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Remove median_absolute_error from METRICS_WITHOUT_SAMPLE_WEIGHT #30787

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Remove median_absolute_error from METRICS_WITHOUT_SAMPLE_WEIGHT #30787

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2e9796e
add len check
lucyleeow Feb 7, 2025
890aa74
fix test list
lucyleeow Feb 7, 2025
5bfc5b6
fix tests
lucyleeow Feb 8, 2025
967212c
lint
lucyleeow Feb 8, 2025
92884bc
use arange sample weight
lucyleeow Feb 12, 2025
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6 changes: 3 additions & 3 deletions sklearn/metrics/_regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -907,9 +907,9 @@ def median_absolute_error(
>>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.85
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
_, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
check_consistent_length(y_true, y_pred, sample_weight)

if sample_weight is None:
output_errors = np.median(np.abs(y_pred - y_true), axis=0)
else:
Expand Down
12 changes: 7 additions & 5 deletions sklearn/metrics/tests/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -552,7 +552,6 @@ def precision_recall_curve_padded_thresholds(*args, **kwargs):

# No Sample weight support
METRICS_WITHOUT_SAMPLE_WEIGHT = {
"median_absolute_error",
"max_error",
"ovo_roc_auc",
"weighted_ovo_roc_auc",
Expand Down Expand Up @@ -1445,9 +1444,10 @@ def test_averaging_multilabel_all_ones(name):
check_averaging(name, y_true, y_true_binarize, y_pred, y_pred_binarize, y_score)


def check_sample_weight_invariance(name, metric, y1, y2):
def check_sample_weight_invariance(name, metric, y1, y2, sample_weight=None):
rng = np.random.RandomState(0)
sample_weight = rng.randint(1, 10, size=len(y1))
if sample_weight is None:
sample_weight = rng.randint(1, 10, size=len(y1))

# top_k_accuracy_score always lead to a perfect score for k > 1 in the
# binary case
Expand Down Expand Up @@ -1550,13 +1550,15 @@ def check_sample_weight_invariance(name, metric, y1, y2):
),
)
def test_regression_sample_weight_invariance(name):
n_samples = 50
n_samples = 51
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This cannot be an even number due to #17370, inspired by @ogrisel last paragraph in #17370 (comment), even though I agree it's a temporary fix.

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Since #29907 was merged, it should be possible to update median_absolute_error to handle even number of data points (with or without weights) as explained below.

random_state = check_random_state(0)
# regression
y_true = random_state.random_sample(size=(n_samples,))
y_pred = random_state.random_sample(size=(n_samples,))
sample_weight = np.arange(len(y_true))
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Changed as suggested in #30787 (comment). This works great.

I've amended check_sample_weight_invariance to optionally take sample_weight as I didn't want to change the sample_weight for all tests that use check_sample_weight_invariance, WDYT @glemaitre ?

metric = ALL_METRICS[name]
check_sample_weight_invariance(name, metric, y_true, y_pred)

check_sample_weight_invariance(name, metric, y_true, y_pred, sample_weight)


@pytest.mark.parametrize(
Expand Down
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