8000 MAINT Param validation: remove n_estimators checks from validate_esti… · scikit-learn/scikit-learn@4114161 · GitHub
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naoise-hMicky774jeremiedbb
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MAINT Param validation: remove n_estimators checks from validate_estimator (#24224)
Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com> Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>
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-38
lines changed

2 files changed

+1
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lines changed

‎sklearn/ensemble/_base.py

Lines changed: 1 addition & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -4,7 +4,6 @@
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# License: BSD 3 clause
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from abc import ABCMeta, abstractmethod
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import numbers
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from typing import List
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import numpy as np
@@ -128,24 +127,10 @@ def __init__(self, base_estimator, *, n_estimators=10, estimator_params=tuple())
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# self.estimators_ needs to be filled by the derived classes in fit.
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def _validate_estimator(self, default=None):
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"""Check the estimator and the n_estimator attribute.
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"""Check the base estimator.
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Sets the base_estimator_` attributes.
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"""
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if not isinstance(self.n_estimators, numbers.Integral):
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raise ValueError(
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"n_estimators must be an integer, got {0}.".format(
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type(self.n_estimators)
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)
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)
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if self.n_estimators <= 0:
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raise ValueError(
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"n_estimators must be greater than zero, got {0}.".format(
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self.n_estimators
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)
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)
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if self.base_estimator is not None:
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self.base_estimator_ = self.base_estimator
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else:

‎sklearn/ensemble/tests/test_base.py

Lines changed: 0 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,6 @@
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# License: BSD 3 clause
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import numpy as np
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import pytest
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from sklearn.datasets import load_iris
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from sklearn.ensemble import BaggingClassifier
@@ -49,27 +48,6 @@ def test_base():
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np_int_ensemble.fit(iris.data, iris.target)
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def test_base_zero_n_estimators():
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# Check that instantiating a BaseEnsemble with n_estimators<=0 raises
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# a ValueError.
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ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=0)
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iris = load_iris()
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with pytest.raises(ValueError):
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ensemble.fit(iris.data, iris.target)
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def test_base_not_int_n_estimators():
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# Check that instantiating a BaseEnsemble with a string as n_estimators
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# raises a ValueError demanding n_estimators to be supplied as an integer.
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string_ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators="3")
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iris = load_iris()
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with pytest.raises(ValueError):
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string_ensemble.fit(iris.data, iris.target)
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float_ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3.0)
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with pytest.raises(ValueError):
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float_ensemble.fit(iris.data, iris.target)
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def test_set_random_states():
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# Linear Discriminant Analysis doesn't have random state: smoke test
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_set_random_states(LinearDiscriminantAnalysis(), random_state=17)

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