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()
)

Answer 3

0.16-dev 中的随机森林现在接受稀疏数据。

Answer 4

you can change pandas Series to arrays using the .values method.

pipeline.fit(df[0].values, df[1].values)

However I think the issue here happens because CountVectorizer() returns a sparse matrix by default, and cannot be piped to the RF classifier. CountVectorizer() does have a dtype parameter to specify the type of array returned. That said usually you need to do some sort of dimensionality reduction to use random forests for text classification, because bag of words feature vectors are very long

Answer 5

with this pipeline add TfidTransformer plus

        pipelinEx = Pipeline([('bow',vectorizer),
                           ('tfidf',TfidfTransformer()),
                           ('to_dense', DenseTransformer()), 
                           ('classifier',classifier)])

The first line above, gets the word counts for the documents in a sparse matrix form. However, in practice, you may be computing tfidf scores with TfidfTransformer on a set of new unseen documents. Then, by calling tfidf transformer.transform(vectorizer) you will finally be computing the tf-idf scores for your docs. Internally this is computing the tf * idf multiplication where term frequency is weighted by its idf values.