def test_is_categorical(self):
x = Series(["True", "False", "True"], name="x", dtype=str)
self.assertTrue(_is_categorical(x.dtype))
x = x.astype(bool)
self.assertTrue(_is_categorical(x.dtype))
x = x.astype(float)
self.assertFalse(_is_categorical(x.dtype))
x = x.astype("category")
self.assertTrue(_is_categorical(x.dtype))
x = x.astype(int)
self.assertFalse(_is_categorical(x.dtype))
def make_xgboost_dataframe_mapper(dtypes, missing_value_aware=True):
"""Construct a DataFrameMapper for feeding complex data into an XGBModel.
Parameters
----------
dtypes: iterable of tuples (column, dtype)
missing_value_aware: boolean
If true, use missing value aware transformers.
Returns
-------
DataFrameMapper
"""
features = list()
for column, dtype in dtypes.items():
if _is_categorical(dtype):
features.append(([column], PMMLLabelBinarizer(
sparse_output=True) if missing_value_aware else LabelBinarizer(
sparse_output=True)))
else:
features.append(([column], None))
return DataFrameMapper(features)
def make_xgboost_column_transformer(dtypes, missing_value_aware=True):
"""Construct a ColumnTransformer for feeding complex data into an XGBModel.
Parameters
----------
dtypes: iterable of tuples (column, dtype)
missing_value_aware: boolean
If true, use missing value aware transformers.
Returns
-------
ColumnTransformer
"""
transformers = list()
for column, dtype in dtypes.items():
if _is_categorical(dtype):
transformers.append(
(column, PMMLLabelBinarizer(sparse_output=True)
if missing_value_aware else Pipeline([("ordinal_encoder",
OrdinalEncoder()),
("one_hot_encoder",
OneHotEncoder())]),
[column]))
else:
transformers.append((column, "passthrough", [column]))
return ColumnTransformer(transformers, remainder="drop")
def make_lightgbm_dataframe_mapper(dtypes, missing_value_aware=True):
"""Construct a DataFrameMapper for feeding complex data into a LGBMModel.
Parameters
----------
dtypes: iterable of tuples (column, dtype)
missing_value_aware: boolean
If true, use missing value aware transformers.
Returns
-------
Tuple (DataFrameMapper, list of categorical columns indices)
"""
features = list()
categorical_features = list()
i = 0
for column, dtype in dtypes.items():
if _is_categorical(dtype):
features.append(([column], PMMLLabelEncoder(
missing_values=-1) if missing_value_aware else LabelEncoder()))
categorical_features.append(i)
else:
features.append(([column], None))
i += 1
return (DataFrameMapper(features), categorical_features)
def make_lightgbm_column_transformer(dtypes, missing_value_aware=True):
"""Construct a ColumnTransformer for feeding complex data into a LGBMModel.
Parameters
----------
dtypes: iterable of tuples (column, dtype)
missing_value_aware: boolean
If true, use missing value aware transformers.
Returns:
Tuple (ColumnTransformer, list of categorical column indices)
"""
transformers = list()
categorical_features = list()
i = 0
for column, dtype in dtypes.items():
if _is_categorical(dtype):
transformers.append(
(column, PMMLLabelEncoder(missing_values=-1)
if missing_value_aware else OrdinalEncoder(), [column]))
categorical_features.append(i)
else:
transformers.append((column, "passthrough", [column]))
i += 1
return (ColumnTransformer(transformers,
remainder="drop"), categorical_features)