How to reverse sklearn.OneHotEncoder transform to recover original data?

Question

I encoded my categorical data using sklearn.OneHotEncoder and fed them to a random forest classifier. Everything seems to work and I got my predicted output bac

Answer 1

How to one-hot encode

See https://stackoverflow.com/a/42874726/562769

import numpy as np
nb_classes = 6
data = [[2, 3, 4, 0]]

def indices_to_one_hot(data, nb_classes):
    """Convert an iterable of indices to one-hot encoded labels."""
    targets = np.array(data).reshape(-1)
    return np.eye(nb_classes)[targets]

How to reverse

def one_hot_to_indices(data):
    indices = []
    for el in data:
        indices.append(list(el).index(1))
    return indices


hot = indices_to_one_hot(orig_data, nb_classes)
indices = one_hot_to_indices(hot)

print(orig_data)
print(indices)

gives:

[[2, 3, 4, 0]]
[2, 3, 4, 0]