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