[scikit-learn] Predict Method of OneVsRestClassifier Integration with Google Cloud ML

[Liam Geron]

Hi Sebastian,

Thanks for the advice! The model actually works on it's own in python fine
luckily, so I don't think that that is the issue exactly. I have tried
rolling my own estimator to wrap the pipeline to have it call the
predict_proba method to return a dense array, however I then came across
the problem that I would have to have that custom estimator defined on the
Cloud ML end, which I'm unsure how to do.

Thanks,
Liam

On Wed, Apr 10, 2019 at 2:06 PM Sebastian Raschka <mail at sebastianraschka.com>
wrote:

> Hi Liam,
>
> not sure what your exact error message is, but it may also be that the
> XGBClassifier only accepts dense arrays? I think the TfidfVectorizer
> returns sparse arrays. You could probably fix your issues by inserting a
> "DenseTransformer" into your pipelone (a simple class that just transforms
> an array from a sparse to a dense format). I've implemented sth like that
> that you can import or copy&paste it from here:
>
>
> https://github.com/rasbt/mlxtend/blob/master/mlxtend/preprocessing/dense_transformer.py
>
> The usage would then basically be
>
> model = Pipeline([('tfidf', TfidfVectorizer()), ('to_dense',
> DenseTransformer()), ('clf', OneVsRestClassifier(XGBClassifier()))])
>
> Best,
> Sebastian
>
>
>
>
> > On Apr 10, 2019, at 12:25 PM, Liam Geron <liam at chatdesk.com> wrote:
> >
> > 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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