FIX: accuracy and zero_loss support for multilabel with Array API (#29336)

Co-authored-by: Omar Salman <[email protected]>
Co-authored-by: Omar Salman <[email protected]>

Author: 2 people

doc/whats_new/v1.5.rst

@@ -48,9 +48,9 @@ Changelog
   instead of implicitly converting those inputs as regular NumPy arrays.
   :pr:`29119` by :user:`Olivier Grisel`.
 
-- |Fix| Fix a regression in :func:`metrics.zero_one_loss` causing an error
-  for Array API dispatch with multilabel inputs.
-  :pr:`29269` by :user:`Yaroslav Korobko <Tialo>`.
+- |Fix| Fix a regression in :func:`metrics.accuracy_score` and in :func:`metrics.zero_one_loss`
+  causing an error for Array API dispatch with multilabel inputs.
+  :pr:`29269` by :user:`Yaroslav Korobko <Tialo>` and :pr:`29336` by :user:`Edoardo Abati <EdAbati>`.
 
 :mod:`sklearn.model_selection`
 ..............................

sklearn/metrics/_classification.py

@@ -40,6 +40,8 @@
 )
 from ..utils._array_api import (
     _average,
+    _count_nonzero,
+    _is_numpy_namespace,
     _union1d,
     get_namespace,
     get_namespace_and_device,
@@ -97,6 +99,7 @@ def _check_targets(y_true, y_pred):
 
     y_pred : array or indicator matrix
     """
+    xp, _ = get_namespace(y_true, y_pred)
     check_consistent_length(y_true, y_pred)
     type_true = type_of_target(y_true, input_name="y_true")
     type_pred = type_of_target(y_pred, input_name="y_pred")
@@ -142,8 +145,13 @@ def _check_targets(y_true, y_pred):
                 y_type = "multiclass"
 
     if y_type.startswith("multilabel"):
-        y_true = csr_matrix(y_true)
-        y_pred = csr_matrix(y_pred)
+        if _is_numpy_namespace(xp):
+            # XXX: do we really want to sparse-encode multilabel indicators when
+            # they are passed as a dense arrays? This is not possible for array
+            # API inputs in general hence we only do it for NumPy inputs. But even
+            # for NumPy the usefulness is questionable.
+            y_true = csr_matrix(y_true)
+            y_pred = csr_matrix(y_pred)
         y_type = "multilabel-indicator"
 
     return y_type, y_true, y_pred
@@ -223,7 +231,12 @@ def accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None):
     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
     check_consistent_length(y_true, y_pred, sample_weight)
     if y_type.startswith("multilabel"):
-        differing_labels = count_nonzero(y_true - y_pred, axis=1)
+        if _is_numpy_namespace(xp):
+            differing_labels = count_nonzero(y_true - y_pred, axis=1)
+        else:
+            differing_labels = _count_nonzero(
+                y_true - y_pred, xp=xp, device=device, axis=1
+            )
         score = xp.asarray(differing_labels == 0, device=device)
     else:
         score = y_true == y_pred

sklearn/utils/_array_api.py

@@ -841,3 +841,20 @@ def indexing_dtype(xp):
     # TODO: once sufficiently adopted, we might want to instead rely on the
     # newer inspection API: https://github.com/data-apis/array-api/issues/640
     return xp.asarray(0).dtype
+
+
+def _count_nonzero(X, xp, device, axis=None, sample_weight=None):
+    """A variant of `sklearn.utils.sparsefuncs.count_nonzero` for the Array API.
+
+    It only supports 2D arrays.
+    """
+    assert X.ndim == 2
+
+    weights = xp.ones_like(X, device=device)
+    if sample_weight is not None:
+        sample_weight = xp.asarray(sample_weight, device=device)
+        sample_weight = xp.reshape(sample_weight, (sample_weight.shape[0], 1))
+        weights = xp.astype(weights, sample_weight.dtype) * sample_weight
+
+    zero_scalar = xp.asarray(0, device=device, dtype=weights.dtype)
+    return xp.sum(xp.where(X != 0, weights, zero_scalar), axis=axis)

sklearn/utils/tests/test_array_api.py

@@ -13,6 +13,7 @@
     _atol_for_type,
     _average,
     _convert_to_numpy,
+    _count_nonzero,
     _estimator_with_converted_arrays,
     _is_numpy_namespace,
     _nanmax,
@@ -30,7 +31,7 @@
     _array_api_for_tests,
     skip_if_array_api_compat_not_configured,
 )
-from sklearn.utils.fixes import _IS_32BIT
+from sklearn.utils.fixes import _IS_32BIT, CSR_CONTAINERS
 
 
 @pytest.mark.parametrize("X", [numpy.asarray([1, 2, 3]), [1, 2, 3]])
@@ -530,3 +531,37 @@ def test_get_namespace_and_device():
         assert namespace is xp_torch
         assert is_array_api
         assert device == some_torch_tensor.device
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+@pytest.mark.parametrize("axis", [0, 1, None, -1, -2])
+@pytest.mark.parametrize("sample_weight_type", [None, "int", "float"])
+def test_count_nonzero(
+    array_namespace, device, dtype_name, csr_container, axis, sample_weight_type
+):
+
+    from sklearn.utils.sparsefuncs import count_nonzero as sparse_count_nonzero
+
+    xp = _array_api_for_tests(array_namespace, device)
+    array = numpy.array([[0, 3, 0], [2, -1, 0], [0, 0, 0], [9, 8, 7], [4, 0, 5]])
+    if sample_weight_type == "int":
+        sample_weight = numpy.asarray([1, 2, 2, 3, 1])
+    elif sample_weight_type == "float":
+        sample_weight = numpy.asarray([0.5, 1.5, 0.8, 3.2, 2.4], dtype=dtype_name)
+    else:
+        sample_weight = None
+    expected = sparse_count_nonzero(
+        csr_container(array), axis=axis, sample_weight=sample_weight
+    )
+    array_xp = xp.asarray(array, device=device)
+
+    with config_context(array_api_dispatch=True):
+        result = _count_nonzero(
+            array_xp, xp=xp, device=device, axis=axis, sample_weight=sample_weight
+        )
+
+    assert_allclose(_convert_to_numpy(result, xp=xp), expected)
+    assert getattr(array_xp, "device", None) == getattr(result, "device", None)