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.

Source Distribution

bace-1.0.1.tar.gz (8.9 kB view hashes)

Uploaded source

Built Distribution

bace-1.0.1-py3-none-any.whl (14.7 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page