mirror of
https://github.com/Steffo99/unimore-bda-6.git
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63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
import logging
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import tensorflow
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from .config import config, DATA_SET_SIZE
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from .database import mongo_reviews_collection_from_config, sample_reviews_polar, sample_reviews_varied
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from .analysis.nltk_sentiment import NLTKSentimentAnalyzer
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from .analysis.tf_text import TensorflowSentimentAnalyzer
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from .tokenizer import NLTKWordTokenizer, PottsTokenizer, PottsTokenizerWithNegation, LowercaseTokenizer
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from .log import install_log_handler
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log = logging.getLogger(__name__)
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def main():
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if len(tensorflow.config.list_physical_devices(device_type="GPU")) == 0:
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log.warning("Tensorflow reports no GPU acceleration available.")
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else:
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log.debug("Tensorflow successfully found GPU acceleration!")
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for dataset_func in [sample_reviews_polar, sample_reviews_varied]:
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# Tensorflow-based
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for Tokenizer in [
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LowercaseTokenizer
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]:
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tokenizer = Tokenizer()
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model = TensorflowSentimentAnalyzer()
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with mongo_reviews_collection_from_config() as collection:
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...
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# NLTK-based
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for Tokenizer in [
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NLTKWordTokenizer,
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PottsTokenizer,
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PottsTokenizerWithNegation,
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LowercaseTokenizer,
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]:
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tokenizer = Tokenizer()
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model = NLTKSentimentAnalyzer(tokenizer=tokenizer)
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with mongo_reviews_collection_from_config() as collection:
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reviews_training = dataset_func(collection=collection, amount=DATA_SET_SIZE.__wrapped__)
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reviews_evaluation = dataset_func(collection=collection, amount=DATA_SET_SIZE.__wrapped__)
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log.info("Training model %s", model)
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model.train(reviews_training)
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log.info("Evaluating model %s", model)
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correct, evaluated = model.evaluate(reviews_evaluation)
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log.info("%d evaluated, %d correct, %0.2d %% accuracy", evaluated, correct, correct / evaluated * 100)
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# try:
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# print("Manual testing for %s" % model)
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# print("Input an empty string to continue to the next model.")
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# while inp := input():
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# print(model.use(inp))
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# except KeyboardInterrupt:
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# pass
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if __name__ == "__main__":
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install_log_handler()
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config.proxies.resolve()
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main()
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