Installation

Usage

importoll# 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 interfaceimportnumpyasnparray=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]fromscipy.sparseimportcsr_matrixmatrix=csr_matrix([[1,2,-1], [0,0,1]])o.fit(matrix, [1,-1])o.predict(matrix)# => [1, -1]# Multi label classificationimporttimeimportollfromsklearn.multiclassimportOutputCodeClassifierfromsklearnimportdatasets,cross_validation,metricsdataset=datasets.load_digits()ALGORITHMS= ("P","AP","PA","PA1","PA2","PAK","CW","AL")foralgorithminALGORITHMS:print(algorithm)occ_predicts= []expected= []start=time.time()for (train_idx,test_idx)incross_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

License