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| # to seprate words and normlize it | ||
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| def decompose(text): |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/feature_extraction/tf-idf.py, please provide doctest for the function decompose
Please provide return type hint for the function: decompose. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: text
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| # creating tfidf class | ||
| class TfIdfVectorizer: | ||
| def __init__(self): |
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Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:
| self.idf = None | ||
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| # these method to compute the tf for each word in given data | ||
| def compute_tf(self, data): |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/feature_extraction/tf-idf.py, please provide doctest for the function compute_tf
Please provide return type hint for the function: compute_tf. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: data
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| return np.array(tfidf, dtype=float) | ||
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| def encode(self, data): |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/feature_extraction/tf-idf.py, please provide doctest for the function encode
Please provide return type hint for the function: encode. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: data
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Describe your change:
Added a pure Python implementation of TF-IDF vectorizer under
machine_learning/feature_extraction/.The vectorizer includes:
Checklist: