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Fix ClassifierChain error message for multiclass-multioutput targets #31797

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Fix ClassifierChain error message for multiclass-multioutput targets #31797

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16 changes: 15 additions & 1 deletion sklearn/multioutput.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@
process_routing,
)
from .utils.metaestimators import available_if
from .utils.multiclass import check_classification_targets
from .utils.multiclass import check_classification_targets, type_of_target
from .utils.parallel import Parallel, delayed
from .utils.validation import (
_check_method_params,
Expand Down Expand Up @@ -1086,6 +1086,20 @@ def fit(self, X, Y, **fit_params):
"""
_raise_for_params(fit_params, self, "fit")

# Validate input data
X, Y = validate_data(self, X, Y, multi_output=True, accept_sparse=True)

# Check if we have multiclass-multioutput targets, which are not supported
target_type = type_of_target(Y)
if target_type == "multiclass-multioutput":
raise ValueError(
"ClassifierChain does not support multiclass-multioutput "
"targets. ClassifierChain is designed for multilabel "
"classification where each target is binary (0 or 1). "
"Your target has multiple classes per output. "
"Consider using MultiOutputClassifier instead."
)

super().fit(X, Y, **fit_params)
self.classes_ = [estimator.classes_ for estimator in self.estimators_]
return self
Expand Down
48 changes: 48 additions & 0 deletions sklearn/tests/test_multioutput.py
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Expand Up @@ -878,3 +878,51 @@ def test_base_estimator_deprecation(Estimator):

with pytest.raises(ValueError):
Estimator(base_estimator=estimator, estimator=estimator).fit(X, y)


def test_classifier_chain_multiclass_multioutput_error():
"""Test that ClassifierChain raises clear error for multiclass-multioutput targets."""
# Create multiclass-multioutput data (3 classes per output)
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
y_multiclass_multioutput = np.array([
[0, 1], # First output: class 0, Second output: class 1
[1, 2], # First output: class 1, Second output: class 2
[2, 0], # First output: class 2, Second output: class 0
[0, 2], # First output: class 0, Second output: class 2
[1, 1], # First output: class 1, Second output: class 1
[2, 0], # First output: class 2, Second output: class 0
])

# This should raise a ValueError with clear message
chain = ClassifierChain(LogisticRegression(random_state=42))

expected_msg = (
"ClassifierChain does not support multiclass-multioutput targets. "
"ClassifierChain is designed for multilabel classification where "
"each target is binary \\(0 or 1\\). Your target has multiple classes "
"per output. Consider using MultiOutputClassifier instead."
)

with pytest.raises(ValueError, match=expected_msg):
chain.fit(X, y_multiclass_multioutput)


def test_classifier_chain_multilabel_still_works():
"""Test that ClassifierChain still works correctly with multilabel data."""
# Create proper multilabel data (binary values only)
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y_multilabel = np.array([
[0, 1], # Not label 1, has label 2
[1, 1], # Has label 1, has label 2
[0, 0], # No labels
[1, 0], # Has label 1, not label 2
])

# This should work fine (no error)
chain = ClassifierChain(LogisticRegression(random_state=42))
chain.fit(X, y_multilabel)

# Basic functionality check
predictions = chain.predict(X)
assert predictions.shape == y_multilabel.shape
assert np.all((predictions == 0) | (predictions == 1)) # Should be binary
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