What is the process of converting a column of one hot labels back to their original column labels?

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To convert a column of one hot labels back to their original column labels, you need to perform a reverse one-hot encoding process. This involves finding the indices of the columns with the highest value and using them to represent the original labels.

Here are the steps to perform reverse one-hot encoding:

1. Create an empty list to store the original labels.
2. Loop through each row in the one-hot encoded column.
3. Find the index of the column with the highest value using the argmax() method from numpy.
4. Use the index to retrieve the corresponding label from the list of original labels.
5. Append the label to the new list.
6. Return the new list of original labels.

Here's an example code snippet that demonstrates the reverse one-hot encoding process:

import numpy as np

# Define the original labels
original_labels = ['Label1', 'Label2', 'Label3']

# Create a sample one-hot encoded column
one_hot_col = np.array([[0, 1, 0],
                        [1, 0, 0],
                        [0, 0, 1],
                        [0, 1, 0]])

# Reverse one-hot encoding process
new_col = []
for row in one_hot_col:
    index = np.argmax(row)
    label = original_labels[index]
    new_col.append(label)

print(new_col)
# Output: ['Label2', 'Label1', 'Label3', 'Label2']

In the above example, the original labels are 'Label1', 'Label2', and 'Label3'. The one-hot encoded column has 3 columns, one for each label. The reverse one-hot encoding process results in the new column with the original labels.