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TST check sample_weight shape added to common tests #11598

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Merged
merged 10 commits into from
Feb 21, 2020
Merged

TST check sample_weight shape added to common tests #11598

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fb33cb3
check sample_weight shape added
sergulaydore Jul 17, 2018
d32e7c8
Merge branch 'master' into ensure_sample_weight_shape
rth Jul 3, 2019
c16fff2
Merge branch 'master' into pr/11598
amueller Aug 7, 2019
1717789
Merge remote-tracking branch 'upstream/master' into ensure_sample_wei…
rth Feb 21, 2020
6bf4c60
Remove redundant checks
rth Feb 21, 2020
6d922a3
Minor fix
rth Feb 21, 2020
4b3c3b0
Also check for incorrect ndim
rth Feb 21, 2020
f5661aa
Remove redundant tests
rth Feb 21, 2020
dc706df
Fix tests
rth Feb 21, 2020
9b97866
Fix failing test
rth Feb 21, 2020
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4 changes: 0 additions & 4 deletions sklearn/ensemble/tests/test_gradient_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -345,10 +345,6 @@ def test_check_inputs():
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.fit, X, y + [0, 1])

clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.fit, X, y,
sample_weight=([1] * len(y)) + [0, 1])

weight = [0, 0, 0, 1, 1, 1]
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
msg = ("y contains 1 class after sample_weight trimmed classes with "
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4 changes: 0 additions & 4 deletions sklearn/neighbors/tests/test_kde.py
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Expand Up @@ -210,10 +210,6 @@ def test_sample_weight_invalid():
kde = KernelDensity()
data = np.reshape([1., 2., 3.], (-1, 1))

sample_weight = [0.1, 0.2]
with pytest.raises(ValueError):
kde.fit(data, sample_weight=sample_weight)

sample_weight = [0.1, -0.2, 0.3]
expected_err = "sample_weight must have positive values"
with pytest.raises(ValueError, match=expected_err):
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5 changes: 0 additions & 5 deletions sklearn/svm/tests/test_svm.py
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Expand Up @@ -639,11 +639,6 @@ def test_bad_input():
with pytest.raises(ValueError):
clf.fit(X, Y)

# sample_weight bad dimensions
clf = svm.SVC()
with pytest.raises(ValueError):
clf.fit(X, Y, sample_weight=range(len(X) - 1))

# predict with sparse input when trained with dense
clf = svm.SVC().fit(X, Y)
with pytest.raises(ValueError):
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8 changes: 0 additions & 8 deletions sklearn/tree/tests/test_tree.py
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Expand Up @@ -1125,14 +1125,6 @@ def test_sample_weight_invalid():
with pytest.raises(TypeError, match=expected_err):
clf.fit(X, y, sample_weight=sample_weight)

sample_weight = np.ones(101)
with pytest.raises(ValueError):
clf.fit(X, y, sample_weight=sample_weight)

sample_weight = np.ones(99)
with pytest.raises(ValueError):
clf.fit(X, y, sample_weight=sample_weight)


def check_class_weights(name):
"""Check class_weights resemble sample_weights behavior."""
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26 changes: 26 additions & 0 deletions sklearn/utils/estimator_checks.py
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Expand Up @@ -80,6 +80,7 @@ def _yield_checks(name, estimator):
yield check_sample_weights_pandas_series
yield check_sample_weights_not_an_array
yield check_sample_weights_list
yield check_sample_weights_shape
yield check_sample_weights_invariance
yield check_estimators_fit_returns_self
yield partial(check_estimators_fit_returns_self, readonly_memmap=True)
Expand Down Expand Up @@ -764,6 +765,31 @@ def check_sample_weights_list(name, estimator_orig):
estimator.fit(X, y, sample_weight=sample_weight)


@ignore_warnings(category=FutureWarning)
def check_sample_weights_shape(name, estimator_orig):
# check that estimators raise an error if sample_weight
# shape mismatches the input
if (has_fit_parameter(estimator_orig, "sample_weight") and
not (hasattr(estimator_orig, "_pairwise")
and estimator_orig._pairwise)):
estimator = clone(estimator_orig)
X = np.array([[1, 3], [1, 3], [1, 3], [1, 3],
[2, 1], [2, 1], [2, 1], [2, 1],
[3, 3], [3, 3], [3, 3], [3, 3],
[4, 1], [4, 1], [4, 1], [4, 1]])
y = np.array([1, 1, 1, 1, 2, 2, 2, 2,
1, 1, 1, 1, 2, 2, 2, 2])
y = _enforce_estimator_tags_y(estimator, y)

estimator.fit(X, y, sample_weight=np.ones(len(y)))

assert_raises(ValueError, estimator.fit, X, y,
sample_weight=np.ones(2*len(y)))

assert_raises(ValueError, estimator.fit, X, y,
sample_weight=np.ones((len(y), 2)))


@ignore_warnings(category=FutureWarning)
def check_sample_weights_invariance(name, estimator_orig):
# check that the estimators yield same results for
Expand Down