from argparse import ArgumentParser
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from fun_text_processing.inverse_text_normalization.inverse_normalize import InverseNormalizer
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from fun_text_processing.text_normalization.data_loader_utils import (
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evaluate,
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known_types,
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load_files,
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training_data_to_sentences,
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training_data_to_tokens,
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)
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"""
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Runs Evaluation on data in the format of : <semiotic class>\t<unnormalized text>\t<`self` if trivial class or normalized text>
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like the Google text normalization data https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish
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"""
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def parse_args():
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parser = ArgumentParser()
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parser.add_argument("--input", help="input file path", type=str, required=True)
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parser.add_argument(
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"--lang",
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help="language",
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choices=["en", "id", "ja", "de", "es", "pt", "ru", "fr", "vi", "ko", "zh", "fil"],
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default="en",
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type=str,
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)
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parser.add_argument(
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"--cat",
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dest="category",
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help="focus on class only (" + ", ".join(known_types) + ")",
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type=str,
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default=None,
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choices=known_types,
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)
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parser.add_argument(
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"--filter", action="store_true", help="clean data for inverse normalization purposes"
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)
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return parser.parse_args()
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if __name__ == "__main__":
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# Example usage:
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# python run_evaluate.py --input=<INPUT> --cat=<CATEGORY> --filter
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args = parse_args()
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if args.lang == "en":
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from fun_text_processing.inverse_text_normalization.en.clean_eval_data import filter_loaded_data
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file_path = args.input
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inverse_normalizer = InverseNormalizer()
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print("Loading training data: " + file_path)
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training_data = load_files([file_path])
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if args.filter:
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training_data = filter_loaded_data(training_data)
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# Evaluate at sentence level if no specific category is provided
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if args.category is None:
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print("Sentence level evaluation...")
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sentences_un_normalized, sentences_normalized, _ = training_data_to_sentences(training_data)
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print("- Data: " + str(len(sentences_normalized)) + " sentences")
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sentences_prediction = inverse_normalizer.inverse_normalize_list(sentences_normalized)
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print("- Denormalized. Evaluating...")
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sentences_accuracy = evaluate(
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preds=sentences_prediction, labels=sentences_un_normalized, input=sentences_normalized
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)
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print("- Accuracy: " + str(sentences_accuracy))
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# Evaluate at token level
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print("Token level evaluation...")
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tokens_per_type = training_data_to_tokens(training_data, category=args.category)
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token_accuracy = {}
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for token_type, (tokens_un_normalized, tokens_normalized) in tokens_per_type.items():
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print("- Token type: " + token_type)
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print(" - Data: " + str(len(tokens_normalized)) + " tokens")
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tokens_prediction = inverse_normalizer.inverse_normalize_list(tokens_normalized)
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print(" - Denormalized. Evaluating...")
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token_accuracy[token_type] = evaluate(
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tokens_prediction, tokens_un_normalized, input=tokens_normalized
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)
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print(" - Accuracy: " + str(token_accuracy[token_type]))
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# Calculate weighted token accuracy
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token_count_per_type = {token_type: len(tokens) for token_type, (tokens, _) in tokens_per_type.items()}
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token_weighted_accuracy = [
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token_count_per_type[token_type] * accuracy
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for token_type, accuracy in token_accuracy.items()
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]
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print("- Accuracy: " + str(sum(token_weighted_accuracy) / sum(token_count_per_type.values())))
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print(" - Total: " + str(sum(token_count_per_type.values())), "\n")
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for token_type in token_accuracy:
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if token_type not in known_types:
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raise ValueError("Unexpected token type: " + token_type)
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# Output table summarizing evaluation results if no specific category is provided
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if args.category is None:
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c1 = ["Class", "sent level"] + known_types
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c2 = ["Num Tokens", len(sentences_normalized)] + [
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str(token_count_per_type.get(known_type, 0)) for known_type in known_types
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]
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c3 = ["Denormalization", str(sentences_accuracy)] + [
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str(token_accuracy.get(known_type, "0")) for known_type in known_types
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]
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for i in range(len(c1)):
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print(f"{c1[i]:10s} | {c2[i]:10s} | {c3[i]:5s}")
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else:
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print(f"numbers\t{token_count_per_type[args.category]}")
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print(f"Denormalization\t{token_accuracy[args.category]}")
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