Which {sparse or numpy array} * {sparse or numpyarray} work ?

sparse-dot-ndarray.py

""" Which {sparse or numpy array} * {sparse or numpy array} work ?
    Which combinations are valid, what's the result type ?
    Here try-it-and-see on N^2 combinations
    with `safe_sparse_dot` from scikit-learn, not "*" .
    See also:
    http://scipy-lectures.github.com/advanced/scipy_sparse/
    https://scipy.github.io/old-wiki/pages/SciPyPackages/Sparse.html
    http://stackoverflow.com/questions/tagged/scipy+sparse-matrix  (lots)
"""
# Keywords: scipy sparse dot-product basics tutorial

from __future__ import division
import sys
import numpy as np
from scipy import sparse

np.set_printoptions( threshold=20, edgeitems=5, linewidth=140,
        formatter = dict( float = lambda x: "%.2g" % x ))  # float arrays %.2g

#...............................................................................
def safe_sparse_dot( a, b ):
    """ -> a * b or np.dot(a, b) """
        # from sklearn
    if sparse.issparse(a) or sparse.issparse(b):
        try:
            return a * b
        except ValueError:  # dimension mismatch: print shapes
            print "Error: %s %s  *  %s %s" % (
                    type(a).__name__, a.shape,
                    type(b).__name__, b.shape )
            raise
    else:
        return np.dot(a, b)

# def asum( ndarray or sparse ): summary, shape type min av max [density]

#...............................................................................
n = 10

    # to change these params in sh or ipython, run this.py  a=1  b=None  c=\"str\" ...
for arg in sys.argv[1:]:
    exec( arg )

n = (n // 5) * 5
numpyvec = np.tile( [0,0,0,0,1.], n // 5 )
numpyarray = np.ones(( n, n ))
rowvec = sparse.csr_matrix( numpyvec )  # (1, n)
colvec = rowvec.T                       # (n, 1) csc
Alil = sparse.lil_matrix((n,n))
Alil.setdiag( np.ones(n) )
Acsr = Alil.tocsr()
Acoo = Acsr.tocoo()
D = sparse.eye( n )  # scipy.sparse.dia.dia_matrix

#...............................................................................
print "\n-- safe_sparse_dot( various scipy sparse, numpy array ) --"

names = "Acsr Acoo D rowvec colvec numpyvec numpyarray ".split()
for Anm in names:
    A = eval(Anm)
    print "%s: %s %s " % (
            Anm, A.shape, type(A).__name__ )

for Anm in names:
    A = eval(Anm)

    for Bnm in names:
        B = eval(Bnm)
        print "%s * %s = " % (Anm, Bnm) ,
        try:
            AB = safe_sparse_dot( A, B )
            # AB = A * B
            print AB.shape, type(AB).__name__
        except StandardError, errmsg:
            # print "Error", errmsg
            pass
    print ""