Skip to main content

bace

Project description

bace

Python 3.7 PyPI version Build Status Documentation Status License: 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 API, so usage is very simple.

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)

Documentation

The full documentation is at http://bace.rtfd.org.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for bace, version 1.0.1
Filename, size File type Python version Upload date Hashes
Filename, size bace-1.0.1-py3-none-any.whl (14.7 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size bace-1.0.1.tar.gz (8.9 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page