if __name__=='__main__':
from modshogun import SparseRealFeatures, RandomFourierDotFeatures, GAUSSIAN
from modshogun import LibSVMFile, BinaryLabels, SVMOcas
from modshogun import Time
from numpy import array
args = parse_arguments()
print 'Loading training data...'
sparse_data = load_sparse_data(args.dataset,args.dimension)
kernel_params = array([args.width], dtype=float)
rf_feats = RandomFourierDotFeatures(sparse_data['data'], args.D, GAUSSIAN,
kernel_params)
svm = SVMOcas(args.C, rf_feats, sparse_data['labels'])
svm.set_epsilon(args.epsilon)
print 'Starting training.'
timer = Time()
svm.train()
timer.stop()
print 'Training completed, took {0:.2f}s.'.format(timer.time_diff_sec())
predicted_labels = svm.apply()
evaluate(predicted_labels, sparse_data['labels'], 'Training results')
if args.testset!=None:
random_coef = rf_feats.get_random_coefficients()
# removing current dataset from memory in order to load the test dataset,
# to avoid running out of memory
rf_feats = None
def classifier_svmocas_modular (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,C=0.9,epsilon=1e-5,num_threads=1): from modshogun import RealFeatures, BinaryLabels from modshogun import SVMOcas, CSVFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) labels=BinaryLabels(CSVFile(label_fname)) svm=SVMOcas(C, feats_train, labels) svm.set_epsilon(epsilon) svm.parallel.set_num_threads(num_threads) svm.set_bias_enabled(False) svm.train() bias=svm.get_bias() w=svm.get_w() predictions = svm.apply(feats_test) return predictions, svm, predictions.get_labels()
if __name__ == '__main__':
from modshogun import SparseRealFeatures, RandomFourierDotFeatures, GAUSSIAN
from modshogun import LibSVMFile, BinaryLabels, SVMOcas
from modshogun import Time
from numpy import array
args = parse_arguments()
print 'Loading training data...'
sparse_data = load_sparse_data(args.dataset, args.dimension)
kernel_params = array([args.width], dtype=float)
rf_feats = RandomFourierDotFeatures(sparse_data['data'], args.D, GAUSSIAN,
kernel_params)
svm = SVMOcas(args.C, rf_feats, sparse_data['labels'])
svm.set_epsilon(args.epsilon)
print 'Starting training.'
timer = Time()
svm.train()
timer.stop()
print 'Training completed, took {0:.2f}s.'.format(timer.time_diff_sec())
predicted_labels = svm.apply()
evaluate(predicted_labels, sparse_data['labels'], 'Training results')
if args.testset != None:
random_coef = rf_feats.get_random_coefficients()
# removing current dataset from memory in order to load the test dataset,
# to avoid running out of memory
rf_feats = None