This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

brew: A Multiple Classifier Systems API

This project was started in 2014 by Dayvid Victor and Thyago Porpino
for the Multiple Classifier Systems class at Federal University of Pernambuco.
The aim of this project is to provide an easy API for Ensembling, Stacking,
Blending, Ensemble Generation, Ensemble Pruning, Dynamic Classifier Selection,
and Dynamic Ensemble Selection.

Features

  • General: Ensembling, Stacking and Blending.
  • Ensemble Classifier Generators: Bagging, Random Subspace, SMOTE-Bagging, ICS-Bagging, SMOTE-ICS-Bagging.
  • Dynamic Selection: Overall Local Accuracy (OLA), Local Class Accuracy (LCA), Multiple Classifier Behavior (MCB), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U), A Priori Dynamic Selection, A Posteriori Dynamic Selection, Dynamic Selection KNN (DSKNN).
  • Ensemble Combination Rules: majority vote, min, max, mean and median.
  • Ensemble Diversity Metrics: Entropy Measure E, Kohavi Wolpert Variance, Q Statistics, Correlation Coefficient p, Disagreement Measure, Agreement Measure, Double Fault Measure.
  • Ensemble Pruning: Ensemble Pruning via Individual Contribution (EPIC).

Example

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import itertools

import sklearn

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier

from brew.base import Ensemble, EnsembleClassifier
from brew.stacking.stacker import EnsembleStack, EnsembleStackClassifier
from brew.combination.combiner import Combiner

from mlxtend.data import iris_data
from mlxtend.evaluate import plot_decision_regions

# Initializing Classifiers
clf1 = LogisticRegression(random_state=0)
clf2 = RandomForestClassifier(random_state=0)
clf3 = SVC(random_state=0, probability=True)

# Creating Ensemble
ensemble = Ensemble([clf1, clf2, clf3])
eclf = EnsembleClassifier(ensemble=ensemble, combiner=Combiner('mean'))

# Creating Stacking
layer_1 = Ensemble([clf1, clf2, clf3])
layer_2 = Ensemble([sklearn.clone(clf1)])

stack = EnsembleStack(cv=3)

stack.add_layer(layer_1)
stack.add_layer(layer_2)

sclf = EnsembleStackClassifier(stack)

clf_list = [clf1, clf2, clf3, eclf, sclf]
lbl_list = ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble', 'Stacking']

# Loading some example data
X, y = iris_data()
X = X[:,[0, 2]]

# Plotting Decision Regions
gs = gridspec.GridSpec(2, 3)
fig = plt.figure(figsize=(10, 8))

itt = itertools.product([0, 1, 2], repeat=2)

for clf, lab, grd in zip(clf_list, lbl_list, itt):
    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
    plt.title(lab)
plt.show()

Dependencies

  • Python 2.7+
  • scikit-learn >= 0.15.2
  • Numpy >= 1.6.1
  • SciPy >= 0.9
  • Matplotlib >= 0.99.1 (examples, only)
  • mlxtend (examples, only)

Installing

You can easily install brew using pip:

pip install brew

or, if you prefer an up-to-date version, get it from here:

pip install git+https://github.com/viisar/brew.git

Important References

  • Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2014.
  • Zhou, Zhi-Hua. Ensemble methods: foundations and algorithms. CRC Press, 2012.

Documentation

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

History

0.1.0 (2014-11-12)

  • First release on PyPI.
Release History

Release History

0.1.4

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.1.3

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.1.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.1.1

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

0.1.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
brew-0.1.4.zip (48.8 kB) Copy SHA256 Checksum SHA256 Source Oct 11, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS HPE HPE Development Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting