I am working on a multi label classification problem and need some guidance on computing class weights using Scikit-Learn.

Problem Context:

I have a dataset with 9973 training samples.The labels are one-hot encoded, representing 13 different classes.The shape of my training labels is (9973, 13).

I want to use this code:

import numpy as np
from sklearn.utils.class_weight import compute_class_weight

y_integers = np.argmax(y, axis=1)
class_weights = compute_class_weight('balanced', np.unique(y_integers), y_integers)
d_class_weights = dict(enumerate(class_weights))

This does not work says, too may positional arguments. My training samples look like this:

                          [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
                          [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
                          [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],

How can i implement in multi class classification problem so that my dataset imbalance can be solved ?