python - Get N maximum values and indices along an axis in a NumPy array

Question

I think this is an easy question for experienced numpy users.

I have a score matrix. The raw index corresponds to samples and column index corresponds to items. For example,

score_matrix = 
  [[ 1. ,  0.3,  0.4],
   [ 0.2,  0.6,  0.8],
   [ 0.1,  0.3,  0.5]]

I want to get top-M indices of items for each samples. Also I want to get top-M scores. For example,

top2_ind = 
  [[0, 2],
   [2, 1],
   [2, 1]]

top2_score = 
  [[1. , 0.4],
   [0,8, 0.6],
   [0.5, 0.3]]

What is the best way to do this using numpy?

Answer 1

I'd use argsort():

top2_ind = score_matrix.argsort()[:,::-1][:,:2]

That is, produce an array which contains the indices which would sort score_matrix:

array([[1, 2, 0],
       [0, 1, 2],
       [0, 1, 2]])

Then reverse the columns with ::-1, then take the first two columns with :2:

array([[0, 2],
       [2, 1],
       [2, 1]])

Then similar but with regular np.sort() to get the values:

top2_score = np.sort(score_matrix)[:,::-1][:,:2]

Which following the same mechanics as above, gives you:

array([[ 1. ,  0.4],
       [ 0.8,  0.6],
       [ 0.5,  0.3]])