array API support for mean_gamma_deviance (scikit-learn#29239)

Co-authored-by: Tim Head <betatim@gmail.com>

Author: EmilyXinyi

doc/modules/array_api.rst

@@ -116,6 +116,7 @@ Metrics
 - :func:`sklearn.metrics.d2_tweedie_score`
 - :func:`sklearn.metrics.max_error`
 - :func:`sklearn.metrics.mean_absolute_error`
+- :func:`sklearn.metrics.mean_gamma_deviance`
 - :func:`sklearn.metrics.mean_squared_error`
 - :func:`sklearn.metrics.mean_tweedie_deviance`
 - :func:`sklearn.metrics.pairwise.cosine_similarity`

doc/whats_new/v1.6.rst

@@ -35,6 +35,7 @@ See :ref:`array_api` for more details.
 - :func:`sklearn.metrics.d2_tweedie_score` :pr:`29207` by :user:`Emily Chen <EmilyXinyi>`;
 - :func:`sklearn.metrics.max_error` :pr:`29212` by :user:`Edoardo Abati <EdAbati>`;
 - :func:`sklearn.metrics.mean_absolute_error` :pr:`27736` by :user:`Edoardo Abati <EdAbati>`;
+- :func:`sklearn.metrics.mean_gamma_deviance` :pr:`29239` by :usser:`Emily Chen <EmilyXinyi>`;
 - :func:`sklearn.metrics.mean_squared_error` :pr:`29142` by :user:`Yaroslav Korobko <Tialo>`;
 - :func:`sklearn.metrics.mean_tweedie_deviance` :pr:`28106` by :user:`Thomas Li <lithomas1>`;
 - :func:`sklearn.metrics.pairwise.cosine_similarity` :pr:`29014` by :user:`Edoardo Abati <EdAbati>`.

sklearn/metrics/_regression.py

@@ -1412,7 +1412,7 @@ def mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0):
             raise ValueError(message + "non-negative y and strictly positive y_pred.")
     elif power >= 2:
         # Gamma and Extreme stable distribution, y and y_pred > 0
-        if (y_true <= 0).any() or (y_pred <= 0).any():
+        if xp.any(y_true <= 0) or xp.any(y_pred <= 0):
             raise ValueError(message + "strictly positive y and y_pred.")
     else:  # pragma: nocover
         # Unreachable statement

sklearn/metrics/tests/test_common.py

@@ -1827,7 +1827,7 @@ def check_array_api_multiclass_classification_metric(
 
 
 def check_array_api_regression_metric(metric, array_namespace, device, dtype_name):
-    y_true_np = np.array([2.0, 0.0, 1.0, 4.0], dtype=dtype_name)
+    y_true_np = np.array([2.0, 0.1, 1.0, 4.0], dtype=dtype_name)
     y_pred_np = np.array([0.5, 0.5, 2, 2], dtype=dtype_name)
 
     metric_kwargs = {}
@@ -1955,6 +1955,7 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name)
         check_array_api_regression_metric,
     ],
     paired_cosine_distances: [check_array_api_metric_pairwise],
+    mean_gamma_deviance: [check_array_api_regression_metric],
     max_error: [check_array_api_regression_metric],
 }