main_ts.py

import os
import argparse
import time
import numpy as np
import utils
from config import get_algo_config, get_algo_class


dataset_root = 'data/'


parser = argparse.ArgumentParser()
parser.add_argument("--runs", type=int, default=1,
                    help="how many times we repeat the experiments to obtain the average performance")
parser.add_argument("--input_dir", type=str, default='ts/',
                    help="not use here for ts data")
parser.add_argument("--output_dir", type=str, default='&ts_record/',
                    help="the output file path")
parser.add_argument("--dataset", type=str, default='UCR_natural_fault_gaitHunt')
parser.add_argument("--entities", type=str,
                    default='FULL',
                    help='FULL represents all the csv file in the folder, or a list of entity names split by comma')
parser.add_argument("--model", type=str, default='dif', help="")
parser.add_argument("--note", type=str, default='')

parser.add_argument('--seq_len', type=int, default=100)
parser.add_argument('--stride', type=int, default=1)
args = parser.parse_args()

model_class = get_algo_class(args.model)
model_configs = get_algo_config(args.model)


if args.model == 'dif':
    ts_model_configs={
        'data_type': 'ts',
        'network_name': 'gru',
        'new_ensemble_method': 0,
        'batch_size': 5000,
        'layers': 1,
        'hidden_dim': 20
    }
    model_configs = dict(model_configs, **ts_model_configs)
    model_configs['data_type'] = 'ts'
    model_configs['network_name'] = 'gru'
print(model_configs)



# create and print results file header
os.makedirs(args.output_dir, exist_ok=True)
cur_time = time.strftime("%m-%d %H.%M.%S", time.localtime())
result_file = os.path.join(args.output_dir, f'{args.model}_{args.dataset}_results.csv')

f = open(result_file, 'a')
print(cur_time, file=f)
print('\n---------------------------------------------------------', file=f)
print(f'model: {args.model}, data dir: {args.input_dir}, dataset: {args.dataset}, {args.runs}runs, ', file=f)
for k in model_configs.keys():
    print(f'Parameters,\t [{k}], \t\t  {model_configs[k]}', file=f)
print(f'args,\t [seq_len], \t\t  {args.seq_len}', file=f)
print(f'args,\t [stride], \t\t  {args.stride}', file=f)
print(f'Note: {args.note}', file=f)
print(f'---------------------------------------------------------', file=f)
print(f'data, adj_auroc, std, adj_ap, std, adj_f1, std, adj_p, std, adj_r, std, time, model', file=f)
f.close()


train_df_lst, test_df_lst, label_lst, name_lst = utils.get_data_lst_ts(os.path.join(dataset_root, args.input_dir),
                                                                       args.dataset, entities=args.entities)
print(name_lst)
for train_df, test_df, labels, dataset_name in zip(train_df_lst, test_df_lst, label_lst, name_lst):
    x_train = train_df.values
    x_test = test_df.values

    entries = []
    t_lst = []
    for i in range(args.runs):
        start_time = time.time()
        print(f'\nRunning [{i+1}/{args.runs}] of [{args.model}] on Dataset [{dataset_name}]')

        clf = model_class(**model_configs, random_state=42+i)

        x = np.concatenate([x_train, x_test])
        if args.model == 'dif':
            x_seq = utils.get_sub_seqs(x, seq_len=args.seq_len, stride=args.stride)
            x_test_seq = utils.get_sub_seqs(x_test, seq_len=args.seq_len, stride=1)
            clf.fit(x_seq)
            scores = clf.decision_function(x_test_seq)

            padding_list = np.zeros(args.seq_len-1)
            scores = np.hstack([padding_list, scores])
        else:
            clf.fit(x)
            scores = clf.decision_function(x_test)

        entry = utils.eval_ts(scores=scores, labels=labels, test_df=test_df)
        entries.append(entry)
        print(dataset_name, end=', ')
        for e in entry:
            print(e, end=', ')
        print()

        t = round(time.time() - start_time, 1)
        t_lst.append(t)

    avg_entry = np.average(np.array(entries), axis=0)
    std_entry = np.std(np.array(entries), axis=0)

    f = open(result_file, 'a')
    txt = '%s, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, ' \
          '%.4f, %.4f, %.4f, %.4f, %.1f, %s ' % (dataset_name,
                                                 avg_entry[0], std_entry[0], avg_entry[1], std_entry[1],
                                                 avg_entry[2], std_entry[2], avg_entry[3], std_entry[3],
                                                 avg_entry[4], std_entry[4],
                                                 np.average(t_lst),  args.model)
    print(txt)
    print(txt, file=f)
    f.close()