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())])
pipeline.fit(numpy.asarray(df[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 pipeline.fit(numpy.asarray(df[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
?