mirror of
https://github.com/Steffo99/unimore-bda-6.git
synced 2024-11-29 03:04:18 +00:00
118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
import nltk
|
|
import nltk.classify
|
|
import nltk.sentiment
|
|
import nltk.sentiment.util
|
|
import logging
|
|
import typing as t
|
|
import itertools
|
|
|
|
from ..database import Text, Category, Review, CachedDatasetFunc
|
|
from .base import BaseSentimentAnalyzer, AlreadyTrainedError, NotTrainedError
|
|
from ..log import count_passage
|
|
from ..tokenizer import BaseTokenizer
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
TokenBag = list[str]
|
|
Features = dict[str, int]
|
|
|
|
|
|
class NLTKSentimentAnalyzer(BaseSentimentAnalyzer):
|
|
"""
|
|
A sentiment analyzer resembling the one implemented in structure the one implemented in the classroom, using the basic sentiment analyzer of NLTK.
|
|
"""
|
|
|
|
def __init__(self, *, tokenizer: BaseTokenizer) -> None:
|
|
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
|
|
|
|
def __repr__(self):
|
|
return f"<{self.__class__.__qualname__} tokenizer={self.tokenizer!r}>"
|
|
|
|
def __tokenize_review(self, datatuple: Review) -> tuple[TokenBag, Category]:
|
|
"""
|
|
Convert the `Text` of a `DataTuple` to a `TokenBag`.
|
|
"""
|
|
count_passage(log, "tokenize_datatuple", 100)
|
|
return self.tokenizer.tokenize_plain(datatuple.text), datatuple.category
|
|
|
|
def _add_feature_unigrams(self, dataset: t.Iterator[tuple[TokenBag, Category]]) -> None:
|
|
"""
|
|
Register the `nltk.sentiment.util.extract_unigram_feats` feature extrator on the model.
|
|
"""
|
|
# Ignore the category and only access the tokens
|
|
tokenbags = map(lambda d: d[0], dataset)
|
|
# Get all words in the documents
|
|
all_words = self.model.all_words(tokenbags, labeled=False)
|
|
# Create unigram `contains(*)` features from the previously gathered words
|
|
unigrams = self.model.unigram_word_feats(words=all_words, min_freq=4)
|
|
# Add the feature extractor to the model
|
|
self.model.add_feat_extractor(nltk.sentiment.util.extract_unigram_feats, unigrams=unigrams)
|
|
|
|
def _add_feature_extractors(self, dataset: t.Iterator[tuple[TokenBag, Category]]):
|
|
"""
|
|
Register new feature extractors on the `.model`.
|
|
"""
|
|
# Add the unigrams feature
|
|
self._add_feature_unigrams(dataset)
|
|
|
|
def __extract_features(self, data: tuple[TokenBag, Category]) -> tuple[Features, Category]:
|
|
"""
|
|
Convert a (TokenBag, Category) tuple to a (Features, Category) tuple.
|
|
|
|
Does not use `SentimentAnalyzer.apply_features` due to unexpected behaviour when using iterators.
|
|
"""
|
|
count_passage(log, "extract_features", 100)
|
|
return self.model.extract_features(data[0]), data[1]
|
|
|
|
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] = training_dataset_func()
|
|
|
|
# Tokenize the dataset
|
|
dataset: t.Iterator[tuple[TokenBag, Category]] = map(self.__tokenize_review, dataset)
|
|
|
|
# Cleanly duplicate the dataset iterator
|
|
# Reduce average memory footprint, but not maximum
|
|
dataset_1, dataset_2 = itertools.tee(dataset, 2)
|
|
dataset_1: t.Iterator[tuple[TokenBag, Category]]
|
|
dataset_2: t.Iterator[tuple[TokenBag, Category]]
|
|
|
|
# Add the feature extractors to the model
|
|
self._add_feature_extractors(dataset_1)
|
|
del dataset_1 # Delete exausted iterator
|
|
|
|
# Extract features from the dataset
|
|
dataset_2: t.Iterator[tuple[Features, Category]] = map(self.__extract_features, dataset_2)
|
|
|
|
# Train the classifier with the extracted features and category
|
|
self.model.classifier = nltk.classify.NaiveBayesClassifier.train(dataset_2)
|
|
|
|
# Toggle the trained flag
|
|
self.trained = True
|
|
|
|
def use(self, text: Text) -> Category:
|
|
# Require the model to be trained
|
|
if not self.trained:
|
|
raise NotTrainedError()
|
|
|
|
# Tokenize the input
|
|
tokens = self.tokenizer.tokenize_plain(text)
|
|
|
|
# Run the classification method
|
|
return self.model.classify(instance=tokens)
|
|
|
|
|
|
__all__ = (
|
|
"NLTKSentimentAnalyzer",
|
|
)
|