8000 Revert "Fix max_depth overshoot in BFS expansion of trees (#12344)" · xhluca/scikit-learn@d2bf084 · GitHub
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Revert "Fix max_depth overshoot in BFS expansion of trees (scikit-learn#12344)"
This reverts commit 784e8c0.
1 parent 2f76ab3 commit d2bf084

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‎doc/whats_new/v0.21.rst

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@@ -18,8 +18,6 @@ occurs due to changes in the modelling logic (bug fixes or enhancements), or in
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random sampling procedures.
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- please add class and reason here (see version 0.20 what's new)
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- Decision trees and derived ensembles when both `max_depth` and
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`max_leaf_nodes` are set. (bug fix)
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Details are listed in the changelog below.
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@@ -127,17 +125,6 @@ Support for Python 3.4 and below has been officially dropped.
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and :class:`tree.ExtraTreeRegressor`.
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:issue:`12300` by :user:`Adrin Jalali <adrinjalali>`.
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- |Fix| Fixed an issue with :class:`tree.BaseDecisionTree`
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and consequently all estimators based
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on it, including :class:`tree.DecisionTreeClassifier`,
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:class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`,
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and :class:`tree.ExtraTreeRegressor`, where they used to exceed the given
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``max_depth`` by 1 while expanding the tree if ``max_leaf_nodes`` and
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``max_depth`` were both specified by the user. Please note that this also
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affects all ensemble methods using decision trees.
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:pr:`12344` by :user:`Adrin Jalali <adrinjalali>`.
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Multiple modules
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................
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‎sklearn/ensemble/tests/test_forest.py

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@@ -711,11 +711,11 @@ def check_max_leaf_nodes_max_depth(name):
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ForestEstimator = FOREST_ESTIMATORS[name]
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est = ForestEstimator(max_depth=1, max_leaf_nodes=4,
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n_estimators=1, random_state=0).fit(X, y)
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assert_equal(est.estimators_[0].get_depth(), 1)
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assert_greater(est.estimators_[0].tree_.max_depth, 1)
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est = ForestEstimator(max_depth=1, n_estimators=1,
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random_state=0).fit(X, y)
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assert_equal(est.estimators_[0].get_depth(), 1)
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assert_equal(est.estimators_[0].tree_.max_depth, 1)
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@pytest.mark.parametrize('name', FOREST_ESTIMATORS)

‎sklearn/ensemble/tests/test_gradient_boosting.py

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@@ -1103,7 +1103,7 @@ def test_max_leaf_nodes_max_depth(GBEstimator):
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est = GBEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
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tree = est.estimators_[0, 0].tree_
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assert_equal(tree.max_depth, 1)
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assert_greater(tree.max_depth, 1)
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est = GBEstimator(max_depth=1).fit(X, y)
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tree = est.estimators_[0, 0].tree_

‎sklearn/tree/_tree.pyx

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@@ -450,7 +450,7 @@ cdef class BestFirstTreeBuilder(TreeBuilder):
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impurity = splitter.node_impurity()
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n_node_samples = end - start
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is_leaf = (depth >= self.max_depth or
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is_leaf = (depth > self.max_depth or
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n_node_samples < self.min_samples_split or
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n_node_samples < 2 * self.min_samples_leaf or
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weighted_n_node_samples < 2 * self.min_weight_leaf or

‎sklearn/tree/tests/test_tree.py

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@@ -1210,6 +1210,7 @@ def test_class_weight_errors(name):
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def test_max_leaf_nodes():
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# Test greedy trees with max_depth + 1 leafs.
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from sklearn.tree._tree import TREE_LEAF
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X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
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k = 4
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for name, TreeEstimator in ALL_TREES.items():
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k = 4
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for name, TreeEstimator in ALL_TREES.items():
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est = TreeEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
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assert_equal(est.get_depth(), 1)
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assert_greater(est.get_depth(), 1)
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def test_arrays_persist():

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