if __name__ == '__main__':
warnings.filterwarnings(module='sklearn*',
action='ignore',
category=DeprecationWarning)
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--dataset', type=str, default='stealing')
parser.add_argument('--clf', type=str, required=True)
opt = parser.parse_args()
all_clf = [
'CID_DTW', 'DD_DTW', 'WDTW', 'ED', 'DTW', 'LearnShapelets',
'FastShapelets', 'BagOfPatterns', 'TSF', 'TSBF', 'LPS', 'SAX', 'ST',
'COTE', 'EE'
]
assert opt.clf in all_clf
fpath = '{}/dataset/{}/Predictions/{}/testFold0.csv'.format(
module_path, opt.clf, opt.dataset)
y_pred, y_test = load_baseline_results(fpath=fpath)
Debugger.info_print('{} test samples with {:.4f} positive'.format(
len(y_test),
sum(y_test) / len(y_test)))
accu = accuracy_score(y_true=y_test, y_pred=y_pred)
prec = precision_score(y_true=y_test, y_pred=y_pred)
recall = recall_score(y_true=y_test, y_pred=y_pred)
f1 = f1_score(y_true=y_test, y_pred=y_pred)
Debugger.info_print(
'res: accu {:.4f}, prec {:.4f}, recall {:.4f}, f1 {:.4f}'.format(
accu, prec, recall, f1))
}
assert opt.target in paras
if opt.target == 'seg_length':
assert opt.total_length != -1
paras.pop(opt.target)
paras.pop('num_segment')
else:
paras.pop(opt.target)
for key, val in paras.items():
cmd += ' --{} {}'.format(key, val)
output = open('evaluate_paras_{}.sh'.format(opt.target), 'w')
output.write('#!/usr/bin/env bash\n')
for p in opt.paras.split(','):
if opt.target == 'K':
tmp = '{} --{} {} --C {}'.format(cmd, opt.target, p, int(p) * 10)
Debugger.info_print('running: {}'.format(tmp))
output.write('{}\n'.format(tmp))
elif opt.target == 'seg_length':
tmp = '{} --{} {} --{} {} --C {}'.format(
cmd, opt.target, p, 'num_segment',
int(opt.total_length // int(p)), int(paras['K'] * 10))
Debugger.info_print('running: {}'.format(tmp))
output.write('{}\n'.format(tmp))
else:
tmp = '{} --{} {} --C {}'.format(cmd, opt.target, p,
int(paras['K'] * 10))
Debugger.info_print('running: {}'.format(tmp))
output.write('{}\n'.format(tmp))
os.system('chmod u+x evaluate_paras_{}.sh'.format(opt.target))
parser.add_argument('--cache', action='store_true', default=False)
parser.add_argument('--embed', type=str, default='aggregate')
parser.add_argument('--embed_size', type=int, default=256)
parser.add_argument('--warp', type=int, default=2)
parser.add_argument('--cmethod', type=str, default='greedy')
parser.add_argument('--kernel', type=str, default='xgb')
parser.add_argument('--percentile', type=int, default=10)
parser.add_argument('--measurement', type=str, default='gdtw')
parser.add_argument('--batch_size', type=int, default=50)
parser.add_argument('--tflag', action='store_false', default=True)
parser.add_argument('--scaled', action='store_true', default=False)
parser.add_argument('--norm', action='store_true', default=False)
parser.add_argument('--no_global', action='store_false', default=True)
args = parser.parse_args()
Debugger.info_print('running with {}'.format(args.__dict__))
if args.dataset.startswith('ucr'):
dataset = args.dataset.rstrip('\n\r').split('-')[-1]
x_train, y_train, x_test, y_test = load_usr_dataset_by_name(
fname=dataset, length=args.seg_length * args.num_segment)
else:
raise NotImplementedError()
Debugger.info_print('training: {:.2f} positive ratio with {}'.format(
float(sum(y_train) / len(y_train)), len(y_train)))
Debugger.info_print('test: {:.2f} positive ratio with {}'.format(
float(sum(y_test) / len(y_test)), len(y_test)))
m = Time2Graph(
kernel=args.kernel,
K=args.K,
C=args.C,
'--top',
type=str,
default='{}/baselines/TimeSeriesClassification/'
'out/production/TimeSeriesClassification'.format(module_path))
parser.add_argument('--gpu_number', type=int, default=0)
parser.add_argument('--clf', type=str, required=True)
opt = parser.parse_args()
all_clf = [
'CID_DTW', 'DD_DTW', 'WDTW', 'ED', 'DTW', 'LearnShapelets',
'FastShapelets', 'BagOfPatterns', 'TSF', 'TSBF', 'LPS', 'ST', 'COTE'
]
classpath = []
for dirpath, dirnames, fnamesList in os.walk(opt.classpath):
Debugger.info_print('{}'.format(dirpath))
for fname in fnamesList:
if fname.endswith('.jar'):
classpath.append('{}{}'.format(dirpath, fname))
break
Debugger.info_print('{}'.format(classpath))
cmd = 'CUDA_VISIBLE_DEVICES={} java -classpath {}'.format(
opt.gpu_number, opt.top)
if opt.clf != 'all':
for p in classpath:
cmd += ':{}'.format(p)
dataset_cmd = cmd + ' development.DataSets -i {} -o {} -t {}'.format(
opt.input, opt.output, opt.dataset)
predict_cmd = cmd + ' timeseriesweka.examples.ClassificationExamples -i {} -o {} -t {} -c {}'.format(
opt.input, opt.output, opt.dataset, opt.clf)