BayesNets

BayesNets contains various implementations of the Naive Bayes through the perspective of Naive Bayes as a special version of a Bayesian Network.

Algorithms

1. Classic Naive Bayes with Nonparametric Density Estimation

2. Selecive Naive Bayes algorithm (k-fold CV)

3. Tree Augmented Naive Bayes (treenb: discrete data and kdetree: continuous data)

4. Forest Augmented Naive Bayes (treenb: discrete data and kdetree: continuous data). An "optimized" version of TAN for ranking.

5. Hierarchical Naive Bayes (in development)

Naive Bayes Usage

import numpy as np
import pandas as pd
import NaiveBayesNets as nbn

df = pd.read_csv("data/Pima.tr.csv")
class_col_name = 'type'
nbmodel = nbn.NaiveBayes(df, class_col_name)

preds = nbmodel.Predict(df) ## prediction probs
preds[class_col_name] = preds.idxmax(axis = 1) ## to get class predictions
preds.head()

accuracy = np.mean(preds[class_col_name].values == preds[class_col_name])
print(accuracy)

Dependencies

1. Numpy

2. Scipy

3. Pandas

4. Itertools

5. Matplotlib

6. Networkx

Reference:

See TAN for an example of a Tree Augmented Naive Bayes.