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in Data Science by (50.2k points)

I'm finding it difficult to understand how to fix a Pipeline I created (read: largely pasted from a tutorial). It's a python 3.4.2:

df = pd.DataFrame

df = DataFrame.from_records(train)

test = [blah1, blah2, blah3]

pipeline = Pipeline([('vectorizer', CountVectorizer()), ('classifier', RandomForestClassifier())])[0]), numpy.asarray(df[1]))

predicted = pipeline.predict(test)

When I run it, I get:

TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.

This is for the line[0]), numpy.asarray(df[1])).

I've experimented a lot with solutions through numpy, scipy, and so forth, but I still don't know how to fix it. And yes, similar questions have come up before, but not inside a pipeline. Where is it that I have to apply toarray or todense?

1 Answer

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by (108k points)
A CountVectorizer provides a sparse matrix and the RandomForestClassifier requires a dense matrix. It is possible to convert using X.todense(). Doing this will essentially increase your memory footprint.

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