one_hot: One-Hot encoding function for class label arrays

A function that performs one-hot encoding for class labels.

from mlxtend.preprocessing import one_hot

Overview

Typical supervised machine learning algorithms for classifications assume that the class labels are nominal (a special case of categorical where no order is implied). A typical example of an nominal feature would be "color" since we can't say (in most applications) that "orange > blue > red".

The one_hot function provides a simple interface to convert class label integers into a so-called one-hot array, where each unique label is represented as a column in the new array.

For example, let's assume we have 5 data points from 3 different classes: 0, 1, and 2.

y = [0, # sample 1, class 0 
     1, # sample 2, class 1 
     0, # sample 3, class 0
     2, # sample 4, class 2
     2] # sample 5, class 2

After one-hot encoding, we then obtain the following array (note that the index position of the "1" in each row denotes the class label of this sample):

y = [[1,  0,  0], # sample 1, class 0 
     [0,  1,  0], # sample 2, class 1  
     [1,  0,  0], # sample 3, class 0
     [0,  0,  1], # sample 4, class 2
     [0,  0,  1]  # sample 5, class 2
     ])

Example 1 - Defaults

from mlxtend.preprocessing import one_hot
import numpy as np

y = np.array([0, 1, 2, 1, 2])
one_hot(y)

array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

Example 2 - Python Lists

from mlxtend.preprocessing import one_hot

y = [0, 1, 2, 1, 2]
one_hot(y)

array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

Example 3 - Integer Arrays

from mlxtend.preprocessing import one_hot

y = [0, 1, 2, 1, 2]
one_hot(y, dtype='int')

array([[1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 1, 0],
       [0, 0, 1]])

Example 4 - Arbitrary Numbers of Class Labels

from mlxtend.preprocessing import one_hot

y = [0, 1, 2, 1, 2]
one_hot(y, num_labels=10)

array([[ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])

API

one_hot(y, num_labels='auto', dtype='float')

One-hot encoding of class labels

Parameters

• y : array-like, shape = [n_classlabels]

  Python list or numpy array consisting of class labels.

• num_labels : int or 'auto'

  Number of unique labels in the class label array. Infers the number of unique labels from the input array if set to 'auto'.

• dtype : str

  NumPy array type (float, float32, float64) of the output array.

Returns

• ary : numpy.ndarray, shape = [n_classlabels]

  One-hot encoded array, where each sample is represented as a row vector in the returned array.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/one_hot/