def test_predict_target_to_feature_dict(self, data, X_y, estimator):
X, y = X_y
X_train, y_train, X_test, y_test = FeatureSplitter().transform(X, y)
target_to_feature_dict = data.draw(
str_target_to_feature_dicts(targets=y.columns, features=X.columns))
multi_feature_gar = MultiFeatureGAR(estimator)
multi_feature_gar.target_to_features_dict = target_to_feature_dict
multi_feature_gar.fit(X_train, y_train)
multi_feature_gar.predict(X_test)
def test_fit_target_to_feature_dict_set_after_consistent(
self, data, X_y, estimator):
X, y = X_y
X_train, y_train, X_test, y_test = FeatureSplitter().transform(X, y)
target_to_feature_dict = data.draw(
str_target_to_feature_dicts(targets=y.columns, features=X.columns))
multi_feature_gar = MultiFeatureGAR(
estimator, target_to_features_dict=target_to_feature_dict)
multi_feature_gar.target_to_features_dict = target_to_feature_dict
multi_feature_gar.fit(X_train, y_train)
for i, estimator_ in enumerate(multi_feature_gar.estimators_):
expected_n_features = len(target_to_feature_dict[y.columns[i]])
assert len(estimator_.coef_) == expected_n_features