Scikit-Learn's Pipeline: A sparse matrix was passed, but dense data is required

Question

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

Answer 1

Unfortunately those two are incompatible. A CountVectorizer produces a sparse matrix and the RandomForestClassifier requires a dense matrix. It is possible to convert using X.todense(). Doing this will substantially increase your memory footprint.

Below is sample code to do this based on http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html which allows you to call .todense() in a pipeline stage.

class DenseTransformer(TransformerMixin):

    def fit(self, X, y=None, **fit_params):
        return self

    def transform(self, X, y=None, **fit_params):
        return X.todense()

Once you have your DenseTransformer, you are able to add it as a pipeline step.

pipeline = Pipeline([
     ('vectorizer', CountVectorizer()), 
     ('to_dense', DenseTransformer()), 
     ('classifier', RandomForestClassifier())
])

Another option would be to use a classifier meant for sparse data like LinearSVC.

from sklearn.svm import LinearSVC
pipeline = Pipeline([('vectorizer', CountVectorizer()), ('classifier', LinearSVC())])

Answer 2

The most terse solution would be use a FunctionTransformer to convert to dense: this will automatically implement the fit, transform and fit_transform methods as in David's answer. Additionally if I don't need special names for my pipeline steps, I like to use the sklearn.pipeline.make_pipeline convenience function to enable a more minimalist language for describing the model:

from sklearn.preprocessing import FunctionTransformer

pipeline = make_pipeline(
     CountVectorizer(), 
     FunctionTransformer(lambda x: x.todense(), accept_sparse=True), 
     RandomForestClassifier()
)