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MNT Revert "MNT Don't validate error messages when strict mode is off" #18481

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Merged
merged 1 commit into from
Sep 28, 2020
Merged

MNT Revert "MNT Don't validate error messages when strict mode is off" #18481

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64 changes: 30 additions & 34 deletions sklearn/utils/estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,17 +67,6 @@
CROSS_DECOMPOSITION = ['PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD']


def _assert_raises(exp_type, strict_mode, func, *args, msg=None):
"""Assert exp_type is raised when calling func(*args)

The error message is validated if strict mode is True.
"""
if strict_mode:
assert_raises_regex(exp_type, msg, func, *args)
else:
assert_raises(exp_type, func, *args)


def _yield_checks(estimator):
name = estimator.__class__.__name__
tags = estimator._get_tags()
Expand Down Expand Up @@ -168,9 +157,18 @@ def check_supervised_y_no_nan(name, estimator_orig, strict_mode=True):
y = np.full(10, np.inf)
y = _enforce_estimator_tags_y(estimator, y)

errmsg = ("Input contains NaN, infinity or a value too large for "
r"dtype\('float64'\).")
_assert_raises(ValueError, strict_mode, estimator.fit, X, y, msg=errmsg)
errmsg = "Input contains NaN, infinity or a value too large for " \
"dtype('float64')."
try:
estimator.fit(X, y)
except ValueError as e:
if str(e) != errmsg:
raise ValueError("Estimator {0} raised error as expected, but "
"does not match expected error message"
.format(name))
else:
raise ValueError("Estimator {0} should have raised error on fitting "
"array y with NaN value.".format(name))


def _yield_regressor_checks(regressor):
Expand Down Expand Up @@ -977,17 +975,16 @@ def check_dtype_object(name, estimator_orig, strict_mode=True):
if hasattr(estimator, "transform"):
estimator.transform(X)

msg = "Unknown label type"
try:
estimator.fit(X, y.astype(object))
except Exception as e:
if strict_mode and "Unknown label type" not in str(e):
if "Unknown label type" not in str(e):
raise

if 'string' not in tags['X_types']:
X[0, 0] = {'foo': 'bar'}
msg = "argument must be a string.* number"
_assert_raises(TypeError, strict_mode, estimator.fit, X, y, msg=msg)
assert_raises_regex(TypeError, msg, estimator.fit, X, y)
else:
# Estimators supporting string will not call np.asarray to convert the
# data to numeric and therefore, the error will not be raised.
Expand All @@ -1002,8 +999,8 @@ def check_complex_data(name, estimator_orig, strict_mode=True):
X = X.reshape(-1, 1)
y = np.random.sample(10) + 1j * np.random.sample(10)
estimator = clone(estimator_orig)
msg = "Complex data not supported"
_assert_raises(ValueError, strict_mode, estimator.fit, X, y, msg=msg)
assert_raises_regex(ValueError, "Complex data not supported",
estimator.fit, X, y)


@ignore_warnings
Expand Down Expand Up @@ -1209,14 +1206,12 @@ def check_fit2d_1sample(name, estimator_orig, strict_mode=True):

msgs = ["1 sample", "n_samples = 1", "n_samples=1", "one sample",
"1 class", "one class"]

try:
estimator.fit(X, y)
except ValueError as e:
if strict_mode and all(msg not in repr(e) for msg in msgs):
raise AssertionError(
"The error message should contain one of the following "
f"patterns: {', '.join(msgs)}."
) from e
if all(msg not in repr(e) for msg in msgs):
raise e


@ignore_warnings
Expand Down Expand Up @@ -1245,14 +1240,12 @@ def check_fit2d_1feature(name, estimator_orig, strict_mode=True):
set_random_state(estimator, 1)

msgs = ["1 feature(s)", "n_features = 1", "n_features=1"]

try:
estimator.fit(X, y)
except ValueError as e:
if strict_mode and all(msg not in repr(e) for msg in msgs):
raise AssertionError(
"The error message should contain one of the following "
f"patterns: {', '.join(msgs)}."
) from e
if all(msg not in repr(e) for msg in msgs):
raise e


@ignore_warnings
Expand Down Expand Up @@ -1533,7 +1526,7 @@ def check_estimators_empty_data_messages(name, estimator_orig,
y = _enforce_estimator_tags_y(e, np.array([1, 0, 1]))
msg = (r"0 feature\(s\) \(shape=\(3, 0\)\) while a minimum of \d* "
"is required.")
_assert_raises(ValueError, strict_mode, e.fit, X_zero_features, y, msg=msg)
assert_raises_regex(ValueError, msg, e.fit, X_zero_features, y)


@ignore_warnings(category=FutureWarning)
Expand Down Expand Up @@ -2928,9 +2921,9 @@ def check_classifiers_regression_target(name, estimator_orig,

X = X + 1 + abs(X.min(axis=0)) # be sure that X is non-negative
e = clone(estimator_orig)
msg = 'Unknown label type: '
if not e._get_tags()["no_validation"]:
msg = 'Unknown label type: '
_assert_raises(ValueError, strict_mode, e.fit, X, y, msg=msg)
assert_raises_regex(ValueError, msg, e.fit, X, y)


@ignore_warnings(category=FutureWarning)
Expand Down Expand Up @@ -3009,8 +3002,11 @@ def check_fit_non_negative(name, estimator_orig, strict_mode=True):
X = np.array([[-1., 1], [-1., 1]])
y = np.array([1, 2])
estimator = clone(estimator_orig)
msg = "Negative values in data passed to"
_assert_raises(ValueError, strict_mode, estimator.fit, X, y, msg=msg)
if strict_mode:
assert_raises_regex(ValueError, "Negative values in data passed to",
estimator.fit, X, y)
else: # Don't check error message if strict mode is off
assert_raises(ValueError, estimator.fit, X, y)


def check_fit_idempotent(name, estimator_orig, strict_mode=True):
Expand Down
36 changes: 0 additions & 36 deletions sklearn/utils/tests/test_estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,8 +23,6 @@
from sklearn.utils.estimator_checks import check_no_attributes_set_in_init
from sklearn.utils.estimator_checks import check_classifier_data_not_an_array
from sklearn.utils.estimator_checks import check_regressor_data_not_an_array
from sklearn.utils.estimator_checks import check_fit2d_1sample
from sklearn.utils.estimator_checks import check_fit2d_1feature
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.estimator_checks import check_outlier_corruption
from sklearn.utils.fixes import np_version, parse_version
Expand Down Expand Up @@ -665,37 +663,3 @@ def test_xfail_ignored_in_check_estimator():
# Make sure checks marked as xfail are just ignored and not run by
# check_estimator(), but still raise a warning.
assert_warns(SkipTestWarning, check_estimator, NuSVC())


def test_check_fit2d_1sample():

class MyEst(SVC):
# raises a bad error message when only 1 sample is passed
def fit(self, X, y):
if X.shape[0] == 1:
raise ValueError("non informative error message")

assert_raises_regex(
AssertionError,
"The error message should contain one of the following",
check_fit2d_1sample,
'estimator_name',
MyEst()
)


def test_check_fit2d_1feature():

class MyEst(SVC):
# raises a bad error message when only 1 feature is passed
def fit(self, X, y):
if X.shape[1] == 1:
raise ValueError("non informative error message")

assert_raises_regex(
AssertionError,
"The error message should contain one of the following",
check_fit2d_1feature,
'estimator_name',
MyEst()
)