remove specific numpy test use comon

Author: jaffourt

sklearn/metrics/tests/test_common.py

@@ -2252,6 +2252,7 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name)
         check_array_api_binary_classification_metric,
         check_array_api_multiclass_classification_metric,
     ],
+    det_curve: [check_array_api_binary_classification_metric],
     f1_score: [
         check_array_api_binary_classification_metric,
         check_array_api_multiclass_classification_metric,

sklearn/metrics/tests/test_ranking.py

@@ -5,7 +5,7 @@
 import pytest
 from scipy import stats
 
-from sklearn import config_context, datasets
+from sklearn import datasets
 from sklearn.datasets import make_multilabel_classification
 from sklearn.exceptions import UndefinedMetricWarning
 from sklearn.linear_model import LogisticRegression
@@ -28,12 +28,7 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import label_binarize
 from sklearn.random_projection import _sparse_random_matrix
-from sklearn.utils._array_api import (
-    _convert_to_numpy,
-    yield_namespace_device_dtype_combinations,
-)
 from sklearn.utils._testing import (
-    _array_api_for_tests,
     _convert_container,
     assert_allclose,
     assert_almost_equal,
@@ -1397,33 +1392,6 @@ def test_det_curve_pos_label():
     assert_allclose(fnr_pos_cancer, fpr_pos_not_cancer[::-1])
 
 
-@pytest.mark.parametrize(
-    "array_namespace, device_, dtype_name", yield_namespace_device_dtype_combinations()
-)
-def test_det_curve_array_api(array_namespace, device_, dtype_name):
-    xp = _array_api_for_tests(array_namespace, device_)
-
-    y_true_np = np.array([0, 1, 0], dtype=dtype_name)
-    y_score_np = np.array([0, 0.5, 1], dtype=dtype_name)
-
-    # baseline numpy results
-    fpr_np, fnr_np, thresholds_np = det_curve(y_true_np, y_score_np)
-
-    y_true_xp = xp.asarray(y_true_np, device=device_)
-    y_score_xp = xp.asarray(y_score_np, device=device_)
-
-    with config_context(array_api_dispatch=True):
-        fpr_xp, fnr_xp, thresholds_xp = det_curve(y_true_xp, y_score_xp)
-
-    fpr_from_xp = _convert_to_numpy(fpr_xp, xp=xp)
-    fnr_from_xp = _convert_to_numpy(fnr_xp, xp=xp)
-    thresholds_from_xp = _convert_to_numpy(thresholds_xp, xp=xp)
-
-    assert_allclose(fpr_np, fpr_from_xp)
-    assert_allclose(fnr_np, fnr_from_xp)
-    assert_allclose(thresholds_np, thresholds_from_xp)
-
-
 def check_lrap_toy(lrap_score):
     # Check on several small example that it works
     assert_almost_equal(lrap_score([[0, 1]], [[0.25, 0.75]]), 1)