scikit-learn · jeremiedbb · Apr 19, 2023 · Apr 8, 2023 · Apr 19, 2023 · Apr 19, 2023
diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
@@ -15,6 +15,7 @@
 import numbers
 import time
 from functools import partial
+from numbers import Real
 from traceback import format_exc
 from contextlib import suppress
 from collections import Counter
@@ -29,7 +30,14 @@
 from ..utils.validation import _num_samples
 from ..utils.parallel import delayed, Parallel
 from ..utils.metaestimators import _safe_split
+from ..utils._param_validation import (
+    HasMethods,
+    Integral,
+    StrOptions,
+    validate_params,
+)
 from ..metrics import check_scoring
+from ..metrics import get_scorer_names
 from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer
 from ..exceptions import FitFailedWarning
 from ._split import check_cv
@@ -46,6 +54,31 @@
 ]
 
 
+@validate_params(
+    {
+        "estimator": [HasMethods("fit")],
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like", None],
+        "groups": ["array-like", None],
+        "scoring": [
+            StrOptions(set(get_scorer_names())),
+            callable,
+            list,
+            tuple,
+            dict,
+            None,
+        ],
+        "cv": ["cv_object"],
+        "n_jobs": [Integral, None],
+        "verbose": ["verbose"],
+        "fit_params": [dict, None],
+        "pre_dispatch": [Integral, str],
+        "return_train_score": ["boolean"],
+        "return_estimator": ["boolean"],
+        "return_indices": ["boolean"],
+        "error_score": [StrOptions({"raise"}), Real],
+    }
+)
 def cross_validate(
     estimator,
     X,
@@ -72,7 +105,7 @@ def cross_validate(
     estimator : estimator object implementing 'fit'
         The object to use to fit the data.
 
-    X : array-like of shape (n_samples, n_features)
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
         The data to fit. Can be for example a list, or an array.
 
     y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
@@ -141,11 +174,6 @@ def cross_validate(
         explosion of memory consumption when more jobs get dispatched
         than CPUs can process. This parameter can be:
 
-            - None, in which case all the jobs are immediately
-              created and spawned. Use this for lightweight and
-              fast-running jobs, to avoid delays due to on-demand
-              spawning of the jobs
-
             - An int, giving the exact number of total jobs that are
               spawned
 

diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
@@ -10,6 +10,7 @@
 import pytest
 import numpy as np
 from scipy.sparse import coo_matrix, csr_matrix
+from scipy.sparse import issparse
 from sklearn.exceptions import FitFailedWarning
 
 from sklearn.model_selection.tests.test_search import FailingClassifier
@@ -354,18 +355,10 @@ def test_cross_validate_invalid_scoring_param():
     with pytest.raises(ValueError, match=error_message_regexp):
         cross_validate(estimator, X, y, scoring=[[make_scorer(precision_score)]])
 
-    error_message_regexp = (
-        ".*scoring is invalid.*Refer to the scoring glossary for details:.*"
-    )
-
     # Empty dict should raise invalid scoring error
     with pytest.raises(ValueError, match="An empty dict"):
         cross_validate(estimator, X, y, scoring=(dict()))
 
-    # And so should any other invalid entry
-    with pytest.raises(ValueError, match=error_message_regexp):
-        cross_validate(estimator, X, y, scoring=5)
-
     multiclass_scorer = make_scorer(precision_recall_fscore_support)
 
     # Multiclass Scorers that return multiple values are not supported yet
@@ -382,9 +375,6 @@ def test_cross_validate_invalid_scoring_param():
     with pytest.warns(UserWarning, match=warning_message):
         cross_validate(estimator, X, y, scoring={"foo": multiclass_scorer})
 
-    with pytest.raises(ValueError, match="'mse' is not a valid scoring value."):
-        cross_validate(SVC(), X, y, scoring="mse")
-
 
 def test_cross_validate_nested_estimator():
     # Non-regression test to ensure that nested
@@ -405,7 +395,8 @@ def test_cross_validate_nested_estimator():
     assert all(isinstance(estimator, Pipeline) for estimator in estimators)
 
 
-def test_cross_validate():
+@pytest.mark.parametrize("use_sparse", [False, True])
+def test_cross_validate(use_sparse: bool):
     # Compute train and test mse/r2 scores
     cv = KFold()
 
@@ -417,6 +408,10 @@ def test_cross_validate():
     X_clf, y_clf = make_classification(n_samples=30, random_state=0)
     clf = SVC(kernel="linear", random_state=0)
 
+    if use_sparse:
+        X_reg = csr_matrix(X_reg)
+        X_clf = csr_matrix(X_clf)
+
     for X, y, est in ((X_reg, y_reg, reg), (X_clf, y_clf, clf)):
         # It's okay to evaluate regression metrics on classification too
         mse_scorer = check_scoring(est, scoring="neg_mean_squared_error")
@@ -510,7 +505,15 @@ def check_cross_validate_single_metric(clf, X, y, scores, cv):
         clf, X, y, scoring="neg_mean_squared_error", return_estimator=True, cv=cv
     )
     for k, est in enumerate(mse_scores_dict["estimator"]):
-        assert_almost_equal(est.coef_, fitted_estimators[k].coef_)
+        est_coef = est.coef_.copy()
+        if issparse(est_coef):
+            est_coef = est_coef.toarray()
+
+        fitted_est_coef = fitted_estimators[k].coef_.copy()
+        if issparse(fitted_est_coef):
+            fitted_est_coef = fitted_est_coef.toarray()
+
+        assert_almost_equal(est_coef, fitted_est_coef)
         assert_almost_equal(est.intercept_, fitted_estimators[k].intercept_)
 
 
@@ -2104,10 +2107,12 @@ def test_fit_and_score_failing():
         "error_score must be the string 'raise' or a numeric value. (Hint: if "
         "using 'raise', please make sure that it has been spelled correctly.)"
     )
-    with pytest.raises(ValueError, match=error_message):
-        cross_validate(failing_clf, X, cv=3, error_score="unvalid-string")
 
-    with pytest.raises(ValueError, match=error_message):
+    error_message_cross_validate = (
+        "The 'error_score' parameter of cross_validate must be .*. Got .* instead."
+    )
+
+    with pytest.raises(ValueError, match=error_message_cross_validate):
         cross_val_score(failing_clf, X, cv=3, error_score="unvalid-string")
 
     with pytest.raises(ValueError, match=error_message):

diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -245,6 +245,7 @@ def _check_function_param_validation(
     "sklearn.metrics.roc_curve",
     "sklearn.metrics.top_k_accuracy_score",
     "sklearn.metrics.zero_one_loss",
+    "sklearn.model_selection.cross_validate",
     "sklearn.model_selection.train_test_split",
     "sklearn.neighbors.sort_graph_by_row_values",
     "sklearn.preprocessing.add_dummy_feature",