Numpy symmetric matrix becomes asymmetric when I applied min-max scaling

Question

I have a symmetric matrix (1877 x 1877), here is the matrix file. I try to standardize the values between 0-1. After I apply this method, the matrix is no longer symmetric. Any help is appreciated.

print((dist.transpose() == dist).all()) # this prints 'True'
def sci_minmax(X):
    minmax_scale = preprocessing.MinMaxScaler()
    return minmax_scale.fit_transform(X)
sci_dist_scaled = sci_minmax(dist)
(sci_dist_scaled.transpose() == sci_dist_scaled).all() # this print 'False'

sci_dist_scaled.dtype, dist.dtype # (dtype('float64'), dtype('float64'))

Answer 1

Looking at this description the minmaxscaler appears to work column-by-column, so, naturally, you can't expect it to preserve symmetry.

What's best to do in your case depends a bit on what you are trying to achieve, really. If having the values between 0 and 1 is all you require you can rescale by hand:

 mn, mx = dist.min(), dist.max()
 dist01 = (dist - mn) / (mx - mn)

but depending on your ultimate problem this may be too simplistic...