One-hot encoding is a common preprocessing step in machine learning, especially when dealing with categorical data. The OneHotEncoder class in scikit-learn is widely used for this purpose. However, there are instances where you need to reverse the transformation and recover the original data from the encoded format. This article will guide you through the process of reversing the OneHotEncoder transform using various methods.

Understanding One-Hot Encoding

One-hot encoding converts categorical variables into a binary matrix. Each category is represented by a unique binary vector, where only one element is 1 (hot) and the rest are 0 (cold). This transformation is beneficial for algorithms that cannot handle categorical data directly.

Why Reverse One-Hot Encoding?

Reversing one-hot encoding is essential in several scenarios:

• Interpreting Model Predictions: After making predictions using a model, you might want to map the encoded predictions back to their original categories.
• Data Analysis: For analyzing and visualizing the results, the original categorical data is often more meaningful than the encoded format.

Step-by-Step Guidance for reversing sklearn.OneHotEncoder Transformation

Therefore, to demolish the one-hot encoding process, we have to go through a one systematic method that will not alter our data and make sure that the data obtained is correct. Here is a detailed breakdown of the steps involved:

1. Initialize the OneHotEncoder

Instantiate an object of the OneHotEncoder class of sklearn. preprocessing. Some of the options which can be set in the encoder include sparse, handle_unknown, and categories. Sparse=False is used for the output so that, the resultant output is in dense form which can be more useful for this demonstration.

2. Fit and Transform the Original Data

Fit_transform the original categorical data using the previously created OneHotEncoder instance. This method performs two operations:This method performs two operations:

• Fitting: The encoder learns about the unique categories existing in the data and how they can be mapped to unique vectors in binary.
• Transforming: To perform this mapping the encoder uses this table to transform the original data into a binary matrix structure where a particular row has a 1 in a certain column if that category is present and 0 for all other values.

Methods to Reverse One-Hot Encoding

1. Use the inverse_transform Method:

Once you get the one-hot encoded data, apply the inverse_transform method on the data which has been encoded using OneHotEncoder and was fitted on the dataset. This method works by:

• Mapping Back: Since the encoder has learned the mapping of the categories to binary vectors, it now does the reverse to bring each binary vector back to the original category.
• Generating Original Data: The method gives a 2D array the rows of which represent the original categorical value for all samples of the input.

from sklearn.preprocessing import OneHotEncoder
import numpy as np

data = np.array(['cat', 'dog', 'fish']).reshape(-1, 1)
encoder = OneHotEncoder()
encoded_data = encoder.fit_transform(data)
original_data = encoder.inverse_transform(encoded_data)
print(encoded_data)
print(original_data)

Output:

 (0, 0)    1.0
  (1, 1)    1.0
  (2, 2)    1.0
[['cat']
 ['dog']
 ['fish']]

• The fit_transform method encodes the data.
• The inverse_transform method decodes the data back to its original form.

2. Using categories_ Attribute

The categories_ attribute of the OneHotEncoder stores the unique categories identified during fitting. This attribute can be used for custom decoding if needed.

Let’s see how we can implement this in Python using sklearn.OneHotEncoder.

from sklearn.preprocessing import OneHotEncoder
import numpy as np

data = np.array(['cat', 'dog', 'fish']).reshape(-1, 1)
encoder = OneHotEncoder()
encoded_data = encoder.fit_transform(data).toarray()
# Get the categories
categories = encoder.categories_[0]
# Reverse the encoding
original_data = categories[np.argmax(encoded_data, axis=1)]
print(original_data)

Output:

['cat' 'dog' 'fish']

3. Using handle_unknown Parameter

The handle_unknown parameter in OneHotEncoder can be set to handle unseen categories during transformation. It can be set to ignore to avoid errors and allow for proper decoding later.

Let’s see practical Implementation using handle_unknown Parameter:

from sklearn.preprocessing import OneHotEncoder
import numpy as np
data = np.array(['cat', 'dog', 'fish']).reshape(-1, 1)

# Initialize the OneHotEncoder with handle_unknown='ignore'
encoder = OneHotEncoder(handle_unknown='ignore')
encoded_data = encoder.fit_transform(data).toarray()

# Simulate unknown category in encoded data
encoded_data_with_unknown = np.vstack([encoded_data, [0, 0, 0]])
original_data = encoder.inverse_transform(encoded_data_with_unknown)

print(original_data)

Output:

[['cat']
 ['dog']
 ['fish']
 [None]]

Conclusion

To revert the one-hot encoding transformation we carry out a simple operation with the help of the inverse_transform attribute available in sklearn. OneHotEncoder. This might prove especially helpful in translating the original categorical data after they have passed through the machine learning algorithms for analysis. Consequently, the procedures demonstrated in this article allow you to reverse the process of one-hot encoding of categorical data and obtain back the indicators in their initial form. Some of the benefits of using GPGPU in machine learning are A, It internalizes soaring hardware costs and stdy and B, it increases the fluidity and readability of GPGPU operations by enabling the processing of encoded and original data formats.

Reversing sklearn.OneHotEncoder Transform to Recover Original Data – FAQs

What is One-Hot Encoding?

One-hot encoding is applied in mapping categorical variables or factors into binary variables, more specifically it is a data preprocessing technique that constructs a binary column for each category that the categorical variable can take thereby reducing the amount of data that must be passed through the model during the transforming step.

Why is One-Hot Encoding important?

Encoding data is very important, because most of the known machine learning algorithms work only with numbers and overlook the categories.

Can you revert any one-hot encoded data?

Yes, it is possible to decode ‘one hot encoded’ data if the given encoder has been fitted on the original data and the one-hot encoding has been done with the help of this very encoder only.

What happens if the encoder is not fitted before transformation?

The problem is if the encoder is not fitted before transformation it doesn’t hold the right mapping to decode the one-hot encoded data into the original form.

How does ‘inverse_transform’ work?

By calling the inverse_transform method one can map back from the one hot encoded data, using the previously fitted encoder.