How to use the kaggler.preprocessing.OneHotEncoder function in Kaggler

trn.drop(TARGET_COL, axis=1, inplace=True)

    cat_cols = [x for x in trn.columns if trn[x].dtype == np.object]
    num_cols = [x for x in trn.columns if trn[x].dtype != np.object]

    logging.info('categorical: {}, numerical: {}'.format(len(cat_cols),
                                                         len(num_cols)))

    df = pd.concat([trn, tst], axis=0)

    logging.info('normalizing numeric features')
    nm = Normalizer()
    df[num_cols] = nm.fit_transform(df[num_cols].values)

    logging.info('label encoding categorical variables')
    ohe = OneHotEncoder(min_obs=10)
    X_ohe = ohe.fit_transform(df[cat_cols])
    ohe_cols = ['ohe{}'.format(i) for i in range(X_ohe.shape[1])]

    X = sparse.hstack((df[num_cols].values, X_ohe), format='csr')

    with open(feature_map_file, 'w') as f:
        for i, col in enumerate(num_cols + ohe_cols):
            f.write('{}\t{}\tq\n'.format(i, col))

    logging.info('saving features')
    save_data(X[:n_trn,], y, train_feature_file)
    save_data(X[n_trn:,], None, test_feature_file)