scikit-learn · jeremiedbb · Mar 14, 2023 · Mar 10, 2023 · Mar 10, 2023 · Mar 10, 2023
diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py
@@ -18,6 +18,7 @@
 from ..utils import as_float_array, check_array
 from ..utils.parallel import delayed, Parallel
 from ..utils._param_validation import Hidden, Interval, StrOptions
+from ..utils._param_validation import validate_params
 from ..model_selection import check_cv
 
 premature = (
@@ -281,6 +282,18 @@ def _gram_omp(
         return gamma, indices[:n_active], n_active
 
 
+@validate_params(
+    {
+        "X": ["array-like"],
+        "y": [np.ndarray],
+        "n_nonzero_coefs": [Interval(Integral, 1, None, closed="left"), None],
+        "tol": [Interval(Real, 0, None, closed="left"), None],
+        "precompute": ["boolean", StrOptions({"auto"})],
+        "copy_X": ["boolean"],
+        "return_path": ["boolean"],
+        "return_n_iter": ["boolean"],
+    }
+)
 def orthogonal_mp(
     X,
     y,
@@ -308,7 +321,7 @@ def orthogonal_mp(
 
     Parameters
     ----------
-    X : ndarray of shape (n_samples, n_features)
+    X : array-like of shape (n_samples, n_features)
         Input data. Columns are assumed to have unit norm.
 
     y : ndarray of shape (n_samples,) or (n_samples, n_targets)
@@ -380,10 +393,6 @@ def orthogonal_mp(
         # default for n_nonzero_coefs is 0.1 * n_features
         # but at least one.
         n_nonzero_coefs = max(int(0.1 * X.shape[1]), 1)
-    if tol is not None and tol < 0:
-        raise ValueError("Epsilon cannot be negative")
-    if tol is None and n_nonzero_coefs <= 0:
-        raise ValueError("The number of atoms must be positive")
     if tol is None and n_nonzero_coefs > X.shape[1]:
         raise ValueError(
             "The number of atoms cannot be more than the number of features"

diff --git a/sklearn/linear_model/tests/test_omp.py b/sklearn/linear_model/tests/test_omp.py
@@ -120,7 +120,7 @@ def test_unreachable_accuracy():
 @pytest.mark.parametrize("positional_params", [(X, y), (G, Xy)])
 @pytest.mark.parametrize(
     "keyword_params",
-    [{"tol": -1}, {"n_nonzero_coefs": -1}, {"n_nonzero_coefs": n_features + 1}],
+    [{"n_nonzero_coefs": n_features + 1}],
 )
 def test_bad_input(positional_params, keyword_params):
     with pytest.raises(ValueError):

diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -126,6 +126,7 @@ def _check_function_param_validation(
     "sklearn.feature_selection.f_regression",
     "sklearn.feature_selection.mutual_info_classif",
     "sklearn.feature_selection.r_regression",
+    "sklearn.linear_model.orthogonal_mp",
     "sklearn.metrics.accuracy_score",
     "sklearn.metrics.auc",
     "sklearn.metrics.average_precision_score",