import pandas as pd
from sklearn.datasets import load_iris
from sklearn.pipeline import make_pipeline
from pandas_feature_union import PandasFeatureUnion
from pandas_transform import PandasTransform
def main():
raw_data = load_iris()
data = pd.DataFrame(raw_data["data"], columns=raw_data["feature_names"])
data.loc[:, "class"] = raw_data["target"]
pipeline = PandasFeatureUnion([
("1", make_pipeline(
PandasTransform(lambda X: X.loc[:, ["sepal length (cm)"]]),
# other transformations
)),
("2", make_pipeline(
PandasTransform(lambda X: X.loc[:, ["sepal width (cm)"]]),
# other transformations
))
])
X = pipeline.fit_transform(data)
print(X["sepal length (cm)"].mean())
print(X["sepal width (cm)"].mean())
if __name__ == '__main__':
main()
