mirror of
https://github.com/Steffo99/unimore-bda-6.git
synced 2024-11-25 01:04:19 +00:00
enough
This commit is contained in:
parent
4d6c8f0fee
commit
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GPG key ID:
2A24051445686895
21 changed files with 309 additions and 149 deletions
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@ -40,6 +40,7 @@
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<option value="E501" />
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<option value="E221" />
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<option value="E203" />
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<option value="E402" />
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</list>
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</option>
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</inspection_tool>
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@ -10,4 +10,7 @@
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<component name="ProjectRootManager" version="2" languageLevel="JDK_19">
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<output url="file://$PROJECT_DIR$/out" />
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<component name="PythonCompatibilityInspectionAdvertiser">
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<option name="version" value="3" />
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</component>
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</project>
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@ -1,12 +1,12 @@
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<component name="ProjectRunConfigurationManager">
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<configuration default="false" name="unimore_bda_6" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
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<module name="unimore-bda-6" />
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<option name="INTERPRETER_OPTIONS" value="" />
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<option name="INTERPRETER_OPTIONS" value="-O" />
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<option name="PARENT_ENVS" value="true" />
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<envs>
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<env name="PYTHONUNBUFFERED" value="1" />
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<env name="CONFIRM_OVERWRITE" value="False" />
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<env name="NLTK_DATA" value="./data/nltk" />
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<env name="PYTHONUNBUFFERED" value="1" />
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<env name="TF_CPP_MIN_LOG_LEVEL" value="2" />
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<env name="WORKING_SET_SIZE" value="1000000" />
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<env name="XLA_FLAGS" value="--xla_gpu_cuda_data_dir=/opt/cuda" />
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57
poetry.lock
generated
57
poetry.lock
generated
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@ -1230,6 +1230,61 @@ files = [
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[package.extras]
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tests = ["pytest", "pytest-cov"]
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[[package]]
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name = "tokenizers"
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version = "0.13.2"
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description = "Fast and Customizable Tokenizers"
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category = "main"
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optional = false
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python-versions = "*"
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||||
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||||
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||||
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{file = "tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3e306b0941ad35087ae7083919a5c410a6b672be0343609d79a1171a364ce79"},
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||||
{file = "tokenizers-0.13.2-cp39-cp39-win32.whl", hash = "sha256:79189e7f706c74dbc6b753450757af172240916d6a12ed4526af5cc6d3ceca26"},
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||||
{file = "tokenizers-0.13.2-cp39-cp39-win_amd64.whl", hash = "sha256:486d637b413fddada845a10a45c74825d73d3725da42ccd8796ccd7a1c07a024"},
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||||
{file = "tokenizers-0.13.2.tar.gz", hash = "sha256:f9525375582fd1912ac3caa2f727d36c86ff8c0c6de45ae1aaff90f87f33b907"},
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]
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[package.extras]
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dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
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docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
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testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
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[[package]]
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name = "tqdm"
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version = "4.64.1"
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@ -1390,4 +1445,4 @@ files = [
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[metadata]
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lock-version = "2.0"
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python-versions = "~3.10"
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content-hash = "b17bed73011c627355660e5ba5ca176a920c1d87915a1ab875e5a5ddd28a9dca"
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content-hash = "d63867d77886e8ae2c09771a5ec1053dae99a5699f7e905a69bd298e1b986a80"
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@ -137,6 +137,7 @@ nltk = "^3.8.1"
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cfig = {extras = ["cli"], version = "^0.3.0"}
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coloredlogs = "^15.0.1"
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tensorflow = "^2.11.0"
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tokenizers = "^0.13.2"
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@ -1,84 +1,81 @@
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import logging
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import tensorflow
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import pymongo.errors
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from .log import install_log_handler
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from .config import config, DATA_SET_SIZE
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from .database import mongo_client_from_config, reviews_collection, sample_reviews_polar, sample_reviews_varied, store_cache, load_cache, delete_cache
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install_log_handler()
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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.nltk_sentiment import NLTKSentimentAnalyzer
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from .analysis.tf_text import TensorflowSentimentAnalyzer
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from .analysis.base import TrainingFailedError
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from .tokenizer import LowercaseTokenizer
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from .log import install_log_handler
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from .tokenizer import PlainTokenizer, LowercaseTokenizer, NLTKWordTokenizer, PottsTokenizer, PottsTokenizerWithNegation
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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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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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log.info("Started unimore-bda-6 in %s mode!", "DEBUG" if __debug__ else "PRODUCTION")
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try:
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delete_cache("./data/training")
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delete_cache("./data/evaluation")
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except FileNotFoundError:
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pass
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log.debug("Validating configuration...")
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config.proxies.resolve()
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for dataset_func in [sample_reviews_polar, sample_reviews_varied]:
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for SentimentAnalyzer in [TensorflowSentimentAnalyzer, NLTKSentimentAnalyzer]:
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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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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 [sample_reviews_varied, sample_reviews_polar]:
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for SentimentAnalyzer in [
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TensorflowSentimentAnalyzer,
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NLTKSentimentAnalyzer
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]:
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while True:
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try:
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tokenizer = Tokenizer()
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model = SentimentAnalyzer(tokenizer=tokenizer)
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with mongo_client_from_config() as db:
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log.debug("Finding the reviews MongoDB collection...")
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collection = reviews_collection(db)
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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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]:
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slog = logging.getLogger(f"{__name__}.{sample_func.__name__}.{SentimentAnalyzer.__name__}.{Tokenizer.__name__}")
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while True:
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try:
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slog.debug("Creating sentiment analyzer...")
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sa = SentimentAnalyzer(tokenizer=Tokenizer())
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except TypeError:
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slog.warning("%s does not support %s, skipping...", Tokenizer.__name__, SentimentAnalyzer.__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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training_cache = load_cache("./data/training")
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evaluation_cache = load_cache("./data/evaluation")
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except FileNotFoundError:
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log.debug("Gathering datasets...")
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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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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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log.debug("Caching datasets...")
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store_cache(reviews_training, "./data/training")
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store_cache(reviews_evaluation, "./data/evaluation")
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del reviews_training
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del reviews_evaluation
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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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training_cache = load_cache("./data/training")
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evaluation_cache = load_cache("./data/evaluation")
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log.debug("Caches stored and loaded successfully!")
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else:
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log.debug("Caches loaded successfully!")
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slog.info("Training succeeded!")
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log.info("Training model: %s", model)
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model.train(training_cache)
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log.info("Evaluating model: %s", model)
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evaluation_results = model.evaluate(evaluation_cache)
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log.info("%s", evaluation_results)
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except TrainingFailedError:
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log.error("Training failed, restarting with a different dataset.")
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continue
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else:
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log.info("Training")
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break
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finally:
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delete_cache("./data/training")
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delete_cache("./data/evaluation")
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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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install_log_handler()
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config.proxies.resolve()
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main()
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||||
|
|
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|||
|
|
@ -1,38 +1,42 @@
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|||
from __future__ import annotations
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||||
|
||||
import abc
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||||
import logging
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import dataclasses
|
||||
|
||||
from ..database import Text, Category, DatasetFunc
|
||||
from ..database import Text, Category, CachedDatasetFunc
|
||||
from ..tokenizer import BaseTokenizer
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||||
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||||
log = logging.getLogger(__name__)
|
||||
|
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@dataclasses.dataclass
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class EvaluationResults:
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||||
correct: int
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||||
evaluated: int
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||||
score: float
|
||||
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def __repr__(self):
|
||||
return f"<EvaluationResults: score of {self.score} out of {self.evaluated} evaluated tuples>"
|
||||
|
||||
def __str__(self):
|
||||
return f"{self.evaluated} evaluated, {self.correct} correct, {self.correct / self.evaluated * 100:.2} % accuracy, {self.score:.2} score, {self.score / self.evaluated * 100:.2} scoreaccuracy"
|
||||
|
||||
|
||||
class BaseSentimentAnalyzer(metaclass=abc.ABCMeta):
|
||||
"""
|
||||
Abstract base class for sentiment analyzers implemented in this project.
|
||||
"""
|
||||
|
||||
# noinspection PyUnusedLocal
|
||||
def __init__(self, *, tokenizer: BaseTokenizer):
|
||||
pass
|
||||
|
||||
def __repr__(self):
|
||||
return f"<{self.__class__.__qualname__}>"
|
||||
|
||||
@abc.abstractmethod
|
||||
def train(self, dataset_func: DatasetFunc) -> None:
|
||||
def train(self, training_dataset_func: CachedDatasetFunc, validation_dataset_func: CachedDatasetFunc) -> None:
|
||||
"""
|
||||
Train the analyzer with the given training dataset.
|
||||
Train the analyzer with the given training and validation datasets.
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
def evaluate(self, dataset_func: DatasetFunc) -> EvaluationResults:
|
||||
@abc.abstractmethod
|
||||
def use(self, text: Text) -> Category:
|
||||
"""
|
||||
Run the model on the given input.
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
def evaluate(self, evaluation_dataset_func: CachedDatasetFunc) -> EvaluationResults:
|
||||
"""
|
||||
Perform a model evaluation by calling repeatedly `.use` on every text of the test dataset and by comparing its resulting category with the expected category.
|
||||
|
||||
|
|
@ -43,23 +47,30 @@ class BaseSentimentAnalyzer(metaclass=abc.ABCMeta):
|
|||
correct: int = 0
|
||||
score: float = 0.0
|
||||
|
||||
for review in dataset_func():
|
||||
for review in evaluation_dataset_func():
|
||||
resulting_category = self.use(review.text)
|
||||
evaluated += 1
|
||||
correct += 1 if resulting_category == review.category else 0
|
||||
score += 1 - (abs(resulting_category - review.category) / 4)
|
||||
if not evaluated % 100:
|
||||
temp_results = EvaluationResults(correct=correct, evaluated=evaluated, score=score)
|
||||
log.debug(f"{temp_results!s}")
|
||||
|
||||
return EvaluationResults(correct=correct, evaluated=evaluated, score=score)
|
||||
|
||||
@abc.abstractmethod
|
||||
def use(self, text: Text) -> Category:
|
||||
"""
|
||||
Run the model on the given input.
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
@dataclasses.dataclass
|
||||
class EvaluationResults:
|
||||
"""
|
||||
Container for the results of a dataset evaluation.
|
||||
"""
|
||||
|
||||
correct: int
|
||||
evaluated: int
|
||||
score: float
|
||||
|
||||
def __repr__(self):
|
||||
return f"<EvaluationResults: {self!s}>"
|
||||
|
||||
def __str__(self):
|
||||
return f"{self.evaluated} evaluated, {self.correct} correct, {self.correct / self.evaluated:.2%} accuracy, {self.score:.2f} score, {self.score / self.evaluated:.2%} scoreaccuracy"
|
||||
|
||||
|
||||
class AlreadyTrainedError(Exception):
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import logging
|
|||
import typing as t
|
||||
import itertools
|
||||
|
||||
from ..database import Text, Category, Review, DatasetFunc
|
||||
from ..database import Text, Category, Review, CachedDatasetFunc
|
||||
from .base import BaseSentimentAnalyzer, AlreadyTrainedError, NotTrainedError
|
||||
from ..log import count_passage
|
||||
from ..tokenizer import BaseTokenizer
|
||||
|
|
@ -23,7 +23,11 @@ class NLTKSentimentAnalyzer(BaseSentimentAnalyzer):
|
|||
"""
|
||||
|
||||
def __init__(self, *, tokenizer: BaseTokenizer) -> None:
|
||||
super().__init__()
|
||||
if not tokenizer.supports_plain():
|
||||
raise TypeError("Tokenizer does not support NLTK")
|
||||
|
||||
super().__init__(tokenizer=tokenizer)
|
||||
|
||||
self.model: nltk.sentiment.SentimentAnalyzer = nltk.sentiment.SentimentAnalyzer()
|
||||
self.trained: bool = False
|
||||
self.tokenizer: BaseTokenizer = tokenizer
|
||||
|
|
@ -36,7 +40,7 @@ class NLTKSentimentAnalyzer(BaseSentimentAnalyzer):
|
|||
Convert the `Text` of a `DataTuple` to a `TokenBag`.
|
||||
"""
|
||||
count_passage(log, "tokenize_datatuple", 100)
|
||||
return self.tokenizer.tokenize_builtins(datatuple.text), datatuple.category
|
||||
return self.tokenizer.tokenize_plain(datatuple.text), datatuple.category
|
||||
|
||||
def _add_feature_unigrams(self, dataset: t.Iterator[tuple[TokenBag, Category]]) -> None:
|
||||
"""
|
||||
|
|
@ -67,13 +71,13 @@ class NLTKSentimentAnalyzer(BaseSentimentAnalyzer):
|
|||
count_passage(log, "extract_features", 100)
|
||||
return self.model.extract_features(data[0]), data[1]
|
||||
|
||||
def train(self, dataset_func: DatasetFunc) -> None:
|
||||
def train(self, training_dataset_func: CachedDatasetFunc, validation_dataset_func: CachedDatasetFunc) -> None:
|
||||
# Forbid retraining the model
|
||||
if self.trained:
|
||||
raise AlreadyTrainedError()
|
||||
|
||||
# Get a generator
|
||||
dataset: t.Generator[Review] = dataset_func()
|
||||
dataset: t.Generator[Review] = training_dataset_func()
|
||||
|
||||
# Tokenize the dataset
|
||||
dataset: t.Iterator[tuple[TokenBag, Category]] = map(self.__tokenize_review, dataset)
|
||||
|
|
@ -103,7 +107,7 @@ class NLTKSentimentAnalyzer(BaseSentimentAnalyzer):
|
|||
raise NotTrainedError()
|
||||
|
||||
# Tokenize the input
|
||||
tokens = self.tokenizer.tokenize_builtins(text)
|
||||
tokens = self.tokenizer.tokenize_plain(text)
|
||||
|
||||
# Run the classification method
|
||||
return self.model.classify(instance=tokens)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import tensorflow
|
||||
import logging
|
||||
|
||||
from ..database import Text, Category, DatasetFunc
|
||||
from ..database import Text, Category, CachedDatasetFunc
|
||||
from ..config import TENSORFLOW_EMBEDDING_SIZE, TENSORFLOW_MAX_FEATURES, TENSORFLOW_EPOCHS
|
||||
from ..tokenizer import BaseTokenizer
|
||||
from .base import BaseSentimentAnalyzer, AlreadyTrainedError, NotTrainedError, TrainingFailedError
|
||||
|
|
@ -9,9 +9,19 @@ from .base import BaseSentimentAnalyzer, AlreadyTrainedError, NotTrainedError, T
|
|||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
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!")
|
||||
|
||||
|
||||
class TensorflowSentimentAnalyzer(BaseSentimentAnalyzer):
|
||||
def __init__(self, tokenizer: BaseTokenizer):
|
||||
super().__init__()
|
||||
def __init__(self, *, tokenizer: BaseTokenizer):
|
||||
if not tokenizer.supports_tensorflow():
|
||||
raise TypeError("Tokenizer does not support Tensorflow")
|
||||
|
||||
super().__init__(tokenizer=tokenizer)
|
||||
|
||||
self.trained: bool = False
|
||||
|
||||
self.text_vectorization_layer: tensorflow.keras.layers.TextVectorization = self._build_vectorizer(tokenizer)
|
||||
|
|
@ -19,7 +29,11 @@ class TensorflowSentimentAnalyzer(BaseSentimentAnalyzer):
|
|||
self.history: tensorflow.keras.callbacks.History | None = None
|
||||
|
||||
@staticmethod
|
||||
def _build_dataset(dataset_func: DatasetFunc) -> tensorflow.data.Dataset:
|
||||
def _build_dataset(dataset_func: CachedDatasetFunc) -> tensorflow.data.Dataset:
|
||||
"""
|
||||
Convert a `CachedDatasetFunc` to a `tensorflow.data.Dataset`.
|
||||
"""
|
||||
|
||||
def dataset_func_with_tensor_tuple():
|
||||
for review in dataset_func():
|
||||
yield review.to_tensor_tuple()
|
||||
|
|
@ -43,15 +57,16 @@ class TensorflowSentimentAnalyzer(BaseSentimentAnalyzer):
|
|||
|
||||
@staticmethod
|
||||
def _build_model() -> tensorflow.keras.Sequential:
|
||||
log.debug("Creating %s model...", tensorflow.keras.Sequential)
|
||||
log.debug("Creating model...")
|
||||
model = tensorflow.keras.Sequential([
|
||||
tensorflow.keras.layers.Embedding(
|
||||
input_dim=TENSORFLOW_MAX_FEATURES.__wrapped__ + 1,
|
||||
output_dim=TENSORFLOW_EMBEDDING_SIZE.__wrapped__,
|
||||
),
|
||||
tensorflow.keras.layers.Dropout(0.2),
|
||||
tensorflow.keras.layers.Dropout(0.25),
|
||||
tensorflow.keras.layers.GlobalAveragePooling1D(),
|
||||
tensorflow.keras.layers.Dropout(0.2),
|
||||
tensorflow.keras.layers.Dropout(0.25),
|
||||
tensorflow.keras.layers.Dense(25),
|
||||
tensorflow.keras.layers.Dense(5, activation="softmax"),
|
||||
])
|
||||
log.debug("Compiling model: %s", model)
|
||||
|
|
@ -72,31 +87,35 @@ class TensorflowSentimentAnalyzer(BaseSentimentAnalyzer):
|
|||
max_tokens=TENSORFLOW_MAX_FEATURES.__wrapped__
|
||||
)
|
||||
|
||||
def train(self, dataset_func: DatasetFunc) -> None:
|
||||
def train(self, training_dataset_func: CachedDatasetFunc, validation_dataset_func: CachedDatasetFunc) -> None:
|
||||
if self.trained:
|
||||
log.error("Tried to train an already trained model.")
|
||||
raise AlreadyTrainedError()
|
||||
|
||||
log.debug("Building dataset...")
|
||||
training_set = self._build_dataset(dataset_func)
|
||||
log.debug("Building datasets...")
|
||||
training_set = self._build_dataset(training_dataset_func)
|
||||
validation_set = self._build_dataset(validation_dataset_func)
|
||||
log.debug("Built dataset: %s", training_set)
|
||||
|
||||
log.debug("Preparing training_set for %s...", self.text_vectorization_layer.adapt)
|
||||
only_text_set = training_set.map(lambda text, category: text)
|
||||
|
||||
log.debug("Adapting text_vectorization_layer: %s", self.text_vectorization_layer)
|
||||
self.text_vectorization_layer.adapt(only_text_set)
|
||||
log.debug("Adapted text_vectorization_layer: %s", self.text_vectorization_layer)
|
||||
|
||||
log.debug("Preparing training_set for %s...", self.model.fit)
|
||||
training_set = training_set.map(lambda text, category: (self.text_vectorization_layer(text), category))
|
||||
validation_set = validation_set.map(lambda text, category: (self.text_vectorization_layer(text), category))
|
||||
log.info("Training: %s", self.model)
|
||||
self.history: tensorflow.keras.callbacks.History | None = self.model.fit(
|
||||
training_set,
|
||||
validation_data=validation_set,
|
||||
epochs=TENSORFLOW_EPOCHS.__wrapped__,
|
||||
callbacks=[
|
||||
tensorflow.keras.callbacks.TerminateOnNaN()
|
||||
])
|
||||
log.info("Trained: %s", self.model)
|
||||
],
|
||||
)
|
||||
|
||||
if len(self.history.epoch) < TENSORFLOW_EPOCHS.__wrapped__:
|
||||
log.error("Model %s training failed: only %d epochs computed", self.model, len(self.history.epoch))
|
||||
|
|
|
|||
|
|
@ -45,14 +45,44 @@ def WORKING_SET_SIZE(val: str | None) -> int:
|
|||
|
||||
|
||||
@config.optional()
|
||||
def DATA_SET_SIZE(val: str | None) -> int:
|
||||
def TRAINING_SET_SIZE(val: str | None) -> int:
|
||||
"""
|
||||
The number of reviews from each category to fetch for the datasets.
|
||||
The number of reviews from each category to fetch for the training dataset.
|
||||
|
||||
Defaults to `1750`.
|
||||
Defaults to `5000`.
|
||||
"""
|
||||
if val is None:
|
||||
return 1750
|
||||
return 5000
|
||||
try:
|
||||
return int(val)
|
||||
except ValueError:
|
||||
raise cfig.InvalidValueError("Not an int.")
|
||||
|
||||
|
||||
@config.optional()
|
||||
def VALIDATION_SET_SIZE(val: str | None) -> int:
|
||||
"""
|
||||
The number of reviews from each category to fetch for the training dataset.
|
||||
|
||||
Defaults to `400`.
|
||||
"""
|
||||
if val is None:
|
||||
return 400
|
||||
try:
|
||||
return int(val)
|
||||
except ValueError:
|
||||
raise cfig.InvalidValueError("Not an int.")
|
||||
|
||||
|
||||
@config.optional()
|
||||
def EVALUATION_SET_SIZE(val: str | None) -> int:
|
||||
"""
|
||||
The number of reviews from each category to fetch for the evaluation dataset.
|
||||
|
||||
Defaults to `1000`.
|
||||
"""
|
||||
if val is None:
|
||||
return 1000
|
||||
try:
|
||||
return int(val)
|
||||
except ValueError:
|
||||
|
|
@ -79,10 +109,10 @@ def TENSORFLOW_EMBEDDING_SIZE(val: str | None) -> int:
|
|||
"""
|
||||
The size of the embeddings tensor to use in Tensorflow models.
|
||||
|
||||
Defaults to `12`.
|
||||
Defaults to `6`.
|
||||
"""
|
||||
if val is None:
|
||||
return 12
|
||||
return 6
|
||||
try:
|
||||
return int(val)
|
||||
except ValueError:
|
||||
|
|
@ -94,10 +124,10 @@ def TENSORFLOW_EPOCHS(val: str | None) -> int:
|
|||
"""
|
||||
The number of epochs to train Tensorflow models for.
|
||||
|
||||
Defaults to `15`.
|
||||
Defaults to `12`.
|
||||
"""
|
||||
if val is None:
|
||||
return 15
|
||||
return 12
|
||||
try:
|
||||
return int(val)
|
||||
except ValueError:
|
||||
|
|
@ -109,7 +139,9 @@ __all__ = (
|
|||
"MONGO_HOST",
|
||||
"MONGO_PORT",
|
||||
"WORKING_SET_SIZE",
|
||||
"DATA_SET_SIZE",
|
||||
"TRAINING_SET_SIZE",
|
||||
"VALIDATION_SET_SIZE",
|
||||
"EVALUATION_SET_SIZE",
|
||||
"TENSORFLOW_MAX_FEATURES",
|
||||
"TENSORFLOW_EMBEDDING_SIZE",
|
||||
"TENSORFLOW_EPOCHS",
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from .datatypes import Review
|
|||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
DatasetFunc = t.Callable[[], t.Generator[Review, t.Any, None]]
|
||||
CachedDatasetFunc = t.Callable[[], t.Generator[Review, t.Any, None]]
|
||||
|
||||
|
||||
def store_cache(reviews: t.Iterator[Review], path: str | pathlib.Path) -> None:
|
||||
|
|
@ -34,7 +34,7 @@ def store_cache(reviews: t.Iterator[Review], path: str | pathlib.Path) -> None:
|
|||
pickle.dump(document, file)
|
||||
|
||||
|
||||
def load_cache(path: str | pathlib.Path) -> DatasetFunc:
|
||||
def load_cache(path: str | pathlib.Path) -> CachedDatasetFunc:
|
||||
"""
|
||||
Load the contents of a directory into a `Review` iterator.
|
||||
"""
|
||||
|
|
@ -69,12 +69,12 @@ def delete_cache(path: str | pathlib.Path) -> None:
|
|||
if not path.exists():
|
||||
raise FileNotFoundError("The specified path does not exist.")
|
||||
|
||||
log.warning("Deleting cache directory: %s", path)
|
||||
log.debug("Deleting cache directory: %s", path)
|
||||
shutil.rmtree(path)
|
||||
|
||||
|
||||
__all__ = (
|
||||
"DatasetFunc",
|
||||
"CachedDatasetFunc",
|
||||
"store_cache",
|
||||
"load_cache",
|
||||
"delete_cache",
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import pymongo
|
||||
import pymongo.errors
|
||||
import contextlib
|
||||
import typing as t
|
||||
import logging
|
||||
|
|
@ -13,12 +14,12 @@ def mongo_client_from_config() -> t.ContextManager[pymongo.MongoClient]:
|
|||
"""
|
||||
Create a new MongoDB client and yield it.
|
||||
"""
|
||||
log.debug("Opening connection to MongoDB...")
|
||||
log.debug("Creating MongoDB client...")
|
||||
client: pymongo.MongoClient = pymongo.MongoClient(
|
||||
host=MONGO_HOST.__wrapped__,
|
||||
port=MONGO_PORT.__wrapped__,
|
||||
)
|
||||
log.info("Opened connection to MongoDB!")
|
||||
log.debug("Created MongoDB client!")
|
||||
|
||||
yield client
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,11 @@ Category = float
|
|||
|
||||
|
||||
class Review:
|
||||
__slots__ = (
|
||||
"text",
|
||||
"category",
|
||||
)
|
||||
|
||||
def __init__(self, text: Text, category: Category):
|
||||
self.text: str = text
|
||||
self.category: float = category
|
||||
|
|
|
|||
|
|
@ -9,6 +9,9 @@ from .datatypes import Review
|
|||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
SampleFunc = t.Callable[[pymongo.collection.Collection, int], t.Iterator[Review]]
|
||||
|
||||
|
||||
def sample_reviews(collection: pymongo.collection.Collection, amount: int) -> t.Iterator[Review]:
|
||||
"""
|
||||
Get ``amount`` random reviews from the ``reviews`` collection.
|
||||
|
|
@ -41,18 +44,20 @@ def sample_reviews_by_rating(collection: pymongo.collection.Collection, rating:
|
|||
|
||||
|
||||
def sample_reviews_polar(collection: pymongo.collection.Collection, amount: int) -> t.Iterator[Review]:
|
||||
log.debug("Getting a sample of %d polar reviews...", amount * 2)
|
||||
category_amount = amount // 2
|
||||
|
||||
log.debug("Getting a sample of %d polar reviews...", category_amount * 2)
|
||||
|
||||
cursor = collection.aggregate([
|
||||
{"$limit": WORKING_SET_SIZE.__wrapped__},
|
||||
{"$match": {"overall": 1.0}},
|
||||
{"$sample": {"size": amount}},
|
||||
{"$sample": {"size": category_amount}},
|
||||
{"$unionWith": {
|
||||
"coll": collection.name,
|
||||
"pipeline": [
|
||||
{"$limit": WORKING_SET_SIZE.__wrapped__},
|
||||
{"$match": {"overall": 5.0}},
|
||||
{"$sample": {"size": amount}},
|
||||
{"$sample": {"size": category_amount}},
|
||||
],
|
||||
}},
|
||||
{"$addFields": {
|
||||
|
|
@ -69,37 +74,39 @@ def sample_reviews_polar(collection: pymongo.collection.Collection, amount: int)
|
|||
|
||||
|
||||
def sample_reviews_varied(collection: pymongo.collection.Collection, amount: int) -> t.Iterator[Review]:
|
||||
log.debug("Getting a sample of %d varied reviews...", amount * 5)
|
||||
category_amount = amount // 5
|
||||
|
||||
log.debug("Getting a sample of %d varied reviews...", category_amount * 5)
|
||||
|
||||
# Wow, this is ugly.
|
||||
cursor = collection.aggregate([
|
||||
{"$limit": WORKING_SET_SIZE.__wrapped__},
|
||||
{"$match": {"overall": 1.0}},
|
||||
{"$sample": {"size": amount}},
|
||||
{"$sample": {"size": category_amount}},
|
||||
{"$unionWith": {
|
||||
"coll": collection.name,
|
||||
"pipeline": [
|
||||
{"$limit": WORKING_SET_SIZE.__wrapped__},
|
||||
{"$match": {"overall": 2.0}},
|
||||
{"$sample": {"size": amount}},
|
||||
{"$sample": {"size": category_amount}},
|
||||
{"$unionWith": {
|
||||
"coll": collection.name,
|
||||
"pipeline": [
|
||||
{"$limit": WORKING_SET_SIZE.__wrapped__},
|
||||
{"$match": {"overall": 3.0}},
|
||||
{"$sample": {"size": amount}},
|
||||
{"$sample": {"size": category_amount}},
|
||||
{"$unionWith": {
|
||||
"coll": collection.name,
|
||||
"pipeline": [
|
||||
{"$limit": WORKING_SET_SIZE.__wrapped__},
|
||||
{"$match": {"overall": 4.0}},
|
||||
{"$sample": {"size": amount}},
|
||||
{"$sample": {"size": category_amount}},
|
||||
{"$unionWith": {
|
||||
"coll": collection.name,
|
||||
"pipeline": [
|
||||
{"$limit": WORKING_SET_SIZE.__wrapped__},
|
||||
{"$match": {"overall": 5.0}},
|
||||
{"$sample": {"size": amount}},
|
||||
{"$sample": {"size": category_amount}},
|
||||
],
|
||||
}}
|
||||
],
|
||||
|
|
@ -122,6 +129,7 @@ def sample_reviews_varied(collection: pymongo.collection.Collection, amount: int
|
|||
|
||||
|
||||
__all__ = (
|
||||
"SampleFunc",
|
||||
"sample_reviews",
|
||||
"sample_reviews_by_rating",
|
||||
"sample_reviews_polar",
|
||||
|
|
|
|||
|
|
@ -15,15 +15,15 @@ def install_log_handler(loggers: list[logging.Logger] = None):
|
|||
for logger in loggers:
|
||||
coloredlogs.install(
|
||||
logger=logger,
|
||||
level="DEBUG",
|
||||
fmt="{asctime} | {name:<32} | {levelname:>8} | {message}",
|
||||
level="DEBUG" if __debug__ else "INFO",
|
||||
fmt="{asctime} | {name:<80} | {levelname:>8} | {message}",
|
||||
style="{",
|
||||
level_styles=dict(
|
||||
debug=dict(color="white"),
|
||||
info=dict(color="cyan"),
|
||||
warning=dict(color="yellow"),
|
||||
error=dict(color="red"),
|
||||
critical=dict(color="red", bold=True),
|
||||
critical=dict(color="black", background="red", bold=True),
|
||||
),
|
||||
field_styles=dict(
|
||||
asctime=dict(color='magenta'),
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
from .base import BaseTokenizer
|
||||
from .nltk_word_tokenize import NLTKWordTokenizer
|
||||
from .potts import PottsTokenizer, PottsTokenizerWithNegation
|
||||
from .plain import PlainTokenizer
|
||||
from .lower import LowercaseTokenizer
|
||||
|
||||
|
||||
|
|
@ -9,5 +10,6 @@ __all__ = (
|
|||
"NLTKWordTokenizer",
|
||||
"PottsTokenizer",
|
||||
"PottsTokenizerWithNegation",
|
||||
"PlainTokenizer",
|
||||
"LowercaseTokenizer",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -14,21 +14,21 @@ class BaseTokenizer:
|
|||
f.__notimplemented__ = True
|
||||
return f
|
||||
|
||||
def can_tokenize_builtins(self) -> bool:
|
||||
return getattr(self.tokenize_builtins, "__notimplemented__", False)
|
||||
def supports_plain(self) -> bool:
|
||||
return not getattr(self.tokenize_plain, "__notimplemented__", False)
|
||||
|
||||
def can_tokenize_tensorflow(self) -> bool:
|
||||
return getattr(self.tokenize_tensorflow, "__notimplemented__", False)
|
||||
def supports_tensorflow(self) -> bool:
|
||||
return not getattr(self.tokenize_tensorflow, "__notimplemented__", False)
|
||||
|
||||
@__not_implemented
|
||||
def tokenize_builtins(self, text: str) -> list[str]:
|
||||
def tokenize_plain(self, text: str) -> list[str]:
|
||||
"""
|
||||
Convert a text string into a list of tokens.
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
@__not_implemented
|
||||
def tokenize_tensorflow(self, text: tensorflow.Tensor) -> tensorflow.Tensor:
|
||||
def tokenize_tensorflow(self, text: "tensorflow.Tensor") -> "tensorflow.Tensor":
|
||||
"""
|
||||
Convert a `tensorflow.Tensor` string into another `tensorflow.Tensor` space-separated string.
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -4,7 +4,11 @@ from .base import BaseTokenizer
|
|||
|
||||
|
||||
class LowercaseTokenizer(BaseTokenizer):
|
||||
def tokenize_builtins(self, text: str) -> list[str]:
|
||||
"""
|
||||
Tokenizer which converts the words to lowercase before splitting them via spaces.
|
||||
"""
|
||||
|
||||
def tokenize_plain(self, text: str) -> list[str]:
|
||||
return text.lower().split()
|
||||
|
||||
def tokenize_tensorflow(self, text: tensorflow.Tensor) -> tensorflow.Tensor:
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ class NLTKWordTokenizer(BaseTokenizer):
|
|||
Tokenizer based on `nltk.word_tokenize`.
|
||||
"""
|
||||
|
||||
def tokenize_builtins(self, text: str) -> t.Iterable[str]:
|
||||
def tokenize_plain(self, text: str) -> t.Iterable[str]:
|
||||
tokens = nltk.word_tokenize(text)
|
||||
nltk.sentiment.util.mark_negation(tokens, shallow=True)
|
||||
return tokens
|
||||
|
|
|
|||
16
unimore_bda_6/tokenizer/plain.py
Normal file
16
unimore_bda_6/tokenizer/plain.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
import tensorflow
|
||||
|
||||
from .base import BaseTokenizer
|
||||
|
||||
|
||||
class PlainTokenizer(BaseTokenizer):
|
||||
"""
|
||||
Tokenizer which just splits the text into tokens by separating them at whitespaces.
|
||||
"""
|
||||
|
||||
def tokenize_plain(self, text: str) -> list[str]:
|
||||
return text.split()
|
||||
|
||||
def tokenize_tensorflow(self, text: tensorflow.Tensor) -> tensorflow.Tensor:
|
||||
text = tensorflow.expand_dims(text, -1, name="tokens")
|
||||
return text
|
||||
|
|
@ -104,7 +104,7 @@ regex_strings = (
|
|||
emoticon_string
|
||||
,
|
||||
# HTML tags:
|
||||
r"""<[^>]+>"""
|
||||
r"""<[^>]+>"""
|
||||
,
|
||||
# Twitter username:
|
||||
r"""(?:@[\w_]+)"""
|
||||
|
|
@ -124,7 +124,7 @@ regex_strings = (
|
|||
|
|
||||
(?:\S) # Everything else that isn't whitespace.
|
||||
"""
|
||||
)
|
||||
)
|
||||
|
||||
######################################################################
|
||||
# This is the core tokenizing regex:
|
||||
|
|
@ -139,6 +139,7 @@ html_entity_digit_re = re.compile(r"&#\d+;")
|
|||
html_entity_alpha_re = re.compile(r"&\w+;")
|
||||
amp = "&"
|
||||
|
||||
|
||||
######################################################################
|
||||
|
||||
|
||||
|
|
@ -165,7 +166,7 @@ class PottsTokenizer(BaseTokenizer):
|
|||
pass
|
||||
# Now the alpha versions:
|
||||
ents = set(html_entity_alpha_re.findall(s))
|
||||
ents = filter((lambda x : x != amp), ents)
|
||||
ents = filter((lambda x: x != amp), ents)
|
||||
for ent in ents:
|
||||
entname = ent[1:-1]
|
||||
try:
|
||||
|
|
@ -175,7 +176,7 @@ class PottsTokenizer(BaseTokenizer):
|
|||
s = s.replace(amp, " and ")
|
||||
return s
|
||||
|
||||
def tokenize_builtins(self, text: str) -> t.Iterable[str]:
|
||||
def tokenize_plain(self, text: str) -> t.Iterable[str]:
|
||||
# Fix HTML character entitites:
|
||||
s = self.__html2string(text)
|
||||
# Tokenize:
|
||||
|
|
@ -187,8 +188,8 @@ class PottsTokenizer(BaseTokenizer):
|
|||
|
||||
|
||||
class PottsTokenizerWithNegation(PottsTokenizer):
|
||||
def tokenize_builtins(self, text: str) -> t.Iterable[str]:
|
||||
words = super().tokenize_builtins(text)
|
||||
def tokenize_plain(self, text: str) -> t.Iterable[str]:
|
||||
words = super().tokenize_plain(text)
|
||||
nltk.sentiment.util.mark_negation(words, shallow=True)
|
||||
return words
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue