mirror of
https://github.com/Steffo99/unimore-bda-6.git
synced 2024-11-21 23:44:19 +00:00
108 lines
4.4 KiB
Python
108 lines
4.4 KiB
Python
import logging
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import pymongo.errors
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import gc
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from .log import install_general_log_handlers
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install_general_log_handlers()
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from .config import config
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from .database import mongo_client_from_config, reviews_collection, sample_reviews_polar, sample_reviews_varied
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from .analysis import NLTKSentimentAnalyzer, TensorflowCategorySentimentAnalyzer, TensorflowPolarSentimentAnalyzer, ThreeCheat
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from .analysis.base import TrainingFailedError
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from .tokenizer import PlainTokenizer, LowercaseTokenizer, NLTKWordTokenizer, PottsTokenizer, PottsTokenizerWithNegation, HuggingBertTokenizer
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from .gathering import Caches
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log = logging.getLogger(__name__)
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def main():
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log.info("Started unimore-bda-6 in %s mode!", "DEBUG" if __debug__ else "PRODUCTION")
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log.debug("Validating configuration...")
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config.proxies.resolve()
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log.debug("Ensuring there are no leftover caches...")
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Caches.ensure_clean()
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with mongo_client_from_config() as db:
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try:
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db.admin.command("ping")
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except pymongo.errors.ServerSelectionTimeoutError:
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log.fatal("MongoDB database is not available, exiting...")
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exit(1)
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reviews = reviews_collection(db)
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for sample_func in [
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sample_reviews_polar,
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sample_reviews_varied,
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]:
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slog = logging.getLogger(f"{__name__}.{sample_func.__name__}")
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slog.debug("Selected sample_func: %s", sample_func.__name__)
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for SentimentAnalyzer in [
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ThreeCheat,
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NLTKSentimentAnalyzer,
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TensorflowPolarSentimentAnalyzer,
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TensorflowCategorySentimentAnalyzer,
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]:
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slog = logging.getLogger(f"{__name__}.{sample_func.__name__}.{SentimentAnalyzer.__name__}")
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slog.debug("Selected SentimentAnalyzer: %s", SentimentAnalyzer.__name__)
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for Tokenizer in [
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PlainTokenizer,
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LowercaseTokenizer,
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NLTKWordTokenizer,
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PottsTokenizer,
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PottsTokenizerWithNegation,
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HuggingBertTokenizer,
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]:
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log.debug("Running garbage collection...")
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garbage_count = gc.collect()
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log.debug("Collected %d pieces of garbage!", garbage_count)
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slog = logging.getLogger(f"{__name__}.{sample_func.__name__}.{SentimentAnalyzer.__name__}.{Tokenizer.__name__}")
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slog.debug("Selected Tokenizer: %s", Tokenizer.__name__)
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run_counter = 0
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while True:
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slog = logging.getLogger(f"{__name__}.{sample_func.__name__}.{SentimentAnalyzer.__name__}.{Tokenizer.__name__}.{run_counter}")
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run_counter += 1
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slog.debug("Run #%d", run_counter)
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if run_counter >= 100:
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slog.fatal("Exceeded 100 runs, giving up and exiting...")
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exit(2)
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try:
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slog.debug("Instantiating %s with %s...", SentimentAnalyzer.__name__, Tokenizer.__name__)
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sa = SentimentAnalyzer(tokenizer=Tokenizer())
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except TypeError:
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slog.warning("%s is not supported by %s, skipping run...", SentimentAnalyzer.__name__, Tokenizer.__name__)
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break
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with Caches.from_database_samples(collection=reviews, sample_func=sample_func) as datasets:
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try:
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slog.info("Training sentiment analyzer: %s", sa)
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sa.train(training_dataset_func=datasets.training, validation_dataset_func=datasets.validation)
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except TrainingFailedError:
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slog.error("Training failed, trying again with a different dataset...")
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continue
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else:
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slog.info("Training succeeded!")
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slog.info("Evaluating sentiment analyzer: %s", sa)
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evaluation_results = sa.evaluate(evaluation_dataset_func=datasets.evaluation)
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slog.info("Evaluation results: %s", evaluation_results)
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break
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if __name__ == "__main__":
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main()
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