Showing memory error while using count vectorizer

#1

the code is showing error :

cv=CountVectorizer()
xcv=cv.fit_transform(xc).toarray()

error:

MemoryError Traceback (most recent call last)
in
1 cv=CountVectorizer()
----> 2 xcv=cv.fit_transform(xc).toarray()

~\Anaconda3\lib\site-packages\scipy\sparse\compressed.py in toarray(self, order, out)
1022 if out is None and order is None:
1023 order = self._swap(‘cf’)[0]
-> 1024 out = self._process_toarray_args(order, out)
1025 if not (out.flags.c_contiguous or out.flags.f_contiguous):
1026 raise ValueError(‘Output array must be C or F contiguous’)

~\Anaconda3\lib\site-packages\scipy\sparse\base.py in _process_toarray_args(self, order, out)
1184 return out
1185 else:
-> 1186 return np.zeros(self.shape, dtype=self.dtype, order=order)
1187
1188

MemoryError:

#2

Hey @dibakarchaudhary58, this is due to ram memory overflow meaning your storage requirements are higher than ram space has. I would recommend switching to google colab they will provide you 25 gb of ram. You can also prefer to use sparse matrices.

#3

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#4

I too faced the same problem in colab while using fit_transform() in machine learning.Can anyone help me??