def _more_tags(self):
tags_dict = _return_tags()
# add additional test that fails
tags_dict["_xfail_checks"][
"check_parameters_default_constructible"
] = "transformer has 1 mandatory parameter"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["allow_nan"] = True
tags_dict["variables"] = "all"
# add additional test that fails
tags_dict["_xfail_checks"][
"check_fit2d_1sample"] = "the transformer raises an error when dropping all columns, ok to fail"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "categorical"
# the below test will fail because sklearn requires to check for inf, but
# you can't check inf of categorical data, numpy returns and error.
# so we need to leave without this test
tags_dict["_xfail_checks"][
"check_estimators_nan_inf"] = "transformer allows NA"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["allow_nan"] = True
tags_dict["variables"] = "numerical"
# add additional test that fails
tags_dict["_xfail_checks"][
"check_methods_subset_invariance"
] = "LagFeatures is not invariant when applied to a subset. Not sure why yet"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["allow_nan"] = True
tags_dict["variables"] = "all"
msg = "transformers need more than 1 feature to work"
tags_dict["_xfail_checks"]["check_fit2d_1feature"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "all"
tags_dict["requires_y"] = True
tags_dict["binary_only"] = True
tags_dict["_xfail_checks"]["check_estimators_nan_inf"] = "transformer allows NA"
msg = "transformers need more than 1 feature to work"
tags_dict["_xfail_checks"]["check_fit2d_1feature"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
# add additional test that fails
tags_dict["_xfail_checks"][
"check_parameters_default_constructible"] = "transformer has 1 mandatory parameter"
tags_dict["_xfail_checks"][
"check_fit2d_1feature"] = "this transformer works with datasets that contain at least 2 variables. \
Otherwise, there is nothing to combine"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["allow_nan"] = True
# add additional test that fails
tags_dict["_xfail_checks"][
"check_parameters_default_constructible"
] = "transformer has 1 mandatory parameter"
tags_dict["_xfail_checks"][
"check_fit2d_1feature"
] = "the transformer raises an error when removing the only column, ok to fail"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "numerical"
# add additional test that fails
tags_dict["_xfail_checks"][
"check_estimators_nan_inf"] = "transformer allows NA"
msg = "transformers need more than 1 feature to work"
tags_dict["_xfail_checks"]["check_fit2d_1feature"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "categorical"
tags_dict["requires_y"] = True
# in the current format, the tests are performed using continuous np.arrays
# this means that when we encode some of the values, the denominator is 0
# and this the transformer raises an error, and the test fails.
# For this reason, most sklearn transformers will fail. And it has nothing to
# do with the class not being compatible, it is just that the inputs passed
# are not suitable
tags_dict["_skip_test"] = True
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["allow_nan"] = True
tags_dict["variables"] = "skip"
# Tests that are OK to fail:
tags_dict["_xfail_checks"][
"check_parameters_default_constructible"] = "transformer has 1 mandatory parameter"
tags_dict["_xfail_checks"][
"check_fit2d_1feature"] = "this transformer works with datasets that contain at least 2 variables. \
Otherwise, there is nothing to combine"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
msg = "input shape of dataframes in fit and transform can differ"
tags_dict["_xfail_checks"]["check_transformer_general"] = msg
msg = (
"transformer takes categorical variables, and inf cannot be determined"
"on these variables. Thus, check is not implemented"
)
tags_dict["_xfail_checks"]["check_estimators_nan_inf"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "numerical"
tags_dict["requires_y"] = True
# add additional test that fails
tags_dict["_xfail_checks"][
"check_estimators_nan_inf"] = "transformer allows NA"
tags_dict["_xfail_checks"][
"check_parameters_default_constructible"] = "transformer has 1 mandatory parameter"
msg = "transformers need more than 1 feature to work"
tags_dict["_xfail_checks"]["check_fit2d_1feature"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
# ======= this tests fail because the transformers throw an error
# when the values are 0. Nothing to do with the test itself but
# mostly with the data created and used in the test
msg = (
"transformers raise errors when data contains zeroes, thus this check fails"
)
tags_dict["_xfail_checks"]["check_estimators_dtypes"] = msg
tags_dict["_xfail_checks"]["check_estimators_fit_returns_self"] = msg
tags_dict["_xfail_checks"]["check_pipeline_consistency"] = msg
tags_dict["_xfail_checks"]["check_estimators_overwrite_params"] = msg
tags_dict["_xfail_checks"]["check_estimators_pickle"] = msg
tags_dict["_xfail_checks"]["check_transformer_general"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "numerical"
# ======= this tests fail because the transformers throw an error
# when the values are 0. Nothing to do with the test itself but
# mostly with the data created and used in the test
msg = (
"transformers raise errors when data contains zeroes, thus this check fails"
)
tags_dict["_xfail_checks"]["check_estimators_dtypes"] = msg
tags_dict["_xfail_checks"]["check_estimators_fit_returns_self"] = msg
tags_dict["_xfail_checks"]["check_pipeline_consistency"] = msg
tags_dict["_xfail_checks"]["check_estimators_overwrite_params"] = msg
tags_dict["_xfail_checks"]["check_estimators_pickle"] = msg
tags_dict["_xfail_checks"]["check_transformer_general"] = msg
# boxcox fails this test as well
msg = "scipy.stats.boxcox does not like the input data"
tags_dict["_xfail_checks"]["check_methods_subset_invariance"] = msg
tags_dict["_xfail_checks"]["check_fit2d_1sample"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "numerical"
# ======= this tests fail because the transformers throw an error when the
# values are less than 0 or greater than 1. Nothing to do with the test itself
# but mostly with the data created and used in the test
msg = (
"transformers raise errors when data is outside [0, 1] range, thus this"
"check fails")
tags_dict["_xfail_checks"]["check_estimators_dtypes"] = msg
tags_dict["_xfail_checks"]["check_estimators_fit_returns_self"] = msg
tags_dict["_xfail_checks"]["check_pipeline_consistency"] = msg
tags_dict["_xfail_checks"]["check_estimators_overwrite_params"] = msg
tags_dict["_xfail_checks"]["check_estimators_pickle"] = msg
tags_dict["_xfail_checks"]["check_transformer_general"] = msg
tags_dict["_xfail_checks"]["check_methods_subset_invariance"] = msg
tags_dict["_xfail_checks"]["check_fit2d_1sample"] = msg
tags_dict["_xfail_checks"]["check_fit2d_1feature"] = msg
tags_dict["_xfail_checks"]["check_dict_unchanged"] = msg
tags_dict["_xfail_checks"]["check_dont_overwrite_parameters"] = msg
tags_dict["_xfail_checks"]["check_fit_check_is_fitted"] = msg
tags_dict["_xfail_checks"]["check_n_features_in"] = msg
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "numerical"
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["variables"] = "numerical"
tags_dict["requires_y"] = True
return tags_dict
def _more_tags(self):
tags_dict = _return_tags()
tags_dict["allow_nan"] = True
tags_dict["variables"] = "all"
return tags_dict
def _more_tags(self):
return _return_tags()
