def build(X_df=None, y_df=None):
"""Build features and target
Args:
X_df (DataFrame): raw variables
y_df (DataFrame): raw target
Returns:
dict with keys X_df, features, mapper_X, X, y_df, encoder_y, y
"""
if X_df is None:
X_df, _ = load_data()
if y_df is None:
_, y_df = load_data()
features = collect_contrib_features()
mapper_X = FeatureEngineeringPipeline(features)
X = mapper_X.fit_transform(X_df)
encoder_y = get_target_encoder()
y = encoder_y.fit_transform(y_df)
return {
'X_df': X_df,
'features': features,
'mapper_X': mapper_X,
'X': X,
'y_df': y_df,
'encoder_y': encoder_y,
'y': y,
}
def test_df_colnames(input, transformer, output):
feature = Feature(input, transformer, output=output)
mapper = FeatureEngineeringPipeline(feature)
entities_df = pd.util.testing.makeCustomDataframe(5, 2)
entities_df.columns = ['foo', 'bar']
feature_matrix = mapper.fit_transform(entities_df)
feature_frame = pd.DataFrame(
feature_matrix,
columns=mapper.transformed_names_,
index=entities_df.index,
)
assert fy.all(fy.isa(str), feature_frame.columns)