feat(nlp): add basic nlp transformations #40
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…ming transformations
cowana-ai
reviewed
Oct 7, 2024
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| class NGrams(Transformation): | ||
| _name_ = "NGrams" |
| class NGrams(Transformation): | ||
| _name_ = "NGrams" | ||
| @beartype | ||
| def __init__(self, n: int): |
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| class Stemming(Transformation): | ||
| _name_ = "Stemming" |
| @beartype | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.stemmer = PorterStemmer() |
| @beartype | ||
| def __init__(self, max_features: int, ngram_range: tuple[int, int], stop_words: list[str] | None = None): | ||
| super().__init__() | ||
| self.vectorizer = TfidfVectorizer(max_features=max_features, ngram_range=ngram_range, stop_words=stop_words) |
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| @beartype | ||
| def execute(self, data: StrArray) -> NumericArray: | ||
| return self.vectorizer.fit_transform(data).toarray() |
There was a problem hiding this comment.
we shouldn't apply fit_transform on test data. should be separate fit & transform
| ngram_range = tuple(ngram_range) | ||
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| self.ngram_range = ngram_range | ||
| self.vectorizer = CountVectorizer( |
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| class BagOfWords(Transformation): | ||
| _name_ = 'BagOfWords' |
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| @beartype | ||
| def execute(self, data: StrArray) -> np.ndarray: | ||
| return self.vectorizer.fit_transform(data).toarray() |
There was a problem hiding this comment.
we shouldn't apply fit_transform on test data. should be separate fit & transform
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