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 <l...@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 <m...@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 <l...@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 > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
