Reordering Cluster Numbers For Correct Correspondence

I have a dataset that I clustered using two different clustering algorithms. The results are about the same, but the cluster numbers are permuted. Now for displaying the color code

Solution 1:

The most well-known algorithm for finding the optimum matching is the hungarian method.

Because it cannot be explained in a few sentences, I have to refer you to a book of your choice, or Wikipedia article "Hungarian algorithm".

You can probably get good results (even perfect if the difference is indeed tiny) by simply picking the maximum of the correspondence matrix and then removing that row and column.

Solution 2:

I have a function that works for me. But it may fail when the two cluster results are very inconsistent, which leads to duplicated max values in the contingency matrix. If your cluster results are about the same, it should work.

Here is my code:

from sklearn.metrics.cluster import contingency_matrix

defalign_cluster_index(ref_cluster, map_cluster):
"""
remap cluster index according the the ref_cluster.
both inputs must be nparray and have same number of unique cluster index values.

Xin Niu Jan-15-2020
"""

ref_values = np.unique(ref_cluster)
map_values = np.unique(map_cluster)

print(ref_values)
print(map_values)

num_values = ref_values.shape[0]

if ref_values.shape[0]!=map_values.shape[0]:
    print('error: both inputs must have same number of unique cluster index values.')
    return()

switched_col = set()
whileTrue:
    cont_mat = contingency_matrix(ref_cluster, map_cluster)
    print(cont_mat)
    # divide contingency_matrix by its row and col sums to avoid potential duplicated values:
    col_sum = np.matmul(np.ones((num_values, 1)), np.sum(cont_mat, axis = 0).reshape(1, num_values))
    row_sum = np.matmul(np.sum(cont_mat, axis = 1).reshape(num_values, 1), np.ones((1, num_values)))
    print(col_sum)
    print(row_sum)

    cont_mat = cont_mat/(col_sum+row_sum)
    print(cont_mat)

    # ignore columns that have been switched:
    cont_mat[:, list(switched_col)]=-1print(cont_mat)

    sort_0 = np.argsort(cont_mat, axis = 0)
    sort_1 = np.argsort(cont_mat, axis = 1)

    print('argsort contmat:')
    print(sort_0)
    print(sort_1)

    if np.array_equal(sort_1[:,-1], np.array(range(num_values))):
        break# switch values according to the max value in the contingency matrix:# get the position of max value:
    idx_max = np.unravel_index(np.argmax(cont_mat, axis=None), cont_mat.shape)
    print(cont_mat)
    print(idx_max)

    if (cont_mat[idx_max]>0) and (idx_max[0] notin switched_col):
        cluster_tmp = map_cluster.copy()
        print('switch', map_values[idx_max[1]], 'and:', ref_values[idx_max[0]])
        map_cluster[cluster_tmp==map_values[idx_max[1]]]=ref_values[idx_max[0]]
        map_cluster[cluster_tmp==map_values[idx_max[0]]]=ref_values[idx_max[1]]

        switched_col.add(idx_max[0])
        print(switched_col)

    else:
        breakprint('final argsort contmat:')
print(sort_0)
print(sort_1)

print('final cont_mat:')
cont_mat = contingency_matrix(ref_cluster, map_cluster)
col_sum = np.matmul(np.ones((num_values, 1)), np.sum(cont_mat, axis = 0).reshape(1, num_values))
row_sum = np.matmul(np.sum(cont_mat, axis = 1).reshape(num_values, 1), np.ones((1, num_values)))
cont_mat = cont_mat/(col_sum+row_sum)

print(cont_mat)

return(map_cluster)

And here is some test code:

ref_cluster = np.array([2,2,3,1,0,0,0,1,2,1,2,2,0,3,3,3,3])
map_cluster = np.array([0,0,0,1,1,3,2,3,2,2,0,0,0,2,0,3,3])

c = align_cluster_index(ref_cluster, map_cluster)
print(ref_cluster)
print(c)

>>>[22310001212203333]
>>>[22211303002220233]