https://github.com/krzjoa/bace

A deck of Naive Bayes algorithms with sklearn-like API 🃏
https://github.com/krzjoa/bace

bayes-classifier machine-learning-algorithms naive-bayes naive-bayes-algorithm naive-bayes-classifier

Last synced: about 2 months ago
JSON representation

A deck of Naive Bayes algorithms with sklearn-like API 🃏

• Host: GitHub
• URL: https://github.com/krzjoa/bace
• Owner: krzjoa
• License: mit
• Created: 2016-04-30T14:29:17.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2020-05-21T05:33:59.000Z (over 4 years ago)
• Last Synced: 2024-10-02T14:38:39.471Z (3 months ago)
• Topics: bayes-classifier, machine-learning-algorithms, naive-bayes, naive-bayes-algorithm, naive-bayes-classifier
• Language: Python
• Homepage: https://bace.readthedocs.io
• Size: 2.28 MB
• Stars: 8
• Watchers: 2
• Forks: 5
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# bace 

![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg) 

[![PyPI version](https://badge.fury.io/py/bace.svg)](https://badge.fury.io/py/bace) 

[![Build Status](https://travis-ci.org/rasbt/mlxtend.svg?branch=master)](https://travis-ci.org/krzjoa/Bayes) 

[![Documentation Status](https://readthedocs.org/projects/bace/badge/?version=latest)](https://bace.readthedocs.io/en/latest/?badge=latest) 

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 

A deck of Naive Bayes algorithms with sklearn-like API.

## Algorithms

* Complement Naive Bayes

* Negation Naive Bayes

* Universal-set Naive Bayes

* Selective Naive Bayes

## Installation

You can install this module directly from GitHub repo with command:

````

python3.7 -m pip install git+https://github.com/krzjoa/bace.git

````

or as a PyPI package

````

python3.7 -m pip install bace

````

## Usage

**bace** API mimics [scikit-learn](http://scikit-learn.org/stable/modules/classes.html) API, so usage is very simple.

```` python

from bace import ComplementNB

from sklearn.datasets import fetch_20newsgroups

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer()

    

# Train set

newsgroups_train = fetch_20newsgroups(subset='train', shuffle=True)

X_train = vectorizer.fit_transform(newsgroups_train.data)

y_train = newsgroups_train.target

    

# Test set

newsgroups_test = fetch_20newsgroups(subset='test', shuffle=True)

X_test = vectorizer.fit_transform(newsgroups_test.data)

y_test = newsgroups_test.target

# Score 

cnb = ComplementNB()

cnb.fit(X_train, y_train).accuracy_score(X_test, y_test)

````