8000 [MRG + 1] Set min_impurity_split in gradient boosting models by sebp · Pull Request #8007 · scikit-learn/scikit-learn · GitHub
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[MRG + 1] Set min_impurity_split in gradient boosting models #8007

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Merged
merged 1 commit into from
Dec 13, 2016
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[MRG + 1] Set min_impurity_split in gradient boosting models #8007

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6 changes: 5 additions & 1 deletion doc/whats_new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -118,6 +118,11 @@ Bug fixes
when a numpy array is passed in for weights. :issue:`7983` by
:user:`Vincent Pham <vincentpham1991>`.

- Fix a bug where :class:`sklearn.ensemble.GradientBoostingClassifier` and
:class:`sklearn.ensemble.GradientBoostingRegressor` ignored the
``min_impurity_split`` parameter.
:issue:`8006` by :user:`Sebastian Pölsterl <sebp>`.

API changes summary
-------------------

Expand All @@ -127,7 +132,6 @@ API changes summary
now only have ``self.estimators_`` available after ``fit``.
:issue:`7464` by `Lars Buitinck`_ and `Loic Esteve`_.


.. _changes_0_18_1:

Version 0.18.1
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1 change: 1 addition & 0 deletions sklearn/ensemble/gradient_boosting.py
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Expand Up @@ -765,6 +765,7 @@ def _fit_stage(self, i, X, y, y_pred, sample_weight, sample_mask,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
min_impurity_split=self.min_impurity_split,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
random_state=random_state,
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13 changes: 13 additions & 0 deletions sklearn/ensemble/tests/test_gradient_boosting.py
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Expand Up @@ -961,6 +961,19 @@ def test_max_leaf_nodes_max_depth():
assert_equal(tree.max_depth, 1)


def test_min_impurity_split():
# Test if min_impurity_split of base estimators is set
# Regression test for #8006
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
all_estimators = [GradientBoostingRegressor,
GradientBoostingClassifier]

for GBEstimator in all_estimators:
est = GBEstimator(min_impurity_split=0.1).fit(X, y)
for tree in est.estimators_.flat:
assert_equal(tree.min_impurity_split, 0.1)


def test_warm_start_wo_nestimators_change():
# Test if warm_start does nothing if n_estimators is not changed.
# Regression test for #3513.
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