Bayes
Python implementations of Naive Bayes algorithm variations 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:
pip install git+https://github.com/krzjoa/Bayes.git
or using pip:
pip install bayes-variants
Usage
Bayes classifiers API mimics [Scikit-Learn](http://scikit-learn.org/stable/modules/classes.html) API, so usage is very simple.
from bayes.classifiers import ComplementNB
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
categories = ['alt.atheism', 'talk.religion.misc',
'comp.graphics', 'sci.space']
# Train set
newsgroups_train = fetch_20newsgroups(subset='train',
categories=categories, shuffle=True)
X_train = vectorizer.fit_transform(newsgroups_train.data)
y_train = newsgroups_train.target
# Test set
newsgroups_test = fetch_20newsgroups(subset='test',
categories=categories, 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)TODO list
- Weighted Complement Naive Bayes
- Locally Weighted Naive Bayes
