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ENH: Make brier_score_loss Array API compatible #31191

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ENH: Make brier_score_loss Array API compatible #31191

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ENH: Make brier score Array API compatible
lithomas1 Apr 13, 2025
28367b5
Merge branch 'main' of github.com:scikit-learn/scikit-learn into brie…
lithomas1 Apr 13, 2025
4020016
whatsnew
lithomas1 Apr 13, 2025
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1 change: 1 addition & 0 deletions 1 doc/modules/array_api.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -131,6 +131,7 @@ base estimator also does:
Metrics
-------

- :func:`sklearn.metrics.brier_score_loss` (only the binary class case is supported)
- :func:`sklearn.metrics.cluster.entropy`
- :func:`sklearn.metrics.accuracy_score`
- :func:`sklearn.metrics.d2_tweedie_score`
Expand Down
2 changes: 2 additions & 0 deletions 2 doc/whats_new/upcoming_changes/array-api/31191.feature.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,2 @@
- :func:`sklearn.metrics.brier_score_loss` now support Array API compatible inputs for the binary class case.
By :user:`Thomas Li <lithomas1>`
40 changes: 28 additions & 12 deletions 40 sklearn/metrics/_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,10 +194,11 @@ def _validate_multiclass_probabilistic_prediction(
y_prob = check_array(
y_prob, ensure_2d=False, dtype=[np.float64, np.float32, np.float16]
)
xp, _ = get_namespace(y_true, y_prob)

if y_prob.max() > 1:
if xp.max(y_prob) > 1:
raise ValueError(f"y_prob contains values greater than 1: {y_prob.max()}")
if y_prob.min() < 0:
if xp.min(y_prob) < 0:
raise ValueError(f"y_prob contains values lower than 0: {y_prob.min()}")

check_consistent_length(y_prob, y_true, sample_weight)
Expand Down Expand Up @@ -3428,6 +3429,7 @@ def _validate_binary_probabilistic_prediction(y_true, y_prob, sample_weight, pos
assert_all_finite(y_prob)

check_consistent_length(y_prob, y_true, sample_weight)
xp, _, device = get_namespace_and_device(y_true, y_prob, sample_weight)

y_type = type_of_target(y_true, input_name="y_true")
if y_type != "binary":
Expand All @@ -3436,16 +3438,16 @@ def _validate_binary_probabilistic_prediction(y_true, y_prob, sample_weight, pos
"binary according to the shape of y_prob."
)

if y_prob.max() > 1:
if xp.max(y_prob) > 1:
raise ValueError(f"y_prob contains values greater than 1: {y_prob.max()}")
if y_prob.min() < 0:
if xp.min(y_prob) < 0:
raise ValueError(f"y_prob contains values less than 0: {y_prob.min()}")

# check that pos_label is consistent with y_true
try:
pos_label = _check_pos_label_consistency(pos_label, y_true)
except ValueError:
classes = np.unique(y_true)
classes = xp.unique_values(y_true)
if classes.dtype.kind not in ("O", "U", "S"):
# for backward compatibility, if classes are not string then
# `pos_label` will correspond to the greater label
Expand All @@ -3454,9 +3456,9 @@ def _validate_binary_probabilistic_prediction(y_true, y_prob, sample_weight, pos
raise

# convert (n_samples,) to (n_samples, 2) shape
y_true = np.array(y_true == pos_label, int)
transformed_labels = np.column_stack((1 - y_true, y_true))
y_prob = np.column_stack((1 - y_prob, y_prob))
y_true = xp.asarray(y_true == pos_label, dtype=xp.int64, device=device)
transformed_labels = xp.stack((1 - y_true, y_true), axis=1)
y_prob = xp.stack((1 - y_prob, y_prob), axis=1)

return transformed_labels, y_prob

Expand Down Expand Up @@ -3589,8 +3591,17 @@ def brier_score_loss(
... )
0.146...
"""
xp, _, device = get_namespace_and_device(
y_true,
y_proba,
sample_weight,
)
y_proba = check_array(
y_proba, ensure_2d=False, dtype=[np.float64, np.float32, np.float16]
y_proba,
ensure_2d=False,
dtype=tuple(
xp.__array_namespace_info__().dtypes(kind="real floating").values()
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Do we really need this change aside from just replacing np with xp in the floatdata types? Is there some other float dtype that we want to support?

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I did this since some libraries like PyTorch MPS don't support xp.float32.
(Also float16 is not in the array API standard. Should we make a special exception for np.float16?)

Maybe it would be good to put this in a helper in the array API utils module?

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So far, we used _find_matching_floating_dtype for this use case. We could update that utility to leverage __array_namespace_info__ as you did here.

We could also improve check_array to accept dtype="floating" and do device/namespace specific conversion when provided with integer inputs.

),
)

if y_proba.ndim == 1 or y_proba.shape[1] == 1:
Expand All @@ -3601,9 +3612,14 @@ def brier_score_loss(
transformed_labels, y_proba = _validate_multiclass_probabilistic_prediction(
y_true, y_proba, sample_weight, labels
)

brier_score = np.average(
np.sum((transformed_labels - y_proba) ** 2, axis=1), weights=sample_weight
transformed_labels = xp.asarray(transformed_labels, device=device)
y_proba = xp.asarray(y_proba, device=device)
Comment on lines +3615 to +3616
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Shouldn't these be on the device already. I think y_proba might be shifted to cpu because of the check_array function but assuming that y_true and y_prob are on the expected device transformed_labels should be on the device as well. Or is this just handling for the array-api-strict?


# If transformed_labels is integer array, cast it to the floating dtype of
# y_proba
transformed_labels = xp.astype(transformed_labels, y_proba.dtype, device=device)
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Here we are again moving it on the device?

brier_score = _average(
xp.sum((transformed_labels - y_proba) ** 2, axis=1), weights=sample_weight
)

if scale_by_half == "auto":
Expand Down
1 change: 1 addition & 0 deletions 1 sklearn/metrics/tests/test_common.py
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Expand Up @@ -2229,6 +2229,7 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name)
check_array_api_regression_metric_multioutput,
],
sigmoid_kernel: [check_array_api_metric_pairwise],
brier_score_loss: [check_array_api_binary_classification_metric],
}


Expand Down
41 changes: 26 additions & 15 deletions 41 sklearn/utils/validation.py
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Expand Up @@ -18,7 +18,14 @@

from .. import get_config as _get_config
from ..exceptions import DataConversionWarning, NotFittedError, PositiveSpectrumWarning
from ..utils._array_api import _asarray_with_order, _is_numpy_namespace, get_namespace
from ..utils._array_api import (
_asarray_with_order,
_convert_to_numpy,
_is_numpy_namespace,
_max_precision_float_dtype,
get_namespace,
get_namespace_and_device,
)
from ..utils.deprecation import _deprecate_force_all_finite
from ..utils.fixes import ComplexWarning, _preserve_dia_indices_dtype
from ._isfinite import FiniteStatus, cy_isfinite
Expand Down Expand Up @@ -2148,9 +2155,10 @@ def _check_sample_weight(
dtype of the validated `sample_weight`.
If None, and `sample_weight` is an array:

- If `sample_weight.dtype` is one of `{np.float64, np.float32}`,
- If `sample_weight.dtype` is one of `{xp.float64, xp.float32}`,
then the dtype is preserved.
- Else the output has NumPy's default dtype: `np.float64`.
- Otherwise, the output has the highest precision floating point dtype
supported by the array namespace/device of the input arrays.

If `dtype` is not `{np.float32, np.float64, None}`, then output will
be `np.float64`.
Expand All @@ -2169,17 +2177,18 @@ def _check_sample_weight(
Validated sample weight. It is guaranteed to be "C" contiguous.
"""
n_samples = _num_samples(X)
xp, _, device = get_namespace_and_device(X, sample_weight)

if dtype is not None and dtype not in [np.float32, np.float64]:
dtype = np.float64
if dtype is not None and dtype not in [xp.float32, xp.float64]:
dtype = _max_precision_float_dtype(xp, device)

if sample_weight is None:
sample_weight = np.ones(n_samples, dtype=dtype)
sample_weight = xp.ones(n_samples, dtype=dtype)
elif isinstance(sample_weight, numbers.Number):
sample_weight = np.full(n_samples, sample_weight, dtype=dtype)
sample_weight = xp.full(n_samples, sample_weight, dtype=dtype)
else:
if dtype is None:
dtype = [np.float64, np.float32]
dtype = [xp.float64, xp.float32]
sample_weight = check_array(
sample_weight,
accept_sparse=False,
Expand Down Expand Up @@ -2629,14 +2638,16 @@ def _check_pos_label_consistency(pos_label, y_true):
# when elements in the two arrays are not comparable.
if pos_label is None:
# Compute classes only if pos_label is not specified:
classes = np.unique(y_true)
if classes.dtype.kind in "OUS" or not (
np.array_equal(classes, [0, 1])
or np.array_equal(classes, [-1, 1])
or np.array_equal(classes, [0])
or np.array_equal(classes, [-1])
or np.array_equal(classes, [1])
xp, _, device = get_namespace_and_device(y_true)
classes = xp.unique_values(y_true)
if (_is_numpy_namespace(xp) and classes.dtype.kind in "OUS") or not (
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I seem to recall seeing a similar kind of change in another PR?

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This is the part from #30878.

xp.all(classes == xp.asarray([0, 1], device=device))
or xp.all(classes == xp.asarray([-1, 1], device=device))
or xp.all(classes == xp.asarray([0], device=device))
or xp.all(classes == xp.asarray([-1], device=device))
or xp.all(classes == xp.asarray([1], device=device))
):
classes = _convert_to_numpy(classes, xp=xp)
classes_repr = ", ".join([repr(c) for c in classes.tolist()])
raise ValueError(
f"y_true takes value in {{{classes_repr}}} and pos_label is not "
Expand Down
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