INNER CODE UNIT · Python

agg_primitives

alteryx/open_source_demos · predict-correct-answer/utils.py:103

                          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):
    k = 0
    for train_index, test_index in splitter.split(fm_enc):
        clf = RandomForestClassifier()
        X_train, X_test = fm_enc.iloc[train_index], fm_enc.iloc[test_index]

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