def decode(fitted_encoder: BaseEstimator, features: List[str],
df: pd.DataFrame) -> pd.DataFrame:
"""
Pipeline function to decode data with fitted sklearn OneHotEncoder.
Parameters
----------
fitted_encoder : sklearn OneHotEncoder
Encodes input data.
features : list
List of categorical feature.
df : pd.DataFrame
Data we want to normalize
Returns
-------
output : pd.DataFrame
Whole DataFrame with encoded values
"""
output = df.copy()
encoded_features = fitted_encoder.get_feature_names(features)
# Prevent errors for datasets without categorical data
# inverse_transform cannot handle these cases
if len(encoded_features) == 0:
return output
output[features] = fitted_encoder.inverse_transform(
output[encoded_features])
output = output.drop(encoded_features, axis=1)
return output
def encode(fitted_encoder: BaseEstimator, features: List[str],
df: pd.DataFrame) -> pd.DataFrame:
"""
Pipeline function to encode data with fitted sklearn OneHotEncoder.
Parameters
----------
fitted_encoder : sklearn OneHotEncoder
Encodes input data.
features : list
List of categorical feature.
df : pd.DataFrame
Data we want to normalize
Returns
-------
output : pd.DataFrame
Whole DataFrame with encoded values
"""
output = df.copy()
encoded_features = fitted_encoder.get_feature_names(features)
output[encoded_features] = fitted_encoder.transform(output[features])
output = output.drop(features, axis=1)
return output
