The code for this entire section is given
in the label_encoder.py file. Here under-
import numpy as np
from sklearn import
preprocessing
# Sample input labels
input_labels = ['red',
'black', 'red', 'green', 'black', 'yellow', 'white']
# Create label encoder and
fit the labels
encoder = preprocessing.LabelEncoder()
encoder.fit(input_labels)
# Print the mapping
print("\nLabel
mapping:")
for i, item in
enumerate(encoder.classes_):
print(item, '-->', i)
# Encode a set of labels
using the encoder
test_labels = ['green',
'red', 'black']
encoded_values =
encoder.transform(test_labels)
print("\nLabels
=", test_labels)
print("Encoded values
=", list(encoded_values))
# Decode a set of values
using the encoder
encoded_values = [3, 0, 4,
1]
decoded_list =
encoder.inverse_transform(encoded_values)
print("\nEncoded
values =", encoded_values)
print("Decoded labels
=", list(decoded_list))
|
and the output is :
