Refactored Count Vectorizer to be more memory efficient on N-grams #7107
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sklearn/feature_extraction/text.py
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| @@ -674,7 +725,7 @@ def __init__(self, input='content', encoding='utf-8', | |||
| self.max_features = max_features | |||
| if max_features is not None: | |||
| if (not isinstance(max_features, numbers.Integral) or | |||
| max_features <= 0): | |||
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can you please post speed benchmarks for varying |
sklearn/feature_extraction/text.py
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| @@ -1222,7 +1292,6 @@ def __init__(self, input='content', encoding='utf-8', | |||
| max_features=None, vocabulary=None, binary=False, | |||
| dtype=np.int64, norm='l2', use_idf=True, smooth_idf=True, | |||
| sublinear_tf=False): | |||
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please try not to make any non-function modifications to the code; it makes it harder to review / in this case the newline should be there.
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travis doesn't seem happy, but i'm very interested to see benchmarks for this as well. |
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Closing, never got response from the OP. |
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Reference Issue
What does this implement/fix? Explain your changes.
Any other comments?
When max_features are specified, CountVectorizer will first count the word occurrence. It will take the the top occurring words up to max_features. It will only make a 2-gram when its 1 gram count is apart of max_features, make a 3-gram when its 2-gram is apart of the max features and so on. This faster and more memory efficient on n-grams when the data sets are large.