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 by fit"
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 for sparse matrix input handling
msg = "Estimator type 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())
# doesn't error on actual estimator
check_estimator(LogisticRegression)
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 by fit"
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 for sparse matrix input handling
msg = "Estimator type 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())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
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_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 sample_weights in fit accepts pandas.Series type
try:
from pandas import Series # noqa
msg = (
"Estimator NoSampleWeightPandasSeriesType raises error if "
"'sample_weight' parameter is of type pandas.Series"
)
assert_raises_regex(ValueError, msg, check_estimator, NoSampleWeightPandasSeriesType)
except ImportError:
pass
# 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 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())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
check_estimator(MultiTaskElasticNet)
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 sample_weights in fit accepts pandas.Series type
try:
from pandas import Series # noqa
msg = ("Estimator NoSampleWeightPandasSeriesType raises error if "
"'sample_weight' parameter is of type pandas.Series")
assert_raises_regex(
ValueError, msg, check_estimator, NoSampleWeightPandasSeriesType)
except ImportError:
pass
# 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 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())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
check_estimator(MultiTaskElasticNet)
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)
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)
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)
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier())
# check that sample_weights in fit accepts pandas.Series type
try:
from pandas import Series # noqa
msg = ("Estimator NoSampleWeightPandasSeriesType raises error if "
"'sample_weight' parameter is of type pandas.Series")
assert_raises_regex(
ValueError, msg, check_estimator, NoSampleWeightPandasSeriesType)
except ImportError:
pass
# 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)
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 attribures that
# are private (start with an _ or end with a _).
msg = ('Estimator ChangesWrongAttribute should not change or mutate '
'the parameter wrong_attribute from 0 to 1 during fit.')
assert_raises_regex(AssertionError, msg,
check_estimator, ChangesWrongAttribute)
check_estimator(ChangesUnderscoreAttribute)
# 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 invariant method
name = NotInvariantPredict.__name__
method = 'predict'
msg = ("{method} of {name} is not invariant when applied "
"to a subset.").format(method=method, name=name)
assert_raises_regex(AssertionError, msg,
check_estimator, NotInvariantPredict)
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = "Estimator %s doesn't seem to fail gracefully on sparse data" % name
# 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())
# doesn't error on actual estimator
check_estimator(AdaBoostClassifier)
check_estimator(AdaBoostClassifier())
check_estimator(MultiTaskElasticNet)
check_estimator(MultiTaskElasticNet())
