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()