def test_check_estimator():
# tests that the estimator actually fails on "bad" estimators.
# not a complete test of all checks, which are very extensive.
# check that we have a set_params and can clone
msg = "it does not implement a 'get_params' methods"
assert_raises_regex(TypeError, msg, check_estimator, object)
# check that we have a fit method
msg = "object has no attribute 'fit'"
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator)
# check that fit does input validation
msg = "TypeError not raised"
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier)
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
assert_raises_regex(AssertionError, msg, check_estimator, NoCheckinPredict)
# check that estimator state does not change
# at transform/predict/predict_proba time
msg = 'Estimator changes __dict__ during predict'
assert_raises_regex(AssertionError, msg, check_estimator, ChangesDict)
# check that `fit` only changes attributes that
# are private (start with an _ or end with a _).
msg = ('Estimator changes public attribute\(s\) during the fit method.'
' Estimators are only allowed to change attributes started'
' or ended with _, but wrong_attribute changed')
assert_raises_regex(AssertionError, msg, check_estimator,
ChangesWrongAttribute)
# check that `fit` doesn't add any public attribute
msg = ('Estimator adds public attribute\(s\) during the fit method.'
' Estimators are only allowed to add private attributes'
' either started with _ or ended'
' with _ but wrong_attribute added')
assert_raises_regex(AssertionError, msg, check_estimator,
SetsWrongAttribute)
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = ("Estimator " + name + " doesn't seem to fail gracefully on"
" sparse data")
# the check for sparse input handling prints to the stdout,
# instead of raising an error, so as not to remove the original traceback.
# that means we need to jump through some hoops to catch it.
old_stdout = sys.stdout
string_buffer = StringIO()
sys.stdout = string_buffer
try:
check_estimator(NoSparseClassifier)
except:
pass
finally:
sys.stdout = old_stdout
assert_true(msg in string_buffer.getvalue())
def test_check_estimator(Estimator, err_type, err_msg):
with pytest.raises(err_type, message=err_msg):
check_estimator(Estimator)
def test_all_estimators(name, Estimator):
# don't run twice the sampler tests. Meta-estimator do not have a
# fit_resample method.
check_estimator(Estimator, run_sampler_tests=False)
def test_check_estimator():
# tests that the estimator actually fails on "bad" estimators.
# not a complete test of all checks, which are very extensive.
# check that we have a set_params and can clone
msg = "it does not implement a 'get_params' methods"
with raises(TypeError, match=msg):
check_estimator(object)
# check that we have a fit method
msg = "object has no attribute 'fit'"
with raises(AttributeError, match=msg):
check_estimator(BaseEstimator)
# check that fit does input validation
msg = "TypeError not raised"
with raises(AssertionError, match=msg):
check_estimator(BaseBadClassifier)
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
with raises(AssertionError, match=msg):
check_estimator(NoCheckinPredict)
# check that estimator state does not change
# at transform/predict/predict_proba time
msg = 'Estimator changes __dict__ during predict'
with raises(AssertionError, match=msg):
check_estimator(ChangesDict)
# check that `fit` only changes attributes that
# are private (start with an _ or end with a _).
msg = ('Estimator changes public attribute\(s\) during the fit method.'
' Estimators are only allowed to change attributes started'
' or ended with _, but wrong_attribute changed')
with raises(AssertionError, match=msg):
check_estimator(ChangesWrongAttribute)
# check that `fit` doesn't add any public attribute
msg = ('Estimator adds public attribute\(s\) during the fit method.'
' Estimators are only allowed to add private attributes'
' either started with _ or ended'
' with _ but wrong_attribute added')
with raises(AssertionError, match=msg):
check_estimator(SetsWrongAttribute)
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = ("Estimator " + name + " doesn't seem to fail gracefully on"
" sparse data")
# the check for sparse input handling prints to the stdout,
# instead of raising an error, so as not to remove the original traceback.
# that means we need to jump through some hoops to catch it.
old_stdout = sys.stdout
string_buffer = StringIO()
sys.stdout = string_buffer
try:
check_estimator(NoSparseClassifier)
except:
pass
finally:
sys.stdout = old_stdout
assert msg in string_buffer.getvalue()
def test_all_estimators(name, Estimator):
# don't run twice the sampler tests. Meta-estimator do not have a
# fit_resample method.
check_estimator(Estimator, run_sampler_tests=False)
def test_check_estimator(Estimator, err_type, err_msg):
with pytest.raises(err_type, message=err_msg):
check_estimator(Estimator)