oll-python

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This is a Python binding of the OLL library for machine learning.

Currently, OLL 0.03 supports following binary classification algorithms:

• Perceptron
• Averaged Perceptron
• Passive Agressive (PA, PA-I, PA-II, Kernelized)
• ALMA (modified slightly from original)
• Confidence Weighted Linear-Classification.

For details of oll, see: http://code.google.com/p/oll

Installation

::

$ pip install oll

OLL library is bundled, so you don't need to install it separately.

Usage

.. code:: python

import oll

You can choose algorithms in

"P" -> Perceptron,

"AP" -> Averaged Perceptron,

"PA" -> Passive Agressive,

"PA1" -> Passive Agressive-I,

"PA2" -> Passive Agressive-II,

"PAK" -> Kernelized Passive Agressive,

"CW" -> Confidence Weighted Linear-Classification,

"AL" -> ALMA

o = oll.oll("CW", C=1.0, bias=0.0) o.add({0: 1.0, 1: 2.0, 2: -1.0}, 1) # train o.classify({0:1.0, 1:1.0}) # predict o.save('oll.model') o.load('oll.model')

scikit-learn like fit/predict interface

import numpy as np array = np.array([[1, 2, -1], [0, 0, 1]]) o.fit(array, [1, -1]) o.predict(np.array([[1, 2, -1], [0, 0, 1]]))

=> [1, -1]

from scipy.sparse import csr_matrix matrix = csr_matrix([[1, 2, -1], [0, 0, 1]]) o.fit(matrix, [1, -1]) o.predict(matrix)

=> [1, -1]

Multi label classification

import time import oll from sklearn.multiclass import OutputCodeClassifier from sklearn import datasets, cross_validation, metrics

dataset = datasets.load_digits() ALGORITHMS = ("P", "AP", "PA", "PA1", "PA2", "PAK", "CW", "AL") for algorithm in ALGORITHMS: print(algorithm) occ_predicts = [] expected = [] start = time.time() for (train_idx, test_idx) in cross_validation.StratifiedKFold(dataset.target, n_folds=10, shuffle=True): clf = OutputCodeClassifier(oll.oll(algorithm)) clf.fit(dataset.data[train_idx], dataset.target[train_idx]) occ_predicts += list(clf.predict(dataset.data[test_idx])) expected += list(dataset.target[test_idx]) print('Elapsed time: %s' % (time.time() - start)) print('Accuracy', metrics.accuracy_score(expected, occ_predicts))

=> P

=> Elapsed time: 109.82188701629639

=> Accuracy 0.770172509738

=> AP

=> Elapsed time: 111.42936396598816

=> Accuracy 0.760155815248

=> PA

=> Elapsed time: 110.95964503288269

=> Accuracy 0.74735670562

=> PA1

=> Elapsed time: 111.39844799041748

=> Accuracy 0.806343906511

=> PA2

=> Elapsed time: 115.12716913223267

=> Accuracy 0.766277128548

=> PAK

=> Elapsed time: 119.53838682174683

=> Accuracy 0.77796327212

=> CW

=> Elapsed time: 121.20785689353943

=> Accuracy 0.771285475793

=> AL

=> Elapsed time: 116.52497220039368

=> Accuracy 0.785754034502

Note

• This module requires C++ compiler to build.
• oll.cpp & oll.hpp : Copyright (c) 2011, Daisuke Okanohara
• oll_swig_wrap.cxx is generated based on 'oll_swig.i' in oll-ruby (https://github.com/syou6162/oll-ruby)

License

New BSD License.

.. |travis| image:: https://travis-ci.org/ikegami-yukino/oll-python.svg?branch=master :target: https://travis-ci.org/ikegami-yukino/oll-python :alt: travis-ci.org .. |coveralls| image:: https://coveralls.io/repos/ikegami-yukino/oll-python/badge.png :target: https://coveralls.io/r/ikegami-yukino/oll-python :alt: coveralls.io

.. |version| image:: https://img.shields.io/pypi/v/oll.svg :target: http://pypi.python.org/pypi/oll/ :alt: latest version

.. |license| image:: https://img.shields.io/pypi/l/oll.svg :target: http://pypi.python.org/pypi/oll/ :alt: license