Spaces:
Running
Running
File size: 9,638 Bytes
d03866e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 |
# -*- coding: utf-8 -*-
# Author: Qinghua Liu <liu.11085@osu.edu>
# License: Apache-2.0 License
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import pandas as pd
import torch
import random, argparse
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from evaluation.metrics import get_metrics
from utils.slidingWindows import find_length_rank
from model_wrapper import *
from HP_list import Optimal_Uni_algo_HP_dict, Optimal_Multi_algo_HP_dict
import os
# Cuda devices
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# seeding
seed = 2024
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
import os
print("CUDA Available: ", torch.cuda.is_available())
print("cuDNN Version: ", torch.backends.cudnn.version())
import pickle
def get_result(filename):
pickle_filename = filename.replace('.csv', '_results.pkl')
df = pickle.load(open(pickle_filename, 'rb'))
return df['anomaly_score'].to_numpy()
if __name__ == '__main__':
# Resolve dataset directory relative to this file (portable across machines)
parser = argparse.ArgumentParser(description='Running TSB-AD')
parser.add_argument('--mode', type=str, default='uni', choices=['uni', 'multi'],
help='Encoder mode: uni for univariate, multi for multivariate')
parser.add_argument('--AD_Name', type=str, default='Time_RCD')
parser.add_argument('--filename', type=str, default='')
parser.add_argument('--data_direc', type=str, default='')
parser.add_argument('--save', type=bool, default=True)
Multi = parser.parse_args().mode == 'multi'
# Initialize list to store all results
all_results = []
all_logits = []
if Multi:
filter_list = [
"GHL",
"Daphnet",
"Exathlon",
"Genesis",
"OPP",
"SMD",
# "SWaT",
# "PSM",
"SMAP",
"MSL",
"CreditCard",
"GECCO",
"MITDB",
"SVDB",
"LTDB",
"CATSv2",
"TAO"
]
base_dir = 'datasets/TSB-AD-M/'
files = os.listdir(base_dir)
else:
filter_list = [
"Daphnet",
"CATSv2",
"SWaT",
"LTDB",
"TAO",
"Exathlon",
"MITDB",
"MSL",
"SMAP",
"SMD",
"SVDB",
"OPP",
# "IOPS",
# "MGAB",
# "NAB",
# "NEK",
# "Power",
# "SED",
# "Stock",
# "TODS",
# "WSD",
# "YAHOO",
# "UCR"
]
base_dir = 'datasets/TSB-AD-U/'
files = os.listdir(base_dir)
# ## ArgumentParser
for file in files:
if any(filter_item in file for filter_item in filter_list):
print(f"Skipping file: {file} due to filter criteria.")
continue
# Clear GPU memory before processing each file
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
args = parser.parse_args()
# Set the file-specific values
args.filename = file
args.data_direc = base_dir
if Multi:
Optimal_Det_HP = Optimal_Multi_algo_HP_dict[args.AD_Name]
else:
Optimal_Det_HP = Optimal_Uni_algo_HP_dict[args.AD_Name]
# try:
# Read data using a proper path join
df_path = os.path.join(args.data_direc, args.filename)
df = pd.read_csv(df_path).dropna()
data = df.iloc[:, 0:-1].values.astype(float)
label = df['Label'].astype(int).to_numpy()
slidingWindow = find_length_rank(data, rank=1)
train_index = args.filename.split('.')[0].split('_')[-3]
data_train = data[:int(train_index), :]
test_data = data[int(train_index):, :]
label_test = label[int(train_index):]
logits = None # ensure defined irrespective of branch
print(f"Running {args.AD_Name} on {args.filename}...")
if args.AD_Name in Semisupervise_AD_Pool:
output = run_Semisupervise_AD(args.AD_Name, data_train, test_data, **Optimal_Det_HP)
elif args.AD_Name in Unsupervise_AD_Pool:
if args.AD_Name == 'Time_RCD':
# For Time_RCD, we need to pass the test data directly
output, logits = run_Unsupervise_AD(args.AD_Name, data_train, test_data, Multi=Multi, **Optimal_Det_HP)
else:
output = run_Unsupervise_AD(args.AD_Name, data_train, test_data, **Optimal_Det_HP)
else:
raise Exception(f"{args.AD_Name} is not defined")
if isinstance(output, np.ndarray):
# output = MinMaxScaler(feature_range=(0,1)).fit_transform(output.reshape(-1,1)).ravel()
# Fix shape mismatch issue - ensure output and labels have the same length
min_length = min(len(output), len(label_test)) # Use label_test instead of label
output_aligned = output[:min_length]
label_aligned = label_test[:min_length]
logits_aligned = None
if logits is not None:
logits_aligned = logits[:min_length]
evaluation_result = get_metrics(output_aligned, label_aligned, slidingWindow=slidingWindow, pred=output_aligned > (np.mean(output_aligned)+3*np.std(output_aligned)))
evaluation_result_logits = None
if logits is not None:
evaluation_result_logits = get_metrics(logits_aligned, label_aligned, slidingWindow=slidingWindow, pred=logits_aligned > (np.mean(logits_aligned)+3*np.std(logits_aligned)))
print(evaluation_result)
# Prepare result dictionary with filename and all metrics
result_dict = {
'filename': args.filename,
'AD_Name': args.AD_Name,
'sliding_window': slidingWindow,
'train_index': train_index,
'data_shape': f"{data.shape[0]}x{data.shape[1]}",
'output_length': len(output),
'label_length': len(label_test), # Use label_test length
'aligned_length': min_length,
**evaluation_result # Unpack all evaluation metrics
}
all_results.append(result_dict)
if logits is not None:
logit_dict = {
'filename': args.filename,
'AD_Name': args.AD_Name,
'sliding_window': slidingWindow,
'train_index': train_index,
'data_shape': f"{data.shape[0]}x{data.shape[1]}",
'output_length': len(logits),
'label_length': len(label_test), # Use label_test length
'aligned_length': min_length,
**evaluation_result_logits # Unpack all evaluation metrics for logits
}
all_logits.append(logit_dict)
# Save value, label, and anomaly scores to pickle file
if args.save:
output_filename = f'{args.filename.split(".")[0]}_results.pkl'
output_path = os.path.join(
os.path.join(os.getcwd(), (f"{'Multi' if Multi else 'Uni'}_"+args.AD_Name), output_filename))
if not os.path.exists(output_path):
os.makedirs(os.path.dirname(output_path), exist_ok=True)
pd.DataFrame({
'value': test_data[:min_length].tolist(),
'label': label_aligned.tolist(),
'anomaly_score': output_aligned.tolist(),
'logits': logits_aligned.tolist() if logits is not None else None
}).to_pickle(output_path)
print(f'Results saved to {output_path}')
else:
print(f'At {args.filename}: '+output)
# Save error information as well
result_dict = {
'filename': args.filename,
'AD_Name': args.AD_Name,
'sliding_window': None,
'train_index': None,
'data_shape': None,
'error_message': output
}
all_results.append(result_dict)
# Convert results to DataFrame and save to CSV
if all_results:
results_df = pd.DataFrame(all_results)
# win_size = str(Optimal_Det_HP['win_size']) if Optimal_Det_HP['win_size'] else ""
output_filename = f'{"Multi" if Multi else "Uni"}_{args.AD_Name}.csv'
results_df.to_csv(output_filename, index=False)
print(f"\nAll results saved to {output_filename}")
print(f"Total file processed: {len(all_results)}")
print(f"Results shape: {results_df.shape}")
if all_logits:
logits_df = pd.DataFrame(all_logits)
logits_output_filename = f'{"Multi" if Multi else "Uni"}_{args.AD_Name}.csv'
logits_df.to_csv(logits_output_filename, index=False)
print(f"Logits results saved to {logits_output_filename}")
else:
print("No results to save.")
|