Source code for asreview.models.feature_extraction.tfidf

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from sklearn.feature_extraction.text import TfidfVectorizer

from asreview.models.feature_extraction.base import BaseFeatureExtraction

[docs]class Tfidf(BaseFeatureExtraction): """TF-IDF feature extraction technique (``tfidf``). Use the standard TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction technique from `SKLearn < generated/sklearn.feature_extraction.text.TfidfVectorizer.html>`__. Gives a sparse matrix as output. Works well in combination with :class:`asreview.models.classifiers.NaiveBayesClassifier` and other fast training models (given that the features vectors are relatively wide). Arguments --------- ngram_max: int Can use up to ngrams up to ngram_max. For example in the case of ngram_max=2, monograms and bigrams could be used. stop_words: str When set to 'english', use stopwords. If set to None or 'none', do not use stop words. """ name = "tfidf" label = "TF-IDF" def __init__(self, *args, ngram_max=1, stop_words="english", **kwargs): """Initialize tfidf class. """ super(Tfidf, self).__init__(*args, **kwargs) self.ngram_max = ngram_max self.stop_words = stop_words if stop_words is None or stop_words.lower() == "none": sklearn_stop_words = None else: sklearn_stop_words = self.stop_words self._model = TfidfVectorizer(ngram_range=(1, ngram_max), stop_words=sklearn_stop_words)
[docs] def fit(self, texts):
[docs] def transform(self, texts): X = self._model.transform(texts).tocsr() return X
[docs] def full_hyper_space(self): from hyperopt import hp hyper_space, hyper_choices = super(Tfidf, self).full_hyper_space() hyper_choices.update({ "fex_stop_words": ["english", "none"] }) hyper_space.update({ "fex_ngram_max": hp.uniformint("fex_ngram_max", 1, 3), "fex_stop_words": hp.choice('fex_stop_words', hyper_choices["fex_stop_words"]), }) return hyper_space, hyper_choices