diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
index 7253287..1c6334f 100644
--- a/.idea/inspectionProfiles/Project_Default.xml
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -40,6 +40,7 @@
+
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 6b0faad..a0e6536 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -10,4 +10,7 @@
+
+
+
\ No newline at end of file
diff --git a/.idea/runConfigurations/unimore_bda_6.xml b/.idea/runConfigurations/unimore_bda_6.xml
index 3718006..f2f821f 100644
--- a/.idea/runConfigurations/unimore_bda_6.xml
+++ b/.idea/runConfigurations/unimore_bda_6.xml
@@ -1,12 +1,12 @@
-
+
+
-
diff --git a/poetry.lock b/poetry.lock
index fb0f6ae..9ea263b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1230,6 +1230,61 @@ files = [
[package.extras]
tests = ["pytest", "pytest-cov"]
+[[package]]
+name = "tokenizers"
+version = "0.13.2"
+description = "Fast and Customizable Tokenizers"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+ {file = "tokenizers-0.13.2-cp310-cp310-macosx_10_11_x86_64.whl", hash = "sha256:a6f36b1b499233bb4443b5e57e20630c5e02fba61109632f5e00dab970440157"},
+ {file = "tokenizers-0.13.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:bc6983282ee74d638a4b6d149e5dadd8bc7ff1d0d6de663d69f099e0c6bddbeb"},
+ {file = "tokenizers-0.13.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16756e6ab264b162f99c0c0a8d3d521328f428b33374c5ee161c0ebec42bf3c0"},
+ {file = "tokenizers-0.13.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b10db6e4b036c78212c6763cb56411566edcf2668c910baa1939afd50095ce48"},
+ {file = "tokenizers-0.13.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:238e879d1a0f4fddc2ce5b2d00f219125df08f8532e5f1f2ba9ad42f02b7da59"},
+ {file = "tokenizers-0.13.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47ef745dbf9f49281e900e9e72915356d69de3a4e4d8a475bda26bfdb5047736"},
+ {file = "tokenizers-0.13.2-cp310-cp310-win32.whl", hash = "sha256:96cedf83864bcc15a3ffd088a6f81a8a8f55b8b188eabd7a7f2a4469477036df"},
+ {file = "tokenizers-0.13.2-cp310-cp310-win_amd64.whl", hash = "sha256:eda77de40a0262690c666134baf19ec5c4f5b8bde213055911d9f5a718c506e1"},
+ {file = "tokenizers-0.13.2-cp311-cp311-macosx_10_11_universal2.whl", hash = "sha256:9eee037bb5aa14daeb56b4c39956164b2bebbe6ab4ca7779d88aa16b79bd4e17"},
+ {file = "tokenizers-0.13.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d1b079c4c9332048fec4cb9c2055c2373c74fbb336716a5524c9a720206d787e"},
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+ {file = "tokenizers-0.13.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3606528c07cda0566cff6cbfbda2b167f923661be595feac95701ffcdcbdbb21"},
+ {file = "tokenizers-0.13.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:41291d0160946084cbd53c8ec3d029df3dc2af2673d46b25ff1a7f31a9d55d51"},
+ {file = "tokenizers-0.13.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7892325f9ca1cc5fca0333d5bfd96a19044ce9b092ce2df625652109a3de16b8"},
+ {file = "tokenizers-0.13.2-cp311-cp311-win32.whl", hash = "sha256:93714958d4ebe5362d3de7a6bd73dc86c36b5af5941ebef6c325ac900fa58865"},
+ {file = "tokenizers-0.13.2-cp311-cp311-win_amd64.whl", hash = "sha256:fa7ef7ee380b1f49211bbcfac8a006b1a3fa2fa4c7f4ee134ae384eb4ea5e453"},
+ {file = "tokenizers-0.13.2-cp37-cp37m-macosx_10_11_x86_64.whl", hash = "sha256:da521bfa94df6a08a6254bb8214ea04854bb9044d61063ae2529361688b5440a"},
+ {file = "tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a739d4d973d422e1073989769723f3b6ad8b11e59e635a63de99aea4b2208188"},
+ {file = "tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cac01fc0b868e4d0a3aa7c5c53396da0a0a63136e81475d32fcf5c348fcb2866"},
+ {file = "tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0901a5c6538d2d2dc752c6b4bde7dab170fddce559ec75662cfad03b3187c8f6"},
+ {file = "tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ba9baa76b5a3eefa78b6cc351315a216232fd727ee5e3ce0f7c6885d9fb531b"},
+ {file = "tokenizers-0.13.2-cp37-cp37m-win32.whl", hash = "sha256:a537061ee18ba104b7f3daa735060c39db3a22c8a9595845c55b6c01d36c5e87"},
+ {file = "tokenizers-0.13.2-cp37-cp37m-win_amd64.whl", hash = "sha256:c82fb87b1cbfa984d8f05b2b3c3c73e428b216c1d4f0e286d0a3b27f521b32eb"},
+ {file = "tokenizers-0.13.2-cp38-cp38-macosx_10_11_x86_64.whl", hash = "sha256:ce298605a833ac7f81b8062d3102a42dcd9fa890493e8f756112c346339fe5c5"},
+ {file = "tokenizers-0.13.2-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:f44d59bafe3d61e8a56b9e0a963075187c0f0091023120b13fbe37a87936f171"},
+ {file = "tokenizers-0.13.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a51b93932daba12ed07060935978a6779593a59709deab04a0d10e6fd5c29e60"},
+ {file = "tokenizers-0.13.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6969e5ea7ccb909ce7d6d4dfd009115dc72799b0362a2ea353267168667408c4"},
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+ {file = "tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4d3bc9f7d7f4c1aa84bb6b8d642a60272c8a2c987669e9bb0ac26daf0c6a9fc8"},
+ {file = "tokenizers-0.13.2-cp38-cp38-win32.whl", hash = "sha256:efbf189fb9cf29bd29e98c0437bdb9809f9de686a1e6c10e0b954410e9ca2142"},
+ {file = "tokenizers-0.13.2-cp38-cp38-win_amd64.whl", hash = "sha256:0b4cb2c60c094f31ea652f6cf9f349aae815f9243b860610c29a69ed0d7a88f8"},
+ {file = "tokenizers-0.13.2-cp39-cp39-macosx_10_11_x86_64.whl", hash = "sha256:b47d6212e7dd05784d7330b3b1e5a170809fa30e2b333ca5c93fba1463dec2b7"},
+ {file = "tokenizers-0.13.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:80a57501b61ec4f94fb7ce109e2b4a1a090352618efde87253b4ede6d458b605"},
+ {file = "tokenizers-0.13.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:61507a9953f6e7dc3c972cbc57ba94c80c8f7f686fbc0876afe70ea2b8cc8b04"},
+ {file = "tokenizers-0.13.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c09f4fa620e879debdd1ec299bb81e3c961fd8f64f0e460e64df0818d29d845c"},
+ {file = "tokenizers-0.13.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:66c892d85385b202893ac6bc47b13390909e205280e5df89a41086cfec76fedb"},
+ {file = "tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3e306b0941ad35087ae7083919a5c410a6b672be0343609d79a1171a364ce79"},
+ {file = "tokenizers-0.13.2-cp39-cp39-win32.whl", hash = "sha256:79189e7f706c74dbc6b753450757af172240916d6a12ed4526af5cc6d3ceca26"},
+ {file = "tokenizers-0.13.2-cp39-cp39-win_amd64.whl", hash = "sha256:486d637b413fddada845a10a45c74825d73d3725da42ccd8796ccd7a1c07a024"},
+ {file = "tokenizers-0.13.2.tar.gz", hash = "sha256:f9525375582fd1912ac3caa2f727d36c86ff8c0c6de45ae1aaff90f87f33b907"},
+]
+
+[package.extras]
+dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+
[[package]]
name = "tqdm"
version = "4.64.1"
@@ -1390,4 +1445,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = "~3.10"
-content-hash = "b17bed73011c627355660e5ba5ca176a920c1d87915a1ab875e5a5ddd28a9dca"
+content-hash = "d63867d77886e8ae2c09771a5ec1053dae99a5699f7e905a69bd298e1b986a80"
diff --git a/pyproject.toml b/pyproject.toml
index 5ff07ae..9cb7937 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -137,6 +137,7 @@ nltk = "^3.8.1"
cfig = {extras = ["cli"], version = "^0.3.0"}
coloredlogs = "^15.0.1"
tensorflow = "^2.11.0"
+tokenizers = "^0.13.2"
diff --git a/unimore_bda_6/__main__.py b/unimore_bda_6/__main__.py
index ea01232..121263a 100644
--- a/unimore_bda_6/__main__.py
+++ b/unimore_bda_6/__main__.py
@@ -1,84 +1,81 @@
import logging
-import tensorflow
+import pymongo.errors
+from .log import install_log_handler
-from .config import config, DATA_SET_SIZE
-from .database import mongo_client_from_config, reviews_collection, sample_reviews_polar, sample_reviews_varied, store_cache, load_cache, delete_cache
+install_log_handler()
+
+from .config import config
+from .database import mongo_client_from_config, reviews_collection, sample_reviews_polar, sample_reviews_varied
from .analysis.nltk_sentiment import NLTKSentimentAnalyzer
from .analysis.tf_text import TensorflowSentimentAnalyzer
from .analysis.base import TrainingFailedError
-from .tokenizer import LowercaseTokenizer
-from .log import install_log_handler
+from .tokenizer import PlainTokenizer, LowercaseTokenizer, NLTKWordTokenizer, PottsTokenizer, PottsTokenizerWithNegation
+from .gathering import Caches
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!")
+ log.info("Started unimore-bda-6 in %s mode!", "DEBUG" if __debug__ else "PRODUCTION")
- try:
- delete_cache("./data/training")
- delete_cache("./data/evaluation")
- except FileNotFoundError:
- pass
+ log.debug("Validating configuration...")
+ config.proxies.resolve()
- for dataset_func in [sample_reviews_polar, sample_reviews_varied]:
- for SentimentAnalyzer in [TensorflowSentimentAnalyzer, NLTKSentimentAnalyzer]:
- for Tokenizer in [
- # NLTKWordTokenizer,
- # PottsTokenizer,
- # PottsTokenizerWithNegation,
- LowercaseTokenizer,
+ log.debug("Ensuring there are no leftover caches...")
+ Caches.ensure_clean()
+
+ with mongo_client_from_config() as db:
+ try:
+ db.admin.command("ping")
+ except pymongo.errors.ServerSelectionTimeoutError:
+ log.fatal("MongoDB database is not available, exiting...")
+ exit(1)
+
+ reviews = reviews_collection(db)
+
+ for sample_func in [sample_reviews_varied, sample_reviews_polar]:
+
+ for SentimentAnalyzer in [
+ TensorflowSentimentAnalyzer,
+ NLTKSentimentAnalyzer
]:
- while True:
- try:
- tokenizer = Tokenizer()
- model = SentimentAnalyzer(tokenizer=tokenizer)
- with mongo_client_from_config() as db:
- log.debug("Finding the reviews MongoDB collection...")
- collection = reviews_collection(db)
+ for Tokenizer in [
+ PlainTokenizer,
+ LowercaseTokenizer,
+ NLTKWordTokenizer,
+ PottsTokenizer,
+ PottsTokenizerWithNegation,
+ ]:
+ slog = logging.getLogger(f"{__name__}.{sample_func.__name__}.{SentimentAnalyzer.__name__}.{Tokenizer.__name__}")
+
+ while True:
+
+ try:
+ slog.debug("Creating sentiment analyzer...")
+ sa = SentimentAnalyzer(tokenizer=Tokenizer())
+ except TypeError:
+ slog.warning("%s does not support %s, skipping...", Tokenizer.__name__, SentimentAnalyzer.__name__)
+ break
+
+ with Caches.from_database_samples(collection=reviews, sample_func=sample_func) as datasets:
try:
- training_cache = load_cache("./data/training")
- evaluation_cache = load_cache("./data/evaluation")
- except FileNotFoundError:
- log.debug("Gathering datasets...")
- reviews_training = dataset_func(collection=collection, amount=DATA_SET_SIZE.__wrapped__)
- reviews_evaluation = dataset_func(collection=collection, amount=DATA_SET_SIZE.__wrapped__)
+ slog.info("Training sentiment analyzer: %s", sa)
+ sa.train(training_dataset_func=datasets.training, validation_dataset_func=datasets.validation)
- log.debug("Caching datasets...")
- store_cache(reviews_training, "./data/training")
- store_cache(reviews_evaluation, "./data/evaluation")
- del reviews_training
- del reviews_evaluation
+ except TrainingFailedError:
+ slog.error("Training failed, trying again with a different dataset...")
+ continue
- training_cache = load_cache("./data/training")
- evaluation_cache = load_cache("./data/evaluation")
- log.debug("Caches stored and loaded successfully!")
else:
- log.debug("Caches loaded successfully!")
+ slog.info("Training succeeded!")
- log.info("Training model: %s", model)
- model.train(training_cache)
- log.info("Evaluating model: %s", model)
- evaluation_results = model.evaluate(evaluation_cache)
- log.info("%s", evaluation_results)
-
- except TrainingFailedError:
- log.error("Training failed, restarting with a different dataset.")
- continue
- else:
- log.info("Training")
- break
- finally:
- delete_cache("./data/training")
- delete_cache("./data/evaluation")
+ slog.info("Evaluating sentiment analyzer: %s", sa)
+ evaluation_results = sa.evaluate(evaluation_dataset_func=datasets.evaluation)
+ slog.info("Evaluation results: %s", evaluation_results)
+ break
if __name__ == "__main__":
- install_log_handler()
- config.proxies.resolve()
main()
diff --git a/unimore_bda_6/analysis/base.py b/unimore_bda_6/analysis/base.py
index b9eb589..3cee461 100644
--- a/unimore_bda_6/analysis/base.py
+++ b/unimore_bda_6/analysis/base.py
@@ -1,38 +1,42 @@
+from __future__ import annotations
+
import abc
import logging
import dataclasses
-from ..database import Text, Category, DatasetFunc
+from ..database import Text, Category, CachedDatasetFunc
+from ..tokenizer import BaseTokenizer
log = logging.getLogger(__name__)
-@dataclasses.dataclass
-class EvaluationResults:
- correct: int
- evaluated: int
- score: float
-
- def __repr__(self):
- return f""
-
- 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""
+
+ 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):
diff --git a/unimore_bda_6/analysis/nltk_sentiment.py b/unimore_bda_6/analysis/nltk_sentiment.py
index 1feaa95..f3d4b86 100644
--- a/unimore_bda_6/analysis/nltk_sentiment.py
+++ b/unimore_bda_6/analysis/nltk_sentiment.py
@@ -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)
diff --git a/unimore_bda_6/analysis/tf_text.py b/unimore_bda_6/analysis/tf_text.py
index ba27c1d..257c106 100644
--- a/unimore_bda_6/analysis/tf_text.py
+++ b/unimore_bda_6/analysis/tf_text.py
@@ -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))
diff --git a/unimore_bda_6/config.py b/unimore_bda_6/config.py
index 8dcc079..c07d29f 100644
--- a/unimore_bda_6/config.py
+++ b/unimore_bda_6/config.py
@@ -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",
diff --git a/unimore_bda_6/database/cache.py b/unimore_bda_6/database/cache.py
index 29f530f..e6a3d45 100644
--- a/unimore_bda_6/database/cache.py
+++ b/unimore_bda_6/database/cache.py
@@ -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",
diff --git a/unimore_bda_6/database/connection.py b/unimore_bda_6/database/connection.py
index 2f8e7e3..5e3d703 100644
--- a/unimore_bda_6/database/connection.py
+++ b/unimore_bda_6/database/connection.py
@@ -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
diff --git a/unimore_bda_6/database/datatypes.py b/unimore_bda_6/database/datatypes.py
index 1af2b7d..980b7e6 100644
--- a/unimore_bda_6/database/datatypes.py
+++ b/unimore_bda_6/database/datatypes.py
@@ -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
diff --git a/unimore_bda_6/database/queries.py b/unimore_bda_6/database/queries.py
index c2cefc4..1d0801c 100644
--- a/unimore_bda_6/database/queries.py
+++ b/unimore_bda_6/database/queries.py
@@ -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",
diff --git a/unimore_bda_6/log.py b/unimore_bda_6/log.py
index f6f0fcd..2a55216 100644
--- a/unimore_bda_6/log.py
+++ b/unimore_bda_6/log.py
@@ -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'),
diff --git a/unimore_bda_6/tokenizer/__init__.py b/unimore_bda_6/tokenizer/__init__.py
index 91253b6..777174f 100644
--- a/unimore_bda_6/tokenizer/__init__.py
+++ b/unimore_bda_6/tokenizer/__init__.py
@@ -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",
)
diff --git a/unimore_bda_6/tokenizer/base.py b/unimore_bda_6/tokenizer/base.py
index 378b324..f4b28e8 100644
--- a/unimore_bda_6/tokenizer/base.py
+++ b/unimore_bda_6/tokenizer/base.py
@@ -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.
"""
diff --git a/unimore_bda_6/tokenizer/lower.py b/unimore_bda_6/tokenizer/lower.py
index 99d9d6e..77177e0 100644
--- a/unimore_bda_6/tokenizer/lower.py
+++ b/unimore_bda_6/tokenizer/lower.py
@@ -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:
diff --git a/unimore_bda_6/tokenizer/nltk_word_tokenize.py b/unimore_bda_6/tokenizer/nltk_word_tokenize.py
index 593cb47..e56776c 100644
--- a/unimore_bda_6/tokenizer/nltk_word_tokenize.py
+++ b/unimore_bda_6/tokenizer/nltk_word_tokenize.py
@@ -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
diff --git a/unimore_bda_6/tokenizer/plain.py b/unimore_bda_6/tokenizer/plain.py
new file mode 100644
index 0000000..206131d
--- /dev/null
+++ b/unimore_bda_6/tokenizer/plain.py
@@ -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
diff --git a/unimore_bda_6/tokenizer/potts.py b/unimore_bda_6/tokenizer/potts.py
index cb4f52c..bc44fb3 100644
--- a/unimore_bda_6/tokenizer/potts.py
+++ b/unimore_bda_6/tokenizer/potts.py
@@ -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