Finding maximum value and their indices in a sparse lil_matrix (Scipy/Python)

Question

What's the best way to find the maximum value and their corresponding row and column indices in a Scipy sparse lil_matrix object ? I can loop through the nonzero entries using itertools.izip , but is there anything better ? I feel like I'm missing something obvious here ..

Answer 1

You could convert to COO format, and then use the data , row and col attributes.

For example, suppose the LIL matrix is x . Here's one way to get the maximum value along with its row and column:

In [41]: x
Out[41]: 
<1000x1000 sparse matrix of type '<type 'numpy.float64'>'
    with 1999 stored elements in LInked List format>

In [42]: y = x.tocoo()

In [43]: k = y.data.argmax()

In [44]: maxval = y.data[k]

In [45]: maxrow = y.row[k]

In [46]: maxcol = y.col[k]

Note: There are two bugs in the above code:

• If all the nonzero values are negative, it will find the largest negative value. But the correct answer should be 0 in that case.
• If there are no nonzero values, then the line k = y.data.argmax() will raise an exception, because y.data is an empty array.

If those cases can't happen in your application, then those bugs can be ignored.