Extra converting in coo_matrix.multiply() when multiplying vector

[gan-lin]

coo_matrix.multiply() method is inherited from spmatrix, which convert self to csr format before calculating.

# in base.py
class spmatrix(object):
    # ...
    def multiply(self, other):
        return self.tocsr().multiply(other)

However, in csr_matrix.multiply(), when multiplying a dense vector, it's done by converting to coo_matrix first. It's inherited from _cs_matrix.

# in compressed.py
class _cs_matrix(_data_matrix, _minmax_mixin, IndexMixin):
    # ...
    def multiply(self, other):
        # ...
        ret = self.tocoo()
        # ...
        # Sparse matrix times dense row vector.
        elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
            data = np.multiply(ret.data, other[:, ret.col].ravel())
        # Sparse matrix times dense column vector.
        elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
            data = np.multiply(ret.data, other[ret.row].ravel())
        # ...

That is to say, coo_matrix.multiply() should be overwritten to avoid these two time-consuming and meaningless converting.

Scipy/Numpy/Python version information:

scipy:1.2.1

numpy:1.16.2

python:3.6.7

[gan-lin]

Sorry, there is one thing I ignored previously. When there are duplicate elements, the behavior will be different. Converting to csr will first sum them, but directly multiplying will keep them.