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[MRG+2] Add a test for sample weights for estimators #11558

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Merged
merged 13 commits into from
Jul 17, 2018
Merged

[MRG+2] Add a test for sample weights for estimators #11558

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5f6c614
test for None and ones for sample_weight added
sergulaydore Jul 16, 2018
e47a059
test for None and ones for sample_weight added
sergulaydore Jul 16, 2018
688be6a
skip KMeans based estimators
sergulaydore Jul 16, 2018
f0c871f
conflict resolved
sergulaydore Jul 16, 2018
3533647
cleaning
sergulaydore Jul 16, 2018
0738abc
dtype and X_trans
sergulaydore Jul 16, 2018
1d93961
after second reviews
sergulaydore Jul 17, 2018
6b7f20a
pyflake errors
sergulaydore Jul 17, 2018
ad6a365
Merge branch 'master' into refactor_sample_weights
sergulaydore Jul 17, 2018
ad50ab9
fixed pairwise error
sergulaydore Jul 17, 2018
033a84a
PEP8: line too long
GaelVaroquaux Jul 17, 2018
ef0371c
add comments and pep8 issues
sergulaydore Jul 17, 2018
1ca44ab
KMeans methods are not skipped anymore
sergulaydore Jul 17, 2018
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35 changes: 35 additions & 0 deletions sklearn/utils/estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,6 +88,7 @@ def _yield_non_meta_checks(name, estimator):
yield check_dtype_object
yield check_sample_weights_pandas_series
yield check_sample_weights_list
yield check_sample_weights_invariance
yield check_estimators_fit_returns_self
yield partial(check_estimators_fit_returns_self, readonly_memmap=True)
yield check_complex_data
Expand Down Expand Up @@ -554,6 +555,40 @@ def check_sample_weights_list(name, estimator_orig):
estimator.fit(X, y, sample_weight=sample_weight)


@ignore_warnings(category=(DeprecationWarning, FutureWarning))
def check_sample_weights_invariance(name, estimator_orig):
# check that the estimators yield same results for
# unit weights and no weights
if (has_fit_parameter(estimator_orig, "sample_weight") and
not (hasattr(estimator_orig, "_pairwise")
and estimator_orig._pairwise)):
# We skip pairwise because the data is not pairwise

estimator1 = clone(estimator_orig)
estimator2 = clone(estimator_orig)
set_random_state(estimator1, random_state=0)
set_random_state(estimator2, random_state=0)

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]], dtype=np.dtype('float'))
y = np.array([1, 1, 1, 1, 2, 2, 2, 2,
1, 1, 1, 1, 2, 2, 2, 2], dtype=np.dtype('int'))

estimator1.fit(X, y=y, sample_weight=np.ones(shape=len(y)))
estimator2.fit(X, y=y, sample_weight=None)

for method in ["predict", "transform"]:
if hasattr(estimator_orig, method):
X_pred1 = getattr(estimator1, method)(X)
X_pred2 = getattr(estimator2, method)(X)
assert_allclose(X_pred1, X_pred2, rtol=0.5,
err_msg="For %s sample_weight=None is not"
" equivalent to sample_weight=ones"
% name)


@ignore_warnings(category=(DeprecationWarning, FutureWarning, UserWarning))
def check_dtype_object(name, estimator_orig):
# check that estimators treat dtype object as numeric if possible
Expand Down