from argparse import ArgumentParser
|
|
from fun_text_processing.inverse_text_normalization.inverse_normalize import InverseNormalizer
|
from fun_text_processing.text_normalization.data_loader_utils import (
|
evaluate,
|
known_types,
|
load_files,
|
training_data_to_sentences,
|
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(
|
"--lang", help="language", choices=['en', 'id', 'ja', 'de', 'es', 'pt', 'ru', 'fr', 'vi', 'ko', 'zh', 'fil'], default="en", type=str
|
)
|
parser.add_argument(
|
"--cat",
|
dest="category",
|
help="focus on class only (" + ", ".join(known_types) + ")",
|
type=str,
|
default=None,
|
choices=known_types,
|
)
|
parser.add_argument("--filter", action='store_true', help="clean data for inverse normalization purposes")
|
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
|
file_path = args.input
|
inverse_normalizer = InverseNormalizer()
|
|
print("Loading training data: " + file_path)
|
training_data = load_files([file_path])
|
|
if args.filter:
|
training_data = filter_loaded_data(training_data)
|
|
if args.category is None:
|
print("Sentence level evaluation...")
|
sentences_un_normalized, sentences_normalized, _ = training_data_to_sentences(training_data)
|
print("- Data: " + str(len(sentences_normalized)) + " sentences")
|
sentences_prediction = inverse_normalizer.inverse_normalize_list(sentences_normalized)
|
print("- Denormalized. Evaluating...")
|
sentences_accuracy = evaluate(
|
preds=sentences_prediction, labels=sentences_un_normalized, input=sentences_normalized
|
)
|
print("- Accuracy: " + str(sentences_accuracy))
|
|
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:
|
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...")
|
token_accuracy[token_type] = evaluate(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}
|
token_weighted_accuracy = [
|
token_count_per_type[token_type] * accuracy for token_type, accuracy in token_accuracy.items()
|
]
|
print("- Accuracy: " + str(sum(token_weighted_accuracy) / sum(token_count_per_type.values())))
|
|
print(" - Total: " + str(sum(token_count_per_type.values())), '\n')
|
|
for token_type in token_accuracy:
|
if token_type not in known_types:
|
raise ValueError("Unexpected token type: " + token_type)
|
|
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
|
]
|
c3 = ["Denormalization", sentences_accuracy] + [
|
token_accuracy[known_type] if known_type in token_accuracy else '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}')
|
else:
|
print(f'numbers\t{token_count_per_type[args.category]}')
|
print(f'Denormalization\t{token_accuracy[args.category]}')
|
