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bda-6-steffo/unimore_bda_6/__main__.py

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2.2 KiB
Python

import logging
import tensorflow
from .config import config, DATA_SET_SIZE
from .database import mongo_reviews_collection_from_config, polar_dataset, varied_dataset
from .analysis.nltk_sentiment import NLTKSentimentAnalyzer
from .analysis.tf_text import TensorflowSentimentAnalyzer
from .tokenizer import NLTKWordTokenizer, PottsTokenizer, PottsTokenizerWithNegation, LowercaseTokenizer
from .log import install_log_handler
log = logging.getLogger(__name__)
def main():
if len(tensorflow.config.list_physical_devices(device_type="GPU")) == 0:
log.warning("Tensorflow reports no GPU acceleration available.")
else:
log.debug("Tensorflow successfully found GPU acceleration!")
for dataset_func in [polar_dataset, varied_dataset]:
for SentimentAnalyzer in [
# NLTKSentimentAnalyzer,
TensorflowSentimentAnalyzer,
]:
for Tokenizer in [
# NLTKWordTokenizer,
# PottsTokenizer,
# PottsTokenizerWithNegation,
LowercaseTokenizer,
]:
tokenizer = Tokenizer()
model = SentimentAnalyzer(tokenizer=tokenizer)
with mongo_reviews_collection_from_config() as reviews:
reviews_training = dataset_func(collection=reviews, amount=DATA_SET_SIZE.__wrapped__)
reviews_evaluation = dataset_func(collection=reviews, amount=DATA_SET_SIZE.__wrapped__)
log.info("Training model %s", model)
model.train(reviews_training)
log.info("Evaluating model %s", model)
correct, evaluated = model.evaluate(reviews_evaluation)
log.info("%d evaluated, %d correct, %0.2d %% accuracy", evaluated, correct, correct / evaluated * 100)
# try:
# print("Manual testing for %s" % model)
# print("Input an empty string to continue to the next model.")
# while inp := input():
# print(model.use(inp))
# except KeyboardInterrupt:
# pass
if __name__ == "__main__":
install_log_handler()
config.proxies.resolve()
main()