oll 0.2.1

Installation

$ pip install oll

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

Usage

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" -> ALMAo = oll.oll("CW", C=1.0, bias=0.0)o.add({0: 1.0, 1: 2.0, 2: -1.0}, 1) # traino.classify({0:1.0, 1:1.0}) # predicto.save('oll.model')o.load('oll.model')# scikit-learn like fit/predict interfaceimport numpy as nparray = 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_matrixmatrix = csr_matrix([[1, 2, -1], [0, 0, 1]])o.fit(matrix, [1, -1])o.predict(matrix)# => [1, -1]# Multi label classificationimport timeimport ollfrom sklearn.multiclass import OutputCodeClassifierfrom sklearn import datasets, cross_validation, metricsdataset = 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.

0.2.1 (2017年6月30日)

• Multi label clasification (using scikit-learn)

• Support Python 3.6

0.2 (2016年11月26日)

• scikit-learn like fit/predict interfaces are available

• Setting C and bias parameters is available in initialization

• Support Python 3.5

• Unsupport Python 2.6 and 3.3

0.1.2 (2015年01月11日)

• Support testFile method

• docstrings are available

0.1.1 (2014年03月29日)

• Compatibility some compilers

0.1 (2013年10月11日)

• Initial release.