INNER CODE UNIT · Python

create_features

alteryx/open_source_demos · predict-correct-answer/utils.py:99

def create_features(es, label='Outcome', custom_agg=[]):
    cutoff_times = es['transactions'].df[['Transaction Id', 'End Time', label]]
    fm, features = ft.dfs(entityset=es,
                          target_entity='transactions',
                          agg_primitives=[Sum, Mean] + custom_agg,
                          trans_primitives=[Hour],
                          max_depth=3,
                          approximate='2m',
                          cutoff_time=cutoff_times,
                          verbose=True)
    fm_enc, _ = ft.encode_features(fm, features)
    fm_enc = fm_enc.fillna(0)
    fm_enc = remove_low_information_features(fm_enc)
    labels = fm.pop(label)
    return (fm_enc, labels)


def estimate_score(fm_enc, label, splitter):

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