array API support for mean_absolute_percentage_error (scikit-learn#29300)

Author: EmilyXinyi

doc/modules/array_api.rst

@@ -117,6 +117,7 @@ Metrics
 - :func:`sklearn.metrics.d2_tweedie_score`
 - :func:`sklearn.metrics.max_error`
 - :func:`sklearn.metrics.mean_absolute_error`
+- :func:`sklearn.metrics.mean_absolute_percentage_error`
 - :func:`sklearn.metrics.mean_gamma_deviance`
 - :func:`sklearn.metrics.mean_squared_error`
 - :func:`sklearn.metrics.mean_tweedie_deviance`

doc/whats_new/v1.6.rst

@@ -37,7 +37,8 @@ See :ref:`array_api` for more details.
 - :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>`
   and :pr:`29143` by :user:`Tialo <Tialo>` and :user:`Loïc Estève <lesteve>`;
-- :func:`sklearn.metrics.mean_gamma_deviance` :pr:`29239` by :usser:`Emily Chen <EmilyXinyi>`;
+- :func:`sklearn.metrics.mean_absolute_percentage_error` :pr:`29300` by :user:`Emily Chen <EmilyXinyi>`;
+- :func:`sklearn.metrics.mean_gamma_deviance` :pr:`29239` by :user:`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.additive_chi2_kernel` :pr:`29144` by :user:`Yaroslav Korobko <Tialo>`;

sklearn/metrics/_regression.py

@@ -395,21 +395,30 @@ def mean_absolute_percentage_error(
     >>> mean_absolute_percentage_error(y_true, y_pred)
     112589990684262.48
     """
+    input_arrays = [y_true, y_pred, sample_weight, multioutput]
+    xp, _ = get_namespace(*input_arrays)
+    dtype = _find_matching_floating_dtype(y_true, y_pred, sample_weight, xp=xp)
+
     y_type, y_true, y_pred, multioutput = _check_reg_targets(
         y_true, y_pred, multioutput
     )
     check_consistent_length(y_true, y_pred, sample_weight)
-    epsilon = np.finfo(np.float64).eps
-    mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon)
-    output_errors = np.average(mape, weights=sample_weight, axis=0)
+    epsilon = xp.asarray(xp.finfo(xp.float64).eps, dtype=dtype)
+    y_true_abs = xp.asarray(xp.abs(y_true), dtype=dtype)
+    mape = xp.asarray(xp.abs(y_pred - y_true), dtype=dtype) / xp.maximum(
+        y_true_abs, epsilon
+    )
+    output_errors = _average(mape, weights=sample_weight, axis=0)
     if isinstance(multioutput, str):
         if multioutput == "raw_values":
             return output_errors
         elif multioutput == "uniform_average":
-            # pass None as weights to np.average: uniform mean
+            # pass None as weights to _average: uniform mean
             multioutput = None
 
-    return np.average(output_errors, weights=multioutput)
+    mean_absolute_percentage_error = _average(output_errors, weights=multioutput)
+    assert mean_absolute_percentage_error.shape == ()
+    return float(mean_absolute_percentage_error)
 
 
 @validate_params(

sklearn/metrics/tests/test_common.py

@@ -2016,6 +2016,10 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name)
     additive_chi2_kernel: [check_array_api_metric_pairwise],
     mean_gamma_deviance: [check_array_api_regression_metric],
     max_error: [check_array_api_regression_metric],
+    mean_absolute_percentage_error: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
     chi2_kernel: [check_array_api_metric_pairwise],
     cosine_distances: [check_array_api_metric_pairwise],
     euclidean_distances: [check_array_api_metric_pairwise],