def test_fit_transform(self): X = _data() ct = make_xgboost_column_transformer(X.dtypes, missing_value_aware = False) dfm = make_xgboost_column_transformer(X.dtypes, missing_value_aware = False) self.assertEqual(ct.fit_transform(X).tolist(), dfm.fit_transform(X).tolist()) ct = make_xgboost_column_transformer(X.dtypes, missing_value_aware = True) dfm = make_xgboost_column_transformer(X.dtypes, missing_value_aware = True) self.assertEqual(ct.fit_transform(X).tolist(), dfm.fit_transform(X).tolist())
for cont_column in cont_columns])
dtypes = Series(dtypes.values, index=[0, 1, 2, 3, 4, 5, 6])
lightgbm_mapper, lightgbm_categorical_feature = make_lightgbm_column_transformer(
dtypes, missing_value_aware=True)
lightgbm_pipeline = Pipeline([
("mapper", lightgbm_mapper),
("classifier",
LGBMClassifier(n_estimators=31,
max_depth=3,
random_state=13,
categorical_feature=lightgbm_categorical_feature))
])
xgboost_mapper = make_xgboost_column_transformer(dtypes,
missing_value_aware=True)
xgboost_pipeline = Pipeline([("mapper", xgboost_mapper),
("classifier",
XGBClassifier(n_estimators=31,
learning_rate=0.1,
max_depth=3,
random_state=13))])
sklearn_mapper = ColumnTransformer(
[(str(cat_index), PMMLLabelBinarizer(sparse_output=False), [cat_index])
for cat_index in range(0, len(cat_columns))] +
[(str(cont_index), "passthrough", [cont_index])
for cont_index in range(len(cat_columns), len(cat_columns + cont_columns))
],
remainder="drop")