def track_execution():
LOGGER.info('Starting training.')
timer = Time()
yield
timer.stop()
LOGGER.info('Training completed, took {0:.2f}s.'.format(
timer.time_diff_sec()))
def track_execution():
LOGGER.info('Starting training.')
timer = Time()
yield
timer.stop()
LOGGER.info('Training completed, took {0:.2f}s.'.format(timer.time_diff_sec()))
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
svm.set_features(None)
svm.set_labels(None)
sparse_data = None
print 'Loading test data...'
sparse_data = load_sparse_data(args.testset, args.dimension)
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
svm.set_features(None)
svm.set_labels(None)
sparse_data = None
print 'Loading test data...'
sparse_data = load_sparse_data(args.testset, args.dimension)
