Description

Hi!

I'm receiving the error below when attempting to pass a sparse matrix to HistGradientBoostingClassifier. The matrix is the result of using CountVectorizer and TfidfTransformer on input text.

In my case, the size of the text prohibits converting the sparse matrix to a dense one (I run out of memory).

Steps/Code to Reproduce

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.ensemble import HistGradientBoostingClassifier

df = pd.read_csv(...)

vectorizer = CountVectorizer()
tfidf = TfidfTransformer()
clf = HistGradientBoostingClassifier()

vecs = vectorizer.fit_transform(df.loc[:, "very_large_text"])
vecs = tfidf.fit_transform(vecs)

clf.fit(vecs, df.loc[:, "label"])

Expected Results

No error is thrown.

Actual Results

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

Versions

System:
python: 3.7.3 (default, Oct 1 2019, 18:28:53) [GCC 5.4.0 20160609]
executable: /local_disk0/pythonVirtualEnvDirs/virtualEnv-3631eab5-084b-4139-952e-5aff594ac1bb/bin/python
machine: Linux-4.15.0-1050-azure-x86_64-with-debian-stretch-sid

Python deps:
pip: 19.0.3
setuptools: 40.8.0
sklearn: 0.21.3
numpy: 1.16.2
scipy: 1.2.1
Cython: 0.29.6
pandas: 0.24.2