8000 [MRG] DEP deprecate "mae" criterion in GadientBoosting estimators by madhuracj · Pull Request #18326 · scikit-learn/scikit-learn · GitHub
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[MRG] DEP deprecate "mae" criterion in GadientBoosting estimators #18326

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Merged
merged 4 commits into from
Sep 4, 2020
Merged

[MRG] DEP deprecate "mae" criterion in GadientBoosting estimators #18326

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e3f758f
Deprecate criterion='mae' in GradientBoostingClassifier and GradientB…
madhuracj Sep 2, 2020
f9f1a3e
Add change log entry for #18263
madhuracj Sep 2, 2020
e04af1c
Explain the reasons for deprecation
madhuracj Sep 3, 2020
7d63e66
Add explanation to the warning as well
madhuracj Sep 4, 2020
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5 changes: 5 additions & 0 deletions doc/whats_new/v0.24.rst
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Expand Up @@ -192,6 +192,11 @@ Changelog
:class:`ensemble.GradientBoostingRegressor` and returns `1`.
:pr:`17702` by :user:`Simona Maggio <simonamaggio>`.

- |API|: Mean absolute error ('mae') is now deprecated for the parameter
``criterion`` in :class:`ensemble.GradientBoostingRegressor` and
:class:`ensemble.GradientBoostingClassifier`.
:pr:`18326` by :user:`Madhura Jayaratne <madhuracj>`.

:mod:`sklearn.exceptions`
.........................

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31 changes: 31 additions & 0 deletions sklearn/ensemble/_gb.py
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Expand Up @@ -358,6 +358,10 @@ def _check_initialized(self):
"""Check that the estimator is initialized, raising an error if not."""
check_is_fitted(self)

@abstractmethod
def _warn_mae_for_criterion(self):
pass

def fit(self, X, y, sample_weight=None, monitor=None):
"""Fit the gradient boosting model.

Expand Down Expand Up @@ -393,6 +397,10 @@ def fit(self, X, y, sample_weight=None, monitor=None):
-------
self : object
"""
if self.criterion == 'mae':
# TODO: This should raise an error from 0.26
self._warn_mae_for_criterion()

# if not warmstart - clear the estimator state
if not self.warm_start:
self._clear_state()
Expand Down Expand Up @@ -802,6 +810,10 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
some cases.

.. versionadded:: 0.18
.. deprecated:: 0.24
`criterion='mae'` is deprecated and will be removed in version
0.26. Use `criterion='friedman_mse'` or `'mse'` instead, as trees
should use a least-square criterion in Gradient Boosting.

min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
Expand Down Expand Up @@ -1102,6 +1114,14 @@ def _validate_y(self, y, sample_weight):
self.n_classes_ = self._n_classes
return y

def _warn_mae_for_criterion(self):
# TODO: This should raise an error from 0.26
warnings.warn("criterion='mae' was deprecated in version 0.24 and "
"will be removed in version 0.26. Use "
"criterion='friedman_mse' or 'mse' instead, as trees "
"should use a least-square criterion in Gradient "
"Boosting.", FutureWarning)

def decision_function(self, X):
"""Compute the decision function of ``X``.

Expand Down Expand Up @@ -1320,6 +1340,10 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
some cases.

.. versionadded:: 0.18
.. deprecated:: 0.24
`criterion='mae'` is deprecated and will be removed in version
0.26. The correct way of minimizing the absolute error is to use
`loss='lad'` instead.

min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
Expand Down Expand Up @@ -1601,6 +1625,13 @@ def _validate_y(self, y, sample_weight=None):
y = y.astype(DOUBLE)
return y

def _warn_mae_for_criterion(self):
# TODO: This should raise an error from 0.26
warnings.warn("criterion='mae' was deprecated in version 0.24 and "
"will be removed in version 0.26. The correct way of "
"minimizing the absolute error is to use loss='lad' "
"instead.", FutureWarning)

def predict(self, X):
"""Predict regression target for X.

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14 changes: 14 additions & 0 deletions sklearn/ensemble/tests/test_gradient_boosting.py
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583D
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Expand Up @@ -1333,3 +1333,17 @@ def test_attr_error_raised_if_not_fitted():
)
with pytest.raises(AttributeError, match=msg):
gbr.n_classes_


# TODO: Update in 0.26 to check for the error raised
@pytest.mark.parametrize('estimator', [
GradientBoostingClassifier(criterion='mae'),
GradientBoostingRegressor(criterion='mae')
])
def test_criterion_mae_deprecation(estimator):
# checks whether a deprecation warning is issues when criterion='mae'
# is used.
msg = ("criterion='mae' was deprecated in version 0.24 and "
"will be removed in version 0.26.")
with pytest.warns(FutureWarning, match=msg):
estimator.fit(X, y)
0