Mean absolute value along all columns of a 3D Numpy array

Question

I always get find myself in state of confusion when dealing with a multi-dimensional array. Imaging having the following array of arrays, where each array contains feature importance scores (3-features), for each class in the dataset (5-classes). The dataset contains 4 samples in all.

arr = np.random.randn(5,4,3).round(1)

arr
array([[[ 0.7, -0.1,  0.6],   # class 0 feature importances
        [-0.8, -0.7,  1.4],
        [ 1.4, -0.1,  1.4],
        [-1.8, -1.2, -1.6]],

       [[-0.3,  2.1,  0.5],  # class 1 feature importances
        [-1.2,  1.4, -0.4],
        [ 0. , -1. ,  0.8],
        [-0.8,  2.3,  0.3]],

       [[ 0.2,  0.6, -0.1],  # class 2 feature importances
        [-1.8, -0.2,  1.2],
        [-0.5,  0.5,  1. ],
        [ 1.3,  0.4, -2.6]],

       [[-1. ,  0.8, -0.4],  # class 3 feature importances
        [ 1.2,  1.5, -0.5],
        [ 0.1, -0.5,  0.8],
        [ 2.5, -1.6, -0.6]],

       [[-1.2,  0.3, -0.9],  # class 4 feature importances
        [ 1. , -1. , -0.5],
        [ 0.3,  1.4,  0.5],
        [-2.3,  0.6,  0.2]]])

I am interested in computing the mean absolute value of feature importances across the classes (overrall). Ideally the resultant arrar should be a rank 1 (3,) since there are three features:

Feature1 = sum( abs(0.7,-0.8, 1.4, -1.8, -0.3, -1.2, 0.0, -0.8, 0.2, -1.8, -0.5, 1.3,  
               -1.0, 1.2, 0.1, 2.5, -1.2, 1.0, 0.3,, -2.3) ) / 12    # n = 12

Vishal Balaji · Accepted Answer · 2023-11-14 11:54:34Z

0

If you do arr[:,:,0], you will get the whole array that you want in 2D format. Which you can just reshape.

arr[:,:,0].reshape(-1)
>>> array([ 0.7, -0.8,  1.4, -1.8, -0.3, -1.2,  0. , -0.8,  0.2, -1.8, -0.5, 1.3, -1. ,  1.2,  0.1,  2.5, -1.2,  1. ,  0.3, -2.3])

You can just run whatever operation you want on this.

This is the operation you ran

np.sum(np.abs(arr[:,:,0].reshape(-1))) / 12
>>> 1.7000000000000002

This is the absolute mean value

np.mean(np.abs(arr[:,:,0].reshape(-1)))
>>> 1.02

edited Nov 14, 2023 at 11:54

answered Nov 14, 2023 at 11:47

Vishal Balaji

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