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

[Joel Nothman]

I think it's a bit weird if we're returning sparse output from
OneVsRestClassifier.predict if it wasn't fit on sparse Y.

Actually, I would be in favour of deprecating multilabel support in
OneVsRestClassifier, since it is performing "binary relevance method" for
multilabel, not actually OvR. MultiOutputClassifier duplicates this
functionality (more or less), outputs a dense array (indeed it doesn't
support sparse Y and perhaps it should) and lives closer to functional
alternatives to binary relevance, such as ClassifierChain.

On Thu, 11 Apr 2019 at 05:32, Liam Geron <liam at chatdesk.com> wrote:

> Unfortunately I don't believe that you get that level of freedom, it's an
> API call that automatically calls the model's predict method so I don't
> think that I get to specify something like model.predict(X).toarray(). I
> could be wrong however, I don't pretend to be an expert on Cloud ML by any
> stretch.
>
> Thanks,
> Liam
>
> On Wed, Apr 10, 2019 at 3:23 PM Sebastian Raschka <
> mail at sebastianraschka.com> wrote:
>
>> Hm, weird that their platform seems to be so picky about it. Have you
>> tried to just make the output of the pipeline dense? I.e.,
>>
>> (model.predict(X)).toarray()
>>
>> Best,
>> Sebastian
>>
>> > On Apr 10, 2019, at 1:10 PM, Liam Geron <liam at chatdesk.com> wrote:
>> >
>> > 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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