utils_nlp/dataset/bbc_hindi.py [127:150]:
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    if test_fraction < 0 or test_fraction >= 1.0:
        logging.warning("Invalid test fraction value: {}, changed to 0.25".format(test_fraction))
        test_fraction = 0.25

    train_df, test_df = train_test_split(all_df, train_size=(1.0 - test_fraction), random_state=random_seed)

    if train_sample_ratio > 1.0:
        train_sample_ratio = 1.0
        logging.warning("Setting the training sample ratio to 1.0")
    elif train_sample_ratio < 0:
        logging.error("Invalid training sample ration: {}".format(train_sample_ratio))
        raise ValueError("Invalid training sample ration: {}".format(train_sample_ratio))

    if test_sample_ratio > 1.0:
        test_sample_ratio = 1.0
        logging.warning("Setting the testing sample ratio to 1.0")
    elif test_sample_ratio < 0:
        logging.error("Invalid testing sample ration: {}".format(test_sample_ratio))
        raise ValueError("Invalid testing sample ration: {}".format(test_sample_ratio))

    if train_sample_ratio < 1.0:
        train_df = train_df.sample(frac=train_sample_ratio).reset_index(drop=True)
    if test_sample_ratio < 1.0:
        test_df = test_df.sample(frac=test_sample_ratio).reset_index(drop=True)
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utils_nlp/dataset/dac.py [120:143]:
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    if test_fraction < 0 or test_fraction >= 1.0:
        logging.warning("Invalid test fraction value: {}, changed to 0.25".format(test_fraction))
        test_fraction = 0.25

    train_df, test_df = train_test_split(all_df, train_size=(1.0 - test_fraction), random_state=random_seed)

    if train_sample_ratio > 1.0:
        train_sample_ratio = 1.0
        logging.warning("Setting the training sample ratio to 1.0")
    elif train_sample_ratio < 0:
        logging.error("Invalid training sample ration: {}".format(train_sample_ratio))
        raise ValueError("Invalid training sample ration: {}".format(train_sample_ratio))

    if test_sample_ratio > 1.0:
        test_sample_ratio = 1.0
        logging.warning("Setting the testing sample ratio to 1.0")
    elif test_sample_ratio < 0:
        logging.error("Invalid testing sample ration: {}".format(test_sample_ratio))
        raise ValueError("Invalid testing sample ration: {}".format(test_sample_ratio))

    if train_sample_ratio < 1.0:
        train_df = train_df.sample(frac=train_sample_ratio).reset_index(drop=True)
    if test_sample_ratio < 1.0:
        test_df = test_df.sample(frac=test_sample_ratio).reset_index(drop=True)
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