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?

@Kasramvd The linked dup target answer for 2D doesn't keep the order from highest to lowest as needed for this question. So, I am reopening, hope that sounds okay. — Divakar
– Divakar, Commented Dec 19, 2016 at 10:32
@Kasramvd Well np.argpartition docs state that to keep the order, we need to provide it a sequence of ints, which I think would be range of those indices, i.e. range(N) inside np.argpartition(). Were you using any such thing there? Sorry if I missed that! — Divakar
– Divakar, Commented Dec 19, 2016 at 10:40
@Divakar No, but certainly still it doesn't justify that this is not a duplicate. Also I don't think that this approach makes any significant difference with argsort version. — Kasravnd
– Kasravnd, Commented Dec 19, 2016 at 10:51
@Kasramvd That would depend a lot on the sizes. But here's a runtime test , where N is significantly smaller than the axis length. — Divakar
– Divakar, Commented Dec 19, 2016 at 10:54

Community · Accepted Answer · 2017-05-23 12:30:52Z

4

Here's an approach using np.argpartition -

idx = np.argpartition(a,range(M))[:,:-M-1:-1] # topM_ind
out = a[np.arange(a.shape[0])[:,None],idx]    # topM_score

Sample run -

In [343]: a
Out[343]: 
array([[ 1. ,  0.3,  0.4],
       [ 0.2,  0.6,  0.8],
       [ 0.1,  0.3,  0.5]])

In [344]: M = 2

In [345]: idx = np.argpartition(a,range(M))[:,:-M-1:-1]

In [346]: idx
Out[346]: 
array([[0, 2],
       [2, 1],
       [2, 1]])

In [347]: a[np.arange(a.shape[0])[:,None],idx]
Out[347]: 
array([[ 1. ,  0.4],
       [ 0.8,  0.6],
       [ 0.5,  0.3]])

Alternatively, possibly slower, but a bit shorter code to get idx would be with np.argsort -

idx = a.argsort(1)[:,:-M-1:-1]

Here's a post containing some runtime test that compares np.argsort and np.argpartition on a similar problem.

edited May 23, 2017 at 12:30

CommunityBot

11 silver badge

answered Nov 13, 2016 at 13:03

Divakar

222k19 gold badges273 silver badges374 bronze badges

Sign up to request clarification or add additional context in comments.

Comments

John Zwinck · Accepted Answer · 2016-11-13 13:10:04Z

3

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]])

answered Nov 13, 2016 at 13:10

John Zwinck

252k44 gold badges346 silver badges459 bronze badges

Comments

Dutse I · Accepted Answer · 2018-09-11 13:27:34Z

In case someone is interested in the both the values and corresponding indices without tempering with the order, the following simple approach will be helpful. Though it could be computationally expensive if working with large data since we are using a list to store tuples of value, index.

import numpy as np
values = np.array([0.01,0.6, 0.4, 0.0, 0.1,0.7, 0.12]) # a simple array
values_indices = [] # define an empty list to store values and indices
while values.shape[0]>1:
    values_indices.append((values.max(), values.argmax()))
    # remove the maximum value from the array:
    values = np.delete(values, values.argmax())

The final output as list of tuples:

values_indices
[(0.7, 5), (0.6, 1), (0.4, 1), (0.12, 3), (0.1, 2), (0.01, 0)]

igorkf · Accepted Answer · 2022-02-06 19:05:40Z

0

Easy way would be:

To get top-2 indices

np.argsort(-score_matrix)[:, :2]

To get top-2 values

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

answered Feb 6, 2022 at 19:05

igorkf

3,6152 gold badges26 silver badges37 bronze badges

Collectives™ on Stack Overflow

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

4 Answers 4

Comments

Comments

Comments

Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

4 Answers 4

Comments

Comments

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related