Naive Bayes

Class Reference

class pykitml.NaiveBayes(input_size, output_size, distributions, reg_param=1)

Implements Naive Bayes classifier.

Note

Consider using GaussianNaiveBayes if all of your features are continuous.

__init__(input_size, output_size, distributions, reg_param=1)
Parameters:
  • input_size (int) – Size of input data or number of input features.
  • output_size (int) – Number of categories or groups.
  • distribution (list) – List of strings describing the distribution to use for each feature. Option are 'gaussian', 'binomial', 'multinomial'.
  • reg_param (int) – If a given class and feature value never occur together in the training data, then the frequency-based probability estimate will be zero. This is problematic because it will wipe out all information in the other probabilities when they are multiplied. So, the probability will become log(reg_param). This is a way to regularize Naive Bayes classifier. See https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes
Raises:
  • InvalidDistributionType – If invalid distribution. Can only be 'gaussian', 'binomial', 'multinomial'.
  • IndexError – If the input_size does not match the length of distribution length.
feed(input_data)

Accepts input array and feeds it to the model.

Parameters:input_data (numpy.array) – The input to feed the model.
Raises:ValueError – If the input data has invalid dimensions/shape.

Note

This function only feeds the input data, to get the output after calling this function use get_output() or get_output_onehot()

get_output()

Returns the output activations of the model.

Returns:The output activations.
Return type:numpy.array
get_output_onehot()

Returns the output layer activations of the model as a one-hot array. A one-hot array is an array of bits in which only one of the bits is high/true. In this case, the corresponding bit to the neuron/node having the highest activation will be high/true.

Returns:The one-hot output activations array.
Return type:numpy.array
train(training_data, targets)

Trains the model on the training data.

Parameters:
  • training_data (numpy.array) – numpy array containing training data.
  • targets (numpy.array) – numpy array containing training targets, corresponding to the training data.
Raises:

numpy.AxisError – If output_size is less than two. Use pykitml.onehot() to change 0/False to [1, 0] and 1/True to [0, 1] for binary classification.
accuracy(testing_data, testing_targets)

Tests the accuracy of the model on the testing data passed to the function. This function should be only used for classification.

Parameters:
  • testing_data (numpy.array) – numpy array containing testing data.
  • testing_targets (numpy.array) – numpy array containing testing targets, corresponding to the testing data.
Returns:

accuracy – The accuracy of the model over the testing data i.e how many testing examples did the model predict correctly.
Return type:

float
confusion_matrix(test_data, test_targets, gnames=[], plot=True)

Returns and plots confusion matrix on the given test data.

Parameters:
  • test_data (numpy.array) – Numpy array containing test data
  • test_targets (numpy.array) – Numpy array containing the targets corresponding to the test data.
  • plot (bool) – If set to false, will not plot the matrix. Default is true.
  • gnames (list) – List of string names for each class/group.
Returns:

confusion_matrix – The confusion matrix.
Return type:

numpy.array

Example: Heart Disease Prediction

Dataset

Heart Disease - pykitml.datasets.heartdisease module

Training

import os.path

import pykitml as pk
from pykitml.datasets import heartdisease

# Download the dataset
if not os.path.exists('heartdisease.pkl'):
    heartdisease.get()

# Load heart data set
inputs, outputs = heartdisease.load()

# Change 0/False to [1, 0]
# Change 1/True to [0, 1]
outputs = pk.onehot(outputs)

distrbutions = [
    'gaussian', 'binomial', 'multinomial',
    'gaussian', 'gaussian', 'binomial', 'multinomial',
    'gaussian', 'binomial', 'gaussian', 'multinomial',
    'multinomial', 'multinomial'
]

# Create model
bayes_heart_classifier = pk.NaiveBayes(13, 2, distrbutions)

# Train
bayes_heart_classifier.train(inputs, outputs)

# Save it
pk.save(bayes_heart_classifier, 'bayes_heart_classifier.pkl')

# Print accuracy
accuracy = bayes_heart_classifier.accuracy(inputs, outputs)
print('Accuracy:', accuracy)

# Plot confusion matrix
bayes_heart_classifier.confusion_matrix(inputs, outputs,
                                        gnames=['False', 'True'])

Predict heartdisease for a person with age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal: 67, 1, 4, 160, 286, 0, 2, 108, 1, 1.5, 2, 3, 3

import numpy as np
import pykitml as pk

# Predict heartdisease for a person with
# age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
# 67, 1, 4, 160, 286, 0, 2, 108, 1, 1.5, 2, 3, 3
input_data = np.array([67, 1, 4, 160, 286, 0, 2, 108, 1, 1.5, 2, 3, 3], dtype=float)

# Load the model
bayes_heart_classifier = pk.load('bayes_heart_classifier.pkl')

# Get output
bayes_heart_classifier.feed(input_data)
model_output = bayes_heart_classifier.get_output()

# Print result (log of probabilities)
print(model_output)

Confusion Matrix

_images/bayes_confusion_matrix.png