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FIX Apply dtype param in `check_array_api_compute_metric` unit test (#27940)

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Franck Charras 2023-12-18 16:43:08 +01:00 committed by GitHub
parent 94b84718f9
commit 77aeb825b6
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8 changed files with 100 additions and 66 deletions
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24
sklearn/decomposition/tests/test_pca.py
View File

@ -817,9 +817,9 @@ def test_variance_correctness(copy):
np.testing.assert_allclose(pca_var, true_var)
def check_array_api_get_precision(name, estimator, array_namespace, device, dtype):
def check_array_api_get_precision(name, estimator, array_namespace, device, dtype_name):
xp = _array_api_for_tests(array_namespace, device)
iris_np = iris.data.astype(dtype)
iris_np = iris.data.astype(dtype_name)
iris_xp = xp.asarray(iris_np, device=device)
estimator.fit(iris_np)
@ -835,7 +835,7 @@ def check_array_api_get_precision(name, estimator, array_namespace, device, dtyp
assert_allclose(
_convert_to_numpy(precision_xp, xp=xp),
precision_np,
atol=_atol_for_type(dtype),
atol=_atol_for_type(dtype_name),
)
covariance_xp = estimator_xp.get_covariance()
assert covariance_xp.shape == (4, 4)
@ -844,12 +844,12 @@ def check_array_api_get_precision(name, estimator, array_namespace, device, dtyp
assert_allclose(
_convert_to_numpy(covariance_xp, xp=xp),
covariance_np,
atol=_atol_for_type(dtype),
atol=_atol_for_type(dtype_name),
)
@pytest.mark.parametrize(
"array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
"array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
)
@pytest.mark.parametrize(
"check",
@ -870,13 +870,15 @@ def check_array_api_get_precision(name, estimator, array_namespace, device, dtyp
],
ids=_get_check_estimator_ids,
)
def test_pca_array_api_compliance(estimator, check, array_namespace, device, dtype):
def test_pca_array_api_compliance(
estimator, check, array_namespace, device, dtype_name
):
name = estimator.__class__.__name__
check(name, estimator, array_namespace, device=device, dtype=dtype)
check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
@pytest.mark.parametrize(
"array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
"array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
)
@pytest.mark.parametrize(
"check",
@ -892,9 +894,11 @@ def test_pca_array_api_compliance(estimator, check, array_namespace, device, dty
],
ids=_get_check_estimator_ids,
)
def test_pca_mle_array_api_compliance(estimator, check, array_namespace, device, dtype):
def test_pca_mle_array_api_compliance(
estimator, check, array_namespace, device, dtype_name
):
name = estimator.__class__.__name__
check(name, estimator, array_namespace, device=device, dtype=dtype)
check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
def test_array_api_error_and_warnings_on_unsupported_params():

86
sklearn/metrics/tests/test_common.py
View File

@ -1733,67 +1733,87 @@ def test_metrics_pos_label_error_str(metric, y_pred_threshold, dtype_y_str):
def check_array_api_metric(
metric, array_namespace, device, dtype, y_true_np, y_pred_np, sample_weight=None
metric, array_namespace, device, dtype_name, y_true_np, y_pred_np, sample_weight
):
xp = _array_api_for_tests(array_namespace, device)
y_true_xp = xp.asarray(y_true_np, device=device)
y_pred_xp = xp.asarray(y_pred_np, device=device)
metric_np = metric(y_true_np, y_pred_np, sample_weight=sample_weight)
if sample_weight is not None:
sample_weight = xp.asarray(sample_weight, device=device)
with config_context(array_api_dispatch=True):
if sample_weight is not None:
sample_weight = xp.asarray(sample_weight, device=device)
metric_xp = metric(y_true_xp, y_pred_xp, sample_weight=sample_weight)
assert_allclose(
metric_xp,
metric_np,
atol=_atol_for_type(dtype),
atol=_atol_for_type(dtype_name),
)
def check_array_api_binary_classification_metric(
metric, array_namespace, device, dtype
metric, array_namespace, device, dtype_name
):
y_true_np = np.array([0, 0, 1, 1])
y_pred_np = np.array([0, 1, 0, 1])
check_array_api_metric(
metric, array_namespace, device, dtype, y_true_np=y_true_np, y_pred_np=y_pred_np
metric,
array_namespace,
device,
dtype_name,
y_true_np=y_true_np,
y_pred_np=y_pred_np,
sample_weight=None,
)
sample_weight = np.array([0.0, 0.1, 2.0, 1.0], dtype=dtype_name)
check_array_api_metric(
metric,
array_namespace,
device,
dtype_name,
y_true_np=y_true_np,
y_pred_np=y_pred_np,
sample_weight=sample_weight,
)
if "sample_weight" in signature(metric).parameters:
check_array_api_metric(
metric,
array_namespace,
device,
dtype,
y_true_np=y_true_np,
y_pred_np=y_pred_np,
sample_weight=np.array([0.0, 0.1, 2.0, 1.0]),
)
def check_array_api_multiclass_classification_metric(
metric, array_namespace, device, dtype
metric, array_namespace, device, dtype_name
):
y_true_np = np.array([0, 1, 2, 3])
y_pred_np = np.array([0, 1, 0, 2])
check_array_api_metric(
metric, array_namespace, device, dtype, y_true_np=y_true_np, y_pred_np=y_pred_np
metric,
array_namespace,
device,
dtype_name,
y_true_np=y_true_np,
y_pred_np=y_pred_np,
sample_weight=None,
)
sample_weight = np.array([0.0, 0.1, 2.0, 1.0], dtype=dtype_name)
check_array_api_metric(
metric,
array_namespace,
device,
dtype_name,
y_true_np=y_true_np,
y_pred_np=y_pred_np,
sample_weight=sample_weight,
)
if "sample_weight" in signature(metric).parameters:
check_array_api_metric(
metric,
array_namespace,
device,
dtype,
y_true_np=y_true_np,
y_pred_np=y_pred_np,
sample_weight=np.array([0.0, 0.1, 2.0, 1.0]),
)
metric_checkers = {
array_api_metric_checkers = {
accuracy_score: [
check_array_api_binary_classification_metric,
check_array_api_multiclass_classification_metric,
@ -1805,15 +1825,15 @@ metric_checkers = {
}
def yield_metric_checker_combinations(metric_checkers=metric_checkers):
def yield_metric_checker_combinations(metric_checkers=array_api_metric_checkers):
for metric, checkers in metric_checkers.items():
for checker in checkers:
yield metric, checker
@pytest.mark.parametrize(
"array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
"array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
)
@pytest.mark.parametrize("metric, check_func", yield_metric_checker_combinations())
def test_array_api_compliance(metric, array_namespace, device, dtype, check_func):
check_func(metric, array_namespace, device, dtype)
def test_array_api_compliance(metric, array_namespace, device, dtype_name, check_func):
check_func(metric, array_namespace, device, dtype_name)

10
sklearn/model_selection/tests/test_split.py
View File

@ -1267,7 +1267,7 @@ def test_train_test_split_default_test_size(train_size, exp_train, exp_test):
@pytest.mark.parametrize(
"array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
"array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
)
@pytest.mark.parametrize(
"shuffle,stratify",
@ -1278,16 +1278,18 @@ def test_train_test_split_default_test_size(train_size, exp_train, exp_test):
(False, None),
),
)
def test_array_api_train_test_split(shuffle, stratify, array_namespace, device, dtype):
def test_array_api_train_test_split(
shuffle, stratify, array_namespace, device, dtype_name
):
xp = _array_api_for_tests(array_namespace, device)
X = np.arange(100).reshape((10, 10))
y = np.arange(10)
X_np = X.astype(dtype)
X_np = X.astype(dtype_name)
X_xp = xp.asarray(X_np, device=device)
y_np = y.astype(dtype)
y_np = y.astype(dtype_name)
y_xp = xp.asarray(y_np, device=device)
X_train_np, X_test_np, y_train_np, y_test_np = train_test_split(

8
sklearn/preprocessing/tests/test_data.py
View File

@ -682,7 +682,7 @@ def test_standard_check_array_of_inverse_transform():
@pytest.mark.parametrize(
"array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
"array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
)
@pytest.mark.parametrize(
"check",
@ -701,9 +701,11 @@ def test_standard_check_array_of_inverse_transform():
],
ids=_get_check_estimator_ids,
)
def test_scaler_array_api_compliance(estimator, check, array_namespace, device, dtype):
def test_scaler_array_api_compliance(
estimator, check, array_namespace, device, dtype_name
):
name = estimator.__class__.__name__
check(name, estimator, array_namespace, device=device, dtype=dtype)
check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
def test_min_max_scaler_iris():

6
sklearn/utils/_array_api.py
View File

@ -24,7 +24,7 @@ def yield_namespace_device_dtype_combinations():
The name of the device on which to allocate the arrays. Can be None to
indicate that the default value should be used.
dtype : str
dtype_name : str
The name of the data type to use for arrays. Can be None to indicate
that the default value should be used.
"""
@ -444,7 +444,9 @@ def _weighted_sum(sample_score, sample_weight, normalize=False, xp=None):
sample_score = xp.astype(xp.asarray(sample_score, device="cpu"), xp.float64)
if sample_weight is not None:
sample_weight = xp.asarray(sample_weight, dtype=sample_score.dtype)
sample_weight = xp.asarray(
sample_weight, dtype=sample_score.dtype, device=device(sample_score)
)
if not xp.isdtype(sample_weight.dtype, "real floating"):
sample_weight = xp.astype(sample_weight, xp.float64)

16
sklearn/utils/estimator_checks.py
View File

@ -311,11 +311,15 @@ def _yield_outliers_checks(estimator):
def _yield_array_api_checks(estimator):
for array_namespace, device, dtype in yield_namespace_device_dtype_combinations():
for (
array_namespace,
device,
dtype_name,
) in yield_namespace_device_dtype_combinations():
yield partial(
check_array_api_input,
array_namespace=array_namespace,
dtype=dtype,
dtype_name=dtype_name,
device=device,
)
@ -864,7 +868,7 @@ def check_array_api_input(
estimator_orig,
array_namespace,
device=None,
dtype="float64",
dtype_name="float64",
check_values=False,
):
"""Check that the estimator can work consistently with the Array API
@ -878,7 +882,7 @@ def check_array_api_input(
xp = _array_api_for_tests(array_namespace, device)
X, y = make_classification(random_state=42)
X = X.astype(dtype, copy=False)
X = X.astype(dtype_name, copy=False)
X = _enforce_estimator_tags_X(estimator_orig, X)
y = _enforce_estimator_tags_y(estimator_orig, y)
@ -1007,14 +1011,14 @@ def check_array_api_input_and_values(
estimator_orig,
array_namespace,
device=None,
dtype="float64",
dtype_name="float64",
):
return check_array_api_input(
name,
estimator_orig,
array_namespace=array_namespace,
device=device,
dtype=dtype,
dtype_name=dtype_name,
check_values=True,
)

10
sklearn/utils/tests/test_array_api.py
View File

@ -129,7 +129,7 @@ def test_asarray_with_order_ignored():
@pytest.mark.parametrize(
"array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
"array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
)
@pytest.mark.parametrize(
"sample_weight, normalize, expected",
@ -143,20 +143,20 @@ def test_asarray_with_order_ignored():
],
)
def test_weighted_sum(
array_namespace, device, dtype, sample_weight, normalize, expected
array_namespace, device, dtype_name, sample_weight, normalize, expected
):
xp = _array_api_for_tests(array_namespace, device)
sample_score = numpy.asarray([1, 2, 3, 4], dtype=dtype)
sample_score = numpy.asarray([1, 2, 3, 4], dtype=dtype_name)
sample_score = xp.asarray(sample_score, device=device)
if sample_weight is not None:
sample_weight = numpy.asarray(sample_weight, dtype=dtype)
sample_weight = numpy.asarray(sample_weight, dtype=dtype_name)
sample_weight = xp.asarray(sample_weight, device=device)
with config_context(array_api_dispatch=True):
result = _weighted_sum(sample_score, sample_weight, normalize)
assert isinstance(result, float)
assert_allclose(result, expected, atol=_atol_for_type(dtype))
assert_allclose(result, expected, atol=_atol_for_type(dtype_name))
@skip_if_array_api_compat_not_configured

6
sklearn/utils/tests/test_multiclass.py
View File

@ -379,17 +379,17 @@ def test_is_multilabel():
@pytest.mark.parametrize(
"array_namespace, device, dtype",
"array_namespace, device, dtype_name",
yield_namespace_device_dtype_combinations(),
)
def test_is_multilabel_array_api_compliance(array_namespace, device, dtype):
def test_is_multilabel_array_api_compliance(array_namespace, device, dtype_name):
xp = _array_api_for_tests(array_namespace, device)
for group, group_examples in ARRAY_API_EXAMPLES.items():
dense_exp = group == "multilabel-indicator"
for example in group_examples:
if np.asarray(example).dtype.kind == "f":
example = np.asarray(example, dtype=dtype)
example = np.asarray(example, dtype=dtype_name)
else:
example = np.asarray(example)
example = xp.asarray(example, device=device)