8000 FIX make CalibratedClassifierCV not enforce sample alignment for fit_… · thomasjpfan/scikit-learn@30bf6f3 · GitHub
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FIX make CalibratedClassifierCV not enforce sample alignment for fit_params (scikit-learn#25805)
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‎doc/whats_new/v1.3.rst‎

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@@ -143,6 +143,12 @@ Changelog
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- |Feature| A `__sklearn_clone__` protocol is now available to override the
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default behavior of :func:`base.clone`. :pr:`24568` by `Thomas Fan`_.
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:mod:`sklearn.calibration`
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..........................
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- |Fix| :class:`calibration.CalibratedClassifierCV` now does not enforce sample
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alignment on `fit_params`. :pr:`25805` by `Adrin Jalali`_.
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:mod:`sklearn.cluster`
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......................
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‎sklearn/calibration.py‎

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@@ -308,9 +308,6 @@ def fit(self, X, y, sample_weight=None, **fit_params):
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if sample_weight is not None:
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sample_weight = _check_sample_weight(sample_weight, X)
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for sample_aligned_params in fit_params.values():
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check_consistent_length(y, sample_aligned_params)
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# TODO(1.4): Remove when base_estimator is removed
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if self.base_estimator != "deprecated":
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if self.estimator is not None:

‎sklearn/tests/test_calibration.py‎

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@@ -974,23 +974,6 @@ def fit(self, X, y, **fit_params):
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pc_clf.fit(X, y, sample_weight=sample_weight)
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def test_calibration_with_fit_params_inconsistent_length(data):
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"""fit_params having different length than data should raise the
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correct error message.
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"""
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X, y = data
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fit_params = {"a": y[:5]}
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clf = CheckingClassifier(expected_fit_params=fit_params)
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pc_clf = CalibratedClassifierCV(clf)
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msg = (
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r"Found input variables with inconsistent numbers of "
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r"samples: \[" + str(N_SAMPLES) + r", 5\]"
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)
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with pytest.raises(ValueError, match=msg):
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pc_clf.fit(X, y, **fit_params)
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@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
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@pytest.mark.parametrize("ensemble", [True, False])
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def test_calibrated_classifier_cv_zeros_sample_weights_equivalence(method, ensemble):
@@ -1054,3 +1037,17 @@ def test_calibrated_classifier_deprecation_base_estimator(data):
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warn_msg = "`base_estimator` was renamed to `estimator`"
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with pytest.warns(FutureWarning, match=warn_msg):
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calibrated_classifier.fit(*data)
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def test_calibration_with_non_sample_aligned_fit_param(data):
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"""Check that CalibratedClassifierCV does not enforce sample alignment
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for fit parameters."""
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class TestClassifier(LogisticRegression):
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def fit(self, X, y, sample_weight=None, fit_param=None):
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assert fit_param is not None
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return super().fit(X, y, sample_weight=sample_weight)
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CalibratedClassifierCV(estimator=TestClassifier()).fit(
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*data, fit_param=np.ones(len(data[1]) + 1)
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)

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