Label encoding

In supervised learning, we usually deal with a variety of labels. These can be in the form of numbers or words. If they are numbers, then the algorithm can use them directly. However, a lot of times, labels need to be in human readable form. So, people usually label the training data with words. Label encoding refers to transforming the word labels into numerical form so that the algorithms can understand how to operate on them. Let's take a look at how to do this.

How to do it…

1. Create a new Python file, and import the preprocessing package:

   from sklearn import preprocessing

2. This package contains various functions that are needed for data preprocessing. Let's define the label encoder, as follows:

   label_encoder = preprocessing.LabelEncoder()

3. The label_encoder object knows how to understand word labels. Let's create some labels:

   input_classes = ['audi', 'ford', 'audi', 'toyota', 'ford', 'bmw']

4. We are now ready to encode these labels:

   label_encoder.fit(input_classes)
   print "\nClass mapping:"
   for i, item in enumerate(label_encoder.classes_):
       print item, '-->', i

5. Run the code, and you will see the following output on your Terminal:

   Class mapping:
   audi --> 0
   bmw --> 1
   ford --> 2
   toyota --> 3
   

6. As shown in the preceding output, the words have been transformed into 0-indexed numbers. Now, when you encounter a set of labels, you can simply transform them, as follows:

   labels = ['toyota', 'ford', 'audi']
   encoded_labels = label_encoder.transform(labels)
   print "\nLabels =", labels 
   print "Encoded labels =", list(encoded_labels)

   Here is the output that you'll see on your Terminal:

   Labels = ['toyota', 'ford', 'audi']
   Encoded labels = [3, 2, 0]
   

7. This is way easier than manually maintaining mapping between words and numbers. You can check the correctness by transforming numbers back to word labels:

   encoded_labels = [2, 1, 0, 3, 1]
   decoded_labels = label_encoder.inverse_transform(encoded_labels)
   print "\nEncoded labels =", encoded_labels
   print "Decoded labels =", list(decoded_labels)

   Here is the output:

   Encoded labels = [2, 1, 0, 3, 1]
   Decoded labels = ['ford', 'bmw', 'audi', 'toyota', 'bmw']
   

   As you can see, the mapping is preserved perfectly.