Added an example of how to implement class TfidfVectorizer #8519
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Travis is returning an error. |
amueller
reviewed
Mar 4, 2017
sklearn/feature_extraction/text.py
Outdated
| @@ -1254,8 +1271,10 @@ class TfidfVectorizer(CountVectorizer): | |||
| The ``stop_words_`` attribute can get large and increase the model size | |||
| when pickling. This attribute is provided only for introspection and can | |||
| be safely removed using delattr or set to None before pickling. | |||
| """ | |||
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amueller
reviewed
Mar 4, 2017
| >>> X = vectorizer.fit_transform(doc_test) | ||
| >>> print(type(X)) | ||
| >>> vectorizer.get_feature_names() | ||
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Looks like the error stems from it not being able to load pandas. I think you can just remove that line to solve it, since you don't actually use it. |
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Reference Issue
Example: Fixes #3846 for class TfidfVectorizer
What does this implement/fix? Explain your changes.
This code provides as an example of how to implement class TfidfVectorizer.
Any other comments?