Introduction

Naive Bayes is a widely used classification algorithm known for its simplicity and efficiency. This package takes naive Bayes to a higher level by providing more flexible and weighted variants, making it suitable for a broader range of applications.

General naive Bayes

Most standard implementations, such as those in sklearn.naive_bayes, assume a single distribution type for all feature likelihoods. This can be restrictive when dealing with mixed data types. WNB overcomes this limitation by allowing users to specify different probability distributions for each feature individually. You can select from a variety of continuous and discrete distributions, enabling greater customization and improved model performance.

Weighted naive Bayes

While naive Bayes is simple and interpretable, its conditional independence assumption often fails in real-world scenarios. To address this, various attribute-weighted naive Bayes methods exist, but most are computationally expensive and lack mechanisms for handling class imbalance.

WNB package provides an optimized implementation of Minimum Log-likelihood Difference Wighted Naive Bayes (MLD-WNB), a novel approach that optimizes feature weights using the Bayes optimal decision rule. It also introduces hyperparameters for controlling model bias, making it more robust for imbalanced classification.

Installation

This library is shipped as an all-in-one module implementation with minimalistic dependencies and requirements. Furthermore, it fully adheres to Scikit-learn API ❤️.

Prerequisites

Ensure that Python 3.8 or higher is installed on your machine before installing WNB.

PyPi

bash pip install wnb

uv

bash uv add wnb

Getting started ⚡️

Here, we show how you can use the library to train general (mixed) and weighted naive Bayes classifiers.

General naive Bayes

A general naive Bayes model can be set up and used in four simple steps:

1. Import the GeneralNB class as well as Distribution enum class python from wnb import GeneralNB, Distribution as D

2. Initialize a classifier with likelihood distributions specified python clf = GeneralNB([D.NORMAL, D.CATEGORICAL, D.EXPONENTIAL, D.EXPONENTIAL]) or ```python

   Columns not explicitly specified will default to Gaussian (normal) distribution

   clf = GeneralNB( distributions=[ (D.CATEGORICAL, [1]), (D.EXPONENTIAL, ["col3", "col4"]), ], ) ```

3. Fit the classifier to a training set (with four features) python clf.fit(X_train, y_train)

4. Predict on test data python clf.predict(X_test)

Weighted naive Bayes

An MLD-WNB model can be set up and used in four simple steps:

1. Import the GaussianWNB class python from wnb import GaussianWNB

2. Initialize a classifier python clf = GaussianWNB(max_iter=25, step_size=1e-2, penalty="l2")

3. Fit the classifier to a training set python clf.fit(X_train, y_train)

4. Predict on test data python clf.predict(X_test)

Compatibility with Scikit-learn 🤝

The wnb library fully adheres to the Scikit-learn API, ensuring seamless integration with other Scikit-learn components and workflows. This means that users familiar with Scikit-learn will find the WNB classifiers intuitive to use.

Both Scikit-learn classifiers and WNB classifiers share these well-known methods:

• fit(X, y)
• predict(X)
• predict_proba(X)
• predict_log_proba(X)
• predict_joint_log_proba(X)
• score(X, y)
• get_params()
• set_params(**params)
• etc.

By maintaining this consistency, WNB classifiers can be easily incorporated into existing machine learning pipelines and processes.

Benchmarks 📊

We conducted benchmarks on four datasets, Wine, Iris, Digits, and Breast Cancer, to evaluate the performance of WNB classifiers and compare them with their Scikit-learn counterpart, GaussianNB. The results show that WNB classifiers generally perform better in certain cases.

| Dataset | Scikit-learn Classifier | Accuracy | WNB Classifier | Accuracy | |------------------|-------------------------|----------|----------------|-----------| | Wine | GaussianNB | 0.9749 | GeneralNB | 0.9812 | | Iris | GaussianNB | 0.9556 | GeneralNB | 0.9602 | | Digits | GaussianNB | 0.8372 | GeneralNB | 0.8905 | | Breast Cancer | GaussianNB | 0.9389 | GaussianWNB | 0.9519 |

These benchmarks highlight the potential of WNB classifiers to provide better performance in certain scenarios by allowing more flexibility in the choice of distributions and incorporating weighting strategies.

The scripts used to generate these benchmark results are available in the benchmarks/ directory.

Support us 💡

You can support the project in the following ways:

⭐ Star WNB on GitHub (click the star button in the top right corner)

💡 Provide your feedback or propose ideas in the Issues section

📰 Post about WNB on LinkedIn or other platforms

Citation 📚

If you utilize this repository, please consider citing it with:

@misc{wnb, author = {Mohammd Mehdi Samsami}, title = {WNB: General and weighted naive Bayes classifiers}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/msamsami/wnb}}, }