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

y = trn[TARGET_COL].values
    n_trn = trn.shape[0]

    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('label encoding categorical variables')
    lbe = LabelEncoder(min_obs=10)
    df[cat_cols] = lbe.fit_transform(df[cat_cols])
    df[num_cols] = df[num_cols].fillna(-1)

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

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