Update run_evaluate.py (#2175)

Vignesh Skanda

2024-10-28 c3e667b217a0ba46ceb559b860c1488b750191a1

 fun_text_processing/inverse_text_normalization/run_evaluate.py

@@ -9,16 +9,14 @@
    training_data_to_tokens,
)


"""
Runs Evaluation on data in the format of : <semiotic class>\t<unnormalized text>\t<`self` if trivial class or normalized text>
like the Google text normalization data https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish
"""


def parse_args():
    parser = ArgumentParser()
    parser.add_argument("--input", help="input file path", type=str)
    parser.add_argument("--input", help="input file path", type=str, required=True)
    parser.add_argument(
        "--lang",
        help="language",
@@ -39,15 +37,13 @@
    )
    return parser.parse_args()


if __name__ == "__main__":
    # Example usage:
    # python run_evaluate.py --input=<INPUT> --cat=<CATEGORY> --filter
    args = parse_args()
    if args.lang == "en":
        from fun_text_processing.inverse_text_normalization.en.clean_eval_data import (
            filter_loaded_data,
        )
        from fun_text_processing.inverse_text_normalization.en.clean_eval_data import filter_loaded_data

    file_path = args.input
    inverse_normalizer = InverseNormalizer()

@@ -57,6 +53,7 @@
    if args.filter:
        training_data = filter_loaded_data(training_data)

    # Evaluate at sentence level if no specific category is provided
    if args.category is None:
        print("Sentence level evaluation...")
        sentences_un_normalized, sentences_normalized, _ = training_data_to_sentences(training_data)
@@ -68,12 +65,12 @@
        )
        print("- Accuracy: " + str(sentences_accuracy))

    # Evaluate at token level
    print("Token level evaluation...")
    tokens_per_type = training_data_to_tokens(training_data, category=args.category)
    token_accuracy = {}
    for token_type in tokens_per_type:
    for token_type, (tokens_un_normalized, tokens_normalized) in tokens_per_type.items():
        print("- Token type: " + token_type)
        tokens_un_normalized, tokens_normalized = tokens_per_type[token_type]
        print("  - Data: " + str(len(tokens_normalized)) + " tokens")
        tokens_prediction = inverse_normalizer.inverse_normalize_list(tokens_normalized)
        print("  - Denormalized. Evaluating...")
@@ -81,9 +78,9 @@
            tokens_prediction, tokens_un_normalized, input=tokens_normalized
        )
        print("  - Accuracy: " + str(token_accuracy[token_type]))
    token_count_per_type = {
        token_type: len(tokens_per_type[token_type][0]) for token_type in tokens_per_type
    }

    # Calculate weighted token accuracy
    token_count_per_type = {token_type: len(tokens) for token_type, (tokens, _) in tokens_per_type.items()}
    token_weighted_accuracy = [
        token_count_per_type[token_type] * accuracy
        for token_type, accuracy in token_accuracy.items()
@@ -96,19 +93,17 @@
        if token_type not in known_types:
            raise ValueError("Unexpected token type: " + token_type)

    # Output table summarizing evaluation results if no specific category is provided
    if args.category is None:
        c1 = ["Class", "sent level"] + known_types
        c2 = ["Num Tokens", len(sentences_normalized)] + [
            token_count_per_type[known_type] if known_type in tokens_per_type else "0"
            for known_type in known_types
            str(token_count_per_type.get(known_type, 0)) for known_type in known_types
        ]
        c3 = ["Denormalization", sentences_accuracy] + [
            token_accuracy[known_type] if known_type in token_accuracy else "0"
            for known_type in known_types
        c3 = ["Denormalization", str(sentences_accuracy)] + [
            str(token_accuracy.get(known_type, "0")) for known_type in known_types
        ]

        for i in range(len(c1)):
            print(f"{str(c1[i]):10s} | {str(c2[i]):10s} | {str(c3[i]):5s}")
            print(f"{c1[i]:10s} | {c2[i]:10s} | {c3[i]:5s}")
    else:
        print(f"numbers\t{token_count_per_type[args.category]}")
        print(f"Denormalization\t{token_accuracy[args.category]}")