FIX: Make compute_sample_weight work on sparse target - #23115

import numpy as np
from scipy import sparse
from sklearn.linear_model import RidgeClassifier
X = np.array([[1, 2],
              [2, 3]])
y = sparse.csr_matrix(np.array([[0, 1],
                                [1, 0]]))
clf = RidgeClassifier(class_weight='balanced')
clf.fit(X, y)

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File ./sklearn/utils/class_weight.py:40, in compute_class_weight(class_weight, classes, y)
     38 from ..preprocessing import LabelEncoder
---> 40 if set(y) - set(classes):
     41     raise ValueError("classes should include all valid labels that can be in y")

TypeError: unhashable type: 'csr_matrix'