8000 API Removes tol=None option from HistGradientBoosting* by thomasjpfan · Pull Request #19296 · scikit-learn/scikit-learn · GitHub
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API Removes tol=None option from HistGradientBoosting* #19296

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Merged
merged 2 commits into from
Jan 29, 2021
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API Removes tol=None option from HistGradientBoosting* #19296

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7cf3494
API Removes tol=None option from HistGradient*
thomasjpfan Jan 29, 2021
ff79227
Pass tol=0.0 instead of tol=None in early stopping tests
ogrisel Jan 29, 2021
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9 changes: 4 additions & 5 deletions sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -82,7 +82,7 @@ def _validate_parameters(self):
raise ValueError(
'validation_fraction={} must be strictly '
'positive, or None.'.format(self.validation_fraction))
if self.tol is not None and self.tol < 0:
if self.tol < 0:
raise ValueError('tol={} '
'must not be smaller than 0.'.format(self.tol))

Expand Down Expand Up @@ -646,8 +646,7 @@ def _should_stop(self, scores):
# harder for subsequent iteration to be considered an improvement upon
# the reference score, and therefore it is more likely to early stop
# because of the lack of significant improvement.
tol = 0 if self.tol is None else self.tol
reference_score = scores[-reference_position] + tol
reference_score = scores[-reference_position] + self.tol
recent_scores = scores[-reference_position + 1:]
recent_improvements = [score > reference_score
for score in recent_scores]
Expand Down Expand Up @@ -992,7 +991,7 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
stopped when none of the last ``n_iter_no_change`` scores are better
than the ``n_iter_no_change - 1`` -th-to-last one, up to some
tolerance. Only used if early stopping is performed.
tol : float or None, default=1e-7
tol : float, default=1e-7
The absolute tolerance to use when comparing scores during early
stopping. The higher the tolerance, the more likely we are to early
stop: higher tolerance means that it will be harder for subsequent
Expand Down Expand Up @@ -1245,7 +1244,7 @@ class HistGradientBoostingClassifier(ClassifierMixin,
stopped when none of the last ``n_iter_no_change`` scores are better
than the ``n_iter_no_change - 1`` -th-to-last one, up to some
tolerance. Only used if early stopping is performed.
tol : float or None, default=1e-7
tol : float, default=1e-7
The absolute tolerance to use when comparing scores. The higher the
tolerance, the more likely we are to early stop: higher tolerance
means that it will be harder for subsequent iterations to be
Expand Down
4 changes: 2 additions & 2 deletions sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
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Expand Up @@ -85,7 +85,7 @@ def test_invalid_classification_loss():
(None, None, True, 5, 1e-1),
('loss', .1, True, 5, 1e-7), # use loss
('loss', None, True, 5, 1e-1), # use loss on training data
(None, None, False, 5, None), # no early stopping
(None, None, False, 5, 0.0), # no early stopping
])
def test_early_stopping_regression(scoring, validation_fraction,
early_stopping, n_iter_no_change, tol):
Expand Down Expand Up @@ -126,7 +126,7 @@ def test_early_stopping_regression(scoring, validation_fraction,
(None, None, True, 5, 1e-1),
('loss', .1, True, 5, 1e-7), # use loss
('loss', None, True, 5, 1e-1), # use loss on training data
(None, None, False, 5, None), # no early stopping
(None, None, False, 5, 0.0), # no early stopping
])
def test_early_stopping_classification(data, scoring, validation_fraction,
early_stopping, n_iter_no_change, tol):
Expand Down
0