Probability estimates.\n\n The returned estimates for all classes are ordered by label of classes.\n\n Note that in the multilabel case, each sample can have any number of\n labels. This returns the marginal probability that the given sample has\n the label in question. For example, it is entirely consistent that two\n labels both have a 90% probability of applying to a given sample.\n\n In the single label multiclass case, the rows of the returned matrix\n sum to 1.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Input data.\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/metrics/tests/test_score_objects.py::test_thresholded_scorers_multilabel_indicator_data", "sklearn/linear_model/tests/test_logistic.py::test_logreg_predict_proba_multinomial", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csr_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csr_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csc_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[csc_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[coo_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[coo_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[dok_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[dok_array]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[lil_matrix]", "sklearn/tests/test_multiclass.py::test_ovr_fit_predict_sparse[lil_array]", "sklearn/tests/test_multiclass.py::test_ovr_always_present", "sklearn/tests/test_multiclass.py::test_ovr_binary", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_with_method_multilabel_ovr", "sklearn/tests/test_multiclass.py::test_ovr_multilabel_predict_proba", "sklearn/tests/test_multiclass.py::test_ovr_single_label_predict_proba", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train]", "sklearn/tests/test_multiclass.py::test_constant_int_target[ones]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_multiclass.py::test_constant_int_target[zeros]", "sklearn/tests/test_common.py::test_estimators[GaussianProcessClassifier()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "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/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/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_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "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_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_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OneVsRestClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OneVsRestClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsRestClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-136	1.0	{ "code": "diff --git b/sklearn/linear_model/_omp.py a/sklearn/linear_model/_omp.py\nindex fe5b17e78..aad9d1184 100644\n--- b/sklearn/linear_model/_omp.py\n+++ a/sklearn/linear_model/_omp.py\n@@ -752,6 +752,7 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):\n self.fit_intercept = fit_intercept\n self.precompute = precompute\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -768,6 +769,51 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):\n self : object\n Returns an instance of self.\n \"\"\"\n+ X, y = validate_data(self, X, y, multi_output=True, y_numeric=True)\n+ n_features = X.shape[1]\n+\n+ X, y, X_offset, y_offset, X_scale, Gram, Xy = _pre_fit(\n+ X, y, None, self.precompute, self.fit_intercept, copy=True\n+ )\n+\n+ if y.ndim == 1:\n+ y = y[:, np.newaxis]\n+\n+ if self.n_nonzero_coefs is None and self.tol is None:\n+ # default for n_nonzero_coefs is 0.1 * n_features\n+ # but at least one.\n+ self.n_nonzero_coefs_ = max(int(0.1 * n_features), 1)\n+ elif self.tol is not None:\n+ self.n_nonzero_coefs_ = None\n+ else:\n+ self.n_nonzero_coefs_ = self.n_nonzero_coefs\n+\n+ if Gram is False:\n+ coef_, self.n_iter_ = orthogonal_mp(\n+ X,\n+ y,\n+ n_nonzero_coefs=self.n_nonzero_coefs_,\n+ tol=self.tol,\n+ precompute=False,\n+ copy_X=True,\n+ return_n_iter=True,\n+ )\n+ else:\n+ norms_sq = np.sum(y**2, axis=0) if self.tol is not None else None\n+\n+ coef_, self.n_iter_ = orthogonal_mp_gram(\n+ Gram,\n+ Xy=Xy,\n+ n_nonzero_coefs=self.n_nonzero_coefs_,\n+ tol=self.tol,\n+ norms_squared=norms_sq,\n+ copy_Gram=True,\n+ copy_Xy=True,\n+ return_n_iter=True,\n+ )\n+ self.coef_ = coef_.T\n+ self._set_intercept(X_offset, y_offset, X_scale)\n+ return self\n \n \n def _omp_path_residues(\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py\nindex aad9d1184..fe5b17e78 100644\n--- a/sklearn/linear_model/_omp.py\n+++ b/sklearn/linear_model/_omp.py\n@@ -752,7 +752,6 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):\n self.fit_intercept = fit_intercept\n self.precompute = precompute\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -769,51 +768,6 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):\n self : object\n Returns an instance of self.\n \"\"\"\n- X, y = validate_data(self, X, y, multi_output=True, y_numeric=True)\n- n_features = X.shape[1]\n-\n- X, y, X_offset, y_offset, X_scale, Gram, Xy = _pre_fit(\n- X, y, None, self.precompute, self.fit_intercept, copy=True\n- )\n-\n- if y.ndim == 1:\n- y = y[:, np.newaxis]\n-\n- if self.n_nonzero_coefs is None and self.tol is None:\n- # default for n_nonzero_coefs is 0.1 * n_features\n- # but at least one.\n- self.n_nonzero_coefs_ = max(int(0.1 * n_features), 1)\n- elif self.tol is not None:\n- self.n_nonzero_coefs_ = None\n- else:\n- self.n_nonzero_coefs_ = self.n_nonzero_coefs\n-\n- if Gram is False:\n- coef_, self.n_iter_ = orthogonal_mp(\n- X,\n- y,\n- n_nonzero_coefs=self.n_nonzero_coefs_,\n- tol=self.tol,\n- precompute=False,\n- copy_X=True,\n- return_n_iter=True,\n- )\n- else:\n- norms_sq = np.sum(y**2, axis=0) if self.tol is not None else None\n-\n- coef_, self.n_iter_ = orthogonal_mp_gram(\n- Gram,\n- Xy=Xy,\n- n_nonzero_coefs=self.n_nonzero_coefs_,\n- tol=self.tol,\n- norms_squared=norms_sq,\n- copy_Gram=True,\n- copy_Xy=True,\n- return_n_iter=True,\n- )\n- self.coef_ = coef_.T\n- self._set_intercept(X_offset, y_offset, X_scale)\n- return self\n \n \n def _omp_path_residues(\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/_omp.py.\nHere is the description for the function:\n def fit(self, X, y):\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,) or (n_samples, n_targets)\n Target values. Will be cast to X's dtype if necessary.\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_common.py::test_balance_property[42-False-OrthogonalMatchingPursuit]", "sklearn/linear_model/tests/test_common.py::test_balance_property[42-False-OrthogonalMatchingPursuitCV]", "sklearn/linear_model/tests/test_omp.py::test_estimator", "sklearn/linear_model/tests/test_omp.py::test_estimator_n_nonzero_coefs", "sklearn/linear_model/tests/test_omp.py::test_omp_cv", "sklearn/linear_model/tests/test_omp.py::test_omp_reaches_least_squares", "sklearn/linear_model/_omp.py::sklearn.linear_model._omp.OrthogonalMatchingPursuit", "sklearn/linear_model/_omp.py::sklearn.linear_model._omp.OrthogonalMatchingPursuitCV", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[OrthogonalMatchingPursuitCV]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_regressor_multioutput]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuit()-check_requires_y_none]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OrthogonalMatchingPursuit()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OrthogonalMatchingPursuitCV(cv=3)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OrthogonalMatchingPursuit()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OrthogonalMatchingPursuitCV(cv=3)]", "sklearn/tests/test_common.py::test_check_param_validation[OrthogonalMatchingPursuit()]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[OrthogonalMatchingPursuitCV(cv=3)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-137	1.0	{ "code": "diff --git b/sklearn/linear_model/_omp.py a/sklearn/linear_model/_omp.py\nindex 504f902cc..aad9d1184 100644\n--- b/sklearn/linear_model/_omp.py\n+++ a/sklearn/linear_model/_omp.py\n@@ -1027,6 +1027,7 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):\n self.n_jobs = n_jobs\n self.verbose = verbose\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, fit_params):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -1053,6 +1054,50 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):\n self : object\n Returns an instance of self.\n \"\"\"\n+ _raise_for_params(fit_params, self, \"fit\")\n+\n+ X, y = validate_data(self, X, y, y_numeric=True, ensure_min_features=2)\n+ X = as_float_array(X, copy=False, ensure_all_finite=False)\n+ cv = check_cv(self.cv, classifier=False)\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+ routed_params.splitter = Bunch(split={})\n+ max_iter = (\n+ min(max(int(0.1 * X.shape[1]), 5), X.shape[1])\n+ if not self.max_iter\n+ else self.max_iter\n+ )\n+ cv_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(\n+ delayed(_omp_path_residues)(\n+ X[train],\n+ y[train],\n+ X[test],\n+ y[test],\n+ self.copy,\n+ self.fit_intercept,\n+ max_iter,\n+ )\n+ for train, test in cv.split(X, routed_params.splitter.split)\n+ )\n+\n+ min_early_stop = min(fold.shape[0] for fold in cv_paths)\n+ mse_folds = np.array(\n+ [(fold[:min_early_stop] 2).mean(axis=1) for fold in cv_paths]\n+ )\n+ best_n_nonzero_coefs = np.argmin(mse_folds.mean(axis=0)) + 1\n+ self.n_nonzero_coefs_ = best_n_nonzero_coefs\n+ omp = OrthogonalMatchingPursuit(\n+ n_nonzero_coefs=best_n_nonzero_coefs,\n+ fit_intercept=self.fit_intercept,\n+ ).fit(X, y)\n+\n+ self.coef_ = omp.coef_\n+ self.intercept_ = omp.intercept_\n+ self.n_iter_ = omp.n_iter_\n+ return self\n \n def get_metadata_routing(self):\n \"\"\"Get metadata routing of this object.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py\nindex aad9d1184..504f902cc 100644\n--- a/sklearn/linear_model/_omp.py\n+++ b/sklearn/linear_model/_omp.py\n@@ -1027,7 +1027,6 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):\n self.n_jobs = n_jobs\n self.verbose = verbose\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, fit_params):\n \"\"\"Fit the model using X, y as training data.\n \n@@ -1054,50 +1053,6 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel):\n self : object\n Returns an instance of self.\n \"\"\"\n- _raise_for_params(fit_params, self, \"fit\")\n-\n- X, y = validate_data(self, X, y, y_numeric=True, ensure_min_features=2)\n- X = as_float_array(X, copy=False, ensure_all_finite=False)\n- cv = check_cv(self.cv, classifier=False)\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- routed_params.splitter = Bunch(split={})\n- max_iter = (\n- min(max(int(0.1 * X.shape[1]), 5), X.shape[1])\n- if not self.max_iter\n- else self.max_iter\n- )\n- cv_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(\n- delayed(_omp_path_residues)(\n- X[train],\n- y[train],\n- X[test],\n- y[test],\n- self.copy,\n- self.fit_intercept,\n- max_iter,\n- )\n- for train, test in cv.split(X, routed_params.splitter.split)\n- )\n-\n- min_early_stop = min(fold.shape[0] for fold in cv_paths)\n- mse_folds = np.array(\n- [(fold[:min_early_stop] 2).mean(axis=1) for fold in cv_paths]\n- )\n- best_n_nonzero_coefs = np.argmin(mse_folds.mean(axis=0)) + 1\n- self.n_nonzero_coefs_ = best_n_nonzero_coefs\n- omp = OrthogonalMatchingPursuit(\n- n_nonzero_coefs=best_n_nonzero_coefs,\n- fit_intercept=self.fit_intercept,\n- ).fit(X, y)\n-\n- self.coef_ = omp.coef_\n- self.intercept_ = omp.intercept_\n- self.n_iter_ = omp.n_iter_\n- return self\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/linear_model/_omp.py.\nHere is the description for the function:\n def fit(self, X, y, fit_params):\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 fit_params : dict\n Parameters to pass to the underlying splitter.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\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_common.py::test_balance_property[42-False-OrthogonalMatchingPursuitCV]", "sklearn/linear_model/tests/test_omp.py::test_omp_cv", "sklearn/linear_model/_omp.py::sklearn.linear_model._omp.OrthogonalMatchingPursuitCV", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[OrthogonalMatchingPursuitCV]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OrthogonalMatchingPursuitCV(cv=3)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OrthogonalMatchingPursuitCV(cv=3)]", "sklearn/tests/test_common.py::test_check_param_validation[OrthogonalMatchingPursuitCV(cv=3)]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[OrthogonalMatchingPursuitCV(cv=3)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-138	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex 626abcd05..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -1150,6 +1150,10 @@ class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self.random_state = random_state\n self.n_jobs = n_jobs\n \n+ @_fit_context(\n+ # OutputCodeClassifier.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n \n@@ -1175,6 +1179,57 @@ class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self : object\n Returns a fitted instance of self.\n \"\"\"\n+ _raise_for_params(fit_params, self, \"fit\")\n+\n+ routed_params = process_routing(\n+ self,\n+ \"fit\",\n+ fit_params,\n+ )\n+\n+ y = validate_data(self, X=\"no_validation\", y=y)\n+\n+ random_state = check_random_state(self.random_state)\n+ check_classification_targets(y)\n+\n+ self.classes_ = np.unique(y)\n+ n_classes = self.classes_.shape[0]\n+ if n_classes == 0:\n+ raise ValueError(\n+ \"OutputCodeClassifier can not be fit when no class is present.\"\n+ )\n+ n_estimators = int(n_classes * self.code_size)\n+\n+ # FIXME: there are more elaborate methods than generating the codebook\n+ # randomly.\n+ self.code_book_ = random_state.uniform(size=(n_classes, n_estimators))\n+ self.code_book_[self.code_book_ > 0.5] = 1.0\n+\n+ if hasattr(self.estimator, \"decision_function\"):\n+ self.code_book_[self.code_book_ != 1] = -1.0\n+ else:\n+ self.code_book_[self.code_book_ != 1] = 0.0\n+\n+ classes_index = {c: i for i, c in enumerate(self.classes_)}\n+\n+ Y = np.array(\n+ [self.code_book_[classes_index[y[i]]] for i in range(_num_samples(y))],\n+ dtype=int,\n+ )\n+\n+ self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n+ delayed(_fit_binary)(\n+ self.estimator, X, Y[:, i], fit_params=routed_params.estimator.fit\n+ )\n+ for i in range(Y.shape[1])\n+ )\n+\n+ if hasattr(self.estimators_[0], \"n_features_in_\"):\n+ self.n_features_in_ = self.estimators_[0].n_features_in_\n+ if hasattr(self.estimators_[0], \"feature_names_in_\"):\n+ self.feature_names_in_ = self.estimators_[0].feature_names_in_\n+\n+ return self\n \n def predict(self, X):\n \"\"\"Predict multi-class targets using underlying estimators.\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..626abcd05 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -1150,10 +1150,6 @@ class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self.random_state = random_state\n self.n_jobs = n_jobs\n \n- @_fit_context(\n- # OutputCodeClassifier.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n \n@@ -1179,57 +1175,6 @@ class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n self : object\n Returns a fitted instance of self.\n \"\"\"\n- _raise_for_params(fit_params, self, \"fit\")\n-\n- routed_params = process_routing(\n- self,\n- \"fit\",\n- fit_params,\n- )\n-\n- y = validate_data(self, X=\"no_validation\", y=y)\n-\n- random_state = check_random_state(self.random_state)\n- check_classification_targets(y)\n-\n- self.classes_ = np.unique(y)\n- n_classes = self.classes_.shape[0]\n- if n_classes == 0:\n- raise ValueError(\n- \"OutputCodeClassifier can not be fit when no class is present.\"\n- )\n- n_estimators = int(n_classes * self.code_size)\n-\n- # FIXME: there are more elaborate methods than generating the codebook\n- # randomly.\n- self.code_book_ = random_state.uniform(size=(n_classes, n_estimators))\n- self.code_book_[self.code_book_ > 0.5] = 1.0\n-\n- if hasattr(self.estimator, \"decision_function\"):\n- self.code_book_[self.code_book_ != 1] = -1.0\n- else:\n- self.code_book_[self.code_book_ != 1] = 0.0\n-\n- classes_index = {c: i for i, c in enumerate(self.classes_)}\n-\n- Y = np.array(\n- [self.code_book_[classes_index[y[i]]] for i in range(_num_samples(y))],\n- dtype=int,\n- )\n-\n- self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n- delayed(_fit_binary)(\n- self.estimator, X, Y[:, i], fit_params=routed_params.estimator.fit\n- )\n- for i in range(Y.shape[1])\n- )\n-\n- if hasattr(self.estimators_[0], \"n_features_in_\"):\n- self.n_features_in_ = self.estimators_[0].n_features_in_\n- if hasattr(self.estimators_[0], \"feature_names_in_\"):\n- self.feature_names_in_ = self.estimators_[0].feature_names_in_\n-\n- return self\n \n def predict(self, X):\n \"\"\"Predict multi-class targets using underlying estimators.\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/multiclass.py.\nHere is the description for the function:\n def fit(self, X, y, fit_params):\n \"\"\"Fit underlying estimators.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Data.\n\n y : array-like of shape (n_samples,)\n Multi-class targets.\n\n fit_params : dict\n Parameters passed to the ``estimator.fit`` method of each\n sub-estimator.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\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_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[OutputCodeClassifier]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[OutputCodeClassifier]", "sklearn/tests/test_multiclass.py::test_ecoc_fit_predict", "sklearn/tests/test_multiclass.py::test_ecoc_gridsearch", "sklearn/tests/test_multiclass.py::test_ecoc_float_y", "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/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[OutputCodeClassifier]", "sklearn/multiclass.py::sklearn.multiclass.OutputCodeClassifier", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[OutputCodeClassifier]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OutputCodeClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OutputCodeClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_param_validation[OutputCodeClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-139	1.0	{ "code": "diff --git b/sklearn/multiclass.py a/sklearn/multiclass.py\nindex ddea001b3..dca055ecb 100644\n--- b/sklearn/multiclass.py\n+++ a/sklearn/multiclass.py\n@@ -1244,6 +1244,17 @@ class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n y : ndarray of shape (n_samples,)\n Predicted multi-class targets.\n \"\"\"\n+ check_is_fitted(self)\n+ # ArgKmin only accepts C-contiguous array. The aggregated predictions need to be\n+ # transposed. We therefore create a F-contiguous array to avoid a copy and have\n+ # a C-contiguous array after the transpose operation.\n+ Y = np.array(\n+ [_predict_binary(e, X) for e in self.estimators_],\n+ order=\"F\",\n+ dtype=np.float64,\n+ ).T\n+ pred = pairwise_distances_argmin(Y, self.code_book_, metric=\"euclidean\")\n+ return self.classes_[pred]\n \n def get_metadata_routing(self):\n \"\"\"Get metadata routing of this object.\n", "test": null }	null	{ "code": "diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py\nindex dca055ecb..ddea001b3 100644\n--- a/sklearn/multiclass.py\n+++ b/sklearn/multiclass.py\n@@ -1244,17 +1244,6 @@ class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n y : ndarray of shape (n_samples,)\n Predicted multi-class targets.\n \"\"\"\n- check_is_fitted(self)\n- # ArgKmin only accepts C-contiguous array. The aggregated predictions need to be\n- # transposed. We therefore create a F-contiguous array to avoid a copy and have\n- # a C-contiguous array after the transpose operation.\n- Y = np.array(\n- [_predict_binary(e, X) for e in self.estimators_],\n- order=\"F\",\n- dtype=np.float64,\n- ).T\n- pred = pairwise_distances_argmin(Y, self.code_book_, metric=\"euclidean\")\n- return self.classes_[pred]\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/multiclass.py.\nHere is the description for the function:\n def predict(self, X):\n \"\"\"Predict multi-class targets using underlying estimators.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Data.\n\n Returns\n -------\n y : ndarray of shape (n_samples,)\n Predicted multi-class targets.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_multiclass.py::test_ecoc_exceptions", "sklearn/tests/test_multiclass.py::test_ecoc_fit_predict", "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/multiclass.py::sklearn.multiclass.OutputCodeClassifier", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[OutputCodeClassifier(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[OutputCodeClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OutputCodeClassifier(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-140	1.0	{ "code": "diff --git b/sklearn/decomposition/_pca.py a/sklearn/decomposition/_pca.py\nindex bd1e58b53..24cb1649c 100644\n--- b/sklearn/decomposition/_pca.py\n+++ a/sklearn/decomposition/_pca.py\n@@ -442,6 +442,7 @@ class PCA(_BasePCA):\n self._fit(X)\n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None):\n \"\"\"Fit the model with X and apply the dimensionality reduction on X.\n \n@@ -464,6 +465,20 @@ class PCA(_BasePCA):\n This method returns a Fortran-ordered array. To convert it to a\n C-ordered array, use 'np.ascontiguousarray'.\n \"\"\"\n+ U, S, _, X, x_is_centered, xp = self._fit(X)\n+ if U is not None:\n+ U = U[:, : self.n_components_]\n+\n+ if self.whiten:\n+ # X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\n+ U = sqrt(X.shape[0] - 1)\n+ else:\n+ # X_new = X V = U * S * Vt * V = U * S\n+ U *= S[: self.n_components_]\n+\n+ return U\n+ else: # solver=\"covariance_eigh\" does not compute U at fit time.\n+ return self._transform(X, xp, x_is_centered=x_is_centered)\n \n def _fit(self, X):\n \"\"\"Dispatch to the right submethod depending on the chosen solver.\"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_pca.py b/sklearn/decomposition/_pca.py\nindex 24cb1649c..bd1e58b53 100644\n--- a/sklearn/decomposition/_pca.py\n+++ b/sklearn/decomposition/_pca.py\n@@ -442,7 +442,6 @@ class PCA(_BasePCA):\n self._fit(X)\n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None):\n \"\"\"Fit the model with X and apply the dimensionality reduction on X.\n \n@@ -465,20 +464,6 @@ class PCA(_BasePCA):\n This method returns a Fortran-ordered array. To convert it to a\n C-ordered array, use 'np.ascontiguousarray'.\n \"\"\"\n- U, S, _, X, x_is_centered, xp = self._fit(X)\n- if U is not None:\n- U = U[:, : self.n_components_]\n-\n- if self.whiten:\n- # X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples)\n- U = sqrt(X.shape[0] - 1)\n- else:\n- # X_new = X V = U * S * Vt * V = U * S\n- U *= S[: self.n_components_]\n-\n- return U\n- else: # solver=\"covariance_eigh\" does not compute U at fit time.\n- return self._transform(X, xp, x_is_centered=x_is_centered)\n \n def _fit(self, X):\n \"\"\"Dispatch to the right submethod depending on the chosen solver.\"\"\"\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/_pca.py.\nHere is the description for the function:\n def fit_transform(self, X, y=None):\n \"\"\"Fit the model with X and apply the dimensionality reduction on X.\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\n and `n_features` is the number of features.\n\n y : Ignored\n Ignored.\n\n Returns\n -------\n X_new : ndarray of shape (n_samples, n_components)\n Transformed values.\n\n Notes\n -----\n This method returns a Fortran-ordered array. To convert it to a\n C-ordered array, use 'np.ascontiguousarray'.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_pca.py::test_pca[1-full]", "sklearn/decomposition/tests/test_pca.py::test_pca[1-covariance_eigh]", "sklearn/decomposition/tests/test_pca.py::test_pca[1-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca[1-randomized]", "sklearn/decomposition/tests/test_pca.py::test_pca[1-auto]", "sklearn/decomposition/tests/test_pca.py::test_pca[2-full]", "sklearn/decomposition/tests/test_pca.py::test_pca[2-covariance_eigh]", "sklearn/decomposition/tests/test_pca.py::test_pca[2-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca[2-randomized]", "sklearn/decomposition/tests/test_pca.py::test_pca[2-auto]", "sklearn/decomposition/tests/test_pca.py::test_pca[3-full]", "sklearn/decomposition/tests/test_pca.py::test_pca[3-covariance_eigh]", "sklearn/decomposition/tests/test_pca.py::test_pca[3-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca[3-randomized]", "sklearn/decomposition/tests/test_pca.py::test_pca[3-auto]", "sklearn/decomposition/tests/test_pca.py::test_pca_sparse_fit_transform[42-csr_matrix]", "sklearn/decomposition/tests/test_pca.py::test_pca_sparse_fit_transform[42-csr_array]", "sklearn/decomposition/tests/test_pca.py::test_pca_sparse_fit_transform[42-csc_matrix]", "sklearn/decomposition/tests/test_pca.py::test_pca_sparse_fit_transform[42-csc_array]", "sklearn/decomposition/tests/test_pca.py::test_whitening[full-True]", "sklearn/decomposition/tests/test_pca.py::test_whitening[full-False]", "sklearn/decomposition/tests/test_pca.py::test_whitening[covariance_eigh-True]", "sklearn/decomposition/tests/test_pca.py::test_whitening[covariance_eigh-False]", "sklearn/decomposition/tests/test_pca.py::test_whitening[arpack-True]", "sklearn/decomposition/tests/test_pca.py::test_whitening[arpack-False]", "sklearn/decomposition/tests/test_pca.py::test_whitening[randomized-True]", "sklearn/decomposition/tests/test_pca.py::test_whitening[randomized-False]", "sklearn/decomposition/tests/test_pca.py::test_whitening[auto-True]", "sklearn/decomposition/tests/test_pca.py::test_whitening[auto-False]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-False-tall-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-False-tall-covariance_eigh]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-False-tall-randomized]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-False-wide-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-False-wide-covariance_eigh]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-False-wide-randomized]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-True-tall-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-True-tall-covariance_eigh]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-True-tall-randomized]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-True-wide-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-True-wide-covariance_eigh]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-False-True-wide-randomized]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-True-False-tall-arpack]", "sklearn/decomposition/tests/test_pca.py::test_pca_solver_equivalence[float64-42-True-False-tall-covariance_eigh]", 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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_methods_subset_invariance]", "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_fit2d_1feature]", "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_dont_overwrite_parameters]", "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[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_check_is_fitted]", "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_n_features_in]", "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_fit1d]", "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_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_pickle(readonly_memmap=True)]", 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"sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)]", 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"sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[PCA()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_check_param_validation[PCA()]", "sklearn/tests/test_common.py::test_set_output_transform[PCA()]", "sklearn/tests/test_common.py::test_set_output_transform[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-PCA()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-PCA()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-TSNE(perplexity=2)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-141	1.0	{ "code": "diff --git b/sklearn/linear_model/_passive_aggressive.py a/sklearn/linear_model/_passive_aggressive.py\nindex efff60ade..61eb06eda 100644\n--- b/sklearn/linear_model/_passive_aggressive.py\n+++ a/sklearn/linear_model/_passive_aggressive.py\n@@ -273,6 +273,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n intercept_init=None,\n )\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -295,6 +296,19 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n self : object\n Fitted estimator.\n \"\"\"\n+ self._more_validate_params()\n+\n+ lr = \"pa1\" if self.loss == \"hinge\" else \"pa2\"\n+ return self._fit(\n+ X,\n+ y,\n+ alpha=1.0,\n+ C=self.C,\n+ loss=\"hinge\",\n+ learning_rate=lr,\n+ coef_init=coef_init,\n+ intercept_init=intercept_init,\n+ )\n \n \n class PassiveAggressiveRegressor(BaseSGDRegressor):\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py\nindex 61eb06eda..efff60ade 100644\n--- a/sklearn/linear_model/_passive_aggressive.py\n+++ b/sklearn/linear_model/_passive_aggressive.py\n@@ -273,7 +273,6 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n intercept_init=None,\n )\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -296,19 +295,6 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n self : object\n Fitted estimator.\n \"\"\"\n- self._more_validate_params()\n-\n- lr = \"pa1\" if self.loss == \"hinge\" else \"pa2\"\n- return self._fit(\n- X,\n- y,\n- alpha=1.0,\n- C=self.C,\n- loss=\"hinge\",\n- learning_rate=lr,\n- coef_init=coef_init,\n- intercept_init=intercept_init,\n- )\n \n \n class PassiveAggressiveRegressor(BaseSGDRegressor):\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/_passive_aggressive.py.\nHere is the description for the function:\n def fit(self, X, y, coef_init=None, intercept_init=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\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,)\n Target values.\n\n coef_init : ndarray of shape (n_classes, n_features)\n The initial coefficients to warm-start the optimization.\n\n intercept_init : ndarray of shape (n_classes,)\n The initial intercept to warm-start the optimization.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[None-True-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[None-True-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[None-False-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[None-False-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_matrix-True-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_matrix-True-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_matrix-False-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_matrix-False-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_array-True-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_array-True-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_array-False-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_accuracy[csr_array-False-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_refit", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_correctness[hinge-None]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_correctness[hinge-csr_matrix]", "sklearn/model_selection/tests/test_validation.py::test_learning_curve_batch_and_incremental_learning_are_equal", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_correctness[hinge-csr_array]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_correctness[squared_hinge-None]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_correctness[squared_hinge-csr_matrix]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_correctness[squared_hinge-csr_array]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_class_weights", "sklearn/linear_model/tests/test_passive_aggressive.py::test_equal_class_weight", "sklearn/linear_model/tests/test_passive_aggressive.py::test_wrong_class_weight_label", "sklearn/model_selection/tests/test_validation.py::test_learning_curve_with_shuffle", "sklearn/linear_model/tests/test_passive_aggressive.py::test_passive_aggressive_deprecated_average[PassiveAggressiveClassifier]", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit", "sklearn/linear_model/_passive_aggressive.py::sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_sparsify_coefficients]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_class_weight_classifiers]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_class_weight_balanced_linear_classifier0]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_class_weight_balanced_linear_classifier1]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PassiveAggressiveClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveClassifier(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_check_param_validation[PassiveAggressiveClassifier(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-142	1.0	{ "code": "diff --git b/sklearn/linear_model/_passive_aggressive.py a/sklearn/linear_model/_passive_aggressive.py\nindex 4cf22f770..61eb06eda 100644\n--- b/sklearn/linear_model/_passive_aggressive.py\n+++ a/sklearn/linear_model/_passive_aggressive.py\n@@ -217,6 +217,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n self.C = C\n self.loss = loss\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y, classes=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -241,6 +242,36 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n self : object\n Fitted estimator.\n \"\"\"\n+ if not hasattr(self, \"classes_\"):\n+ self._more_validate_params(for_partial_fit=True)\n+\n+ if self.class_weight == \"balanced\":\n+ raise ValueError(\n+ \"class_weight 'balanced' is not supported for \"\n+ \"partial_fit. For 'balanced' weights, use \"\n+ \"`sklearn.utils.compute_class_weight` with \"\n+ \"`class_weight='balanced'`. In place of y you \"\n+ \"can use a large enough subset of the full \"\n+ \"training set target to properly estimate the \"\n+ \"class frequency distributions. Pass the \"\n+ \"resulting weights as the class_weight \"\n+ \"parameter.\"\n+ )\n+\n+ lr = \"pa1\" if self.loss == \"hinge\" else \"pa2\"\n+ return self._partial_fit(\n+ X,\n+ y,\n+ alpha=1.0,\n+ C=self.C,\n+ loss=\"hinge\",\n+ learning_rate=lr,\n+ max_iter=1,\n+ classes=classes,\n+ sample_weight=None,\n+ coef_init=None,\n+ intercept_init=None,\n+ )\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py\nindex 61eb06eda..4cf22f770 100644\n--- a/sklearn/linear_model/_passive_aggressive.py\n+++ b/sklearn/linear_model/_passive_aggressive.py\n@@ -217,7 +217,6 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n self.C = C\n self.loss = loss\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y, classes=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -242,36 +241,6 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):\n self : object\n Fitted estimator.\n \"\"\"\n- if not hasattr(self, \"classes_\"):\n- self._more_validate_params(for_partial_fit=True)\n-\n- if self.class_weight == \"balanced\":\n- raise ValueError(\n- \"class_weight 'balanced' is not supported for \"\n- \"partial_fit. For 'balanced' weights, use \"\n- \"`sklearn.utils.compute_class_weight` with \"\n- \"`class_weight='balanced'`. In place of y you \"\n- \"can use a large enough subset of the full \"\n- \"training set target to properly estimate the \"\n- \"class frequency distributions. Pass the \"\n- \"resulting weights as the class_weight \"\n- \"parameter.\"\n- )\n-\n- lr = \"pa1\" if self.loss == \"hinge\" else \"pa2\"\n- return self._partial_fit(\n- X,\n- y,\n- alpha=1.0,\n- C=self.C,\n- loss=\"hinge\",\n- learning_rate=lr,\n- max_iter=1,\n- classes=classes,\n- sample_weight=None,\n- coef_init=None,\n- intercept_init=None,\n- )\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=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/linear_model/_passive_aggressive.py.\nHere is the description for the function:\n def partial_fit(self, X, y, classes=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Subset of the training data.\n\n y : array-like of shape (n_samples,)\n Subset of the target values.\n\n classes : ndarray of shape (n_classes,)\n Classes across all calls to partial_fit.\n Can be obtained by 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 Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_validation.py::test_learning_curve_batch_and_incremental_learning_are_equal", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_partial_fit[None-False]", "sklearn/model_selection/tests/test_validation.py::test_learning_curve_with_shuffle", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_partial_fit[None-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_partial_fit[csr_matrix-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_partial_fit[csr_matrix-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_partial_fit[csr_array-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_classifier_partial_fit[csr_array-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_partial_fit_weight_class_balanced", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveClassifier(max_iter=5)-check_estimators_partial_fit_n_features]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PassiveAggressiveClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[PassiveAggressiveClassifier(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-143	1.0	{ "code": "diff --git b/sklearn/linear_model/_passive_aggressive.py a/sklearn/linear_model/_passive_aggressive.py\nindex c7be1df8c..61eb06eda 100644\n--- b/sklearn/linear_model/_passive_aggressive.py\n+++ a/sklearn/linear_model/_passive_aggressive.py\n@@ -535,6 +535,7 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n intercept_init=None,\n )\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -557,3 +558,16 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n self : object\n Fitted estimator.\n \"\"\"\n+ self._more_validate_params()\n+\n+ lr = \"pa1\" if self.loss == \"epsilon_insensitive\" else \"pa2\"\n+ return self._fit(\n+ X,\n+ y,\n+ alpha=1.0,\n+ C=self.C,\n+ loss=\"epsilon_insensitive\",\n+ learning_rate=lr,\n+ coef_init=coef_init,\n+ intercept_init=intercept_init,\n+ )\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py\nindex 61eb06eda..c7be1df8c 100644\n--- a/sklearn/linear_model/_passive_aggressive.py\n+++ b/sklearn/linear_model/_passive_aggressive.py\n@@ -535,7 +535,6 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n intercept_init=None,\n )\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -558,16 +557,3 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n self : object\n Fitted estimator.\n \"\"\"\n- self._more_validate_params()\n-\n- lr = \"pa1\" if self.loss == \"epsilon_insensitive\" else \"pa2\"\n- return self._fit(\n- X,\n- y,\n- alpha=1.0,\n- C=self.C,\n- loss=\"epsilon_insensitive\",\n- learning_rate=lr,\n- coef_init=coef_init,\n- intercept_init=intercept_init,\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/_passive_aggressive.py.\nHere is the description for the function:\n def fit(self, X, y, coef_init=None, intercept_init=None):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n\n y : numpy array of shape [n_samples]\n Target values.\n\n coef_init : array, shape = [n_features]\n The initial coefficients to warm-start the optimization.\n\n intercept_init : array, shape = [1]\n The initial intercept to warm-start the optimization.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[None-True-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[None-True-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[None-False-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[None-False-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_matrix-True-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_matrix-True-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_matrix-False-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_matrix-False-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_array-True-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_array-True-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_array-False-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_mse[csr_array-False-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_correctness[epsilon_insensitive-None]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_correctness[epsilon_insensitive-csr_matrix]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_correctness[epsilon_insensitive-csr_array]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_correctness[squared_epsilon_insensitive-None]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_correctness[squared_epsilon_insensitive-csr_matrix]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_correctness[squared_epsilon_insensitive-csr_array]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_passive_aggressive_deprecated_average[PassiveAggressiveRegressor]", "sklearn/linear_model/_passive_aggressive.py::sklearn.linear_model._passive_aggressive.PassiveAggressiveRegressor", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_sparsify_coefficients]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PassiveAggressiveRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveRegressor(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_check_param_validation[PassiveAggressiveRegressor(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-144	1.0	{ "code": "diff --git b/sklearn/linear_model/_passive_aggressive.py a/sklearn/linear_model/_passive_aggressive.py\nindex 9f5a80d0f..61eb06eda 100644\n--- b/sklearn/linear_model/_passive_aggressive.py\n+++ a/sklearn/linear_model/_passive_aggressive.py\n@@ -501,6 +501,7 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n self.C = C\n self.loss = loss\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -517,6 +518,22 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n self : object\n Fitted estimator.\n \"\"\"\n+ if not hasattr(self, \"coef_\"):\n+ self._more_validate_params(for_partial_fit=True)\n+\n+ lr = \"pa1\" if self.loss == \"epsilon_insensitive\" else \"pa2\"\n+ return self._partial_fit(\n+ X,\n+ y,\n+ alpha=1.0,\n+ C=self.C,\n+ loss=\"epsilon_insensitive\",\n+ learning_rate=lr,\n+ max_iter=1,\n+ sample_weight=None,\n+ coef_init=None,\n+ intercept_init=None,\n+ )\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py\nindex 61eb06eda..9f5a80d0f 100644\n--- a/sklearn/linear_model/_passive_aggressive.py\n+++ b/sklearn/linear_model/_passive_aggressive.py\n@@ -501,7 +501,6 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n self.C = C\n self.loss = loss\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def partial_fit(self, X, y):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n \n@@ -518,22 +517,6 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):\n self : object\n Fitted estimator.\n \"\"\"\n- if not hasattr(self, \"coef_\"):\n- self._more_validate_params(for_partial_fit=True)\n-\n- lr = \"pa1\" if self.loss == \"epsilon_insensitive\" else \"pa2\"\n- return self._partial_fit(\n- X,\n- y,\n- alpha=1.0,\n- C=self.C,\n- loss=\"epsilon_insensitive\",\n- learning_rate=lr,\n- max_iter=1,\n- sample_weight=None,\n- coef_init=None,\n- intercept_init=None,\n- )\n \n @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, coef_init=None, intercept_init=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/linear_model/_passive_aggressive.py.\nHere is the description for the function:\n def partial_fit(self, X, y):\n \"\"\"Fit linear model with Passive Aggressive algorithm.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Subset of training data.\n\n y : numpy array of shape [n_samples]\n Subset of target values.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_partial_fit[None-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_partial_fit[None-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_partial_fit[csr_matrix-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_partial_fit[csr_matrix-True]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_partial_fit[csr_array-False]", "sklearn/linear_model/tests/test_passive_aggressive.py::test_regressor_partial_fit[csr_array-True]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_estimators_partial_fit_n_features]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PassiveAggressiveRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[PassiveAggressiveRegressor(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-145	1.0	{ "code": "diff --git b/sklearn/feature_extraction/image.py a/sklearn/feature_extraction/image.py\nindex 08ca7c532..ae7325d52 100644\n--- b/sklearn/feature_extraction/image.py\n+++ a/sklearn/feature_extraction/image.py\n@@ -632,6 +632,50 @@ class PatchExtractor(TransformerMixin, BaseEstimator):\n `n_patches` is either `n_samples * max_patches` or the total\n number of patches that can be extracted.\n \"\"\"\n+ X = validate_data(\n+ self,\n+ X=X,\n+ ensure_2d=False,\n+ allow_nd=True,\n+ ensure_min_samples=1,\n+ ensure_min_features=1,\n+ reset=False,\n+ )\n+ random_state = check_random_state(self.random_state)\n+ n_imgs, img_height, img_width = X.shape[:3]\n+ if self.patch_size is None:\n+ patch_size = img_height // 10, img_width // 10\n+ else:\n+ if len(self.patch_size) != 2:\n+ raise ValueError(\n+ \"patch_size must be a tuple of two integers. Got\"\n+ f\" {self.patch_size} instead.\"\n+ )\n+ patch_size = self.patch_size\n+\n+ n_imgs, img_height, img_width = X.shape[:3]\n+ X = np.reshape(X, (n_imgs, img_height, img_width, -1))\n+ n_channels = X.shape[-1]\n+\n+ # compute the dimensions of the patches array\n+ patch_height, patch_width = patch_size\n+ n_patches = _compute_n_patches(\n+ img_height, img_width, patch_height, patch_width, self.max_patches\n+ )\n+ patches_shape = (n_imgs * n_patches,) + patch_size\n+ if n_channels > 1:\n+ patches_shape += (n_channels,)\n+\n+ # extract the patches\n+ patches = np.empty(patches_shape)\n+ for ii, image in enumerate(X):\n+ patches[ii * n_patches : (ii + 1) * n_patches] = extract_patches_2d(\n+ image,\n+ patch_size,\n+ max_patches=self.max_patches,\n+ random_state=random_state,\n+ )\n+ return patches\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_extraction/image.py b/sklearn/feature_extraction/image.py\nindex ae7325d52..08ca7c532 100644\n--- a/sklearn/feature_extraction/image.py\n+++ b/sklearn/feature_extraction/image.py\n@@ -632,50 +632,6 @@ class PatchExtractor(TransformerMixin, BaseEstimator):\n `n_patches` is either `n_samples * max_patches` or the total\n number of patches that can be extracted.\n \"\"\"\n- X = validate_data(\n- self,\n- X=X,\n- ensure_2d=False,\n- allow_nd=True,\n- ensure_min_samples=1,\n- ensure_min_features=1,\n- reset=False,\n- )\n- random_state = check_random_state(self.random_state)\n- n_imgs, img_height, img_width = X.shape[:3]\n- if self.patch_size is None:\n- patch_size = img_height // 10, img_width // 10\n- else:\n- if len(self.patch_size) != 2:\n- raise ValueError(\n- \"patch_size must be a tuple of two integers. Got\"\n- f\" {self.patch_size} instead.\"\n- )\n- patch_size = self.patch_size\n-\n- n_imgs, img_height, img_width = X.shape[:3]\n- X = np.reshape(X, (n_imgs, img_height, img_width, -1))\n- n_channels = X.shape[-1]\n-\n- # compute the dimensions of the patches array\n- patch_height, patch_width = patch_size\n- n_patches = _compute_n_patches(\n- img_height, img_width, patch_height, patch_width, self.max_patches\n- )\n- patches_shape = (n_imgs * n_patches,) + patch_size\n- if n_channels > 1:\n- patches_shape += (n_channels,)\n-\n- # extract the patches\n- patches = np.empty(patches_shape)\n- for ii, image in enumerate(X):\n- patches[ii * n_patches : (ii + 1) * n_patches] = extract_patches_2d(\n- image,\n- patch_size,\n- max_patches=self.max_patches,\n- random_state=random_state,\n- )\n- return patches\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/image.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Transform the image samples in `X` into a matrix of patch data.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, image_height, image_width) or \\\n (n_samples, image_height, image_width, n_channels)\n Array of images from which to extract patches. For color images,\n the last dimension specifies the channel: a RGB image would have\n `n_channels=3`.\n\n Returns\n -------\n patches : array of shape (n_patches, patch_height, patch_width) or \\\n (n_patches, patch_height, patch_width, n_channels)\n The collection of patches extracted from the images, where\n `n_patches` is either `n_samples * max_patches` or the total\n number of patches that can be extracted.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/feature_extraction/image.py::sklearn.feature_extraction.image.PatchExtractor" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-146	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex e8d7dda8e..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -702,6 +702,7 @@ class Pipeline(_BaseComposition):\n Xt, routed_params[self.steps[-1][0]].predict_proba\n )\n \n+ @available_if(_final_estimator_has(\"decision_function\"))\n def decision_function(self, X, params):\n \"\"\"Transform the data, and apply `decision_function` with the final estimator.\n \n@@ -731,6 +732,20 @@ class Pipeline(_BaseComposition):\n y_score : ndarray of shape (n_samples, n_classes)\n Result of calling `decision_function` on the final estimator.\n \"\"\"\n+ _raise_for_params(params, self, \"decision_function\")\n+\n+ # not branching here since params is only available if\n+ # enable_metadata_routing=True\n+ routed_params = process_routing(self, \"decision_function\", params)\n+\n+ Xt = X\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(\n+ Xt, routed_params.get(name, {}).get(\"transform\", {})\n+ )\n+ return self.steps[-1][1].decision_function(\n+ Xt, **routed_params.get(self.steps[-1][0], {}).get(\"decision_function\", {})\n+ )\n \n @available_if(_final_estimator_has(\"score_samples\"))\n def score_samples(self, X):\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..e8d7dda8e 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -702,7 +702,6 @@ class Pipeline(_BaseComposition):\n Xt, routed_params[self.steps[-1][0]].predict_proba\n )\n \n- @available_if(_final_estimator_has(\"decision_function\"))\n def decision_function(self, X, params):\n \"\"\"Transform the data, and apply `decision_function` with the final estimator.\n \n@@ -732,20 +731,6 @@ class Pipeline(_BaseComposition):\n y_score : ndarray of shape (n_samples, n_classes)\n Result of calling `decision_function` on the final estimator.\n \"\"\"\n- _raise_for_params(params, self, \"decision_function\")\n-\n- # not branching here since params is only available if\n- # enable_metadata_routing=True\n- routed_params = process_routing(self, \"decision_function\", params)\n-\n- Xt = X\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(\n- Xt, routed_params.get(name, {}).get(\"transform\", {})\n- )\n- return self.steps[-1][1].decision_function(\n- Xt, **routed_params.get(self.steps[-1][0], {}).get(\"decision_function\", {})\n- )\n \n @available_if(_final_estimator_has(\"score_samples\"))\n def score_samples(self, 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/pipeline.py.\nHere is the description for the function:\n def decision_function(self, X, params):\n \"\"\"Transform the data, and apply `decision_function` with the final estimator.\n\n Call `transform` of each transformer in the pipeline. The transformed\n data are finally passed to the final estimator that calls\n `decision_function` method. Only valid if the final estimator\n implements `decision_function`.\n\n Parameters\n ----------\n X : iterable\n Data to predict on. Must fulfill input requirements of first step\n of the pipeline.\n\n params : dict of string -> object\n Parameters requested and accepted by steps. Each step must have\n requested certain metadata for these parameters to be forwarded to\n them.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n y_score : ndarray of shape (n_samples, n_classes)\n Result of calling `decision_function` on the final estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_sgd.py::test_ocsvm_vs_sgdocsvm", "sklearn/tests/test_pipeline.py::test_pipeline_methods_preprocessing_svm", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[None]", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[passthrough]", "sklearn/tests/test_pipeline.py::test_metadata_routing_for_pipeline[decision_function]", "sklearn/tests/test_pipeline.py::test_metadata_routing_error_for_pipeline[decision_function]", "sklearn/tests/test_pipeline.py::test_routing_passed_metadata_not_supported[decision_function]", "sklearn/tests/test_calibration.py::test_parallel_execution[True-sigmoid]", "sklearn/tests/test_calibration.py::test_parallel_execution[True-isotonic]", "sklearn/tests/test_multiclass.py::test_ovr_pipeline", "sklearn/tests/test_calibration.py::test_parallel_execution[False-sigmoid]", "sklearn/tests/test_multiclass.py::test_support_missing_values[OneVsRestClassifier]", "sklearn/tests/test_multiclass.py::test_support_missing_values[OneVsOneClassifier]", "sklearn/tests/test_calibration.py::test_parallel_execution[False-isotonic]", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_without_constraint_value[decision_function]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[CalibratedClassifierCV]", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_with_string_targets[metric0-decision_function]", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_with_string_targets[metric1-decision_function]", "sklearn/ensemble/_stacking.py::sklearn.ensemble._stacking.StackingClassifier", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_dict_unchanged]", 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"sklearn/tests/test_common.py::test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]", "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_estimators_dtypes]", "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_estimators_pickle]", "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_estimators_pickle(readonly_memmap=True)]", "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_classifiers_classes]", "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_classifiers_train]", "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_classifiers_train(readonly_memmap=True)]", "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_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "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_decision_proba_consistency]", "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_methods_sample_order_invariance]", "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_methods_subset_invariance]", "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[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_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])]", "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[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('logisticregression',LogisticRegression(C=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-147	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 74cb3c6f1..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -412,6 +412,10 @@ class Pipeline(_BaseComposition):\n self.steps[step_idx] = (name, fitted_transformer)\n return X\n \n+ @_fit_context(\n+ # estimators in Pipeline.steps are not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y=None, params):\n \"\"\"Fit the model.\n \n@@ -451,6 +455,14 @@ class Pipeline(_BaseComposition):\n self : object\n Pipeline with fitted steps.\n \"\"\"\n+ routed_params = self._check_method_params(method=\"fit\", props=params)\n+ Xt = self._fit(X, y, routed_params)\n+ with _print_elapsed_time(\"Pipeline\", self._log_message(len(self.steps) - 1)):\n+ if self._final_estimator != \"passthrough\":\n+ last_step_params = routed_params[self.steps[-1][0]]\n+ self._final_estimator.fit(Xt, y, last_step_params[\"fit\"])\n+\n+ return self\n \n def _can_fit_transform(self):\n return (\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..74cb3c6f1 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -412,10 +412,6 @@ class Pipeline(_BaseComposition):\n self.steps[step_idx] = (name, fitted_transformer)\n return X\n \n- @_fit_context(\n- # estimators in Pipeline.steps are not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y=None, params):\n \"\"\"Fit the model.\n \n@@ -455,14 +451,6 @@ class Pipeline(_BaseComposition):\n self : object\n Pipeline with fitted steps.\n \"\"\"\n- routed_params = self._check_method_params(method=\"fit\", props=params)\n- Xt = self._fit(X, y, routed_params)\n- with _print_elapsed_time(\"Pipeline\", self._log_message(len(self.steps) - 1)):\n- if self._final_estimator != \"passthrough\":\n- last_step_params = routed_params[self.steps[-1][0]]\n- self._final_estimator.fit(Xt, y, last_step_params[\"fit\"])\n-\n- return self\n \n def _can_fit_transform(self):\n return (\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, params):\n \"\"\"Fit the model.\n\n Fit all the transformers one after the other and sequentially transform the\n data. Finally, fit the transformed data using the final estimator.\n\n Parameters\n ----------\n X : iterable\n Training data. Must fulfill input requirements of first step of the\n pipeline.\n\n y : iterable, default=None\n Training targets. Must fulfill label requirements for all steps of\n the pipeline.\n\n params : dict of str -> object\n - If `enable_metadata_routing=False` (default): Parameters passed to the\n ``fit`` method of each step, where each parameter name is prefixed such\n that parameter ``p`` for step ``s`` has key ``s__p``.\n\n - If `enable_metadata_routing=True`: Parameters requested and accepted by\n steps. Each step must have requested certain metadata for these parameters\n to be forwarded to them.\n\n .. versionchanged:: 1.4\n Parameters are now passed to the ``transform`` method of the\n intermediate steps as well, if requested, and if\n `enable_metadata_routing=True` is set via\n :func:`~sklearn.set_config`.\n\n See :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n self : object\n Pipeline with fitted steps.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[ARDRegression(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_pipeline_consistency]", "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/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/tests/test_common.py::test_estimators[AdditiveChi2Sampler()-check_pipeline_consistency]", "sklearn/impute/tests/test_impute.py::test_imputation_pipeline_grid_search", "sklearn/tests/test_common.py::test_estimators[AffinityPropagation(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AgglomerativeClustering()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BaggingClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/linear_model/tests/test_sgd.py::test_ocsvm_vs_sgdocsvm", "sklearn/tests/test_common.py::test_estimators[BaggingRegressor(n_estimators=5)-check_pipeline_consistency]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_pipeline", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-None-estimator-brute]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-None-estimator-recursion]", "sklearn/tests/test_common.py::test_estimators[BayesianGaussianMixture(max_iter=5,n_init=2)-check_pipeline_consistency]", "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/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-None-estimator-brute]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-string-None-estimator-recursion]", "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]", 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scikit-learn__scikit-learn-148	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 1698e1e95..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -471,6 +471,11 @@ class Pipeline(_BaseComposition):\n or hasattr(self._final_estimator, \"fit_transform\")\n )\n \n+ @available_if(_can_fit_transform)\n+ @_fit_context(\n+ # estimators in Pipeline.steps are not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit the model and transform with the final estimator.\n \n@@ -510,6 +515,22 @@ class Pipeline(_BaseComposition):\n Xt : ndarray of shape (n_samples, n_transformed_features)\n Transformed samples.\n \"\"\"\n+ routed_params = self._check_method_params(method=\"fit_transform\", props=params)\n+ Xt = self._fit(X, y, routed_params)\n+\n+ last_step = self._final_estimator\n+ with _print_elapsed_time(\"Pipeline\", self._log_message(len(self.steps) - 1)):\n+ if last_step == \"passthrough\":\n+ return Xt\n+ last_step_params = routed_params[self.steps[-1][0]]\n+ if hasattr(last_step, \"fit_transform\"):\n+ return last_step.fit_transform(\n+ Xt, y, last_step_params[\"fit_transform\"]\n+ )\n+ else:\n+ return last_step.fit(Xt, y, last_step_params[\"fit\"]).transform(\n+ Xt, last_step_params[\"transform\"]\n+ )\n \n @available_if(_final_estimator_has(\"predict\"))\n def predict(self, X, **params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..1698e1e95 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -471,11 +471,6 @@ class Pipeline(_BaseComposition):\n or hasattr(self._final_estimator, \"fit_transform\")\n )\n \n- @available_if(_can_fit_transform)\n- @_fit_context(\n- # estimators in Pipeline.steps are not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit_transform(self, X, y=None, params):\n \"\"\"Fit the model and transform with the final estimator.\n \n@@ -515,22 +510,6 @@ class Pipeline(_BaseComposition):\n Xt : ndarray of shape (n_samples, n_transformed_features)\n Transformed samples.\n \"\"\"\n- routed_params = self._check_method_params(method=\"fit_transform\", props=params)\n- Xt = self._fit(X, y, routed_params)\n-\n- last_step = self._final_estimator\n- with _print_elapsed_time(\"Pipeline\", self._log_message(len(self.steps) - 1)):\n- if last_step == \"passthrough\":\n- return Xt\n- last_step_params = routed_params[self.steps[-1][0]]\n- if hasattr(last_step, \"fit_transform\"):\n- return last_step.fit_transform(\n- Xt, y, last_step_params[\"fit_transform\"]\n- )\n- else:\n- return last_step.fit(Xt, y, last_step_params[\"fit\"]).transform(\n- Xt, last_step_params[\"transform\"]\n- )\n \n @available_if(_final_estimator_has(\"predict\"))\n def predict(self, X, **params):\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 the model and transform with the final estimator.\n\n Fit all the transformers one after the other and sequentially transform\n the data. Only valid if the final estimator either implements\n `fit_transform` or `fit` and `transform`.\n\n Parameters\n ----------\n X : iterable\n Training data. Must fulfill input requirements of first step of the\n pipeline.\n\n y : iterable, default=None\n Training targets. Must fulfill label requirements for all steps of\n the pipeline.\n\n params : dict of str -> object\n - If `enable_metadata_routing=False` (default): Parameters passed to the\n ``fit`` method of each step, where each parameter name is prefixed such\n that parameter ``p`` for step ``s`` has key ``s__p``.\n\n - If `enable_metadata_routing=True`: Parameters requested and accepted by\n steps. Each step must have requested certain metadata for these parameters\n to be forwarded to them.\n\n .. versionchanged:: 1.4\n Parameters are now passed to the ``transform`` method of the\n intermediate steps as well, if requested, and if\n `enable_metadata_routing=True`.\n\n See :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n Xt : ndarray of shape (n_samples, n_transformed_features)\n Transformed samples.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[AdditiveChi2Sampler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BernoulliRBM(n_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Binarizer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Birch(n_clusters=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BisectingKMeans(max_iter=5,n_clusters=2,n_init=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[ColumnTransformer(transformers=[('trans1',StandardScaler(),[0,1])])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[DictionaryLearning(max_iter=20,transform_algorithm='lasso_lars')-check_pipeline_consistency]", "sklearn/feature_extraction/tests/test_text.py::test_countvectorizer_custom_vocabulary_pipeline", "sklearn/tests/test_pipeline.py::test_pipeline_transform", "sklearn/tests/test_pipeline.py::test_pipeline_fit_transform", "sklearn/tests/test_pipeline.py::test_set_pipeline_steps", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[None]", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[passthrough]", "sklearn/tests/test_pipeline.py::test_step_name_validation", "sklearn/tests/test_pipeline.py::test_verbose[est7-\\\\[Pipeline\\\\].\\\$step 1 of 2\\\$ Processing transf. total=.\\\\n\\\\[Pipeline\\\\].\\\$step 2 of 2\\\$ Processing clf.* total=.\\\\n$-fit_transform]", "sklearn/tests/test_pipeline.py::test_verbose[est9-\\\\[Pipeline\\\\].\\\$step 1 of 2\\\$ Processing transf.* total=.\\\\n\\\\[Pipeline\\\\].\\\$step 2 of 2\\\$ Processing mult.* total=.\\\\n$-fit_transform]", "sklearn/tests/test_pipeline.py::test_verbose[est11-\\\\[Pipeline\\\\].\\\$step 1 of 2\\\$ Processing transf.* total=.\\\\n\\\\[Pipeline\\\\].\\\$step 2 of 2\\\$ Processing mult.* total=.*\\\\n$-fit_transform]", "sklearn/tests/test_pipeline.py::test_pipeline_inverse_transform_Xt_deprecation", "sklearn/tests/test_pipeline.py::test_metadata_routing_for_pipeline[fit_transform]", "sklearn/tests/test_pipeline.py::test_metadata_routing_error_for_pipeline[fit_transform]", "sklearn/tests/test_metadata_routing.py::test_unsetmetadatapassederror_correct_for_composite_methods", "sklearn/manifold/tests/test_isomap.py::test_pipeline_with_nearest_neighbors_transformer[float64]", "sklearn/tests/test_common.py::test_estimators[FactorAnalysis(max_iter=5)-check_pipeline_consistency]", "sklearn/preprocessing/tests/test_function_transformer.py::test_get_feature_names_out_dataframe_with_string_data[True-one-to-one-expected0]", "sklearn/tests/test_common.py::test_estimators[FastICA(max_iter=5)-check_pipeline_consistency]", "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_consistence_column_name_between_steps", "sklearn/tests/test_common.py::test_estimators[FeatureAgglomeration()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[FeatureUnion(transformer_list=[('trans1',StandardScaler())])-check_pipeline_consistency]", "sklearn/utils/tests/test_estimator_checks.py::test_check_estimator", "sklearn/tests/test_common.py::test_estimators[FunctionTransformer()-check_pipeline_consistency]", "sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones", "sklearn/tests/test_common.py::test_estimators[GaussianRandomProjection(n_components=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[GenericUnivariateSelect()-check_pipeline_consistency]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_mixed_types_pandas", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_spectral_embedding", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_isomap", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_tsne", "sklearn/tests/test_common.py::test_estimators[IncrementalPCA(batch_size=10)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Isomap()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KBinsDiscretizer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KMeans(max_iter=5,n_clusters=2,n_init=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KNNImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KNeighborsTransformer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KernelCenterer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[KernelPCA()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LatentDirichletAllocation(batch_size=10,max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LinearDiscriminantAnalysis()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[LocallyLinearEmbedding(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MDS(max_iter=5,n_init=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MaxAbsScaler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MinMaxScaler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MiniBatchDictionaryLearning(batch_size=10,max_iter=5)-check_pipeline_consistency]", "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[MiniBatchNMF(batch_size=10,fresh_restarts=True,max_iter=20)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MiniBatchSparsePCA(batch_size=10,max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[MissingIndicator()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[NMF(max_iter=500)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[NeighborhoodComponentsAnalysis(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Normalizer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Nystroem()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OneHotEncoder(handle_unknown='ignore')-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[OrdinalEncoder()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[PCA()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[PowerTransformer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[QuantileTransformer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RadiusNeighborsTransformer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RobustScaler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelectFdr(alpha=0.5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelectFpr()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelectFwe()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelectKBest(k=1)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelectPercentile()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SparsePCA(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SparseRandomProjection(n_components=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SpectralEmbedding(eigen_tol=1e-05)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-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_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,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[StandardScaler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TargetEncoder(cv=3)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TfidfTransformer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[VarianceThreshold()-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_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_pipeline_consistency]", "sklearn/tests/test_common.py::test_check_param_validation[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])]", "sklearn/tests/test_common.py::test_check_param_validation[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])]", "sklearn/tests/test_common.py::test_set_output_transform[Pipeline(steps=[('standardscaler',StandardScaler()),('minmaxscaler',MinMaxScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-Pipeline(steps=[('standardscaler',StandardScaler()),('minmaxscaler',MinMaxScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-Pipeline(steps=[('standardscaler',StandardScaler()),('minmaxscaler',MinMaxScaler())])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-149	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 72ecc2626..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -1035,6 +1035,16 @@ class Pipeline(_BaseComposition):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n+ feature_names_out = input_features\n+ for _, name, transform in self._iter():\n+ if not hasattr(transform, \"get_feature_names_out\"):\n+ raise AttributeError(\n+ \"Estimator {} does not provide get_feature_names_out. \"\n+ \"Did you mean to call pipeline[:-1].get_feature_names_out\"\n+ \"()?\".format(name)\n+ )\n+ feature_names_out = transform.get_feature_names_out(feature_names_out)\n+ return feature_names_out\n \n @property\n def n_features_in_(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..72ecc2626 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -1035,16 +1035,6 @@ class Pipeline(_BaseComposition):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n- feature_names_out = input_features\n- for _, name, transform in self._iter():\n- if not hasattr(transform, \"get_feature_names_out\"):\n- raise AttributeError(\n- \"Estimator {} does not provide get_feature_names_out. \"\n- \"Did you mean to call pipeline[:-1].get_feature_names_out\"\n- \"()?\".format(name)\n- )\n- feature_names_out = transform.get_feature_names_out(feature_names_out)\n- return feature_names_out\n \n @property\n def n_features_in_(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 get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\n\n Transform input features using the pipeline.\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_features_names_passthrough", "sklearn/tests/test_pipeline.py::test_feature_names_count_vectorizer", "sklearn/tests/test_pipeline.py::test_pipeline_feature_names_out_error_without_definition", "sklearn/tests/test_pipeline.py::test_pipeline_get_feature_names_out_passes_names_through", "sklearn/tests/test_pipeline.py::test_pipeline_set_output_integration", "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_consistence_column_name_between_steps", "sklearn/tests/test_common.py::test_estimators_get_feature_names_out_error[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])]", "sklearn/tests/test_common.py::test_estimators_get_feature_names_out_error[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-Pipeline(steps=[('standardscaler',StandardScaler()),('minmaxscaler',MinMaxScaler())])]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-Pipeline(steps=[('standardscaler',StandardScaler()),('minmaxscaler',MinMaxScaler())])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-150	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex ac66cf3da..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -877,6 +877,7 @@ class Pipeline(_BaseComposition):\n def _can_inverse_transform(self):\n return all(hasattr(t, \"inverse_transform\") for _, _, t in self._iter())\n \n+ @available_if(_can_inverse_transform)\n def inverse_transform(self, X=None, , Xt=None, params):\n \"\"\"Apply `inverse_transform` for each step in a reverse order.\n \n@@ -915,6 +916,17 @@ class Pipeline(_BaseComposition):\n Inverse transformed data, that is, data in the original feature\n space.\n \"\"\"\n+ _raise_for_params(params, self, \"inverse_transform\")\n+\n+ X = _deprecate_Xt_in_inverse_transform(X, Xt)\n+\n+ # we don't have to branch here, since params is only non-empty if\n+ # enable_metadata_routing=True.\n+ routed_params = process_routing(self, \"inverse_transform\", params)\n+ reverse_iter = reversed(list(self._iter()))\n+ for _, name, transform in reverse_iter:\n+ X = transform.inverse_transform(X, routed_params[name].inverse_transform)\n+ return X\n \n @available_if(_final_estimator_has(\"score\"))\n def score(self, X, y=None, sample_weight=None, *params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..ac66cf3da 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -877,7 +877,6 @@ class Pipeline(_BaseComposition):\n def _can_inverse_transform(self):\n return all(hasattr(t, \"inverse_transform\") for _, _, t in self._iter())\n \n- @available_if(_can_inverse_transform)\n def inverse_transform(self, X=None, , Xt=None, params):\n \"\"\"Apply `inverse_transform` for each step in a reverse order.\n \n@@ -916,17 +915,6 @@ class Pipeline(_BaseComposition):\n Inverse transformed data, that is, data in the original feature\n space.\n \"\"\"\n- _raise_for_params(params, self, \"inverse_transform\")\n-\n- X = _deprecate_Xt_in_inverse_transform(X, Xt)\n-\n- # we don't have to branch here, since params is only non-empty if\n- # enable_metadata_routing=True.\n- routed_params = process_routing(self, \"inverse_transform\", params)\n- reverse_iter = reversed(list(self._iter()))\n- for _, name, transform in reverse_iter:\n- X = transform.inverse_transform(X, routed_params[name].inverse_transform)\n- return X\n \n @available_if(_final_estimator_has(\"score\"))\n def score(self, X, y=None, sample_weight=None, *params):\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 inverse_transform(self, X=None, , Xt=None, params):\n \"\"\"Apply `inverse_transform` for each step in a reverse order.\n\n All estimators in the pipeline must support `inverse_transform`.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_transformed_features)\n Data samples, where ``n_samples`` is the number of samples and\n ``n_features`` is the number of features. Must fulfill\n input requirements of last step of pipeline's\n ``inverse_transform`` method.\n\n Xt : array-like of shape (n_samples, n_transformed_features)\n Data samples, where ``n_samples`` is the number of samples and\n ``n_features`` is the number of features. Must fulfill\n input requirements of last step of pipeline's\n ``inverse_transform`` method.\n\n .. deprecated:: 1.5\n `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.\n\n *params : dict of str -> object\n Parameters requested and accepted by steps. Each step must have\n requested certain metadata for these parameters to be forwarded to\n them.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n Xt : ndarray of shape (n_samples, n_features)\n Inverse transformed data, that is, data in the original feature\n space.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_pipeline_transform", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[None]", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[passthrough]", "sklearn/tests/test_pipeline.py::test_pipeline_ducktyping", "sklearn/tests/test_pipeline.py::test_pipeline_inverse_transform_Xt_deprecation", "sklearn/tests/test_pipeline.py::test_metadata_routing_for_pipeline[inverse_transform]", "sklearn/tests/test_pipeline.py::test_metadata_routing_error_for_pipeline[inverse_transform]", "sklearn/tests/test_pipeline.py::test_routing_passed_metadata_not_supported[inverse_transform]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-151	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 8cd1e6f3e..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -532,6 +532,7 @@ class Pipeline(_BaseComposition):\n Xt, last_step_params[\"transform\"]\n )\n \n+ @available_if(_final_estimator_has(\"predict\"))\n def predict(self, X, params):\n \"\"\"Transform the data, and apply `predict` with the final estimator.\n \n@@ -574,6 +575,18 @@ class Pipeline(_BaseComposition):\n y_pred : ndarray\n Result of calling `predict` on the final estimator.\n \"\"\"\n+ Xt = X\n+\n+ if not _routing_enabled():\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt)\n+ return self.steps[-1][1].predict(Xt, params)\n+\n+ # metadata routing enabled\n+ routed_params = process_routing(self, \"predict\", params)\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt, routed_params[name].transform)\n+ return self.steps[-1][1].predict(Xt, routed_params[self.steps[-1][0]].predict)\n \n @available_if(_final_estimator_has(\"fit_predict\"))\n @_fit_context(\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..8cd1e6f3e 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -532,7 +532,6 @@ class Pipeline(_BaseComposition):\n Xt, last_step_params[\"transform\"]\n )\n \n- @available_if(_final_estimator_has(\"predict\"))\n def predict(self, X, params):\n \"\"\"Transform the data, and apply `predict` with the final estimator.\n \n@@ -575,18 +574,6 @@ class Pipeline(_BaseComposition):\n y_pred : ndarray\n Result of calling `predict` on the final estimator.\n \"\"\"\n- Xt = X\n-\n- if not _routing_enabled():\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt)\n- return self.steps[-1][1].predict(Xt, params)\n-\n- # metadata routing enabled\n- routed_params = process_routing(self, \"predict\", params)\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt, routed_params[name].transform)\n- return self.steps[-1][1].predict(Xt, routed_params[self.steps[-1][0]].predict)\n \n @available_if(_final_estimator_has(\"fit_predict\"))\n @_fit_context(\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 predict(self, X, params):\n \"\"\"Transform the data, and apply `predict` with the final estimator.\n\n Call `transform` of each transformer in the pipeline. The transformed\n data are finally passed to the final estimator that calls `predict`\n method. Only valid if the final estimator implements `predict`.\n\n Parameters\n ----------\n X : iterable\n Data to predict on. Must fulfill input requirements of first step\n of the pipeline.\n\n params : dict of str -> object\n - If `enable_metadata_routing=False` (default): Parameters to the\n ``predict`` called at the end of all transformations in the pipeline.\n\n - If `enable_metadata_routing=True`: Parameters requested and accepted by\n steps. Each step must have requested certain metadata for these parameters\n to be forwarded to them.\n\n .. versionadded:: 0.20\n\n .. versionchanged:: 1.4\n Parameters are now passed to the ``transform`` method of the\n intermediate steps as well, if requested, and if\n `enable_metadata_routing=True` is set via\n :func:`~sklearn.set_config`.\n\n See :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Note that while this may be used to return uncertainties from some\n models with ``return_std`` or ``return_cov``, uncertainties that are\n generated by the transformations in the pipeline are not propagated\n to the final estimator.\n\n Returns\n -------\n y_pred : ndarray\n Result of calling `predict` on the final estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "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/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/linear_model/tests/test_sgd.py::test_ocsvm_vs_sgdocsvm", "sklearn/preprocessing/tests/test_data.py::test_cv_pipeline_precomputed", "sklearn/neighbors/tests/test_neighbors.py::test_pipeline_with_nearest_neighbors_transformer", "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-estimator2]", "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-estimator2]", "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]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-ElasticNetCV-params2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[-1-estimator0]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-RidgeClassifier-params3]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-ElasticNet-params4]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-ElasticNet-params5]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_non_null_weight_idx[-1-estimator2]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-Ridge-params6]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-LinearRegression-params7]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-RidgeCV-params8]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_matrix-RidgeClassifierCV-params9]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-Lasso-params0]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-LassoCV-params1]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-ElasticNetCV-params2]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-RidgeClassifier-params3]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-ElasticNet-params4]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-ElasticNet-params5]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-Ridge-params6]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-LinearRegression-params7]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-RidgeCV-params8]", "sklearn/linear_model/tests/test_coordinate_descent.py::test_model_pipeline_same_dense_and_sparse[csr_array-RidgeClassifierCV-params9]", "sklearn/feature_extraction/tests/test_text.py::test_count_vectorizer_pipeline_grid_selection", "sklearn/tests/test_pipeline.py::test_pipeline_methods_anova", "sklearn/feature_extraction/tests/test_text.py::test_vectorizer_pipeline_grid_selection", "sklearn/tests/test_pipeline.py::test_pipeline_fit_params", "sklearn/tests/test_pipeline.py::test_pipeline_methods_pca_svm", "sklearn/tests/test_pipeline.py::test_pipeline_methods_preprocessing_svm", "sklearn/tests/test_pipeline.py::test_predict_methods_with_predict_params[predict]", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[None]", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[passthrough]", "sklearn/tests/test_pipeline.py::test_pipeline_ducktyping", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_minmax_imputation", "sklearn/tests/test_pipeline.py::test_pipeline_memory", "sklearn/tests/test_pipeline.py::test_search_cv_using_minimal_compatible_estimator[MinimalRegressor]", "sklearn/tests/test_pipeline.py::test_search_cv_using_minimal_compatible_estimator[MinimalClassifier]", "sklearn/tests/test_pipeline.py::test_metadata_routing_for_pipeline[predict]", "sklearn/tests/test_pipeline.py::test_metadata_routing_error_for_pipeline[predict]", "sklearn/tests/test_pipeline.py::test_pipeline_with_estimator_with_len", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_with_categorical[categorical_features0-dataframe]", "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/model_selection/tests/test_search.py::test_search_cv_using_minimal_compatible_estimator[MinimalRegressor-GridSearchCV]", "sklearn/model_selection/tests/test_search.py::test_search_cv_using_minimal_compatible_estimator[MinimalRegressor-RandomizedSearchCV]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_legend", "sklearn/model_selection/tests/test_search.py::test_search_cv_using_minimal_compatible_estimator[MinimalClassifier-GridSearchCV]", "sklearn/model_selection/tests/test_search.py::test_search_cv_using_minimal_compatible_estimator[MinimalClassifier-RandomizedSearchCV]", "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/tests/test_multiclass.py::test_support_missing_values[OneVsOneClassifier]", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_without_constraint_value[auto]", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_without_constraint_value[decision_function]", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_without_constraint_value[predict_proba]", "sklearn/metrics/_plot/tests/test_confusion_matrix_display.py::test_confusion_matrix_pipeline[pipeline-clf]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/tests/test_multioutput.py::test_support_missing_values[MultiOutputClassifier-LogisticRegression]", "sklearn/metrics/_plot/tests/test_confusion_matrix_display.py::test_confusion_matrix_pipeline[pipeline-column_transformer-clf]", 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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_classifiers_train(readonly_memmap=True)]", "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_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "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_methods_sample_order_invariance]", "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_methods_subset_invariance]", "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[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_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)]", 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"sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])]", "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[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('logisticregression',LogisticRegression(C=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-152	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 3b0398319..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -772,6 +772,7 @@ class Pipeline(_BaseComposition):\n Xt = transformer.transform(Xt)\n return self.steps[-1][1].score_samples(Xt)\n \n+ @available_if(_final_estimator_has(\"predict_log_proba\"))\n def predict_log_proba(self, X, params):\n \"\"\"Transform the data, and apply `predict_log_proba` with the final estimator.\n \n@@ -810,6 +811,20 @@ class Pipeline(_BaseComposition):\n y_log_proba : ndarray of shape (n_samples, n_classes)\n Result of calling `predict_log_proba` on the final estimator.\n \"\"\"\n+ Xt = X\n+\n+ if not _routing_enabled():\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt)\n+ return self.steps[-1][1].predict_log_proba(Xt, params)\n+\n+ # metadata routing enabled\n+ routed_params = process_routing(self, \"predict_log_proba\", params)\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt, routed_params[name].transform)\n+ return self.steps[-1][1].predict_log_proba(\n+ Xt, **routed_params[self.steps[-1][0]].predict_log_proba\n+ )\n \n def _can_transform(self):\n return self._final_estimator == \"passthrough\" or hasattr(\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..3b0398319 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -772,7 +772,6 @@ class Pipeline(_BaseComposition):\n Xt = transformer.transform(Xt)\n return self.steps[-1][1].score_samples(Xt)\n \n- @available_if(_final_estimator_has(\"predict_log_proba\"))\n def predict_log_proba(self, X, params):\n \"\"\"Transform the data, and apply `predict_log_proba` with the final estimator.\n \n@@ -811,20 +810,6 @@ class Pipeline(_BaseComposition):\n y_log_proba : ndarray of shape (n_samples, n_classes)\n Result of calling `predict_log_proba` on the final estimator.\n \"\"\"\n- Xt = X\n-\n- if not _routing_enabled():\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt)\n- return self.steps[-1][1].predict_log_proba(Xt, params)\n-\n- # metadata routing enabled\n- routed_params = process_routing(self, \"predict_log_proba\", params)\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt, routed_params[name].transform)\n- return self.steps[-1][1].predict_log_proba(\n- Xt, **routed_params[self.steps[-1][0]].predict_log_proba\n- )\n \n def _can_transform(self):\n return self._final_estimator == \"passthrough\" or hasattr(\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 predict_log_proba(self, X, params):\n \"\"\"Transform the data, and apply `predict_log_proba` with the final estimator.\n\n Call `transform` of each transformer in the pipeline. The transformed\n data are finally passed to the final estimator that calls\n `predict_log_proba` method. Only valid if the final estimator\n implements `predict_log_proba`.\n\n Parameters\n ----------\n X : iterable\n Data to predict on. Must fulfill input requirements of first step\n of the pipeline.\n\n params : dict of str -> object\n - If `enable_metadata_routing=False` (default): Parameters to the\n `predict_log_proba` called at the end of all transformations in the\n pipeline.\n\n - If `enable_metadata_routing=True`: Parameters requested and accepted by\n steps. Each step must have requested certain metadata for these parameters\n to be forwarded to them.\n\n .. versionadded:: 0.20\n\n .. versionchanged:: 1.4\n Parameters are now passed to the ``transform`` method of the\n intermediate steps as well, if requested, and if\n `enable_metadata_routing=True`.\n\n See :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n y_log_proba : ndarray of shape (n_samples, n_classes)\n Result of calling `predict_log_proba` on the final estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_pipeline.py::test_pipeline_methods_anova", "sklearn/tests/test_pipeline.py::test_pipeline_methods_pca_svm", "sklearn/tests/test_pipeline.py::test_pipeline_methods_preprocessing_svm", "sklearn/tests/test_pipeline.py::test_predict_methods_with_predict_params[predict_log_proba]", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[None]", "sklearn/tests/test_pipeline.py::test_set_pipeline_step_passthrough[passthrough]", "sklearn/tests/test_pipeline.py::test_pipeline_memory", "sklearn/tests/test_pipeline.py::test_metadata_routing_for_pipeline[predict_log_proba]", "sklearn/tests/test_pipeline.py::test_metadata_routing_error_for_pipeline[predict_log_proba]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/ensemble/tests/test_bagging.py::test_bagging_classifier_with_missing_inputs", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[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)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[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)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[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)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", 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"sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('logisticregression',LogisticRegression(C=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-153	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 861f75457..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -649,6 +649,7 @@ class Pipeline(_BaseComposition):\n )\n return y_pred\n \n+ @available_if(_final_estimator_has(\"predict_proba\"))\n def predict_proba(self, X, params):\n \"\"\"Transform the data, and apply `predict_proba` with the final estimator.\n \n@@ -686,6 +687,20 @@ class Pipeline(_BaseComposition):\n y_proba : ndarray of shape (n_samples, n_classes)\n Result of calling `predict_proba` on the final estimator.\n \"\"\"\n+ Xt = X\n+\n+ if not _routing_enabled():\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt)\n+ return self.steps[-1][1].predict_proba(Xt, params)\n+\n+ # metadata routing enabled\n+ routed_params = process_routing(self, \"predict_proba\", params)\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt, routed_params[name].transform)\n+ return self.steps[-1][1].predict_proba(\n+ Xt, routed_params[self.steps[-1][0]].predict_proba\n+ )\n \n @available_if(_final_estimator_has(\"decision_function\"))\n def decision_function(self, X, params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..861f75457 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -649,7 +649,6 @@ class Pipeline(_BaseComposition):\n )\n return y_pred\n \n- @available_if(_final_estimator_has(\"predict_proba\"))\n def predict_proba(self, X, params):\n \"\"\"Transform the data, and apply `predict_proba` with the final estimator.\n \n@@ -687,20 +686,6 @@ class Pipeline(_BaseComposition):\n y_proba : ndarray of shape (n_samples, n_classes)\n Result of calling `predict_proba` on the final estimator.\n \"\"\"\n- Xt = X\n-\n- if not _routing_enabled():\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt)\n- return self.steps[-1][1].predict_proba(Xt, params)\n-\n- # metadata routing enabled\n- routed_params = process_routing(self, \"predict_proba\", params)\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt, routed_params[name].transform)\n- return self.steps[-1][1].predict_proba(\n- Xt, routed_params[self.steps[-1][0]].predict_proba\n- )\n \n @available_if(_final_estimator_has(\"decision_function\"))\n def decision_function(self, X, params):\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 predict_proba(self, X, params):\n \"\"\"Transform the data, and apply `predict_proba` with the final estimator.\n\n Call `transform` of each transformer in the pipeline. The transformed\n data are finally passed to the final estimator that calls\n `predict_proba` method. Only valid if the final estimator implements\n `predict_proba`.\n\n Parameters\n ----------\n X : iterable\n Data to predict on. Must fulfill input requirements of first step\n of the pipeline.\n\n params : dict of str -> object\n - If `enable_metadata_routing=False` (default): Parameters to the\n `predict_proba` called at the end of all transformations in the pipeline.\n\n - If `enable_metadata_routing=True`: Parameters requested and accepted by\n steps. Each step must have requested certain metadata for these parameters\n to be forwarded to them.\n\n .. versionadded:: 0.20\n\n .. versionchanged:: 1.4\n Parameters are now passed to the ``transform`` method of the\n intermediate steps as well, if requested, and if\n `enable_metadata_routing=True`.\n\n See :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n y_proba : ndarray of shape (n_samples, n_classes)\n Result of calling `predict_proba` on the final estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_pipeline", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-None-estimator-brute]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_dataframe[features-integer-None-estimator-recursion]", 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"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]", 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"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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[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)]", 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"sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('logisticregression',LogisticRegression(C=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-154	1.0	{ "code": "diff --git b/sklearn/pipeline.py a/sklearn/pipeline.py\nindex 7dcb74cee..6ea44888c 100644\n--- b/sklearn/pipeline.py\n+++ a/sklearn/pipeline.py\n@@ -928,6 +928,7 @@ class Pipeline(_BaseComposition):\n X = transform.inverse_transform(X, routed_params[name].inverse_transform)\n return X\n \n+ @available_if(_final_estimator_has(\"score\"))\n def score(self, X, y=None, sample_weight=None, params):\n \"\"\"Transform the data, and apply `score` with the final estimator.\n \n@@ -964,6 +965,24 @@ class Pipeline(_BaseComposition):\n score : float\n Result of calling `score` on the final estimator.\n \"\"\"\n+ Xt = X\n+ if not _routing_enabled():\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt)\n+ score_params = {}\n+ if sample_weight is not None:\n+ score_params[\"sample_weight\"] = sample_weight\n+ return self.steps[-1][1].score(Xt, y, score_params)\n+\n+ # metadata routing is enabled.\n+ routed_params = process_routing(\n+ self, \"score\", sample_weight=sample_weight, params\n+ )\n+\n+ Xt = X\n+ for _, name, transform in self._iter(with_final=False):\n+ Xt = transform.transform(Xt, routed_params[name].transform)\n+ return self.steps[-1][1].score(Xt, y, routed_params[self.steps[-1][0]].score)\n \n @property\n def classes_(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py\nindex 6ea44888c..7dcb74cee 100644\n--- a/sklearn/pipeline.py\n+++ b/sklearn/pipeline.py\n@@ -928,7 +928,6 @@ class Pipeline(_BaseComposition):\n X = transform.inverse_transform(X, routed_params[name].inverse_transform)\n return X\n \n- @available_if(_final_estimator_has(\"score\"))\n def score(self, X, y=None, sample_weight=None, params):\n \"\"\"Transform the data, and apply `score` with the final estimator.\n \n@@ -965,24 +964,6 @@ class Pipeline(_BaseComposition):\n score : float\n Result of calling `score` on the final estimator.\n \"\"\"\n- Xt = X\n- if not _routing_enabled():\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt)\n- score_params = {}\n- if sample_weight is not None:\n- score_params[\"sample_weight\"] = sample_weight\n- return self.steps[-1][1].score(Xt, y, score_params)\n-\n- # metadata routing is enabled.\n- routed_params = process_routing(\n- self, \"score\", sample_weight=sample_weight, params\n- )\n-\n- Xt = X\n- for _, name, transform in self._iter(with_final=False):\n- Xt = transform.transform(Xt, routed_params[name].transform)\n- return self.steps[-1][1].score(Xt, y, routed_params[self.steps[-1][0]].score)\n \n @property\n def classes_(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 score(self, X, y=None, sample_weight=None, params):\n \"\"\"Transform the data, and apply `score` with the final estimator.\n\n Call `transform` of each transformer in the pipeline. The transformed\n data are finally passed to the final estimator that calls\n `score` method. Only valid if the final estimator implements `score`.\n\n Parameters\n ----------\n X : iterable\n Data to predict on. Must fulfill input requirements of first step\n of the pipeline.\n\n y : iterable, default=None\n Targets used for scoring. Must fulfill label requirements for all\n steps of the pipeline.\n\n sample_weight : array-like, default=None\n If not None, this argument is passed as ``sample_weight`` keyword\n argument to the ``score`` method of the final estimator.\n\n params : dict of str -> object\n Parameters requested and accepted by steps. Each step must have\n requested certain metadata for these parameters to be forwarded to\n them.\n\n .. versionadded:: 1.4\n Only available if `enable_metadata_routing=True`. See\n :ref:`Metadata Routing User Guide <metadata_routing>` for more\n details.\n\n Returns\n -------\n score : float\n Result of calling `score` on the final estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[ARDRegression(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[AdaBoostRegressor(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BaggingClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BaggingRegressor(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BayesianGaussianMixture(max_iter=5,n_init=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BayesianRidge(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[BernoulliNB()-check_pipeline_consistency]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-None-make_friedman1-DecisionTreeRegressor-0]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-None-make_friedman1-ExtraTreeRegressor-0.07]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-None-make_friedman1_classification-DecisionTreeClassifier-0.03]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-None-make_friedman1_classification-ExtraTreeClassifier-0.12]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-ones-make_friedman1-DecisionTreeRegressor-0]", "sklearn/tests/test_common.py::test_estimators[BisectingKMeans(max_iter=5,n_clusters=2,n_init=2)-check_pipeline_consistency]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-ones-make_friedman1-ExtraTreeRegressor-0.07]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-ones-make_friedman1_classification-DecisionTreeClassifier-0.03]", "sklearn/tree/tests/test_tree.py::test_missing_values_is_resilience[42-ones-make_friedman1_classification-ExtraTreeClassifier-0.12]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[CategoricalNB()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[ComplementNB()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[DecisionTreeClassifier()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[DecisionTreeRegressor()-check_pipeline_consistency]", 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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_dont_overwrite_parameters]", "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[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_check_is_fitted]", "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_n_features_in]", "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_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[RegressorChain(base_estimator=Ridge(),cv=3)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[Ridge()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RidgeCV()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifier()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifierCV()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SGDClassifier(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SGDRegressor(max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SVC()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SVR()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-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_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,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[TheilSenRegressor(max_iter=5,max_subpopulation=100)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-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_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_pipeline_consistency]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[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=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])]", "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[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[Pipeline(steps=[('logisticregression',LogisticRegression(C=1))])]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-155	1.0	{ "code": "diff --git b/sklearn/kernel_approximation.py a/sklearn/kernel_approximation.py\nindex f693b356b..92d906dde 100644\n--- b/sklearn/kernel_approximation.py\n+++ a/sklearn/kernel_approximation.py\n@@ -187,6 +187,54 @@ class PolynomialCountSketch(\n Returns the instance itself.\n \"\"\"\n \n+ check_is_fitted(self)\n+ X = validate_data(self, X, accept_sparse=\"csc\", reset=False)\n+\n+ X_gamma = np.sqrt(self.gamma) * X\n+\n+ if sp.issparse(X_gamma) and self.coef0 != 0:\n+ X_gamma = sp.hstack(\n+ [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))],\n+ format=\"csc\",\n+ )\n+\n+ elif not sp.issparse(X_gamma) and self.coef0 != 0:\n+ X_gamma = np.hstack(\n+ [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))]\n+ )\n+\n+ if X_gamma.shape[1] != self.indexHash_.shape[1]:\n+ raise ValueError(\n+ \"Number of features of test samples does not\"\n+ \" match that of training samples.\"\n+ )\n+\n+ count_sketches = np.zeros((X_gamma.shape[0], self.degree, self.n_components))\n+\n+ if sp.issparse(X_gamma):\n+ for j in range(X_gamma.shape[1]):\n+ for d in range(self.degree):\n+ iHashIndex = self.indexHash_[d, j]\n+ iHashBit = self.bitHash_[d, j]\n+ count_sketches[:, d, iHashIndex] += (\n+ (iHashBit * X_gamma[:, [j]]).toarray().ravel()\n+ )\n+\n+ else:\n+ for j in range(X_gamma.shape[1]):\n+ for d in range(self.degree):\n+ iHashIndex = self.indexHash_[d, j]\n+ iHashBit = self.bitHash_[d, j]\n+ count_sketches[:, d, iHashIndex] += iHashBit * X_gamma[:, j]\n+\n+ # For each same, compute a count sketch of phi(x) using the polynomial\n+ # multiplication (via FFT) of p count sketches of x.\n+ count_sketches_fft = fft(count_sketches, axis=2, overwrite_x=True)\n+ count_sketches_fft_prod = np.prod(count_sketches_fft, axis=1)\n+ data_sketch = np.real(ifft(count_sketches_fft_prod, overwrite_x=True))\n+\n+ return data_sketch\n+\n \n class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n \"\"\"Approximate a RBF kernel feature map using random Fourier features.\n", "test": null }	null	{ "code": "diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py\nindex 92d906dde..f693b356b 100644\n--- a/sklearn/kernel_approximation.py\n+++ b/sklearn/kernel_approximation.py\n@@ -187,54 +187,6 @@ class PolynomialCountSketch(\n Returns the instance itself.\n \"\"\"\n \n- check_is_fitted(self)\n- X = validate_data(self, X, accept_sparse=\"csc\", reset=False)\n-\n- X_gamma = np.sqrt(self.gamma) * X\n-\n- if sp.issparse(X_gamma) and self.coef0 != 0:\n- X_gamma = sp.hstack(\n- [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))],\n- format=\"csc\",\n- )\n-\n- elif not sp.issparse(X_gamma) and self.coef0 != 0:\n- X_gamma = np.hstack(\n- [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))]\n- )\n-\n- if X_gamma.shape[1] != self.indexHash_.shape[1]:\n- raise ValueError(\n- \"Number of features of test samples does not\"\n- \" match that of training samples.\"\n- )\n-\n- count_sketches = np.zeros((X_gamma.shape[0], self.degree, self.n_components))\n-\n- if sp.issparse(X_gamma):\n- for j in range(X_gamma.shape[1]):\n- for d in range(self.degree):\n- iHashIndex = self.indexHash_[d, j]\n- iHashBit = self.bitHash_[d, j]\n- count_sketches[:, d, iHashIndex] += (\n- (iHashBit * X_gamma[:, [j]]).toarray().ravel()\n- )\n-\n- else:\n- for j in range(X_gamma.shape[1]):\n- for d in range(self.degree):\n- iHashIndex = self.indexHash_[d, j]\n- iHashBit = self.bitHash_[d, j]\n- count_sketches[:, d, iHashIndex] += iHashBit * X_gamma[:, j]\n-\n- # For each same, compute a count sketch of phi(x) using the polynomial\n- # multiplication (via FFT) of p count sketches of x.\n- count_sketches_fft = fft(count_sketches, axis=2, overwrite_x=True)\n- count_sketches_fft_prod = np.prod(count_sketches_fft, axis=1)\n- data_sketch = np.real(ifft(count_sketches_fft_prod, overwrite_x=True))\n-\n- return data_sketch\n-\n \n class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n \"\"\"Approximate a RBF kernel feature map using random Fourier features.\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/kernel_approximation.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Generate the feature map approximation for X.\n\n Parameters\n ----------\n X : {array-like}, shape (n_samples, n_features)\n New data, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n Returns\n -------\n X_new : array-like, shape (n_samples, n_components)\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-1-500-0.1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-1-500-1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-1-500-2.5]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-2-500-0.1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-2-500-1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-2-500-2.5]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-3-5000-0.1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-3-5000-1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[0-3-5000-2.5]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-1-500-0.1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-1-500-1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-1-500-2.5]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-2-500-0.1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-2-500-1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-2-500-2.5]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-3-5000-0.1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-3-5000-1]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch[2.5-3-5000-2.5]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-1-0.1-csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-1-0.1-csr_array]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-1-1.0-csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-1-1.0-csr_array]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-2-0.1-csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-2-0.1-csr_array]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-2-1.0-csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-2-1.0-csr_array]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-3-0.1-csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-3-0.1-csr_array]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-3-1.0-csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[0-3-1.0-csr_array]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[2.5-1-0.1-csr_matrix]", 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"sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[2.5-3-1.0-csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_polynomial_count_sketch_dense_sparse[2.5-3-1.0-csr_array]", "sklearn/tests/test_kernel_approximation.py::test_get_feature_names_out[PolynomialCountSketch]", "sklearn/kernel_approximation.py::sklearn.kernel_approximation.PolynomialCountSketch", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[PolynomialCountSketch()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PolynomialCountSketch()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PolynomialCountSketch()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[PolynomialCountSketch()]", "sklearn/tests/test_common.py::test_set_output_transform[PolynomialCountSketch()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-PolynomialCountSketch()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-PolynomialCountSketch()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-156	1.0	{ "code": "diff --git b/sklearn/preprocessing/_polynomial.py a/sklearn/preprocessing/_polynomial.py\nindex c0530d1e0..5a3239f11 100644\n--- b/sklearn/preprocessing/_polynomial.py\n+++ a/sklearn/preprocessing/_polynomial.py\n@@ -306,6 +306,7 @@ class PolynomialFeatures(TransformerMixin, BaseEstimator):\n feature_names.append(name)\n return np.asarray(feature_names, dtype=object)\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"\n Compute number of output features.\n@@ -323,6 +324,84 @@ class PolynomialFeatures(TransformerMixin, BaseEstimator):\n self : object\n Fitted transformer.\n \"\"\"\n+ _, n_features = validate_data(self, X, accept_sparse=True).shape\n+\n+ if isinstance(self.degree, Integral):\n+ if self.degree == 0 and not self.include_bias:\n+ raise ValueError(\n+ \"Setting degree to zero and include_bias to False would result in\"\n+ \" an empty output array.\"\n+ )\n+\n+ self._min_degree = 0\n+ self._max_degree = self.degree\n+ elif (\n+ isinstance(self.degree, collections.abc.Iterable) and len(self.degree) == 2\n+ ):\n+ self._min_degree, self._max_degree = self.degree\n+ if not (\n+ isinstance(self._min_degree, Integral)\n+ and isinstance(self._max_degree, Integral)\n+ and self._min_degree >= 0\n+ and self._min_degree <= self._max_degree\n+ ):\n+ raise ValueError(\n+ \"degree=(min_degree, max_degree) must \"\n+ \"be non-negative integers that fulfil \"\n+ \"min_degree <= max_degree, got \"\n+ f\"{self.degree}.\"\n+ )\n+ elif self._max_degree == 0 and not self.include_bias:\n+ raise ValueError(\n+ \"Setting both min_degree and max_degree to zero and include_bias to\"\n+ \" False would result in an empty output array.\"\n+ )\n+ else:\n+ raise ValueError(\n+ \"degree must be a non-negative int or tuple \"\n+ \"(min_degree, max_degree), got \"\n+ f\"{self.degree}.\"\n+ )\n+\n+ self.n_output_features_ = self._num_combinations(\n+ n_features=n_features,\n+ min_degree=self._min_degree,\n+ max_degree=self._max_degree,\n+ interaction_only=self.interaction_only,\n+ include_bias=self.include_bias,\n+ )\n+ if self.n_output_features_ > np.iinfo(np.intp).max:\n+ msg = (\n+ \"The output that would result from the current configuration would\"\n+ f\" have {self.n_output_features_} features which is too large to be\"\n+ f\" indexed by {np.intp().dtype.name}. Please change some or all of the\"\n+ \" following:\\n- The number of features in the input, currently\"\n+ f\" {n_features=}\\n- The range of degrees to calculate, currently\"\n+ f\" [{self._min_degree}, {self._max_degree}]\\n- Whether to include only\"\n+ f\" interaction terms, currently {self.interaction_only}\\n- Whether to\"\n+ f\" include a bias term, currently {self.include_bias}.\"\n+ )\n+ if (\n+ np.intp == np.int32\n+ and self.n_output_features_ <= np.iinfo(np.int64).max\n+ ): # pragma: nocover\n+ msg += (\n+ \"\\nNote that the current Python runtime has a limited 32 bit \"\n+ \"address space and that this configuration would have been \"\n+ \"admissible if run on a 64 bit Python runtime.\"\n+ )\n+ raise ValueError(msg)\n+ # We also record the number of output features for\n+ # _max_degree = 0\n+ self._n_out_full = self._num_combinations(\n+ n_features=n_features,\n+ min_degree=0,\n+ max_degree=self._max_degree,\n+ interaction_only=self.interaction_only,\n+ include_bias=self.include_bias,\n+ )\n+\n+ return self\n \n def transform(self, X):\n \"\"\"Transform data to polynomial features.\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_polynomial.py b/sklearn/preprocessing/_polynomial.py\nindex 5a3239f11..c0530d1e0 100644\n--- a/sklearn/preprocessing/_polynomial.py\n+++ b/sklearn/preprocessing/_polynomial.py\n@@ -306,7 +306,6 @@ class PolynomialFeatures(TransformerMixin, BaseEstimator):\n feature_names.append(name)\n return np.asarray(feature_names, dtype=object)\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"\n Compute number of output features.\n@@ -324,84 +323,6 @@ class PolynomialFeatures(TransformerMixin, BaseEstimator):\n self : object\n Fitted transformer.\n \"\"\"\n- _, n_features = validate_data(self, X, accept_sparse=True).shape\n-\n- if isinstance(self.degree, Integral):\n- if self.degree == 0 and not self.include_bias:\n- raise ValueError(\n- \"Setting degree to zero and include_bias to False would result in\"\n- \" an empty output array.\"\n- )\n-\n- self._min_degree = 0\n- self._max_degree = self.degree\n- elif (\n- isinstance(self.degree, collections.abc.Iterable) and len(self.degree) == 2\n- ):\n- self._min_degree, self._max_degree = self.degree\n- if not (\n- isinstance(self._min_degree, Integral)\n- and isinstance(self._max_degree, Integral)\n- and self._min_degree >= 0\n- and self._min_degree <= self._max_degree\n- ):\n- raise ValueError(\n- \"degree=(min_degree, max_degree) must \"\n- \"be non-negative integers that fulfil \"\n- \"min_degree <= max_degree, got \"\n- f\"{self.degree}.\"\n- )\n- elif self._max_degree == 0 and not self.include_bias:\n- raise ValueError(\n- \"Setting both min_degree and max_degree to zero and include_bias to\"\n- \" False would result in an empty output array.\"\n- )\n- else:\n- raise ValueError(\n- \"degree must be a non-negative int or tuple \"\n- \"(min_degree, max_degree), got \"\n- f\"{self.degree}.\"\n- )\n-\n- self.n_output_features_ = self._num_combinations(\n- n_features=n_features,\n- min_degree=self._min_degree,\n- max_degree=self._max_degree,\n- interaction_only=self.interaction_only,\n- include_bias=self.include_bias,\n- )\n- if self.n_output_features_ > np.iinfo(np.intp).max:\n- msg = (\n- \"The output that would result from the current configuration would\"\n- f\" have {self.n_output_features_} features which is too large to be\"\n- f\" indexed by {np.intp().dtype.name}. Please change some or all of the\"\n- \" following:\\n- The number of features in the input, currently\"\n- f\" {n_features=}\\n- The range of degrees to calculate, currently\"\n- f\" [{self._min_degree}, {self._max_degree}]\\n- Whether to include only\"\n- f\" interaction terms, currently {self.interaction_only}\\n- Whether to\"\n- f\" include a bias term, currently {self.include_bias}.\"\n- )\n- if (\n- np.intp == np.int32\n- and self.n_output_features_ <= np.iinfo(np.int64).max\n- ): # pragma: nocover\n- msg += (\n- \"\\nNote that the current Python runtime has a limited 32 bit \"\n- \"address space and that this configuration would have been \"\n- \"admissible if run on a 64 bit Python runtime.\"\n- )\n- raise ValueError(msg)\n- # We also record the number of output features for\n- # _max_degree = 0\n- self._n_out_full = self._num_combinations(\n- n_features=n_features,\n- min_degree=0,\n- max_degree=self._max_degree,\n- interaction_only=self.interaction_only,\n- include_bias=self.include_bias,\n- )\n-\n- return self\n \n def transform(self, X):\n \"\"\"Transform data to polynomial features.\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/_polynomial.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"\n Compute number of output features.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The data.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n self : object\n Fitted transformer.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_and_spline_array_order[PolynomialFeatures]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_input_validation[params0-degree=\\\$min_degree, max_degree\\\$ must]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_input_validation[params1-degree=\\\$min_degree, max_degree\\\$ must]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_input_validation[params2-degree=\\\$min_degree, max_degree\\\$ must]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_input_validation[params3-int or tuple \\\$min_degree, max_degree\\\$]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-3-True-False-indices0]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-3-False-False-indices1]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-3-True-True-indices2]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-3-False-True-indices3]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-degree4-True-False-indices4]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-degree5-False-False-indices5]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-degree6-True-True-indices6]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[None-degree7-False-True-indices7]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-3-True-False-indices0]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-3-False-False-indices1]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-3-True-True-indices2]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-3-False-True-indices3]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-degree4-True-False-indices4]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-degree5-False-False-indices5]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-degree6-True-True-indices6]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_matrix-degree7-False-True-indices7]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[1-est0]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_array-3-True-False-indices0]", "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_features_one_feature[csr_array-3-False-False-indices1]", 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"sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[PolynomialFeatures()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PolynomialFeatures()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PolynomialFeatures()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[PolynomialFeatures()]", "sklearn/tests/test_common.py::test_check_param_validation[PolynomialFeatures()]", "sklearn/tests/test_common.py::test_set_output_transform[PolynomialFeatures()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-PolynomialFeatures()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-PolynomialFeatures()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-157	1.0	{ "code": "diff --git b/sklearn/discriminant_analysis.py a/sklearn/discriminant_analysis.py\nindex 74ada12ce..69339491d 100644\n--- b/sklearn/discriminant_analysis.py\n+++ a/sklearn/discriminant_analysis.py\n@@ -878,6 +878,7 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):\n self.store_covariance = store_covariance\n self.tol = tol\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit the model according to the given training data and parameters.\n \n@@ -902,6 +903,61 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n+ X, y = validate_data(self, X, y)\n+ check_classification_targets(y)\n+ self.classes_, y = np.unique(y, return_inverse=True)\n+ n_samples, n_features = X.shape\n+ n_classes = len(self.classes_)\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+ if self.priors is None:\n+ self.priors_ = np.bincount(y) / float(n_samples)\n+ else:\n+ self.priors_ = np.array(self.priors)\n+\n+ cov = None\n+ store_covariance = self.store_covariance\n+ if store_covariance:\n+ cov = []\n+ means = []\n+ scalings = []\n+ rotations = []\n+ for ind in range(n_classes):\n+ Xg = X[y == ind, :]\n+ meang = Xg.mean(0)\n+ means.append(meang)\n+ if len(Xg) == 1:\n+ raise ValueError(\n+ \"y has only 1 sample in class %s, covariance is ill defined.\"\n+ % str(self.classes_[ind])\n+ )\n+ Xgc = Xg - meang\n+ # Xgc = U * S * V.T\n+ _, S, Vt = np.linalg.svd(Xgc, full_matrices=False)\n+ S2 = (S*2) / (len(Xg) - 1)\n+ S2 = ((1 - self.reg_param) S2) + self.reg_param\n+ rank = np.sum(S2 > self.tol)\n+ if rank < n_features:\n+ warnings.warn(\n+ f\"The covariance matrix of class {ind} is not full rank. \"\n+ \"Increasing the value of parameter `reg_param` might help\"\n+ \" reducing the collinearity.\",\n+ linalg.LinAlgWarning,\n+ )\n+ if self.store_covariance or store_covariance:\n+ # cov = V * (S^2 / (n-1)) * V.T\n+ cov.append(np.dot(S2 * Vt.T, Vt))\n+ scalings.append(S2)\n+ rotations.append(Vt.T)\n+ if self.store_covariance or store_covariance:\n+ self.covariance_ = cov\n+ self.means_ = np.asarray(means)\n+ self.scalings_ = scalings\n+ self.rotations_ = rotations\n+ return self\n \n def _decision_function(self, X):\n # return log posterior, see eq (4.12) p. 110 of the ESL.\n", "test": null }	null	{ "code": "diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py\nindex 69339491d..74ada12ce 100644\n--- a/sklearn/discriminant_analysis.py\n+++ b/sklearn/discriminant_analysis.py\n@@ -878,7 +878,6 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):\n self.store_covariance = store_covariance\n self.tol = tol\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y):\n \"\"\"Fit the model according to the given training data and parameters.\n \n@@ -903,61 +902,6 @@ class QuadraticDiscriminantAnalysis(ClassifierMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n- X, y = validate_data(self, X, y)\n- check_classification_targets(y)\n- self.classes_, y = np.unique(y, return_inverse=True)\n- n_samples, n_features = X.shape\n- n_classes = len(self.classes_)\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- if self.priors is None:\n- self.priors_ = np.bincount(y) / float(n_samples)\n- else:\n- self.priors_ = np.array(self.priors)\n-\n- cov = None\n- store_covariance = self.store_covariance\n- if store_covariance:\n- cov = []\n- means = []\n- scalings = []\n- rotations = []\n- for ind in range(n_classes):\n- Xg = X[y == ind, :]\n- meang = Xg.mean(0)\n- means.append(meang)\n- if len(Xg) == 1:\n- raise ValueError(\n- \"y has only 1 sample in class %s, covariance is ill defined.\"\n- % str(self.classes_[ind])\n- )\n- Xgc = Xg - meang\n- # Xgc = U * S * V.T\n- _, S, Vt = np.linalg.svd(Xgc, full_matrices=False)\n- S2 = (S*2) / (len(Xg) - 1)\n- S2 = ((1 - self.reg_param) S2) + self.reg_param\n- rank = np.sum(S2 > self.tol)\n- if rank < n_features:\n- warnings.warn(\n- f\"The covariance matrix of class {ind} is not full rank. \"\n- \"Increasing the value of parameter `reg_param` might help\"\n- \" reducing the collinearity.\",\n- linalg.LinAlgWarning,\n- )\n- if self.store_covariance or store_covariance:\n- # cov = V * (S^2 / (n-1)) * V.T\n- cov.append(np.dot(S2 * Vt.T, Vt))\n- scalings.append(S2)\n- rotations.append(Vt.T)\n- if self.store_covariance or store_covariance:\n- self.covariance_ = cov\n- self.means_ = np.asarray(means)\n- self.scalings_ = scalings\n- self.rotations_ = rotations\n- return self\n \n def _decision_function(self, X):\n # return log posterior, see eq (4.12) p. 110 of the ESL.\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 model according to the given training data and parameters.\n\n .. versionchanged:: 0.19\n ``store_covariances`` has been moved to main constructor as\n ``store_covariance``.\n\n .. versionchanged:: 0.19\n ``tol`` has been moved to main constructor.\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 and\n `n_features` is the number of features.\n\n y : array-like of shape (n_samples,)\n Target values (integers).\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_discriminant_analysis.py::test_qda", "sklearn/tests/test_discriminant_analysis.py::test_qda_priors", "sklearn/tests/test_discriminant_analysis.py::test_qda_prior_type[list]", "sklearn/tests/test_discriminant_analysis.py::test_qda_prior_type[tuple]", "sklearn/tests/test_discriminant_analysis.py::test_qda_prior_type[array]", "sklearn/tests/test_discriminant_analysis.py::test_qda_prior_copy", "sklearn/tests/test_discriminant_analysis.py::test_qda_store_covariance", "sklearn/tests/test_discriminant_analysis.py::test_qda_regularization", "sklearn/discriminant_analysis.py::sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[QuadraticDiscriminantAnalysis()-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[QuadraticDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[QuadraticDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_check_param_validation[QuadraticDiscriminantAnalysis()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-158	1.0	{ "code": "diff --git b/sklearn/linear_model/_quantile.py a/sklearn/linear_model/_quantile.py\nindex d6e8002f4..883a41558 100644\n--- b/sklearn/linear_model/_quantile.py\n+++ a/sklearn/linear_model/_quantile.py\n@@ -139,6 +139,7 @@ class QuantileRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self.solver = solver\n self.solver_options = solver_options\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@@ -158,3 +159,138 @@ class QuantileRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self : object\n Returns self.\n \"\"\"\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=[\"csc\", \"csr\", \"coo\"],\n+ y_numeric=True,\n+ multi_output=False,\n+ )\n+ sample_weight = _check_sample_weight(sample_weight, X)\n+\n+ n_features = X.shape[1]\n+ n_params = n_features\n+\n+ if self.fit_intercept:\n+ n_params += 1\n+ # Note that centering y and X with _preprocess_data does not work\n+ # for quantile regression.\n+\n+ # The objective is defined as 1/n * sum(pinball loss) + alpha * L1.\n+ # So we rescale the penalty term, which is equivalent.\n+ alpha = np.sum(sample_weight) * self.alpha\n+\n+ if self.solver == \"interior-point\" and sp_version >= parse_version(\"1.11.0\"):\n+ raise ValueError(\n+ f\"Solver {self.solver} is not anymore available in SciPy >= 1.11.0.\"\n+ )\n+\n+ if sparse.issparse(X) and self.solver not in [\"highs\", \"highs-ds\", \"highs-ipm\"]:\n+ raise ValueError(\n+ f\"Solver {self.solver} does not support sparse X. \"\n+ \"Use solver 'highs' for example.\"\n+ )\n+ # make default solver more stable\n+ if self.solver_options is None and self.solver == \"interior-point\":\n+ solver_options = {\"lstsq\": True}\n+ else:\n+ solver_options = self.solver_options\n+\n+ # After rescaling alpha, the minimization problem is\n+ # min sum(pinball loss) + alpha * L1\n+ # Use linear programming formulation of quantile regression\n+ # min_x c x\n+ # A_eq x = b_eq\n+ # 0 <= x\n+ # x = (s0, s, t0, t, u, v) = slack variables >= 0\n+ # intercept = s0 - t0\n+ # coef = s - t\n+ # c = (0, alpha * 1_p, 0, alpha * 1_p, quantile * 1_n, (1-quantile) * 1_n)\n+ # residual = y - X@coef - intercept = u - v\n+ # A_eq = (1_n, X, -1_n, -X, diag(1_n), -diag(1_n))\n+ # b_eq = y\n+ # p = n_features\n+ # n = n_samples\n+ # 1_n = vector of length n with entries equal one\n+ # see https://stats.stackexchange.com/questions/384909/\n+ #\n+ # Filtering out zero sample weights from the beginning makes life\n+ # easier for the linprog solver.\n+ indices = np.nonzero(sample_weight)[0]\n+ n_indices = len(indices) # use n_mask instead of n_samples\n+ if n_indices < len(sample_weight):\n+ sample_weight = sample_weight[indices]\n+ X = _safe_indexing(X, indices)\n+ y = _safe_indexing(y, indices)\n+ c = np.concatenate(\n+ [\n+ np.full(2 * n_params, fill_value=alpha),\n+ sample_weight * self.quantile,\n+ sample_weight * (1 - self.quantile),\n+ ]\n+ )\n+ if self.fit_intercept:\n+ # do not penalize the intercept\n+ c[0] = 0\n+ c[n_params] = 0\n+\n+ if self.solver in [\"highs\", \"highs-ds\", \"highs-ipm\"]:\n+ # Note that highs methods always use a sparse CSC memory layout internally,\n+ # even for optimization problems parametrized using dense numpy arrays.\n+ # Therefore, we work with CSC matrices as early as possible to limit\n+ # unnecessary repeated memory copies.\n+ eye = sparse.eye(n_indices, dtype=X.dtype, format=\"csc\")\n+ if self.fit_intercept:\n+ ones = sparse.csc_matrix(np.ones(shape=(n_indices, 1), dtype=X.dtype))\n+ A_eq = sparse.hstack([ones, X, -ones, -X, eye, -eye], format=\"csc\")\n+ else:\n+ A_eq = sparse.hstack([X, -X, eye, -eye], format=\"csc\")\n+ else:\n+ eye = np.eye(n_indices)\n+ if self.fit_intercept:\n+ ones = np.ones((n_indices, 1))\n+ A_eq = np.concatenate([ones, X, -ones, -X, eye, -eye], axis=1)\n+ else:\n+ A_eq = np.concatenate([X, -X, eye, -eye], axis=1)\n+\n+ b_eq = y\n+\n+ result = linprog(\n+ c=c,\n+ A_eq=A_eq,\n+ b_eq=b_eq,\n+ method=self.solver,\n+ options=solver_options,\n+ )\n+ solution = result.x\n+ if not result.success:\n+ failure = {\n+ 1: \"Iteration limit reached.\",\n+ 2: \"Problem appears to be infeasible.\",\n+ 3: \"Problem appears to be unbounded.\",\n+ 4: \"Numerical difficulties encountered.\",\n+ }\n+ warnings.warn(\n+ \"Linear programming for QuantileRegressor did not succeed.\\n\"\n+ f\"Status is {result.status}: \"\n+ + failure.setdefault(result.status, \"unknown reason\")\n+ + \"\\n\"\n+ + \"Result message of linprog:\\n\"\n+ + result.message,\n+ ConvergenceWarning,\n+ )\n+\n+ # positive slack - negative slack\n+ # solution is an array with (params_pos, params_neg, u, v)\n+ params = solution[:n_params] - solution[n_params : 2 * n_params]\n+\n+ self.n_iter_ = result.nit\n+\n+ if self.fit_intercept:\n+ self.coef_ = params[1:]\n+ self.intercept_ = params[0]\n+ else:\n+ self.coef_ = params\n+ self.intercept_ = 0.0\n+ return self\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_quantile.py b/sklearn/linear_model/_quantile.py\nindex 883a41558..d6e8002f4 100644\n--- a/sklearn/linear_model/_quantile.py\n+++ b/sklearn/linear_model/_quantile.py\n@@ -139,7 +139,6 @@ class QuantileRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self.solver = solver\n self.solver_options = solver_options\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@@ -159,138 +158,3 @@ class QuantileRegressor(LinearModel, RegressorMixin, BaseEstimator):\n self : object\n Returns self.\n \"\"\"\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=[\"csc\", \"csr\", \"coo\"],\n- y_numeric=True,\n- multi_output=False,\n- )\n- sample_weight = _check_sample_weight(sample_weight, X)\n-\n- n_features = X.shape[1]\n- n_params = n_features\n-\n- if self.fit_intercept:\n- n_params += 1\n- # Note that centering y and X with _preprocess_data does not work\n- # for quantile regression.\n-\n- # The objective is defined as 1/n * sum(pinball loss) + alpha * L1.\n- # So we rescale the penalty term, which is equivalent.\n- alpha = np.sum(sample_weight) * self.alpha\n-\n- if self.solver == \"interior-point\" and sp_version >= parse_version(\"1.11.0\"):\n- raise ValueError(\n- f\"Solver {self.solver} is not anymore available in SciPy >= 1.11.0.\"\n- )\n-\n- if sparse.issparse(X) and self.solver not in [\"highs\", \"highs-ds\", \"highs-ipm\"]:\n- raise ValueError(\n- f\"Solver {self.solver} does not support sparse X. \"\n- \"Use solver 'highs' for example.\"\n- )\n- # make default solver more stable\n- if self.solver_options is None and self.solver == \"interior-point\":\n- solver_options = {\"lstsq\": True}\n- else:\n- solver_options = self.solver_options\n-\n- # After rescaling alpha, the minimization problem is\n- # min sum(pinball loss) + alpha * L1\n- # Use linear programming formulation of quantile regression\n- # min_x c x\n- # A_eq x = b_eq\n- # 0 <= x\n- # x = (s0, s, t0, t, u, v) = slack variables >= 0\n- # intercept = s0 - t0\n- # coef = s - t\n- # c = (0, alpha * 1_p, 0, alpha * 1_p, quantile * 1_n, (1-quantile) * 1_n)\n- # residual = y - X@coef - intercept = u - v\n- # A_eq = (1_n, X, -1_n, -X, diag(1_n), -diag(1_n))\n- # b_eq = y\n- # p = n_features\n- # n = n_samples\n- # 1_n = vector of length n with entries equal one\n- # see https://stats.stackexchange.com/questions/384909/\n- #\n- # Filtering out zero sample weights from the beginning makes life\n- # easier for the linprog solver.\n- indices = np.nonzero(sample_weight)[0]\n- n_indices = len(indices) # use n_mask instead of n_samples\n- if n_indices < len(sample_weight):\n- sample_weight = sample_weight[indices]\n- X = _safe_indexing(X, indices)\n- y = _safe_indexing(y, indices)\n- c = np.concatenate(\n- [\n- np.full(2 * n_params, fill_value=alpha),\n- sample_weight * self.quantile,\n- sample_weight * (1 - self.quantile),\n- ]\n- )\n- if self.fit_intercept:\n- # do not penalize the intercept\n- c[0] = 0\n- c[n_params] = 0\n-\n- if self.solver in [\"highs\", \"highs-ds\", \"highs-ipm\"]:\n- # Note that highs methods always use a sparse CSC memory layout internally,\n- # even for optimization problems parametrized using dense numpy arrays.\n- # Therefore, we work with CSC matrices as early as possible to limit\n- # unnecessary repeated memory copies.\n- eye = sparse.eye(n_indices, dtype=X.dtype, format=\"csc\")\n- if self.fit_intercept:\n- ones = sparse.csc_matrix(np.ones(shape=(n_indices, 1), dtype=X.dtype))\n- A_eq = sparse.hstack([ones, X, -ones, -X, eye, -eye], format=\"csc\")\n- else:\n- A_eq = sparse.hstack([X, -X, eye, -eye], format=\"csc\")\n- else:\n- eye = np.eye(n_indices)\n- if self.fit_intercept:\n- ones = np.ones((n_indices, 1))\n- A_eq = np.concatenate([ones, X, -ones, -X, eye, -eye], axis=1)\n- else:\n- A_eq = np.concatenate([X, -X, eye, -eye], axis=1)\n-\n- b_eq = y\n-\n- result = linprog(\n- c=c,\n- A_eq=A_eq,\n- b_eq=b_eq,\n- method=self.solver,\n- options=solver_options,\n- )\n- solution = result.x\n- if not result.success:\n- failure = {\n- 1: \"Iteration limit reached.\",\n- 2: \"Problem appears to be infeasible.\",\n- 3: \"Problem appears to be unbounded.\",\n- 4: \"Numerical difficulties encountered.\",\n- }\n- warnings.warn(\n- \"Linear programming for QuantileRegressor did not succeed.\\n\"\n- f\"Status is {result.status}: \"\n- + failure.setdefault(result.status, \"unknown reason\")\n- + \"\\n\"\n- + \"Result message of linprog:\\n\"\n- + result.message,\n- ConvergenceWarning,\n- )\n-\n- # positive slack - negative slack\n- # solution is an array with (params_pos, params_neg, u, v)\n- params = solution[:n_params] - solution[n_params : 2 * n_params]\n-\n- self.n_iter_ = result.nit\n-\n- if self.fit_intercept:\n- self.coef_ = params[1:]\n- self.intercept_ = params[0]\n- else:\n- self.coef_ = params\n- self.intercept_ = 0.0\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/_quantile.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 data.\n\n y : array-like of shape (n_samples,)\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 self.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_quantile.py::test_quantile_toy_example[0.5-0-1-None]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_toy_example[0.51-0-1-10]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_toy_example[0.49-0-1-1]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_toy_example[0.5-0.01-1-1]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_toy_example[0.5-100-2-0]", "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_quantile.py::test_quantile_estimates_calibration[0.5]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_estimates_calibration[0.9]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_estimates_calibration[0.05]", "sklearn/linear_model/tests/test_quantile.py::test_quantile_sample_weight", "sklearn/linear_model/tests/test_quantile.py::test_asymmetric_error[0.2]", "sklearn/linear_model/tests/test_quantile.py::test_asymmetric_error[0.5]", "sklearn/linear_model/tests/test_quantile.py::test_asymmetric_error[0.8]", "sklearn/linear_model/tests/test_quantile.py::test_equivariance[0.2]", "sklearn/linear_model/tests/test_quantile.py::test_equivariance[0.5]", "sklearn/linear_model/tests/test_quantile.py::test_equivariance[0.8]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-csc_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-csc_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-csr_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-csr_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-coo_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-coo_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ds-csc_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ds-csc_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ds-csr_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ds-csr_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ds-coo_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ds-coo_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ipm-csc_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ipm-csc_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ipm-csr_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ipm-csr_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ipm-coo_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[True-highs-ipm-coo_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-csc_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-csc_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-csr_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-csr_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-coo_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-coo_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ds-csc_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ds-csc_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ds-csr_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ds-csr_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ds-coo_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ds-coo_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ipm-csc_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ipm-csc_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ipm-csr_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ipm-csr_array]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ipm-coo_matrix]", "sklearn/linear_model/tests/test_quantile.py::test_sparse_input[False-highs-ipm-coo_array]", "sklearn/linear_model/tests/test_quantile.py::test_error_interior_point_future", "sklearn/linear_model/_quantile.py::sklearn.linear_model._quantile.QuantileRegressor", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[QuantileRegressor()-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[QuantileRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[QuantileRegressor()]", "sklearn/tests/test_common.py::test_check_param_validation[QuantileRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-159	1.0	{ "code": "diff --git b/sklearn/linear_model/_ransac.py a/sklearn/linear_model/_ransac.py\nindex b604e9a0f..f3144f7e7 100644\n--- b/sklearn/linear_model/_ransac.py\n+++ a/sklearn/linear_model/_ransac.py\n@@ -314,6 +314,14 @@ class RANSACRegressor(\n self.random_state = random_state\n self.loss = loss\n \n+ @_fit_context(\n+ # RansacRegressor.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n+ # TODO(1.7): remove `sample_weight` from the signature after deprecation\n+ # cycle; for backwards compatibility: pop it from `fit_params` before the\n+ # `_raise_for_params` check and reinsert it after the check\n+ @_deprecate_positional_args(version=\"1.7\")\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit estimator using RANSAC algorithm.\n \n@@ -355,6 +363,247 @@ class RANSACRegressor(\n `is_data_valid` and `is_model_valid` return False for all\n `max_trials` randomly chosen sub-samples.\n \"\"\"\n+ # Need to validate separately here. We can't pass multi_output=True\n+ # because that would allow y to be csr. Delay expensive finiteness\n+ # check to the estimator's own input validation.\n+ _raise_for_params(fit_params, self, \"fit\")\n+ check_X_params = dict(accept_sparse=\"csr\", ensure_all_finite=False)\n+ check_y_params = dict(ensure_2d=False)\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+\n+ if self.estimator is not None:\n+ estimator = clone(self.estimator)\n+ else:\n+ estimator = LinearRegression()\n+\n+ if self.min_samples is None:\n+ if not isinstance(estimator, LinearRegression):\n+ raise ValueError(\n+ \"`min_samples` needs to be explicitly set when estimator \"\n+ \"is not a LinearRegression.\"\n+ )\n+ min_samples = X.shape[1] + 1\n+ elif 0 < self.min_samples < 1:\n+ min_samples = np.ceil(self.min_samples X.shape[0])\n+ elif self.min_samples >= 1:\n+ min_samples = self.min_samples\n+ if min_samples > X.shape[0]:\n+ raise ValueError(\n+ \"`min_samples` may not be larger than number \"\n+ \"of samples: n_samples = %d.\" % (X.shape[0])\n+ )\n+\n+ if self.residual_threshold is None:\n+ # MAD (median absolute deviation)\n+ residual_threshold = np.median(np.abs(y - np.median(y)))\n+ else:\n+ residual_threshold = self.residual_threshold\n+\n+ if self.loss == \"absolute_error\":\n+ if y.ndim == 1:\n+ loss_function = lambda y_true, y_pred: np.abs(y_true - y_pred)\n+ else:\n+ loss_function = lambda y_true, y_pred: np.sum(\n+ np.abs(y_true - y_pred), axis=1\n+ )\n+ elif self.loss == \"squared_error\":\n+ if y.ndim == 1:\n+ loss_function = lambda y_true, y_pred: (y_true - y_pred) 2\n+ else:\n+ loss_function = lambda y_true, y_pred: np.sum(\n+ (y_true - y_pred) 2, axis=1\n+ )\n+\n+ elif callable(self.loss):\n+ loss_function = self.loss\n+\n+ random_state = check_random_state(self.random_state)\n+\n+ try: # Not all estimator accept a random_state\n+ estimator.set_params(random_state=random_state)\n+ except ValueError:\n+ pass\n+\n+ estimator_fit_has_sample_weight = has_fit_parameter(estimator, \"sample_weight\")\n+ estimator_name = type(estimator).__name__\n+ if sample_weight is not None and not estimator_fit_has_sample_weight:\n+ raise ValueError(\n+ \"%s does not support sample_weight. Sample\"\n+ \" weights are only used for the calibration\"\n+ \" itself.\" % estimator_name\n+ )\n+\n+ if sample_weight is not None:\n+ fit_params[\"sample_weight\"] = sample_weight\n+\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"fit\", fit_params)\n+ else:\n+ routed_params = Bunch()\n+ routed_params.estimator = Bunch(fit={}, predict={}, score={})\n+ if sample_weight is not None:\n+ sample_weight = _check_sample_weight(sample_weight, X)\n+ routed_params.estimator.fit = {\"sample_weight\": sample_weight}\n+\n+ n_inliers_best = 1\n+ score_best = -np.inf\n+ inlier_mask_best = None\n+ X_inlier_best = None\n+ y_inlier_best = None\n+ inlier_best_idxs_subset = None\n+ self.n_skips_no_inliers_ = 0\n+ self.n_skips_invalid_data_ = 0\n+ self.n_skips_invalid_model_ = 0\n+\n+ # number of data samples\n+ n_samples = X.shape[0]\n+ sample_idxs = np.arange(n_samples)\n+\n+ self.n_trials_ = 0\n+ max_trials = self.max_trials\n+ while self.n_trials_ < max_trials:\n+ self.n_trials_ += 1\n+\n+ if (\n+ self.n_skips_no_inliers_\n+ + self.n_skips_invalid_data_\n+ + self.n_skips_invalid_model_\n+ ) > self.max_skips:\n+ break\n+\n+ # choose random sample set\n+ subset_idxs = sample_without_replacement(\n+ n_samples, min_samples, random_state=random_state\n+ )\n+ X_subset = X[subset_idxs]\n+ y_subset = y[subset_idxs]\n+\n+ # check if random sample set is valid\n+ if self.is_data_valid is not None and not self.is_data_valid(\n+ X_subset, y_subset\n+ ):\n+ self.n_skips_invalid_data_ += 1\n+ continue\n+\n+ # cut `fit_params` down to `subset_idxs`\n+ fit_params_subset = _check_method_params(\n+ X, params=routed_params.estimator.fit, indices=subset_idxs\n+ )\n+\n+ # fit model for current random sample set\n+ estimator.fit(X_subset, y_subset, fit_params_subset)\n+\n+ # check if estimated model is valid\n+ if self.is_model_valid is not None and not self.is_model_valid(\n+ estimator, X_subset, y_subset\n+ ):\n+ self.n_skips_invalid_model_ += 1\n+ continue\n+\n+ # residuals of all data for current random sample model\n+ y_pred = estimator.predict(X)\n+ residuals_subset = loss_function(y, y_pred)\n+\n+ # classify data into inliers and outliers\n+ inlier_mask_subset = residuals_subset <= residual_threshold\n+ n_inliers_subset = np.sum(inlier_mask_subset)\n+\n+ # less inliers -> skip current random sample\n+ if n_inliers_subset < n_inliers_best:\n+ self.n_skips_no_inliers_ += 1\n+ continue\n+\n+ # extract inlier data set\n+ inlier_idxs_subset = sample_idxs[inlier_mask_subset]\n+ X_inlier_subset = X[inlier_idxs_subset]\n+ y_inlier_subset = y[inlier_idxs_subset]\n+\n+ # cut `fit_params` down to `inlier_idxs_subset`\n+ score_params_inlier_subset = _check_method_params(\n+ X, params=routed_params.estimator.score, indices=inlier_idxs_subset\n+ )\n+\n+ # score of inlier data set\n+ score_subset = estimator.score(\n+ X_inlier_subset,\n+ y_inlier_subset,\n+ *score_params_inlier_subset,\n+ )\n+\n+ # same number of inliers but worse score -> skip current random\n+ # sample\n+ if n_inliers_subset == n_inliers_best and score_subset < score_best:\n+ continue\n+\n+ # save current random sample as best sample\n+ n_inliers_best = n_inliers_subset\n+ score_best = score_subset\n+ inlier_mask_best = inlier_mask_subset\n+ X_inlier_best = X_inlier_subset\n+ y_inlier_best = y_inlier_subset\n+ inlier_best_idxs_subset = inlier_idxs_subset\n+\n+ max_trials = min(\n+ max_trials,\n+ _dynamic_max_trials(\n+ n_inliers_best, n_samples, min_samples, self.stop_probability\n+ ),\n+ )\n+\n+ # break if sufficient number of inliers or score is reached\n+ if n_inliers_best >= self.stop_n_inliers or score_best >= self.stop_score:\n+ break\n+\n+ # if none of the iterations met the required criteria\n+ if inlier_mask_best is None:\n+ if (\n+ self.n_skips_no_inliers_\n+ + self.n_skips_invalid_data_\n+ + self.n_skips_invalid_model_\n+ ) > self.max_skips:\n+ raise ValueError(\n+ \"RANSAC skipped more iterations than `max_skips` without\"\n+ \" finding a valid consensus set. Iterations were skipped\"\n+ \" because each randomly chosen sub-sample failed the\"\n+ \" passing criteria. See estimator attributes for\"\n+ \" diagnostics (n_skips).\"\n+ )\n+ else:\n+ raise ValueError(\n+ \"RANSAC could not find a valid consensus set. All\"\n+ \" `max_trials` iterations were skipped because each\"\n+ \" randomly chosen sub-sample failed the passing criteria.\"\n+ \" See estimator attributes for diagnostics (n_skips).\"\n+ )\n+ else:\n+ if (\n+ self.n_skips_no_inliers_\n+ + self.n_skips_invalid_data_\n+ + self.n_skips_invalid_model_\n+ ) > self.max_skips:\n+ warnings.warn(\n+ (\n+ \"RANSAC found a valid consensus set but exited\"\n+ \" early due to skipping more iterations than\"\n+ \" `max_skips`. See estimator attributes for\"\n+ \" diagnostics (n_skips).\"\n+ ),\n+ ConvergenceWarning,\n+ )\n+\n+ # estimate final model using all inliers\n+ fit_params_best_idxs_subset = _check_method_params(\n+ X, params=routed_params.estimator.fit, indices=inlier_best_idxs_subset\n+ )\n+\n+ estimator.fit(X_inlier_best, y_inlier_best, fit_params_best_idxs_subset)\n+\n+ self.estimator_ = estimator\n+ self.inlier_mask_ = inlier_mask_best\n+ return self\n \n def predict(self, X, params):\n \"\"\"Predict using the estimated model.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py\nindex f3144f7e7..b604e9a0f 100644\n--- a/sklearn/linear_model/_ransac.py\n+++ b/sklearn/linear_model/_ransac.py\n@@ -314,14 +314,6 @@ class RANSACRegressor(\n self.random_state = random_state\n self.loss = loss\n \n- @_fit_context(\n- # RansacRegressor.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n- # TODO(1.7): remove `sample_weight` from the signature after deprecation\n- # cycle; for backwards compatibility: pop it from `fit_params` before the\n- # `_raise_for_params` check and reinsert it after the check\n- @_deprecate_positional_args(version=\"1.7\")\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit estimator using RANSAC algorithm.\n \n@@ -363,247 +355,6 @@ class RANSACRegressor(\n `is_data_valid` and `is_model_valid` return False for all\n `max_trials` randomly chosen sub-samples.\n \"\"\"\n- # Need to validate separately here. We can't pass multi_output=True\n- # because that would allow y to be csr. Delay expensive finiteness\n- # check to the estimator's own input validation.\n- _raise_for_params(fit_params, self, \"fit\")\n- check_X_params = dict(accept_sparse=\"csr\", ensure_all_finite=False)\n- check_y_params = dict(ensure_2d=False)\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-\n- if self.estimator is not None:\n- estimator = clone(self.estimator)\n- else:\n- estimator = LinearRegression()\n-\n- if self.min_samples is None:\n- if not isinstance(estimator, LinearRegression):\n- raise ValueError(\n- \"`min_samples` needs to be explicitly set when estimator \"\n- \"is not a LinearRegression.\"\n- )\n- min_samples = X.shape[1] + 1\n- elif 0 < self.min_samples < 1:\n- min_samples = np.ceil(self.min_samples X.shape[0])\n- elif self.min_samples >= 1:\n- min_samples = self.min_samples\n- if min_samples > X.shape[0]:\n- raise ValueError(\n- \"`min_samples` may not be larger than number \"\n- \"of samples: n_samples = %d.\" % (X.shape[0])\n- )\n-\n- if self.residual_threshold is None:\n- # MAD (median absolute deviation)\n- residual_threshold = np.median(np.abs(y - np.median(y)))\n- else:\n- residual_threshold = self.residual_threshold\n-\n- if self.loss == \"absolute_error\":\n- if y.ndim == 1:\n- loss_function = lambda y_true, y_pred: np.abs(y_true - y_pred)\n- else:\n- loss_function = lambda y_true, y_pred: np.sum(\n- np.abs(y_true - y_pred), axis=1\n- )\n- elif self.loss == \"squared_error\":\n- if y.ndim == 1:\n- loss_function = lambda y_true, y_pred: (y_true - y_pred) 2\n- else:\n- loss_function = lambda y_true, y_pred: np.sum(\n- (y_true - y_pred) 2, axis=1\n- )\n-\n- elif callable(self.loss):\n- loss_function = self.loss\n-\n- random_state = check_random_state(self.random_state)\n-\n- try: # Not all estimator accept a random_state\n- estimator.set_params(random_state=random_state)\n- except ValueError:\n- pass\n-\n- estimator_fit_has_sample_weight = has_fit_parameter(estimator, \"sample_weight\")\n- estimator_name = type(estimator).__name__\n- if sample_weight is not None and not estimator_fit_has_sample_weight:\n- raise ValueError(\n- \"%s does not support sample_weight. Sample\"\n- \" weights are only used for the calibration\"\n- \" itself.\" % estimator_name\n- )\n-\n- if sample_weight is not None:\n- fit_params[\"sample_weight\"] = sample_weight\n-\n- if _routing_enabled():\n- routed_params = process_routing(self, \"fit\", fit_params)\n- else:\n- routed_params = Bunch()\n- routed_params.estimator = Bunch(fit={}, predict={}, score={})\n- if sample_weight is not None:\n- sample_weight = _check_sample_weight(sample_weight, X)\n- routed_params.estimator.fit = {\"sample_weight\": sample_weight}\n-\n- n_inliers_best = 1\n- score_best = -np.inf\n- inlier_mask_best = None\n- X_inlier_best = None\n- y_inlier_best = None\n- inlier_best_idxs_subset = None\n- self.n_skips_no_inliers_ = 0\n- self.n_skips_invalid_data_ = 0\n- self.n_skips_invalid_model_ = 0\n-\n- # number of data samples\n- n_samples = X.shape[0]\n- sample_idxs = np.arange(n_samples)\n-\n- self.n_trials_ = 0\n- max_trials = self.max_trials\n- while self.n_trials_ < max_trials:\n- self.n_trials_ += 1\n-\n- if (\n- self.n_skips_no_inliers_\n- + self.n_skips_invalid_data_\n- + self.n_skips_invalid_model_\n- ) > self.max_skips:\n- break\n-\n- # choose random sample set\n- subset_idxs = sample_without_replacement(\n- n_samples, min_samples, random_state=random_state\n- )\n- X_subset = X[subset_idxs]\n- y_subset = y[subset_idxs]\n-\n- # check if random sample set is valid\n- if self.is_data_valid is not None and not self.is_data_valid(\n- X_subset, y_subset\n- ):\n- self.n_skips_invalid_data_ += 1\n- continue\n-\n- # cut `fit_params` down to `subset_idxs`\n- fit_params_subset = _check_method_params(\n- X, params=routed_params.estimator.fit, indices=subset_idxs\n- )\n-\n- # fit model for current random sample set\n- estimator.fit(X_subset, y_subset, fit_params_subset)\n-\n- # check if estimated model is valid\n- if self.is_model_valid is not None and not self.is_model_valid(\n- estimator, X_subset, y_subset\n- ):\n- self.n_skips_invalid_model_ += 1\n- continue\n-\n- # residuals of all data for current random sample model\n- y_pred = estimator.predict(X)\n- residuals_subset = loss_function(y, y_pred)\n-\n- # classify data into inliers and outliers\n- inlier_mask_subset = residuals_subset <= residual_threshold\n- n_inliers_subset = np.sum(inlier_mask_subset)\n-\n- # less inliers -> skip current random sample\n- if n_inliers_subset < n_inliers_best:\n- self.n_skips_no_inliers_ += 1\n- continue\n-\n- # extract inlier data set\n- inlier_idxs_subset = sample_idxs[inlier_mask_subset]\n- X_inlier_subset = X[inlier_idxs_subset]\n- y_inlier_subset = y[inlier_idxs_subset]\n-\n- # cut `fit_params` down to `inlier_idxs_subset`\n- score_params_inlier_subset = _check_method_params(\n- X, params=routed_params.estimator.score, indices=inlier_idxs_subset\n- )\n-\n- # score of inlier data set\n- score_subset = estimator.score(\n- X_inlier_subset,\n- y_inlier_subset,\n- *score_params_inlier_subset,\n- )\n-\n- # same number of inliers but worse score -> skip current random\n- # sample\n- if n_inliers_subset == n_inliers_best and score_subset < score_best:\n- continue\n-\n- # save current random sample as best sample\n- n_inliers_best = n_inliers_subset\n- score_best = score_subset\n- inlier_mask_best = inlier_mask_subset\n- X_inlier_best = X_inlier_subset\n- y_inlier_best = y_inlier_subset\n- inlier_best_idxs_subset = inlier_idxs_subset\n-\n- max_trials = min(\n- max_trials,\n- _dynamic_max_trials(\n- n_inliers_best, n_samples, min_samples, self.stop_probability\n- ),\n- )\n-\n- # break if sufficient number of inliers or score is reached\n- if n_inliers_best >= self.stop_n_inliers or score_best >= self.stop_score:\n- break\n-\n- # if none of the iterations met the required criteria\n- if inlier_mask_best is None:\n- if (\n- self.n_skips_no_inliers_\n- + self.n_skips_invalid_data_\n- + self.n_skips_invalid_model_\n- ) > self.max_skips:\n- raise ValueError(\n- \"RANSAC skipped more iterations than `max_skips` without\"\n- \" finding a valid consensus set. Iterations were skipped\"\n- \" because each randomly chosen sub-sample failed the\"\n- \" passing criteria. See estimator attributes for\"\n- \" diagnostics (n_skips).\"\n- )\n- else:\n- raise ValueError(\n- \"RANSAC could not find a valid consensus set. All\"\n- \" `max_trials` iterations were skipped because each\"\n- \" randomly chosen sub-sample failed the passing criteria.\"\n- \" See estimator attributes for diagnostics (n_skips).\"\n- )\n- else:\n- if (\n- self.n_skips_no_inliers_\n- + self.n_skips_invalid_data_\n- + self.n_skips_invalid_model_\n- ) > self.max_skips:\n- warnings.warn(\n- (\n- \"RANSAC found a valid consensus set but exited\"\n- \" early due to skipping more iterations than\"\n- \" `max_skips`. See estimator attributes for\"\n- \" diagnostics (n_skips).\"\n- ),\n- ConvergenceWarning,\n- )\n-\n- # estimate final model using all inliers\n- fit_params_best_idxs_subset = _check_method_params(\n- X, params=routed_params.estimator.fit, indices=inlier_best_idxs_subset\n- )\n-\n- estimator.fit(X_inlier_best, y_inlier_best, fit_params_best_idxs_subset)\n-\n- self.estimator_ = estimator\n- self.inlier_mask_ = inlier_mask_best\n- return self\n \n def predict(self, X, params):\n \"\"\"Predict using the estimated 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/_ransac.py.\nHere is the description for the function:\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit estimator using RANSAC algorithm.\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.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Individual weights for each sample\n raises error if sample_weight is passed and estimator\n fit method does not support it.\n\n .. versionadded:: 0.18\n\n *fit_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\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 self : object\n Fitted `RANSACRegressor` estimator.\n\n Raises\n ------\n ValueError\n If no valid consensus set could be found. This occurs if\n `is_data_valid` and `is_model_valid` return False for all\n `max_trials` randomly chosen sub-samples.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[RANSACRegressor]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[RANSACRegressor]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[RANSACRegressor]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_inliers_outliers", "sklearn/linear_model/tests/test_ransac.py::test_ransac_is_data_valid", "sklearn/linear_model/tests/test_ransac.py::test_ransac_is_model_valid", "sklearn/linear_model/tests/test_ransac.py::test_ransac_max_trials", "sklearn/linear_model/tests/test_ransac.py::test_ransac_stop_n_inliers", "sklearn/linear_model/tests/test_ransac.py::test_ransac_stop_score", "sklearn/linear_model/tests/test_ransac.py::test_ransac_score", "sklearn/linear_model/tests/test_ransac.py::test_ransac_predict", "sklearn/linear_model/tests/test_ransac.py::test_ransac_no_valid_data", "sklearn/linear_model/tests/test_ransac.py::test_ransac_no_valid_model", "sklearn/linear_model/tests/test_ransac.py::test_ransac_exceed_max_skips", "sklearn/linear_model/tests/test_ransac.py::test_ransac_warn_exceed_max_skips", "sklearn/linear_model/tests/test_ransac.py::test_ransac_sparse[coo_matrix]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_sparse[coo_array]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_sparse[csr_matrix]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_sparse[csr_array]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_sparse[csc_matrix]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_sparse[csc_array]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_none_estimator", "sklearn/linear_model/tests/test_ransac.py::test_ransac_min_n_samples", "sklearn/linear_model/tests/test_ransac.py::test_ransac_multi_dimensional_targets", "sklearn/linear_model/tests/test_ransac.py::test_ransac_residual_loss", "sklearn/linear_model/tests/test_ransac.py::test_ransac_default_residual_threshold", "sklearn/linear_model/tests/test_ransac.py::test_ransac_fit_sample_weight", "sklearn/linear_model/tests/test_ransac.py::test_ransac_final_model_fit_sample_weight", "sklearn/linear_model/tests/test_ransac.py::test_perfect_horizontal_line", "sklearn/linear_model/_ransac.py::sklearn.linear_model._ransac.RANSACRegressor", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_sample_weights_pandas_series]", "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_complex_data]", "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_estimators_empty_data_messages]", "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_supervised_y_no_nan]", "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_1sample]", "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_fit1d]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]", "sklearn/tests/test_common.py::test_check_param_validation[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-160	1.0	{ "code": "diff --git b/sklearn/linear_model/_ransac.py a/sklearn/linear_model/_ransac.py\nindex 081a33626..f3144f7e7 100644\n--- b/sklearn/linear_model/_ransac.py\n+++ a/sklearn/linear_model/_ransac.py\n@@ -631,6 +631,25 @@ class RANSACRegressor(\n y : array, shape = [n_samples] or [n_samples, n_targets]\n Returns predicted values.\n \"\"\"\n+ check_is_fitted(self)\n+ X = validate_data(\n+ self,\n+ X,\n+ ensure_all_finite=False,\n+ accept_sparse=True,\n+ reset=False,\n+ )\n+\n+ _raise_for_params(params, self, \"predict\")\n+\n+ if _routing_enabled():\n+ predict_params = process_routing(self, \"predict\", params).estimator[\n+ \"predict\"\n+ ]\n+ else:\n+ predict_params = {}\n+\n+ return self.estimator_.predict(X, predict_params)\n \n def score(self, X, y, **params):\n \"\"\"Return the score of the prediction.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py\nindex f3144f7e7..081a33626 100644\n--- a/sklearn/linear_model/_ransac.py\n+++ b/sklearn/linear_model/_ransac.py\n@@ -631,25 +631,6 @@ class RANSACRegressor(\n y : array, shape = [n_samples] or [n_samples, n_targets]\n Returns predicted values.\n \"\"\"\n- check_is_fitted(self)\n- X = validate_data(\n- self,\n- X,\n- ensure_all_finite=False,\n- accept_sparse=True,\n- reset=False,\n- )\n-\n- _raise_for_params(params, self, \"predict\")\n-\n- if _routing_enabled():\n- predict_params = process_routing(self, \"predict\", params).estimator[\n- \"predict\"\n- ]\n- else:\n- predict_params = {}\n-\n- return self.estimator_.predict(X, predict_params)\n \n def score(self, X, y, **params):\n \"\"\"Return the score of the prediction.\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/_ransac.py.\nHere is the description for the function:\n def predict(self, X, params):\n \"\"\"Predict using the estimated model.\n\n This is a wrapper for `estimator_.predict(X)`.\n\n Parameters\n ----------\n X : {array-like or sparse matrix} of shape (n_samples, n_features)\n Input data.\n\n params : dict\n Parameters routed to the `predict` method of the sub-estimator via\n the metadata routing API.\n\n .. versionadded:: 1.5\n\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 y : array, shape = [n_samples] or [n_samples, n_targets]\n Returns predicted values.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[RANSACRegressor]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_predict", "sklearn/linear_model/tests/test_ransac.py::test_ransac_none_estimator", "sklearn/linear_model/tests/test_ransac.py::test_ransac_min_n_samples", "sklearn/linear_model/tests/test_ransac.py::test_ransac_residual_loss", "sklearn/linear_model/_ransac.py::sklearn.linear_model._ransac.RANSACRegressor", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[RANSACRegressor]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_estimators_dtypes]", "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_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_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_estimators_unfitted]", "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_dict_unchanged]", "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_fit2d_predict1d]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[RANSACRegressor]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-161	1.0	{ "code": "diff --git b/sklearn/linear_model/_ransac.py a/sklearn/linear_model/_ransac.py\nindex 96310fa3c..f3144f7e7 100644\n--- b/sklearn/linear_model/_ransac.py\n+++ a/sklearn/linear_model/_ransac.py\n@@ -680,6 +680,22 @@ class RANSACRegressor(\n z : float\n Score of the prediction.\n \"\"\"\n+ check_is_fitted(self)\n+ X = validate_data(\n+ self,\n+ X,\n+ ensure_all_finite=False,\n+ accept_sparse=True,\n+ reset=False,\n+ )\n+\n+ _raise_for_params(params, self, \"score\")\n+ if _routing_enabled():\n+ score_params = process_routing(self, \"score\", params).estimator[\"score\"]\n+ else:\n+ score_params = {}\n+\n+ return self.estimator_.score(X, y, score_params)\n \n def get_metadata_routing(self):\n \"\"\"Get metadata routing of this object.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py\nindex f3144f7e7..96310fa3c 100644\n--- a/sklearn/linear_model/_ransac.py\n+++ b/sklearn/linear_model/_ransac.py\n@@ -680,22 +680,6 @@ class RANSACRegressor(\n z : float\n Score of the prediction.\n \"\"\"\n- check_is_fitted(self)\n- X = validate_data(\n- self,\n- X,\n- ensure_all_finite=False,\n- accept_sparse=True,\n- reset=False,\n- )\n-\n- _raise_for_params(params, self, \"score\")\n- if _routing_enabled():\n- score_params = process_routing(self, \"score\", params).estimator[\"score\"]\n- else:\n- score_params = {}\n-\n- return self.estimator_.score(X, y, score_params)\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/linear_model/_ransac.py.\nHere is the description for the function:\n def score(self, X, y, params):\n \"\"\"Return the score of the prediction.\n\n This is a wrapper for `estimator_.score(X, y)`.\n\n Parameters\n ----------\n X : (array-like or 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.\n\n params : dict\n Parameters routed to the `score` method of the sub-estimator via\n the metadata routing API.\n\n .. versionadded:: 1.5\n\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 z : float\n Score of the prediction.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[RANSACRegressor]", "sklearn/linear_model/tests/test_ransac.py::test_ransac_score", "sklearn/linear_model/_ransac.py::sklearn.linear_model._ransac.RANSACRegressor", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[RANSACRegressor]", "sklearn/tests/test_common.py::test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_fit_score_takes_y]", "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_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_check_n_features_in_after_fitting[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RANSACRegressor(estimator=LinearRegression(),max_trials=10)]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[RANSACRegressor]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-162	1.0	{ "code": "diff --git b/sklearn/kernel_approximation.py a/sklearn/kernel_approximation.py\nindex cf989de06..92d906dde 100644\n--- b/sklearn/kernel_approximation.py\n+++ a/sklearn/kernel_approximation.py\n@@ -333,6 +333,7 @@ class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato\n self.n_components = n_components\n self.random_state = random_state\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model with X.\n \n@@ -353,6 +354,30 @@ class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X, accept_sparse=\"csr\")\n+ random_state = check_random_state(self.random_state)\n+ n_features = X.shape[1]\n+ sparse = sp.issparse(X)\n+ if self.gamma == \"scale\":\n+ # var = E[X^2] - E[X]^2 if sparse\n+ X_var = (X.multiply(X)).mean() - (X.mean()) ** 2 if sparse else X.var()\n+ self._gamma = 1.0 / (n_features * X_var) if X_var != 0 else 1.0\n+ else:\n+ self._gamma = self.gamma\n+ self.random_weights_ = (2.0 * self._gamma) ** 0.5 * random_state.normal(\n+ size=(n_features, self.n_components)\n+ )\n+\n+ self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components)\n+\n+ if X.dtype == np.float32:\n+ # Setting the data type of the fitted attribute will ensure the\n+ # output data type during `transform`.\n+ self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False)\n+ self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False)\n+\n+ self._n_features_out = self.n_components\n+ return self\n \n def transform(self, X):\n \"\"\"Apply the approximate feature map to X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py\nindex 92d906dde..cf989de06 100644\n--- a/sklearn/kernel_approximation.py\n+++ b/sklearn/kernel_approximation.py\n@@ -333,7 +333,6 @@ class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato\n self.n_components = n_components\n self.random_state = random_state\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model with X.\n \n@@ -354,30 +353,6 @@ class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X, accept_sparse=\"csr\")\n- random_state = check_random_state(self.random_state)\n- n_features = X.shape[1]\n- sparse = sp.issparse(X)\n- if self.gamma == \"scale\":\n- # var = E[X^2] - E[X]^2 if sparse\n- X_var = (X.multiply(X)).mean() - (X.mean()) ** 2 if sparse else X.var()\n- self._gamma = 1.0 / (n_features * X_var) if X_var != 0 else 1.0\n- else:\n- self._gamma = self.gamma\n- self.random_weights_ = (2.0 * self._gamma) ** 0.5 * random_state.normal(\n- size=(n_features, self.n_components)\n- )\n-\n- self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components)\n-\n- if X.dtype == np.float32:\n- # Setting the data type of the fitted attribute will ensure the\n- # output data type during `transform`.\n- self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False)\n- self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False)\n-\n- self._n_features_out = self.n_components\n- return self\n \n def transform(self, X):\n \"\"\"Apply the approximate feature map 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/kernel_approximation.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the model with X.\n\n Samples random projection according to n_features.\n\n Parameters\n ----------\n X : {array-like, sparse matrix}, 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 : array-like, shape (n_samples,) or (n_samples, n_outputs), \\\n default=None\n Target values (None for unsupervised transformations).\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_kernel_approximation.py::test_rbf_sampler", "sklearn/tests/test_kernel_approximation.py::test_rbf_sampler_fitted_attributes_dtype[float64]", "sklearn/tests/test_kernel_approximation.py::test_rbf_sampler_dtype_equivalence", "sklearn/tests/test_kernel_approximation.py::test_rbf_sampler_gamma_scale", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_array]", "sklearn/tests/test_kernel_approximation.py::test_get_feature_names_out[RBFSampler]", "sklearn/kernel_approximation.py::sklearn.kernel_approximation.RBFSampler", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[RBFSampler(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RBFSampler()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RBFSampler()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RBFSampler()]", "sklearn/tests/test_common.py::test_check_param_validation[RBFSampler()]", "sklearn/tests/test_common.py::test_set_output_transform[RBFSampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-RBFSampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-RBFSampler()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-163	1.0	{ "code": "diff --git b/sklearn/kernel_approximation.py a/sklearn/kernel_approximation.py\nindex 86334a7ba..92d906dde 100644\n--- b/sklearn/kernel_approximation.py\n+++ a/sklearn/kernel_approximation.py\n@@ -393,6 +393,14 @@ class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato\n X_new : array-like, shape (n_samples, n_components)\n Returns the instance itself.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ X = validate_data(self, X, accept_sparse=\"csr\", reset=False)\n+ projection = safe_sparse_dot(X, self.random_weights_)\n+ projection += self.random_offset_\n+ np.cos(projection, projection)\n+ projection = (2.0 / self.n_components) * 0.5\n+ return projection\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py\nindex 92d906dde..86334a7ba 100644\n--- a/sklearn/kernel_approximation.py\n+++ b/sklearn/kernel_approximation.py\n@@ -393,14 +393,6 @@ class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimato\n X_new : array-like, shape (n_samples, n_components)\n Returns the instance itself.\n \"\"\"\n- check_is_fitted(self)\n-\n- X = validate_data(self, X, accept_sparse=\"csr\", reset=False)\n- projection = safe_sparse_dot(X, self.random_weights_)\n- projection += self.random_offset_\n- np.cos(projection, projection)\n- projection = (2.0 / self.n_components) * 0.5\n- return projection\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/kernel_approximation.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Apply the approximate feature map to X.\n\n Parameters\n ----------\n X : {array-like, sparse matrix}, shape (n_samples, n_features)\n New data, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n Returns\n -------\n X_new : array-like, shape (n_samples, n_components)\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_kernel_approximation.py::test_rbf_sampler", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_array]", "sklearn/tests/test_kernel_approximation.py::test_get_feature_names_out[RBFSampler]", "sklearn/kernel_approximation.py::sklearn.kernel_approximation.RBFSampler", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RBFSampler(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RBFSampler()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RBFSampler()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RBFSampler()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RBFSampler()]", "sklearn/tests/test_common.py::test_set_output_transform[RBFSampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-RBFSampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-RBFSampler()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-164	1.0	{ "code": "diff --git b/sklearn/feature_selection/_rfe.py a/sklearn/feature_selection/_rfe.py\nindex 4850fc269..5cf787631 100644\n--- b/sklearn/feature_selection/_rfe.py\n+++ a/sklearn/feature_selection/_rfe.py\n@@ -385,6 +385,7 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n \n return self\n \n+ @available_if(_estimator_has(\"predict\"))\n def predict(self, X, predict_params):\n \"\"\"Reduce X to the selected features and predict using the estimator.\n \n@@ -409,6 +410,16 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n y : array of shape [n_samples]\n The predicted target values.\n \"\"\"\n+ _raise_for_params(predict_params, self, \"predict\")\n+ check_is_fitted(self)\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"predict\", predict_params)\n+ else:\n+ routed_params = Bunch(estimator=Bunch(predict={}))\n+\n+ return self.estimator_.predict(\n+ self.transform(X), routed_params.estimator.predict\n+ )\n \n @available_if(_estimator_has(\"score\"))\n def score(self, X, y, score_params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py\nindex 5cf787631..4850fc269 100644\n--- a/sklearn/feature_selection/_rfe.py\n+++ b/sklearn/feature_selection/_rfe.py\n@@ -385,7 +385,6 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n \n return self\n \n- @available_if(_estimator_has(\"predict\"))\n def predict(self, X, predict_params):\n \"\"\"Reduce X to the selected features and predict using the estimator.\n \n@@ -410,16 +409,6 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n y : array of shape [n_samples]\n The predicted target values.\n \"\"\"\n- _raise_for_params(predict_params, self, \"predict\")\n- check_is_fitted(self)\n- if _routing_enabled():\n- routed_params = process_routing(self, \"predict\", predict_params)\n- else:\n- routed_params = Bunch(estimator=Bunch(predict={}))\n-\n- return self.estimator_.predict(\n- self.transform(X), routed_params.estimator.predict\n- )\n \n @available_if(_estimator_has(\"score\"))\n def score(self, X, y, score_params):\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_selection/_rfe.py.\nHere is the description for the function:\n def predict(self, X, predict_params):\n \"\"\"Reduce X to the selected features and predict using the estimator.\n\n Parameters\n ----------\n X : array of shape [n_samples, n_features]\n The input samples.\n\n predict_params : dict\n Parameters to route to the ``predict`` method of the\n underlying estimator.\n\n .. versionadded:: 1.6\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>`\n for more details.\n\n Returns\n -------\n y : array of shape [n_samples]\n The predicted target values.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[RFE]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe[csr_matrix]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe[csr_array]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[CCA-RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[PLSCanonical-RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[PLSRegression-RFECV]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[RFE]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[RFE]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_scorer[RFECV]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RFE(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "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))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-165	1.0	{ "code": "diff --git b/sklearn/feature_selection/_rfe.py a/sklearn/feature_selection/_rfe.py\nindex 55689c056..5cf787631 100644\n--- b/sklearn/feature_selection/_rfe.py\n+++ a/sklearn/feature_selection/_rfe.py\n@@ -421,6 +421,7 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n self.transform(X), routed_params.estimator.predict\n )\n \n+ @available_if(_estimator_has(\"score\"))\n def score(self, X, y, score_params):\n \"\"\"Reduce X to the selected features and return the score of the estimator.\n \n@@ -451,6 +452,15 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n Score of the underlying base estimator computed with the selected\n features returned by `rfe.transform(X)` and `y`.\n \"\"\"\n+ check_is_fitted(self)\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"score\", score_params)\n+ else:\n+ routed_params = Bunch(estimator=Bunch(score=score_params))\n+\n+ return self.estimator_.score(\n+ self.transform(X), y, routed_params.estimator.score\n+ )\n \n def _get_support_mask(self):\n check_is_fitted(self)\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py\nindex 5cf787631..55689c056 100644\n--- a/sklearn/feature_selection/_rfe.py\n+++ b/sklearn/feature_selection/_rfe.py\n@@ -421,7 +421,6 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n self.transform(X), routed_params.estimator.predict\n )\n \n- @available_if(_estimator_has(\"score\"))\n def score(self, X, y, score_params):\n \"\"\"Reduce X to the selected features and return the score of the estimator.\n \n@@ -452,15 +451,6 @@ class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):\n Score of the underlying base estimator computed with the selected\n features returned by `rfe.transform(X)` and `y`.\n \"\"\"\n- check_is_fitted(self)\n- if _routing_enabled():\n- routed_params = process_routing(self, \"score\", score_params)\n- else:\n- routed_params = Bunch(estimator=Bunch(score=score_params))\n-\n- return self.estimator_.score(\n- self.transform(X), y, routed_params.estimator.score\n- )\n \n def _get_support_mask(self):\n check_is_fitted(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/feature_selection/_rfe.py.\nHere is the description for the function:\n def score(self, X, y, score_params):\n \"\"\"Reduce X to the selected features and return the score of the estimator.\n\n Parameters\n ----------\n X : array of shape [n_samples, n_features]\n The input samples.\n\n y : array of shape [n_samples]\n The target values.\n\n score_params : dict\n - If `enable_metadata_routing=False` (default): Parameters directly passed\n to the ``score`` method of the underlying estimator.\n\n - If `enable_metadata_routing=True`: Parameters safely routed to the `score`\n method of the underlying estimator.\n\n .. versionadded:: 1.0\n\n .. versionchanged:: 1.6\n See :ref:`Metadata Routing User Guide <metadata_routing>`\n for more details.\n\n Returns\n -------\n score : float\n Score of the underlying base estimator computed with the selected\n features returned by `rfe.transform(X)` and `y`.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[RFE]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe[csr_matrix]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe[csr_array]", "sklearn/feature_selection/tests/test_rfe.py::test_RFE_fit_score_params", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_estimator_tags", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[CCA-RFE]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[PLSCanonical-RFE]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[PLSRegression-RFE]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[RFE]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[RFE]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[RFE(estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RFE(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RFE(estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-166	1.0	{ "code": "diff --git b/sklearn/feature_selection/_rfe.py a/sklearn/feature_selection/_rfe.py\nindex eec2887e8..5cf787631 100644\n--- b/sklearn/feature_selection/_rfe.py\n+++ a/sklearn/feature_selection/_rfe.py\n@@ -773,6 +773,11 @@ class RFECV(RFE):\n self.min_features_to_select = min_features_to_select\n \n # TODO(1.8): remove `groups` from the signature after deprecation cycle.\n+ @_deprecate_positional_args(version=\"1.8\")\n+ @_fit_context(\n+ # RFECV.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y, , groups=None, params):\n \"\"\"Fit the RFE model and automatically tune the number of selected features.\n \n@@ -809,6 +814,110 @@ class RFECV(RFE):\n self : object\n Fitted estimator.\n \"\"\"\n+ _raise_for_params(params, self, \"fit\")\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=\"csr\",\n+ ensure_min_features=2,\n+ ensure_all_finite=False,\n+ multi_output=True,\n+ )\n+\n+ if _routing_enabled():\n+ if groups is not None:\n+ params.update({\"groups\": groups})\n+ routed_params = process_routing(self, \"fit\", params)\n+ else:\n+ routed_params = Bunch(\n+ estimator=Bunch(fit={}),\n+ splitter=Bunch(split={\"groups\": groups}),\n+ scorer=Bunch(score={}),\n+ )\n+\n+ # Initialization\n+ cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))\n+ scorer = self._get_scorer()\n+\n+ # Build an RFE object, which will evaluate and score each possible\n+ # feature count, down to self.min_features_to_select\n+ n_features = X.shape[1]\n+ if self.min_features_to_select > n_features:\n+ warnings.warn(\n+ (\n+ f\"Found min_features_to_select={self.min_features_to_select} > \"\n+ f\"{n_features=}. There will be no feature selection and all \"\n+ \"features will be kept.\"\n+ ),\n+ UserWarning,\n+ )\n+ rfe = RFE(\n+ estimator=self.estimator,\n+ n_features_to_select=min(self.min_features_to_select, n_features),\n+ importance_getter=self.importance_getter,\n+ step=self.step,\n+ verbose=self.verbose,\n+ )\n+\n+ # Determine the number of subsets of features by fitting across\n+ # the train folds and choosing the \"features_to_select\" parameter\n+ # that gives the least averaged error across all folds.\n+\n+ # Note that joblib raises a non-picklable error for bound methods\n+ # even if n_jobs is set to 1 with the default multiprocessing\n+ # backend.\n+ # This branching is done so that to\n+ # make sure that user code that sets n_jobs to 1\n+ # and provides bound methods as scorers is not broken with the\n+ # addition of n_jobs parameter in version 0.18.\n+\n+ if effective_n_jobs(self.n_jobs) == 1:\n+ parallel, func = list, _rfe_single_fit\n+ else:\n+ parallel = Parallel(n_jobs=self.n_jobs)\n+ func = delayed(_rfe_single_fit)\n+\n+ scores_features = parallel(\n+ func(rfe, self.estimator, X, y, train, test, scorer, routed_params)\n+ for train, test in cv.split(X, y, routed_params.splitter.split)\n+ )\n+ scores, step_n_features = zip(scores_features)\n+\n+ step_n_features_rev = np.array(step_n_features[0])[::-1]\n+ scores = np.array(scores)\n+\n+ # Reverse order such that lowest number of features is selected in case of tie.\n+ scores_sum_rev = np.sum(scores, axis=0)[::-1]\n+ n_features_to_select = step_n_features_rev[np.argmax(scores_sum_rev)]\n+\n+ # Re-execute an elimination with best_k over the whole set\n+ rfe = RFE(\n+ estimator=self.estimator,\n+ n_features_to_select=n_features_to_select,\n+ step=self.step,\n+ importance_getter=self.importance_getter,\n+ verbose=self.verbose,\n+ )\n+\n+ rfe.fit(X, y, routed_params.estimator.fit)\n+\n+ # Set final attributes\n+ self.support_ = rfe.support_\n+ self.n_features_ = rfe.n_features_\n+ self.ranking_ = rfe.ranking_\n+ self.estimator_ = clone(self.estimator)\n+ self.estimator_.fit(self._transform(X), y, routed_params.estimator.fit)\n+\n+ # reverse to stay consistent with before\n+ scores_rev = scores[:, ::-1]\n+ self.cv_results_ = {\n+ \"mean_test_score\": np.mean(scores_rev, axis=0),\n+ \"std_test_score\": np.std(scores_rev, axis=0),\n+ {f\"split{i}_test_score\": scores_rev[i] for i in range(scores.shape[0])},\n+ \"n_features\": step_n_features_rev,\n+ }\n+ return self\n \n def score(self, X, y, score_params):\n \"\"\"Score using the `scoring` option on the given test data and labels.\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py\nindex 5cf787631..eec2887e8 100644\n--- a/sklearn/feature_selection/_rfe.py\n+++ b/sklearn/feature_selection/_rfe.py\n@@ -773,11 +773,6 @@ class RFECV(RFE):\n self.min_features_to_select = min_features_to_select\n \n # TODO(1.8): remove `groups` from the signature after deprecation cycle.\n- @_deprecate_positional_args(version=\"1.8\")\n- @_fit_context(\n- # RFECV.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y, , groups=None, params):\n \"\"\"Fit the RFE model and automatically tune the number of selected features.\n \n@@ -814,110 +809,6 @@ class RFECV(RFE):\n self : object\n Fitted estimator.\n \"\"\"\n- _raise_for_params(params, self, \"fit\")\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=\"csr\",\n- ensure_min_features=2,\n- ensure_all_finite=False,\n- multi_output=True,\n- )\n-\n- if _routing_enabled():\n- if groups is not None:\n- params.update({\"groups\": groups})\n- routed_params = process_routing(self, \"fit\", params)\n- else:\n- routed_params = Bunch(\n- estimator=Bunch(fit={}),\n- splitter=Bunch(split={\"groups\": groups}),\n- scorer=Bunch(score={}),\n- )\n-\n- # Initialization\n- cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))\n- scorer = self._get_scorer()\n-\n- # Build an RFE object, which will evaluate and score each possible\n- # feature count, down to self.min_features_to_select\n- n_features = X.shape[1]\n- if self.min_features_to_select > n_features:\n- warnings.warn(\n- (\n- f\"Found min_features_to_select={self.min_features_to_select} > \"\n- f\"{n_features=}. There will be no feature selection and all \"\n- \"features will be kept.\"\n- ),\n- UserWarning,\n- )\n- rfe = RFE(\n- estimator=self.estimator,\n- n_features_to_select=min(self.min_features_to_select, n_features),\n- importance_getter=self.importance_getter,\n- step=self.step,\n- verbose=self.verbose,\n- )\n-\n- # Determine the number of subsets of features by fitting across\n- # the train folds and choosing the \"features_to_select\" parameter\n- # that gives the least averaged error across all folds.\n-\n- # Note that joblib raises a non-picklable error for bound methods\n- # even if n_jobs is set to 1 with the default multiprocessing\n- # backend.\n- # This branching is done so that to\n- # make sure that user code that sets n_jobs to 1\n- # and provides bound methods as scorers is not broken with the\n- # addition of n_jobs parameter in version 0.18.\n-\n- if effective_n_jobs(self.n_jobs) == 1:\n- parallel, func = list, _rfe_single_fit\n- else:\n- parallel = Parallel(n_jobs=self.n_jobs)\n- func = delayed(_rfe_single_fit)\n-\n- scores_features = parallel(\n- func(rfe, self.estimator, X, y, train, test, scorer, routed_params)\n- for train, test in cv.split(X, y, routed_params.splitter.split)\n- )\n- scores, step_n_features = zip(scores_features)\n-\n- step_n_features_rev = np.array(step_n_features[0])[::-1]\n- scores = np.array(scores)\n-\n- # Reverse order such that lowest number of features is selected in case of tie.\n- scores_sum_rev = np.sum(scores, axis=0)[::-1]\n- n_features_to_select = step_n_features_rev[np.argmax(scores_sum_rev)]\n-\n- # Re-execute an elimination with best_k over the whole set\n- rfe = RFE(\n- estimator=self.estimator,\n- n_features_to_select=n_features_to_select,\n- step=self.step,\n- importance_getter=self.importance_getter,\n- verbose=self.verbose,\n- )\n-\n- rfe.fit(X, y, routed_params.estimator.fit)\n-\n- # Set final attributes\n- self.support_ = rfe.support_\n- self.n_features_ = rfe.n_features_\n- self.ranking_ = rfe.ranking_\n- self.estimator_ = clone(self.estimator)\n- self.estimator_.fit(self._transform(X), y, routed_params.estimator.fit)\n-\n- # reverse to stay consistent with before\n- scores_rev = scores[:, ::-1]\n- self.cv_results_ = {\n- \"mean_test_score\": np.mean(scores_rev, axis=0),\n- \"std_test_score\": np.std(scores_rev, axis=0),\n- {f\"split{i}_test_score\": scores_rev[i] for i in range(scores.shape[0])},\n- \"n_features\": step_n_features_rev,\n- }\n- return self\n \n def score(self, X, y, score_params):\n \"\"\"Score using the `scoring` option on the given test data and 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/feature_selection/_rfe.py.\nHere is the description for the function:\n def fit(self, X, y, , groups=None, params):\n \"\"\"Fit the RFE model and automatically tune the number of selected features.\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 total number of features.\n\n y : array-like of shape (n_samples,)\n Target values (integers for classification, real numbers for\n regression).\n\n groups : array-like of shape (n_samples,) or None, default=None\n Group labels for the samples used while splitting the dataset into\n train/test set. Only used in conjunction with a \"Group\" :term:`cv`\n instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).\n\n .. versionadded:: 0.20\n\n *params : dict of str -> object\n Parameters passed to the ``fit`` method of the estimator,\n the scorer, and the CV splitter.\n\n .. versionadded:: 1.6\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>`\n for more details.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv[csr_matrix]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv[csr_array]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_mockclassifier", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_verbose_output", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_size[42]", "sklearn/feature_selection/tests/test_rfe.py::test_number_of_subsets_of_features[42]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_cv_n_jobs[42]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_cv_groups", "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[RFECV-auto-ValueError]", "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/feature_selection/tests/test_rfe.py::test_rfe_allow_nan_inf_in_x[5]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_std_and_mean[42]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-4-1-cv_results_n_features0]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-5-1-cv_results_n_features1]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-4-2-cv_results_n_features2]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-5-2-cv_results_n_features3]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-4-3-cv_results_n_features4]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-5-3-cv_results_n_features5]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-4-4-cv_results_n_features6]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[1-5-4-cv_results_n_features7]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[4-4-2-cv_results_n_features8]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[4-5-1-cv_results_n_features9]", "sklearn/feature_selection/tests/test_rfe.py::test_rfecv_cv_results_n_features[4-5-2-cv_results_n_features10]", "sklearn/feature_selection/tests/test_rfe.py::test_multioutput[RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_pipeline_with_nans[RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[CCA-RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[PLSCanonical-RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_pls[PLSRegression-RFECV]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_n_features_to_select_warning[RFECV-min_features_to_select]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/feature_selection/_rfe.py::sklearn.feature_selection._rfe.RFECV", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[RFECV]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[RFECV]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_scorer[RFECV]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[RFECV]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[RFECV(cv=3,estimator=LogisticRegression(C=1))-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RFECV(cv=3,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_transformers_get_feature_names_out[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_param_validation[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-RFECV(cv=3,estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-167	1.0	{ "code": "diff --git b/sklearn/ensemble/_forest.py a/sklearn/ensemble/_forest.py\nindex 5650fcbd3..ae729f4df 100644\n--- b/sklearn/ensemble/_forest.py\n+++ a/sklearn/ensemble/_forest.py\n@@ -2900,6 +2900,7 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):\n self.fit_transform(X, y, sample_weight=sample_weight)\n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit estimator and transform dataset.\n@@ -2925,6 +2926,14 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):\n X_transformed : sparse matrix of shape (n_samples, n_out)\n Transformed dataset.\n \"\"\"\n+ rnd = check_random_state(self.random_state)\n+ y = rnd.uniform(size=_num_samples(X))\n+ super().fit(X, y, sample_weight=sample_weight)\n+\n+ self.one_hot_encoder_ = OneHotEncoder(sparse_output=self.sparse_output)\n+ output = self.one_hot_encoder_.fit_transform(self.apply(X))\n+ self._n_features_out = output.shape[1]\n+ return output\n \n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py\nindex ae729f4df..5650fcbd3 100644\n--- a/sklearn/ensemble/_forest.py\n+++ b/sklearn/ensemble/_forest.py\n@@ -2900,7 +2900,6 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):\n self.fit_transform(X, y, sample_weight=sample_weight)\n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit estimator and transform dataset.\n@@ -2926,14 +2925,6 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):\n X_transformed : sparse matrix of shape (n_samples, n_out)\n Transformed dataset.\n \"\"\"\n- rnd = check_random_state(self.random_state)\n- y = rnd.uniform(size=_num_samples(X))\n- super().fit(X, y, sample_weight=sample_weight)\n-\n- self.one_hot_encoder_ = OneHotEncoder(sparse_output=self.sparse_output)\n- output = self.one_hot_encoder_.fit_transform(self.apply(X))\n- self._n_features_out = output.shape[1]\n- return output\n \n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\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/_forest.py.\nHere is the description for the function:\n def fit_transform(self, X, y=None, sample_weight=None):\n \"\"\"\n Fit estimator and transform dataset.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Input data used to build forests. Use ``dtype=np.float32`` for\n 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. Splits\n that would create child nodes with net zero or negative weight are\n ignored while searching for a split in each node. In the case of\n classification, splits are also ignored if they would result in any\n single class carrying a negative weight in either child node.\n\n Returns\n -------\n X_transformed : sparse matrix of shape (n_samples, n_out)\n Transformed dataset.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/tests/test_forest.py::test_random_trees_dense_type", "sklearn/ensemble/tests/test_forest.py::test_random_trees_dense_equal", "sklearn/ensemble/tests/test_forest.py::test_random_hasher", "sklearn/ensemble/tests/test_forest.py::test_random_hasher_sparse_data[csc_matrix]", "sklearn/ensemble/tests/test_forest.py::test_random_hasher_sparse_data[csc_array]", "sklearn/ensemble/tests/test_forest.py::test_max_leaf_nodes_max_depth[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_min_samples_split[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_min_samples_leaf[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_min_weight_fraction_leaf[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_sparse_input[coo_matrix-RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_sparse_input[coo_array-RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_sparse_input[csc_matrix-RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_sparse_input[csc_array-RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_sparse_input[csr_matrix-RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_sparse_input[csr_array-RandomTreesEmbedding]", "sklearn/tests/test_pipeline.py::test_pipeline_with_estimator_with_len", "sklearn/ensemble/tests/test_forest.py::test_1d_input[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_warm_start[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_warm_start_clear[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_warm_start_smaller_n_estimators[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_warm_start_equal_n_estimators[RandomTreesEmbedding]", "sklearn/ensemble/tests/test_forest.py::test_random_trees_embedding_feature_names_out", "sklearn/ensemble/_forest.py::sklearn.ensemble._forest.RandomTreesEmbedding", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_list]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_shape]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[RandomTreesEmbedding(n_estimators=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_param_validation[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform[RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-RandomTreesEmbedding(n_estimators=5)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-RandomTreesEmbedding(n_estimators=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-168	1.0	{ "code": "diff --git b/sklearn/feature_selection/_from_model.py a/sklearn/feature_selection/_from_model.py\nindex d2b91a69d..d5476e3f0 100644\n--- b/sklearn/feature_selection/_from_model.py\n+++ a/sklearn/feature_selection/_from_model.py\n@@ -335,6 +335,10 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n )\n self.max_features_ = max_features\n \n+ @_fit_context(\n+ # SelectFromModel.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y=None, fit_params):\n \"\"\"Fit the SelectFromModel meta-transformer.\n \n@@ -364,6 +368,33 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n+ self._check_max_features(X)\n+\n+ if self.prefit:\n+ try:\n+ check_is_fitted(self.estimator)\n+ except NotFittedError as exc:\n+ raise NotFittedError(\n+ \"When `prefit=True`, `estimator` is expected to be a fitted \"\n+ \"estimator.\"\n+ ) from exc\n+ self.estimator_ = deepcopy(self.estimator)\n+ else:\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"fit\", fit_params)\n+ self.estimator_ = clone(self.estimator)\n+ self.estimator_.fit(X, y, routed_params.estimator.fit)\n+ else:\n+ # TODO(SLEP6): remove when metadata routing cannot be disabled.\n+ self.estimator_ = clone(self.estimator)\n+ self.estimator_.fit(X, y, fit_params)\n+\n+ if hasattr(self.estimator_, \"feature_names_in_\"):\n+ self.feature_names_in_ = self.estimator_.feature_names_in_\n+ else:\n+ _check_feature_names(self, X, reset=True)\n+\n+ return self\n \n @property\n def threshold_(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_selection/_from_model.py b/sklearn/feature_selection/_from_model.py\nindex d5476e3f0..d2b91a69d 100644\n--- a/sklearn/feature_selection/_from_model.py\n+++ b/sklearn/feature_selection/_from_model.py\n@@ -335,10 +335,6 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n )\n self.max_features_ = max_features\n \n- @_fit_context(\n- # SelectFromModel.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y=None, fit_params):\n \"\"\"Fit the SelectFromModel meta-transformer.\n \n@@ -368,33 +364,6 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n- self._check_max_features(X)\n-\n- if self.prefit:\n- try:\n- check_is_fitted(self.estimator)\n- except NotFittedError as exc:\n- raise NotFittedError(\n- \"When `prefit=True`, `estimator` is expected to be a fitted \"\n- \"estimator.\"\n- ) from exc\n- self.estimator_ = deepcopy(self.estimator)\n- else:\n- if _routing_enabled():\n- routed_params = process_routing(self, \"fit\", fit_params)\n- self.estimator_ = clone(self.estimator)\n- self.estimator_.fit(X, y, routed_params.estimator.fit)\n- else:\n- # TODO(SLEP6): remove when metadata routing cannot be disabled.\n- self.estimator_ = clone(self.estimator)\n- self.estimator_.fit(X, y, fit_params)\n-\n- if hasattr(self.estimator_, \"feature_names_in_\"):\n- self.feature_names_in_ = self.estimator_.feature_names_in_\n- else:\n- _check_feature_names(self, X, reset=True)\n-\n- return self\n \n @property\n def threshold_(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/feature_selection/_from_model.py.\nHere is the description for the function:\n def fit(self, X, y=None, fit_params):\n \"\"\"Fit the SelectFromModel meta-transformer.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The training input samples.\n\n y : array-like of shape (n_samples,), default=None\n The target values (integers that correspond to classes in\n classification, real numbers in regression).\n\n fit_params : dict\n - If `enable_metadata_routing=False` (default): Parameters directly passed\n to the `fit` method of the sub-estimator. They are ignored if\n `prefit=True`.\n\n - If `enable_metadata_routing=True`: Parameters safely routed to the `fit`\n method of the sub-estimator. They are ignored if `prefit=True`.\n\n .. versionchanged:: 1.4\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[SelectFromModel]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[SelectFromModel]", "sklearn/feature_selection/tests/test_from_model.py::test_invalid_input", "sklearn/feature_selection/tests/test_from_model.py::test_input_estimator_unchanged", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_error[5-ValueError-max_features ==]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_error[<lambda>-TypeError-max_features must be an instance of int, not float.]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_error[<lambda>-ValueError-max_features ==0]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_error[<lambda>-ValueError-max_features ==1]", "sklearn/feature_selection/tests/test_from_model.py::test_inferred_max_features_integer[0]", "sklearn/feature_selection/tests/test_from_model.py::test_inferred_max_features_integer[2]", "sklearn/feature_selection/tests/test_from_model.py::test_inferred_max_features_integer[4]", "sklearn/feature_selection/tests/test_from_model.py::test_inferred_max_features_integer[None]", "sklearn/feature_selection/tests/test_from_model.py::test_inferred_max_features_callable[<lambda>0]", "sklearn/feature_selection/tests/test_from_model.py::test_inferred_max_features_callable[<lambda>1]", "sklearn/feature_selection/tests/test_from_model.py::test_inferred_max_features_callable[<lambda>2]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_array_like[<lambda>]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_array_like[2]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_callable_data[<lambda>0]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_callable_data[<lambda>1]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_callable_data[<lambda>2]", "sklearn/feature_selection/tests/test_from_model.py::test_max_features", "sklearn/feature_selection/tests/test_from_model.py::test_max_features_tiebreak", "sklearn/feature_selection/tests/test_from_model.py::test_threshold_and_max_features", "sklearn/feature_selection/tests/test_from_model.py::test_feature_importances", "sklearn/feature_selection/tests/test_from_model.py::test_sample_weight", "sklearn/feature_selection/tests/test_from_model.py::test_coef_default_threshold[estimator0]", "sklearn/feature_selection/tests/test_from_model.py::test_coef_default_threshold[estimator1]", "sklearn/feature_selection/tests/test_from_model.py::test_coef_default_threshold[estimator2]", "sklearn/feature_selection/tests/test_from_model.py::test_coef_default_threshold[estimator3]", "sklearn/feature_selection/tests/test_from_model.py::test_2d_coef", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit", "sklearn/feature_selection/tests/test_from_model.py::test_calling_fit_reinitializes", "sklearn/feature_selection/tests/test_from_model.py::test_prefit", "sklearn/feature_selection/tests/test_from_model.py::test_prefit_get_feature_names_out", "sklearn/feature_selection/tests/test_from_model.py::test_threshold_string", "sklearn/feature_selection/tests/test_from_model.py::test_threshold_without_refitting", "sklearn/feature_selection/tests/test_from_model.py::test_fit_accepts_nan_inf", "sklearn/feature_selection/tests/test_from_model.py::test_transform_accepts_nan_inf", "sklearn/feature_selection/tests/test_from_model.py::test_importance_getter[estimator0-named_steps.logisticregression.coef_]", "sklearn/feature_selection/tests/test_from_model.py::test_importance_getter[estimator1-_pca_importances]", "sklearn/feature_selection/tests/test_from_model.py::test_select_from_model_pls[CCA]", "sklearn/feature_selection/tests/test_from_model.py::test_select_from_model_pls[PLSCanonical]", "sklearn/feature_selection/tests/test_from_model.py::test_select_from_model_pls[PLSRegression]", "sklearn/feature_selection/tests/test_from_model.py::test_estimator_does_not_support_feature_names", "sklearn/feature_selection/tests/test_from_model.py::test_from_model_estimator_attribute_error", "sklearn/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[SelectFromModel]", "sklearn/feature_selection/_from_model.py::sklearn.feature_selection._from_model.SelectFromModel", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[SelectFromModel]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_check_param_validation[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_set_output_transform[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SelectFromModel(estimator=SGDRegressor(random_state=0))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-169	1.0	{ "code": "diff --git b/sklearn/feature_selection/_from_model.py a/sklearn/feature_selection/_from_model.py\nindex 11a38d226..d5476e3f0 100644\n--- b/sklearn/feature_selection/_from_model.py\n+++ a/sklearn/feature_selection/_from_model.py\n@@ -407,6 +407,11 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n )\n return _calculate_threshold(self.estimator, scores, self.threshold)\n \n+ @available_if(_estimator_has(\"partial_fit\"))\n+ @_fit_context(\n+ # SelectFromModel.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def partial_fit(self, X, y=None, partial_fit_params):\n \"\"\"Fit the SelectFromModel meta-transformer only once.\n \n@@ -440,6 +445,39 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n+ first_call = not hasattr(self, \"estimator_\")\n+\n+ if first_call:\n+ self._check_max_features(X)\n+\n+ if self.prefit:\n+ if first_call:\n+ try:\n+ check_is_fitted(self.estimator)\n+ except NotFittedError as exc:\n+ raise NotFittedError(\n+ \"When `prefit=True`, `estimator` is expected to be a fitted \"\n+ \"estimator.\"\n+ ) from exc\n+ self.estimator_ = deepcopy(self.estimator)\n+ return self\n+\n+ if first_call:\n+ self.estimator_ = clone(self.estimator)\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"partial_fit\", partial_fit_params)\n+ self.estimator_ = clone(self.estimator)\n+ self.estimator_.partial_fit(X, y, routed_params.estimator.partial_fit)\n+ else:\n+ # TODO(SLEP6): remove when metadata routing cannot be disabled.\n+ self.estimator_.partial_fit(X, y, partial_fit_params)\n+\n+ if hasattr(self.estimator_, \"feature_names_in_\"):\n+ self.feature_names_in_ = self.estimator_.feature_names_in_\n+ else:\n+ _check_feature_names(self, X, reset=first_call)\n+\n+ return self\n \n @property\n def n_features_in_(self):\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_selection/_from_model.py b/sklearn/feature_selection/_from_model.py\nindex d5476e3f0..11a38d226 100644\n--- a/sklearn/feature_selection/_from_model.py\n+++ b/sklearn/feature_selection/_from_model.py\n@@ -407,11 +407,6 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n )\n return _calculate_threshold(self.estimator, scores, self.threshold)\n \n- @available_if(_estimator_has(\"partial_fit\"))\n- @_fit_context(\n- # SelectFromModel.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def partial_fit(self, X, y=None, partial_fit_params):\n \"\"\"Fit the SelectFromModel meta-transformer only once.\n \n@@ -445,39 +440,6 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n- first_call = not hasattr(self, \"estimator_\")\n-\n- if first_call:\n- self._check_max_features(X)\n-\n- if self.prefit:\n- if first_call:\n- try:\n- check_is_fitted(self.estimator)\n- except NotFittedError as exc:\n- raise NotFittedError(\n- \"When `prefit=True`, `estimator` is expected to be a fitted \"\n- \"estimator.\"\n- ) from exc\n- self.estimator_ = deepcopy(self.estimator)\n- return self\n-\n- if first_call:\n- self.estimator_ = clone(self.estimator)\n- if _routing_enabled():\n- routed_params = process_routing(self, \"partial_fit\", partial_fit_params)\n- self.estimator_ = clone(self.estimator)\n- self.estimator_.partial_fit(X, y, routed_params.estimator.partial_fit)\n- else:\n- # TODO(SLEP6): remove when metadata routing cannot be disabled.\n- self.estimator_.partial_fit(X, y, partial_fit_params)\n-\n- if hasattr(self.estimator_, \"feature_names_in_\"):\n- self.feature_names_in_ = self.estimator_.feature_names_in_\n- else:\n- _check_feature_names(self, X, reset=first_call)\n-\n- return self\n \n @property\n def n_features_in_(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/feature_selection/_from_model.py.\nHere is the description for the function:\n def partial_fit(self, X, y=None, partial_fit_params):\n \"\"\"Fit the SelectFromModel meta-transformer only once.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The training input samples.\n\n y : array-like of shape (n_samples,), default=None\n The target values (integers that correspond to classes in\n classification, real numbers in regression).\n\n partial_fit_params : dict\n - If `enable_metadata_routing=False` (default): Parameters directly passed\n to the `partial_fit` method of the sub-estimator.\n\n - If `enable_metadata_routing=True`: Parameters passed to the `partial_fit`\n method of the sub-estimator. They are ignored if `prefit=True`.\n\n .. versionchanged:: 1.4\n\n `**partial_fit_params` are routed to the sub-estimator, if\n `enable_metadata_routing=True` is set via\n :func:`~sklearn.set_config`, which allows for aliasing.\n\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[SelectFromModel]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[SelectFromModel]", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit", "sklearn/feature_selection/tests/test_from_model.py::test_prefit", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit_validate_max_features[ValueError-max_features == 10, must be <= 4-10]", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit_validate_max_features[ValueError-max_features == 5, must be <= 4-<lambda>]", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit_validate_feature_names[True]", "sklearn/feature_selection/tests/test_from_model.py::test_partial_fit_validate_feature_names[False]", "sklearn/feature_selection/tests/test_from_model.py::test_from_model_estimator_attribute_error", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[SelectFromModel]", "sklearn/tests/test_common.py::test_estimators[SelectFromModel(estimator=SGDRegressor(random_state=0))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_check_param_validation[SelectFromModel(estimator=SGDRegressor(random_state=0))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-170	1.0	{ "code": "diff --git b/sklearn/semi_supervised/_self_training.py a/sklearn/semi_supervised/_self_training.py\nindex c970d44a5..59bd93cb9 100644\n--- b/sklearn/semi_supervised/_self_training.py\n+++ a/sklearn/semi_supervised/_self_training.py\n@@ -244,6 +244,10 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n estimator_ = clone(self.estimator)\n return estimator_\n \n+ @_fit_context(\n+ # SelfTrainingClassifier.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y, params):\n \"\"\"\n Fit self-training classifier using `X`, `y` as training data.\n@@ -272,6 +276,112 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n+ _raise_for_params(params, self, \"fit\")\n+\n+ self.estimator_ = self._get_estimator()\n+\n+ # we need row slicing support for sparse matrices, but costly finiteness check\n+ # can be delegated to the base estimator.\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=[\"csr\", \"csc\", \"lil\", \"dok\"],\n+ ensure_all_finite=False,\n+ )\n+\n+ if y.dtype.kind in [\"U\", \"S\"]:\n+ raise ValueError(\n+ \"y has dtype string. If you wish to predict on \"\n+ \"string targets, use dtype object, and use -1\"\n+ \" as the label for unlabeled samples.\"\n+ )\n+\n+ has_label = y != -1\n+\n+ if np.all(has_label):\n+ warnings.warn(\"y contains no unlabeled samples\", UserWarning)\n+\n+ if self.criterion == \"k_best\" and (\n+ self.k_best > X.shape[0] - np.sum(has_label)\n+ ):\n+ warnings.warn(\n+ (\n+ \"k_best is larger than the amount of unlabeled \"\n+ \"samples. All unlabeled samples will be labeled in \"\n+ \"the first iteration\"\n+ ),\n+ UserWarning,\n+ )\n+\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"fit\", params)\n+ else:\n+ routed_params = Bunch(estimator=Bunch(fit={}))\n+\n+ self.transduction_ = np.copy(y)\n+ self.labeled_iter_ = np.full_like(y, -1)\n+ self.labeled_iter_[has_label] = 0\n+\n+ self.n_iter_ = 0\n+\n+ while not np.all(has_label) and (\n+ self.max_iter is None or self.n_iter_ < self.max_iter\n+ ):\n+ self.n_iter_ += 1\n+ self.estimator_.fit(\n+ X[safe_mask(X, has_label)],\n+ self.transduction_[has_label],\n+ routed_params.estimator.fit,\n+ )\n+\n+ # Predict on the unlabeled samples\n+ prob = self.estimator_.predict_proba(X[safe_mask(X, ~has_label)])\n+ pred = self.estimator_.classes_[np.argmax(prob, axis=1)]\n+ max_proba = np.max(prob, axis=1)\n+\n+ # Select new labeled samples\n+ if self.criterion == \"threshold\":\n+ selected = max_proba > self.threshold\n+ else:\n+ n_to_select = min(self.k_best, max_proba.shape[0])\n+ if n_to_select == max_proba.shape[0]:\n+ selected = np.ones_like(max_proba, dtype=bool)\n+ else:\n+ # NB these are indices, not a mask\n+ selected = np.argpartition(-max_proba, n_to_select)[:n_to_select]\n+\n+ # Map selected indices into original array\n+ selected_full = np.nonzero(~has_label)[0][selected]\n+\n+ # Add newly labeled confident predictions to the dataset\n+ self.transduction_[selected_full] = pred[selected]\n+ has_label[selected_full] = True\n+ self.labeled_iter_[selected_full] = self.n_iter_\n+\n+ if selected_full.shape[0] == 0:\n+ # no changed labels\n+ self.termination_condition_ = \"no_change\"\n+ break\n+\n+ if self.verbose:\n+ print(\n+ f\"End of iteration {self.n_iter_},\"\n+ f\" added {selected_full.shape[0]} new labels.\"\n+ )\n+\n+ if self.n_iter_ == self.max_iter:\n+ self.termination_condition_ = \"max_iter\"\n+ if np.all(has_label):\n+ self.termination_condition_ = \"all_labeled\"\n+\n+ self.estimator_.fit(\n+ X[safe_mask(X, has_label)],\n+ self.transduction_[has_label],\n+ routed_params.estimator.fit,\n+ )\n+ self.classes_ = self.estimator_.classes_\n+ return self\n \n @available_if(_estimator_has(\"predict\"))\n def predict(self, X, **params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/semi_supervised/_self_training.py b/sklearn/semi_supervised/_self_training.py\nindex 59bd93cb9..c970d44a5 100644\n--- a/sklearn/semi_supervised/_self_training.py\n+++ b/sklearn/semi_supervised/_self_training.py\n@@ -244,10 +244,6 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n estimator_ = clone(self.estimator)\n return estimator_\n \n- @_fit_context(\n- # SelfTrainingClassifier.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y, params):\n \"\"\"\n Fit self-training classifier using `X`, `y` as training data.\n@@ -276,112 +272,6 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n- _raise_for_params(params, self, \"fit\")\n-\n- self.estimator_ = self._get_estimator()\n-\n- # we need row slicing support for sparse matrices, but costly finiteness check\n- # can be delegated to the base estimator.\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=[\"csr\", \"csc\", \"lil\", \"dok\"],\n- ensure_all_finite=False,\n- )\n-\n- if y.dtype.kind in [\"U\", \"S\"]:\n- raise ValueError(\n- \"y has dtype string. If you wish to predict on \"\n- \"string targets, use dtype object, and use -1\"\n- \" as the label for unlabeled samples.\"\n- )\n-\n- has_label = y != -1\n-\n- if np.all(has_label):\n- warnings.warn(\"y contains no unlabeled samples\", UserWarning)\n-\n- if self.criterion == \"k_best\" and (\n- self.k_best > X.shape[0] - np.sum(has_label)\n- ):\n- warnings.warn(\n- (\n- \"k_best is larger than the amount of unlabeled \"\n- \"samples. All unlabeled samples will be labeled in \"\n- \"the first iteration\"\n- ),\n- UserWarning,\n- )\n-\n- if _routing_enabled():\n- routed_params = process_routing(self, \"fit\", params)\n- else:\n- routed_params = Bunch(estimator=Bunch(fit={}))\n-\n- self.transduction_ = np.copy(y)\n- self.labeled_iter_ = np.full_like(y, -1)\n- self.labeled_iter_[has_label] = 0\n-\n- self.n_iter_ = 0\n-\n- while not np.all(has_label) and (\n- self.max_iter is None or self.n_iter_ < self.max_iter\n- ):\n- self.n_iter_ += 1\n- self.estimator_.fit(\n- X[safe_mask(X, has_label)],\n- self.transduction_[has_label],\n- routed_params.estimator.fit,\n- )\n-\n- # Predict on the unlabeled samples\n- prob = self.estimator_.predict_proba(X[safe_mask(X, ~has_label)])\n- pred = self.estimator_.classes_[np.argmax(prob, axis=1)]\n- max_proba = np.max(prob, axis=1)\n-\n- # Select new labeled samples\n- if self.criterion == \"threshold\":\n- selected = max_proba > self.threshold\n- else:\n- n_to_select = min(self.k_best, max_proba.shape[0])\n- if n_to_select == max_proba.shape[0]:\n- selected = np.ones_like(max_proba, dtype=bool)\n- else:\n- # NB these are indices, not a mask\n- selected = np.argpartition(-max_proba, n_to_select)[:n_to_select]\n-\n- # Map selected indices into original array\n- selected_full = np.nonzero(~has_label)[0][selected]\n-\n- # Add newly labeled confident predictions to the dataset\n- self.transduction_[selected_full] = pred[selected]\n- has_label[selected_full] = True\n- self.labeled_iter_[selected_full] = self.n_iter_\n-\n- if selected_full.shape[0] == 0:\n- # no changed labels\n- self.termination_condition_ = \"no_change\"\n- break\n-\n- if self.verbose:\n- print(\n- f\"End of iteration {self.n_iter_},\"\n- f\" added {selected_full.shape[0]} new labels.\"\n- )\n-\n- if self.n_iter_ == self.max_iter:\n- self.termination_condition_ = \"max_iter\"\n- if np.all(has_label):\n- self.termination_condition_ = \"all_labeled\"\n-\n- self.estimator_.fit(\n- X[safe_mask(X, has_label)],\n- self.transduction_[has_label],\n- routed_params.estimator.fit,\n- )\n- self.classes_ = self.estimator_.classes_\n- return self\n \n @available_if(_estimator_has(\"predict\"))\n def predict(self, X, **params):\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/semi_supervised/_self_training.py.\nHere is the description for the function:\n def fit(self, X, y, params):\n \"\"\"\n Fit self-training classifier using `X`, `y` as training data.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Array representing the data.\n\n y : {array-like, sparse matrix} of shape (n_samples,)\n Array representing the labels. Unlabeled samples should have the\n label -1.\n\n params : dict\n Parameters to pass to the underlying estimators.\n\n .. versionadded:: 1.6\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[SelfTrainingClassifier]", "sklearn/semi_supervised/tests/test_self_training.py::test_warns_k_best", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[threshold-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[threshold-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[k_best-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[k_best-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_k_best", "sklearn/semi_supervised/tests/test_self_training.py::test_sanity_classification", "sklearn/semi_supervised/tests/test_self_training.py::test_none_iter", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y0-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y0-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y1-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y1-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_labeled_iter[1]", "sklearn/semi_supervised/tests/test_self_training.py::test_labeled_iter[2]", "sklearn/semi_supervised/tests/test_self_training.py::test_labeled_iter[3]", "sklearn/semi_supervised/tests/test_self_training.py::test_labeled_iter[4]", "sklearn/semi_supervised/tests/test_self_training.py::test_no_unlabeled", "sklearn/semi_supervised/tests/test_self_training.py::test_early_stopping", "sklearn/semi_supervised/tests/test_self_training.py::test_strings_dtype", "sklearn/semi_supervised/tests/test_self_training.py::test_verbose[True]", "sklearn/semi_supervised/tests/test_self_training.py::test_verbose[False]", "sklearn/semi_supervised/tests/test_self_training.py::test_verbose_k_best", "sklearn/semi_supervised/tests/test_self_training.py::test_k_best_selects_best", "sklearn/semi_supervised/tests/test_self_training.py::test_estimator_meta_estimator", "sklearn/semi_supervised/tests/test_self_training.py::test_self_training_estimator_attribute_error", "sklearn/semi_supervised/tests/test_self_training.py::test_deprecation_warning_base_estimator", "sklearn/semi_supervised/tests/test_self_training.py::test_routing_passed_metadata_not_supported[decision_function]", "sklearn/semi_supervised/tests/test_self_training.py::test_routing_passed_metadata_not_supported[predict_log_proba]", "sklearn/semi_supervised/tests/test_self_training.py::test_routing_passed_metadata_not_supported[predict_proba]", "sklearn/semi_supervised/tests/test_self_training.py::test_routing_passed_metadata_not_supported[predict]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/semi_supervised/_self_training.py::sklearn.semi_supervised._self_training.SelfTrainingClassifier", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[SelfTrainingClassifier]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[SelfTrainingClassifier]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_overwrite_params]", "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_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_nan_inf]", "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_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifiers_one_label]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifiers_classes]", "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_classifiers_regression_target]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_supervised_y_no_nan]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_supervised_y_2d]", "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_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit2d_1feature]", "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_dont_overwrite_parameters]", "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[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-171	1.0	{ "code": "diff --git b/sklearn/semi_supervised/_self_training.py a/sklearn/semi_supervised/_self_training.py\nindex cc61c9861..59bd93cb9 100644\n--- b/sklearn/semi_supervised/_self_training.py\n+++ a/sklearn/semi_supervised/_self_training.py\n@@ -383,6 +383,7 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n self.classes_ = self.estimator_.classes_\n return self\n \n+ @available_if(_estimator_has(\"predict\"))\n def predict(self, X, params):\n \"\"\"Predict the classes of `X`.\n \n@@ -406,6 +407,23 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n y : ndarray of shape (n_samples,)\n Array with predicted labels.\n \"\"\"\n+ check_is_fitted(self)\n+ _raise_for_params(params, self, \"predict\")\n+\n+ if _routing_enabled():\n+ # metadata routing is enabled.\n+ routed_params = process_routing(self, \"predict\", params)\n+ else:\n+ routed_params = Bunch(estimator=Bunch(predict={}))\n+\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=True,\n+ ensure_all_finite=False,\n+ reset=False,\n+ )\n+ return self.estimator_.predict(X, routed_params.estimator.predict)\n \n @available_if(_estimator_has(\"predict_proba\"))\n def predict_proba(self, X, params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/semi_supervised/_self_training.py b/sklearn/semi_supervised/_self_training.py\nindex 59bd93cb9..cc61c9861 100644\n--- a/sklearn/semi_supervised/_self_training.py\n+++ b/sklearn/semi_supervised/_self_training.py\n@@ -383,7 +383,6 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n self.classes_ = self.estimator_.classes_\n return self\n \n- @available_if(_estimator_has(\"predict\"))\n def predict(self, X, params):\n \"\"\"Predict the classes of `X`.\n \n@@ -407,23 +406,6 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n y : ndarray of shape (n_samples,)\n Array with predicted labels.\n \"\"\"\n- check_is_fitted(self)\n- _raise_for_params(params, self, \"predict\")\n-\n- if _routing_enabled():\n- # metadata routing is enabled.\n- routed_params = process_routing(self, \"predict\", params)\n- else:\n- routed_params = Bunch(estimator=Bunch(predict={}))\n-\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=True,\n- ensure_all_finite=False,\n- reset=False,\n- )\n- return self.estimator_.predict(X, routed_params.estimator.predict)\n \n @available_if(_estimator_has(\"predict_proba\"))\n def predict_proba(self, X, params):\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/semi_supervised/_self_training.py.\nHere is the description for the function:\n def predict(self, X, params):\n \"\"\"Predict the classes of `X`.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Array representing the data.\n\n params : dict of str -> object\n Parameters to pass to the underlying estimator's ``predict`` method.\n\n .. versionadded:: 1.6\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n y : ndarray of shape (n_samples,)\n Array with predicted labels.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[SelfTrainingClassifier]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[threshold-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[threshold-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[k_best-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[k_best-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_sanity_classification", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y0-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y0-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y1-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_zero_iterations[y1-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_prefitted_throws_error", "sklearn/semi_supervised/tests/test_self_training.py::test_no_unlabeled", "sklearn/semi_supervised/tests/test_self_training.py::test_routing_passed_metadata_not_supported[predict]", "sklearn/tests/test_metaestimators.py::test_metaestimator_delegation", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[SelfTrainingClassifier]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[SelfTrainingClassifier]", "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_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_nan_inf]", "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_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_classifiers_classes]", "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_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_estimators_unfitted]", "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[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-172	1.0	{ "code": "diff --git b/sklearn/semi_supervised/_self_training.py a/sklearn/semi_supervised/_self_training.py\nindex 19407145f..59bd93cb9 100644\n--- b/sklearn/semi_supervised/_self_training.py\n+++ a/sklearn/semi_supervised/_self_training.py\n@@ -425,6 +425,7 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n )\n return self.estimator_.predict(X, routed_params.estimator.predict)\n \n+ @available_if(_estimator_has(\"predict_proba\"))\n def predict_proba(self, X, params):\n \"\"\"Predict probability for each possible outcome.\n \n@@ -449,6 +450,23 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n y : ndarray of shape (n_samples, n_features)\n Array with prediction probabilities.\n \"\"\"\n+ check_is_fitted(self)\n+ _raise_for_params(params, self, \"predict_proba\")\n+\n+ if _routing_enabled():\n+ # metadata routing is enabled.\n+ routed_params = process_routing(self, \"predict_proba\", params)\n+ else:\n+ routed_params = Bunch(estimator=Bunch(predict_proba={}))\n+\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=True,\n+ ensure_all_finite=False,\n+ reset=False,\n+ )\n+ return self.estimator_.predict_proba(X, routed_params.estimator.predict_proba)\n \n @available_if(_estimator_has(\"decision_function\"))\n def decision_function(self, X, **params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/semi_supervised/_self_training.py b/sklearn/semi_supervised/_self_training.py\nindex 59bd93cb9..19407145f 100644\n--- a/sklearn/semi_supervised/_self_training.py\n+++ b/sklearn/semi_supervised/_self_training.py\n@@ -425,7 +425,6 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n )\n return self.estimator_.predict(X, routed_params.estimator.predict)\n \n- @available_if(_estimator_has(\"predict_proba\"))\n def predict_proba(self, X, params):\n \"\"\"Predict probability for each possible outcome.\n \n@@ -450,23 +449,6 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):\n y : ndarray of shape (n_samples, n_features)\n Array with prediction probabilities.\n \"\"\"\n- check_is_fitted(self)\n- _raise_for_params(params, self, \"predict_proba\")\n-\n- if _routing_enabled():\n- # metadata routing is enabled.\n- routed_params = process_routing(self, \"predict_proba\", params)\n- else:\n- routed_params = Bunch(estimator=Bunch(predict_proba={}))\n-\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=True,\n- ensure_all_finite=False,\n- reset=False,\n- )\n- return self.estimator_.predict_proba(X, routed_params.estimator.predict_proba)\n \n @available_if(_estimator_has(\"decision_function\"))\n def decision_function(self, X, **params):\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/semi_supervised/_self_training.py.\nHere is the description for the function:\n def predict_proba(self, X, params):\n \"\"\"Predict probability for each possible outcome.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Array representing the data.\n\n params : dict of str -> object\n Parameters to pass to the underlying estimator's\n ``predict_proba`` method.\n\n .. versionadded:: 1.6\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n y : ndarray of shape (n_samples, n_features)\n Array with prediction probabilities.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[SelfTrainingClassifier]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[threshold-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[threshold-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[k_best-estimator0]", "sklearn/semi_supervised/tests/test_self_training.py::test_classification[k_best-estimator1]", "sklearn/semi_supervised/tests/test_self_training.py::test_estimator_meta_estimator", "sklearn/semi_supervised/tests/test_self_training.py::test_routing_passed_metadata_not_supported[predict_proba]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[SelfTrainingClassifier]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[SelfTrainingClassifier]", "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_estimators_unfitted]", "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[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SelfTrainingClassifier(estimator=LogisticRegression(C=1),max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-173	1.0	{ "code": "diff --git b/sklearn/feature_selection/_sequential.py a/sklearn/feature_selection/_sequential.py\nindex ed99624d1..ac5f13fd0 100644\n--- b/sklearn/feature_selection/_sequential.py\n+++ a/sklearn/feature_selection/_sequential.py\n@@ -192,6 +192,10 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator\n self.cv = cv\n self.n_jobs = n_jobs\n \n+ @_fit_context(\n+ # SequentialFeatureSelector.estimator is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y=None, *params):\n \"\"\"Learn the features to select from X.\n \n@@ -222,6 +226,75 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator\n self : object\n Returns the instance itself.\n \"\"\"\n+ _raise_for_params(params, self, \"fit\")\n+ tags = self.__sklearn_tags__()\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=\"csc\",\n+ ensure_min_features=2,\n+ ensure_all_finite=not tags.input_tags.allow_nan,\n+ )\n+ n_features = X.shape[1]\n+\n+ if self.n_features_to_select == \"auto\":\n+ if self.tol is not None:\n+ # With auto feature selection, `n_features_to_select_` will be updated\n+ # to `support_.sum()` after features are selected.\n+ self.n_features_to_select_ = n_features - 1\n+ else:\n+ self.n_features_to_select_ = n_features // 2\n+ elif isinstance(self.n_features_to_select, Integral):\n+ if self.n_features_to_select >= n_features:\n+ raise ValueError(\"n_features_to_select must be < n_features.\")\n+ self.n_features_to_select_ = self.n_features_to_select\n+ elif isinstance(self.n_features_to_select, Real):\n+ self.n_features_to_select_ = int(n_features self.n_features_to_select)\n+\n+ if self.tol is not None and self.tol < 0 and self.direction == \"forward\":\n+ raise ValueError(\n+ \"tol must be strictly positive when doing forward selection\"\n+ )\n+\n+ cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))\n+\n+ cloned_estimator = clone(self.estimator)\n+\n+ # the current mask corresponds to the set of features:\n+ # - that we have already selected if we do forward selection\n+ # - that we have already excluded if we do backward selection\n+ current_mask = np.zeros(shape=n_features, dtype=bool)\n+ n_iterations = (\n+ self.n_features_to_select_\n+ if self.n_features_to_select == \"auto\" or self.direction == \"forward\"\n+ else n_features - self.n_features_to_select_\n+ )\n+\n+ old_score = -np.inf\n+ is_auto_select = self.tol is not None and self.n_features_to_select == \"auto\"\n+\n+ # We only need to verify the routing here and not use the routed params\n+ # because internally the actual routing will also take place inside the\n+ # `cross_val_score` function.\n+ if _routing_enabled():\n+ process_routing(self, \"fit\", params)\n+ for _ in range(n_iterations):\n+ new_feature_idx, new_score = self._get_best_new_feature_score(\n+ cloned_estimator, X, y, cv, current_mask, params\n+ )\n+ if is_auto_select and ((new_score - old_score) < self.tol):\n+ break\n+\n+ old_score = new_score\n+ current_mask[new_feature_idx] = True\n+\n+ if self.direction == \"backward\":\n+ current_mask = ~current_mask\n+\n+ self.support_ = current_mask\n+ self.n_features_to_select_ = self.support_.sum()\n+\n+ return self\n \n def _get_best_new_feature_score(self, estimator, X, y, cv, current_mask, **params):\n # Return the best new feature and its score to add to the current_mask,\n", "test": null }	null	{ "code": "diff --git a/sklearn/feature_selection/_sequential.py b/sklearn/feature_selection/_sequential.py\nindex ac5f13fd0..ed99624d1 100644\n--- a/sklearn/feature_selection/_sequential.py\n+++ b/sklearn/feature_selection/_sequential.py\n@@ -192,10 +192,6 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator\n self.cv = cv\n self.n_jobs = n_jobs\n \n- @_fit_context(\n- # SequentialFeatureSelector.estimator is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y=None, *params):\n \"\"\"Learn the features to select from X.\n \n@@ -226,75 +222,6 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator\n self : object\n Returns the instance itself.\n \"\"\"\n- _raise_for_params(params, self, \"fit\")\n- tags = self.__sklearn_tags__()\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=\"csc\",\n- ensure_min_features=2,\n- ensure_all_finite=not tags.input_tags.allow_nan,\n- )\n- n_features = X.shape[1]\n-\n- if self.n_features_to_select == \"auto\":\n- if self.tol is not None:\n- # With auto feature selection, `n_features_to_select_` will be updated\n- # to `support_.sum()` after features are selected.\n- self.n_features_to_select_ = n_features - 1\n- else:\n- self.n_features_to_select_ = n_features // 2\n- elif isinstance(self.n_features_to_select, Integral):\n- if self.n_features_to_select >= n_features:\n- raise ValueError(\"n_features_to_select must be < n_features.\")\n- self.n_features_to_select_ = self.n_features_to_select\n- elif isinstance(self.n_features_to_select, Real):\n- self.n_features_to_select_ = int(n_features self.n_features_to_select)\n-\n- if self.tol is not None and self.tol < 0 and self.direction == \"forward\":\n- raise ValueError(\n- \"tol must be strictly positive when doing forward selection\"\n- )\n-\n- cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))\n-\n- cloned_estimator = clone(self.estimator)\n-\n- # the current mask corresponds to the set of features:\n- # - that we have already selected if we do forward selection\n- # - that we have already excluded if we do backward selection\n- current_mask = np.zeros(shape=n_features, dtype=bool)\n- n_iterations = (\n- self.n_features_to_select_\n- if self.n_features_to_select == \"auto\" or self.direction == \"forward\"\n- else n_features - self.n_features_to_select_\n- )\n-\n- old_score = -np.inf\n- is_auto_select = self.tol is not None and self.n_features_to_select == \"auto\"\n-\n- # We only need to verify the routing here and not use the routed params\n- # because internally the actual routing will also take place inside the\n- # `cross_val_score` function.\n- if _routing_enabled():\n- process_routing(self, \"fit\", params)\n- for _ in range(n_iterations):\n- new_feature_idx, new_score = self._get_best_new_feature_score(\n- cloned_estimator, X, y, cv, current_mask, params\n- )\n- if is_auto_select and ((new_score - old_score) < self.tol):\n- break\n-\n- old_score = new_score\n- current_mask[new_feature_idx] = True\n-\n- if self.direction == \"backward\":\n- current_mask = ~current_mask\n-\n- self.support_ = current_mask\n- self.n_features_to_select_ = self.support_.sum()\n-\n- return self\n \n def _get_best_new_feature_score(self, estimator, X, y, cv, current_mask, **params):\n # Return the best new feature and its score to add to the current_mask,\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_selection/_sequential.py.\nHere is the description for the function:\n def fit(self, X, y=None, params):\n \"\"\"Learn the features to select from X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training vectors, where `n_samples` is the number of samples and\n `n_features` is the number of predictors.\n\n y : array-like of shape (n_samples,), default=None\n Target values. This parameter may be ignored for\n unsupervised learning.\n\n params : dict, default=None\n Parameters to be passed to the underlying `estimator`, `cv`\n and `scorer` objects.\n\n .. versionadded:: 1.6\n\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[SequentialFeatureSelector]", "sklearn/feature_selection/tests/test_sequential.py::test_bad_n_features_to_select", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[1-forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[1-backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[5-forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[5-backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[9-forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[9-backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[auto-forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select[auto-backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_auto[forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_auto[backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_stopping_criterion[forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_stopping_criterion[backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_float[0.1-1-forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_float[0.1-1-backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_float[1.0-10-forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_float[1.0-10-backward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_float[0.5-5-forward]", "sklearn/feature_selection/tests/test_sequential.py::test_n_features_to_select_float[0.5-5-backward]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-0]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-1]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-2]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-3]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-4]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-5]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-6]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-7]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-8]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-forward-9]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-0]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-1]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-2]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-3]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-4]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-5]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-6]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-7]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-8]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[2-expected_selected_features0-backward-9]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-0]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-1]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-2]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-3]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-4]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-5]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-6]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-7]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-8]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-forward-9]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-0]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-1]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-2]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-3]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-4]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-5]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-6]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-7]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-8]", "sklearn/feature_selection/tests/test_sequential.py::test_sanity[1-expected_selected_features1-backward-9]", "sklearn/feature_selection/tests/test_sequential.py::test_sparse_support[csr_matrix]", "sklearn/feature_selection/tests/test_sequential.py::test_sparse_support[csr_array]", "sklearn/feature_selection/tests/test_sequential.py::test_nan_support", "sklearn/feature_selection/tests/test_sequential.py::test_pipeline_support", "sklearn/feature_selection/tests/test_sequential.py::test_unsupervised_model_fit[2]", "sklearn/feature_selection/tests/test_sequential.py::test_unsupervised_model_fit[3]", "sklearn/feature_selection/tests/test_sequential.py::test_no_y_validation_model_fit[no_validation]", "sklearn/feature_selection/tests/test_sequential.py::test_no_y_validation_model_fit[1j]", "sklearn/feature_selection/tests/test_sequential.py::test_no_y_validation_model_fit[99.9]", "sklearn/feature_selection/tests/test_sequential.py::test_no_y_validation_model_fit[nan]", "sklearn/feature_selection/tests/test_sequential.py::test_no_y_validation_model_fit[3]", "sklearn/feature_selection/tests/test_sequential.py::test_forward_neg_tol_error", "sklearn/feature_selection/tests/test_sequential.py::test_backward_neg_tol", "sklearn/feature_selection/tests/test_sequential.py::test_cv_generator_support", "sklearn/feature_selection/tests/test_sequential.py::test_fit_rejects_params_with_no_routing_enabled", "sklearn/feature_selection/_sequential.py::sklearn.feature_selection._sequential.SequentialFeatureSelector", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[SequentialFeatureSelector]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[SequentialFeatureSelector]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_scorer[SequentialFeatureSelector]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[SequentialFeatureSelector]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_param_validation[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-174	1.0	{ "code": "diff --git b/sklearn/covariance/_shrunk_covariance.py a/sklearn/covariance/_shrunk_covariance.py\nindex e04189da8..2a5e09f2c 100644\n--- b/sklearn/covariance/_shrunk_covariance.py\n+++ a/sklearn/covariance/_shrunk_covariance.py\n@@ -251,6 +251,7 @@ class ShrunkCovariance(EmpiricalCovariance):\n )\n self.shrinkage = shrinkage\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the shrunk covariance model to X.\n \n@@ -268,6 +269,18 @@ class ShrunkCovariance(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X)\n+ # Not calling the parent object to fit, to avoid a potential\n+ # matrix inversion when setting the precision\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+ covariance = shrunk_covariance(covariance, self.shrinkage)\n+ self._set_covariance(covariance)\n+\n+ return self\n \n \n # Ledoit-Wolf estimator\n", "test": null }	null	{ "code": "diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py\nindex 2a5e09f2c..e04189da8 100644\n--- a/sklearn/covariance/_shrunk_covariance.py\n+++ b/sklearn/covariance/_shrunk_covariance.py\n@@ -251,7 +251,6 @@ class ShrunkCovariance(EmpiricalCovariance):\n )\n self.shrinkage = shrinkage\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the shrunk covariance model to X.\n \n@@ -269,18 +268,6 @@ class ShrunkCovariance(EmpiricalCovariance):\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X)\n- # Not calling the parent object to fit, to avoid a potential\n- # matrix inversion when setting the precision\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- covariance = shrunk_covariance(covariance, self.shrinkage)\n- self._set_covariance(covariance)\n-\n- return self\n \n \n # Ledoit-Wolf 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 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\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_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/covariance/tests/test_covariance.py::test_shrunk_covariance", "sklearn/covariance/tests/test_covariance.py::test_ledoit_wolf", "sklearn/covariance/tests/test_covariance.py::test_oas", "sklearn/covariance/_shrunk_covariance.py::sklearn.covariance._shrunk_covariance.ShrunkCovariance", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[ShrunkCovariance()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[ShrunkCovariance()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ShrunkCovariance()]", "sklearn/tests/test_common.py::test_check_param_validation[ShrunkCovariance()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-175	1.0	{ "code": "diff --git b/sklearn/impute/_base.py a/sklearn/impute/_base.py\nindex de4579daf..aecc235ec 100644\n--- b/sklearn/impute/_base.py\n+++ a/sklearn/impute/_base.py\n@@ -408,6 +408,7 @@ class SimpleImputer(_BaseImputer):\n \n return X\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the imputer on `X`.\n \n@@ -425,6 +426,28 @@ class SimpleImputer(_BaseImputer):\n self : object\n Fitted estimator.\n \"\"\"\n+ X = self._validate_input(X, in_fit=True)\n+\n+ # default fill_value is 0 for numerical input and \"missing_value\"\n+ # otherwise\n+ if self.fill_value is None:\n+ if X.dtype.kind in (\"i\", \"u\", \"f\"):\n+ fill_value = 0\n+ else:\n+ fill_value = \"missing_value\"\n+ else:\n+ fill_value = self.fill_value\n+\n+ if sp.issparse(X):\n+ self.statistics_ = self._sparse_fit(\n+ X, self.strategy, self.missing_values, fill_value\n+ )\n+ else:\n+ self.statistics_ = self._dense_fit(\n+ X, self.strategy, self.missing_values, fill_value\n+ )\n+\n+ return self\n \n def _sparse_fit(self, X, strategy, missing_values, fill_value):\n \"\"\"Fit the transformer on sparse data.\"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py\nindex aecc235ec..de4579daf 100644\n--- a/sklearn/impute/_base.py\n+++ b/sklearn/impute/_base.py\n@@ -408,7 +408,6 @@ class SimpleImputer(_BaseImputer):\n \n return X\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the imputer on `X`.\n \n@@ -426,28 +425,6 @@ class SimpleImputer(_BaseImputer):\n self : object\n Fitted estimator.\n \"\"\"\n- X = self._validate_input(X, in_fit=True)\n-\n- # default fill_value is 0 for numerical input and \"missing_value\"\n- # otherwise\n- if self.fill_value is None:\n- if X.dtype.kind in (\"i\", \"u\", \"f\"):\n- fill_value = 0\n- else:\n- fill_value = \"missing_value\"\n- else:\n- fill_value = self.fill_value\n-\n- if sp.issparse(X):\n- self.statistics_ = self._sparse_fit(\n- X, self.strategy, self.missing_values, fill_value\n- )\n- else:\n- self.statistics_ = self._dense_fit(\n- X, self.strategy, self.missing_values, fill_value\n- )\n-\n- return self\n \n def _sparse_fit(self, X, strategy, missing_values, fill_value):\n \"\"\"Fit the transformer on sparse data.\"\"\"\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 fit(self, X, y=None):\n \"\"\"Fit the imputer on `X`.\n\n Parameters\n ----------\n X : {array-like, sparse matrix}, 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 here for API consistency by convention.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null 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"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[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/tests/test_multioutput.py::test_support_missing_values[MultiOutputClassifier-LogisticRegression]", "sklearn/impute/tests/test_common.py::test_imputers_add_indicator[IterativeImputer-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/tests/test_multioutput.py::test_support_missing_values[MultiOutputRegressor-Ridge]", "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-SimpleImputer]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[False-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputers_pandas_na_integer_array_support[False-SimpleImputer]", "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[True-SimpleImputer]", "sklearn/impute/tests/test_common.py::test_imputers_feature_names_out_pandas[False-IterativeImputer]", "sklearn/impute/tests/test_common.py::test_imputers_feature_names_out_pandas[False-SimpleImputer]", "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_keep_empty_features[SimpleImputer-True]", "sklearn/impute/tests/test_common.py::test_keep_empty_features[SimpleImputer-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[nan-SimpleImputer]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-IterativeImputer]", "sklearn/feature_selection/tests/test_rfe.py::test_pipeline_with_nans[RFE]", "sklearn/feature_selection/tests/test_rfe.py::test_pipeline_with_nans[RFECV]", "sklearn/impute/tests/test_common.py::test_imputation_adds_missing_indicator_if_add_indicator_is_true[1-SimpleImputer]", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[StackingClassifier-LogisticRegression-X0-y0]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_mixed_types", "sklearn/tests/test_calibration.py::test_calibration_nan_imputer[True]", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[StackingRegressor-LinearRegression-X1-y1]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_mixed_types_pandas", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[VotingClassifier-LogisticRegression-X2-y2]", "sklearn/tests/test_calibration.py::test_calibration_nan_imputer[False]", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[VotingRegressor-LinearRegression-X3-y3]", "sklearn/impute/_iterative.py::sklearn.impute._iterative.IterativeImputer", "sklearn/impute/_base.py::sklearn.impute._base.SimpleImputer", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[IterativeImputer]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[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_dtype_object]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_pipeline_consistency]", "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_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IterativeImputer()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SimpleImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IterativeImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SimpleImputer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[IterativeImputer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SimpleImputer()]", "sklearn/tests/test_common.py::test_check_param_validation[SimpleImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[SimpleImputer()]", "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_set_output_transform_pandas-SimpleImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SimpleImputer()]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[SimpleImputer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-176	1.0	{ "code": "diff --git b/sklearn/impute/_base.py a/sklearn/impute/_base.py\nindex dd808d967..aecc235ec 100644\n--- b/sklearn/impute/_base.py\n+++ a/sklearn/impute/_base.py\n@@ -674,6 +674,39 @@ class SimpleImputer(_BaseImputer):\n The original `X` with missing values as it was prior\n to imputation.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ if not self.add_indicator:\n+ raise ValueError(\n+ \"'inverse_transform' works only when \"\n+ \"'SimpleImputer' is instantiated with \"\n+ \"'add_indicator=True'. \"\n+ f\"Got 'add_indicator={self.add_indicator}' \"\n+ \"instead.\"\n+ )\n+\n+ n_features_missing = len(self.indicator_.features_)\n+ non_empty_feature_count = X.shape[1] - n_features_missing\n+ array_imputed = X[:, :non_empty_feature_count].copy()\n+ missing_mask = X[:, non_empty_feature_count:].astype(bool)\n+\n+ n_features_original = len(self.statistics_)\n+ shape_original = (X.shape[0], n_features_original)\n+ X_original = np.zeros(shape_original)\n+ X_original[:, self.indicator_.features_] = missing_mask\n+ full_mask = X_original.astype(bool)\n+\n+ imputed_idx, original_idx = 0, 0\n+ while imputed_idx < len(array_imputed.T):\n+ if not np.all(X_original[:, original_idx]):\n+ X_original[:, original_idx] = array_imputed.T[imputed_idx]\n+ imputed_idx += 1\n+ original_idx += 1\n+ else:\n+ original_idx += 1\n+\n+ X_original[full_mask] = self.missing_values\n+ return X_original\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py\nindex aecc235ec..dd808d967 100644\n--- a/sklearn/impute/_base.py\n+++ b/sklearn/impute/_base.py\n@@ -674,39 +674,6 @@ class SimpleImputer(_BaseImputer):\n The original `X` with missing values as it was prior\n to imputation.\n \"\"\"\n- check_is_fitted(self)\n-\n- if not self.add_indicator:\n- raise ValueError(\n- \"'inverse_transform' works only when \"\n- \"'SimpleImputer' is instantiated with \"\n- \"'add_indicator=True'. \"\n- f\"Got 'add_indicator={self.add_indicator}' \"\n- \"instead.\"\n- )\n-\n- n_features_missing = len(self.indicator_.features_)\n- non_empty_feature_count = X.shape[1] - n_features_missing\n- array_imputed = X[:, :non_empty_feature_count].copy()\n- missing_mask = X[:, non_empty_feature_count:].astype(bool)\n-\n- n_features_original = len(self.statistics_)\n- shape_original = (X.shape[0], n_features_original)\n- X_original = np.zeros(shape_original)\n- X_original[:, self.indicator_.features_] = missing_mask\n- full_mask = X_original.astype(bool)\n-\n- imputed_idx, original_idx = 0, 0\n- while imputed_idx < len(array_imputed.T):\n- if not np.all(X_original[:, original_idx]):\n- X_original[:, original_idx] = array_imputed.T[imputed_idx]\n- imputed_idx += 1\n- original_idx += 1\n- else:\n- original_idx += 1\n-\n- X_original[full_mask] = self.missing_values\n- return X_original\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/impute/_base.py.\nHere is the description for the function:\n def inverse_transform(self, X):\n \"\"\"Convert the data back to the original representation.\n\n Inverts the `transform` operation performed on an array.\n This operation can only be performed after :class:`SimpleImputer` is\n instantiated with `add_indicator=True`.\n\n Note that `inverse_transform` can only invert the transform in\n features that have binary indicators for missing values. If a feature\n has no missing values at `fit` time, the feature won't have a binary\n indicator, and the imputation done at `transform` time won't be\n inverted.\n\n .. versionadded:: 0.24\n\n Parameters\n ----------\n X : array-like of shape \\\n (n_samples, n_features + n_features_missing_indicator)\n The imputed data to be reverted to original data. It has to be\n an augmented array of imputed data and the missing indicator mask.\n\n Returns\n -------\n X_original : ndarray of shape (n_samples, n_features)\n The original `X` with missing values as it was prior\n to imputation.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "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_impute.py::test_simple_imputation_inverse_transform_exceptions[-1]", "sklearn/impute/tests/test_impute.py::test_simple_imputation_inverse_transform_exceptions[nan]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-177	1.0	{ "code": "diff --git b/sklearn/impute/_base.py a/sklearn/impute/_base.py\nindex 10ecc9cbd..aecc235ec 100644\n--- b/sklearn/impute/_base.py\n+++ a/sklearn/impute/_base.py\n@@ -573,6 +573,78 @@ class SimpleImputer(_BaseImputer):\n (n_samples, n_features_out)\n `X` with imputed values.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ X = self._validate_input(X, in_fit=False)\n+ statistics = self.statistics_\n+\n+ if X.shape[1] != statistics.shape[0]:\n+ raise ValueError(\n+ \"X has %d features per sample, expected %d\"\n+ % (X.shape[1], self.statistics_.shape[0])\n+ )\n+\n+ # compute mask before eliminating invalid features\n+ missing_mask = _get_mask(X, self.missing_values)\n+\n+ # Decide whether to keep missing features\n+ if self.strategy == \"constant\" or self.keep_empty_features:\n+ valid_statistics = statistics\n+ valid_statistics_indexes = None\n+ else:\n+ # same as np.isnan but also works for object dtypes\n+ invalid_mask = _get_mask(statistics, np.nan)\n+ valid_mask = np.logical_not(invalid_mask)\n+ valid_statistics = statistics[valid_mask]\n+ valid_statistics_indexes = np.flatnonzero(valid_mask)\n+\n+ if invalid_mask.any():\n+ invalid_features = np.arange(X.shape[1])[invalid_mask]\n+ # use feature names warning if features are provided\n+ if hasattr(self, \"feature_names_in_\"):\n+ invalid_features = self.feature_names_in_[invalid_features]\n+ warnings.warn(\n+ \"Skipping features without any observed values:\"\n+ f\" {invalid_features}. At least one non-missing value is needed\"\n+ f\" for imputation with strategy='{self.strategy}'.\"\n+ )\n+ X = X[:, valid_statistics_indexes]\n+\n+ # Do actual imputation\n+ if sp.issparse(X):\n+ if self.missing_values == 0:\n+ raise ValueError(\n+ \"Imputation not possible when missing_values \"\n+ \"== 0 and input is sparse. Provide a dense \"\n+ \"array instead.\"\n+ )\n+ else:\n+ # if no invalid statistics are found, use the mask computed\n+ # before, else recompute mask\n+ if valid_statistics_indexes is None:\n+ mask = missing_mask.data\n+ else:\n+ mask = _get_mask(X.data, self.missing_values)\n+ indexes = np.repeat(\n+ np.arange(len(X.indptr) - 1, dtype=int), np.diff(X.indptr)\n+ )[mask]\n+\n+ X.data[mask] = valid_statistics[indexes].astype(X.dtype, copy=False)\n+ else:\n+ # use mask computed before eliminating invalid mask\n+ if valid_statistics_indexes is None:\n+ mask_valid_features = missing_mask\n+ else:\n+ mask_valid_features = missing_mask[:, valid_statistics_indexes]\n+ n_missing = np.sum(mask_valid_features, axis=0)\n+ values = np.repeat(valid_statistics, n_missing)\n+ coordinates = np.where(mask_valid_features.transpose())[::-1]\n+\n+ X[coordinates] = values\n+\n+ X_indicator = super()._transform_indicator(missing_mask)\n+\n+ return super()._concatenate_indicator(X, X_indicator)\n \n def inverse_transform(self, X):\n \"\"\"Convert the data back to the original representation.\n", "test": null }	null	{ "code": "diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py\nindex aecc235ec..10ecc9cbd 100644\n--- a/sklearn/impute/_base.py\n+++ b/sklearn/impute/_base.py\n@@ -573,78 +573,6 @@ class SimpleImputer(_BaseImputer):\n (n_samples, n_features_out)\n `X` with imputed values.\n \"\"\"\n- check_is_fitted(self)\n-\n- X = self._validate_input(X, in_fit=False)\n- statistics = self.statistics_\n-\n- if X.shape[1] != statistics.shape[0]:\n- raise ValueError(\n- \"X has %d features per sample, expected %d\"\n- % (X.shape[1], self.statistics_.shape[0])\n- )\n-\n- # compute mask before eliminating invalid features\n- missing_mask = _get_mask(X, self.missing_values)\n-\n- # Decide whether to keep missing features\n- if self.strategy == \"constant\" or self.keep_empty_features:\n- valid_statistics = statistics\n- valid_statistics_indexes = None\n- else:\n- # same as np.isnan but also works for object dtypes\n- invalid_mask = _get_mask(statistics, np.nan)\n- valid_mask = np.logical_not(invalid_mask)\n- valid_statistics = statistics[valid_mask]\n- valid_statistics_indexes = np.flatnonzero(valid_mask)\n-\n- if invalid_mask.any():\n- invalid_features = np.arange(X.shape[1])[invalid_mask]\n- # use feature names warning if features are provided\n- if hasattr(self, \"feature_names_in_\"):\n- invalid_features = self.feature_names_in_[invalid_features]\n- warnings.warn(\n- \"Skipping features without any observed values:\"\n- f\" {invalid_features}. At least one non-missing value is needed\"\n- f\" for imputation with strategy='{self.strategy}'.\"\n- )\n- X = X[:, valid_statistics_indexes]\n-\n- # Do actual imputation\n- if sp.issparse(X):\n- if self.missing_values == 0:\n- raise ValueError(\n- \"Imputation not possible when missing_values \"\n- \"== 0 and input is sparse. Provide a dense \"\n- \"array instead.\"\n- )\n- else:\n- # if no invalid statistics are found, use the mask computed\n- # before, else recompute mask\n- if valid_statistics_indexes is None:\n- mask = missing_mask.data\n- else:\n- mask = _get_mask(X.data, self.missing_values)\n- indexes = np.repeat(\n- np.arange(len(X.indptr) - 1, dtype=int), np.diff(X.indptr)\n- )[mask]\n-\n- X.data[mask] = valid_statistics[indexes].astype(X.dtype, copy=False)\n- else:\n- # use mask computed before eliminating invalid mask\n- if valid_statistics_indexes is None:\n- mask_valid_features = missing_mask\n- else:\n- mask_valid_features = missing_mask[:, valid_statistics_indexes]\n- n_missing = np.sum(mask_valid_features, axis=0)\n- values = np.repeat(valid_statistics, n_missing)\n- coordinates = np.where(mask_valid_features.transpose())[::-1]\n-\n- X[coordinates] = values\n-\n- X_indicator = super()._transform_indicator(missing_mask)\n-\n- return super()._concatenate_indicator(X, X_indicator)\n \n def inverse_transform(self, X):\n \"\"\"Convert the data back to the original representation.\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 \"\"\"Impute all missing values in `X`.\n\n Parameters\n ----------\n X : {array-like, sparse matrix}, shape (n_samples, n_features)\n The input data to complete.\n\n Returns\n -------\n X_imputed : {ndarray, sparse matrix} of shape \\\n (n_samples, n_features_out)\n `X` with imputed values.\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]", 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"sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[StackingRegressor-LinearRegression-X1-y1]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_mixed_types_pandas", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[VotingClassifier-LogisticRegression-X2-y2]", "sklearn/tests/test_calibration.py::test_calibration_nan_imputer[False]", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[VotingRegressor-LinearRegression-X3-y3]", "sklearn/impute/_iterative.py::sklearn.impute._iterative.IterativeImputer", "sklearn/impute/_base.py::sklearn.impute._base.SimpleImputer", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[IterativeImputer]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[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_dtype_object]", "sklearn/tests/test_common.py::test_estimators[IterativeImputer()-check_pipeline_consistency]", "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_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SimpleImputer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[IterativeImputer()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SimpleImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[IterativeImputer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SimpleImputer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[IterativeImputer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SimpleImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform[SimpleImputer()]", "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_set_output_transform_pandas-SimpleImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-IterativeImputer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SimpleImputer()]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[SimpleImputer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-178	1.0	{ "code": "diff --git b/sklearn/kernel_approximation.py a/sklearn/kernel_approximation.py\nindex ea3fdcd07..92d906dde 100644\n--- b/sklearn/kernel_approximation.py\n+++ a/sklearn/kernel_approximation.py\n@@ -494,6 +494,7 @@ class SkewedChi2Sampler(\n self.n_components = n_components\n self.random_state = random_state\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model with X.\n \n@@ -514,6 +515,22 @@ class SkewedChi2Sampler(\n self : object\n Returns the instance itself.\n \"\"\"\n+ X = validate_data(self, X)\n+ random_state = check_random_state(self.random_state)\n+ n_features = X.shape[1]\n+ uniform = random_state.uniform(size=(n_features, self.n_components))\n+ # transform by inverse CDF of sech\n+ self.random_weights_ = 1.0 / np.pi * np.log(np.tan(np.pi / 2.0 * uniform))\n+ self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components)\n+\n+ if X.dtype == np.float32:\n+ # Setting the data type of the fitted attribute will ensure the\n+ # output data type during `transform`.\n+ self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False)\n+ self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False)\n+\n+ self._n_features_out = self.n_components\n+ return self\n \n def transform(self, X):\n \"\"\"Apply the approximate feature map to X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py\nindex 92d906dde..ea3fdcd07 100644\n--- a/sklearn/kernel_approximation.py\n+++ b/sklearn/kernel_approximation.py\n@@ -494,7 +494,6 @@ class SkewedChi2Sampler(\n self.n_components = n_components\n self.random_state = random_state\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Fit the model with X.\n \n@@ -515,22 +514,6 @@ class SkewedChi2Sampler(\n self : object\n Returns the instance itself.\n \"\"\"\n- X = validate_data(self, X)\n- random_state = check_random_state(self.random_state)\n- n_features = X.shape[1]\n- uniform = random_state.uniform(size=(n_features, self.n_components))\n- # transform by inverse CDF of sech\n- self.random_weights_ = 1.0 / np.pi * np.log(np.tan(np.pi / 2.0 * uniform))\n- self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components)\n-\n- if X.dtype == np.float32:\n- # Setting the data type of the fitted attribute will ensure the\n- # output data type during `transform`.\n- self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False)\n- self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False)\n-\n- self._n_features_out = self.n_components\n- return self\n \n def transform(self, X):\n \"\"\"Apply the approximate feature map 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/kernel_approximation.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit the model with X.\n\n Samples random projection according to n_features.\n\n Parameters\n ----------\n X : array-like, 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 : array-like, shape (n_samples,) or (n_samples, n_outputs), \\\n default=None\n Target values (None for unsupervised transformations).\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_kernel_approximation.py::test_skewed_chi2_sampler", "sklearn/tests/test_kernel_approximation.py::test_skewed_chi2_sampler_fitted_attributes_dtype[float64]", "sklearn/tests/test_kernel_approximation.py::test_skewed_chi2_sampler_dtype_equivalence", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_array]", "sklearn/tests/test_kernel_approximation.py::test_get_feature_names_out[SkewedChi2Sampler]", "sklearn/kernel_approximation.py::sklearn.kernel_approximation.SkewedChi2Sampler", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_check_param_validation[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_set_output_transform[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SkewedChi2Sampler()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-179	1.0	{ "code": "diff --git b/sklearn/kernel_approximation.py a/sklearn/kernel_approximation.py\nindex d6d620838..92d906dde 100644\n--- b/sklearn/kernel_approximation.py\n+++ a/sklearn/kernel_approximation.py\n@@ -547,6 +547,20 @@ class SkewedChi2Sampler(\n X_new : array-like, shape (n_samples, n_components)\n Returns the instance itself.\n \"\"\"\n+ check_is_fitted(self)\n+ X = validate_data(\n+ self, X, copy=True, dtype=[np.float64, np.float32], reset=False\n+ )\n+ if (X <= -self.skewedness).any():\n+ raise ValueError(\"X may not contain entries smaller than -skewedness.\")\n+\n+ X += self.skewedness\n+ np.log(X, X)\n+ projection = safe_sparse_dot(X, self.random_weights_)\n+ projection += self.random_offset_\n+ np.cos(projection, projection)\n+ projection *= np.sqrt(2.0) / np.sqrt(self.n_components)\n+ return projection\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py\nindex 92d906dde..d6d620838 100644\n--- a/sklearn/kernel_approximation.py\n+++ b/sklearn/kernel_approximation.py\n@@ -547,20 +547,6 @@ class SkewedChi2Sampler(\n X_new : array-like, shape (n_samples, n_components)\n Returns the instance itself.\n \"\"\"\n- check_is_fitted(self)\n- X = validate_data(\n- self, X, copy=True, dtype=[np.float64, np.float32], reset=False\n- )\n- if (X <= -self.skewedness).any():\n- raise ValueError(\"X may not contain entries smaller than -skewedness.\")\n-\n- X += self.skewedness\n- np.log(X, X)\n- projection = safe_sparse_dot(X, self.random_weights_)\n- projection += self.random_offset_\n- np.cos(projection, projection)\n- projection *= np.sqrt(2.0) / np.sqrt(self.n_components)\n- return projection\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/kernel_approximation.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Apply the approximate feature map to X.\n\n Parameters\n ----------\n X : array-like, shape (n_samples, n_features)\n New data, where `n_samples` is the number of samples\n and `n_features` is the number of features. All values of X must be\n strictly greater than \"-skewedness\".\n\n Returns\n -------\n X_new : array-like, shape (n_samples, n_components)\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_kernel_approximation.py::test_skewed_chi2_sampler", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_matrix]", "sklearn/tests/test_kernel_approximation.py::test_input_validation[csr_array]", "sklearn/tests/test_kernel_approximation.py::test_get_feature_names_out[SkewedChi2Sampler]", "sklearn/kernel_approximation.py::sklearn.kernel_approximation.SkewedChi2Sampler", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SkewedChi2Sampler()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_set_output_transform[SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-SkewedChi2Sampler()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SkewedChi2Sampler()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-180	1.0	{ "code": "diff --git b/sklearn/cluster/_spectral.py a/sklearn/cluster/_spectral.py\nindex 6cb68f2f2..e588aa6ce 100644\n--- b/sklearn/cluster/_spectral.py\n+++ a/sklearn/cluster/_spectral.py\n@@ -663,6 +663,7 @@ class SpectralClustering(ClusterMixin, BaseEstimator):\n self.n_jobs = n_jobs\n self.verbose = verbose\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Perform spectral clustering from features, or affinity matrix.\n \n@@ -685,6 +686,85 @@ class SpectralClustering(ClusterMixin, BaseEstimator):\n self : object\n A fitted instance of the estimator.\n \"\"\"\n+ X = validate_data(\n+ self,\n+ X,\n+ accept_sparse=[\"csr\", \"csc\", \"coo\"],\n+ dtype=np.float64,\n+ ensure_min_samples=2,\n+ )\n+ allow_squared = self.affinity in [\n+ \"precomputed\",\n+ \"precomputed_nearest_neighbors\",\n+ ]\n+ if X.shape[0] == X.shape[1] and not allow_squared:\n+ warnings.warn(\n+ \"The spectral clustering API has changed. ``fit``\"\n+ \"now constructs an affinity matrix from data. To use\"\n+ \" a custom affinity matrix, \"\n+ \"set ``affinity=precomputed``.\"\n+ )\n+\n+ if self.affinity == \"nearest_neighbors\":\n+ connectivity = kneighbors_graph(\n+ X, n_neighbors=self.n_neighbors, include_self=True, n_jobs=self.n_jobs\n+ )\n+ self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)\n+ elif self.affinity == \"precomputed_nearest_neighbors\":\n+ estimator = NearestNeighbors(\n+ n_neighbors=self.n_neighbors, n_jobs=self.n_jobs, metric=\"precomputed\"\n+ ).fit(X)\n+ connectivity = estimator.kneighbors_graph(X=X, mode=\"connectivity\")\n+ self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)\n+ elif self.affinity == \"precomputed\":\n+ self.affinity_matrix_ = X\n+ else:\n+ params = self.kernel_params\n+ if params is None:\n+ params = {}\n+ if not callable(self.affinity):\n+ params[\"gamma\"] = self.gamma\n+ params[\"degree\"] = self.degree\n+ params[\"coef0\"] = self.coef0\n+ self.affinity_matrix_ = pairwise_kernels(\n+ X, metric=self.affinity, filter_params=True, **params\n+ )\n+\n+ random_state = check_random_state(self.random_state)\n+ n_components = (\n+ self.n_clusters if self.n_components is None else self.n_components\n+ )\n+ # We now obtain the real valued solution matrix to the\n+ # relaxed Ncut problem, solving the eigenvalue problem\n+ # L_sym x = lambda x and recovering u = D^-1/2 x.\n+ # The first eigenvector is constant only for fully connected graphs\n+ # and should be kept for spectral clustering (drop_first = False)\n+ # See spectral_embedding documentation.\n+ maps = _spectral_embedding(\n+ self.affinity_matrix_,\n+ n_components=n_components,\n+ eigen_solver=self.eigen_solver,\n+ random_state=random_state,\n+ eigen_tol=self.eigen_tol,\n+ drop_first=False,\n+ )\n+ if self.verbose:\n+ print(f\"Computing label assignment using {self.assign_labels}\")\n+\n+ if self.assign_labels == \"kmeans\":\n+ _, self.labels_, _ = k_means(\n+ maps,\n+ self.n_clusters,\n+ random_state=random_state,\n+ n_init=self.n_init,\n+ verbose=self.verbose,\n+ )\n+ elif self.assign_labels == \"cluster_qr\":\n+ self.labels_ = cluster_qr(maps)\n+ else:\n+ self.labels_ = discretize(maps, random_state=random_state)\n+\n+ return self\n \n def fit_predict(self, X, y=None):\n \"\"\"Perform spectral clustering on `X` and return cluster labels.\n", "test": null }	null	{ "code": "diff --git a/sklearn/cluster/_spectral.py b/sklearn/cluster/_spectral.py\nindex e588aa6ce..6cb68f2f2 100644\n--- a/sklearn/cluster/_spectral.py\n+++ b/sklearn/cluster/_spectral.py\n@@ -663,7 +663,6 @@ class SpectralClustering(ClusterMixin, BaseEstimator):\n self.n_jobs = n_jobs\n self.verbose = verbose\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None):\n \"\"\"Perform spectral clustering from features, or affinity matrix.\n \n@@ -686,85 +685,6 @@ class SpectralClustering(ClusterMixin, BaseEstimator):\n self : object\n A fitted instance of the estimator.\n \"\"\"\n- X = validate_data(\n- self,\n- X,\n- accept_sparse=[\"csr\", \"csc\", \"coo\"],\n- dtype=np.float64,\n- ensure_min_samples=2,\n- )\n- allow_squared = self.affinity in [\n- \"precomputed\",\n- \"precomputed_nearest_neighbors\",\n- ]\n- if X.shape[0] == X.shape[1] and not allow_squared:\n- warnings.warn(\n- \"The spectral clustering API has changed. ``fit``\"\n- \"now constructs an affinity matrix from data. To use\"\n- \" a custom affinity matrix, \"\n- \"set ``affinity=precomputed``.\"\n- )\n-\n- if self.affinity == \"nearest_neighbors\":\n- connectivity = kneighbors_graph(\n- X, n_neighbors=self.n_neighbors, include_self=True, n_jobs=self.n_jobs\n- )\n- self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)\n- elif self.affinity == \"precomputed_nearest_neighbors\":\n- estimator = NearestNeighbors(\n- n_neighbors=self.n_neighbors, n_jobs=self.n_jobs, metric=\"precomputed\"\n- ).fit(X)\n- connectivity = estimator.kneighbors_graph(X=X, mode=\"connectivity\")\n- self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)\n- elif self.affinity == \"precomputed\":\n- self.affinity_matrix_ = X\n- else:\n- params = self.kernel_params\n- if params is None:\n- params = {}\n- if not callable(self.affinity):\n- params[\"gamma\"] = self.gamma\n- params[\"degree\"] = self.degree\n- params[\"coef0\"] = self.coef0\n- self.affinity_matrix_ = pairwise_kernels(\n- X, metric=self.affinity, filter_params=True, **params\n- )\n-\n- random_state = check_random_state(self.random_state)\n- n_components = (\n- self.n_clusters if self.n_components is None else self.n_components\n- )\n- # We now obtain the real valued solution matrix to the\n- # relaxed Ncut problem, solving the eigenvalue problem\n- # L_sym x = lambda x and recovering u = D^-1/2 x.\n- # The first eigenvector is constant only for fully connected graphs\n- # and should be kept for spectral clustering (drop_first = False)\n- # See spectral_embedding documentation.\n- maps = _spectral_embedding(\n- self.affinity_matrix_,\n- n_components=n_components,\n- eigen_solver=self.eigen_solver,\n- random_state=random_state,\n- eigen_tol=self.eigen_tol,\n- drop_first=False,\n- )\n- if self.verbose:\n- print(f\"Computing label assignment using {self.assign_labels}\")\n-\n- if self.assign_labels == \"kmeans\":\n- _, self.labels_, _ = k_means(\n- maps,\n- self.n_clusters,\n- random_state=random_state,\n- n_init=self.n_init,\n- verbose=self.verbose,\n- )\n- elif self.assign_labels == \"cluster_qr\":\n- self.labels_ = cluster_qr(maps)\n- else:\n- self.labels_ = discretize(maps, random_state=random_state)\n-\n- return self\n \n def fit_predict(self, X, y=None):\n \"\"\"Perform spectral clustering on `X` and return cluster 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/_spectral.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Perform spectral clustering from features, or affinity 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, similarities / affinities between\n instances if ``affinity='precomputed'``, or distances between\n instances if ``affinity='precomputed_nearest_neighbors``. If a\n sparse matrix is provided in a format other than ``csr_matrix``,\n ``csc_matrix``, or ``coo_matrix``, it will be converted into a\n sparse ``csr_matrix``.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n self : object\n A fitted instance of the estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_public_functions.py::test_class_wrapper_param_validation[sklearn.cluster.spectral_clustering-sklearn.cluster.SpectralClustering]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[kmeans-arpack-csr_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[kmeans-arpack-csr_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[kmeans-lobpcg-csr_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[kmeans-lobpcg-csr_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[discretize-arpack-csr_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[discretize-arpack-csr_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[discretize-lobpcg-csr_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[discretize-lobpcg-csr_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[cluster_qr-arpack-csr_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[cluster_qr-arpack-csr_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[cluster_qr-lobpcg-csr_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering[cluster_qr-lobpcg-csr_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_sparse[kmeans-coo_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_sparse[kmeans-coo_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_sparse[discretize-coo_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_sparse[discretize-coo_array]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_sparse[cluster_qr-coo_matrix]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_sparse[cluster_qr-coo_array]", "sklearn/cluster/tests/test_spectral.py::test_precomputed_nearest_neighbors_filtering", "sklearn/cluster/tests/test_spectral.py::test_affinities", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_with_arpack_amg_solvers", "sklearn/cluster/tests/test_spectral.py::test_n_components", "sklearn/cluster/tests/test_spectral.py::test_verbose[kmeans]", "sklearn/cluster/tests/test_spectral.py::test_verbose[discretize]", "sklearn/cluster/tests/test_spectral.py::test_verbose[cluster_qr]", "sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_np_matrix_raises", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_spectral_clustering", "sklearn/cluster/_spectral.py::sklearn.cluster._spectral.SpectralClustering", "sklearn/cluster/_spectral.py::sklearn.cluster._spectral.spectral_clustering", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_clustering]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_clustering(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=1,n_components=1,n_init=2)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[SpectralClustering(n_clusters=2,n_init=2)-check_fit2d_predict1d]", "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_pandas_column_name_consistency[SpectralClustering(n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_param_validation[SpectralClustering(n_clusters=2,n_init=2)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-181	1.0	{ "code": "diff --git b/sklearn/preprocessing/_polynomial.py a/sklearn/preprocessing/_polynomial.py\nindex df30c9db3..5a3239f11 100644\n--- b/sklearn/preprocessing/_polynomial.py\n+++ a/sklearn/preprocessing/_polynomial.py\n@@ -817,6 +817,7 @@ class SplineTransformer(TransformerMixin, BaseEstimator):\n feature_names.append(f\"{input_features[i]}_sp_{j}\")\n return np.asarray(feature_names, dtype=object)\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute knot positions of splines.\n \n@@ -838,6 +839,118 @@ class SplineTransformer(TransformerMixin, BaseEstimator):\n self : object\n Fitted transformer.\n \"\"\"\n+ X = validate_data(\n+ self,\n+ X,\n+ reset=True,\n+ accept_sparse=False,\n+ ensure_min_samples=2,\n+ ensure_2d=True,\n+ )\n+ if sample_weight is not None:\n+ sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n+\n+ _, n_features = X.shape\n+\n+ if isinstance(self.knots, str):\n+ base_knots = self._get_base_knot_positions(\n+ X, n_knots=self.n_knots, knots=self.knots, sample_weight=sample_weight\n+ )\n+ else:\n+ base_knots = check_array(self.knots, dtype=np.float64)\n+ if base_knots.shape[0] < 2:\n+ raise ValueError(\"Number of knots, knots.shape[0], must be >= 2.\")\n+ elif base_knots.shape[1] != n_features:\n+ raise ValueError(\"knots.shape[1] == n_features is violated.\")\n+ elif not np.all(np.diff(base_knots, axis=0) > 0):\n+ raise ValueError(\"knots must be sorted without duplicates.\")\n+\n+ if self.sparse_output and sp_version < parse_version(\"1.8.0\"):\n+ raise ValueError(\n+ \"Option sparse_output=True is only available with scipy>=1.8.0, \"\n+ f\"but here scipy=={sp_version} is used.\"\n+ )\n+\n+ # number of knots for base interval\n+ n_knots = base_knots.shape[0]\n+\n+ if self.extrapolation == \"periodic\" and n_knots <= self.degree:\n+ raise ValueError(\n+ \"Periodic splines require degree < n_knots. Got n_knots=\"\n+ f\"{n_knots} and degree={self.degree}.\"\n+ )\n+\n+ # number of splines basis functions\n+ if self.extrapolation != \"periodic\":\n+ n_splines = n_knots + self.degree - 1\n+ else:\n+ # periodic splines have self.degree less degrees of freedom\n+ n_splines = n_knots - 1\n+\n+ degree = self.degree\n+ n_out = n_features * n_splines\n+ # We have to add degree number of knots below, and degree number knots\n+ # above the base knots in order to make the spline basis complete.\n+ if self.extrapolation == \"periodic\":\n+ # For periodic splines the spacing of the first / last degree knots\n+ # needs to be a continuation of the spacing of the last / first\n+ # base knots.\n+ period = base_knots[-1] - base_knots[0]\n+ knots = np.r_[\n+ base_knots[-(degree + 1) : -1] - period,\n+ base_knots,\n+ base_knots[1 : (degree + 1)] + period,\n+ ]\n+\n+ else:\n+ # Eilers & Marx in \"Flexible smoothing with B-splines and\n+ # penalties\" https://doi.org/10.1214/ss/1038425655 advice\n+ # against repeating first and last knot several times, which\n+ # would have inferior behaviour at boundaries if combined with\n+ # a penalty (hence P-Spline). We follow this advice even if our\n+ # splines are unpenalized. Meaning we do not:\n+ # knots = np.r_[\n+ # np.tile(base_knots.min(axis=0), reps=[degree, 1]),\n+ # base_knots,\n+ # np.tile(base_knots.max(axis=0), reps=[degree, 1])\n+ # ]\n+ # Instead, we reuse the distance of the 2 fist/last knots.\n+ dist_min = base_knots[1] - base_knots[0]\n+ dist_max = base_knots[-1] - base_knots[-2]\n+\n+ knots = np.r_[\n+ np.linspace(\n+ base_knots[0] - degree * dist_min,\n+ base_knots[0] - dist_min,\n+ num=degree,\n+ ),\n+ base_knots,\n+ np.linspace(\n+ base_knots[-1] + dist_max,\n+ base_knots[-1] + degree * dist_max,\n+ num=degree,\n+ ),\n+ ]\n+\n+ # With a diagonal coefficient matrix, we get back the spline basis\n+ # elements, i.e. the design matrix of the spline.\n+ # Note, BSpline appreciates C-contiguous float64 arrays as c=coef.\n+ coef = np.eye(n_splines, dtype=np.float64)\n+ if self.extrapolation == \"periodic\":\n+ coef = np.concatenate((coef, coef[:degree, :]))\n+\n+ extrapolate = self.extrapolation in [\"periodic\", \"continue\"]\n+\n+ bsplines = [\n+ BSpline.construct_fast(\n+ knots[:, i], coef, self.degree, extrapolate=extrapolate\n+ )\n+ for i in range(n_features)\n+ ]\n+ self.bsplines_ = bsplines\n+\n+ self.n_features_out_ = n_out - n_features * (1 - self.include_bias)\n+ return self\n \n def transform(self, X):\n \"\"\"Transform each feature data to B-splines.\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_polynomial.py b/sklearn/preprocessing/_polynomial.py\nindex 5a3239f11..df30c9db3 100644\n--- a/sklearn/preprocessing/_polynomial.py\n+++ b/sklearn/preprocessing/_polynomial.py\n@@ -817,7 +817,6 @@ class SplineTransformer(TransformerMixin, BaseEstimator):\n feature_names.append(f\"{input_features[i]}_sp_{j}\")\n return np.asarray(feature_names, dtype=object)\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute knot positions of splines.\n \n@@ -839,118 +838,6 @@ class SplineTransformer(TransformerMixin, BaseEstimator):\n self : object\n Fitted transformer.\n \"\"\"\n- X = validate_data(\n- self,\n- X,\n- reset=True,\n- accept_sparse=False,\n- ensure_min_samples=2,\n- ensure_2d=True,\n- )\n- if sample_weight is not None:\n- sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)\n-\n- _, n_features = X.shape\n-\n- if isinstance(self.knots, str):\n- base_knots = self._get_base_knot_positions(\n- X, n_knots=self.n_knots, knots=self.knots, sample_weight=sample_weight\n- )\n- else:\n- base_knots = check_array(self.knots, dtype=np.float64)\n- if base_knots.shape[0] < 2:\n- raise ValueError(\"Number of knots, knots.shape[0], must be >= 2.\")\n- elif base_knots.shape[1] != n_features:\n- raise ValueError(\"knots.shape[1] == n_features is violated.\")\n- elif not np.all(np.diff(base_knots, axis=0) > 0):\n- raise ValueError(\"knots must be sorted without duplicates.\")\n-\n- if self.sparse_output and sp_version < parse_version(\"1.8.0\"):\n- raise ValueError(\n- \"Option sparse_output=True is only available with scipy>=1.8.0, \"\n- f\"but here scipy=={sp_version} is used.\"\n- )\n-\n- # number of knots for base interval\n- n_knots = base_knots.shape[0]\n-\n- if self.extrapolation == \"periodic\" and n_knots <= self.degree:\n- raise ValueError(\n- \"Periodic splines require degree < n_knots. Got n_knots=\"\n- f\"{n_knots} and degree={self.degree}.\"\n- )\n-\n- # number of splines basis functions\n- if self.extrapolation != \"periodic\":\n- n_splines = n_knots + self.degree - 1\n- else:\n- # periodic splines have self.degree less degrees of freedom\n- n_splines = n_knots - 1\n-\n- degree = self.degree\n- n_out = n_features * n_splines\n- # We have to add degree number of knots below, and degree number knots\n- # above the base knots in order to make the spline basis complete.\n- if self.extrapolation == \"periodic\":\n- # For periodic splines the spacing of the first / last degree knots\n- # needs to be a continuation of the spacing of the last / first\n- # base knots.\n- period = base_knots[-1] - base_knots[0]\n- knots = np.r_[\n- base_knots[-(degree + 1) : -1] - period,\n- base_knots,\n- base_knots[1 : (degree + 1)] + period,\n- ]\n-\n- else:\n- # Eilers & Marx in \"Flexible smoothing with B-splines and\n- # penalties\" https://doi.org/10.1214/ss/1038425655 advice\n- # against repeating first and last knot several times, which\n- # would have inferior behaviour at boundaries if combined with\n- # a penalty (hence P-Spline). We follow this advice even if our\n- # splines are unpenalized. Meaning we do not:\n- # knots = np.r_[\n- # np.tile(base_knots.min(axis=0), reps=[degree, 1]),\n- # base_knots,\n- # np.tile(base_knots.max(axis=0), reps=[degree, 1])\n- # ]\n- # Instead, we reuse the distance of the 2 fist/last knots.\n- dist_min = base_knots[1] - base_knots[0]\n- dist_max = base_knots[-1] - base_knots[-2]\n-\n- knots = np.r_[\n- np.linspace(\n- base_knots[0] - degree * dist_min,\n- base_knots[0] - dist_min,\n- num=degree,\n- ),\n- base_knots,\n- np.linspace(\n- base_knots[-1] + dist_max,\n- base_knots[-1] + degree * dist_max,\n- num=degree,\n- ),\n- ]\n-\n- # With a diagonal coefficient matrix, we get back the spline basis\n- # elements, i.e. the design matrix of the spline.\n- # Note, BSpline appreciates C-contiguous float64 arrays as c=coef.\n- coef = np.eye(n_splines, dtype=np.float64)\n- if self.extrapolation == \"periodic\":\n- coef = np.concatenate((coef, coef[:degree, :]))\n-\n- extrapolate = self.extrapolation in [\"periodic\", \"continue\"]\n-\n- bsplines = [\n- BSpline.construct_fast(\n- knots[:, i], coef, self.degree, extrapolate=extrapolate\n- )\n- for i in range(n_features)\n- ]\n- self.bsplines_ = bsplines\n-\n- self.n_features_out_ = n_out - n_features * (1 - self.include_bias)\n- return self\n \n def transform(self, X):\n \"\"\"Transform each feature data to B-splines.\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/_polynomial.py.\nHere is the description for the function:\n def fit(self, X, y=None, sample_weight=None):\n \"\"\"Compute knot positions of splines.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data.\n\n y : None\n Ignored.\n\n sample_weight : array-like of shape (n_samples,), default = None\n Individual weights for each sample. Used to calculate quantiles if\n `knots=\"quantile\"`. For `knots=\"uniform\"`, zero weighted\n observations are ignored for finding the min and max of `X`.\n\n Returns\n -------\n self : object\n Fitted transformer.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_and_spline_array_order[SplineTransformer]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_input_validation[params0-Number of knots, knots.shape\\\\[0\\\\], must be >= 2.]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_input_validation[params1-knots.shape\\\\[1\\\\] == n_features is violated]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_input_validation[params2-knots must be sorted without duplicates.]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_integer_knots[continue]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_integer_knots[periodic]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_feature_names", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-constant]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-linear]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-continue]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-periodic]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[3-constant]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[3-linear]", 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scikit-learn__scikit-learn-182	1.0	{ "code": "diff --git b/sklearn/preprocessing/_polynomial.py a/sklearn/preprocessing/_polynomial.py\nindex f4205e7de..5a3239f11 100644\n--- b/sklearn/preprocessing/_polynomial.py\n+++ a/sklearn/preprocessing/_polynomial.py\n@@ -966,6 +966,211 @@ class SplineTransformer(TransformerMixin, BaseEstimator):\n The matrix of features, where n_splines is the number of bases\n elements of the B-splines, n_knots + degree - 1.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ X = validate_data(self, X, reset=False, accept_sparse=False, ensure_2d=True)\n+\n+ n_samples, n_features = X.shape\n+ n_splines = self.bsplines_[0].c.shape[1]\n+ degree = self.degree\n+\n+ # TODO: Remove this condition, once scipy 1.10 is the minimum version.\n+ # Only scipy => 1.10 supports design_matrix(.., extrapolate=..).\n+ # The default (implicit in scipy < 1.10) is extrapolate=False.\n+ scipy_1_10 = sp_version >= parse_version(\"1.10.0\")\n+ # Note: self.bsplines_[0].extrapolate is True for extrapolation in\n+ # [\"periodic\", \"continue\"]\n+ if scipy_1_10:\n+ use_sparse = self.sparse_output\n+ kwargs_extrapolate = {\"extrapolate\": self.bsplines_[0].extrapolate}\n+ else:\n+ use_sparse = self.sparse_output and not self.bsplines_[0].extrapolate\n+ kwargs_extrapolate = dict()\n+\n+ # Note that scipy BSpline returns float64 arrays and converts input\n+ # x=X[:, i] to c-contiguous float64.\n+ n_out = self.n_features_out_ + n_features * (1 - self.include_bias)\n+ if X.dtype in FLOAT_DTYPES:\n+ dtype = X.dtype\n+ else:\n+ dtype = np.float64\n+ if use_sparse:\n+ output_list = []\n+ else:\n+ XBS = np.zeros((n_samples, n_out), dtype=dtype, order=self.order)\n+\n+ for i in range(n_features):\n+ spl = self.bsplines_[i]\n+\n+ if self.extrapolation in (\"continue\", \"error\", \"periodic\"):\n+ if self.extrapolation == \"periodic\":\n+ # With periodic extrapolation we map x to the segment\n+ # [spl.t[k], spl.t[n]].\n+ # This is equivalent to BSpline(.., extrapolate=\"periodic\")\n+ # for scipy>=1.0.0.\n+ n = spl.t.size - spl.k - 1\n+ # Assign to new array to avoid inplace operation\n+ x = spl.t[spl.k] + (X[:, i] - spl.t[spl.k]) % (\n+ spl.t[n] - spl.t[spl.k]\n+ )\n+ else:\n+ x = X[:, i]\n+\n+ if use_sparse:\n+ XBS_sparse = BSpline.design_matrix(\n+ x, spl.t, spl.k, *kwargs_extrapolate\n+ )\n+ if self.extrapolation == \"periodic\":\n+ # See the construction of coef in fit. We need to add the last\n+ # degree spline basis function to the first degree ones and\n+ # then drop the last ones.\n+ # Note: See comment about SparseEfficiencyWarning below.\n+ XBS_sparse = XBS_sparse.tolil()\n+ XBS_sparse[:, :degree] += XBS_sparse[:, -degree:]\n+ XBS_sparse = XBS_sparse[:, :-degree]\n+ else:\n+ XBS[:, (i n_splines) : ((i + 1) * n_splines)] = spl(x)\n+ else: # extrapolation in (\"constant\", \"linear\")\n+ xmin, xmax = spl.t[degree], spl.t[-degree - 1]\n+ # spline values at boundaries\n+ f_min, f_max = spl(xmin), spl(xmax)\n+ mask = (xmin <= X[:, i]) & (X[:, i] <= xmax)\n+ if use_sparse:\n+ mask_inv = ~mask\n+ x = X[:, i].copy()\n+ # Set some arbitrary values outside boundary that will be reassigned\n+ # later.\n+ x[mask_inv] = spl.t[self.degree]\n+ XBS_sparse = BSpline.design_matrix(x, spl.t, spl.k)\n+ # Note: Without converting to lil_matrix we would get:\n+ # scipy.sparse._base.SparseEfficiencyWarning: Changing the sparsity\n+ # structure of a csr_matrix is expensive. lil_matrix is more\n+ # efficient.\n+ if np.any(mask_inv):\n+ XBS_sparse = XBS_sparse.tolil()\n+ XBS_sparse[mask_inv, :] = 0\n+ else:\n+ XBS[mask, (i * n_splines) : ((i + 1) * n_splines)] = spl(X[mask, i])\n+\n+ # Note for extrapolation:\n+ # 'continue' is already returned as is by scipy BSplines\n+ if self.extrapolation == \"error\":\n+ # BSpline with extrapolate=False does not raise an error, but\n+ # outputs np.nan.\n+ if (use_sparse and np.any(np.isnan(XBS_sparse.data))) or (\n+ not use_sparse\n+ and np.any(\n+ np.isnan(XBS[:, (i * n_splines) : ((i + 1) * n_splines)])\n+ )\n+ ):\n+ raise ValueError(\n+ \"X contains values beyond the limits of the knots.\"\n+ )\n+ elif self.extrapolation == \"constant\":\n+ # Set all values beyond xmin and xmax to the value of the\n+ # spline basis functions at those two positions.\n+ # Only the first degree and last degree number of splines\n+ # have non-zero values at the boundaries.\n+\n+ mask = X[:, i] < xmin\n+ if np.any(mask):\n+ if use_sparse:\n+ # Note: See comment about SparseEfficiencyWarning above.\n+ XBS_sparse = XBS_sparse.tolil()\n+ XBS_sparse[mask, :degree] = f_min[:degree]\n+\n+ else:\n+ XBS[mask, (i * n_splines) : (i * n_splines + degree)] = f_min[\n+ :degree\n+ ]\n+\n+ mask = X[:, i] > xmax\n+ if np.any(mask):\n+ if use_sparse:\n+ # Note: See comment about SparseEfficiencyWarning above.\n+ XBS_sparse = XBS_sparse.tolil()\n+ XBS_sparse[mask, -degree:] = f_max[-degree:]\n+ else:\n+ XBS[\n+ mask,\n+ ((i + 1) * n_splines - degree) : ((i + 1) * n_splines),\n+ ] = f_max[-degree:]\n+\n+ elif self.extrapolation == \"linear\":\n+ # Continue the degree first and degree last spline bases\n+ # linearly beyond the boundaries, with slope = derivative at\n+ # the boundary.\n+ # Note that all others have derivative = value = 0 at the\n+ # boundaries.\n+\n+ # spline derivatives = slopes at boundaries\n+ fp_min, fp_max = spl(xmin, nu=1), spl(xmax, nu=1)\n+ # Compute the linear continuation.\n+ if degree <= 1:\n+ # For degree=1, the derivative of 2nd spline is not zero at\n+ # boundary. For degree=0 it is the same as 'constant'.\n+ degree += 1\n+ for j in range(degree):\n+ mask = X[:, i] < xmin\n+ if np.any(mask):\n+ linear_extr = f_min[j] + (X[mask, i] - xmin) * fp_min[j]\n+ if use_sparse:\n+ # Note: See comment about SparseEfficiencyWarning above.\n+ XBS_sparse = XBS_sparse.tolil()\n+ XBS_sparse[mask, j] = linear_extr\n+ else:\n+ XBS[mask, i * n_splines + j] = linear_extr\n+\n+ mask = X[:, i] > xmax\n+ if np.any(mask):\n+ k = n_splines - 1 - j\n+ linear_extr = f_max[k] + (X[mask, i] - xmax) * fp_max[k]\n+ if use_sparse:\n+ # Note: See comment about SparseEfficiencyWarning above.\n+ XBS_sparse = XBS_sparse.tolil()\n+ XBS_sparse[mask, k : k + 1] = linear_extr[:, None]\n+ else:\n+ XBS[mask, i * n_splines + k] = linear_extr\n+\n+ if use_sparse:\n+ XBS_sparse = XBS_sparse.tocsr()\n+ output_list.append(XBS_sparse)\n+\n+ if use_sparse:\n+ # TODO: Remove this conditional error when the minimum supported version of\n+ # SciPy is 1.9.2\n+ # `scipy.sparse.hstack` breaks in scipy<1.9.2\n+ # when `n_features_out_ > max_int32`\n+ max_int32 = np.iinfo(np.int32).max\n+ all_int32 = True\n+ for mat in output_list:\n+ all_int32 &= mat.indices.dtype == np.int32\n+ if (\n+ sp_version < parse_version(\"1.9.2\")\n+ and self.n_features_out_ > max_int32\n+ and all_int32\n+ ):\n+ raise ValueError(\n+ \"In scipy versions `<1.9.2`, the function `scipy.sparse.hstack`\"\n+ \" produces negative columns when:\\n1. The output shape contains\"\n+ \" `n_cols` too large to be represented by a 32bit signed\"\n+ \" integer.\\n. All sub-matrices to be stacked have indices of\"\n+ \" dtype `np.int32`.\\nTo avoid this error, either use a version\"\n+ \" of scipy `>=1.9.2` or alter the `SplineTransformer`\"\n+ \" transformer to produce fewer than 2^31 output features\"\n+ )\n+ XBS = sparse.hstack(output_list, format=\"csr\")\n+ elif self.sparse_output:\n+ # TODO: Remove ones scipy 1.10 is the minimum version. See comments above.\n+ XBS = sparse.csr_matrix(XBS)\n+\n+ if self.include_bias:\n+ return XBS\n+ else:\n+ # We throw away one spline basis per feature.\n+ # We chose the last one.\n+ indices = [j for j in range(XBS.shape[1]) if (j + 1) % n_splines != 0]\n+ return XBS[:, indices]\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_polynomial.py b/sklearn/preprocessing/_polynomial.py\nindex 5a3239f11..f4205e7de 100644\n--- a/sklearn/preprocessing/_polynomial.py\n+++ b/sklearn/preprocessing/_polynomial.py\n@@ -966,211 +966,6 @@ class SplineTransformer(TransformerMixin, BaseEstimator):\n The matrix of features, where n_splines is the number of bases\n elements of the B-splines, n_knots + degree - 1.\n \"\"\"\n- check_is_fitted(self)\n-\n- X = validate_data(self, X, reset=False, accept_sparse=False, ensure_2d=True)\n-\n- n_samples, n_features = X.shape\n- n_splines = self.bsplines_[0].c.shape[1]\n- degree = self.degree\n-\n- # TODO: Remove this condition, once scipy 1.10 is the minimum version.\n- # Only scipy => 1.10 supports design_matrix(.., extrapolate=..).\n- # The default (implicit in scipy < 1.10) is extrapolate=False.\n- scipy_1_10 = sp_version >= parse_version(\"1.10.0\")\n- # Note: self.bsplines_[0].extrapolate is True for extrapolation in\n- # [\"periodic\", \"continue\"]\n- if scipy_1_10:\n- use_sparse = self.sparse_output\n- kwargs_extrapolate = {\"extrapolate\": self.bsplines_[0].extrapolate}\n- else:\n- use_sparse = self.sparse_output and not self.bsplines_[0].extrapolate\n- kwargs_extrapolate = dict()\n-\n- # Note that scipy BSpline returns float64 arrays and converts input\n- # x=X[:, i] to c-contiguous float64.\n- n_out = self.n_features_out_ + n_features * (1 - self.include_bias)\n- if X.dtype in FLOAT_DTYPES:\n- dtype = X.dtype\n- else:\n- dtype = np.float64\n- if use_sparse:\n- output_list = []\n- else:\n- XBS = np.zeros((n_samples, n_out), dtype=dtype, order=self.order)\n-\n- for i in range(n_features):\n- spl = self.bsplines_[i]\n-\n- if self.extrapolation in (\"continue\", \"error\", \"periodic\"):\n- if self.extrapolation == \"periodic\":\n- # With periodic extrapolation we map x to the segment\n- # [spl.t[k], spl.t[n]].\n- # This is equivalent to BSpline(.., extrapolate=\"periodic\")\n- # for scipy>=1.0.0.\n- n = spl.t.size - spl.k - 1\n- # Assign to new array to avoid inplace operation\n- x = spl.t[spl.k] + (X[:, i] - spl.t[spl.k]) % (\n- spl.t[n] - spl.t[spl.k]\n- )\n- else:\n- x = X[:, i]\n-\n- if use_sparse:\n- XBS_sparse = BSpline.design_matrix(\n- x, spl.t, spl.k, *kwargs_extrapolate\n- )\n- if self.extrapolation == \"periodic\":\n- # See the construction of coef in fit. We need to add the last\n- # degree spline basis function to the first degree ones and\n- # then drop the last ones.\n- # Note: See comment about SparseEfficiencyWarning below.\n- XBS_sparse = XBS_sparse.tolil()\n- XBS_sparse[:, :degree] += XBS_sparse[:, -degree:]\n- XBS_sparse = XBS_sparse[:, :-degree]\n- else:\n- XBS[:, (i n_splines) : ((i + 1) * n_splines)] = spl(x)\n- else: # extrapolation in (\"constant\", \"linear\")\n- xmin, xmax = spl.t[degree], spl.t[-degree - 1]\n- # spline values at boundaries\n- f_min, f_max = spl(xmin), spl(xmax)\n- mask = (xmin <= X[:, i]) & (X[:, i] <= xmax)\n- if use_sparse:\n- mask_inv = ~mask\n- x = X[:, i].copy()\n- # Set some arbitrary values outside boundary that will be reassigned\n- # later.\n- x[mask_inv] = spl.t[self.degree]\n- XBS_sparse = BSpline.design_matrix(x, spl.t, spl.k)\n- # Note: Without converting to lil_matrix we would get:\n- # scipy.sparse._base.SparseEfficiencyWarning: Changing the sparsity\n- # structure of a csr_matrix is expensive. lil_matrix is more\n- # efficient.\n- if np.any(mask_inv):\n- XBS_sparse = XBS_sparse.tolil()\n- XBS_sparse[mask_inv, :] = 0\n- else:\n- XBS[mask, (i * n_splines) : ((i + 1) * n_splines)] = spl(X[mask, i])\n-\n- # Note for extrapolation:\n- # 'continue' is already returned as is by scipy BSplines\n- if self.extrapolation == \"error\":\n- # BSpline with extrapolate=False does not raise an error, but\n- # outputs np.nan.\n- if (use_sparse and np.any(np.isnan(XBS_sparse.data))) or (\n- not use_sparse\n- and np.any(\n- np.isnan(XBS[:, (i * n_splines) : ((i + 1) * n_splines)])\n- )\n- ):\n- raise ValueError(\n- \"X contains values beyond the limits of the knots.\"\n- )\n- elif self.extrapolation == \"constant\":\n- # Set all values beyond xmin and xmax to the value of the\n- # spline basis functions at those two positions.\n- # Only the first degree and last degree number of splines\n- # have non-zero values at the boundaries.\n-\n- mask = X[:, i] < xmin\n- if np.any(mask):\n- if use_sparse:\n- # Note: See comment about SparseEfficiencyWarning above.\n- XBS_sparse = XBS_sparse.tolil()\n- XBS_sparse[mask, :degree] = f_min[:degree]\n-\n- else:\n- XBS[mask, (i * n_splines) : (i * n_splines + degree)] = f_min[\n- :degree\n- ]\n-\n- mask = X[:, i] > xmax\n- if np.any(mask):\n- if use_sparse:\n- # Note: See comment about SparseEfficiencyWarning above.\n- XBS_sparse = XBS_sparse.tolil()\n- XBS_sparse[mask, -degree:] = f_max[-degree:]\n- else:\n- XBS[\n- mask,\n- ((i + 1) * n_splines - degree) : ((i + 1) * n_splines),\n- ] = f_max[-degree:]\n-\n- elif self.extrapolation == \"linear\":\n- # Continue the degree first and degree last spline bases\n- # linearly beyond the boundaries, with slope = derivative at\n- # the boundary.\n- # Note that all others have derivative = value = 0 at the\n- # boundaries.\n-\n- # spline derivatives = slopes at boundaries\n- fp_min, fp_max = spl(xmin, nu=1), spl(xmax, nu=1)\n- # Compute the linear continuation.\n- if degree <= 1:\n- # For degree=1, the derivative of 2nd spline is not zero at\n- # boundary. For degree=0 it is the same as 'constant'.\n- degree += 1\n- for j in range(degree):\n- mask = X[:, i] < xmin\n- if np.any(mask):\n- linear_extr = f_min[j] + (X[mask, i] - xmin) * fp_min[j]\n- if use_sparse:\n- # Note: See comment about SparseEfficiencyWarning above.\n- XBS_sparse = XBS_sparse.tolil()\n- XBS_sparse[mask, j] = linear_extr\n- else:\n- XBS[mask, i * n_splines + j] = linear_extr\n-\n- mask = X[:, i] > xmax\n- if np.any(mask):\n- k = n_splines - 1 - j\n- linear_extr = f_max[k] + (X[mask, i] - xmax) * fp_max[k]\n- if use_sparse:\n- # Note: See comment about SparseEfficiencyWarning above.\n- XBS_sparse = XBS_sparse.tolil()\n- XBS_sparse[mask, k : k + 1] = linear_extr[:, None]\n- else:\n- XBS[mask, i * n_splines + k] = linear_extr\n-\n- if use_sparse:\n- XBS_sparse = XBS_sparse.tocsr()\n- output_list.append(XBS_sparse)\n-\n- if use_sparse:\n- # TODO: Remove this conditional error when the minimum supported version of\n- # SciPy is 1.9.2\n- # `scipy.sparse.hstack` breaks in scipy<1.9.2\n- # when `n_features_out_ > max_int32`\n- max_int32 = np.iinfo(np.int32).max\n- all_int32 = True\n- for mat in output_list:\n- all_int32 &= mat.indices.dtype == np.int32\n- if (\n- sp_version < parse_version(\"1.9.2\")\n- and self.n_features_out_ > max_int32\n- and all_int32\n- ):\n- raise ValueError(\n- \"In scipy versions `<1.9.2`, the function `scipy.sparse.hstack`\"\n- \" produces negative columns when:\\n1. The output shape contains\"\n- \" `n_cols` too large to be represented by a 32bit signed\"\n- \" integer.\\n. All sub-matrices to be stacked have indices of\"\n- \" dtype `np.int32`.\\nTo avoid this error, either use a version\"\n- \" of scipy `>=1.9.2` or alter the `SplineTransformer`\"\n- \" transformer to produce fewer than 2^31 output features\"\n- )\n- XBS = sparse.hstack(output_list, format=\"csr\")\n- elif self.sparse_output:\n- # TODO: Remove ones scipy 1.10 is the minimum version. See comments above.\n- XBS = sparse.csr_matrix(XBS)\n-\n- if self.include_bias:\n- return XBS\n- else:\n- # We throw away one spline basis per feature.\n- # We chose the last one.\n- indices = [j for j in range(XBS.shape[1]) if (j + 1) % n_splines != 0]\n- return XBS[:, indices]\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/_polynomial.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Transform each feature data to B-splines.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data to transform.\n\n Returns\n -------\n XBS : {ndarray, sparse matrix} of shape (n_samples, n_features * n_splines)\n The matrix of features, where n_splines is the number of bases\n elements of the B-splines, n_knots + degree - 1.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_polynomial.py::test_polynomial_and_spline_array_order[SplineTransformer]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_integer_knots[continue]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_integer_knots[periodic]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-constant]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-linear]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-continue]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[2-periodic]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[3-constant]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[3-linear]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[3-continue]", "sklearn/preprocessing/tests/test_polynomial.py::test_split_transform_feature_names_extrapolation_degree[3-periodic]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-3-1]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-3-2]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-3-3]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-3-4]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-4-1]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-4-2]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-4-3]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-uniform-4-4]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-quantile-3-1]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-quantile-3-2]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-quantile-3-3]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-quantile-3-4]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_unity_decomposition[constant-quantile-4-1]", 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"sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-error-4-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-error-4-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-error-4-False-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-3-True-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-3-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-3-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-3-False-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-4-True-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-4-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-4-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-constant-4-False-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-3-True-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-3-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-3-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-3-False-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-4-True-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-4-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-4-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-linear-4-False-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-3-True-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-3-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-3-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-3-False-10]", "sklearn/model_selection/tests/test_search.py::test_cv_results_multi_size_array", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-4-True-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-4-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-4-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-continue-4-False-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-periodic-3-True-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-periodic-3-True-10]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-periodic-3-False-5]", "sklearn/preprocessing/tests/test_polynomial.py::test_spline_transformer_n_features_out[False-periodic-3-False-10]", 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"sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_transformers_unfitted]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SplineTransformer()-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SplineTransformer()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SplineTransformer()]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SplineTransformer()]", "sklearn/tests/test_common.py::test_set_output_transform[SplineTransformer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-SplineTransformer()]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SplineTransformer()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-183	1.0	{ "code": "diff --git b/sklearn/ensemble/_stacking.py a/sklearn/ensemble/_stacking.py\nindex 45c19f45c..57bc63a18 100644\n--- b/sklearn/ensemble/_stacking.py\n+++ a/sklearn/ensemble/_stacking.py\n@@ -676,6 +676,7 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n # TODO(1.7): remove `sample_weight` from the signature after deprecation\n # cycle; pop it from `fit_params` before the `_raise_for_params` check and\n # reinsert afterwards, for backwards compatibility\n+ @_deprecate_positional_args(version=\"1.7\")\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit the estimators.\n \n@@ -711,6 +712,25 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n self : object\n Returns a fitted instance of estimator.\n \"\"\"\n+ _raise_for_params(fit_params, self, \"fit\")\n+ check_classification_targets(y)\n+ if type_of_target(y) == \"multilabel-indicator\":\n+ self._label_encoder = [LabelEncoder().fit(yk) for yk in y.T]\n+ self.classes_ = [le.classes_ for le in self._label_encoder]\n+ y_encoded = np.array(\n+ [\n+ self._label_encoder[target_idx].transform(target)\n+ for target_idx, target in enumerate(y.T)\n+ ]\n+ ).T\n+ else:\n+ self._label_encoder = LabelEncoder().fit(y)\n+ self.classes_ = self._label_encoder.classes_\n+ y_encoded = self._label_encoder.transform(y)\n+\n+ if sample_weight is not None:\n+ fit_params[\"sample_weight\"] = sample_weight\n+ return super().fit(X, y_encoded, fit_params)\n \n @available_if(_estimator_has(\"predict\"))\n def predict(self, X, *predict_params):\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_stacking.py b/sklearn/ensemble/_stacking.py\nindex 57bc63a18..45c19f45c 100644\n--- a/sklearn/ensemble/_stacking.py\n+++ b/sklearn/ensemble/_stacking.py\n@@ -676,7 +676,6 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n # TODO(1.7): remove `sample_weight` from the signature after deprecation\n # cycle; pop it from `fit_params` before the `_raise_for_params` check and\n # reinsert afterwards, for backwards compatibility\n- @_deprecate_positional_args(version=\"1.7\")\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit the estimators.\n \n@@ -712,25 +711,6 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n self : object\n Returns a fitted instance of estimator.\n \"\"\"\n- _raise_for_params(fit_params, self, \"fit\")\n- check_classification_targets(y)\n- if type_of_target(y) == \"multilabel-indicator\":\n- self._label_encoder = [LabelEncoder().fit(yk) for yk in y.T]\n- self.classes_ = [le.classes_ for le in self._label_encoder]\n- y_encoded = np.array(\n- [\n- self._label_encoder[target_idx].transform(target)\n- for target_idx, target in enumerate(y.T)\n- ]\n- ).T\n- else:\n- self._label_encoder = LabelEncoder().fit(y)\n- self.classes_ = self._label_encoder.classes_\n- y_encoded = self._label_encoder.transform(y)\n-\n- if sample_weight is not None:\n- fit_params[\"sample_weight\"] = sample_weight\n- return super().fit(X, y_encoded, fit_params)\n \n @available_if(_estimator_has(\"predict\"))\n def predict(self, X, *predict_params):\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/_stacking.py.\nHere is the description for the function:\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit the estimators.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vectors, 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 values. Note that `y` will be internally encoded in\n numerically increasing order or lexicographic order. If the order\n matter (e.g. for ordinal regression), one should numerically encode\n the target `y` before calling :term:`fit`.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights. If None, then samples are equally weighted.\n Note that this is supported only if all underlying estimators\n support sample weights.\n\n *fit_params : dict\n Parameters to pass to the underlying estimators.\n\n .. versionadded:: 1.6\n\n Only available if `enable_metadata_routing=True`, which can be\n set by using ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n self : object\n Returns a fitted instance of estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "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_column_binary_classification", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_drop_estimator", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[coo_matrix]", "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/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[csr_matrix]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_sparse_passthrough[csr_array]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_drop_binary_prob", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_error[y0-params0-ValueError-Invalid 'estimators' attribute,]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_error[y1-params1-ValueError-does not implement the method predict_proba]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_error[y2-params2-TypeError-does not support sample weight]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_error[y3-params3-TypeError-does not support sample weight]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_randomness[StackingClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_stratify_default", 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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_empty_data_messages]", "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]", 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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_requires_y_none]", "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[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[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_param_validation[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-184	1.0	{ "code": "diff --git b/sklearn/ensemble/_stacking.py a/sklearn/ensemble/_stacking.py\nindex 6d9db31c3..57bc63a18 100644\n--- b/sklearn/ensemble/_stacking.py\n+++ a/sklearn/ensemble/_stacking.py\n@@ -732,6 +732,7 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n fit_params[\"sample_weight\"] = sample_weight\n return super().fit(X, y_encoded, fit_params)\n \n+ @available_if(_estimator_has(\"predict\"))\n def predict(self, X, predict_params):\n \"\"\"Predict target for X.\n \n@@ -763,6 +764,26 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)\n Predicted targets.\n \"\"\"\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"predict\", predict_params)\n+ else:\n+ # TODO(SLEP6): remove when metadata routing cannot be disabled.\n+ routed_params = Bunch()\n+ routed_params.final_estimator_ = Bunch(predict={})\n+ routed_params.final_estimator_.predict = predict_params\n+\n+ y_pred = super().predict(X, routed_params.final_estimator_[\"predict\"])\n+ if isinstance(self._label_encoder, list):\n+ # Handle the multilabel-indicator case\n+ y_pred = np.array(\n+ [\n+ self._label_encoder[target_idx].inverse_transform(target)\n+ for target_idx, target in enumerate(y_pred.T)\n+ ]\n+ ).T\n+ else:\n+ y_pred = self._label_encoder.inverse_transform(y_pred)\n+ return y_pred\n \n @available_if(_estimator_has(\"predict_proba\"))\n def predict_proba(self, X):\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_stacking.py b/sklearn/ensemble/_stacking.py\nindex 57bc63a18..6d9db31c3 100644\n--- a/sklearn/ensemble/_stacking.py\n+++ b/sklearn/ensemble/_stacking.py\n@@ -732,7 +732,6 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n fit_params[\"sample_weight\"] = sample_weight\n return super().fit(X, y_encoded, fit_params)\n \n- @available_if(_estimator_has(\"predict\"))\n def predict(self, X, predict_params):\n \"\"\"Predict target for X.\n \n@@ -764,26 +763,6 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):\n y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)\n Predicted targets.\n \"\"\"\n- if _routing_enabled():\n- routed_params = process_routing(self, \"predict\", predict_params)\n- else:\n- # TODO(SLEP6): remove when metadata routing cannot be disabled.\n- routed_params = Bunch()\n- routed_params.final_estimator_ = Bunch(predict={})\n- routed_params.final_estimator_.predict = predict_params\n-\n- y_pred = super().predict(X, routed_params.final_estimator_[\"predict\"])\n- if isinstance(self._label_encoder, list):\n- # Handle the multilabel-indicator case\n- y_pred = np.array(\n- [\n- self._label_encoder[target_idx].inverse_transform(target)\n- for target_idx, target in enumerate(y_pred.T)\n- ]\n- ).T\n- else:\n- y_pred = self._label_encoder.inverse_transform(y_pred)\n- return y_pred\n \n @available_if(_estimator_has(\"predict_proba\"))\n def predict_proba(self, 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/_stacking.py.\nHere is the description for the function:\n def predict(self, X, predict_params):\n \"\"\"Predict target for X.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vectors, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n predict_params : dict of str -> obj\n Parameters to the `predict` called by the `final_estimator`. Note\n that this may be used to return uncertainties from some estimators\n with `return_std` or `return_cov`. Be aware that it will only\n account for uncertainty in the final estimator.\n\n - If `enable_metadata_routing=False` (default):\n Parameters directly passed to the `predict` method of the\n `final_estimator`.\n\n - If `enable_metadata_routing=True`: Parameters safely routed to\n the `predict` method of the `final_estimator`. See :ref:`Metadata\n Routing User Guide <metadata_routing>` for more details.\n\n .. versionchanged:: 1.6\n `**predict_params` can be routed via metadata routing API.\n\n Returns\n -------\n y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)\n Predicted targets.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "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/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_common.py::test_heterogeneous_ensemble_support_missing_values[StackingClassifier-LogisticRegression-X0-y0]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[False-predict]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-auto]", "sklearn/ensemble/_stacking.py::sklearn.ensemble._stacking.StackingClassifier", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-predict]", "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/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_sample_weights_invariance(kind=ones)]", "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_sample_weights_invariance(kind=zeros)]", "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_estimators_unfitted]", "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[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[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[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-185	1.0	{ "code": "diff --git b/sklearn/ensemble/_stacking.py a/sklearn/ensemble/_stacking.py\nindex 59a87f593..57bc63a18 100644\n--- b/sklearn/ensemble/_stacking.py\n+++ a/sklearn/ensemble/_stacking.py\n@@ -1125,6 +1125,7 @@ class StackingRegressor(RegressorMixin, _BaseStacking):\n fit_params[\"sample_weight\"] = sample_weight\n return super().fit_transform(X, y, fit_params)\n \n+ @available_if(_estimator_has(\"predict\"))\n def predict(self, X, predict_params):\n \"\"\"Predict target for X.\n \n@@ -1156,6 +1157,17 @@ class StackingRegressor(RegressorMixin, _BaseStacking):\n y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)\n Predicted targets.\n \"\"\"\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"predict\", predict_params)\n+ else:\n+ # TODO(SLEP6): remove when metadata routing cannot be disabled.\n+ routed_params = Bunch()\n+ routed_params.final_estimator_ = Bunch(predict={})\n+ routed_params.final_estimator_.predict = predict_params\n+\n+ y_pred = super().predict(X, routed_params.final_estimator_[\"predict\"])\n+\n+ return y_pred\n \n def _sk_visual_block_(self):\n # If final_estimator's default changes then this should be\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_stacking.py b/sklearn/ensemble/_stacking.py\nindex 57bc63a18..59a87f593 100644\n--- a/sklearn/ensemble/_stacking.py\n+++ b/sklearn/ensemble/_stacking.py\n@@ -1125,7 +1125,6 @@ class StackingRegressor(RegressorMixin, _BaseStacking):\n fit_params[\"sample_weight\"] = sample_weight\n return super().fit_transform(X, y, fit_params)\n \n- @available_if(_estimator_has(\"predict\"))\n def predict(self, X, predict_params):\n \"\"\"Predict target for X.\n \n@@ -1157,17 +1156,6 @@ class StackingRegressor(RegressorMixin, _BaseStacking):\n y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)\n Predicted targets.\n \"\"\"\n- if _routing_enabled():\n- routed_params = process_routing(self, \"predict\", predict_params)\n- else:\n- # TODO(SLEP6): remove when metadata routing cannot be disabled.\n- routed_params = Bunch()\n- routed_params.final_estimator_ = Bunch(predict={})\n- routed_params.final_estimator_.predict = predict_params\n-\n- y_pred = super().predict(X, routed_params.final_estimator_[\"predict\"])\n-\n- return y_pred\n \n def _sk_visual_block_(self):\n # If final_estimator's default changes then this should be\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/_stacking.py.\nHere is the description for the function:\n def predict(self, X, predict_params):\n \"\"\"Predict target for X.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vectors, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n predict_params : dict of str -> obj\n Parameters to the `predict` called by the `final_estimator`. Note\n that this may be used to return uncertainties from some estimators\n with `return_std` or `return_cov`. Be aware that it will only\n account for uncertainty in the final estimator.\n\n - If `enable_metadata_routing=False` (default):\n Parameters directly passed to the `predict` method of the\n `final_estimator`.\n\n - If `enable_metadata_routing=True`: Parameters safely routed to\n the `predict` method of the `final_estimator`. See :ref:`Metadata\n Routing User Guide <metadata_routing>` for more details.\n\n .. versionchanged:: 1.6\n `**predict_params` can be routed via metadata routing API.\n\n Returns\n -------\n y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)\n Predicted targets.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_drop_estimator", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-None-predict_params0-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-None-predict_params0-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-final_estimator1-predict_params1-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-final_estimator1-predict_params1-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-final_estimator2-predict_params2-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[False-final_estimator2-predict_params2-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[True-None-predict_params0-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[True-None-predict_params0-cv1]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[True-final_estimator1-predict_params1-3]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_regressor_diabetes[True-final_estimator1-predict_params1-cv1]", "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/ensemble/tests/test_stacking.py::test_stacking_with_sample_weight[StackingRegressor]", "sklearn/tests/test_multioutput.py::test_multioutputregressor_ducktypes_fitted_estimator", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[StackingRegressor-LinearRegression-X1-y1]", "sklearn/ensemble/_stacking.py::sklearn.ensemble._stacking.StackingRegressor", "sklearn/ensemble/tests/test_stacking.py::test_metadata_routing_for_stacking_estimators[sample_weight-prop_value0-StackingRegressor-ConsumingRegressor]", "sklearn/ensemble/tests/test_stacking.py::test_metadata_routing_for_stacking_estimators[metadata-a-StackingRegressor-ConsumingRegressor]", "sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_estimators_unfitted]", "sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[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[StackingRegressor(cv=3,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-186	1.0	{ "code": "diff --git b/sklearn/preprocessing/_data.py a/sklearn/preprocessing/_data.py\nindex fca29a784..007e5b002 100644\n--- b/sklearn/preprocessing/_data.py\n+++ a/sklearn/preprocessing/_data.py\n@@ -1098,6 +1098,32 @@ class StandardScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):\n X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)\n Transformed array.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ copy = copy if copy is not None else self.copy\n+ X = check_array(\n+ X,\n+ accept_sparse=\"csr\",\n+ copy=copy,\n+ dtype=FLOAT_DTYPES,\n+ force_writeable=True,\n+ ensure_all_finite=\"allow-nan\",\n+ )\n+\n+ if sparse.issparse(X):\n+ if self.with_mean:\n+ raise ValueError(\n+ \"Cannot uncenter sparse matrices: pass `with_mean=False` \"\n+ \"instead See docstring for motivation and alternatives.\"\n+ )\n+ if self.scale_ is not None:\n+ inplace_column_scale(X, self.scale_)\n+ else:\n+ if self.with_std:\n+ X *= self.scale_\n+ if self.with_mean:\n+ X += self.mean_\n+ return X\n \n def __sklearn_tags__(self):\n tags = super().__sklearn_tags__()\n", "test": null }	null	{ "code": "diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py\nindex 007e5b002..fca29a784 100644\n--- a/sklearn/preprocessing/_data.py\n+++ b/sklearn/preprocessing/_data.py\n@@ -1098,32 +1098,6 @@ class StandardScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):\n X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)\n Transformed array.\n \"\"\"\n- check_is_fitted(self)\n-\n- copy = copy if copy is not None else self.copy\n- X = check_array(\n- X,\n- accept_sparse=\"csr\",\n- copy=copy,\n- dtype=FLOAT_DTYPES,\n- force_writeable=True,\n- ensure_all_finite=\"allow-nan\",\n- )\n-\n- if sparse.issparse(X):\n- if self.with_mean:\n- raise ValueError(\n- \"Cannot uncenter sparse matrices: pass `with_mean=False` \"\n- \"instead See docstring for motivation and alternatives.\"\n- )\n- if self.scale_ is not None:\n- inplace_column_scale(X, self.scale_)\n- else:\n- if self.with_std:\n- X *= self.scale_\n- if self.with_mean:\n- X += self.mean_\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/preprocessing/_data.py.\nHere is the description for the function:\n def inverse_transform(self, X, copy=None):\n \"\"\"Scale back the data to the original representation.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The data used to scale along the features axis.\n copy : bool, default=None\n Copy the input X or not.\n\n Returns\n -------\n X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)\n Transformed array.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/preprocessing/tests/test_data.py::test_standard_scaler_1d", "sklearn/preprocessing/tests/test_data.py::test_scaler_2d_arrays", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csc_matrix-True]", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csc_matrix-None]", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csc_array-True]", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csc_array-None]", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csr_matrix-True]", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csr_matrix-None]", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csr_array-True]", "sklearn/preprocessing/tests/test_data.py::test_partial_fit_sparse_input[csr_array-None]", "sklearn/preprocessing/tests/test_data.py::test_standard_scaler_trasform_with_partial_fit[True]", "sklearn/preprocessing/tests/test_data.py::test_standard_scaler_trasform_with_partial_fit[None]", "sklearn/preprocessing/tests/test_data.py::test_standard_check_array_of_inverse_transform", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csc_matrix-True]", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csc_matrix-None]", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csc_array-True]", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csc_array-None]", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csr_matrix-True]", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csr_matrix-None]", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csr_array-True]", "sklearn/preprocessing/tests/test_data.py::test_scaler_without_centering[csr_array-None]", "sklearn/preprocessing/tests/test_data.py::test_scaler_int[csc_matrix]", "sklearn/preprocessing/tests/test_data.py::test_scaler_int[csc_array]", "sklearn/preprocessing/tests/test_data.py::test_scaler_int[csr_matrix]", "sklearn/preprocessing/tests/test_data.py::test_scaler_int[csr_array]", "sklearn/preprocessing/tests/test_data.py::test_scale_sparse_with_mean_raise_exception[csr_matrix]", "sklearn/preprocessing/tests/test_data.py::test_scale_sparse_with_mean_raise_exception[csr_array]", "sklearn/preprocessing/tests/test_data.py::test_scale_sparse_with_mean_raise_exception[csc_matrix]", "sklearn/preprocessing/tests/test_data.py::test_scale_sparse_with_mean_raise_exception[csc_array]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_inverse[X0-True-box-cox]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_inverse[X0-True-yeo-johnson]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_inverse[X1-True-box-cox]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_inverse[X1-True-yeo-johnson]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_1d", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_2d", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_copy_True[True-box-cox]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_copy_True[True-yeo-johnson]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_copy_False[True-box-cox]", "sklearn/preprocessing/tests/test_data.py::test_power_transformer_copy_False[True-yeo-johnson]", "sklearn/preprocessing/tests/test_data.py::test_standard_scaler_raise_error_for_1d_input", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_error", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_2d_transformer[X0-y0]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_2d_transformer[X1-y1]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_2d_transformer_multioutput", "sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est2-scale-False-False-omit_kwargs2]", "sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est3-scale-True-False-omit_kwargs3]", "sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est4-power_transform-False-False-omit_kwargs4]", "sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est5-power_transform-False-True-omit_kwargs5]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-187	1.0	{ "code": "diff --git b/sklearn/model_selection/_split.py a/sklearn/model_selection/_split.py\nindex 27cb6683f..1453e42b8 100644\n--- b/sklearn/model_selection/_split.py\n+++ a/sklearn/model_selection/_split.py\n@@ -847,6 +847,13 @@ class StratifiedKFold(_BaseKFold):\n split. You can make the results identical by setting `random_state`\n to an integer.\n \"\"\"\n+ if groups is not None:\n+ warnings.warn(\n+ f\"The groups parameter is ignored by {self.__class__.__name__}\",\n+ UserWarning,\n+ )\n+ y = check_array(y, input_name=\"y\", ensure_2d=False, dtype=None)\n+ return super().split(X, y, groups)\n \n \n class StratifiedGroupKFold(GroupsConsumerMixin, _BaseKFold):\n", "test": null }	null	{ "code": "diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py\nindex 1453e42b8..27cb6683f 100644\n--- a/sklearn/model_selection/_split.py\n+++ b/sklearn/model_selection/_split.py\n@@ -847,13 +847,6 @@ class StratifiedKFold(_BaseKFold):\n split. You can make the results identical by setting `random_state`\n to an integer.\n \"\"\"\n- if groups is not None:\n- warnings.warn(\n- f\"The groups parameter is ignored by {self.__class__.__name__}\",\n- UserWarning,\n- )\n- y = check_array(y, input_name=\"y\", ensure_2d=False, dtype=None)\n- return super().split(X, y, groups)\n \n \n class StratifiedGroupKFold(GroupsConsumerMixin, _BaseKFold):\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/_split.py.\nHere is the description for the function:\n def split(self, X, y, groups=None):\n \"\"\"Generate indices to split data into training and test set.\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 Note that providing ``y`` is sufficient to generate the splits and\n hence ``np.zeros(n_samples)`` may be used as a placeholder for\n ``X`` instead of actual training data.\n\n y : array-like of shape (n_samples,)\n The target variable for supervised learning problems.\n Stratification is done based on the y labels.\n\n groups : object\n Always ignored, exists for compatibility.\n\n Yields\n ------\n train : ndarray\n The training set indices for that split.\n\n test : ndarray\n The testing set indices for that split.\n\n Notes\n -----\n Randomized CV splitters may return different results for each call of\n split. You can make the results identical by setting `random_state`\n to an integer.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_split.py::test_cross_validator_with_default_params", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_mock_scorer", "sklearn/model_selection/tests/test_split.py::test_2d_y", "sklearn/model_selection/tests/test_split.py::test_kfold_valueerrors", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_no_shuffle", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-4-False]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-4-True]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-5-False]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-5-True]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-6-False]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-6-True]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-7-False]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-7-True]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-8-False]", "sklearn/linear_model/tests/test_logistic.py::test_multinomial_logistic_regression_string_inputs", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_sparse[csr_matrix]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-8-True]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_sparse[csr_array]", "sklearn/linear_model/tests/test_logistic.py::test_ovr_multinomial_iris", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-9-False]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-9-True]", "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/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-10-False]", "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/model_selection/tests/test_split.py::test_stratified_kfold_ratios[StratifiedKFold-10-True]", "sklearn/metrics/tests/test_score_objects.py::test_raises_on_score_list", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multinomial", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_label_invariance[StratifiedKFold-4-False]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_label_invariance[StratifiedKFold-4-True]", "sklearn/linear_model/tests/test_logistic.py::test_liblinear_logregcv_sparse[csr_matrix]", "sklearn/linear_model/tests/test_logistic.py::test_liblinear_logregcv_sparse[csr_array]", "sklearn/linear_model/tests/test_logistic.py::test_saga_sparse[csr_matrix]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_label_invariance[StratifiedKFold-6-False]", "sklearn/linear_model/tests/test_logistic.py::test_saga_sparse[csr_array]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_label_invariance[StratifiedKFold-6-True]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_label_invariance[StratifiedKFold-7-False]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_cv_refit[l1-42]", "sklearn/model_selection/tests/test_split.py::test_stratified_kfold_label_invariance[StratifiedKFold-7-True]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_cv_refit[l2-42]", "sklearn/linear_model/tests/test_sgd.py::test_multi_core_gridsearch_and_early_stopping", "sklearn/model_selection/tests/test_split.py::test_stratifiedkfold_balance[StratifiedKFold]", "sklearn/linear_model/tests/test_logistic.py::test_n_iter[lbfgs]", "sklearn/linear_model/tests/test_logistic.py::test_n_iter[liblinear]", "sklearn/model_selection/tests/test_split.py::test_shuffle_kfold_stratifiedkfold_reproducibility[StratifiedKFold]", "sklearn/linear_model/tests/test_logistic.py::test_n_iter[newton-cg]", "sklearn/linear_model/tests/test_logistic.py::test_n_iter[newton-cholesky]", "sklearn/model_selection/tests/test_split.py::test_shuffle_stratifiedkfold", "sklearn/linear_model/tests/test_logistic.py::test_n_iter[sag]", "sklearn/linear_model/tests/test_logistic.py::test_n_iter[saga]", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring0]", "sklearn/model_selection/tests/test_split.py::test_kfold_can_detect_dependent_samples_on_digits", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring1]", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring2]", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring3]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_cross_validation", "sklearn/model_selection/tests/test_split.py::test_repeated_stratified_kfold_determinstic_split", "sklearn/linear_model/tests/test_logistic.py::test_elastic_net_vs_l1_l2[0.001]", "sklearn/linear_model/tests/test_logistic.py::test_elastic_net_vs_l1_l2[1]", "sklearn/linear_model/tests/test_logistic.py::test_elastic_net_vs_l1_l2[100]", "sklearn/linear_model/tests/test_logistic.py::test_elastic_net_vs_l1_l2[1000000.0]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_GridSearchCV_elastic_net[2]", "sklearn/model_selection/tests/test_split.py::test_check_cv", "sklearn/ensemble/tests/test_forest.py::test_gridsearch[ExtraTreesClassifier]", "sklearn/model_selection/tests/test_split.py::test_nested_cv", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_GridSearchCV_elastic_net[3]", "sklearn/ensemble/tests/test_forest.py::test_gridsearch[RandomForestClassifier]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_GridSearchCV_elastic_net_ovr", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_no_refit[ovr-l2]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_no_refit[ovr-elasticnet]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_no_refit[multinomial-l2]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_no_refit[multinomial-elasticnet]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_no_refit[auto-l2]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_no_refit[auto-elasticnet]", "sklearn/linear_model/tests/test_logistic.py::test_LogisticRegressionCV_elasticnet_attribute_shapes", "sklearn/model_selection/tests/test_split.py::test_no_group_splitters_warns_with_groups[StratifiedKFold(n_splits=5, random_state=None, shuffle=False)]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[lbfgs-LogisticRegressionCV]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[liblinear-LogisticRegressionCV]", "sklearn/tree/tests/test_tree.py::test_missing_value_is_predictive[42-DecisionTreeClassifier-0.85]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[newton-cg-LogisticRegressionCV]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[newton-cholesky-LogisticRegressionCV]", "sklearn/tree/tests/test_tree.py::test_missing_value_is_predictive[42-ExtraTreeClassifier-0.53]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[sag-LogisticRegressionCV]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_regression_multi_class_auto[saga-LogisticRegressionCV]", "sklearn/linear_model/tests/test_logistic.py::test_scores_attribute_layout_elasticnet", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/linear_model/tests/test_logistic.py::test_lr_cv_scores_differ_when_sample_weight_is_requested", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/linear_model/tests/test_logistic.py::test_lr_cv_scores_without_enabling_metadata_routing", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_sample_weights_pandas_series]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_sample_weights_not_an_array]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_sample_weights_list]", 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"sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-TargetEncoder(cv=3)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-188	1.0	{ "code": "diff --git b/sklearn/model_selection/_split.py a/sklearn/model_selection/_split.py\nindex 4aa16b2e6..1453e42b8 100644\n--- b/sklearn/model_selection/_split.py\n+++ a/sklearn/model_selection/_split.py\n@@ -2371,6 +2371,13 @@ class StratifiedShuffleSplit(BaseShuffleSplit):\n split. You can make the results identical by setting `random_state`\n to an integer.\n \"\"\"\n+ if groups is not None:\n+ warnings.warn(\n+ f\"The groups parameter is ignored by {self.__class__.__name__}\",\n+ UserWarning,\n+ )\n+ y = check_array(y, input_name=\"y\", ensure_2d=False, dtype=None)\n+ return super().split(X, y, groups)\n \n \n def _validate_shuffle_split(n_samples, test_size, train_size, default_test_size=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py\nindex 1453e42b8..4aa16b2e6 100644\n--- a/sklearn/model_selection/_split.py\n+++ b/sklearn/model_selection/_split.py\n@@ -2371,13 +2371,6 @@ class StratifiedShuffleSplit(BaseShuffleSplit):\n split. You can make the results identical by setting `random_state`\n to an integer.\n \"\"\"\n- if groups is not None:\n- warnings.warn(\n- f\"The groups parameter is ignored by {self.__class__.__name__}\",\n- UserWarning,\n- )\n- y = check_array(y, input_name=\"y\", ensure_2d=False, dtype=None)\n- return super().split(X, y, groups)\n \n \n def _validate_shuffle_split(n_samples, test_size, train_size, default_test_size=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/model_selection/_split.py.\nHere is the description for the function:\n def split(self, X, y, groups=None):\n \"\"\"Generate indices to split data into training and test set.\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 Note that providing ``y`` is sufficient to generate the splits and\n hence ``np.zeros(n_samples)`` may be used as a placeholder for\n ``X`` instead of actual training data.\n\n y : array-like of shape (n_samples,) or (n_samples, n_labels)\n The target variable for supervised learning problems.\n Stratification is done based on the y labels.\n\n groups : object\n Always ignored, exists for compatibility.\n\n Yields\n ------\n train : ndarray\n The training set indices for that split.\n\n test : ndarray\n The testing set indices for that split.\n\n Notes\n -----\n Randomized CV splitters may return different results for each call of\n split. You can make the results identical by setting `random_state`\n to an integer.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_split.py::test_2d_y", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[accuracy-accuracy_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[balanced_accuracy-balanced_accuracy_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_weighted-metric2]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_macro-metric3]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_micro-metric4]", "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]", "sklearn/linear_model/tests/test_sgd.py::test_early_stopping[SGDClassifier]", "sklearn/linear_model/tests/test_sgd.py::test_early_stopping[SparseSGDClassifier]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[precision_micro-metric7]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[recall_weighted-metric8]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[recall_macro-metric9]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[recall_micro-metric10]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[jaccard_weighted-metric11]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[jaccard_macro-metric12]", "sklearn/linear_model/tests/test_sgd.py::test_validation_set_not_used_for_training[SGDClassifier]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[jaccard_micro-metric13]", "sklearn/linear_model/tests/test_sgd.py::test_validation_set_not_used_for_training[SparseSGDClassifier]", "sklearn/linear_model/tests/test_sgd.py::test_n_iter_no_change[SGDClassifier]", "sklearn/linear_model/tests/test_sgd.py::test_n_iter_no_change[SparseSGDClassifier]", "sklearn/linear_model/tests/test_sgd.py::test_not_enough_sample_for_early_stopping[SGDClassifier]", "sklearn/linear_model/tests/test_sgd.py::test_not_enough_sample_for_early_stopping[SparseSGDClassifier]", "sklearn/metrics/tests/test_score_objects.py::test_average_precision_pos_label", "sklearn/metrics/tests/test_score_objects.py::test_brier_score_loss_pos_label", "sklearn/metrics/tests/test_score_objects.py::test_non_symmetric_metric_pos_label[f1_score]", "sklearn/metrics/tests/test_score_objects.py::test_non_symmetric_metric_pos_label[precision_score]", "sklearn/metrics/tests/test_score_objects.py::test_non_symmetric_metric_pos_label[recall_score]", "sklearn/metrics/tests/test_score_objects.py::test_non_symmetric_metric_pos_label[jaccard_score]", "sklearn/model_selection/tests/test_split.py::test_shuffle_split_default_test_size[None-9-1-StratifiedShuffleSplit]", "sklearn/model_selection/tests/test_split.py::test_shuffle_split_default_test_size[8-8-2-StratifiedShuffleSplit]", "sklearn/model_selection/tests/test_split.py::test_shuffle_split_default_test_size[0.8-8-2-StratifiedShuffleSplit]", "sklearn/model_selection/tests/test_split.py::test_stratified_shuffle_split_init", "sklearn/model_selection/tests/test_split.py::test_stratified_shuffle_split_respects_test_size", "sklearn/model_selection/tests/test_split.py::test_stratified_shuffle_split_iter", "sklearn/model_selection/tests/test_split.py::test_stratified_shuffle_split_even", "sklearn/model_selection/tests/test_split.py::test_stratified_shuffle_split_overlap_train_test_bug", "sklearn/model_selection/tests/test_split.py::test_stratified_shuffle_split_multilabel", "sklearn/model_selection/tests/test_split.py::test_stratified_shuffle_split_multilabel_many_labels", "sklearn/linear_model/tests/test_sgd.py::test_multi_thread_multi_class_and_early_stopping", "sklearn/model_selection/tests/test_split.py::test_train_test_split[coo_matrix]", "sklearn/model_selection/tests/test_split.py::test_train_test_split[coo_array]", "sklearn/linear_model/tests/test_sgd.py::test_multi_core_gridsearch_and_early_stopping", "sklearn/model_selection/tests/test_split.py::test_train_test_split_32bit_overflow", "sklearn/model_selection/tests/test_split.py::test_train_test_split_list_input", "sklearn/model_selection/tests/test_split.py::test_stratifiedshufflesplit_list_input", "sklearn/model_selection/tests/test_split.py::test_nested_cv", "sklearn/model_selection/tests/test_split.py::test_shuffle_split_empty_trainset[StratifiedShuffleSplit]", "sklearn/model_selection/tests/test_split.py::test_no_group_splitters_warns_with_groups[StratifiedShuffleSplit(n_splits=10, random_state=None, test_size=0.5,\\n train_size=None)]", "sklearn/linear_model/tests/test_sgd.py::test_validation_mask_correctly_subsets", "sklearn/linear_model/tests/test_sgd.py::test_sgd_error_on_zero_validation_weight", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingClassifier-X1-y1]", "sklearn/model_selection/tests/test_search.py::test_grid_search_groups", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_binning_train_validation_are_separated", "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_gradient_boosting.py::test_oob_scores[GradientBoostingClassifier]", "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_column_binary_classification", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_drop_estimator", "sklearn/ensemble/tests/test_gradient_boosting.py::test_oob_multilcass_iris", "sklearn/metrics/_plot/tests/test_roc_curve_display.py::test_plot_roc_curve_pos_label[from_estimator-predict_proba]", "sklearn/metrics/_plot/tests/test_roc_curve_display.py::test_plot_roc_curve_pos_label[from_estimator-decision_function]", "sklearn/metrics/_plot/tests/test_roc_curve_display.py::test_plot_roc_curve_pos_label[from_predictions-predict_proba]", "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/metrics/_plot/tests/test_roc_curve_display.py::test_plot_roc_curve_pos_label[from_predictions-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/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-auto]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_multilabel_auto_predict[True-predict]", "sklearn/neural_network/tests/test_mlp.py::test_early_stopping[MLPClassifier]", "sklearn/ensemble/tests/test_stacking.py::test_stacking_classifier_base_regressor", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[predict_proba-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[predict_proba-from_predictions]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[decision_function-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[decision_function-from_predictions]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_early_stopping[None-HistGradientBoostingClassifier-X0-y0]", "sklearn/neural_network/tests/test_mlp.py::test_early_stopping_stratified", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_early_stopping[loss-HistGradientBoostingClassifier-X0-y0]", "sklearn/model_selection/_split.py::sklearn.model_selection._split.StratifiedShuffleSplit", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[none-HistGradientBoostingClassifier-X0-y0]", "sklearn/neural_network/tests/test_mlp.py::test_preserve_feature_names[MLPClassifier]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[int-HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[instance-HistGradientBoostingClassifier-X0-y0]", "sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingGridSearchCV]", "sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_gradient_boosting_early_stopping[GradientBoostingClassifier]", "sklearn/ensemble/tests/test_gradient_boosting.py::test_gradient_boosting_validation_fraction", "sklearn/neural_network/_multilayer_perceptron.py::sklearn.neural_network._multilayer_perceptron.MLPClassifier", "sklearn/ensemble/tests/test_gradient_boosting.py::test_early_stopping_stratified", "sklearn/ensemble/tests/test_forest.py::test_max_samples_boundary_classifiers[ExtraTreesClassifier]", "sklearn/ensemble/_stacking.py::sklearn.ensemble._stacking.StackingClassifier", "sklearn/model_selection/_classification_threshold.py::sklearn.model_selection._classification_threshold.FixedThresholdClassifier", "sklearn/model_selection/_classification_threshold.py::sklearn.model_selection._classification_threshold.TunedThresholdClassifierCV", "sklearn/ensemble/tests/test_forest.py::test_max_samples_boundary_classifiers[RandomForestClassifier]", "sklearn/neighbors/_nca.py::sklearn.neighbors._nca.NeighborhoodComponentsAnalysis", "sklearn/ensemble/tests/test_gradient_boosting.py::test_early_stopping_n_classes", "sklearn/model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_cv_float", "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[HistGradientBoostingClassifier(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveClassifier(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Perceptron(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDClassifier(early_stopping=True,max_iter=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-189	1.0	{ "code": "diff --git b/sklearn/manifold/_t_sne.py a/sklearn/manifold/_t_sne.py\nindex 9d582d1b1..71125d8b9 100644\n--- b/sklearn/manifold/_t_sne.py\n+++ a/sklearn/manifold/_t_sne.py\n@@ -1127,6 +1127,10 @@ class TSNE(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n \n return X_embedded\n \n+ @_fit_context(\n+ # TSNE.metric is not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n def fit_transform(self, X, y=None):\n \"\"\"Fit X into an embedded space and return that transformed output.\n \n@@ -1148,6 +1152,32 @@ class TSNE(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n X_new : ndarray of shape (n_samples, n_components)\n Embedding of the training data in low-dimensional space.\n \"\"\"\n+ # TODO(1.7): remove\n+ # Also make sure to change `max_iter` default back to 1000 and deprecate None\n+ if self.n_iter != \"deprecated\":\n+ if self.max_iter is not None:\n+ raise ValueError(\n+ \"Both 'n_iter' and 'max_iter' attributes were set. Attribute\"\n+ \" 'n_iter' was deprecated in version 1.5 and will be removed in\"\n+ \" 1.7. To avoid this error, only set the 'max_iter' attribute.\"\n+ )\n+ warnings.warn(\n+ (\n+ \"'n_iter' was renamed to 'max_iter' in version 1.5 and \"\n+ \"will be removed in 1.7.\"\n+ ),\n+ FutureWarning,\n+ )\n+ self._max_iter = self.n_iter\n+ elif self.max_iter is None:\n+ self._max_iter = 1000\n+ else:\n+ self._max_iter = self.max_iter\n+\n+ self._check_params_vs_input(X)\n+ embedding = self._fit(X)\n+ self.embedding_ = embedding\n+ return self.embedding_\n \n @_fit_context(\n # TSNE.metric is not validated yet\n", "test": null }	null	{ "code": "diff --git a/sklearn/manifold/_t_sne.py b/sklearn/manifold/_t_sne.py\nindex 71125d8b9..9d582d1b1 100644\n--- a/sklearn/manifold/_t_sne.py\n+++ b/sklearn/manifold/_t_sne.py\n@@ -1127,10 +1127,6 @@ class TSNE(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n \n return X_embedded\n \n- @_fit_context(\n- # TSNE.metric is not validated yet\n- prefer_skip_nested_validation=False\n- )\n def fit_transform(self, X, y=None):\n \"\"\"Fit X into an embedded space and return that transformed output.\n \n@@ -1152,32 +1148,6 @@ class TSNE(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):\n X_new : ndarray of shape (n_samples, n_components)\n Embedding of the training data in low-dimensional space.\n \"\"\"\n- # TODO(1.7): remove\n- # Also make sure to change `max_iter` default back to 1000 and deprecate None\n- if self.n_iter != \"deprecated\":\n- if self.max_iter is not None:\n- raise ValueError(\n- \"Both 'n_iter' and 'max_iter' attributes were set. Attribute\"\n- \" 'n_iter' was deprecated in version 1.5 and will be removed in\"\n- \" 1.7. To avoid this error, only set the 'max_iter' attribute.\"\n- )\n- warnings.warn(\n- (\n- \"'n_iter' was renamed to 'max_iter' in version 1.5 and \"\n- \"will be removed in 1.7.\"\n- ),\n- FutureWarning,\n- )\n- self._max_iter = self.n_iter\n- elif self.max_iter is None:\n- self._max_iter = 1000\n- else:\n- self._max_iter = self.max_iter\n-\n- self._check_params_vs_input(X)\n- embedding = self._fit(X)\n- self.embedding_ = embedding\n- return self.embedding_\n \n @_fit_context(\n # TSNE.metric is not validated yet\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/_t_sne.py.\nHere is the description for the function:\n def fit_transform(self, X, y=None):\n \"\"\"Fit X into an embedded space and return that transformed output.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features) or \\\n (n_samples, n_samples)\n If the metric is 'precomputed' X must be a square distance\n matrix. Otherwise it contains a sample per row. If the method\n is 'exact', X may be a sparse matrix of type 'csr', 'csc'\n or 'coo'. If the method is 'barnes_hut' and the metric is\n 'precomputed', X may be a precomputed sparse graph.\n\n y : None\n Ignored.\n\n Returns\n -------\n X_new : ndarray of shape (n_samples, n_components)\n Embedding of the training data in low-dimensional space.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately[random-exact]", "sklearn/manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately[random-barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately[pca-exact]", "sklearn/manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately[pca-barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_optimization_minimizes_kl_divergence", "sklearn/manifold/tests/test_t_sne.py::test_fit_transform_csr_matrix[csr_matrix-exact]", "sklearn/manifold/tests/test_t_sne.py::test_fit_transform_csr_matrix[csr_matrix-barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_fit_transform_csr_matrix[csr_array-exact]", "sklearn/manifold/tests/test_t_sne.py::test_fit_transform_csr_matrix[csr_array-barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately_with_precomputed_distances", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D0-.* square distance matrix-exact-asarray]", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D0-.* square distance matrix-barnes_hut-asarray]", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D0-.* square distance matrix-barnes_hut-csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D0-.* square distance matrix-barnes_hut-csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D1-.* positive.-exact-asarray]", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D1-. positive.-barnes_hut-asarray]", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D1-. positive.-barnes_hut-csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_bad_precomputed_distances[D1-. positive.*-barnes_hut-csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_exact_no_precomputed_sparse[csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_exact_no_precomputed_sparse[csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_high_perplexity_precomputed_sparse_distances[csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_high_perplexity_precomputed_sparse_distances[csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[lil_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[lil_array]", "sklearn/manifold/tests/test_t_sne.py::test_non_positive_computed_distances", "sklearn/manifold/tests/test_t_sne.py::test_init_ndarray", "sklearn/manifold/tests/test_t_sne.py::test_init_ndarray_precomputed", "sklearn/manifold/tests/test_t_sne.py::test_pca_initialization_not_compatible_with_precomputed_kernel", "sklearn/manifold/tests/test_t_sne.py::test_pca_initialization_not_compatible_with_sparse_input[csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_pca_initialization_not_compatible_with_sparse_input[csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_n_components_range", "sklearn/manifold/tests/test_t_sne.py::test_early_exaggeration_used", "sklearn/manifold/tests/test_t_sne.py::test_max_iter_used", "sklearn/manifold/tests/test_t_sne.py::test_verbose", "sklearn/manifold/tests/test_t_sne.py::test_chebyshev_metric", "sklearn/manifold/tests/test_t_sne.py::test_reduction_to_one_component", "sklearn/manifold/tests/test_t_sne.py::test_64bit[float32-barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_64bit[float32-exact]", "sklearn/manifold/tests/test_t_sne.py::test_64bit[float64-barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_64bit[float64-exact]", "sklearn/manifold/tests/test_t_sne.py::test_kl_divergence_not_nan[barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_kl_divergence_not_nan[exact]", "sklearn/manifold/tests/test_t_sne.py::test_n_iter_without_progress", "sklearn/manifold/tests/test_t_sne.py::test_min_grad_norm", "sklearn/manifold/tests/test_t_sne.py::test_accessible_kl_divergence", "sklearn/manifold/tests/test_t_sne.py::test_uniform_grid[barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_uniform_grid[exact]", "sklearn/manifold/tests/test_t_sne.py::test_bh_match_exact", "sklearn/manifold/tests/test_t_sne.py::test_tsne_with_different_distance_metrics[barnes_hut-cosine-cosine_distances]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_with_different_distance_metrics[exact-manhattan-manhattan_distances]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_with_different_distance_metrics[exact-cosine-cosine_distances]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_n_jobs[exact]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_n_jobs[barnes_hut]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_with_mahalanobis_distance", "sklearn/manifold/tests/test_t_sne.py::test_tsne_perplexity_validation[20]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_perplexity_validation[30]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_works_with_pandas_output", "sklearn/manifold/tests/test_t_sne.py::test_tnse_n_iter_deprecated", "sklearn/manifold/tests/test_t_sne.py::test_tnse_n_iter_max_iter_both_set", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_tsne", "sklearn/manifold/_t_sne.py::sklearn.manifold._t_sne.TSNE", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[TSNE(n_components=1,perplexity=2)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[TSNE(perplexity=2)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_check_param_validation[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_set_output_transform[TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-TSNE(perplexity=2)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-TSNE(perplexity=2)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-190	1.0	{ "code": "diff --git b/sklearn/compose/_target.py a/sklearn/compose/_target.py\nindex e5fb6de0a..d90ee17d1 100644\n--- b/sklearn/compose/_target.py\n+++ a/sklearn/compose/_target.py\n@@ -217,6 +217,10 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):\n UserWarning,\n )\n \n+ @_fit_context(\n+ # TransformedTargetRegressor.regressor/transformer are not validated yet.\n+ prefer_skip_nested_validation=False\n+ )\n def fit(self, X, y, fit_params):\n \"\"\"Fit the model according to the given training data.\n \n@@ -245,6 +249,53 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n+ if y is None:\n+ raise ValueError(\n+ f\"This {self.__class__.__name__} estimator \"\n+ \"requires y to be passed, but the target y is None.\"\n+ )\n+ y = check_array(\n+ y,\n+ input_name=\"y\",\n+ accept_sparse=False,\n+ ensure_all_finite=True,\n+ ensure_2d=False,\n+ dtype=\"numeric\",\n+ allow_nd=True,\n+ )\n+\n+ # store the number of dimension of the target to predict an array of\n+ # similar shape at predict\n+ self._training_dim = y.ndim\n+\n+ # transformers are designed to modify X which is 2d dimensional, we\n+ # need to modify y accordingly.\n+ if y.ndim == 1:\n+ y_2d = y.reshape(-1, 1)\n+ else:\n+ y_2d = y\n+ self._fit_transformer(y_2d)\n+\n+ # transform y and convert back to 1d array if needed\n+ y_trans = self.transformer_.transform(y_2d)\n+ # FIXME: a FunctionTransformer can return a 1D array even when validate\n+ # is set to True. Therefore, we need to check the number of dimension\n+ # first.\n+ if y_trans.ndim == 2 and y_trans.shape[1] == 1:\n+ y_trans = y_trans.squeeze(axis=1)\n+\n+ self.regressor_ = self._get_regressor(get_clone=True)\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"fit\", fit_params)\n+ else:\n+ routed_params = Bunch(regressor=Bunch(fit=fit_params))\n+\n+ self.regressor_.fit(X, y_trans, routed_params.regressor.fit)\n+\n+ if hasattr(self.regressor_, \"feature_names_in_\"):\n+ self.feature_names_in_ = self.regressor_.feature_names_in_\n+\n+ return self\n \n def predict(self, X, predict_params):\n \"\"\"Predict using the base regressor, applying inverse.\n", "test": null }	null	{ "code": "diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py\nindex d90ee17d1..e5fb6de0a 100644\n--- a/sklearn/compose/_target.py\n+++ b/sklearn/compose/_target.py\n@@ -217,10 +217,6 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):\n UserWarning,\n )\n \n- @_fit_context(\n- # TransformedTargetRegressor.regressor/transformer are not validated yet.\n- prefer_skip_nested_validation=False\n- )\n def fit(self, X, y, fit_params):\n \"\"\"Fit the model according to the given training data.\n \n@@ -249,53 +245,6 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):\n self : object\n Fitted estimator.\n \"\"\"\n- if y is None:\n- raise ValueError(\n- f\"This {self.__class__.__name__} estimator \"\n- \"requires y to be passed, but the target y is None.\"\n- )\n- y = check_array(\n- y,\n- input_name=\"y\",\n- accept_sparse=False,\n- ensure_all_finite=True,\n- ensure_2d=False,\n- dtype=\"numeric\",\n- allow_nd=True,\n- )\n-\n- # store the number of dimension of the target to predict an array of\n- # similar shape at predict\n- self._training_dim = y.ndim\n-\n- # transformers are designed to modify X which is 2d dimensional, we\n- # need to modify y accordingly.\n- if y.ndim == 1:\n- y_2d = y.reshape(-1, 1)\n- else:\n- y_2d = y\n- self._fit_transformer(y_2d)\n-\n- # transform y and convert back to 1d array if needed\n- y_trans = self.transformer_.transform(y_2d)\n- # FIXME: a FunctionTransformer can return a 1D array even when validate\n- # is set to True. Therefore, we need to check the number of dimension\n- # first.\n- if y_trans.ndim == 2 and y_trans.shape[1] == 1:\n- y_trans = y_trans.squeeze(axis=1)\n-\n- self.regressor_ = self._get_regressor(get_clone=True)\n- if _routing_enabled():\n- routed_params = process_routing(self, \"fit\", fit_params)\n- else:\n- routed_params = Bunch(regressor=Bunch(fit=fit_params))\n-\n- self.regressor_.fit(X, y_trans, routed_params.regressor.fit)\n-\n- if hasattr(self.regressor_, \"feature_names_in_\"):\n- self.feature_names_in_ = self.regressor_.feature_names_in_\n-\n- return self\n \n def predict(self, X, predict_params):\n \"\"\"Predict using the base regressor, applying inverse.\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/_target.py.\nHere is the description for the function:\n def fit(self, X, y, fit_params):\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 values.\n\n fit_params : dict\n - If `enable_metadata_routing=False` (default): Parameters directly passed\n to the `fit` method of the underlying regressor.\n\n - If `enable_metadata_routing=True`: Parameters safely routed to the `fit`\n method of the underlying regressor.\n\n .. versionchanged:: 1.6\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n self : object\n Fitted estimator.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[TransformedTargetRegressor]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_error", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_invertible", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions_multioutput", "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_2d_transformer[X0-y0]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_2d_transformer[X1-y1]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_2d_transformer_multioutput", "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_ensure_y_array", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_count_fit[False]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_count_fit[True]", "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/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/compose/tests/test_target.py::test_transform_target_regressor_not_warns_with_global_output_set[polars]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFE-5-importance_getter0]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFE-5-regressor_.coef_]", "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/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/compose/_target.py::sklearn.compose._target.TransformedTargetRegressor", "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_complex_data]", "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_supervised_y_no_nan]", "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_estimators[TransformedTargetRegressor()-check_requires_y_none]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[TransformedTargetRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[TransformedTargetRegressor()]", "sklearn/tests/test_common.py::test_check_param_validation[TransformedTargetRegressor()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-191	1.0	{ "code": "diff --git b/sklearn/compose/_target.py a/sklearn/compose/_target.py\nindex 1feb3356c..d90ee17d1 100644\n--- b/sklearn/compose/_target.py\n+++ a/sklearn/compose/_target.py\n@@ -324,6 +324,25 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):\n y_hat : ndarray of shape (n_samples,)\n Predicted values.\n \"\"\"\n+ check_is_fitted(self)\n+ if _routing_enabled():\n+ routed_params = process_routing(self, \"predict\", predict_params)\n+ else:\n+ routed_params = Bunch(regressor=Bunch(predict=predict_params))\n+\n+ pred = self.regressor_.predict(X, routed_params.regressor.predict)\n+ if pred.ndim == 1:\n+ pred_trans = self.transformer_.inverse_transform(pred.reshape(-1, 1))\n+ else:\n+ pred_trans = self.transformer_.inverse_transform(pred)\n+ if (\n+ self._training_dim == 1\n+ and pred_trans.ndim == 2\n+ and pred_trans.shape[1] == 1\n+ ):\n+ pred_trans = pred_trans.squeeze(axis=1)\n+\n+ return pred_trans\n \n def __sklearn_tags__(self):\n regressor = self._get_regressor()\n", "test": null }	null	{ "code": "diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py\nindex d90ee17d1..1feb3356c 100644\n--- a/sklearn/compose/_target.py\n+++ b/sklearn/compose/_target.py\n@@ -324,25 +324,6 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):\n y_hat : ndarray of shape (n_samples,)\n Predicted values.\n \"\"\"\n- check_is_fitted(self)\n- if _routing_enabled():\n- routed_params = process_routing(self, \"predict\", predict_params)\n- else:\n- routed_params = Bunch(regressor=Bunch(predict=predict_params))\n-\n- pred = self.regressor_.predict(X, routed_params.regressor.predict)\n- if pred.ndim == 1:\n- pred_trans = self.transformer_.inverse_transform(pred.reshape(-1, 1))\n- else:\n- pred_trans = self.transformer_.inverse_transform(pred)\n- if (\n- self._training_dim == 1\n- and pred_trans.ndim == 2\n- and pred_trans.shape[1] == 1\n- ):\n- pred_trans = pred_trans.squeeze(axis=1)\n-\n- return pred_trans\n \n def __sklearn_tags__(self):\n regressor = self._get_regressor()\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/_target.py.\nHere is the description for the function:\n def predict(self, X, predict_params):\n \"\"\"Predict using the base regressor, applying inverse.\n\n The regressor is used to predict and the `inverse_func` or\n `inverse_transform` is applied before returning the prediction.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Samples.\n\n predict_params : dict of str -> object\n - If `enable_metadata_routing=False` (default): Parameters directly passed\n to the `predict` method of the underlying regressor.\n\n - If `enable_metadata_routing=True`: Parameters safely routed to the\n `predict` method of the underlying regressor.\n\n .. versionchanged:: 1.6\n See :ref:`Metadata Routing User Guide <metadata_routing>`\n for more details.\n\n Returns\n -------\n y_hat : ndarray of shape (n_samples,)\n Predicted values.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_metaestimators_metadata_routing.py::test_error_on_missing_requests_for_sub_estimator[TransformedTargetRegressor]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions_multioutput", "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_2d_transformer[X0-y0]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_2d_transformer[X1-y1]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_2d_transformer_multioutput", "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_ensure_y_array", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_pass_extra_predict_parameters", "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/compose/_target.py::sklearn.compose._target.TransformedTargetRegressor", "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_dtypes]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_dtype_object]", "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_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_estimators_unfitted]", "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_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[TransformedTargetRegressor()-check_fit_idempotent]", "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-192	1.0	{ "code": "diff --git b/sklearn/ensemble/_hist_gradient_boosting/grower.py a/sklearn/ensemble/_hist_gradient_boosting/grower.py\nindex 93ce330c6..a71e56405 100644\n--- b/sklearn/ensemble/_hist_gradient_boosting/grower.py\n+++ a/sklearn/ensemble/_hist_gradient_boosting/grower.py\n@@ -709,6 +709,24 @@ class TreeGrower:\n -------\n A TreePredictor object.\n \"\"\"\n+ predictor_nodes = np.zeros(self.n_nodes, dtype=PREDICTOR_RECORD_DTYPE)\n+ binned_left_cat_bitsets = np.zeros(\n+ (self.n_categorical_splits, 8), dtype=X_BITSET_INNER_DTYPE\n+ )\n+ raw_left_cat_bitsets = np.zeros(\n+ (self.n_categorical_splits, 8), dtype=X_BITSET_INNER_DTYPE\n+ )\n+ _fill_predictor_arrays(\n+ predictor_nodes,\n+ binned_left_cat_bitsets,\n+ raw_left_cat_bitsets,\n+ self.root,\n+ binning_thresholds,\n+ self.n_bins_non_missing,\n+ )\n+ return TreePredictor(\n+ predictor_nodes, binned_left_cat_bitsets, raw_left_cat_bitsets\n+ )\n \n \n def _fill_predictor_arrays(\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_hist_gradient_boosting/grower.py b/sklearn/ensemble/_hist_gradient_boosting/grower.py\nindex a71e56405..93ce330c6 100644\n--- a/sklearn/ensemble/_hist_gradient_boosting/grower.py\n+++ b/sklearn/ensemble/_hist_gradient_boosting/grower.py\n@@ -709,24 +709,6 @@ class TreeGrower:\n -------\n A TreePredictor object.\n \"\"\"\n- predictor_nodes = np.zeros(self.n_nodes, dtype=PREDICTOR_RECORD_DTYPE)\n- binned_left_cat_bitsets = np.zeros(\n- (self.n_categorical_splits, 8), dtype=X_BITSET_INNER_DTYPE\n- )\n- raw_left_cat_bitsets = np.zeros(\n- (self.n_categorical_splits, 8), dtype=X_BITSET_INNER_DTYPE\n- )\n- _fill_predictor_arrays(\n- predictor_nodes,\n- binned_left_cat_bitsets,\n- raw_left_cat_bitsets,\n- self.root,\n- binning_thresholds,\n- self.n_bins_non_missing,\n- )\n- return TreePredictor(\n- predictor_nodes, binned_left_cat_bitsets, raw_left_cat_bitsets\n- )\n \n \n def _fill_predictor_arrays(\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/_hist_gradient_boosting/grower.py.\nHere is the description for the function:\n def make_predictor(self, binning_thresholds):\n \"\"\"Make a TreePredictor object out of the current tree.\n\n Parameters\n ----------\n binning_thresholds : array-like of floats\n Corresponds to the bin_thresholds_ attribute of the BinMapper.\n For each feature, this stores:\n\n - the bin frontiers for continuous features\n - the unique raw category values for categorical features\n\n Returns\n -------\n A TreePredictor object.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[neg_mean_squared_error-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[neg_mean_squared_error-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[loss-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[loss-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-None-False-5-0.0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-None-True-5-0.1-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-None-True-5-0.1-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-True-5-0.1-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-True-5-0.1-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-None-True-5-0.1-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-None-True-5-0.1-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-False-5-0.0-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-False-5-0.0-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingClassifier-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingRegressor-X2-y2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingRegressor-X3-y3]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_absolute_error", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_absolute_error_sample_weight", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_gamma", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.5]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.8]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_poisson", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_binning_train_validation_are_separated", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_trivial", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.1-0.97-0.89-classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.1-0.97-0.89-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.2-0.93-0.81-classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.2-0.93-0.81-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.5-0.79-0.52-classification]", "sklearn/tests/test_pipeline.py::test_n_features_in_pipeline", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.5-0.79-0.52-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_division_hessians[binary_log_loss]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_division_hessians[multiclass_log_loss]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_minmax_imputation", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_infinite_values", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_infinite_values_missing_values", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_string_target_early_stopping[None]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_string_target_early_stopping[loss]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_sample_weights_regression", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_sample_weights_classification", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[half-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[half-binary_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[half-multiclass_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[all-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[all-binary_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[all-multiclass_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_max_depth_max_leaf_nodes", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_on_test_set_with_warm_start", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_with_sample_weights", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_raw_predict_is_called_with_custom_scorer", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_single_node_trees[HistGradientBoostingClassifier]", "sklearn/model_selection/tests/test_search.py::test_n_features_in", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_single_node_trees[HistGradientBoostingRegressor]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_custom_loss[HistGradientBoostingClassifier-loss0-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_custom_loss[HistGradientBoostingRegressor-loss1-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_staged_predict[HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_staged_predict[HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_staged_predict[HistGradientBoostingClassifier-X2-y2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_unknown_categories_nan[nan-True-HistGradientBoostingRegressor-False]", 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"sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-193	1.0	{ "code": "diff --git b/sklearn/ensemble/_hist_gradient_boosting/predictor.py a/sklearn/ensemble/_hist_gradient_boosting/predictor.py\nindex 243199f1a..59bb6499c 100644\n--- b/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n+++ a/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n@@ -66,6 +66,18 @@ class TreePredictor:\n y : ndarray, shape (n_samples,)\n The raw predicted values.\n \"\"\"\n+ out = np.empty(X.shape[0], dtype=Y_DTYPE)\n+\n+ _predict_from_raw_data(\n+ self.nodes,\n+ X,\n+ self.raw_left_cat_bitsets,\n+ known_cat_bitsets,\n+ f_idx_map,\n+ n_threads,\n+ out,\n+ )\n+ return out\n \n def predict_binned(self, X, missing_values_bin_idx, n_threads):\n \"\"\"Predict raw values for binned data.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_hist_gradient_boosting/predictor.py b/sklearn/ensemble/_hist_gradient_boosting/predictor.py\nindex 59bb6499c..243199f1a 100644\n--- a/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n+++ b/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n@@ -66,18 +66,6 @@ class TreePredictor:\n y : ndarray, shape (n_samples,)\n The raw predicted values.\n \"\"\"\n- out = np.empty(X.shape[0], dtype=Y_DTYPE)\n-\n- _predict_from_raw_data(\n- self.nodes,\n- X,\n- self.raw_left_cat_bitsets,\n- known_cat_bitsets,\n- f_idx_map,\n- n_threads,\n- out,\n- )\n- return out\n \n def predict_binned(self, X, missing_values_bin_idx, n_threads):\n \"\"\"Predict raw values for binned data.\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/_hist_gradient_boosting/predictor.py.\nHere is the description for the function:\n def predict(self, X, known_cat_bitsets, f_idx_map, n_threads):\n \"\"\"Predict raw values for non-binned data.\n\n Parameters\n ----------\n X : ndarray, shape (n_samples, n_features)\n The input samples.\n\n known_cat_bitsets : ndarray of shape (n_categorical_features, 8)\n Array of bitsets of known categories, for each categorical feature.\n\n f_idx_map : ndarray of shape (n_features,)\n Map from original feature index to the corresponding index in the\n known_cat_bitsets array.\n\n n_threads : int\n Number of OpenMP threads to use.\n\n Returns\n -------\n y : ndarray, shape (n_samples,)\n The raw predicted values.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-0]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-4]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-0]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-4]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[0-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[1-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[2-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[3-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[4-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[5-est1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_absolute_error", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_gamma", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.5]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.8]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_poisson", 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"sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_class_weight_classifiers]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_decision_proba_consistency]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-194	1.0	{ "code": "diff --git b/sklearn/ensemble/_hist_gradient_boosting/predictor.py a/sklearn/ensemble/_hist_gradient_boosting/predictor.py\nindex 0ae39c764..59bb6499c 100644\n--- b/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n+++ a/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n@@ -98,6 +98,16 @@ class TreePredictor:\n y : ndarray, shape (n_samples,)\n The raw predicted values.\n \"\"\"\n+ out = np.empty(X.shape[0], dtype=Y_DTYPE)\n+ _predict_from_binned_data(\n+ self.nodes,\n+ X,\n+ self.binned_left_cat_bitsets,\n+ missing_values_bin_idx,\n+ n_threads,\n+ out,\n+ )\n+ return out\n \n def compute_partial_dependence(self, grid, target_features, out):\n \"\"\"Fast partial dependence computation.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_hist_gradient_boosting/predictor.py b/sklearn/ensemble/_hist_gradient_boosting/predictor.py\nindex 59bb6499c..0ae39c764 100644\n--- a/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n+++ b/sklearn/ensemble/_hist_gradient_boosting/predictor.py\n@@ -98,16 +98,6 @@ class TreePredictor:\n y : ndarray, shape (n_samples,)\n The raw predicted values.\n \"\"\"\n- out = np.empty(X.shape[0], dtype=Y_DTYPE)\n- _predict_from_binned_data(\n- self.nodes,\n- X,\n- self.binned_left_cat_bitsets,\n- missing_values_bin_idx,\n- n_threads,\n- out,\n- )\n- return out\n \n def compute_partial_dependence(self, grid, target_features, out):\n \"\"\"Fast partial dependence computation.\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/_hist_gradient_boosting/predictor.py.\nHere is the description for the function:\n def predict_binned(self, X, missing_values_bin_idx, n_threads):\n \"\"\"Predict raw values for binned data.\n\n Parameters\n ----------\n X : ndarray, shape (n_samples, n_features)\n The input samples.\n missing_values_bin_idx : uint8\n Index of the bin that is used for missing values. This is the\n index of the last bin and is always equal to max_bins (as passed\n to the GBDT classes), or equivalently to n_bins - 1.\n n_threads : int\n Number of OpenMP threads to use.\n\n Returns\n -------\n y : ndarray, shape (n_samples,)\n The raw predicted values.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[neg_mean_squared_error-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[loss-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-True-5-0.1-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-True-5-0.1-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingClassifier-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingRegressor-X3-y3]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_binning_train_validation_are_separated", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_on_test_set_with_warm_start", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_with_sample_weights", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_raw_predict_is_called_with_custom_scorer", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_no_user_warning_with_scoring", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_predictor_from_grower", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_split_on_nan_with_infinite_values", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_grow_tree_categories", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[binary-2-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[binary-2-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[binary-10-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[binary-10-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[binary-100-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[binary-100-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[random-2-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[random-2-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[random-10-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[random-10-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[random-100-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[random-100-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[equal-2-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[equal-2-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[equal-10-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[equal-10-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[equal-100-1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py::test_ohe_equivalence[equal-100-20]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_yields_identical_results[HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_yields_identical_results[HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_max_depth[HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_max_depth[HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_early_stopping[None-HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_early_stopping[None-HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_early_stopping[loss-HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_warm_start_early_stopping[loss-HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[none-HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[none-HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py::test_categorical_predictor[bins_go_left0-expected_predictions0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[int-HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py::test_categorical_predictor[bins_go_left1-expected_predictions1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py::test_categorical_predictor[bins_go_left2-expected_predictions2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[int-HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[instance-HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py::test_random_seeds_warm_start[instance-HistGradientBoostingRegressor-X1-y1]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-195	1.0	{ "code": "diff --git b/sklearn/decomposition/_truncated_svd.py a/sklearn/decomposition/_truncated_svd.py\nindex 18182c4ad..b87a53684 100644\n--- b/sklearn/decomposition/_truncated_svd.py\n+++ a/sklearn/decomposition/_truncated_svd.py\n@@ -206,6 +206,7 @@ class TruncatedSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstima\n self.fit_transform(X)\n return self\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None):\n \"\"\"Fit model to X and perform dimensionality reduction on X.\n \n@@ -222,6 +223,57 @@ class TruncatedSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstima\n X_new : ndarray of shape (n_samples, n_components)\n Reduced version of X. This will always be a dense array.\n \"\"\"\n+ X = validate_data(self, X, accept_sparse=[\"csr\", \"csc\"], ensure_min_features=2)\n+ random_state = check_random_state(self.random_state)\n+\n+ if self.algorithm == \"arpack\":\n+ v0 = _init_arpack_v0(min(X.shape), random_state)\n+ U, Sigma, VT = svds(X, k=self.n_components, tol=self.tol, v0=v0)\n+ # svds doesn't abide by scipy.linalg.svd/randomized_svd\n+ # conventions, so reverse its outputs.\n+ Sigma = Sigma[::-1]\n+ # u_based_decision=False is needed to be consistent with PCA.\n+ U, VT = svd_flip(U[:, ::-1], VT[::-1], u_based_decision=False)\n+\n+ elif self.algorithm == \"randomized\":\n+ if self.n_components > X.shape[1]:\n+ raise ValueError(\n+ f\"n_components({self.n_components}) must be <=\"\n+ f\" n_features({X.shape[1]}).\"\n+ )\n+ U, Sigma, VT = randomized_svd(\n+ X,\n+ self.n_components,\n+ n_iter=self.n_iter,\n+ n_oversamples=self.n_oversamples,\n+ power_iteration_normalizer=self.power_iteration_normalizer,\n+ random_state=random_state,\n+ flip_sign=False,\n+ )\n+ U, VT = svd_flip(U, VT, u_based_decision=False)\n+\n+ self.components_ = VT\n+\n+ # As a result of the SVD approximation error on X ~ U @ Sigma @ V.T,\n+ # X @ V is not the same as U @ Sigma\n+ if self.algorithm == \"randomized\" or (\n+ self.algorithm == \"arpack\" and self.tol > 0\n+ ):\n+ X_transformed = safe_sparse_dot(X, self.components_.T)\n+ else:\n+ X_transformed = U * Sigma\n+\n+ # Calculate explained variance & explained variance ratio\n+ self.explained_variance_ = exp_var = np.var(X_transformed, axis=0)\n+ if sp.issparse(X):\n+ _, full_var = mean_variance_axis(X, axis=0)\n+ full_var = full_var.sum()\n+ else:\n+ full_var = np.var(X, axis=0).sum()\n+ self.explained_variance_ratio_ = exp_var / full_var\n+ self.singular_values_ = Sigma # Store the singular values.\n+\n+ return X_transformed\n \n def transform(self, X):\n \"\"\"Perform dimensionality reduction on X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_truncated_svd.py b/sklearn/decomposition/_truncated_svd.py\nindex b87a53684..18182c4ad 100644\n--- a/sklearn/decomposition/_truncated_svd.py\n+++ b/sklearn/decomposition/_truncated_svd.py\n@@ -206,7 +206,6 @@ class TruncatedSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstima\n self.fit_transform(X)\n return self\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit_transform(self, X, y=None):\n \"\"\"Fit model to X and perform dimensionality reduction on X.\n \n@@ -223,57 +222,6 @@ class TruncatedSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstima\n X_new : ndarray of shape (n_samples, n_components)\n Reduced version of X. This will always be a dense array.\n \"\"\"\n- X = validate_data(self, X, accept_sparse=[\"csr\", \"csc\"], ensure_min_features=2)\n- random_state = check_random_state(self.random_state)\n-\n- if self.algorithm == \"arpack\":\n- v0 = _init_arpack_v0(min(X.shape), random_state)\n- U, Sigma, VT = svds(X, k=self.n_components, tol=self.tol, v0=v0)\n- # svds doesn't abide by scipy.linalg.svd/randomized_svd\n- # conventions, so reverse its outputs.\n- Sigma = Sigma[::-1]\n- # u_based_decision=False is needed to be consistent with PCA.\n- U, VT = svd_flip(U[:, ::-1], VT[::-1], u_based_decision=False)\n-\n- elif self.algorithm == \"randomized\":\n- if self.n_components > X.shape[1]:\n- raise ValueError(\n- f\"n_components({self.n_components}) must be <=\"\n- f\" n_features({X.shape[1]}).\"\n- )\n- U, Sigma, VT = randomized_svd(\n- X,\n- self.n_components,\n- n_iter=self.n_iter,\n- n_oversamples=self.n_oversamples,\n- power_iteration_normalizer=self.power_iteration_normalizer,\n- random_state=random_state,\n- flip_sign=False,\n- )\n- U, VT = svd_flip(U, VT, u_based_decision=False)\n-\n- self.components_ = VT\n-\n- # As a result of the SVD approximation error on X ~ U @ Sigma @ V.T,\n- # X @ V is not the same as U @ Sigma\n- if self.algorithm == \"randomized\" or (\n- self.algorithm == \"arpack\" and self.tol > 0\n- ):\n- X_transformed = safe_sparse_dot(X, self.components_.T)\n- else:\n- X_transformed = U * Sigma\n-\n- # Calculate explained variance & explained variance ratio\n- self.explained_variance_ = exp_var = np.var(X_transformed, axis=0)\n- if sp.issparse(X):\n- _, full_var = mean_variance_axis(X, axis=0)\n- full_var = full_var.sum()\n- else:\n- full_var = np.var(X, axis=0).sum()\n- self.explained_variance_ratio_ = exp_var / full_var\n- self.singular_values_ = Sigma # Store the singular values.\n-\n- return X_transformed\n \n def transform(self, X):\n \"\"\"Perform dimensionality reduction on 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/_truncated_svd.py.\nHere is the description for the function:\n def fit_transform(self, X, y=None):\n \"\"\"Fit model to X and perform dimensionality reduction on X.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training data.\n\n y : Ignored\n Not used, present here for API consistency by convention.\n\n Returns\n -------\n X_new : ndarray of shape (n_samples, n_components)\n Reduced version of X. This will always be a dense array.\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/ensemble/tests/test_forest.py::test_random_hasher", "sklearn/decomposition/tests/test_truncated_svd.py::test_solvers[dense-randomized]", "sklearn/decomposition/tests/test_truncated_svd.py::test_solvers[sparse-randomized]", "sklearn/decomposition/tests/test_truncated_svd.py::test_attributes[10]", "sklearn/decomposition/tests/test_truncated_svd.py::test_attributes[25]", "sklearn/decomposition/tests/test_truncated_svd.py::test_attributes[41]", "sklearn/decomposition/tests/test_truncated_svd.py::test_attributes[55]", "sklearn/decomposition/tests/test_truncated_svd.py::test_too_many_components[arpack-55]", "sklearn/decomposition/tests/test_truncated_svd.py::test_too_many_components[arpack-56]", "sklearn/decomposition/tests/test_truncated_svd.py::test_too_many_components[randomized-56]", "sklearn/decomposition/tests/test_truncated_svd.py::test_sparse_formats[array]", "sklearn/decomposition/tests/test_truncated_svd.py::test_sparse_formats[csr]", "sklearn/decomposition/tests/test_truncated_svd.py::test_sparse_formats[csc]", "sklearn/decomposition/tests/test_truncated_svd.py::test_sparse_formats[coo]", "sklearn/decomposition/tests/test_truncated_svd.py::test_sparse_formats[lil]", "sklearn/decomposition/tests/test_truncated_svd.py::test_inverse_transform[arpack]", "sklearn/decomposition/tests/test_truncated_svd.py::test_inverse_transform[randomized]", "sklearn/decomposition/tests/test_truncated_svd.py::test_integers", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[arpack-10-dense]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[arpack-10-sparse]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[arpack-20-dense]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[arpack-20-sparse]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[randomized-10-dense]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[randomized-10-sparse]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[randomized-20-dense]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance[randomized-20-sparse]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance_components_10_20[arpack-dense]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance_components_10_20[arpack-sparse]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance_components_10_20[randomized-dense]", "sklearn/decomposition/tests/test_truncated_svd.py::test_explained_variance_components_10_20[randomized-sparse]", "sklearn/decomposition/tests/test_truncated_svd.py::test_singular_values_consistency[arpack]", "sklearn/decomposition/tests/test_truncated_svd.py::test_singular_values_consistency[randomized]", "sklearn/decomposition/tests/test_truncated_svd.py::test_singular_values_expected[arpack]", "sklearn/decomposition/tests/test_truncated_svd.py::test_singular_values_expected[randomized]", "sklearn/decomposition/tests/test_truncated_svd.py::test_truncated_svd_eq_pca", "sklearn/decomposition/tests/test_truncated_svd.py::test_fit_transform[dense-randomized-0.0]", "sklearn/decomposition/tests/test_truncated_svd.py::test_fit_transform[dense-arpack-1e-06]", "sklearn/decomposition/tests/test_truncated_svd.py::test_fit_transform[dense-arpack-0.0]", "sklearn/decomposition/tests/test_truncated_svd.py::test_fit_transform[sparse-randomized-0.0]", "sklearn/decomposition/tests/test_truncated_svd.py::test_fit_transform[sparse-arpack-1e-06]", "sklearn/decomposition/tests/test_truncated_svd.py::test_fit_transform[sparse-arpack-0.0]", "sklearn/pipeline.py::sklearn.pipeline.FeatureUnion", "sklearn/decomposition/_truncated_svd.py::sklearn.decomposition._truncated_svd.TruncatedSVD", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_complex_data]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_empty_data_messages]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_fit1d]", "sklearn/tests/test_common.py::test_estimators[TruncatedSVD(n_components=1)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[TruncatedSVD(n_components=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[TruncatedSVD(n_components=1)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[TruncatedSVD(n_components=1)]", "sklearn/tests/test_common.py::test_check_param_validation[TruncatedSVD(n_components=1)]", "sklearn/tests/test_common.py::test_set_output_transform[TruncatedSVD(n_components=1)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-TruncatedSVD(n_components=1)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-TruncatedSVD(n_components=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-196	1.0	{ "code": "diff --git b/sklearn/ensemble/_voting.py a/sklearn/ensemble/_voting.py\nindex 46affae1c..5c9a4de58 100644\n--- b/sklearn/ensemble/_voting.py\n+++ a/sklearn/ensemble/_voting.py\n@@ -348,6 +348,14 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n self.flatten_transform = flatten_transform\n self.verbose = verbose\n \n+ @_fit_context(\n+ # estimators in VotingClassifier.estimators are not validated yet\n+ prefer_skip_nested_validation=False\n+ )\n+ # TODO(1.7): remove `sample_weight` from the signature after deprecation\n+ # cycle; pop it from `fit_params` before the `_raise_for_params` check and\n+ # reinsert later, for backwards compatibility\n+ @_deprecate_positional_args(version=\"1.7\")\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit the estimators.\n \n@@ -383,6 +391,32 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n self : object\n Returns the instance itself.\n \"\"\"\n+ _raise_for_params(fit_params, self, \"fit\")\n+ y_type = type_of_target(y, input_name=\"y\")\n+ if y_type in (\"unknown\", \"continuous\"):\n+ # raise a specific ValueError for non-classification tasks\n+ raise ValueError(\n+ f\"Unknown label type: {y_type}. Maybe you are trying to fit a \"\n+ \"classifier, which expects discrete classes on a \"\n+ \"regression target with continuous values.\"\n+ )\n+ elif y_type not in (\"binary\", \"multiclass\"):\n+ # raise a NotImplementedError for backward compatibility for non-supported\n+ # classification tasks\n+ raise NotImplementedError(\n+ f\"{self.__class__.__name__} only supports binary or multiclass \"\n+ \"classification. Multilabel and multi-output classification are not \"\n+ \"supported.\"\n+ )\n+\n+ self.le_ = LabelEncoder().fit(y)\n+ self.classes_ = self.le_.classes_\n+ transformed_y = self.le_.transform(y)\n+\n+ if sample_weight is not None:\n+ fit_params[\"sample_weight\"] = sample_weight\n+\n+ return super().fit(X, transformed_y, *fit_params)\n \n def predict(self, X):\n \"\"\"Predict class labels for X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py\nindex 5c9a4de58..46affae1c 100644\n--- a/sklearn/ensemble/_voting.py\n+++ b/sklearn/ensemble/_voting.py\n@@ -348,14 +348,6 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n self.flatten_transform = flatten_transform\n self.verbose = verbose\n \n- @_fit_context(\n- # estimators in VotingClassifier.estimators are not validated yet\n- prefer_skip_nested_validation=False\n- )\n- # TODO(1.7): remove `sample_weight` from the signature after deprecation\n- # cycle; pop it from `fit_params` before the `_raise_for_params` check and\n- # reinsert later, for backwards compatibility\n- @_deprecate_positional_args(version=\"1.7\")\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit the estimators.\n \n@@ -391,32 +383,6 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n self : object\n Returns the instance itself.\n \"\"\"\n- _raise_for_params(fit_params, self, \"fit\")\n- y_type = type_of_target(y, input_name=\"y\")\n- if y_type in (\"unknown\", \"continuous\"):\n- # raise a specific ValueError for non-classification tasks\n- raise ValueError(\n- f\"Unknown label type: {y_type}. Maybe you are trying to fit a \"\n- \"classifier, which expects discrete classes on a \"\n- \"regression target with continuous values.\"\n- )\n- elif y_type not in (\"binary\", \"multiclass\"):\n- # raise a NotImplementedError for backward compatibility for non-supported\n- # classification tasks\n- raise NotImplementedError(\n- f\"{self.__class__.__name__} only supports binary or multiclass \"\n- \"classification. Multilabel and multi-output classification are not \"\n- \"supported.\"\n- )\n-\n- self.le_ = LabelEncoder().fit(y)\n- self.classes_ = self.le_.classes_\n- transformed_y = self.le_.transform(y)\n-\n- if sample_weight is not None:\n- fit_params[\"sample_weight\"] = sample_weight\n-\n- return super().fit(X, transformed_y, *fit_params)\n \n def predict(self, X):\n \"\"\"Predict class labels 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/_voting.py.\nHere is the description for the function:\n def fit(self, X, y, , sample_weight=None, fit_params):\n \"\"\"Fit the estimators.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vectors, 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 values.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights. If None, then samples are equally weighted.\n Note that this is supported only if all underlying estimators\n support sample weights.\n\n .. versionadded:: 0.18\n\n *fit_params : dict\n Parameters to pass to the underlying estimators.\n\n .. versionadded:: 1.5\n\n Only available if `enable_metadata_routing=True`,\n which can be set by using\n ``sklearn.set_config(enable_metadata_routing=True)``.\n See :ref:`Metadata Routing User Guide <metadata_routing>` for\n more details.\n\n Returns\n -------\n self : object\n Returns the instance itself.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/tests/test_voting.py::test_voting_classifier_estimator_init[params0-Invalid 'estimators' attribute, 'estimators' should be a non-empty list]", "sklearn/ensemble/tests/test_voting.py::test_voting_classifier_estimator_init[params1-Number of `estimators` and weights must be equal]", "sklearn/ensemble/tests/test_voting.py::test_predictproba_hardvoting", "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_voting.py::test_predict_proba_on_toy_problem", "sklearn/ensemble/tests/test_voting.py::test_multilabel", "sklearn/ensemble/tests/test_voting.py::test_gridsearch", "sklearn/ensemble/tests/test_voting.py::test_parallel_fit[42]", "sklearn/ensemble/tests/test_voting.py::test_sample_weight[42]", "sklearn/ensemble/tests/test_voting.py::test_sample_weight_kwargs", "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_estimator_weights_format[42]", "sklearn/ensemble/tests/test_voting.py::test_transform[42]", "sklearn/ensemble/tests/test_voting.py::test_none_estimator_with_weights[X0-y0-voter0]", "sklearn/ensemble/tests/test_voting.py::test_n_features_in[VotingClassifier]", "sklearn/ensemble/tests/test_voting.py::test_voting_verbose[estimator1]", "sklearn/ensemble/tests/test_voting.py::test_get_features_names_out_classifier[kwargs0-expected_names0]", "sklearn/ensemble/tests/test_voting.py::test_get_features_names_out_classifier[kwargs1-expected_names1]", "sklearn/ensemble/tests/test_voting.py::test_get_features_names_out_classifier_error", "sklearn/ensemble/tests/test_voting.py::test_routing_passed_metadata_not_supported[VotingClassifier-ConsumingClassifier]", "sklearn/ensemble/tests/test_voting.py::test_metadata_routing_for_voting_estimators[sample_weight-VotingClassifier-ConsumingClassifier]", "sklearn/ensemble/tests/test_voting.py::test_metadata_routing_for_voting_estimators[metadata-VotingClassifier-ConsumingClassifier]", "sklearn/ensemble/tests/test_voting.py::test_metadata_routing_error_for_voting_estimators[VotingClassifier-ConsumingClassifier]", "sklearn/ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_behavior[voting-classifier]", "sklearn/ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_type[VotingClassifier]", "sklearn/ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_name_validation[X1-y1-VotingClassifier]", "sklearn/ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_all_dropped[voting-classifier]", "sklearn/ensemble/tests/test_common.py::test_heterogeneous_ensemble_support_missing_values[VotingClassifier-LogisticRegression-X2-y2]", "sklearn/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[VotingClassifier]", "sklearn/tests/test_calibration.py::test_calibration_votingclassifier", "sklearn/ensemble/_voting.py::sklearn.ensemble._voting.VotingClassifier", "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_overwrite_params]", "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_pandas_series]", "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_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_sample_weights_list]", "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_shape]", "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_not_overwritten]", "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_estimators_fit_returns_self]", "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_fit_returns_self(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_complex_data]", "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_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_classifiers_regression_target]", "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_no_nan]", "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_estimators[VotingClassifier(estimators=[('est1',DecisionTreeClassifier(max_depth=3,random_state=0)),('est2',DecisionTreeClassifier(max_depth=3,random_state=1))])-check_requires_y_none]", "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[VotingClassifier(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[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[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-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-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-197	1.0	{ "code": "diff --git b/sklearn/ensemble/_voting.py a/sklearn/ensemble/_voting.py\nindex 35ce129a4..5c9a4de58 100644\n--- b/sklearn/ensemble/_voting.py\n+++ a/sklearn/ensemble/_voting.py\n@@ -523,6 +523,29 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n+ check_is_fitted(self, \"n_features_in_\")\n+ if self.voting == \"soft\" and not self.flatten_transform:\n+ raise ValueError(\n+ \"get_feature_names_out is not supported when `voting='soft'` and \"\n+ \"`flatten_transform=False`\"\n+ )\n+\n+ _check_feature_names_in(self, input_features, generate_names=False)\n+ class_name = self.__class__.__name__.lower()\n+\n+ active_names = [name for name, est in self.estimators if est != \"drop\"]\n+\n+ if self.voting == \"hard\":\n+ return np.asarray(\n+ [f\"{class_name}_{name}\" for name in active_names], dtype=object\n+ )\n+\n+ # voting == \"soft\"\n+ n_classes = len(self.classes_)\n+ names_out = [\n+ f\"{class_name}_{name}{i}\" for name in active_names for i in range(n_classes)\n+ ]\n+ return np.asarray(names_out, dtype=object)\n \n \n class VotingRegressor(RegressorMixin, _BaseVoting):\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py\nindex 5c9a4de58..35ce129a4 100644\n--- a/sklearn/ensemble/_voting.py\n+++ b/sklearn/ensemble/_voting.py\n@@ -523,29 +523,6 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n- check_is_fitted(self, \"n_features_in_\")\n- if self.voting == \"soft\" and not self.flatten_transform:\n- raise ValueError(\n- \"get_feature_names_out is not supported when `voting='soft'` and \"\n- \"`flatten_transform=False`\"\n- )\n-\n- _check_feature_names_in(self, input_features, generate_names=False)\n- class_name = self.__class__.__name__.lower()\n-\n- active_names = [name for name, est in self.estimators if est != \"drop\"]\n-\n- if self.voting == \"hard\":\n- return np.asarray(\n- [f\"{class_name}_{name}\" for name in active_names], dtype=object\n- )\n-\n- # voting == \"soft\"\n- n_classes = len(self.classes_)\n- names_out = [\n- f\"{class_name}_{name}{i}\" for name in active_names for i in range(n_classes)\n- ]\n- return np.asarray(names_out, dtype=object)\n \n \n class VotingRegressor(RegressorMixin, _BaseVoting):\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/_voting.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 Not used, present here for API consistency by convention.\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/ensemble/tests/test_voting.py::test_get_features_names_out_classifier[kwargs0-expected_names0]", "sklearn/ensemble/tests/test_voting.py::test_get_features_names_out_classifier[kwargs1-expected_names1]", "sklearn/ensemble/tests/test_voting.py::test_get_features_names_out_classifier_error", "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_estimators_get_feature_names_out_error[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-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-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-198	1.0	{ "code": "diff --git b/sklearn/ensemble/_voting.py a/sklearn/ensemble/_voting.py\nindex e0affca13..5c9a4de58 100644\n--- b/sklearn/ensemble/_voting.py\n+++ a/sklearn/ensemble/_voting.py\n@@ -431,6 +431,21 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n maj : array-like of shape (n_samples,)\n Predicted class labels.\n \"\"\"\n+ check_is_fitted(self)\n+ if self.voting == \"soft\":\n+ maj = np.argmax(self.predict_proba(X), axis=1)\n+\n+ else: # 'hard' voting\n+ predictions = self._predict(X)\n+ maj = np.apply_along_axis(\n+ lambda x: np.argmax(np.bincount(x, weights=self._weights_not_none)),\n+ axis=1,\n+ arr=predictions,\n+ )\n+\n+ maj = self.le_.inverse_transform(maj)\n+\n+ return maj\n \n def _collect_probas(self, X):\n \"\"\"Collect results from clf.predict calls.\"\"\"\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py\nindex 5c9a4de58..e0affca13 100644\n--- a/sklearn/ensemble/_voting.py\n+++ b/sklearn/ensemble/_voting.py\n@@ -431,21 +431,6 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n maj : array-like of shape (n_samples,)\n Predicted class labels.\n \"\"\"\n- check_is_fitted(self)\n- if self.voting == \"soft\":\n- maj = np.argmax(self.predict_proba(X), axis=1)\n-\n- else: # 'hard' voting\n- predictions = self._predict(X)\n- maj = np.apply_along_axis(\n- lambda x: np.argmax(np.bincount(x, weights=self._weights_not_none)),\n- axis=1,\n- arr=predictions,\n- )\n-\n- maj = self.le_.inverse_transform(maj)\n-\n- return maj\n \n def _collect_probas(self, X):\n \"\"\"Collect results from clf.predict calls.\"\"\"\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/_voting.py.\nHere is the description for the function:\n def predict(self, X):\n \"\"\"Predict class labels for X.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n The input samples.\n\n Returns\n -------\n maj : array-like of shape (n_samples,)\n Predicted class labels.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/tests/test_voting.py::test_notfitted", "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_voting.py::test_parallel_fit[42]", "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_common.py::test_heterogeneous_ensemble_support_missing_values[VotingClassifier-LogisticRegression-X2-y2]", "sklearn/ensemble/_voting.py::sklearn.ensemble._voting.VotingClassifier", "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_estimators_unfitted]", "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_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[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[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-199	1.0	{ "code": "diff --git b/sklearn/ensemble/_voting.py a/sklearn/ensemble/_voting.py\nindex 764f6c2b6..5c9a4de58 100644\n--- b/sklearn/ensemble/_voting.py\n+++ a/sklearn/ensemble/_voting.py\n@@ -499,6 +499,16 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n ndarray of shape (n_samples, n_classifiers), being\n class labels predicted by each classifier.\n \"\"\"\n+ check_is_fitted(self)\n+\n+ if self.voting == \"soft\":\n+ probas = self._collect_probas(X)\n+ if not self.flatten_transform:\n+ return probas\n+ return np.hstack(probas)\n+\n+ else:\n+ return self._predict(X)\n \n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py\nindex 5c9a4de58..764f6c2b6 100644\n--- a/sklearn/ensemble/_voting.py\n+++ b/sklearn/ensemble/_voting.py\n@@ -499,16 +499,6 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):\n ndarray of shape (n_samples, n_classifiers), being\n class labels predicted by each classifier.\n \"\"\"\n- check_is_fitted(self)\n-\n- if self.voting == \"soft\":\n- probas = self._collect_probas(X)\n- if not self.flatten_transform:\n- return probas\n- return np.hstack(probas)\n-\n- else:\n- return self._predict(X)\n \n def get_feature_names_out(self, input_features=None):\n \"\"\"Get output feature names for transformation.\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/_voting.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Return class labels or probabilities for X for each estimator.\n\n Parameters\n ----------\n X : {array-like, sparse matrix} of shape (n_samples, n_features)\n Training vectors, where `n_samples` is the number of samples and\n `n_features` is the number of features.\n\n Returns\n -------\n probabilities_or_labels\n If `voting='soft'` and `flatten_transform=True`:\n returns ndarray of shape (n_samples, n_classifiers * n_classes),\n being class probabilities calculated by each classifier.\n If `voting='soft' and `flatten_transform=False`:\n ndarray of shape (n_classifiers, n_samples, n_classes)\n If `voting='hard'`:\n ndarray of shape (n_samples, n_classifiers), being\n class labels predicted by each classifier.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/tests/test_voting.py::test_notfitted", "sklearn/ensemble/tests/test_voting.py::test_set_estimator_drop", "sklearn/ensemble/tests/test_voting.py::test_transform[42]", "sklearn/ensemble/tests/test_voting.py::test_get_features_names_out_classifier[kwargs0-expected_names0]", "sklearn/ensemble/tests/test_voting.py::test_get_features_names_out_classifier[kwargs1-expected_names1]", "sklearn/ensemble/_voting.py::sklearn.ensemble._voting.VotingClassifier", "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_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_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_transformers_unfitted]", "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_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[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[VotingClassifier(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_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-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-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-200	1.0	{ "code": "diff --git b/sklearn/ensemble/_voting.py a/sklearn/ensemble/_voting.py\nindex 6e98efd83..5c9a4de58 100644\n--- b/sklearn/ensemble/_voting.py\n+++ a/sklearn/ensemble/_voting.py\n@@ -743,3 +743,10 @@ class VotingRegressor(RegressorMixin, _BaseVoting):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n+ check_is_fitted(self, \"n_features_in_\")\n+ _check_feature_names_in(self, input_features, generate_names=False)\n+ class_name = self.__class__.__name__.lower()\n+ return np.asarray(\n+ [f\"{class_name}_{name}\" for name, est in self.estimators if est != \"drop\"],\n+ dtype=object,\n+ )\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py\nindex 5c9a4de58..6e98efd83 100644\n--- a/sklearn/ensemble/_voting.py\n+++ b/sklearn/ensemble/_voting.py\n@@ -743,10 +743,3 @@ class VotingRegressor(RegressorMixin, _BaseVoting):\n feature_names_out : ndarray of str objects\n Transformed feature names.\n \"\"\"\n- check_is_fitted(self, \"n_features_in_\")\n- _check_feature_names_in(self, input_features, generate_names=False)\n- class_name = self.__class__.__name__.lower()\n- return np.asarray(\n- [f\"{class_name}_{name}\" for name, est in self.estimators if est != \"drop\"],\n- dtype=object,\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/_voting.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 Not used, present here for API consistency by convention.\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/ensemble/tests/test_voting.py::test_get_features_names_out_regressor", "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_estimators_get_feature_names_out_error[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-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-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-201	1.0	{ "code": "diff --git b/sklearn/ensemble/_hist_gradient_boosting/binning.py a/sklearn/ensemble/_hist_gradient_boosting/binning.py\nindex b5e81112b..c428c742a 100644\n--- b/sklearn/ensemble/_hist_gradient_boosting/binning.py\n+++ a/sklearn/ensemble/_hist_gradient_boosting/binning.py\n@@ -186,6 +186,74 @@ class _BinMapper(TransformerMixin, BaseEstimator):\n -------\n self : object\n \"\"\"\n+ if not (3 <= self.n_bins <= 256):\n+ # min is 3: at least 2 distinct bins and a missing values bin\n+ raise ValueError(\n+ \"n_bins={} should be no smaller than 3 and no larger than 256.\".format(\n+ self.n_bins\n+ )\n+ )\n+\n+ X = check_array(X, dtype=[X_DTYPE], ensure_all_finite=False)\n+ max_bins = self.n_bins - 1\n+\n+ rng = check_random_state(self.random_state)\n+ if self.subsample is not None and X.shape[0] > self.subsample:\n+ subset = rng.choice(X.shape[0], self.subsample, replace=False)\n+ X = X.take(subset, axis=0)\n+\n+ if self.is_categorical is None:\n+ self.is_categorical_ = np.zeros(X.shape[1], dtype=np.uint8)\n+ else:\n+ self.is_categorical_ = np.asarray(self.is_categorical, dtype=np.uint8)\n+\n+ n_features = X.shape[1]\n+ known_categories = self.known_categories\n+ if known_categories is None:\n+ known_categories = [None] * n_features\n+\n+ # validate is_categorical and known_categories parameters\n+ for f_idx in range(n_features):\n+ is_categorical = self.is_categorical_[f_idx]\n+ known_cats = known_categories[f_idx]\n+ if is_categorical and known_cats is None:\n+ raise ValueError(\n+ f\"Known categories for feature {f_idx} must be provided.\"\n+ )\n+ if not is_categorical and known_cats is not None:\n+ raise ValueError(\n+ f\"Feature {f_idx} isn't marked as a categorical feature, \"\n+ \"but categories were passed.\"\n+ )\n+\n+ self.missing_values_bin_idx_ = self.n_bins - 1\n+\n+ self.bin_thresholds_ = [None] * n_features\n+ n_bins_non_missing = [None] * n_features\n+\n+ non_cat_thresholds = Parallel(n_jobs=self.n_threads, backend=\"threading\")(\n+ delayed(_find_binning_thresholds)(X[:, f_idx], max_bins)\n+ for f_idx in range(n_features)\n+ if not self.is_categorical_[f_idx]\n+ )\n+\n+ non_cat_idx = 0\n+ for f_idx in range(n_features):\n+ if self.is_categorical_[f_idx]:\n+ # Since categories are assumed to be encoded in\n+ # [0, n_cats] and since n_cats <= max_bins,\n+ # the thresholds are the unique categorical values. This will\n+ # lead to the correct mapping in transform()\n+ thresholds = known_categories[f_idx]\n+ n_bins_non_missing[f_idx] = thresholds.shape[0]\n+ self.bin_thresholds_[f_idx] = thresholds\n+ else:\n+ self.bin_thresholds_[f_idx] = non_cat_thresholds[non_cat_idx]\n+ n_bins_non_missing[f_idx] = self.bin_thresholds_[f_idx].shape[0] + 1\n+ non_cat_idx += 1\n+\n+ self.n_bins_non_missing_ = np.array(n_bins_non_missing, dtype=np.uint32)\n+ return self\n \n def transform(self, X):\n \"\"\"Bin data X.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_hist_gradient_boosting/binning.py b/sklearn/ensemble/_hist_gradient_boosting/binning.py\nindex c428c742a..b5e81112b 100644\n--- a/sklearn/ensemble/_hist_gradient_boosting/binning.py\n+++ b/sklearn/ensemble/_hist_gradient_boosting/binning.py\n@@ -186,74 +186,6 @@ class _BinMapper(TransformerMixin, BaseEstimator):\n -------\n self : object\n \"\"\"\n- if not (3 <= self.n_bins <= 256):\n- # min is 3: at least 2 distinct bins and a missing values bin\n- raise ValueError(\n- \"n_bins={} should be no smaller than 3 and no larger than 256.\".format(\n- self.n_bins\n- )\n- )\n-\n- X = check_array(X, dtype=[X_DTYPE], ensure_all_finite=False)\n- max_bins = self.n_bins - 1\n-\n- rng = check_random_state(self.random_state)\n- if self.subsample is not None and X.shape[0] > self.subsample:\n- subset = rng.choice(X.shape[0], self.subsample, replace=False)\n- X = X.take(subset, axis=0)\n-\n- if self.is_categorical is None:\n- self.is_categorical_ = np.zeros(X.shape[1], dtype=np.uint8)\n- else:\n- self.is_categorical_ = np.asarray(self.is_categorical, dtype=np.uint8)\n-\n- n_features = X.shape[1]\n- known_categories = self.known_categories\n- if known_categories is None:\n- known_categories = [None] * n_features\n-\n- # validate is_categorical and known_categories parameters\n- for f_idx in range(n_features):\n- is_categorical = self.is_categorical_[f_idx]\n- known_cats = known_categories[f_idx]\n- if is_categorical and known_cats is None:\n- raise ValueError(\n- f\"Known categories for feature {f_idx} must be provided.\"\n- )\n- if not is_categorical and known_cats is not None:\n- raise ValueError(\n- f\"Feature {f_idx} isn't marked as a categorical feature, \"\n- \"but categories were passed.\"\n- )\n-\n- self.missing_values_bin_idx_ = self.n_bins - 1\n-\n- self.bin_thresholds_ = [None] * n_features\n- n_bins_non_missing = [None] * n_features\n-\n- non_cat_thresholds = Parallel(n_jobs=self.n_threads, backend=\"threading\")(\n- delayed(_find_binning_thresholds)(X[:, f_idx], max_bins)\n- for f_idx in range(n_features)\n- if not self.is_categorical_[f_idx]\n- )\n-\n- non_cat_idx = 0\n- for f_idx in range(n_features):\n- if self.is_categorical_[f_idx]:\n- # Since categories are assumed to be encoded in\n- # [0, n_cats] and since n_cats <= max_bins,\n- # the thresholds are the unique categorical values. This will\n- # lead to the correct mapping in transform()\n- thresholds = known_categories[f_idx]\n- n_bins_non_missing[f_idx] = thresholds.shape[0]\n- self.bin_thresholds_[f_idx] = thresholds\n- else:\n- self.bin_thresholds_[f_idx] = non_cat_thresholds[non_cat_idx]\n- n_bins_non_missing[f_idx] = self.bin_thresholds_[f_idx].shape[0] + 1\n- non_cat_idx += 1\n-\n- self.n_bins_non_missing_ = np.array(n_bins_non_missing, dtype=np.uint32)\n- return self\n \n def transform(self, X):\n \"\"\"Bin 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/ensemble/_hist_gradient_boosting/binning.py.\nHere is the description for the function:\n def fit(self, X, y=None):\n \"\"\"Fit data X by computing the binning thresholds.\n\n The last bin is reserved for missing values, whether missing values\n are present in the data or not.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data to bin.\n y: None\n Ignored.\n\n Returns\n -------\n self : object\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[neg_mean_squared_error-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[neg_mean_squared_error-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[loss-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[loss-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-None-False-5-0.0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-None-True-5-0.1-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-None-True-5-0.1-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-True-5-0.1-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-True-5-0.1-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-None-True-5-0.1-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[loss-None-True-5-0.1-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-False-5-0.0-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[None-None-False-5-0.0-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingClassifier-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingRegressor-X2-y2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_default[HistGradientBoostingRegressor-X3-y3]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_absolute_error", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_absolute_error_sample_weight", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_gamma", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.2]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.5]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_quantile_asymmetric_error[0.8]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_poisson", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_binning_train_validation_are_separated", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_trivial", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.1-0.97-0.89-classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.1-0.97-0.89-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.2-0.93-0.81-classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.2-0.93-0.81-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.5-0.79-0.52-classification]", "sklearn/tests/test_pipeline.py::test_n_features_in_pipeline", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_resilience[0.5-0.79-0.52-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_division_hessians[binary_log_loss]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_division_hessians[multiclass_log_loss]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_missing_values_minmax_imputation", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_infinite_values", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_infinite_values_missing_values", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_string_target_early_stopping[None]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_string_target_early_stopping[loss]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_sample_weights_regression", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_zero_sample_weights_classification", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[half-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[half-binary_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[half-multiclass_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[all-regression]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[all-binary_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sample_weight_effect[all-multiclass_classification]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sum_hessians_are_sample_weight[HalfSquaredError]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_sum_hessians_are_sample_weight[AbsoluteError]", "sklearn/model_selection/tests/test_search.py::test_n_features_in", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_max_depth_max_leaf_nodes", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_on_test_set_with_warm_start", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_with_sample_weights", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_raw_predict_is_called_with_custom_scorer", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_single_node_trees[HistGradientBoostingClassifier]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_single_node_trees[HistGradientBoostingRegressor]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_custom_loss[HistGradientBoostingClassifier-loss0-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_custom_loss[HistGradientBoostingRegressor-loss1-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_staged_predict[HistGradientBoostingClassifier-X0-y0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_staged_predict[HistGradientBoostingRegressor-X1-y1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_staged_predict[HistGradientBoostingClassifier-X2-y2]", "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]", 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"sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-202	1.0	{ "code": "diff --git b/sklearn/ensemble/_hist_gradient_boosting/binning.py a/sklearn/ensemble/_hist_gradient_boosting/binning.py\nindex 3d6aff650..c428c742a 100644\n--- b/sklearn/ensemble/_hist_gradient_boosting/binning.py\n+++ a/sklearn/ensemble/_hist_gradient_boosting/binning.py\n@@ -275,6 +275,25 @@ class _BinMapper(TransformerMixin, BaseEstimator):\n X_binned : array-like of shape (n_samples, n_features)\n The binned data (fortran-aligned).\n \"\"\"\n+ X = check_array(X, dtype=[X_DTYPE], ensure_all_finite=False)\n+ check_is_fitted(self)\n+ if X.shape[1] != self.n_bins_non_missing_.shape[0]:\n+ raise ValueError(\n+ \"This estimator was fitted with {} features but {} got passed \"\n+ \"to transform()\".format(self.n_bins_non_missing_.shape[0], X.shape[1])\n+ )\n+\n+ n_threads = _openmp_effective_n_threads(self.n_threads)\n+ binned = np.zeros_like(X, dtype=X_BINNED_DTYPE, order=\"F\")\n+ _map_to_bins(\n+ X,\n+ self.bin_thresholds_,\n+ self.is_categorical_,\n+ self.missing_values_bin_idx_,\n+ n_threads,\n+ binned,\n+ )\n+ return binned\n \n def make_known_categories_bitsets(self):\n \"\"\"Create bitsets of known categories.\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_hist_gradient_boosting/binning.py b/sklearn/ensemble/_hist_gradient_boosting/binning.py\nindex c428c742a..3d6aff650 100644\n--- a/sklearn/ensemble/_hist_gradient_boosting/binning.py\n+++ b/sklearn/ensemble/_hist_gradient_boosting/binning.py\n@@ -275,25 +275,6 @@ class _BinMapper(TransformerMixin, BaseEstimator):\n X_binned : array-like of shape (n_samples, n_features)\n The binned data (fortran-aligned).\n \"\"\"\n- X = check_array(X, dtype=[X_DTYPE], ensure_all_finite=False)\n- check_is_fitted(self)\n- if X.shape[1] != self.n_bins_non_missing_.shape[0]:\n- raise ValueError(\n- \"This estimator was fitted with {} features but {} got passed \"\n- \"to transform()\".format(self.n_bins_non_missing_.shape[0], X.shape[1])\n- )\n-\n- n_threads = _openmp_effective_n_threads(self.n_threads)\n- binned = np.zeros_like(X, dtype=X_BINNED_DTYPE, order=\"F\")\n- _map_to_bins(\n- X,\n- self.bin_thresholds_,\n- self.is_categorical_,\n- self.missing_values_bin_idx_,\n- n_threads,\n- binned,\n- )\n- return binned\n \n def make_known_categories_bitsets(self):\n \"\"\"Create bitsets of known categories.\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/_hist_gradient_boosting/binning.py.\nHere is the description for the function:\n def transform(self, X):\n \"\"\"Bin data X.\n\n Missing values will be mapped to the last bin.\n\n For categorical features, the mapping will be incorrect for unknown\n categories. Since the BinMapper is given known_categories of the\n entire training data (i.e. before the call to train_test_split() in\n case of early-stopping), this never happens.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data to bin.\n\n Returns\n -------\n X_binned : array-like of shape (n_samples, n_features)\n The binned data (fortran-aligned).\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-0]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est3-brute-4]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-0]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-3]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_helpers[est4-recursion-4]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[0-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[1-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[2-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[3-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[4-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_recursion_decision_function[5-est1]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[1-est2]", "sklearn/inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[2-est2]", "sklearn/inspection/tests/test_partial_dependence.py::test_hist_gbdt_sw_not_supported", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[neg_mean_squared_error-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[neg_mean_squared_error-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[loss-0.1-True-5-1e-07]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[loss-None-True-5-0.1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_regression[None-None-False-5-0.0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data0]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-0.1-True-5-1e-07-data1]", "sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py::test_early_stopping_classification[accuracy-None-True-5-0.1-data0]", 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"sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressor_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_no_decision_function]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_int]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_non_transformer_estimators_n_iter]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_1feature]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingRegressor(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-203	1.0	{ "code": "diff --git b/sklearn/calibration.py a/sklearn/calibration.py\nindex ad23676d9..8b053f538 100644\n--- b/sklearn/calibration.py\n+++ a/sklearn/calibration.py\n@@ -707,6 +707,47 @@ class _CalibratedClassifier:\n proba : array, shape (n_samples, n_classes)\n The predicted probabilities. Can be exact zeros.\n \"\"\"\n+ predictions, _ = _get_response_values(\n+ self.estimator,\n+ X,\n+ response_method=[\"decision_function\", \"predict_proba\"],\n+ )\n+ if predictions.ndim == 1:\n+ # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`\n+ predictions = predictions.reshape(-1, 1)\n+\n+ n_classes = len(self.classes)\n+\n+ label_encoder = LabelEncoder().fit(self.classes)\n+ pos_class_indices = label_encoder.transform(self.estimator.classes_)\n+\n+ proba = np.zeros((_num_samples(X), n_classes))\n+ for class_idx, this_pred, calibrator in zip(\n+ pos_class_indices, predictions.T, self.calibrators\n+ ):\n+ if n_classes == 2:\n+ # When binary, `predictions` consists only of predictions for\n+ # clf.classes_[1] but `pos_class_indices` = 0\n+ class_idx += 1\n+ proba[:, class_idx] = calibrator.predict(this_pred)\n+\n+ # Normalize the probabilities\n+ if n_classes == 2:\n+ proba[:, 0] = 1.0 - proba[:, 1]\n+ else:\n+ denominator = np.sum(proba, axis=1)[:, np.newaxis]\n+ # In the edge case where for each class calibrator returns a null\n+ # probability for a given sample, use the uniform distribution\n+ # instead.\n+ uniform_proba = np.full_like(proba, 1 / n_classes)\n+ proba = np.divide(\n+ proba, denominator, out=uniform_proba, where=denominator != 0\n+ )\n+\n+ # Deal with cases where the predicted probability minimally exceeds 1.0\n+ proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0\n+\n+ return proba\n \n \n # The max_abs_prediction_threshold was approximated using\n", "test": null }	null	{ "code": "diff --git a/sklearn/calibration.py b/sklearn/calibration.py\nindex 8b053f538..ad23676d9 100644\n--- a/sklearn/calibration.py\n+++ b/sklearn/calibration.py\n@@ -707,47 +707,6 @@ class _CalibratedClassifier:\n proba : array, shape (n_samples, n_classes)\n The predicted probabilities. Can be exact zeros.\n \"\"\"\n- predictions, _ = _get_response_values(\n- self.estimator,\n- X,\n- response_method=[\"decision_function\", \"predict_proba\"],\n- )\n- if predictions.ndim == 1:\n- # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`\n- predictions = predictions.reshape(-1, 1)\n-\n- n_classes = len(self.classes)\n-\n- label_encoder = LabelEncoder().fit(self.classes)\n- pos_class_indices = label_encoder.transform(self.estimator.classes_)\n-\n- proba = np.zeros((_num_samples(X), n_classes))\n- for class_idx, this_pred, calibrator in zip(\n- pos_class_indices, predictions.T, self.calibrators\n- ):\n- if n_classes == 2:\n- # When binary, `predictions` consists only of predictions for\n- # clf.classes_[1] but `pos_class_indices` = 0\n- class_idx += 1\n- proba[:, class_idx] = calibrator.predict(this_pred)\n-\n- # Normalize the probabilities\n- if n_classes == 2:\n- proba[:, 0] = 1.0 - proba[:, 1]\n- else:\n- denominator = np.sum(proba, axis=1)[:, np.newaxis]\n- # In the edge case where for each class calibrator returns a null\n- # probability for a given sample, use the uniform distribution\n- # instead.\n- uniform_proba = np.full_like(proba, 1 / n_classes)\n- proba = np.divide(\n- proba, denominator, out=uniform_proba, where=denominator != 0\n- )\n-\n- # Deal with cases where the predicted probability minimally exceeds 1.0\n- proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0\n-\n- return proba\n \n \n # The max_abs_prediction_threshold was approximated using\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/calibration.py.\nHere is the description for the function:\n def predict_proba(self, X):\n \"\"\"Calculate calibrated probabilities.\n\n Calculates classification calibrated probabilities\n for each class, in a one-vs-all manner, for `X`.\n\n Parameters\n ----------\n X : ndarray of shape (n_samples, n_features)\n The sample data.\n\n Returns\n -------\n proba : array, shape (n_samples, n_classes)\n The predicted probabilities. Can be exact zeros.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_sample_weights_invariance(kind=ones)]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_sample_weights_invariance(kind=zeros)]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifier_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_classes]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_supervised_y_2d]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_calibration.py::test_calibration[True-sigmoid-csr_matrix]", "sklearn/tests/test_calibration.py::test_calibration[True-sigmoid-csr_array]", "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-sigmoid-csr_matrix]", "sklearn/tests/test_calibration.py::test_calibration[False-sigmoid-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-sigmoid]", "sklearn/tests/test_calibration.py::test_sample_weight[True-isotonic]", "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/tests/test_calibration.py::test_parallel_execution[True-isotonic]", "sklearn/tests/test_calibration.py::test_parallel_execution[False-sigmoid]", "sklearn/tests/test_calibration.py::test_parallel_execution[False-isotonic]", "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_zero_probability", "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[sigmoid]", "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/tests/test_calibration.py::test_calibration_prob_sum[True]", "sklearn/tests/test_calibration.py::test_calibration_prob_sum[False]", "sklearn/tests/test_calibration.py::test_calibration_less_classes[True]", "sklearn/tests/test_calibration.py::test_calibration_less_classes[False]", "sklearn/tests/test_calibration.py::test_calibration_dict_pipeline", "sklearn/tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence[True-sigmoid]", "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-sigmoid]", "sklearn/tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence[False-isotonic]", "sklearn/calibration.py::sklearn.calibration.CalibratedClassifierCV", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-204	1.0	{ "code": "diff --git b/sklearn/linear_model/_glm/glm.py a/sklearn/linear_model/_glm/glm.py\nindex b721bedee..093a813f6 100644\n--- b/sklearn/linear_model/_glm/glm.py\n+++ a/sklearn/linear_model/_glm/glm.py\n@@ -167,6 +167,7 @@ class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):\n self.warm_start = warm_start\n self.verbose = verbose\n \n+ @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit a Generalized Linear Model.\n \n@@ -186,6 +187,138 @@ class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):\n self : object\n Fitted model.\n \"\"\"\n+ X, y = validate_data(\n+ self,\n+ X,\n+ y,\n+ accept_sparse=[\"csc\", \"csr\"],\n+ dtype=[np.float64, np.float32],\n+ y_numeric=True,\n+ multi_output=False,\n+ )\n+\n+ # required by losses\n+ if self.solver == \"lbfgs\":\n+ # lbfgs will force coef and therefore raw_prediction to be float64. The\n+ # base_loss needs y, X @ coef and sample_weight all of same dtype\n+ # (and contiguous).\n+ loss_dtype = np.float64\n+ else:\n+ loss_dtype = min(max(y.dtype, X.dtype), np.float64)\n+ y = check_array(y, dtype=loss_dtype, order=\"C\", ensure_2d=False)\n+\n+ if sample_weight is not None:\n+ # Note that _check_sample_weight calls check_array(order=\"C\") required by\n+ # losses.\n+ sample_weight = _check_sample_weight(sample_weight, X, dtype=loss_dtype)\n+\n+ n_samples, n_features = X.shape\n+ self._base_loss = self._get_loss()\n+\n+ linear_loss = LinearModelLoss(\n+ base_loss=self._base_loss,\n+ fit_intercept=self.fit_intercept,\n+ )\n+\n+ if not linear_loss.base_loss.in_y_true_range(y):\n+ raise ValueError(\n+ \"Some value(s) of y are out of the valid range of the loss\"\n+ f\" {self._base_loss.__class__.__name__!r}.\"\n+ )\n+\n+ # TODO: if alpha=0 check that X is not rank deficient\n+\n+ # NOTE: Rescaling of sample_weight:\n+ # We want to minimize\n+ # obj = 1/(2 * sum(sample_weight)) * sum(sample_weight * deviance)\n+ # + 1/2 * alpha * L2,\n+ # with\n+ # deviance = 2 * loss.\n+ # The objective is invariant to multiplying sample_weight by a constant. We\n+ # could choose this constant such that sum(sample_weight) = 1 in order to end\n+ # up with\n+ # obj = sum(sample_weight * loss) + 1/2 * alpha * L2.\n+ # But LinearModelLoss.loss() already computes\n+ # average(loss, weights=sample_weight)\n+ # Thus, without rescaling, we have\n+ # obj = LinearModelLoss.loss(...)\n+\n+ if self.warm_start and hasattr(self, \"coef_\"):\n+ if self.fit_intercept:\n+ # LinearModelLoss needs intercept at the end of coefficient array.\n+ coef = np.concatenate((self.coef_, np.array([self.intercept_])))\n+ else:\n+ coef = self.coef_\n+ coef = coef.astype(loss_dtype, copy=False)\n+ else:\n+ coef = linear_loss.init_zero_coef(X, dtype=loss_dtype)\n+ if self.fit_intercept:\n+ coef[-1] = linear_loss.base_loss.link.link(\n+ np.average(y, weights=sample_weight)\n+ )\n+\n+ l2_reg_strength = self.alpha\n+ n_threads = _openmp_effective_n_threads()\n+\n+ # Algorithms for optimization:\n+ # Note again that our losses implement 1/2 * deviance.\n+ if self.solver == \"lbfgs\":\n+ func = linear_loss.loss_gradient\n+\n+ opt_res = scipy.optimize.minimize(\n+ func,\n+ coef,\n+ method=\"L-BFGS-B\",\n+ jac=True,\n+ options={\n+ \"maxiter\": self.max_iter,\n+ \"maxls\": 50, # default is 20\n+ \"iprint\": self.verbose - 1,\n+ \"gtol\": self.tol,\n+ # The constant 64 was found empirically to pass the test suite.\n+ # The point is that ftol is very small, but a bit larger than\n+ # machine precision for float64, which is the dtype used by lbfgs.\n+ \"ftol\": 64 * np.finfo(float).eps,\n+ },\n+ args=(X, y, sample_weight, l2_reg_strength, n_threads),\n+ )\n+ self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n+ coef = opt_res.x\n+ elif self.solver == \"newton-cholesky\":\n+ sol = NewtonCholeskySolver(\n+ coef=coef,\n+ linear_loss=linear_loss,\n+ l2_reg_strength=l2_reg_strength,\n+ tol=self.tol,\n+ max_iter=self.max_iter,\n+ n_threads=n_threads,\n+ verbose=self.verbose,\n+ )\n+ coef = sol.solve(X, y, sample_weight)\n+ self.n_iter_ = sol.iteration\n+ elif issubclass(self.solver, NewtonSolver):\n+ sol = self.solver(\n+ coef=coef,\n+ linear_loss=linear_loss,\n+ l2_reg_strength=l2_reg_strength,\n+ tol=self.tol,\n+ max_iter=self.max_iter,\n+ n_threads=n_threads,\n+ )\n+ coef = sol.solve(X, y, sample_weight)\n+ self.n_iter_ = sol.iteration\n+ else:\n+ raise ValueError(f\"Invalid solver={self.solver}.\")\n+\n+ if self.fit_intercept:\n+ self.intercept_ = coef[-1]\n+ self.coef_ = coef[:-1]\n+ else:\n+ # set intercept to zero as the other linear models do\n+ self.intercept_ = 0.0\n+ self.coef_ = coef\n+\n+ return self\n \n def _linear_predictor(self, X):\n \"\"\"Compute the linear_predictor = `X @ coef_ + intercept_`.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py\nindex 093a813f6..b721bedee 100644\n--- a/sklearn/linear_model/_glm/glm.py\n+++ b/sklearn/linear_model/_glm/glm.py\n@@ -167,7 +167,6 @@ class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):\n self.warm_start = warm_start\n self.verbose = verbose\n \n- @_fit_context(prefer_skip_nested_validation=True)\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit a Generalized Linear Model.\n \n@@ -187,138 +186,6 @@ class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):\n self : object\n Fitted model.\n \"\"\"\n- X, y = validate_data(\n- self,\n- X,\n- y,\n- accept_sparse=[\"csc\", \"csr\"],\n- dtype=[np.float64, np.float32],\n- y_numeric=True,\n- multi_output=False,\n- )\n-\n- # required by losses\n- if self.solver == \"lbfgs\":\n- # lbfgs will force coef and therefore raw_prediction to be float64. The\n- # base_loss needs y, X @ coef and sample_weight all of same dtype\n- # (and contiguous).\n- loss_dtype = np.float64\n- else:\n- loss_dtype = min(max(y.dtype, X.dtype), np.float64)\n- y = check_array(y, dtype=loss_dtype, order=\"C\", ensure_2d=False)\n-\n- if sample_weight is not None:\n- # Note that _check_sample_weight calls check_array(order=\"C\") required by\n- # losses.\n- sample_weight = _check_sample_weight(sample_weight, X, dtype=loss_dtype)\n-\n- n_samples, n_features = X.shape\n- self._base_loss = self._get_loss()\n-\n- linear_loss = LinearModelLoss(\n- base_loss=self._base_loss,\n- fit_intercept=self.fit_intercept,\n- )\n-\n- if not linear_loss.base_loss.in_y_true_range(y):\n- raise ValueError(\n- \"Some value(s) of y are out of the valid range of the loss\"\n- f\" {self._base_loss.__class__.__name__!r}.\"\n- )\n-\n- # TODO: if alpha=0 check that X is not rank deficient\n-\n- # NOTE: Rescaling of sample_weight:\n- # We want to minimize\n- # obj = 1/(2 * sum(sample_weight)) * sum(sample_weight * deviance)\n- # + 1/2 * alpha * L2,\n- # with\n- # deviance = 2 * loss.\n- # The objective is invariant to multiplying sample_weight by a constant. We\n- # could choose this constant such that sum(sample_weight) = 1 in order to end\n- # up with\n- # obj = sum(sample_weight * loss) + 1/2 * alpha * L2.\n- # But LinearModelLoss.loss() already computes\n- # average(loss, weights=sample_weight)\n- # Thus, without rescaling, we have\n- # obj = LinearModelLoss.loss(...)\n-\n- if self.warm_start and hasattr(self, \"coef_\"):\n- if self.fit_intercept:\n- # LinearModelLoss needs intercept at the end of coefficient array.\n- coef = np.concatenate((self.coef_, np.array([self.intercept_])))\n- else:\n- coef = self.coef_\n- coef = coef.astype(loss_dtype, copy=False)\n- else:\n- coef = linear_loss.init_zero_coef(X, dtype=loss_dtype)\n- if self.fit_intercept:\n- coef[-1] = linear_loss.base_loss.link.link(\n- np.average(y, weights=sample_weight)\n- )\n-\n- l2_reg_strength = self.alpha\n- n_threads = _openmp_effective_n_threads()\n-\n- # Algorithms for optimization:\n- # Note again that our losses implement 1/2 * deviance.\n- if self.solver == \"lbfgs\":\n- func = linear_loss.loss_gradient\n-\n- opt_res = scipy.optimize.minimize(\n- func,\n- coef,\n- method=\"L-BFGS-B\",\n- jac=True,\n- options={\n- \"maxiter\": self.max_iter,\n- \"maxls\": 50, # default is 20\n- \"iprint\": self.verbose - 1,\n- \"gtol\": self.tol,\n- # The constant 64 was found empirically to pass the test suite.\n- # The point is that ftol is very small, but a bit larger than\n- # machine precision for float64, which is the dtype used by lbfgs.\n- \"ftol\": 64 * np.finfo(float).eps,\n- },\n- args=(X, y, sample_weight, l2_reg_strength, n_threads),\n- )\n- self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n- coef = opt_res.x\n- elif self.solver == \"newton-cholesky\":\n- sol = NewtonCholeskySolver(\n- coef=coef,\n- linear_loss=linear_loss,\n- l2_reg_strength=l2_reg_strength,\n- tol=self.tol,\n- max_iter=self.max_iter,\n- n_threads=n_threads,\n- verbose=self.verbose,\n- )\n- coef = sol.solve(X, y, sample_weight)\n- self.n_iter_ = sol.iteration\n- elif issubclass(self.solver, NewtonSolver):\n- sol = self.solver(\n- coef=coef,\n- linear_loss=linear_loss,\n- l2_reg_strength=l2_reg_strength,\n- tol=self.tol,\n- max_iter=self.max_iter,\n- n_threads=n_threads,\n- )\n- coef = sol.solve(X, y, sample_weight)\n- self.n_iter_ = sol.iteration\n- else:\n- raise ValueError(f\"Invalid solver={self.solver}.\")\n-\n- if self.fit_intercept:\n- self.intercept_ = coef[-1]\n- self.coef_ = coef[:-1]\n- else:\n- # set intercept to zero as the other linear models do\n- self.intercept_ = 0.0\n- self.coef_ = coef\n-\n- return self\n \n def _linear_predictor(self, X):\n \"\"\"Compute the linear_predictor = `X @ coef_ + 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/_glm/glm.py.\nHere is the description for the function:\n def fit(self, X, y, sample_weight=None):\n \"\"\"Fit a Generalized 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,)\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 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/_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]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-PoissonRegressor()-42-True-lbfgs]", "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-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-GammaRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-GammaRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-GammaRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-TweedieRegressor(power=1.5)-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-TweedieRegressor(power=1.5)-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-TweedieRegressor(power=1.5)-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[long-TweedieRegressor(power=1.5)-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-BinomialRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-PoissonRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-PoissonRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-PoissonRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-PoissonRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-GammaRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-GammaRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-GammaRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-GammaRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-TweedieRegressor(power=1.5)-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-TweedieRegressor(power=1.5)-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-TweedieRegressor(power=1.5)-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression[wide-TweedieRegressor(power=1.5)-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-BinomialRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-PoissonRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-PoissonRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-PoissonRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-PoissonRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-GammaRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-GammaRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-GammaRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-GammaRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-TweedieRegressor(power=1.5)-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-TweedieRegressor(power=1.5)-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-TweedieRegressor(power=1.5)-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[long-TweedieRegressor(power=1.5)-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-BinomialRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-BinomialRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-PoissonRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-PoissonRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-PoissonRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-PoissonRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-GammaRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-GammaRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-GammaRegressor()-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-GammaRegressor()-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-TweedieRegressor(power=1.5)-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-TweedieRegressor(power=1.5)-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-TweedieRegressor(power=1.5)-42-False-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_hstacked_X[wide-TweedieRegressor(power=1.5)-42-False-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_vstacked_X[long-BinomialRegressor()-42-True-lbfgs]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_vstacked_X[long-BinomialRegressor()-42-True-newton-cholesky]", "sklearn/linear_model/_glm/tests/test_glm.py::test_glm_regression_vstacked_X[long-BinomialRegressor()-42-False-lbfgs]", 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"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_fit1d]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_estimators[TweedieRegressor(max_iter=5)-check_requires_y_none]", "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)]", "sklearn/tests/test_common.py::test_check_param_validation[GammaRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[PoissonRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_param_validation[TweedieRegressor(max_iter=5)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-205	1.0	{ "code": "diff --git b/sklearn/utils/_estimator_html_repr.py a/sklearn/utils/_estimator_html_repr.py\nindex 1aad599a9..90a700a26 100644\n--- b/sklearn/utils/_estimator_html_repr.py\n+++ a/sklearn/utils/_estimator_html_repr.py\n@@ -529,3 +529,21 @@ class _HTMLDocumentationLinkMixin:\n not belong to module `_doc_link_module`, the empty string (i.e. `\"\"`) is\n returned.\n \"\"\"\n+ if self.__class__.__module__.split(\".\")[0] != self._doc_link_module:\n+ return \"\"\n+\n+ if self._doc_link_url_param_generator is None:\n+ estimator_name = self.__class__.__name__\n+ # Construct the estimator's module name, up to the first private submodule.\n+ # This works because in scikit-learn all public estimators are exposed at\n+ # that level, even if they actually live in a private sub-module.\n+ estimator_module = \".\".join(\n+ itertools.takewhile(\n+ lambda part: not part.startswith(\"_\"),\n+ self.__class__.__module__.split(\".\"),\n+ )\n+ )\n+ return self._doc_link_template.format(\n+ estimator_module=estimator_module, estimator_name=estimator_name\n+ )\n+ return self._doc_link_template.format(**self._doc_link_url_param_generator())\n", "test": null }	null	{ "code": "diff --git a/sklearn/utils/_estimator_html_repr.py b/sklearn/utils/_estimator_html_repr.py\nindex 90a700a26..1aad599a9 100644\n--- a/sklearn/utils/_estimator_html_repr.py\n+++ b/sklearn/utils/_estimator_html_repr.py\n@@ -529,21 +529,3 @@ class _HTMLDocumentationLinkMixin:\n not belong to module `_doc_link_module`, the empty string (i.e. `\"\"`) is\n returned.\n \"\"\"\n- if self.__class__.__module__.split(\".\")[0] != self._doc_link_module:\n- return \"\"\n-\n- if self._doc_link_url_param_generator is None:\n- estimator_name = self.__class__.__name__\n- # Construct the estimator's module name, up to the first private submodule.\n- # This works because in scikit-learn all public estimators are exposed at\n- # that level, even if they actually live in a private sub-module.\n- estimator_module = \".\".join(\n- itertools.takewhile(\n- lambda part: not part.startswith(\"_\"),\n- self.__class__.__module__.split(\".\"),\n- )\n- )\n- return self._doc_link_template.format(\n- estimator_module=estimator_module, estimator_name=estimator_name\n- )\n- return self._doc_link_template.format(**self._doc_link_url_param_generator())\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/utils/_estimator_html_repr.py.\nHere is the description for the function:\n def _get_doc_link(self):\n \"\"\"Generates a link to the API documentation for a given estimator.\n\n This method generates the link to the estimator's documentation page\n by using the template defined by the attribute `_doc_link_template`.\n\n Returns\n -------\n url : str\n The URL to the API documentation for this estimator. If the estimator does\n not belong to module `_doc_link_module`, the empty string (i.e. `\"\"`) is\n returned.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_search.py::test_search_html_repr", "sklearn/tests/test_base.py::test_repr_mimebundle_", "sklearn/tests/test_base.py::test_repr_html_wraps", "sklearn/utils/tests/test_estimator_html_repr.py::test_estimator_html_repr_pipeline", "sklearn/utils/tests/test_estimator_html_repr.py::test_stacking_classifier[None]", "sklearn/utils/tests/test_estimator_html_repr.py::test_stacking_classifier[final_estimator1]", "sklearn/utils/tests/test_estimator_html_repr.py::test_stacking_regressor[None]", "sklearn/utils/tests/test_estimator_html_repr.py::test_stacking_regressor[final_estimator1]", "sklearn/utils/tests/test_estimator_html_repr.py::test_birch_duck_typing_meta", "sklearn/utils/tests/test_estimator_html_repr.py::test_ovo_classifier_duck_typing_meta", "sklearn/utils/tests/test_estimator_html_repr.py::test_duck_typing_nested_estimator", "sklearn/utils/tests/test_estimator_html_repr.py::test_one_estimator_print_change_only[True]", "sklearn/utils/tests/test_estimator_html_repr.py::test_one_estimator_print_change_only[False]", "sklearn/utils/tests/test_estimator_html_repr.py::test_fallback_exists", "sklearn/utils/tests/test_estimator_html_repr.py::test_show_arrow_pipeline", "sklearn/utils/tests/test_estimator_html_repr.py::test_invalid_parameters_in_stacking", "sklearn/utils/tests/test_estimator_html_repr.py::test_estimator_html_repr_unfitted_vs_fitted", "sklearn/utils/tests/test_estimator_html_repr.py::test_estimator_html_repr_fitted_icon[estimator0]", "sklearn/utils/tests/test_estimator_html_repr.py::test_estimator_html_repr_fitted_icon[estimator1]", "sklearn/utils/tests/test_estimator_html_repr.py::test_estimator_html_repr_fitted_icon[estimator2]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_sklearn[1.3.0.dev0]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_sklearn[1.3.0]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_instance[prefix.mymodule-prefix.mymodule]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_instance[prefix._mymodule-prefix]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_instance[prefix.mypackage._mymodule-prefix.mypackage]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_instance[prefix.mypackage._mymodule.submodule-prefix.mypackage]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_instance[prefix.mypackage.mymodule.submodule-prefix.mypackage.mymodule.submodule]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_class[prefix.mymodule-prefix.mymodule]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_class[prefix._mymodule-prefix]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_class[prefix.mypackage._mymodule-prefix.mypackage]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_class[prefix.mypackage._mymodule.submodule-prefix.mypackage]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_class[prefix.mypackage.mymodule.submodule-prefix.mypackage.mymodule.submodule]", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_get_doc_link_out_of_library", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_doc_link_url_param_generator_instance", "sklearn/utils/tests/test_estimator_html_repr.py::test_html_documentation_link_mixin_doc_link_url_param_generator_class", "sklearn/utils/tests/test_estimator_html_repr.py::test_function_transformer_show_caption[<lambda>-<lambda>]", "sklearn/utils/tests/test_estimator_html_repr.py::test_function_transformer_show_caption[dummy_function-dummy_function]", "sklearn/utils/tests/test_estimator_html_repr.py::test_function_transformer_show_caption[func2-dummy_function]", "sklearn/utils/tests/test_estimator_html_repr.py::test_function_transformer_show_caption[func3-vectorize\\\$\\\\.\\\\.\\\\.\\\$]", "sklearn/utils/_estimator_html_repr.py::sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin", "sklearn/utils/_estimator_html_repr.py::sklearn.utils._estimator_html_repr.estimator_html_repr" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-206	1.0	{ "code": "diff --git b/sklearn/linear_model/_ridge.py a/sklearn/linear_model/_ridge.py\nindex 3dea4a0bc..827366fab 100644\n--- b/sklearn/linear_model/_ridge.py\n+++ a/sklearn/linear_model/_ridge.py\n@@ -1843,6 +1843,25 @@ class _RidgeGCV(LinearModel):\n The centered X is never actually computed because centering would break\n the sparsity of X.\n \"\"\"\n+ if not self.fit_intercept:\n+ # in this case centering has been done in preprocessing\n+ # or we are not fitting an intercept.\n+ X_mean = np.zeros(X.shape[1], dtype=X.dtype)\n+ return safe_sparse_dot(X.T, X, dense_output=True), X_mean\n+ # this function only gets called for sparse X\n+ n_samples = X.shape[0]\n+ sample_weight_matrix = sparse.dia_matrix(\n+ (sqrt_sw, 0), shape=(n_samples, n_samples)\n+ )\n+ X_weighted = sample_weight_matrix.dot(X)\n+ X_mean, _ = mean_variance_axis(X_weighted, axis=0)\n+ X_mean = X_mean * n_samples / sqrt_sw.dot(sqrt_sw)\n+ weight_sum = sqrt_sw.dot(sqrt_sw)\n+ return (\n+ safe_sparse_dot(X.T, X, dense_output=True)\n+ - weight_sum * np.outer(X_mean, X_mean),\n+ X_mean,\n+ )\n \n def _sparse_multidot_diag(self, X, A, X_mean, sqrt_sw):\n \"\"\"Compute the diagonal of (X - X_mean).dot(A).dot((X - X_mean).T)\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py\nindex 827366fab..3dea4a0bc 100644\n--- a/sklearn/linear_model/_ridge.py\n+++ b/sklearn/linear_model/_ridge.py\n@@ -1843,25 +1843,6 @@ class _RidgeGCV(LinearModel):\n The centered X is never actually computed because centering would break\n the sparsity of X.\n \"\"\"\n- if not self.fit_intercept:\n- # in this case centering has been done in preprocessing\n- # or we are not fitting an intercept.\n- X_mean = np.zeros(X.shape[1], dtype=X.dtype)\n- return safe_sparse_dot(X.T, X, dense_output=True), X_mean\n- # this function only gets called for sparse X\n- n_samples = X.shape[0]\n- sample_weight_matrix = sparse.dia_matrix(\n- (sqrt_sw, 0), shape=(n_samples, n_samples)\n- )\n- X_weighted = sample_weight_matrix.dot(X)\n- X_mean, _ = mean_variance_axis(X_weighted, axis=0)\n- X_mean = X_mean * n_samples / sqrt_sw.dot(sqrt_sw)\n- weight_sum = sqrt_sw.dot(sqrt_sw)\n- return (\n- safe_sparse_dot(X.T, X, dense_output=True)\n- - weight_sum * np.outer(X_mean, X_mean),\n- X_mean,\n- )\n \n def _sparse_multidot_diag(self, X, A, X_mean, sqrt_sw):\n \"\"\"Compute the diagonal of (X - X_mean).dot(A).dot((X - X_mean).T)\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/_ridge.py.\nHere is the description for the function:\n def _compute_covariance(self, X, sqrt_sw):\n \"\"\"Computes covariance matrix X^TX with possible centering.\n\n Parameters\n ----------\n X : sparse matrix of shape (n_samples, n_features)\n The preprocessed design matrix.\n\n sqrt_sw : ndarray of shape (n_samples,)\n square roots of sample weights\n\n Returns\n -------\n covariance : ndarray of shape (n_features, n_features)\n The covariance matrix.\n X_mean : ndarray of shape (n_feature,)\n The weighted mean of ``X`` for each feature.\n\n Notes\n -----\n Since X is sparse it has not been centered in preprocessing, but it has\n been scaled by sqrt(sample weights).\n\n When self.fit_intercept is False no centering is done.\n\n The centered X is never actually computed because centering would break\n the sparsity of X.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-True-shape0]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-True-shape1]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-True-shape2]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-True-shape3]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-True-shape4]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-False-shape0]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-False-shape1]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-False-shape2]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-False-shape3]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_matrix-False-shape4]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-True-shape0]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-True-shape1]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-True-shape2]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-True-shape3]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-True-shape4]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-False-shape0]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-False-shape1]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-False-shape2]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-False-shape3]", "sklearn/linear_model/tests/test_ridge.py::test_compute_covariance[csr_array-False-shape4]", "sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[seed0-40-float32-1.0-ridgecv-csr_matrix]", "sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[seed0-40-float32-1.0-ridgecv-csr_array]", "sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[seed1-40-float32-1.0-ridgecv-csr_matrix]", "sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[seed1-40-float32-1.0-ridgecv-csr_array]", "sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[seed2-40-float32-1.0-ridgecv-csr_matrix]", "sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[seed2-40-float32-1.0-ridgecv-csr_array]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-True-X_shape0-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-True-X_shape0-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-True-X_shape1-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-True-X_shape1-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-False-X_shape0-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-False-X_shape0-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-False-X_shape1-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape0-1.0-False-X_shape1-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-True-X_shape0-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-True-X_shape0-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-True-X_shape1-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-True-X_shape1-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-False-X_shape0-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-False-X_shape0-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-False-X_shape1-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape1-30.0-False-X_shape1-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-True-X_shape0-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-True-X_shape0-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-True-X_shape1-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-True-X_shape1-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-False-X_shape0-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-False-X_shape0-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-False-X_shape1-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_vs_ridge_loo_cv[y_shape2-150.0-False-X_shape1-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-8-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-8-csr_array-svd]", 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"sklearn/linear_model/tests/test_ridge.py::test_dense_sparse[csr_array-_test_ridge_loo]", "sklearn/linear_model/tests/test_ridge.py::test_dense_sparse[csr_array-_test_ridge_cv]", "sklearn/linear_model/tests/test_ridge.py::test_dense_sparse[csr_array-_test_ridge_classifiers]", "sklearn/tests/test_common.py::test_estimators[RidgeCV()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RidgeCV()-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifierCV()-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifierCV()-check_estimator_sparse_matrix]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-207	1.0	{ "code": "diff --git b/sklearn/linear_model/_ridge.py a/sklearn/linear_model/_ridge.py\nindex 54d17acfd..827366fab 100644\n--- b/sklearn/linear_model/_ridge.py\n+++ a/sklearn/linear_model/_ridge.py\n@@ -1794,6 +1794,26 @@ class _RidgeGCV(LinearModel):\n The centered X is never actually computed because centering would break\n the sparsity of X.\n \"\"\"\n+ center = self.fit_intercept and sparse.issparse(X)\n+ if not center:\n+ # in this case centering has been done in preprocessing\n+ # or we are not fitting an intercept.\n+ X_mean = np.zeros(X.shape[1], dtype=X.dtype)\n+ return safe_sparse_dot(X, X.T, dense_output=True), X_mean\n+ # X is sparse\n+ n_samples = X.shape[0]\n+ sample_weight_matrix = sparse.dia_matrix(\n+ (sqrt_sw, 0), shape=(n_samples, n_samples)\n+ )\n+ X_weighted = sample_weight_matrix.dot(X)\n+ X_mean, _ = mean_variance_axis(X_weighted, axis=0)\n+ X_mean = n_samples / sqrt_sw.dot(sqrt_sw)\n+ X_mX = sqrt_sw[:, None] safe_sparse_dot(X_mean, X.T, dense_output=True)\n+ X_mX_m = np.outer(sqrt_sw, sqrt_sw) * np.dot(X_mean, X_mean)\n+ return (\n+ safe_sparse_dot(X, X.T, dense_output=True) + X_mX_m - X_mX - X_mX.T,\n+ X_mean,\n+ )\n \n def _compute_covariance(self, X, sqrt_sw):\n \"\"\"Computes covariance matrix X^TX with possible centering.\n", "test": null }	null	{ "code": "diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py\nindex 827366fab..54d17acfd 100644\n--- a/sklearn/linear_model/_ridge.py\n+++ b/sklearn/linear_model/_ridge.py\n@@ -1794,26 +1794,6 @@ class _RidgeGCV(LinearModel):\n The centered X is never actually computed because centering would break\n the sparsity of X.\n \"\"\"\n- center = self.fit_intercept and sparse.issparse(X)\n- if not center:\n- # in this case centering has been done in preprocessing\n- # or we are not fitting an intercept.\n- X_mean = np.zeros(X.shape[1], dtype=X.dtype)\n- return safe_sparse_dot(X, X.T, dense_output=True), X_mean\n- # X is sparse\n- n_samples = X.shape[0]\n- sample_weight_matrix = sparse.dia_matrix(\n- (sqrt_sw, 0), shape=(n_samples, n_samples)\n- )\n- X_weighted = sample_weight_matrix.dot(X)\n- X_mean, _ = mean_variance_axis(X_weighted, axis=0)\n- X_mean = n_samples / sqrt_sw.dot(sqrt_sw)\n- X_mX = sqrt_sw[:, None] safe_sparse_dot(X_mean, X.T, dense_output=True)\n- X_mX_m = np.outer(sqrt_sw, sqrt_sw) * np.dot(X_mean, X_mean)\n- return (\n- safe_sparse_dot(X, X.T, dense_output=True) + X_mX_m - X_mX - X_mX.T,\n- X_mean,\n- )\n \n def _compute_covariance(self, X, sqrt_sw):\n \"\"\"Computes covariance matrix X^TX with possible centering.\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/_ridge.py.\nHere is the description for the function:\n def _compute_gram(self, X, sqrt_sw):\n \"\"\"Computes the Gram matrix XX^T with possible centering.\n\n Parameters\n ----------\n X : {ndarray, sparse matrix} of shape (n_samples, n_features)\n The preprocessed design matrix.\n\n sqrt_sw : ndarray of shape (n_samples,)\n square roots of sample weights\n\n Returns\n -------\n gram : ndarray of shape (n_samples, n_samples)\n The Gram matrix.\n X_mean : ndarray of shape (n_feature,)\n The weighted mean of ``X`` for each feature.\n\n Notes\n -----\n When X is dense the centering has been done in preprocessing\n so the mean is 0 and we just compute XX^T.\n\n When X is sparse it has not been centered in preprocessing, but it has\n been scaled by sqrt(sample weights).\n\n When self.fit_intercept is False no centering is done.\n\n The centered X is never actually computed because centering would break\n the sparsity of X.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/impute/tests/test_impute.py::test_iterative_imputer_estimators[estimator4]", 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"sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-8-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-8-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-8-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-20-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-20-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-20-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape2-True-150.0-8-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape2-True-150.0-8-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape2-True-150.0-8-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape2-True-150.0-20-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape2-True-150.0-20-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape2-True-150.0-20-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape3-False-30.0-8-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape3-False-30.0-8-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape3-False-30.0-8-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape3-False-30.0-20-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape3-False-30.0-20-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape3-False-30.0-20-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_cv_results_not_stored[ridge0-make_regression]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_cv_results_not_stored[ridge1-make_classification]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_best_score[None-ridge0-make_regression]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_best_score[None-ridge1-make_classification]", "sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_conversion[RidgeCV]", "sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_conversion[RidgeClassifierCV]", "sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_scalar[RidgeCV]", "sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_scalar[RidgeClassifierCV]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_store_cv_values_deprecated", "sklearn/linear_model/tests/test_ridge.py::test_ridge_cv_values_deprecated", "sklearn/tests/test_common.py::test_estimators[RidgeCV()-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifierCV()-check_classifiers_one_label_sample_weights]", "sklearn/tests/test_common.py::test_estimators[RidgeClassifierCV()-check_fit2d_1sample]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-208	1.0	{ "code": "diff --git b/sklearn/utils/validation.py a/sklearn/utils/validation.py\nindex b8d878a60..401e72cef 100644\n--- b/sklearn/utils/validation.py\n+++ a/sklearn/utils/validation.py\n@@ -2154,6 +2154,21 @@ def _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9):\n more tolerant than the default for numpy.testing.assert_allclose, where\n atol=0.\n \"\"\"\n+ if sp.issparse(x) and sp.issparse(y):\n+ x = x.tocsr()\n+ y = y.tocsr()\n+ x.sum_duplicates()\n+ y.sum_duplicates()\n+ return (\n+ np.array_equal(x.indices, y.indices)\n+ and np.array_equal(x.indptr, y.indptr)\n+ and np.allclose(x.data, y.data, rtol=rtol, atol=atol)\n+ )\n+ elif not sp.issparse(x) and not sp.issparse(y):\n+ return np.allclose(x, y, rtol=rtol, atol=atol)\n+ raise ValueError(\n+ \"Can only compare two sparse matrices, not a sparse matrix and an array\"\n+ )\n \n \n def _check_response_method(estimator, response_method):\n", "test": null }	null	{ "code": "diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py\nindex 401e72cef..b8d878a60 100644\n--- a/sklearn/utils/validation.py\n+++ b/sklearn/utils/validation.py\n@@ -2154,21 +2154,6 @@ def _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9):\n more tolerant than the default for numpy.testing.assert_allclose, where\n atol=0.\n \"\"\"\n- if sp.issparse(x) and sp.issparse(y):\n- x = x.tocsr()\n- y = y.tocsr()\n- x.sum_duplicates()\n- y.sum_duplicates()\n- return (\n- np.array_equal(x.indices, y.indices)\n- and np.array_equal(x.indptr, y.indptr)\n- and np.allclose(x.data, y.data, rtol=rtol, atol=atol)\n- )\n- elif not sp.issparse(x) and not sp.issparse(y):\n- return np.allclose(x, y, rtol=rtol, atol=atol)\n- raise ValueError(\n- \"Can only compare two sparse matrices, not a sparse matrix and an array\"\n- )\n \n \n def _check_response_method(estimator, response_method):\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/utils/validation.py.\nHere is the description for the function:\ndef _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9):\n \"\"\"Check allclose for sparse and dense data.\n\n Both x and y need to be either sparse or dense, they\n can't be mixed.\n\n Parameters\n ----------\n x : {array-like, sparse matrix}\n First array to compare.\n\n y : {array-like, sparse matrix}\n Second array to compare.\n\n rtol : float, default=1e-7\n Relative tolerance; see numpy.allclose.\n\n atol : float, default=1e-9\n absolute tolerance; see numpy.allclose. Note that the default here is\n more tolerant than the default for numpy.testing.assert_allclose, where\n atol=0.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_equals[array]", "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_equals[csr_matrix]", "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_equals[csc_matrix]", "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_not_equals[array]", "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_not_equals[csr_matrix]", "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_not_equals[csc_matrix]", "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_raise[csr_matrix]", "sklearn/utils/tests/test_validation.py::test_allclose_dense_sparse_raise[csc_matrix]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_distance_matrix", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distance_matrix[csr_matrix]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distance_matrix[csr_array]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distance_matrix[csc_matrix]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distance_matrix[csc_array]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_usable_inputs[kwargs0-X0]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_usable_inputs[kwargs1-X1]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distances_too_few_nonzero[csr_matrix]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distances_too_few_nonzero[csr_array]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distances_disconnected_graph[csr_matrix]", "sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_sparse_distances_disconnected_graph[csr_array]", "sklearn/preprocessing/tests/test_function_transformer.py::test_check_inverse[None]", "sklearn/preprocessing/tests/test_function_transformer.py::test_check_inverse[csc_matrix]", "sklearn/preprocessing/tests/test_function_transformer.py::test_check_inverse[csc_array]", "sklearn/preprocessing/tests/test_function_transformer.py::test_check_inverse[csr_matrix]", "sklearn/preprocessing/tests/test_function_transformer.py::test_check_inverse[csr_array]", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_support_all_nummerical_dataframes_check_inverse_True", "sklearn/preprocessing/tests/test_function_transformer.py::test_function_transformer_validate_inverse", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_invertible", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_functions_multioutput", "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/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFE-5-importance_getter0]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_3d_target", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_not_warns_with_global_output_set[pandas]", "sklearn/compose/tests/test_target.py::test_transform_target_regressor_not_warns_with_global_output_set[polars]", "sklearn/feature_selection/tests/test_rfe.py::test_rfe_wrapped_estimator[RFE-5-regressor_.coef_]", "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/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/compose/_target.py::sklearn.compose._target.TransformedTargetRegressor", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[HDBSCAN(min_samples=1)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-209	1.0	{ "code": "diff --git b/sklearn/decomposition/_pca.py a/sklearn/decomposition/_pca.py\nindex 378addad6..24cb1649c 100644\n--- b/sklearn/decomposition/_pca.py\n+++ a/sklearn/decomposition/_pca.py\n@@ -52,6 +52,50 @@ def _assess_dimension(spectrum, rank, n_samples):\n Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604\n <https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_\n \"\"\"\n+ xp, _ = get_namespace(spectrum)\n+\n+ n_features = spectrum.shape[0]\n+ if not 1 <= rank < n_features:\n+ raise ValueError(\"the tested rank should be in [1, n_features - 1]\")\n+\n+ eps = 1e-15\n+\n+ if spectrum[rank - 1] < eps:\n+ # When the tested rank is associated with a small eigenvalue, there's\n+ # no point in computing the log-likelihood: it's going to be very\n+ # small and won't be the max anyway. Also, it can lead to numerical\n+ # issues below when computing pa, in particular in log((spectrum[i] -\n+ # spectrum[j]) because this will take the log of something very small.\n+ return -xp.inf\n+\n+ pu = -rank * log(2.0)\n+ for i in range(1, rank + 1):\n+ pu += (\n+ gammaln((n_features - i + 1) / 2.0)\n+ - log(xp.pi) * (n_features - i + 1) / 2.0\n+ )\n+\n+ pl = xp.sum(xp.log(spectrum[:rank]))\n+ pl = -pl * n_samples / 2.0\n+\n+ v = max(eps, xp.sum(spectrum[rank:]) / (n_features - rank))\n+ pv = -log(v) * n_samples * (n_features - rank) / 2.0\n+\n+ m = n_features * rank - rank * (rank + 1.0) / 2.0\n+ pp = log(2.0 * xp.pi) * (m + rank) / 2.0\n+\n+ pa = 0.0\n+ spectrum_ = xp.asarray(spectrum, copy=True)\n+ spectrum_[rank:n_features] = v\n+ for i in range(rank):\n+ for j in range(i + 1, spectrum.shape[0]):\n+ pa += log(\n+ (spectrum[i] - spectrum[j]) * (1.0 / spectrum_[j] - 1.0 / spectrum_[i])\n+ ) + log(n_samples)\n+\n+ ll = pu + pl + pv + pp - pa / 2.0 - rank * log(n_samples) / 2.0\n+\n+ return ll\n \n \n def _infer_dimension(spectrum, n_samples):\n", "test": null }	null	{ "code": "diff --git a/sklearn/decomposition/_pca.py b/sklearn/decomposition/_pca.py\nindex 24cb1649c..378addad6 100644\n--- a/sklearn/decomposition/_pca.py\n+++ b/sklearn/decomposition/_pca.py\n@@ -52,50 +52,6 @@ def _assess_dimension(spectrum, rank, n_samples):\n Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604\n <https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_\n \"\"\"\n- xp, _ = get_namespace(spectrum)\n-\n- n_features = spectrum.shape[0]\n- if not 1 <= rank < n_features:\n- raise ValueError(\"the tested rank should be in [1, n_features - 1]\")\n-\n- eps = 1e-15\n-\n- if spectrum[rank - 1] < eps:\n- # When the tested rank is associated with a small eigenvalue, there's\n- # no point in computing the log-likelihood: it's going to be very\n- # small and won't be the max anyway. Also, it can lead to numerical\n- # issues below when computing pa, in particular in log((spectrum[i] -\n- # spectrum[j]) because this will take the log of something very small.\n- return -xp.inf\n-\n- pu = -rank * log(2.0)\n- for i in range(1, rank + 1):\n- pu += (\n- gammaln((n_features - i + 1) / 2.0)\n- - log(xp.pi) * (n_features - i + 1) / 2.0\n- )\n-\n- pl = xp.sum(xp.log(spectrum[:rank]))\n- pl = -pl * n_samples / 2.0\n-\n- v = max(eps, xp.sum(spectrum[rank:]) / (n_features - rank))\n- pv = -log(v) * n_samples * (n_features - rank) / 2.0\n-\n- m = n_features * rank - rank * (rank + 1.0) / 2.0\n- pp = log(2.0 * xp.pi) * (m + rank) / 2.0\n-\n- pa = 0.0\n- spectrum_ = xp.asarray(spectrum, copy=True)\n- spectrum_[rank:n_features] = v\n- for i in range(rank):\n- for j in range(i + 1, spectrum.shape[0]):\n- pa += log(\n- (spectrum[i] - spectrum[j]) * (1.0 / spectrum_[j] - 1.0 / spectrum_[i])\n- ) + log(n_samples)\n-\n- ll = pu + pl + pv + pp - pa / 2.0 - rank * log(n_samples) / 2.0\n-\n- return ll\n \n \n def _infer_dimension(spectrum, n_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/_pca.py.\nHere is the description for the function:\ndef _assess_dimension(spectrum, rank, n_samples):\n \"\"\"Compute the log-likelihood of a rank ``rank`` dataset.\n\n The dataset is assumed to be embedded in gaussian noise of shape(n,\n dimf) having spectrum ``spectrum``. This implements the method of\n T. P. Minka.\n\n Parameters\n ----------\n spectrum : ndarray of shape (n_features,)\n Data spectrum.\n rank : int\n Tested rank value. It should be strictly lower than n_features,\n otherwise the method isn't specified (division by zero in equation\n (31) from the paper).\n n_samples : int\n Number of samples.\n\n Returns\n -------\n ll : float\n The log-likelihood.\n\n References\n ----------\n This implements the method of `Thomas P. Minka:\n Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604\n <https://proceedings.neurips.cc/paper/2000/file/7503cfacd12053d309b6bed5c89de212-Paper.pdf>`_\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/decomposition/tests/test_pca.py::test_n_components_mle[auto]", "sklearn/decomposition/tests/test_pca.py::test_n_components_mle[full]", "sklearn/decomposition/tests/test_pca.py::test_pca_dim", "sklearn/decomposition/tests/test_pca.py::test_infer_dim_1", "sklearn/decomposition/tests/test_pca.py::test_infer_dim_2", "sklearn/decomposition/tests/test_pca.py::test_infer_dim_3", "sklearn/decomposition/tests/test_pca.py::test_assess_dimension_bad_rank", "sklearn/decomposition/tests/test_pca.py::test_small_eigenvalues_mle", "sklearn/decomposition/tests/test_pca.py::test_mle_redundant_data", "sklearn/decomposition/tests/test_pca.py::test_mle_simple_case", "sklearn/decomposition/tests/test_pca.py::test_assess_dimesion_rank_one", "sklearn/tests/test_pipeline.py::test_pipeline_methods_pca_svm", "sklearn/tests/test_pipeline.py::test_pipeline_score_samples_pca_lof" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-210	1.0	{ "code": "diff --git b/sklearn/metrics/_base.py a/sklearn/metrics/_base.py\nindex 5ee16bcc4..ee797e1bc 100644\n--- b/sklearn/metrics/_base.py\n+++ a/sklearn/metrics/_base.py\n@@ -57,6 +57,70 @@ def _average_binary_score(binary_metric, y_true, y_score, average, sample_weight\n classes.\n \n \"\"\"\n+ average_options = (None, \"micro\", \"macro\", \"weighted\", \"samples\")\n+ if average not in average_options:\n+ raise ValueError(\"average has to be one of {0}\".format(average_options))\n+\n+ y_type = type_of_target(y_true)\n+ if y_type not in (\"binary\", \"multilabel-indicator\"):\n+ raise ValueError(\"{0} format is not supported\".format(y_type))\n+\n+ if y_type == \"binary\":\n+ return binary_metric(y_true, y_score, sample_weight=sample_weight)\n+\n+ check_consistent_length(y_true, y_score, sample_weight)\n+ y_true = check_array(y_true)\n+ y_score = check_array(y_score)\n+\n+ not_average_axis = 1\n+ score_weight = sample_weight\n+ average_weight = None\n+\n+ if average == \"micro\":\n+ if score_weight is not None:\n+ score_weight = np.repeat(score_weight, y_true.shape[1])\n+ y_true = y_true.ravel()\n+ y_score = y_score.ravel()\n+\n+ elif average == \"weighted\":\n+ if score_weight is not None:\n+ average_weight = np.sum(\n+ np.multiply(y_true, np.reshape(score_weight, (-1, 1))), axis=0\n+ )\n+ else:\n+ average_weight = np.sum(y_true, axis=0)\n+ if np.isclose(average_weight.sum(), 0.0):\n+ return 0\n+\n+ elif average == \"samples\":\n+ # swap average_weight <-> score_weight\n+ average_weight = score_weight\n+ score_weight = None\n+ not_average_axis = 0\n+\n+ if y_true.ndim == 1:\n+ y_true = y_true.reshape((-1, 1))\n+\n+ if y_score.ndim == 1:\n+ y_score = y_score.reshape((-1, 1))\n+\n+ n_classes = y_score.shape[not_average_axis]\n+ score = np.zeros((n_classes,))\n+ for c in range(n_classes):\n+ y_true_c = y_true.take([c], axis=not_average_axis).ravel()\n+ y_score_c = y_score.take([c], axis=not_average_axis).ravel()\n+ score[c] = binary_metric(y_true_c, y_score_c, sample_weight=score_weight)\n+\n+ # Average the results\n+ if average is not None:\n+ if average_weight is not None:\n+ # Scores with 0 weights are forced to be 0, preventing the average\n+ # score from being affected by 0-weighted NaN elements.\n+ average_weight = np.asarray(average_weight)\n+ score[average_weight == 0] = 0\n+ return np.average(score, weights=average_weight)\n+ else:\n+ return score\n \n \n def _average_multiclass_ovo_score(binary_metric, y_true, y_score, average=\"macro\"):\n", "test": null }	null	{ "code": "diff --git a/sklearn/metrics/_base.py b/sklearn/metrics/_base.py\nindex ee797e1bc..5ee16bcc4 100644\n--- a/sklearn/metrics/_base.py\n+++ b/sklearn/metrics/_base.py\n@@ -57,70 +57,6 @@ def _average_binary_score(binary_metric, y_true, y_score, average, sample_weight\n classes.\n \n \"\"\"\n- average_options = (None, \"micro\", \"macro\", \"weighted\", \"samples\")\n- if average not in average_options:\n- raise ValueError(\"average has to be one of {0}\".format(average_options))\n-\n- y_type = type_of_target(y_true)\n- if y_type not in (\"binary\", \"multilabel-indicator\"):\n- raise ValueError(\"{0} format is not supported\".format(y_type))\n-\n- if y_type == \"binary\":\n- return binary_metric(y_true, y_score, sample_weight=sample_weight)\n-\n- check_consistent_length(y_true, y_score, sample_weight)\n- y_true = check_array(y_true)\n- y_score = check_array(y_score)\n-\n- not_average_axis = 1\n- score_weight = sample_weight\n- average_weight = None\n-\n- if average == \"micro\":\n- if score_weight is not None:\n- score_weight = np.repeat(score_weight, y_true.shape[1])\n- y_true = y_true.ravel()\n- y_score = y_score.ravel()\n-\n- elif average == \"weighted\":\n- if score_weight is not None:\n- average_weight = np.sum(\n- np.multiply(y_true, np.reshape(score_weight, (-1, 1))), axis=0\n- )\n- else:\n- average_weight = np.sum(y_true, axis=0)\n- if np.isclose(average_weight.sum(), 0.0):\n- return 0\n-\n- elif average == \"samples\":\n- # swap average_weight <-> score_weight\n- average_weight = score_weight\n- score_weight = None\n- not_average_axis = 0\n-\n- if y_true.ndim == 1:\n- y_true = y_true.reshape((-1, 1))\n-\n- if y_score.ndim == 1:\n- y_score = y_score.reshape((-1, 1))\n-\n- n_classes = y_score.shape[not_average_axis]\n- score = np.zeros((n_classes,))\n- for c in range(n_classes):\n- y_true_c = y_true.take([c], axis=not_average_axis).ravel()\n- y_score_c = y_score.take([c], axis=not_average_axis).ravel()\n- score[c] = binary_metric(y_true_c, y_score_c, sample_weight=score_weight)\n-\n- # Average the results\n- if average is not None:\n- if average_weight is not None:\n- # Scores with 0 weights are forced to be 0, preventing the average\n- # score from being affected by 0-weighted NaN elements.\n- average_weight = np.asarray(average_weight)\n- score[average_weight == 0] = 0\n- return np.average(score, weights=average_weight)\n- else:\n- return score\n \n \n def _average_multiclass_ovo_score(binary_metric, y_true, y_score, average=\"macro\"):\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/metrics/_base.py.\nHere is the description for the function:\ndef _average_binary_score(binary_metric, y_true, y_score, average, sample_weight=None):\n \"\"\"Average a binary metric for multilabel classification.\n\n Parameters\n ----------\n y_true : array, shape = [n_samples] or [n_samples, n_classes]\n True binary labels in binary label indicators.\n\n y_score : array, shape = [n_samples] or [n_samples, n_classes]\n Target scores, can either be probability estimates of the positive\n class, confidence values, or binary decisions.\n\n average : {None, 'micro', 'macro', 'samples', 'weighted'}, default='macro'\n If ``None``, the scores for each class are returned. Otherwise,\n this determines the type of averaging performed on the data:\n\n ``'micro'``:\n Calculate metrics globally by considering each element of the label\n indicator matrix as a label.\n ``'macro'``:\n Calculate metrics for each label, and find their unweighted\n mean. This does not take label imbalance into account.\n ``'weighted'``:\n Calculate metrics for each label, and find their average, weighted\n by support (the number of true instances for each label).\n ``'samples'``:\n Calculate metrics for each instance, and find their average.\n\n Will be ignored when ``y_true`` is binary.\n\n sample_weight : array-like of shape (n_samples,), default=None\n Sample weights.\n\n binary_metric : callable, returns shape [n_classes]\n The binary metric function to use.\n\n Returns\n -------\n score : float or array of shape [n_classes]\n If not ``None``, average the score, else return the score for each\n classes.\n\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[average_precision_score]", "sklearn/metrics/tests/test_common.py::test_sample_order_invariance[micro_average_precision_score]", 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"sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[average_precision_score]", "sklearn/neighbors/tests/test_lof.py::test_lof_performance[float64]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[micro_average_precision_score]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[micro_roc_auc]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[partial_roc_auc]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[roc_auc_score]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[samples_average_precision_score]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[samples_roc_auc]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[weighted_average_precision_score]", "sklearn/metrics/tests/test_common.py::test_thresholded_multilabel_multioutput_permutations_invariance[weighted_roc_auc]", "sklearn/metrics/tests/test_common.py::test_thresholded_metric_permutation_invariance[average_precision_score]", "sklearn/metrics/tests/test_common.py::test_thresholded_metric_permutation_invariance[micro_average_precision_score]", "sklearn/metrics/tests/test_common.py::test_thresholded_metric_permutation_invariance[ovr_roc_auc]", "sklearn/metrics/tests/test_common.py::test_thresholded_metric_permutation_invariance[samples_average_precision_score]", "sklearn/model_selection/tests/test_search.py::test_grid_search_correct_score_results", "sklearn/metrics/tests/test_common.py::test_thresholded_metric_permutation_invariance[weighted_average_precision_score]", "sklearn/metrics/tests/test_common.py::test_thresholded_metric_permutation_invariance[weighted_ovr_roc_auc]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[average_precision_score-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[micro_average_precision_score-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[micro_roc_auc-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[ovo_roc_auc-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[ovr_roc_auc-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[partial_roc_auc-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[roc_auc_score-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[samples_average_precision_score-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[samples_roc_auc-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[weighted_average_precision_score-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[weighted_ovo_roc_auc-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[weighted_ovr_roc_auc-pandas]", "sklearn/metrics/tests/test_common.py::test_metrics_dataframe_series[weighted_roc_auc-pandas]", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_display_curve_estimator_name_multiple_calls[from_estimator-PrecisionRecallDisplay]", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_display_curve_estimator_name_multiple_calls[from_predictions-PrecisionRecallDisplay]", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_display_curve_not_fitted_errors[PrecisionRecallDisplay-clf0]", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_display_curve_not_fitted_errors[PrecisionRecallDisplay-clf1]", "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_classifier_display_curve_named_constructor_return_type[from_predictions-PrecisionRecallDisplay]", "sklearn/metrics/_plot/tests/test_common_curve_display.py::test_classifier_display_curve_named_constructor_return_type[from_estimator-PrecisionRecallDisplay]", "sklearn/neural_network/tests/test_mlp.py::test_predict_proba_binary", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[True-predict_proba-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[True-predict_proba-from_predictions]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[True-decision_function-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[True-decision_function-from_predictions]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[False-predict_proba-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[False-predict_proba-from_predictions]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[False-decision_function-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_plotting[False-decision_function-from_predictions]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_chance_level_line[from_estimator-None]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_chance_level_line[from_estimator-chance_level_kw1]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_chance_level_line[from_predictions-None]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_chance_level_line[from_predictions-chance_level_kw1]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_name[from_estimator-LogisticRegression (AP = {:.2f})]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_name[from_predictions-Classifier (AP = {:.2f})]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_pipeline[clf0]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_pipeline[clf1]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_display_string_labels", "sklearn/ensemble/tests/test_iforest.py::test_iforest_performance[42]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[predict_proba-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[predict_proba-from_predictions]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[decision_function-from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_plot_precision_recall_pos_label[decision_function-from_predictions]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_prevalence_pos_label_reusable[from_estimator]", "sklearn/metrics/_plot/tests/test_precision_recall_display.py::test_precision_recall_prevalence_pos_label_reusable[from_predictions]", "sklearn/metrics/_ranking.py::sklearn.metrics._ranking.average_precision_score", "sklearn/metrics/_ranking.py::sklearn.metrics._ranking.roc_auc_score", "sklearn/metrics/_plot/precision_recall_curve.py::sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay.from_estimator", "sklearn/metrics/_plot/precision_recall_curve.py::sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay.from_predictions" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-211	1.0	{ "code": "diff --git b/sklearn/ensemble/_iforest.py a/sklearn/ensemble/_iforest.py\nindex f5dda981b..d7d9a06eb 100644\n--- b/sklearn/ensemble/_iforest.py\n+++ a/sklearn/ensemble/_iforest.py\n@@ -657,3 +657,22 @@ def _average_path_length(n_samples_leaf):\n -------\n average_path_length : ndarray of shape (n_samples,)\n \"\"\"\n+\n+ n_samples_leaf = check_array(n_samples_leaf, ensure_2d=False)\n+\n+ n_samples_leaf_shape = n_samples_leaf.shape\n+ n_samples_leaf = n_samples_leaf.reshape((1, -1))\n+ average_path_length = np.zeros(n_samples_leaf.shape)\n+\n+ mask_1 = n_samples_leaf <= 1\n+ mask_2 = n_samples_leaf == 2\n+ not_mask = ~np.logical_or(mask_1, mask_2)\n+\n+ average_path_length[mask_1] = 0.0\n+ average_path_length[mask_2] = 1.0\n+ average_path_length[not_mask] = (\n+ 2.0 * (np.log(n_samples_leaf[not_mask] - 1.0) + np.euler_gamma)\n+ - 2.0 * (n_samples_leaf[not_mask] - 1.0) / n_samples_leaf[not_mask]\n+ )\n+\n+ return average_path_length.reshape(n_samples_leaf_shape)\n", "test": null }	null	{ "code": "diff --git a/sklearn/ensemble/_iforest.py b/sklearn/ensemble/_iforest.py\nindex d7d9a06eb..f5dda981b 100644\n--- a/sklearn/ensemble/_iforest.py\n+++ b/sklearn/ensemble/_iforest.py\n@@ -657,22 +657,3 @@ def _average_path_length(n_samples_leaf):\n -------\n average_path_length : ndarray of shape (n_samples,)\n \"\"\"\n-\n- n_samples_leaf = check_array(n_samples_leaf, ensure_2d=False)\n-\n- n_samples_leaf_shape = n_samples_leaf.shape\n- n_samples_leaf = n_samples_leaf.reshape((1, -1))\n- average_path_length = np.zeros(n_samples_leaf.shape)\n-\n- mask_1 = n_samples_leaf <= 1\n- mask_2 = n_samples_leaf == 2\n- not_mask = ~np.logical_or(mask_1, mask_2)\n-\n- average_path_length[mask_1] = 0.0\n- average_path_length[mask_2] = 1.0\n- average_path_length[not_mask] = (\n- 2.0 * (np.log(n_samples_leaf[not_mask] - 1.0) + np.euler_gamma)\n- - 2.0 * (n_samples_leaf[not_mask] - 1.0) / n_samples_leaf[not_mask]\n- )\n-\n- return average_path_length.reshape(n_samples_leaf_shape)\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:\ndef _average_path_length(n_samples_leaf):\n \"\"\"\n The average path length in a n_samples iTree, which is equal to\n the average path length of an unsuccessful BST search since the\n latter has the same structure as an isolation tree.\n Parameters\n ----------\n n_samples_leaf : array-like of shape (n_samples,)\n The number of training samples in each test sample leaf, for\n each estimators.\n\n Returns\n -------\n average_path_length : ndarray of shape (n_samples,)\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_iforest_average_path_length", "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_dtype_object]", "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_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)]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-212	1.0	{ "code": "diff --git b/sklearn/inspection/_plot/decision_boundary.py a/sklearn/inspection/_plot/decision_boundary.py\nindex ae3b6ac61..d8be2ef5d 100644\n--- b/sklearn/inspection/_plot/decision_boundary.py\n+++ a/sklearn/inspection/_plot/decision_boundary.py\n@@ -43,6 +43,29 @@ def _check_boundary_response_method(estimator, response_method, class_of_interes\n prediction_method : list of str or str\n The name or list of names of the response methods to use.\n \"\"\"\n+ has_classes = hasattr(estimator, \"classes_\")\n+ if has_classes and _is_arraylike_not_scalar(estimator.classes_[0]):\n+ msg = \"Multi-label and multi-output multi-class classifiers are not supported\"\n+ raise ValueError(msg)\n+\n+ if has_classes and len(estimator.classes_) > 2:\n+ if response_method not in {\"auto\", \"predict\"} and class_of_interest is None:\n+ msg = (\n+ \"Multiclass classifiers are only supported when `response_method` is \"\n+ \"'predict' or 'auto'. Else you must provide `class_of_interest` to \"\n+ \"plot the decision boundary of a specific class.\"\n+ )\n+ raise ValueError(msg)\n+ prediction_method = \"predict\" if response_method == \"auto\" else response_method\n+ elif response_method == \"auto\":\n+ if is_regressor(estimator):\n+ prediction_method = \"predict\"\n+ else:\n+ prediction_method = [\"decision_function\", \"predict_proba\", \"predict\"]\n+ else:\n+ prediction_method = response_method\n+\n+ return prediction_method\n \n \n class DecisionBoundaryDisplay:\n", "test": null }	null	{ "code": "diff --git a/sklearn/inspection/_plot/decision_boundary.py b/sklearn/inspection/_plot/decision_boundary.py\nindex d8be2ef5d..ae3b6ac61 100644\n--- a/sklearn/inspection/_plot/decision_boundary.py\n+++ b/sklearn/inspection/_plot/decision_boundary.py\n@@ -43,29 +43,6 @@ def _check_boundary_response_method(estimator, response_method, class_of_interes\n prediction_method : list of str or str\n The name or list of names of the response methods to use.\n \"\"\"\n- has_classes = hasattr(estimator, \"classes_\")\n- if has_classes and _is_arraylike_not_scalar(estimator.classes_[0]):\n- msg = \"Multi-label and multi-output multi-class classifiers are not supported\"\n- raise ValueError(msg)\n-\n- if has_classes and len(estimator.classes_) > 2:\n- if response_method not in {\"auto\", \"predict\"} and class_of_interest is None:\n- msg = (\n- \"Multiclass classifiers are only supported when `response_method` is \"\n- \"'predict' or 'auto'. Else you must provide `class_of_interest` to \"\n- \"plot the decision boundary of a specific class.\"\n- )\n- raise ValueError(msg)\n- prediction_method = \"predict\" if response_method == \"auto\" else response_method\n- elif response_method == \"auto\":\n- if is_regressor(estimator):\n- prediction_method = \"predict\"\n- else:\n- prediction_method = [\"decision_function\", \"predict_proba\", \"predict\"]\n- else:\n- prediction_method = response_method\n-\n- return prediction_method\n \n \n class DecisionBoundaryDisplay:\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/inspection/_plot/decision_boundary.py.\nHere is the description for the function:\ndef _check_boundary_response_method(estimator, response_method, class_of_interest):\n \"\"\"Validate the response methods to be used with the fitted estimator.\n\n Parameters\n ----------\n estimator : object\n Fitted estimator to check.\n\n response_method : {'auto', 'predict_proba', 'decision_function', 'predict'}\n Specifies whether to use :term:`predict_proba`,\n :term:`decision_function`, :term:`predict` as the target response.\n If set to 'auto', the response method is tried in the following order:\n :term:`decision_function`, :term:`predict_proba`, :term:`predict`.\n\n class_of_interest : int, float, bool, str or None\n The class considered when plotting the decision. Cannot be None if\n multiclass and `response_method` is 'predict_proba' or 'decision_function'.\n\n .. versionadded:: 1.4\n\n Returns\n -------\n prediction_method : list of str or str\n The name or list of names of the response methods to use.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method_error", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator0-predict-None-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator1-auto-None-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator2-predict-None-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator3-auto-None-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator4-predict_proba-0-predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator5-decision_function-0-decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator6-auto-None-expected_prediction_method6]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator7-predict-None-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_check_boundary_response_method[estimator8-response_method8-None-expected_prediction_method8]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multiclass_error[predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multiclass_error[decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multiclass[auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multiclass[predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_display_plot_input_error", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contourf-auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contourf-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contourf-predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contourf-decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contour-auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contour-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contour-predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_classifier[contour-decision_function]", "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/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_regressor[contourf-auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_regressor[contourf-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_regressor[contour-auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_decision_boundary_display_regressor[contour-predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_error_bad_response[predict_proba-MyClassifier has none of the following attributes: predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_error_bad_response[decision_function-MyClassifier has none of the following attributes: decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_error_bad_response[auto-MyClassifier has none of the following attributes: decision_function, predict_proba, predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_error_bad_response[bad_method-MyClassifier has none of the following attributes: bad_method]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multilabel_classifier_error[auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multilabel_classifier_error[predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multilabel_classifier_error[predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multi_output_multi_class_classifier_error[auto]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multi_output_multi_class_classifier_error[predict]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multi_output_multi_class_classifier_error[predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_multioutput_regressor_error", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_regressor_unsupported_response[predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_regressor_unsupported_response[decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_regressor_unsupported_response[response_method2]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_dataframe_labels_used", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_string_target", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_dataframe_support[pandas]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_class_of_interest_binary[predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_class_of_interest_binary[decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_class_of_interest_multiclass[predict_proba]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_class_of_interest_multiclass[decision_function]", "sklearn/inspection/_plot/tests/test_boundary_decision_display.py::test_subclass_named_constructors_return_type_is_subclass", "sklearn/inspection/_plot/decision_boundary.py::sklearn.inspection._plot.decision_boundary.DecisionBoundaryDisplay.from_estimator" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-213	1.0	{ "code": "diff --git b/sklearn/inspection/_pd_utils.py a/sklearn/inspection/_pd_utils.py\nindex 11b20585e..a48ba4d9a 100644\n--- b/sklearn/inspection/_pd_utils.py\n+++ a/sklearn/inspection/_pd_utils.py\n@@ -21,6 +21,20 @@ def _check_feature_names(X, feature_names=None):\n or a generic list of feature names (e.g. `[\"x0\", \"x1\", ...]`) for a\n NumPy array.\n \"\"\"\n+ if feature_names is None:\n+ if hasattr(X, \"columns\") and hasattr(X.columns, \"tolist\"):\n+ # get the column names for a pandas dataframe\n+ feature_names = X.columns.tolist()\n+ else:\n+ # define a list of numbered indices for a numpy array\n+ feature_names = [f\"x{i}\" for i in range(X.shape[1])]\n+ elif hasattr(feature_names, \"tolist\"):\n+ # convert numpy array or pandas index to a list\n+ feature_names = feature_names.tolist()\n+ if len(set(feature_names)) != len(feature_names):\n+ raise ValueError(\"feature_names should not contain duplicates.\")\n+\n+ return feature_names\n \n \n def _get_feature_index(fx, feature_names=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/inspection/_pd_utils.py b/sklearn/inspection/_pd_utils.py\nindex a48ba4d9a..11b20585e 100644\n--- a/sklearn/inspection/_pd_utils.py\n+++ b/sklearn/inspection/_pd_utils.py\n@@ -21,20 +21,6 @@ def _check_feature_names(X, feature_names=None):\n or a generic list of feature names (e.g. `[\"x0\", \"x1\", ...]`) for a\n NumPy array.\n \"\"\"\n- if feature_names is None:\n- if hasattr(X, \"columns\") and hasattr(X.columns, \"tolist\"):\n- # get the column names for a pandas dataframe\n- feature_names = X.columns.tolist()\n- else:\n- # define a list of numbered indices for a numpy array\n- feature_names = [f\"x{i}\" for i in range(X.shape[1])]\n- elif hasattr(feature_names, \"tolist\"):\n- # convert numpy array or pandas index to a list\n- feature_names = feature_names.tolist()\n- if len(set(feature_names)) != len(feature_names):\n- raise ValueError(\"feature_names should not contain duplicates.\")\n-\n- return feature_names\n \n \n def _get_feature_index(fx, feature_names=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/inspection/_pd_utils.py.\nHere is the description for the function:\ndef _check_feature_names(X, feature_names=None):\n \"\"\"Check feature names.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Input data.\n\n feature_names : None or array-like of shape (n_names,), dtype=str\n Feature names to check or `None`.\n\n Returns\n -------\n feature_names : list of str\n Feature names validated. If `feature_names` is `None`, then a list of\n feature names is provided, i.e. the column names of a pandas dataframe\n or a generic list of feature names (e.g. `[\"x0\", \"x1\", ...]`) for a\n NumPy array.\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/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-features0-5-GradientBoostingRegressor-auto-data4]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-GradientBoostingRegressor-brute-data5]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-DecisionTreeRegressor-brute-data6]", "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-features0-5-LogisticRegression-brute-data9]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-5-LogisticRegression-brute-data10]", "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-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/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-features0-10-DecisionTreeRegressor-brute-data6]", "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-features0-10-LogisticRegression-brute-data9]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features0-10-LogisticRegression-brute-data10]", "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-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-GradientBoostingClassifier-auto-data1]", "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-features1-5-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-features1-5-DecisionTreeRegressor-brute-data6]", "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-features1-5-LogisticRegression-brute-data9]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-5-LogisticRegression-brute-data10]", "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-GradientBoostingClassifier-auto-data0]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-GradientBoostingClassifier-auto-data1]", "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[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[average-features1-10-DecisionTreeRegressor-brute-data6]", "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/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-LogisticRegression-brute-data9]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[average-features1-10-LogisticRegression-brute-data10]", "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-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-features0-5-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-features0-5-DecisionTreeRegressor-brute-data6]", "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/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-LogisticRegression-brute-data9]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-5-LogisticRegression-brute-data10]", "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-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-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-features0-10-DecisionTreeRegressor-brute-data6]", "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-features0-10-LogisticRegression-brute-data9]", "sklearn/inspection/tests/test_partial_dependence.py::test_output_shape[individual-features0-10-LogisticRegression-brute-data10]", "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-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]", 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"sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_overwrite_labels[both-line_kw5-xxx]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_grid_resolution_with_categorical[categorical_features0-dataframe]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_grid_resolution_with_categorical[categorical_features1-array]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_grid_resolution_with_categorical[categorical_features2-array]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_one_way[True-individual]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_one_way[True-average]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_one_way[True-both]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_one_way[False-individual]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_one_way[False-average]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_one_way[False-both]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_two_way[True]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_plot_limits_two_way[False]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_kind_list", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_kind_error[features0-individual]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_kind_error[features1-both]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_kind_error[features2-individual]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_kind_error[features3-both]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_kind_error[features4-kind4]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_kind_error[features5-kind5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_lines_kw[line_kw0-pd_line_kw0-ice_lines_kw0-expected_colors0]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_lines_kw[None-pd_line_kw1-ice_lines_kw1-expected_colors1]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_lines_kw[line_kw2-None-ice_lines_kw2-expected_colors2]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_lines_kw[line_kw3-pd_line_kw3-None-expected_colors3]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_lines_kw[line_kw4-None-None-expected_colors4]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_plot_partial_dependence_lines_kw[line_kw5-pd_line_kw5-ice_lines_kw5-expected_colors5]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_wrong_len_kind", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_kind_centered_interaction[individual]", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_kind_centered_interaction[both]", "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/_plot/tests/test_plot_partial_dependence.py::test_partial_dependence_display_with_constant_sample_weight", "sklearn/inspection/_plot/tests/test_plot_partial_dependence.py::test_subclass_named_constructors_return_type_is_subclass", "sklearn/inspection/tests/test_pd_utils.py::test_check_feature_names[None-array-expected_feature_names0]", "sklearn/inspection/tests/test_pd_utils.py::test_check_feature_names[None-dataframe-expected_feature_names1]", "sklearn/inspection/tests/test_pd_utils.py::test_check_feature_names[feature_names2-array-expected_feature_names2]", "sklearn/inspection/tests/test_pd_utils.py::test_check_feature_names_error", "sklearn/inspection/_plot/partial_dependence.py::sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay", "sklearn/inspection/_plot/partial_dependence.py::sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay.from_estimator" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-214	1.0	{ "code": "diff --git b/sklearn/model_selection/_validation.py a/sklearn/model_selection/_validation.py\nindex 73753bc6d..f412d0012 100644\n--- b/sklearn/model_selection/_validation.py\n+++ a/sklearn/model_selection/_validation.py\n@@ -1458,6 +1458,13 @@ def _check_is_permutation(indices, n_samples):\n is_partition : bool\n True iff sorted(indices) is np.arange(n)\n \"\"\"\n+ if len(indices) != n_samples:\n+ return False\n+ hit = np.zeros(n_samples, dtype=bool)\n+ hit[indices] = True\n+ if not np.all(hit):\n+ return False\n+ return True\n \n \n @validate_params(\n", "test": null }	null	{ "code": "diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py\nindex f412d0012..73753bc6d 100644\n--- a/sklearn/model_selection/_validation.py\n+++ b/sklearn/model_selection/_validation.py\n@@ -1458,13 +1458,6 @@ def _check_is_permutation(indices, n_samples):\n is_partition : bool\n True iff sorted(indices) is np.arange(n)\n \"\"\"\n- if len(indices) != n_samples:\n- return False\n- hit = np.zeros(n_samples, dtype=bool)\n- hit[indices] = True\n- if not np.all(hit):\n- return False\n- return True\n \n \n @validate_params(\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/_validation.py.\nHere is the description for the function:\ndef _check_is_permutation(indices, n_samples):\n \"\"\"Check whether indices is a reordering of the array np.arange(n_samples)\n\n Parameters\n ----------\n indices : ndarray\n int array to test\n n_samples : int\n number of expected elements\n\n Returns\n -------\n is_partition : bool\n True iff sorted(indices) is np.arange(n)\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict[coo_matrix]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict[coo_array]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_decision_function_shape", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_predict_proba_shape", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_predict_log_proba_shape", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_input_types[coo_matrix]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_input_types[coo_array]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_pandas", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_unbalanced", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_y_none", "sklearn/model_selection/tests/test_validation.py::test_check_is_permutation", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction[csr_matrix]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction[csr_array]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_with_method", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_method_checking", "sklearn/model_selection/tests/test_validation.py::test_gridsearchcv_cross_val_predict_with_method", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_with_method_multilabel_ovr", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_with_method_multilabel_rf", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_with_method_rare_class", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_with_method_multilabel_rf_rare_class", "sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset", "sklearn/model_selection/tests/test_validation.py::test_fit_param_deprecation[cross_val_predict-extra_args2]", "sklearn/preprocessing/tests/test_data.py::test_cv_pipeline_precomputed", "sklearn/model_selection/tests/test_validation.py::test_validation_functions_routing[cross_val_predict-extra_args2]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-asarray-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-8-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-asarray-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-asarray-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-csr_matrix-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-csr_matrix-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-csr_array-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape0-True-1.0-20-csr_array-eigen]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-8-asarray-svd]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_gcv_sample_weights[y_shape1-True-20.0-8-asarray-eigen]", 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"sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[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[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[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[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[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[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[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[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-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-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-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-StackingRegressor(cv=3,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-215	1.0	{ "code": "diff --git b/sklearn/utils/validation.py a/sklearn/utils/validation.py\nindex c00987eb1..401e72cef 100644\n--- b/sklearn/utils/validation.py\n+++ a/sklearn/utils/validation.py\n@@ -2237,6 +2237,27 @@ def _check_method_params(X, params, indices=None):\n method_params_validated : dict\n Validated parameters. We ensure that the values support indexing.\n \"\"\"\n+ from . import _safe_indexing\n+\n+ method_params_validated = {}\n+ for param_key, param_value in params.items():\n+ if (\n+ not _is_arraylike(param_value)\n+ and not sp.issparse(param_value)\n+ or _num_samples(param_value) != _num_samples(X)\n+ ):\n+ # Non-indexable pass-through (for now for backward-compatibility).\n+ # https://github.com/scikit-learn/scikit-learn/issues/15805\n+ method_params_validated[param_key] = param_value\n+ else:\n+ # Any other method_params should support indexing\n+ # (e.g. for cross-validation).\n+ method_params_validated[param_key] = _make_indexable(param_value)\n+ method_params_validated[param_key] = _safe_indexing(\n+ method_params_validated[param_key], indices\n+ )\n+\n+ return method_params_validated\n \n \n def _is_pandas_df_or_series(X):\n", "test": null }	null	{ "code": "diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py\nindex 401e72cef..c00987eb1 100644\n--- a/sklearn/utils/validation.py\n+++ b/sklearn/utils/validation.py\n@@ -2237,27 +2237,6 @@ def _check_method_params(X, params, indices=None):\n method_params_validated : dict\n Validated parameters. We ensure that the values support indexing.\n \"\"\"\n- from . import _safe_indexing\n-\n- method_params_validated = {}\n- for param_key, param_value in params.items():\n- if (\n- not _is_arraylike(param_value)\n- and not sp.issparse(param_value)\n- or _num_samples(param_value) != _num_samples(X)\n- ):\n- # Non-indexable pass-through (for now for backward-compatibility).\n- # https://github.com/scikit-learn/scikit-learn/issues/15805\n- method_params_validated[param_key] = param_value\n- else:\n- # Any other method_params should support indexing\n- # (e.g. for cross-validation).\n- method_params_validated[param_key] = _make_indexable(param_value)\n- method_params_validated[param_key] = _safe_indexing(\n- method_params_validated[param_key], indices\n- )\n-\n- return method_params_validated\n \n \n def _is_pandas_df_or_series(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/utils/validation.py.\nHere is the description for the function:\ndef _check_method_params(X, params, indices=None):\n \"\"\"Check and validate the parameters passed to a specific\n method like `fit`.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Data array.\n\n params : dict\n Dictionary containing the parameters passed to the method.\n\n indices : array-like of shape (n_samples,), default=None\n Indices to be selected if the parameter has the same size as `X`.\n\n Returns\n -------\n method_params_validated : dict\n Validated parameters. We ensure that the values support indexing.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/model_selection/tests/test_validation.py::test_cross_val_score[coo_matrix]", "sklearn/ensemble/tests/test_bagging.py::test_classification", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[fit-HalvingGridSearchCV]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_mock_scorer", "sklearn/model_selection/tests/test_validation.py::test_cross_val_score[coo_array]", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[fit-HalvingRandomSearchCV]", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params0-predict]", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[predict-HalvingGridSearchCV]", "sklearn/model_selection/tests/test_validation.py::test_cross_validate_many_jobs", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params1-predict_proba]", "sklearn/model_selection/tests/test_successive_halving.py::test_nan_handling[predict-HalvingRandomSearchCV]", "sklearn/model_selection/tests/test_search.py::test_grid_search", "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/model_selection/tests/test_successive_halving.py::test_aggressive_elimination[True-limited-4-4-3-1-expected_n_candidates0-expected_n_resources0-HalvingRandomSearchCV]", "sklearn/model_selection/tests/test_search.py::test_grid_search_pipeline_steps", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params2-predict_log_proba]", "sklearn/model_selection/tests/test_search.py::test_SearchCV_with_fit_params[GridSearchCV]", "sklearn/model_selection/tests/test_search.py::test_SearchCV_with_fit_params[RandomizedSearchCV]", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params3-decision_function]", "sklearn/model_selection/tests/test_search.py::test_grid_search_no_score", "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/model_selection/tests/test_search.py::test_grid_search_score_method", "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/metrics/tests/test_score_objects.py::test_check_scoring_gridsearchcv", "sklearn/model_selection/tests/test_search.py::test_grid_search_groups", "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/model_selection/tests/test_search.py::test_classes__property", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params4-predict]", "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/model_selection/tests/test_search.py::test_trivial_cv_results_attr", "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/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params5-predict_proba]", "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/model_selection/tests/test_search.py::test_no_refit", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params6-predict_log_proba]", "sklearn/model_selection/tests/test_successive_halving.py::test_min_max_resources[smallest-auto-2-4-expected_n_resources0-HalvingGridSearchCV]", "sklearn/model_selection/tests/test_successive_halving.py::test_min_max_resources[smallest-auto-2-4-expected_n_resources0-HalvingRandomSearchCV]", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params7-decision_function]", "sklearn/ensemble/tests/test_bagging.py::test_sparse_classification[csr_matrix-params8-predict]", "sklearn/model_selection/tests/test_search.py::test_grid_search_one_grid_point", 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"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]", 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"sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[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_check_n_features_in_after_fitting[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "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[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[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GraphicalLassoCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GridSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})]", "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()),('ridge',Ridge())]),param_grid={'ridge__alpha':[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=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=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()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[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=Ridge(),param_distributions={'alpha':[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()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[MultiOutputClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultiOutputRegressor(estimator=Ridge())]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsOneClassifier(estimator=LogisticRegression(C=1))]", "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[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomizedSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)]", "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()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[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[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "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[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[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "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[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[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=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[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-RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=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-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-RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=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-StackingRegressor(cv=3,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-216	1.0	{ "code": "diff --git b/sklearn/metrics/_scorer.py a/sklearn/metrics/_scorer.py\nindex eaa4e4797..f09b4e6d7 100644\n--- b/sklearn/metrics/_scorer.py\n+++ a/sklearn/metrics/_scorer.py\n@@ -546,6 +546,63 @@ def _check_multimetric_scoring(estimator, scoring):\n scorers_dict : dict\n A dict mapping each scorer name to its validated scorer.\n \"\"\"\n+ err_msg_generic = (\n+ f\"scoring is invalid (got {scoring!r}). Refer to the \"\n+ \"scoring glossary for details: \"\n+ \"https://scikit-learn.org/stable/glossary.html#term-scoring\"\n+ )\n+\n+ if isinstance(scoring, (list, tuple, set)):\n+ err_msg = (\n+ \"The list/tuple elements must be unique strings of predefined scorers. \"\n+ )\n+ try:\n+ keys = set(scoring)\n+ except TypeError as e:\n+ raise ValueError(err_msg) from e\n+\n+ if len(keys) != len(scoring):\n+ raise ValueError(\n+ f\"{err_msg} Duplicate elements were found in\"\n+ f\" the given list. {scoring!r}\"\n+ )\n+ elif len(keys) > 0:\n+ if not all(isinstance(k, str) for k in keys):\n+ if any(callable(k) for k in keys):\n+ raise ValueError(\n+ f\"{err_msg} One or more of the elements \"\n+ \"were callables. Use a dict of score \"\n+ \"name mapped to the scorer callable. \"\n+ f\"Got {scoring!r}\"\n+ )\n+ else:\n+ raise ValueError(\n+ f\"{err_msg} Non-string types were found \"\n+ f\"in the given list. Got {scoring!r}\"\n+ )\n+ scorers = {\n+ scorer: check_scoring(estimator, scoring=scorer) for scorer in scoring\n+ }\n+ else:\n+ raise ValueError(f\"{err_msg} Empty list was given. {scoring!r}\")\n+\n+ elif isinstance(scoring, dict):\n+ keys = set(scoring)\n+ if not all(isinstance(k, str) for k in keys):\n+ raise ValueError(\n+ \"Non-string types were found in the keys of \"\n+ f\"the given dict. scoring={scoring!r}\"\n+ )\n+ if len(keys) == 0:\n+ raise ValueError(f\"An empty dict was passed. {scoring!r}\")\n+ scorers = {\n+ key: check_scoring(estimator, scoring=scorer)\n+ for key, scorer in scoring.items()\n+ }\n+ else:\n+ raise ValueError(err_msg_generic)\n+\n+ return scorers\n \n \n def _get_response_method(response_method, needs_threshold, needs_proba):\n", "test": null }	null	{ "code": "diff --git a/sklearn/metrics/_scorer.py b/sklearn/metrics/_scorer.py\nindex f09b4e6d7..eaa4e4797 100644\n--- a/sklearn/metrics/_scorer.py\n+++ b/sklearn/metrics/_scorer.py\n@@ -546,63 +546,6 @@ def _check_multimetric_scoring(estimator, scoring):\n scorers_dict : dict\n A dict mapping each scorer name to its validated scorer.\n \"\"\"\n- err_msg_generic = (\n- f\"scoring is invalid (got {scoring!r}). Refer to the \"\n- \"scoring glossary for details: \"\n- \"https://scikit-learn.org/stable/glossary.html#term-scoring\"\n- )\n-\n- if isinstance(scoring, (list, tuple, set)):\n- err_msg = (\n- \"The list/tuple elements must be unique strings of predefined scorers. \"\n- )\n- try:\n- keys = set(scoring)\n- except TypeError as e:\n- raise ValueError(err_msg) from e\n-\n- if len(keys) != len(scoring):\n- raise ValueError(\n- f\"{err_msg} Duplicate elements were found in\"\n- f\" the given list. {scoring!r}\"\n- )\n- elif len(keys) > 0:\n- if not all(isinstance(k, str) for k in keys):\n- if any(callable(k) for k in keys):\n- raise ValueError(\n- f\"{err_msg} One or more of the elements \"\n- \"were callables. Use a dict of score \"\n- \"name mapped to the scorer callable. \"\n- f\"Got {scoring!r}\"\n- )\n- else:\n- raise ValueError(\n- f\"{err_msg} Non-string types were found \"\n- f\"in the given list. Got {scoring!r}\"\n- )\n- scorers = {\n- scorer: check_scoring(estimator, scoring=scorer) for scorer in scoring\n- }\n- else:\n- raise ValueError(f\"{err_msg} Empty list was given. {scoring!r}\")\n-\n- elif isinstance(scoring, dict):\n- keys = set(scoring)\n- if not all(isinstance(k, str) for k in keys):\n- raise ValueError(\n- \"Non-string types were found in the keys of \"\n- f\"the given dict. scoring={scoring!r}\"\n- )\n- if len(keys) == 0:\n- raise ValueError(f\"An empty dict was passed. {scoring!r}\")\n- scorers = {\n- key: check_scoring(estimator, scoring=scorer)\n- for key, scorer in scoring.items()\n- }\n- else:\n- raise ValueError(err_msg_generic)\n-\n- return scorers\n \n \n def _get_response_method(response_method, needs_threshold, needs_proba):\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/metrics/_scorer.py.\nHere is the description for the function:\ndef _check_multimetric_scoring(estimator, scoring):\n \"\"\"Check the scoring parameter in cases when multiple metrics are allowed.\n\n In addition, multimetric scoring leverages a caching mechanism to not call the same\n estimator response method multiple times. Hence, the scorer is modified to only use\n a single response method given a list of response methods and the estimator.\n\n Parameters\n ----------\n estimator : sklearn estimator instance\n The estimator for which the scoring will be applied.\n\n scoring : list, tuple or dict\n Strategy to evaluate the performance of the cross-validated model on\n the test set.\n\n The possibilities are:\n\n - a list or tuple of unique strings;\n - a callable returning a dictionary where they keys are the metric\n names and the values are the metric scores;\n - a dictionary with metric names as keys and callables a values.\n\n See :ref:`multimetric_grid_search` for an example.\n\n Returns\n -------\n scorers_dict : dict\n A dict mapping each scorer name to its validated scorer.\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_check_scoring_and_check_multimetric_scoring_errors[tuple of callables]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring_errors[list of int]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring_errors[tuple of one callable]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring_errors[empty tuple]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring_errors[non-unique str]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring_errors[non-string key dict]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_and_check_multimetric_scoring_errors[empty dict]", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_gridsearchcv", "sklearn/metrics/tests/test_score_objects.py::test_raises_on_score_list", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_calls_method_once[scorers0-1-1-1]", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_calls_method_once[scorers1-1-0-1]", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_calls_method_once[scorers2-1-1-0]", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_calls_method_once_classifier_no_decision[scorers0]", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_calls_method_once_classifier_no_decision[scorers1]", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_calls_method_once_regressor_threshold", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_sanity_check", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_exception_handling[True]", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_exception_handling[False]", "sklearn/model_selection/tests/test_split.py::test_kfold_can_detect_dependent_samples_on_digits", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring0]", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring1]", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring2]", "sklearn/metrics/tests/test_classification.py::test_classification_metric_division_by_zero_nan_validaton[scoring3]", "sklearn/model_selection/tests/test_split.py::test_nested_cv", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scoring_metadata_routing", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_cross_validation", "sklearn/metrics/tests/test_score_objects.py::test_get_scorer_multimetric[True]", "sklearn/metrics/tests/test_score_objects.py::test_get_scorer_multimetric[False]", "sklearn/metrics/tests/test_score_objects.py::test_multimetric_scorer_repr", "sklearn/metrics/tests/test_score_objects.py::test_check_scoring_multimetric_raise_exc", "sklearn/linear_model/tests/test_logistic.py::test_scores_attribute_layout_elasticnet", "sklearn/tree/tests/test_tree.py::test_missing_value_is_predictive[42-DecisionTreeClassifier-0.85]", "sklearn/tree/tests/test_tree.py::test_missing_value_is_predictive[42-ExtraTreeClassifier-0.53]", "sklearn/feature_extraction/tests/test_text.py::test_vectorizer_pipeline_cross_validation", "sklearn/model_selection/tests/test_validation.py::test_cross_val_score[coo_matrix]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_score[coo_array]", "sklearn/model_selection/tests/test_validation.py::test_cross_validate_invalid_scoring_param", "sklearn/model_selection/tests/test_validation.py::test_cross_validate[csr_matrix-False]", "sklearn/model_selection/tests/test_validation.py::test_cross_validate[csr_matrix-True]", "sklearn/model_selection/tests/test_search.py::test_no_refit", "sklearn/model_selection/tests/test_validation.py::test_cross_validate[csr_array-False]", "sklearn/model_selection/tests/test_validation.py::test_cross_validate[csr_array-True]", "sklearn/model_selection/tests/test_validation.py::test_cross_val_score_predict_groups", 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"sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_alphas_iterable[array]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_pipeline_consistency]", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_scores", "sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv_scores_with_routing[42]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_estimators_nan_inf]", "sklearn/ensemble/tests/test_forest.py::test_read_only_buffer[csr_matrix]", "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/ensemble/tests/test_forest.py::test_read_only_buffer[csr_array]", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_f_contiguous_array_estimator]", "sklearn/model_selection/_validation.py::sklearn.model_selection._validation.cross_val_score", "sklearn/model_selection/_validation.py::sklearn.model_selection._validation.cross_validate", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_methods_sample_order_invariance]", "sklearn/metrics/_scorer.py::sklearn.metrics._scorer.check_scoring", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_multi_metric[list_single_scorer0-multi_scorer0]", "sklearn/covariance/_graph_lasso.py::sklearn.covariance._graph_lasso.GraphicalLassoCV", "sklearn/tests/test_common.py::test_estimators[GraphicalLassoCV(cv=3,max_iter=5)-check_methods_subset_invariance]", "sklearn/inspection/tests/test_permutation_importance.py::test_permutation_importance_multi_metric[list_single_scorer1-multi_scorer1]", "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/feature_selection/_sequential.py::sklearn.feature_selection._sequential.SequentialFeatureSelector", "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_calibration.py::test_calibrated_classifier_cv_works_with_large_confidence_scores[42]", "sklearn/model_selection/tests/test_validation.py::test_passed_unrequested_metadata[cross_val_score-extra_args1]", "sklearn/model_selection/tests/test_validation.py::test_validation_functions_routing[cross_validate-extra_args0]", "sklearn/model_selection/tests/test_validation.py::test_validation_functions_routing[cross_val_score-extra_args1]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[SequentialFeatureSelector]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[SequentialFeatureSelector]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_scorer[SequentialFeatureSelector]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[GraphicalLassoCV]", "sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[SequentialFeatureSelector]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit_score_takes_y]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_overwrite_params]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_dtypes]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_fit_returns_self(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_dtype_object]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_pipeline_consistency]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_nan_inf]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_array]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimator_sparse_matrix]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_estimators_pickle(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_f_contiguous_array_estimator]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_data_not_an_array]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_general]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_preserve_dtypes]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_transformer_general(readonly_memmap=True)]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_methods_sample_order_invariance]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_methods_subset_invariance]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_1sample]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_dict_unchanged]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_dont_overwrite_parameters]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit_idempotent]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit_check_is_fitted]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_n_features_in]", "sklearn/tests/test_common.py::test_estimators[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))-check_fit2d_predict1d]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[GraphicalLassoCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GraphicalLassoCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform[SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SequentialFeatureSelector(cv=3,estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-217	1.0	{ "code": "diff --git b/sklearn/neighbors/_base.py a/sklearn/neighbors/_base.py\nindex 2f8d555a8..e8647d1a1 100644\n--- b/sklearn/neighbors/_base.py\n+++ a/sklearn/neighbors/_base.py\n@@ -166,6 +166,27 @@ def _check_precomputed(X):\n Distance matrix to other samples. X may be a sparse matrix, in which\n case only non-zero elements may be considered neighbors.\n \"\"\"\n+ if not issparse(X):\n+ X = check_array(X, ensure_non_negative=True, input_name=\"X\")\n+ return X\n+ else:\n+ graph = X\n+\n+ if graph.format not in (\"csr\", \"csc\", \"coo\", \"lil\"):\n+ raise TypeError(\n+ \"Sparse matrix in {!r} format is not supported due to \"\n+ \"its handling of explicit zeros\".format(graph.format)\n+ )\n+ copied = graph.format != \"csr\"\n+ graph = check_array(\n+ graph,\n+ accept_sparse=\"csr\",\n+ ensure_non_negative=True,\n+ input_name=\"precomputed distance matrix\",\n+ )\n+ graph = sort_graph_by_row_values(graph, copy=not copied, warn_when_not_sorted=True)\n+\n+ return graph\n \n \n @validate_params(\n", "test": null }	null	{ "code": "diff --git a/sklearn/neighbors/_base.py b/sklearn/neighbors/_base.py\nindex e8647d1a1..2f8d555a8 100644\n--- a/sklearn/neighbors/_base.py\n+++ b/sklearn/neighbors/_base.py\n@@ -166,27 +166,6 @@ def _check_precomputed(X):\n Distance matrix to other samples. X may be a sparse matrix, in which\n case only non-zero elements may be considered neighbors.\n \"\"\"\n- if not issparse(X):\n- X = check_array(X, ensure_non_negative=True, input_name=\"X\")\n- return X\n- else:\n- graph = X\n-\n- if graph.format not in (\"csr\", \"csc\", \"coo\", \"lil\"):\n- raise TypeError(\n- \"Sparse matrix in {!r} format is not supported due to \"\n- \"its handling of explicit zeros\".format(graph.format)\n- )\n- copied = graph.format != \"csr\"\n- graph = check_array(\n- graph,\n- accept_sparse=\"csr\",\n- ensure_non_negative=True,\n- input_name=\"precomputed distance matrix\",\n- )\n- graph = sort_graph_by_row_values(graph, copy=not copied, warn_when_not_sorted=True)\n-\n- return graph\n \n \n @validate_params(\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/_base.py.\nHere is the description for the function:\ndef _check_precomputed(X):\n \"\"\"Check precomputed distance matrix.\n\n If the precomputed distance matrix is sparse, it checks that the non-zero\n entries are sorted by distances. If not, the matrix is copied and sorted.\n\n Parameters\n ----------\n X : {sparse matrix, array-like}, (n_samples, n_samples)\n Distance matrix to other samples. X may be a sparse matrix, in which\n case only non-zero elements may be considered neighbors.\n\n Returns\n -------\n X : {sparse matrix, array-like}, (n_samples, n_samples)\n Distance matrix to other samples. X may be a sparse matrix, in which\n case only non-zero elements may be considered neighbors.\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "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_precomputed_sparse_radius[csr]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_sparse_radius[lil]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values[csr_matrix-_check_precomputed]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values[csr_array-_check_precomputed]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_copy[csr_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_copy[csr_array]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_warning[csr_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_warning[csr_array]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_bad_sparse_format[dok_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_bad_sparse_format[dok_array]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_bad_sparse_format[bsr_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_bad_sparse_format[bsr_array]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_bad_sparse_format[dia_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_sort_graph_by_row_values_bad_sparse_format[dia_array]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_sparse_invalid[csr_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_sparse_invalid[csr_array]", "sklearn/neighbors/tests/test_neighbors.py::test_precomputed_cross_validation", "sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_returns_array_of_objects[csr_matrix]", "sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_returns_array_of_objects[csr_array]", "sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_sort_results[brute-precomputed]", "sklearn/neighbors/tests/test_neighbors.py::test_kneighbors_regressor_sparse", "sklearn/neighbors/tests/test_neighbors.py::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-precomputed]", "sklearn/neighbors/tests/test_neighbors.py::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-precomputed]", "sklearn/neighbors/tests/test_neighbors.py::test_pipeline_with_nearest_neighbors_transformer", "sklearn/neighbors/tests/test_neighbors.py::test_auto_algorithm[X0-precomputed-None-brute]", "sklearn/neighbors/tests/test_lof.py::test_lof_precomputed[float64]", "sklearn/manifold/tests/test_isomap.py::test_pipeline_with_nearest_neighbors_transformer[float64]", "sklearn/manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately_with_precomputed_distances", "sklearn/manifold/tests/test_isomap.py::test_isomap_fit_precomputed_radius_graph[float64]", "sklearn/manifold/tests/test_isomap.py::test_multiple_connected_components_metric_precomputed[float64]", "sklearn/manifold/tests/test_t_sne.py::test_high_perplexity_precomputed_sparse_distances[csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_high_perplexity_precomputed_sparse_distances[csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[csr_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[csr_array]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[lil_matrix]", "sklearn/manifold/tests/test_t_sne.py::test_sparse_precomputed_distance[lil_array]", "sklearn/manifold/tests/test_t_sne.py::test_init_ndarray_precomputed", "sklearn/manifold/tests/test_t_sne.py::test_tsne_with_different_distance_metrics[barnes_hut-cosine-cosine_distances]", "sklearn/manifold/tests/test_t_sne.py::test_tsne_with_mahalanobis_distance", "sklearn/cluster/tests/test_optics.py::test_precomputed_dists[float64-None]", "sklearn/cluster/tests/test_optics.py::test_precomputed_dists[float64-csr_matrix]", "sklearn/cluster/tests/test_optics.py::test_precomputed_dists[float64-csr_array]", "sklearn/cluster/tests/test_optics.py::test_optics_input_not_modified_precomputed_sparse_nodiag[csr_matrix]", "sklearn/cluster/tests/test_optics.py::test_optics_input_not_modified_precomputed_sparse_nodiag[csr_array]", "sklearn/cluster/tests/test_spectral.py::test_precomputed_nearest_neighbors_filtering", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_similarity", "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_array-precomputed]", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_input_not_modified[None-precomputed]", "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_balltree", "sklearn/cluster/tests/test_dbscan.py::test_weighted_dbscan[42]", "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/manifold/tests/test_spectral_embedding.py::test_precomputed_nearest_neighbors_filtering", "sklearn/cluster/tests/test_dbscan.py::test_dbscan_precomputed_metric_with_initial_rows_zero[csr_array]", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_dbscan", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_isomap", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_tsne", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_lof_novelty_false", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_lof_novelty_true", "sklearn/neighbors/_graph.py::sklearn.neighbors._graph.RadiusNeighborsTransformer", "sklearn/neighbors/tests/test_neighbors_pipeline.py::test_kneighbors_regressor", "sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_pairwise" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-218	1.0	{ "code": "diff --git b/sklearn/utils/validation.py a/sklearn/utils/validation.py\nindex 970f5929c..401e72cef 100644\n--- b/sklearn/utils/validation.py\n+++ a/sklearn/utils/validation.py\n@@ -1966,6 +1966,96 @@ def _check_psd_eigenvalues(lambdas, enable_warnings=False):\n \n \"\"\"\n \n+ lambdas = np.array(lambdas)\n+ is_double_precision = lambdas.dtype == np.float64\n+\n+ # note: the minimum value available is\n+ # - single-precision: np.finfo('float32').eps = 1.2e-07\n+ # - double-precision: np.finfo('float64').eps = 2.2e-16\n+\n+ # the various thresholds used for validation\n+ # we may wish to change the value according to precision.\n+ significant_imag_ratio = 1e-5\n+ significant_neg_ratio = 1e-5 if is_double_precision else 5e-3\n+ significant_neg_value = 1e-10 if is_double_precision else 1e-6\n+ small_pos_ratio = 1e-12 if is_double_precision else 2e-7\n+\n+ # Check that there are no significant imaginary parts\n+ if not np.isreal(lambdas).all():\n+ max_imag_abs = np.abs(np.imag(lambdas)).max()\n+ max_real_abs = np.abs(np.real(lambdas)).max()\n+ if max_imag_abs > significant_imag_ratio * max_real_abs:\n+ raise ValueError(\n+ \"There are significant imaginary parts in eigenvalues (%g \"\n+ \"of the maximum real part). Either the matrix is not PSD, or \"\n+ \"there was an issue while computing the eigendecomposition \"\n+ \"of the matrix.\" % (max_imag_abs / max_real_abs)\n+ )\n+\n+ # warn about imaginary parts being removed\n+ if enable_warnings:\n+ warnings.warn(\n+ \"There are imaginary parts in eigenvalues (%g \"\n+ \"of the maximum real part). Either the matrix is not\"\n+ \" PSD, or there was an issue while computing the \"\n+ \"eigendecomposition of the matrix. Only the real \"\n+ \"parts will be kept.\" % (max_imag_abs / max_real_abs),\n+ PositiveSpectrumWarning,\n+ )\n+\n+ # Remove all imaginary parts (even if zero)\n+ lambdas = np.real(lambdas)\n+\n+ # Check that there are no significant negative eigenvalues\n+ max_eig = lambdas.max()\n+ if max_eig < 0:\n+ raise ValueError(\n+ \"All eigenvalues are negative (maximum is %g). \"\n+ \"Either the matrix is not PSD, or there was an \"\n+ \"issue while computing the eigendecomposition of \"\n+ \"the matrix.\" % max_eig\n+ )\n+\n+ else:\n+ min_eig = lambdas.min()\n+ if (\n+ min_eig < -significant_neg_ratio * max_eig\n+ and min_eig < -significant_neg_value\n+ ):\n+ raise ValueError(\n+ \"There are significant negative eigenvalues (%g\"\n+ \" of the maximum positive). Either the matrix is \"\n+ \"not PSD, or there was an issue while computing \"\n+ \"the eigendecomposition of the matrix.\" % (-min_eig / max_eig)\n+ )\n+ elif min_eig < 0:\n+ # Remove all negative values and warn about it\n+ if enable_warnings:\n+ warnings.warn(\n+ \"There are negative eigenvalues (%g of the \"\n+ \"maximum positive). Either the matrix is not \"\n+ \"PSD, or there was an issue while computing the\"\n+ \" eigendecomposition of the matrix. Negative \"\n+ \"eigenvalues will be replaced with 0.\" % (-min_eig / max_eig),\n+ PositiveSpectrumWarning,\n+ )\n+ lambdas[lambdas < 0] = 0\n+\n+ # Check for conditioning (small positive non-zeros)\n+ too_small_lambdas = (0 < lambdas) & (lambdas < small_pos_ratio * max_eig)\n+ if too_small_lambdas.any():\n+ if enable_warnings:\n+ warnings.warn(\n+ \"Badly conditioned PSD matrix spectrum: the largest \"\n+ \"eigenvalue is more than %g times the smallest. \"\n+ \"Small eigenvalues will be replaced with 0.\"\n+ \"\" % (1 / small_pos_ratio),\n+ PositiveSpectrumWarning,\n+ )\n+ lambdas[too_small_lambdas] = 0\n+\n+ return lambdas\n+\n \n def _check_sample_weight(\n sample_weight, X, dtype=None, copy=False, ensure_non_negative=False\n", "test": null }	null	{ "code": "diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py\nindex 401e72cef..970f5929c 100644\n--- a/sklearn/utils/validation.py\n+++ b/sklearn/utils/validation.py\n@@ -1966,96 +1966,6 @@ def _check_psd_eigenvalues(lambdas, enable_warnings=False):\n \n \"\"\"\n \n- lambdas = np.array(lambdas)\n- is_double_precision = lambdas.dtype == np.float64\n-\n- # note: the minimum value available is\n- # - single-precision: np.finfo('float32').eps = 1.2e-07\n- # - double-precision: np.finfo('float64').eps = 2.2e-16\n-\n- # the various thresholds used for validation\n- # we may wish to change the value according to precision.\n- significant_imag_ratio = 1e-5\n- significant_neg_ratio = 1e-5 if is_double_precision else 5e-3\n- significant_neg_value = 1e-10 if is_double_precision else 1e-6\n- small_pos_ratio = 1e-12 if is_double_precision else 2e-7\n-\n- # Check that there are no significant imaginary parts\n- if not np.isreal(lambdas).all():\n- max_imag_abs = np.abs(np.imag(lambdas)).max()\n- max_real_abs = np.abs(np.real(lambdas)).max()\n- if max_imag_abs > significant_imag_ratio * max_real_abs:\n- raise ValueError(\n- \"There are significant imaginary parts in eigenvalues (%g \"\n- \"of the maximum real part). Either the matrix is not PSD, or \"\n- \"there was an issue while computing the eigendecomposition \"\n- \"of the matrix.\" % (max_imag_abs / max_real_abs)\n- )\n-\n- # warn about imaginary parts being removed\n- if enable_warnings:\n- warnings.warn(\n- \"There are imaginary parts in eigenvalues (%g \"\n- \"of the maximum real part). Either the matrix is not\"\n- \" PSD, or there was an issue while computing the \"\n- \"eigendecomposition of the matrix. Only the real \"\n- \"parts will be kept.\" % (max_imag_abs / max_real_abs),\n- PositiveSpectrumWarning,\n- )\n-\n- # Remove all imaginary parts (even if zero)\n- lambdas = np.real(lambdas)\n-\n- # Check that there are no significant negative eigenvalues\n- max_eig = lambdas.max()\n- if max_eig < 0:\n- raise ValueError(\n- \"All eigenvalues are negative (maximum is %g). \"\n- \"Either the matrix is not PSD, or there was an \"\n- \"issue while computing the eigendecomposition of \"\n- \"the matrix.\" % max_eig\n- )\n-\n- else:\n- min_eig = lambdas.min()\n- if (\n- min_eig < -significant_neg_ratio * max_eig\n- and min_eig < -significant_neg_value\n- ):\n- raise ValueError(\n- \"There are significant negative eigenvalues (%g\"\n- \" of the maximum positive). Either the matrix is \"\n- \"not PSD, or there was an issue while computing \"\n- \"the eigendecomposition of the matrix.\" % (-min_eig / max_eig)\n- )\n- elif min_eig < 0:\n- # Remove all negative values and warn about it\n- if enable_warnings:\n- warnings.warn(\n- \"There are negative eigenvalues (%g of the \"\n- \"maximum positive). Either the matrix is not \"\n- \"PSD, or there was an issue while computing the\"\n- \" eigendecomposition of the matrix. Negative \"\n- \"eigenvalues will be replaced with 0.\" % (-min_eig / max_eig),\n- PositiveSpectrumWarning,\n- )\n- lambdas[lambdas < 0] = 0\n-\n- # Check for conditioning (small positive non-zeros)\n- too_small_lambdas = (0 < lambdas) & (lambdas < small_pos_ratio * max_eig)\n- if too_small_lambdas.any():\n- if enable_warnings:\n- warnings.warn(\n- \"Badly conditioned PSD matrix spectrum: the largest \"\n- \"eigenvalue is more than %g times the smallest. \"\n- \"Small eigenvalues will be replaced with 0.\"\n- \"\" % (1 / small_pos_ratio),\n- PositiveSpectrumWarning,\n- )\n- lambdas[too_small_lambdas] = 0\n-\n- return lambdas\n-\n \n def _check_sample_weight(\n sample_weight, X, dtype=None, copy=False, ensure_non_negative=False\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/utils/validation.py.\nHere is the description for the function:\ndef _check_psd_eigenvalues(lambdas, enable_warnings=False):\n \"\"\"Check the eigenvalues of a positive semidefinite (PSD) matrix.\n\n Checks the provided array of PSD matrix eigenvalues for numerical or\n conditioning issues and returns a fixed validated version. This method\n should typically be used if the PSD matrix is user-provided (e.g. a\n Gram matrix) or computed using a user-provided dissimilarity metric\n (e.g. kernel function), or if the decomposition process uses approximation\n methods (randomized SVD, etc.).\n\n It checks for three things:\n\n - that there are no significant imaginary parts in eigenvalues (more than\n 1e-5 times the maximum real part). If this check fails, it raises a\n ``ValueError``. Otherwise all non-significant imaginary parts that may\n remain are set to zero. This operation is traced with a\n ``PositiveSpectrumWarning`` when ``enable_warnings=True``.\n\n - that eigenvalues are not all negative. If this check fails, it raises a\n ``ValueError``\n\n - that there are no significant negative eigenvalues with absolute value\n more than 1e-10 (1e-6) and more than 1e-5 (5e-3) times the largest\n positive eigenvalue in double (simple) precision. If this check fails,\n it raises a ``ValueError``. Otherwise all negative eigenvalues that may\n remain are set to zero. This operation is traced with a\n ``PositiveSpectrumWarning`` when ``enable_warnings=True``.\n\n Finally, all the positive eigenvalues that are too small (with a value\n smaller than the maximum eigenvalue multiplied by 1e-12 (2e-7)) are set to\n zero. This operation is traced with a ``PositiveSpectrumWarning`` when\n ``enable_warnings=True``.\n\n Parameters\n ----------\n lambdas : array-like of shape (n_eigenvalues,)\n Array of eigenvalues to check / fix.\n\n enable_warnings : bool, default=False\n When this is set to ``True``, a ``PositiveSpectrumWarning`` will be\n raised when there are imaginary parts, negative eigenvalues, or\n extremely small non-zero eigenvalues. Otherwise no warning will be\n raised. In both cases, imaginary parts, negative eigenvalues, and\n extremely small non-zero eigenvalues will be set to zero.\n\n Returns\n -------\n lambdas_fixed : ndarray of shape (n_eigenvalues,)\n A fixed validated copy of the array of eigenvalues.\n\n Examples\n --------\n >>> from sklearn.utils.validation import _check_psd_eigenvalues\n >>> _check_psd_eigenvalues([1, 2]) # nominal case\n array([1, 2])\n >>> _check_psd_eigenvalues([5, 5j]) # significant imag part\n Traceback (most recent call last):\n ...\n ValueError: There are significant imaginary parts in eigenvalues (1\n of the maximum real part). Either the matrix is not PSD, or there was\n an issue while computing the eigendecomposition of the matrix.\n >>> _check_psd_eigenvalues([5, 5e-5j]) # insignificant imag part\n array([5., 0.])\n >>> _check_psd_eigenvalues([-5, -1]) # all negative\n Traceback (most recent call last):\n ...\n ValueError: All eigenvalues are negative (maximum is -1). Either the\n matrix is not PSD, or there was an issue while computing the\n eigendecomposition of the matrix.\n >>> _check_psd_eigenvalues([5, -1]) # significant negative\n Traceback (most recent call last):\n ...\n ValueError: There are significant negative eigenvalues (0.2 of the\n maximum positive). Either the matrix is not PSD, or there was an issue\n while computing the eigendecomposition of the matrix.\n >>> _check_psd_eigenvalues([5, -5e-5]) # insignificant negative\n array([5., 0.])\n >>> _check_psd_eigenvalues([5, 4e-12]) # bad conditioning (too small)\n array([5., 0.])\n\n \"\"\"\n", "test": null }	c71340fd74280408b84be7ca008e1205e10c7830	{ "FAIL_TO_PASS": [ "sklearn/_loss/tests/test_loss.py::test_binomial_vs_alternative_formulation[float32-y_pred0-y_true0]", "sklearn/_loss/tests/test_loss.py::test_binomial_vs_alternative_formulation[float32-y_pred0-y_true1]", "sklearn/_loss/tests/test_loss.py::test_binomial_vs_alternative_formulation[float32-y_pred1-y_true0]", "sklearn/_loss/tests/test_loss.py::test_binomial_vs_alternative_formulation[float32-y_pred1-y_true1]", "sklearn/cluster/tests/test_affinity_propagation.py::test_affinity_propagation[float32-42]", "sklearn/cluster/tests/test_affinity_propagation.py::test_affinity_propagation_predict[float32-42]", 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"sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_read_only_axis_1[1-expected_array1-series-polars-True-True]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_read_only_axis_1[1-expected_array1-series-polars-True-False]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_read_only_axis_1[1-expected_array1-series-polars-False-True]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_read_only_axis_1[1-expected_array1-series-polars-False-False]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_1d_container_mask[list-polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_1d_container_mask[tuple-polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_1d_container_mask[array-polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_1d_container_mask[series-polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[0-expected_subset0-list-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[0-expected_subset0-tuple-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[0-expected_subset0-array-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[0-expected_subset0-series-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[1-expected_subset1-list-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[1-expected_subset1-tuple-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[1-expected_subset1-array-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_mask[1-expected_subset1-series-polars]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_scalar_axis_0[polars-polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_1d_scalar[polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_scalar_axis_1[2-polars-polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_2d_scalar_axis_1[col_2-polars-polars_series]", "sklearn/utils/tests/test_indexing.py::test_safe_indexing_1d_array_error[polars_series]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[numpy-None-None]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[array_api_strict-None-None]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[cupy-None-None]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[torch-cpu-float64]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[torch-cpu-float32]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[torch-cuda-float64]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[torch-cuda-float32]", "sklearn/utils/tests/test_multiclass.py::test_is_multilabel_array_api_compliance[torch-mps-float32]", "sklearn/utils/tests/test_set_output.py::test_polars_adapter", "sklearn/utils/tests/test_set_output.py::test_set_output_method[polars]", "sklearn/utils/tests/test_set_output.py::test_set_output_list_input[polars]", "sklearn/utils/tests/test_testing.py::test_check_docstring_parameters", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency_object_type", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency_arg_checks[objects0-kwargs0-The 'exclude_params' argument]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency_arg_checks[objects1-kwargs1-The 'exclude_returns' argument]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency[whitespace]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency[incl_all]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency[2-1 group]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency[1-1-1 group]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency[empty type]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency[skip warn]", "sklearn/utils/tests/test_testing.py::test_assert_docstring_consistency_error_msg", "sklearn/utils/tests/test_testing.py::test_convert_container_categories_polars", "sklearn/utils/tests/test_testing.py::test_convert_container_categories_pyarrow", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[int32-sparse_csr_array]", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[int32-sparse_csc_array]", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[int64-sparse_csr_array]", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[int64-sparse_csc_array]", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[float32-sparse_csr_array]", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[float32-sparse_csc_array]", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[float64-sparse_csr_array]", "sklearn/utils/tests/test_testing.py::test_convert_container_raise_when_sparray_not_available[float64-sparse_csc_array]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[True-nominal]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[True-nominal_np_array]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[True-insignificant_imag]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[True-insignificant neg]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[True-insignificant neg float32]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[True-insignificant neg float64]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[True-insignificant pos]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[False-nominal]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[False-nominal_np_array]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[False-insignificant_imag]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[False-insignificant neg]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[False-insignificant neg float32]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[False-insignificant neg float64]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_valid[False-insignificant pos]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_invalid[significant_imag]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_invalid[all negative]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_invalid[significant neg]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_invalid[significant neg float32]", "sklearn/utils/tests/test_validation.py::test_check_psd_eigenvalues_invalid[significant neg float64]", "sklearn/utils/tests/test_validation.py::test_get_feature_names_dataframe_protocol[pyarrow-12.0.0]", "sklearn/utils/tests/test_validation.py::test_get_feature_names_dataframe_protocol[polars-0.18.2]", "sklearn/utils/tests/test_validation.py::test_is_pandas_df_other_libraries[pyarrow]", "sklearn/utils/tests/test_validation.py::test_is_pandas_df_other_libraries[polars]", "sklearn/utils/tests/test_validation.py::test_is_polars_df_other_libraries[pyarrow-12.0.0]", "sklearn/utils/tests/test_validation.py::test_is_polars_df_other_libraries[polars-0.20.30]", "sklearn/utils/tests/test_validation.py::test_check_array_array_api_has_non_finite", "sklearn/utils/tests/test_validation.py::test_num_samples_dataframe_protocol", "sklearn/utils/tests/test_validation.py::test_check_array_on_sparse_inputs_with_array_api_enabled" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-219	1.0	{ "code": "diff --git b/sklearn/utils/validation.py a/sklearn/utils/validation.py\nindex c0274bcb7..401e72cef 100644\n--- b/sklearn/utils/validation.py\n+++ a/sklearn/utils/validation.py\n@@ -2201,6 +2201,20 @@ def _check_response_method(estimator, response_method):\n AttributeError\n If `response_method` is not available in `estimator`.\n \"\"\"\n+ if isinstance(response_method, str):\n+ list_methods = [response_method]\n+ else:\n+ list_methods = response_method\n+\n+ prediction_method = [getattr(estimator, method, None) for method in list_methods]\n+ prediction_method = reduce(lambda x, y: x or y, prediction_method)\n+ if prediction_method is None:\n+ raise AttributeError(\n+ f\"{estimator.__class__.__name__} has none of the following attributes: \"\n+ f\"{', '.join(list_methods)}.\"\n+ )\n+\n+ return prediction_method\n \n \n def _check_method_params(X, params, indices=None):\n", "test": null }	null	{ "code": "diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py\nindex 401e72cef..c0274bcb7 100644\n--- a/sklearn/utils/validation.py\n+++ b/sklearn/utils/validation.py\n@@ -2201,20 +2201,6 @@ def _check_response_method(estimator, response_method):\n AttributeError\n If `response_method` is not available in `estimator`.\n \"\"\"\n- if isinstance(response_method, str):\n- list_methods = [response_method]\n- else:\n- list_methods = response_method\n-\n- prediction_method = [getattr(estimator, method, None) for method in list_methods]\n- prediction_method = reduce(lambda x, y: x or y, prediction_method)\n- if prediction_method is None:\n- raise AttributeError(\n- f\"{estimator.__class__.__name__} has none of the following attributes: \"\n- f\"{', '.join(list_methods)}.\"\n- )\n-\n- return prediction_method\n \n \n def _check_method_params(X, params, indices=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/utils/validation.py.\nHere is the description for the function:\ndef _check_response_method(estimator, response_method):\n \"\"\"Check if `response_method` is available in estimator and return it.\n\n .. versionadded:: 1.3\n\n Parameters\n ----------\n estimator : estimator instance\n Classifier or regressor to check.\n\n response_method : {\"predict_proba\", \"predict_log_proba\", \"decision_function\",\n \"predict\"} or list of such str\n Specifies the response method to use get prediction from an estimator\n (i.e. :term:`predict_proba`, :term:`predict_log_proba`,\n :term:`decision_function` or :term:`predict`). Possible choices are:\n - if `str`, it corresponds to the name to the method to return;\n - if a list of `str`, it provides the method names in order of\n preference. The method returned corresponds to the first method in\n the list and which is implemented by `estimator`.\n\n Returns\n -------\n prediction_method : callable\n Prediction method of estimator.\n\n Raises\n ------\n AttributeError\n If `response_method` is not available in `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/linear_model/tests/test_logistic.py::test_logistic_cv_multinomial_score[accuracy-multiclass_agg_list0]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_multinomial_score[precision-multiclass_agg_list1]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[jaccard_weighted-metric13]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_multinomial_score[f1-multiclass_agg_list2]", "sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[jaccard_macro-metric14]", "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_classification_binary_scores[jaccard_micro-metric15]", "sklearn/linear_model/tests/test_logistic.py::test_logistic_cv_multinomial_score[recall-multiclass_agg_list4]", "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_classification_multiclass_scores[accuracy-accuracy_score]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[balanced_accuracy-balanced_accuracy_score]", "sklearn/linear_model/tests/test_logistic.py::test_ovr_multinomial_iris", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_weighted-metric2]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_macro-metric3]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[f1_micro-metric4]", "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]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[precision_micro-metric7]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[recall_weighted-metric8]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[recall_macro-metric9]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[recall_micro-metric10]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[jaccard_weighted-metric11]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[jaccard_macro-metric12]", "sklearn/metrics/tests/test_score_objects.py::test_classification_multiclass_scores[jaccard_micro-metric13]", "sklearn/metrics/tests/test_score_objects.py::test_custom_scorer_pickling", "sklearn/metrics/tests/test_score_objects.py::test_regression_scorers", "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers", "sklearn/metrics/tests/test_score_objects.py::test_thresholded_scorers_multilabel_indicator_data", "sklearn/metrics/tests/test_score_objects.py::test_supervised_cluster_scorers", "sklearn/metrics/tests/test_score_objects.py::test_raises_on_score_list", "sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight", "sklearn/metrics/tests/test_score_objects.py::test_regression_scorer_sample_weight", 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"sklearn/tests/test_common.py::test_estimators[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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[StackingRegressor(cv=3,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_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[CalibratedClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[ClassifierChain(base_estimator=LogisticRegression(C=1),cv=3)]", "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[LogisticRegressionCV(cv=3,max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "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[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[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[CalibratedClassifierCV(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[LogisticRegressionCV(cv=3,max_iter=5)]", "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[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[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[TunedThresholdClassifierCV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "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[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[RFECV(cv=3,estimator=LogisticRegression(C=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[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-RFECV(cv=3,estimator=LogisticRegression(C=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-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-RFECV(cv=3,estimator=LogisticRegression(C=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-StackingRegressor(cv=3,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-220	1.0	{ "code": "diff --git b/sklearn/utils/validation.py a/sklearn/utils/validation.py\nindex 2071327f1..401e72cef 100644\n--- b/sklearn/utils/validation.py\n+++ a/sklearn/utils/validation.py\n@@ -2095,6 +2095,41 @@ def _check_sample_weight(\n sample_weight : ndarray of shape (n_samples,)\n Validated sample weight. It is guaranteed to be \"C\" contiguous.\n \"\"\"\n+ n_samples = _num_samples(X)\n+\n+ if dtype is not None and dtype not in [np.float32, np.float64]:\n+ dtype = np.float64\n+\n+ if sample_weight is None:\n+ sample_weight = np.ones(n_samples, dtype=dtype)\n+ elif isinstance(sample_weight, numbers.Number):\n+ sample_weight = np.full(n_samples, sample_weight, dtype=dtype)\n+ else:\n+ if dtype is None:\n+ dtype = [np.float64, np.float32]\n+ sample_weight = check_array(\n+ sample_weight,\n+ accept_sparse=False,\n+ ensure_2d=False,\n+ dtype=dtype,\n+ order=\"C\",\n+ copy=copy,\n+ input_name=\"sample_weight\",\n+ )\n+ if sample_weight.ndim != 1:\n+ raise ValueError(\"Sample weights must be 1D array or scalar\")\n+\n+ if sample_weight.shape != (n_samples,):\n+ raise ValueError(\n+ \"sample_weight.shape == {}, expected {}!\".format(\n+ sample_weight.shape, (n_samples,)\n+ )\n+ )\n+\n+ if ensure_non_negative:\n+ check_non_negative(sample_weight, \"`sample_weight`\")\n+\n+ return sample_weight\n \n \n def _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9):\n", "test": null }	null	{ "code": "diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py\nindex 401e72cef..2071327f1 100644\n--- a/sklearn/utils/validation.py\n+++ b/sklearn/utils/validation.py\n@@ -2095,41 +2095,6 @@ def _check_sample_weight(\n sample_weight : ndarray of shape (n_samples,)\n Validated sample weight. It is guaranteed to be \"C\" contiguous.\n \"\"\"\n- n_samples = _num_samples(X)\n-\n- if dtype is not None and dtype not in [np.float32, np.float64]:\n- dtype = np.float64\n-\n- if sample_weight is None:\n- sample_weight = np.ones(n_samples, dtype=dtype)\n- elif isinstance(sample_weight, numbers.Number):\n- sample_weight = np.full(n_samples, sample_weight, dtype=dtype)\n- else:\n- if dtype is None:\n- dtype = [np.float64, np.float32]\n- sample_weight = check_array(\n- sample_weight,\n- accept_sparse=False,\n- ensure_2d=False,\n- dtype=dtype,\n- order=\"C\",\n- copy=copy,\n- input_name=\"sample_weight\",\n- )\n- if sample_weight.ndim != 1:\n- raise ValueError(\"Sample weights must be 1D array or scalar\")\n-\n- if sample_weight.shape != (n_samples,):\n- raise ValueError(\n- \"sample_weight.shape == {}, expected {}!\".format(\n- sample_weight.shape, (n_samples,)\n- )\n- )\n-\n- if ensure_non_negative:\n- check_non_negative(sample_weight, \"`sample_weight`\")\n-\n- return sample_weight\n \n \n def _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9):\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/utils/validation.py.\nHere is the description for the function:\ndef _check_sample_weight(\n sample_weight, X, dtype=None, copy=False, ensure_non_negative=False\n):\n \"\"\"Validate sample weights.\n\n Note that passing sample_weight=None will output an array of ones.\n Therefore, in some cases, you may want to protect the call with:\n if sample_weight is not None:\n sample_weight = _check_sample_weight(...)\n\n Parameters\n ----------\n sample_weight : {ndarray, Number or None}, shape (n_samples,)\n Input sample weights.\n\n X : {ndarray, list, sparse matrix}\n Input data.\n\n ensure_non_negative : bool, default=False,\n Whether or not the weights are expected to be non-negative.\n\n .. versionadded:: 1.0\n\n dtype : dtype, default=None\n dtype of the validated `sample_weight`.\n If None, and the input `sample_weight` is an array, the dtype of the\n input is preserved; otherwise an array with the default numpy dtype\n is be allocated. If `dtype` is not one of `float32`, `float64`,\n `None`, the output will be of dtype `float64`.\n\n copy : bool, default=False\n If True, a copy of sample_weight will be created.\n\n Returns\n -------\n sample_weight : ndarray of shape (n_samples,)\n Validated sample weight. It is guaranteed to be \"C\" contiguous.\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/linear_model/tests/test_ridge.py::test_ridge_regression[long-42-True-svd]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_pandas_series]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_regression[long-42-True-sparse_cg]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_not_an_array]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_regression[long-42-True-cholesky]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_list]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_regression[long-42-True-lsqr]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_shape]", "sklearn/preprocessing/tests/test_data.py::test_raises_value_error_if_sample_weights_greater_than_1d", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_not_overwritten]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_regression[long-42-True-sag]", "sklearn/tree/tests/test_tree.py::test_weighted_classification_toy", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_sample_weights_invariance(kind=ones)]", "sklearn/linear_model/tests/test_ridge.py::test_ridge_regression[long-42-True-saga]", "sklearn/ensemble/tests/test_forest.py::test_classification_toy[RandomForestClassifier]", 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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_pandas_series]", "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_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_sample_weights_list]", "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_shape]", "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_not_overwritten]", "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_check_n_features_in_after_fitting[AdaBoostClassifier(n_estimators=5)]", "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[BaggingClassifier(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[BayesianGaussianMixture(max_iter=5,n_init=2)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[BisectingKMeans(max_iter=5,n_clusters=2,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[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[HuberRegressor(max_iter=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[KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LinearSVC(max_iter=20)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[LinearSVR(max_iter=20)]", "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_check_n_features_in_after_fitting[PassiveAggressiveClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[PassiveAggressiveRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[Perceptron(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[QuantileRegressor()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomForestClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RandomForestRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RidgeClassifier()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[RidgeClassifierCV()]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SGDClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SGDOneClassSVM(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SGDRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_check_n_features_in_after_fitting[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "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[AdaBoostClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[AdaBoostRegressor(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[BaggingRegressor(n_estimators=5)]", "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[BisectingKMeans(max_iter=5,n_clusters=2,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[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[HuberRegressor(max_iter=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[KMeans(max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearSVC(max_iter=20)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearSVR(max_iter=20)]", "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_pandas_column_name_consistency[PassiveAggressiveClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Perceptron(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[QuantileRegressor()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestClassifier(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestRegressor(n_estimators=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RidgeClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RidgeClassifierCV()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDOneClassSVM(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDRegressor(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "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_pandas_column_name_consistency[BaggingClassifier(n_estimators=5,oob_score=True)]", "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[ExtraTreesClassifier(bootstrap=True,n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[ExtraTreesRegressor(bootstrap=True,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_pandas_column_name_consistency[PassiveAggressiveClassifier(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveRegressor(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Perceptron(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestClassifier(n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestRegressor(n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDClassifier(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDRegressor(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[BisectingKMeans(max_iter=5,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_transformers_get_feature_names_out[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_set_output_transform[BisectingKMeans(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[MiniBatchKMeans(batch_size=10,max_iter=5,n_clusters=2,n_init=2)]", "sklearn/tests/test_common.py::test_set_output_transform[SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-BisectingKMeans(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_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_set_output_transform_pandas-SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-BisectingKMeans(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)]", "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)]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-SelectFromModel(estimator=SGDRegressor(random_state=0))]", "sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[RidgeClassifier()]" ], "PASS_TO_PASS": null }
scikit-learn__scikit-learn-221	1.0	{ "code": "diff --git b/sklearn/metrics/_classification.py a/sklearn/metrics/_classification.py\nindex ae294f1cc..65bedb99d 100644\n--- b/sklearn/metrics/_classification.py\n+++ a/sklearn/metrics/_classification.py\n@@ -88,6 +88,62 @@ def _check_targets(y_true, y_pred):\n \n y_pred : array or indicator matrix\n \"\"\"\n+ xp, _ = get_namespace(y_true, y_pred)\n+ check_consistent_length(y_true, y_pred)\n+ type_true = type_of_target(y_true, input_name=\"y_true\")\n+ type_pred = type_of_target(y_pred, input_name=\"y_pred\")\n+\n+ y_type = {type_true, type_pred}\n+ if y_type == {\"binary\", \"multiclass\"}:\n+ y_type = {\"multiclass\"}\n+\n+ if len(y_type) > 1:\n+ raise ValueError(\n+ \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n+ type_true, type_pred\n+ )\n+ )\n+\n+ # We can't have more than one value on y_type => The set is no more needed\n+ y_type = y_type.pop()\n+\n+ # No metrics support \"multiclass-multioutput\" format\n+ if y_type not in [\"binary\", \"multiclass\", \"multilabel-indicator\"]:\n+ raise ValueError(\"{0} is not supported\".format(y_type))\n+\n+ if y_type in [\"binary\", \"multiclass\"]:\n+ xp, _ = get_namespace(y_true, y_pred)\n+ y_true = column_or_1d(y_true)\n+ y_pred = column_or_1d(y_pred)\n+ if y_type == \"binary\":\n+ try:\n+ unique_values = _union1d(y_true, y_pred, xp)\n+ except TypeError as e:\n+ # We expect y_true and y_pred to be of the same data type.\n+ # If `y_true` was provided to the classifier as strings,\n+ # `y_pred` given by the classifier will also be encoded with\n+ # strings. So we raise a meaningful error\n+ raise TypeError(\n+ \"Labels in y_true and y_pred should be of the same type. \"\n+ f\"Got y_true={xp.unique(y_true)} and \"\n+ f\"y_pred={xp.unique(y_pred)}. Make sure that the \"\n+ \"predictions provided by the classifier coincides with \"\n+ \"the true labels.\"\n+ ) from e\n+ if unique_values.shape[0] > 2:\n+ y_type = \"multiclass\"\n+\n+ if y_type.startswith(\"multilabel\"):\n+ if _is_numpy_namespace(xp):\n+ # XXX: do we really want to sparse-encode multilabel indicators when\n+ # they are passed as a dense arrays? This is not possible for array\n+ # API inputs in general hence we only do it for NumPy inputs. But even\n+ # for NumPy the usefulness is questionable.\n+ y_true = csr_matrix(y_true)\n+ y_pred = csr_matrix(y_pred)\n+ y_type = \"multilabel-indicator\"\n+\n+ return y_type, y_true, y_pred\n \n \n @validate_params(\n", "test": null }	null	{ "code": "diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py\nindex 65bedb99d..ae294f1cc 100644\n--- a/sklearn/metrics/_classification.py\n+++ b/sklearn/metrics/_classification.py\n@@ -88,62 +88,6 @@ def _check_targets(y_true, y_pred):\n \n y_pred : array or indicator matrix\n \"\"\"\n- xp, _ = get_namespace(y_true, y_pred)\n- check_consistent_length(y_true, y_pred)\n- type_true = type_of_target(y_true, input_name=\"y_true\")\n- type_pred = type_of_target(y_pred, input_name=\"y_pred\")\n-\n- y_type = {type_true, type_pred}\n- if y_type == {\"binary\", \"multiclass\"}:\n- y_type = {\"multiclass\"}\n-\n- if len(y_type) > 1:\n- raise ValueError(\n- \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n- type_true, type_pred\n- )\n- )\n-\n- # We can't have more than one value on y_type => The set is no more needed\n- y_type = y_type.pop()\n-\n- # No metrics support \"multiclass-multioutput\" format\n- if y_type not in [\"binary\", \"multiclass\", \"multilabel-indicator\"]:\n- raise ValueError(\"{0} is not supported\".format(y_type))\n-\n- if y_type in [\"binary\", \"multiclass\"]:\n- xp, _ = get_namespace(y_true, y_pred)\n- y_true = column_or_1d(y_true)\n- y_pred = column_or_1d(y_pred)\n- if y_type == \"binary\":\n- try:\n- unique_values = _union1d(y_true, y_pred, xp)\n- except TypeError as e:\n- # We expect y_true and y_pred to be of the same data type.\n- # If `y_true` was provided to the classifier as strings,\n- # `y_pred` given by the classifier will also be encoded with\n- # strings. So we raise a meaningful error\n- raise TypeError(\n- \"Labels in y_true and y_pred should be of the same type. \"\n- f\"Got y_true={xp.unique(y_true)} and \"\n- f\"y_pred={xp.unique(y_pred)}. Make sure that the \"\n- \"predictions provided by the classifier coincides with \"\n- \"the true labels.\"\n- ) from e\n- if unique_values.shape[0] > 2:\n- y_type = \"multiclass\"\n-\n- if y_type.startswith(\"multilabel\"):\n- if _is_numpy_namespace(xp):\n- # XXX: do we really want to sparse-encode multilabel indicators when\n- # they are passed as a dense arrays? This is not possible for array\n- # API inputs in general hence we only do it for NumPy inputs. But even\n- # for NumPy the usefulness is questionable.\n- y_true = csr_matrix(y_true)\n- y_pred = csr_matrix(y_pred)\n- y_type = \"multilabel-indicator\"\n-\n- return y_type, y_true, y_pred\n \n \n @validate_params(\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/metrics/_classification.py.\nHere is the description for the function:\ndef _check_targets(y_true, y_pred):\n \"\"\"Check that y_true and y_pred belong to the same classification task.\n\n This converts multiclass or binary types to a common shape, and raises a\n ValueError for a mix of multilabel and multiclass targets, a mix of\n multilabel formats, for the presence of continuous-valued or multioutput\n targets, or for targets of different lengths.\n\n Column vectors are squeezed to 1d, while multilabel formats are returned\n as CSR sparse label indicators.\n\n Parameters\n ----------\n y_true : array-like\n\n y_pred : array-like\n\n Returns\n -------\n type_true : one of {'multilabel-indicator', 'multiclass', 'binary'}\n The type of the true target data, as output by\n ``utils.multiclass.type_of_target``.\n\n y_true : array or indicator matrix\n\n y_pred : array or indicator matrix\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/metrics/tests/test_common.py::test_symmetric_metric[accuracy_score]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_pipeline_consistency]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[cohen_kappa_score]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[f1_score]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[hamming_loss]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[jaccard_score]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_train]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[macro_f1_score]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[macro_jaccard_score]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True)]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[matthews_corrcoef_score]", "sklearn/tests/test_common.py::test_estimators[AdaBoostClassifier(n_estimators=5)-check_classifiers_train(readonly_memmap=True,X_dtype=float32)]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[micro_f0.5_score]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[micro_f1_score]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[micro_f2_score]", "sklearn/metrics/tests/test_common.py::test_symmetric_metric[micro_jaccard_score]", 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"sklearn/tests/test_common.py::test_pandas_column_name_consistency[ExtraTreesClassifier(n_estimators=5)]", "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[GaussianNB()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GaussianProcessClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[GradientBoostingClassifier(n_estimators=5)]", "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[HistGradientBoostingClassifier(max_iter=5,min_samples_leaf=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[KNeighborsClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LabelPropagation(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LabelSpreading(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearDiscriminantAnalysis()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[LinearSVC(max_iter=20)]", "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[MLPClassifier(max_iter=100)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[MultinomialNB()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[NearestCentroid()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[NuSVC()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[OneVsOneClassifier(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[OutputCodeClassifier(estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Perceptron(max_iter=5)]", "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[QuadraticDiscriminantAnalysis()]", "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[RadiusNeighborsClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestClassifier(n_estimators=5)]", "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[RidgeClassifier()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RidgeClassifierCV()]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDClassifier(max_iter=5)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SVC()]", "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[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[Pipeline(steps=[('logisticregression',LogisticRegression(C=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[GradientBoostingClassifier(n_estimators=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[HistGradientBoostingClassifier(early_stopping=True,max_iter=5,min_samples_leaf=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[PassiveAggressiveClassifier(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[Perceptron(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[RandomForestClassifier(n_estimators=5,oob_score=True)]", "sklearn/tests/test_common.py::test_pandas_column_name_consistency[SGDClassifier(early_stopping=True,max_iter=5,n_iter_no_change=1)]", "sklearn/tests/test_common.py::test_transformers_get_feature_names_out[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform[RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_set_output_transform_pandas-RFECV(cv=3,estimator=LogisticRegression(C=1))]", "sklearn/tests/test_common.py::test_set_output_transform_configured[check_global_output_transform_pandas-RFECV(cv=3,estimator=LogisticRegression(C=1))]" ], "PASS_TO_PASS": null }

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