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[MRG+1] Throw an error with explicit message if n_estimators is not an integer. #7457

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Merged
merged 8 commits into from
Oct 14, 2016
Merged

[MRG+1] Throw an error with explicit message if n_estimators is not an integer. #7457

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f3533a0
Throw an error with explicit message if n_estimators is not an integer.
fabianegli Sep 19, 2016
0a4bf16
Testing for explicit message if n_estimators is not an integer.
fabianegli Sep 19, 2016
24a0379
Fixed typo in test for explicit message if n_estimators is an integer.
fabianegli Sep 19, 2016
652c161
Added tests for np.int32 and float input.
fabianegli Sep 19, 2016
c73f7a6
pep8 compliance
fabianegli Sep 19, 2016
05c1dcd
fix function name
fabianegli Sep 19, 2016
1f7c367
Import numpy to test n_estimators suplied as numpy int32.
fabianegli Sep 19, 2016
d0666e4
rebased to current master
fabianegli Oct 10, 2016
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5 changes: 5 additions & 0 deletions sklearn/ensemble/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
# License: BSD 3 clause

import numpy as np
import numbers

from ..base import clone
from ..base import BaseEstimator
Expand Down Expand Up @@ -93,6 +94,10 @@ def __init__(self, base_estimator, n_estimators=10,
def _validate_estimator(self, default=None):
"""Check the estimator and the n_estimator attribute, set the
`base_estimator_` attribute."""
if not isinstance(self.n_estimators, (numbers.Integral, np.integer)):
raise ValueError("n_estimators must be an integer, "
"got {0}.".format(type(self.n_estimators)))

if self.n_estimators <= 0:
raise ValueError("n_estimators must be greater than zero, "
"got {0}.".format(self.n_estimators))
Expand Down
23 changes: 22 additions & 1 deletion sklearn/ensemble/tests/test_base.py
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Expand Up @@ -46,17 +46,38 @@ def test_base():
assert_true(isinstance(ensemble[2].random_state, int))
assert_not_equal(ensemble[1].random_state, ensemble[2].random_state)

np_int_ensemble = BaggingClassifier(base_estimator=Perceptron(),
n_estimators=np.int32(3))
np_int_ensemble.fit(iris.data, iris.target)


def test_base_zero_n_estimators():
# Check that instantiating a BaseEnsemble with n_estimators<=0 raises
# a ValueError.
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=0)
ensemble = BaggingClassifier(base_estimator=Perceptron(),
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why the newline?

n_estimators=0)
iris = load_iris()
assert_raise_message(ValueError,
"n_estimators must be greater than zero, got 0.",
ensemble.fit, iris.data, iris.target)


def test_base_not_int_n_estimators():
# Check that instantiating a BaseEnsemble with a string as n_estimators
# raises a ValueError demanding n_estimators to be supplied as an integer.
string_ensemble = BaggingClassifier(base_estimator=Perceptron(),
n_estimators='3')
iris = load_iris()
assert_raise_message(ValueError,
"n_estimators must be an integer",
string_ensemble.fit, iris.data, iris.target)
float_ensemble = BaggingClassifier(base_estimator=Perceptron(),
n_estimators=3.0)
assert_raise_message(ValueError,
"n_estimators must be an integer",
float_ensemble.fit, iris.data, iris.target)


def test_set_random_states():
# Linear Discriminant Analysis doesn't have random state: smoke test
_set_random_states(LinearDiscriminantAnalysis(), random_state=17)
Expand Down