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ENH add predict_log_proba to ClassifierChain #27720

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Dec 6, 2023
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ENH add predict_log_proba to ClassifierChain #27720

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ENH add predict_log_proba to ClassifierChain
glemaitre Nov 3, 2023
c2eb71c
update pr number
glemaitre Nov 3, 2023
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6 changes: 6 additions & 0 deletions doc/whats_new/v1.4.rst
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Expand Up @@ -417,6 +417,12 @@ Changelog
object in the parameter grid if it's an estimator. :pr:`26786` by `Adrin
Jalali`_.

:mod:`sklearn.multioutput`
..........................

- |Enhancement| Add method `predict_log_proba` to :class:`multioutput.ClassifierChain`.
:pr:`27720` by :user:`Guillaume Lemaitre <glemaitre>`.

:mod:`sklearn.neighbors`
........................

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15 changes: 15 additions & 0 deletions sklearn/multioutput.py
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Expand Up @@ -956,6 +956,21 @@ def predict_proba(self, X):

return Y_prob

def predict_log_proba(self, X):
"""Predict logarithm of probability estimates.

Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.

Returns
-------
Y_log_prob : array-like of shape (n_samples, n_classes)
The predicted logarithm of the probabilities.
"""
return np.log(self.predict_proba(X))

@_available_if_base_estimator_has("decision_function")
def decision_function(self, X):
"""Evaluate the decision_function of the models in the chain.
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7 changes: 5 additions & 2 deletions sklearn/tests/test_multioutput.py
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Expand Up @@ -548,7 +548,8 @@ def test_classifier_chain_vs_independent_models():
)


def test_base_chain_fit_and_predict():
@pytest.mark.parametrize("response_method", ["predict_proba", "predict_log_proba"])
def test_base_chain_fit_and_predict(response_method):
# Fit base chain and verify predict performance
X, Y = generate_multilabel_dataset_with_correlations()
chains = [RegressorChain(Ridge()), ClassifierChain(LogisticRegression())]
Expand All @@ -560,7 +561,9 @@ def test_base_chain_fit_and_predict():
range(X.shape[1], X.shape[1] + Y.shape[1])
)

Y_prob = chains[1].predict_proba(X)
Y_prob = getattr(chains[1], response_method)(X)
if response_method == "predict_log_proba":
Y_prob = np.exp(Y_prob)
Y_binary = Y_prob >= 0.5
assert_array_equal(Y_binary, Y_pred)

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