I think you may be reinventing sklearn.pipeline.FeatureUnion. If one of the
transformers returns sparse, it will hstack all the outputs to a sparse
format.
On 3 July 2014 02:35, ZORAIDA HIDALGO SANCHEZ <
[email protected]> wrote:
> Dear all,
>
> For a given dataset, I can have more than one source(differente csv) and
> each of these sources needs a different transformer(for instance, one
> source could be text and
> uses TfidfVectorizer whereas other is composed by attributes of type float
> and needs to be normalized using StandardScaler). What the fit_transform
> function returns depends
> on the transformer. Some transformers return narrays whereas others
> scipy.sparces matrix. I need to generalize the way I transform each
> source:
>
> for train, test in kfolds:
> X_train = []
> for src in sources:
> transformer.fit_transform(X_src_train, y_train)
> transformer.transform(X_src_test)
> X_train = np.concatenate(X_train, X_src_train, axis=1)
> X_test = np.concatenate(X_test, X_src_test, axis=1)
>
>
> for k in range(0, len(score_funcs)):
> ...
> for i in range(0, len(classifiers)):
> ...
>
>
> Does it make sense? I know there are different methods for concatenating
> narrays and sparse matrix but separately. How do you deal with this?
>
> Thanks in advance.
>
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