Just compute the dot-product of the encoded values with enc.active_features_. It would work both for sparse and dense representation.
from sklearn.preprocessing import OneHotEncoder
import numpy as np
orig = np.array([6, 9, 8, 2, 5, 4, 5, 3, 3, 6])
enc = OneHotEncoder()
encoded = enc.fit_transform(orig.reshape(-1, 1))
decoded = encoded.dot(ohe.active_features_).astype(int)
assert np.allclose(orig, decoded)
The key insight is that the active_features_ attribute of the OHE model, that represents the original values for each binary column. Thus we can decode the binary-encoded number by simply computing a dot-product with active_features_. For each data point, there's just a single 1 the position of the original value.
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