python - How to use own algorithm to extract features in scikit-learn ( text feature extraction) -


i want use own algorithm extract features training data , fit , transform using countvectorize in scikit-learn.

currently doing:

from sklearn.feature_extraction.text import countvectorizer cvect_obj = countvectorizer() vects = cvect_obj.fit_transform(traning_data)

fit_transform(traning_data)automatically extracts features , transforms it, want use own algorithm extract features.

actually quite not possible using directly though.as rule scikit-learn add well-established algorithms. rule of thumb @ least 3 years since publications, 200+ citations , wide use , usefullness. technique provides clear-cut improvement (e.g. enhanced data structure or efficient approximation) on widely-used method considered inclusion.

moreover, implementation doesn’t need in scikit-learn used scikit-learn tools, though. implement favorite algorithm in scikit-learn compatible way, upload github , listed under related projects.


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