# counter vector fit transform cosine similarity memory error

count_matrix = count.fit_transform(off_data3['bag_of_words'])


I have count_matrix shape with

count_matrix.shape (476147, 482824)

cosine_sim = cosine_similarity(count_matrix, count_matrix)


I think the matrix size is too big to cause this memory error

--------------------------------------------------------------------------- MemoryError Traceback (most recent call last) in

~/venv/lib/python3.6/site-packages/sklearn/metrics/pairwise.py in cosine_similarity(X, Y, dense_output) 1034 1035 K = safe_sparse_dot(X_normalized, Y_normalized.T, -> 1036 dense_output=dense_output) 1037 1038 return K

~/venv/lib/python3.6/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output) 135 """ 136 if sparse.issparse(a) or sparse.issparse(b): --> 137 ret = a * b 138 if dense_output and hasattr(ret, "toarray"): 139 ret = ret.toarray()

~/venv/lib/python3.6/site-packages/scipy/sparse/base.py in mul(self, other) 479 if self.shape[1] != other.shape[0]: 480 raise ValueError('dimension mismatch') --> 481 return self._mul_sparse_matrix(other) 482 483 # If it's a list or whatever, treat it like a matrix

~/venv/lib/python3.6/site-packages/scipy/sparse/compressed.py in _mul_sparse_matrix(self, other) 514 maxval=nnz) 515 indptr = np.asarray(indptr, dtype=idx_dtype) --> 516 indices = np.empty(nnz, dtype=idx_dtype) 517 data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype)) 518

MemoryError:

Any tips to avoid this memory error when You have large matrix?

It's not clear to me what is your data and what you are trying to do with it, but from what I gather you are trying to calculate cosine similarity for each pair in a cartesian product, right?

If yes then you might want to use "blocking" to reduce the number of comparisons, see https://datascience.stackexchange.com/a/54582/64377.