Hi all,
I was hoping to get some guidance re: changing the result of the predict
method of the OneVsRestClassifier to return a dense array rather than a
sparse array, given that Google Cloud ML only accepts dense numpy arrays as
a result of a given models predict method. Right now my model architecture
looks like:
model = Pipeline([('tfidf', TfidfVectorizer()), ('clf',
OneVsRestClassifier(XGBClassifier()))])
Which returns a sparse array with the predict method. I saw the Stack
Overflow post here:
https://stackoverflow.com/questions/52151548/google-cloud-ml-engine-scikit-learn-prediction-probability-predict-proba
which recommends overwriting the predict method with the predict_proba
method, however I found that I can't serialize the model after doing so. I
also have a stack overflow post here:
https://stackoverflow.com/questions/55366454/how-to-convert-scikit-learn-onevsrestclassifier-predict-method-output-to-dense-a
which
details the specific pickling error.
Is this a known issue? Is there an accepted way to convert this into a
dense array?
Thanks,
Liam Geron
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