def evaluation_cross_validation_multiclass_storage(traindat=traindat, label_traindat=label_traindat): from shogun.Evaluation import CrossValidation, CrossValidationResult from shogun.Evaluation import CrossValidationPrintOutput from shogun.Evaluation import CrossValidationMKLStorage, CrossValidationMulticlassStorage from shogun.Evaluation import MulticlassAccuracy, F1Measure from shogun.Evaluation import StratifiedCrossValidationSplitting from shogun.Features import MulticlassLabels from shogun.Features import RealFeatures, CombinedFeatures from shogun.Kernel import GaussianKernel, CombinedKernel from shogun.Classifier import MKLMulticlass from shogun.Mathematics import Statistics, MSG_DEBUG # training data, combined features all on same data features=RealFeatures(traindat) comb_features=CombinedFeatures() comb_features.append_feature_obj(features) comb_features.append_feature_obj(features) comb_features.append_feature_obj(features) labels=MulticlassLabels(label_traindat) # kernel, different Gaussians combined kernel=CombinedKernel() kernel.append_kernel(GaussianKernel(10, 0.1)) kernel.append_kernel(GaussianKernel(10, 1)) kernel.append_kernel(GaussianKernel(10, 2)) # create mkl using libsvm, due to a mem-bug, interleaved is not possible svm=MKLMulticlass(1.0,kernel,labels); svm.set_kernel(kernel); # splitting strategy for 5 fold cross-validation (for classification its better # to use "StratifiedCrossValidation", but the standard # "StratifiedCrossValidationSplitting" is also available splitting_strategy=StratifiedCrossValidationSplitting(labels, 5) # evaluation method evaluation_criterium=MulticlassAccuracy() # cross-validation instance cross_validation=CrossValidation(svm, comb_features, labels, splitting_strategy, evaluation_criterium) cross_validation.set_autolock(False) # append cross vlaidation output classes #cross_validation.add_cross_validation_output(CrossValidationPrintOutput()) #mkl_storage=CrossValidationMKLStorage() #cross_validation.add_cross_validation_output(mkl_storage) multiclass_storage=CrossValidationMulticlassStorage() multiclass_storage.append_binary_evaluation(F1Measure()) cross_validation.add_cross_validation_output(multiclass_storage) cross_validation.set_num_runs(3) # perform cross-validation result=cross_validation.evaluate() roc_0_0_0 = multiclass_storage.get_fold_ROC(0,0,0) #print roc_0_0_0 auc_0_0_0 = multiclass_storage.get_fold_evaluation_result(0,0,0,0) #print auc_0_0_0 return roc_0_0_0, auc_0_0_0
def mkl_multiclass_modular(fm_train_real, fm_test_real, label_train_multiclass, width, C, epsilon, num_threads, mkl_epsilon, mkl_norm): from shogun.Features import CombinedFeatures, RealFeatures, MulticlassLabels from shogun.Kernel import CombinedKernel, GaussianKernel, LinearKernel, PolyKernel from shogun.Classifier import MKLMulticlass kernel = CombinedKernel() feats_train = CombinedFeatures() feats_test = CombinedFeatures() subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = GaussianKernel(10, width) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = LinearKernel() feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = PolyKernel(10, 2) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) labels = MulticlassLabels(label_train_multiclass) mkl = MKLMulticlass(C, kernel, labels) mkl.set_epsilon(epsilon) mkl.parallel.set_num_threads(num_threads) mkl.set_mkl_epsilon(mkl_epsilon) mkl.set_mkl_norm(mkl_norm) mkl.train() kernel.init(feats_train, feats_test) out = mkl.apply().get_labels() return out
def evaluation_cross_validation_multiclass_storage( traindat=traindat, label_traindat=label_traindat): from shogun.Evaluation import CrossValidation, CrossValidationResult from shogun.Evaluation import CrossValidationPrintOutput from shogun.Evaluation import CrossValidationMKLStorage, CrossValidationMulticlassStorage from shogun.Evaluation import MulticlassAccuracy, F1Measure from shogun.Evaluation import StratifiedCrossValidationSplitting from shogun.Features import MulticlassLabels from shogun.Features import RealFeatures, CombinedFeatures from shogun.Kernel import GaussianKernel, CombinedKernel from shogun.Classifier import MKLMulticlass from shogun.Mathematics import Statistics, MSG_DEBUG # training data, combined features all on same data features = RealFeatures(traindat) comb_features = CombinedFeatures() comb_features.append_feature_obj(features) comb_features.append_feature_obj(features) comb_features.append_feature_obj(features) labels = MulticlassLabels(label_traindat) # kernel, different Gaussians combined kernel = CombinedKernel() kernel.append_kernel(GaussianKernel(10, 0.1)) kernel.append_kernel(GaussianKernel(10, 1)) kernel.append_kernel(GaussianKernel(10, 2)) # create mkl using libsvm, due to a mem-bug, interleaved is not possible svm = MKLMulticlass(1.0, kernel, labels) svm.set_kernel(kernel) # splitting strategy for 5 fold cross-validation (for classification its better # to use "StratifiedCrossValidation", but the standard # "StratifiedCrossValidationSplitting" is also available splitting_strategy = StratifiedCrossValidationSplitting(labels, 5) # evaluation method evaluation_criterium = MulticlassAccuracy() # cross-validation instance cross_validation = CrossValidation(svm, comb_features, labels, splitting_strategy, evaluation_criterium) cross_validation.set_autolock(False) # append cross vlaidation output classes #cross_validation.add_cross_validation_output(CrossValidationPrintOutput()) #mkl_storage=CrossValidationMKLStorage() #cross_validation.add_cross_validation_output(mkl_storage) multiclass_storage = CrossValidationMulticlassStorage() multiclass_storage.append_binary_evaluation(F1Measure()) cross_validation.add_cross_validation_output(multiclass_storage) cross_validation.set_num_runs(3) # perform cross-validation result = cross_validation.evaluate() roc_0_0_0 = multiclass_storage.get_fold_ROC(0, 0, 0) #print roc_0_0_0 auc_0_0_0 = multiclass_storage.get_fold_evaluation_result(0, 0, 0, 0) #print auc_0_0_0 return roc_0_0_0, auc_0_0_0
def mkl_multiclass_modular(fm_train_real, fm_test_real, label_train_multiclass, width, C, epsilon, num_threads, mkl_epsilon, mkl_norm): from shogun.Features import CombinedFeatures, RealFeatures, Labels from shogun.Kernel import CombinedKernel, GaussianKernel, LinearKernel, PolyKernel from shogun.Classifier import MKLMulticlass kernel = CombinedKernel() feats_train = CombinedFeatures() feats_test = CombinedFeatures() subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = GaussianKernel(10, width) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = LinearKernel() feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = PolyKernel(10, 2) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) labels = Labels(label_train_multiclass) mkl = MKLMulticlass(C, kernel, labels) mkl.set_epsilon(epsilon) mkl.parallel.set_num_threads(num_threads) mkl.set_mkl_epsilon(mkl_epsilon) mkl.set_mkl_norm(mkl_norm) mkl.train() kernel.init(feats_train, feats_test) out = mkl.apply().get_labels() return out