Find out if two symmetric matrices are the same up to a permutation of the rows/columns

Here's a vectorized solution based on sorting and leveraging searchsorted -

import pandas as pd

# Sort rows for a and b
aS = np.sort(a,axis=1)
bS = np.sort(b,axis=1)

# Scale down each row to a scalar each
scale = np.r_[(np.maximum(aS.max(0),bS.max(0))+1)[::-1].cumprod()[::-1][1:],1]
aS1D = aS.dot(scale)
bS1D = bS.dot(scale)

# Use searchsorted to get the correspondence on indexing
sidx = aS1D.argsort()
searchsorted_idx = np.searchsorted(aS1D,bS1D,sorter=sidx)
searchsorted_idx[searchsorted_idx==len(aS1D)] = len(aS1D)-1
df = pd.DataFrame({'A':searchsorted_idx})
new_order = sidx[df.groupby('A').cumcount().values+searchsorted_idx]
# new_order is the permuted order, i.e. [5, 7, 1, 3, 2, 4, 6]

# Finally index into a with the new_order and compare against b
out = np.array_equal(a[new_order[:,None], new_order],b)

I will assume that you have the list of permutation of the rows/columns of a which gives the b, e.g. something like this

p = np.array([5, 7, 1, 3, 2, 4, 6]) - 1

Then you can simply do the following on a

a_p = a[p]
a_p = a_p[:, p]

and check if b and the permuted a_p are equal:

(a_p == b).all()

Edit: since you don't have a list like the above p, you could (at least for small arrays a and b) generate the permutations of the indices and check for each one:

from itertools import permutations

def a_p(a, b, p):
    p = np.array(p)
    a_p = a[p]
    a_p = a_p[:, p]
    return a_p

for p in permutations(range(a.shape[0])):
    if (a_p(a, b, p) == b).all():
        print('True')
        break
else:
    print('False')

Note that this brute-force method works for non-symmetric matrices as well. But since the number of permutations is huge for large arrays a and b, this method can be very slow. So your solution with computing eigenvalues is much better.

Here's a benchmark:

def Yduqoli(a, b):
    ''' I suppose your solution is similar'''
    if (np.array(np.unique(a, return_counts=True)) == np.array(np.unique(b, return_counts=True))).all():
        a_eigs = np.sort(np.linalg.eigvals(a))
        b_eigs = np.sort(np.linalg.eigvals(b))
        return np.allclose(a_eigs, b_eigs)
    else:
        return False

def AndyK(a, b):
    for p in permutations(range(a.shape[0])):
        if (a_p(a, b, p) == b).all():
            return True
    return False  

%timeit AndyK(a,b)
103 ms ± 4.54 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit Yduqoli(a,b)
408 µs ± 65.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

where I used the symmetric matrices a and b provided by the OP.

Update: As mentioned by Paul Panzer, simply checking the eigenvalues can give incorrect result in some cases, e.g. a = np.array([[4, 0], [0, 0]]), b = np.array([[2, 2], [2, 2]]) have the same eigenvalues, but cannot be shuffled one into the other. So we first need to check if the arrays a and b have the same elements (regardless their positions).

You could always sort the matrix by row-norm and see if they are different. If two rows have the same norm, you'd have to check permutations of the rows that have the same norm though. But this reduces the problem to only rows with the same norm. In many cases you can first sort by 2-norm, then 1-norm and finally brute force the remaining permutations.

import numpy as np

def get_row_norm(a):
    """
    Sort by 2-norm
    """
    row_norms = np.sum(a**2, axis=1)
    return row_norms

def sort(a):
    """
    Return the matrix a sorted by 2-norm
    """
    n = a.shape[0]
    # Get the norms
    row_norms = get_row_norm(a)
    # Get the order
    order = np.argsort(row_norms)[::-1]

    sorted_a = a.copy()

    for m in range(n):
        i = order[m]
        for k in range(m+1): 
            j = order[k]
            sorted_a[m, k] = a[i, j]
            sorted_a[k, m] = a[i, j]

    return sorted_a


a = np.array([
    #1 #2 #3 #4 #5 #6 #7
    [0, 1, 1, 0, 0, 0, 1], #1
    [1, 0, 1, 2, 1, 1, 2], #2
    [1, 1, 0, 0, 0, 0, 1], #3
    [0, 2, 0, 0, 4, 0, 4], #4
    [0, 1, 0, 4, 0, 1, 2], #5
    [0, 1, 0, 0, 1, 0, 0], #6
    [1, 2, 1, 4, 2, 0, 0]  #7
])  
b = np.array([
    #5 #7 #1,3#3,1#2 #4 #6 
    [0, 2, 0, 0, 1, 4, 1], #5
    [2, 0, 1, 1, 2, 4, 0], #7
    [0, 1, 0, 1, 1, 0, 0], #1,3 could be either
    [0, 1, 1, 0, 1, 0, 0], #1,3 could be either
    [1, 2, 1, 1, 0, 2, 1], #2
    [4, 4, 0, 0, 2, 0, 0], #4
    [1, 0, 0, 0, 1, 0, 0]  #6
])

# Sort a and b
A = sort(a)
B = sort(b)
# Print the norms
print(get_row_norm(a)) # [ 3. 12.  3. 36. 22.  2. 26.]
print(get_row_norm(A)) # [36. 26. 22. 12.  3.  3.  2.]
print(get_row_norm(B)) # [36. 26. 22. 12.  3.  3.  2.]
# Assert that they are equal
print( (A == B).all())

Note that if they are not equal, you still have to check permutation of the fifth and sixth row, since their norms are equal.