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.


Comments

Popular posts from this blog

c# - Binding a comma separated list to a List<int> in asp.net web api -

Delphi 7 and decode UTF-8 base64 -

html - Is there any way to exclude a single element from the style? (Bootstrap) -