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2_anomaly_detection.py
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2_anomaly_detection.py
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import argparse
import torch
import pickle
import preprocess_data
from model import model
from torch import optim
from pathlib import Path
from matplotlib import pyplot as plt
import numpy as np
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
from anomalyDetector import fit_norm_distribution_param
from anomalyDetector import anomalyScore
from anomalyDetector import get_precision_recall
parser = argparse.ArgumentParser(description='PyTorch RNN Anomaly Detection Model')
parser.add_argument('--prediction_window_size', type=int, default=10,
help='prediction_window_size')
parser.add_argument('--data', type=str, default='ecg',
help='type of the dataset (ecg, gesture, power_demand, space_shuttle, respiration, nyc_taxi')
parser.add_argument('--filename', type=str, default='chfdb_chf13_45590.pkl',
help='filename of the dataset')
parser.add_argument('--save_fig', action='store_true',
help='save results as figures')
parser.add_argument('--compensate', action='store_true',
help='compensate anomaly score using anomaly score esimation')
parser.add_argument('--beta', type=float, default=1.0,
help='beta value for f-beta score')
args_ = parser.parse_args()
print('-' * 89)
print("=> loading checkpoint ")
checkpoint = torch.load(str(Path('save',args_.data,'checkpoint',args_.filename).with_suffix('.pth')))
args = checkpoint['args']
args.prediction_window_size= args_.prediction_window_size
args.beta = args_.beta
args.save_fig = args_.save_fig
args.compensate = args_.compensate
print("=> loaded checkpoint")
# Set the random seed manually for reproducibility.
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
###############################################################################
# Load data
###############################################################################
TimeseriesData = preprocess_data.PickleDataLoad(data_type=args.data,filename=args.filename, augment_test_data=False)
train_dataset = TimeseriesData.batchify(args,TimeseriesData.trainData[:TimeseriesData.length], bsz=1)
test_dataset = TimeseriesData.batchify(args,TimeseriesData.testData, bsz=1)
###############################################################################
# Build the model
###############################################################################
nfeatures = TimeseriesData.trainData.size(-1)
model = model.RNNPredictor(rnn_type = args.model,
enc_inp_size=nfeatures,
rnn_inp_size = args.emsize,
rnn_hid_size = args.nhid,
dec_out_size=nfeatures,
nlayers = args.nlayers,
res_connection=args.res_connection).to(args.device)
model.load_state_dict(checkpoint['state_dict'])
#del checkpoint
scores, predicted_scores, precisions, recalls, f_betas = list(), list(), list(), list(), list()
targets, mean_predictions, oneStep_predictions, Nstep_predictions = list(), list(), list(), list()
try:
# For each channel in the dataset
for channel_idx in range(nfeatures):
''' 1. Load mean and covariance if they are pre-calculated, if not calculate them. '''
# Mean and covariance are calculated on train dataset.
if 'means' in checkpoint.keys() and 'covs' in checkpoint.keys():
print('=> loading pre-calculated mean and covariance')
mean, cov = checkpoint['means'][channel_idx], checkpoint['covs'][channel_idx]
else:
print('=> calculating mean and covariance')
mean, cov = fit_norm_distribution_param(args, model, train_dataset, channel_idx=channel_idx)
''' 2. Train anomaly score predictor using support vector regression (SVR). (Optional) '''
# An anomaly score predictor is trained
# given hidden layer output and the corresponding anomaly score on train dataset.
# Predicted anomaly scores on test dataset can be used for the baseline of the adaptive threshold.
if args.compensate:
print('=> training an SVR as anomaly score predictor')
train_score, _, _, hiddens, _ = anomalyScore(args, model, train_dataset, mean, cov, channel_idx=channel_idx)
score_predictor = GridSearchCV(SVR(), cv=5,param_grid={"C": [1e0, 1e1, 1e2],"gamma": np.logspace(-1, 1, 3)})
score_predictor.fit(torch.cat(hiddens,dim=0).numpy(), train_score.cpu().numpy())
else:
score_predictor=None
''' 3. Calculate anomaly scores'''
# Anomaly scores are calculated on the test dataset
# given the mean and the covariance calculated on the train dataset
print('=> calculating anomaly scores')
score, sorted_prediction, sorted_error, _, predicted_score = anomalyScore(args, model, test_dataset, mean, cov,
score_predictor=score_predictor,
channel_idx=channel_idx)
''' 4. Evaluate the result '''
# The obtained anomaly scores are evaluated by measuring precision, recall, and f_beta scores
# The precision, recall, f_beta scores are are calculated repeatedly,
# sampling the threshold from 1 to the maximum anomaly score value, either equidistantly or logarithmically.
print('=> calculating precision, recall, and f_beta')
precision, recall, f_beta = get_precision_recall(args, score, num_samples=1000, beta=args.beta,
label=TimeseriesData.testLabel.to(args.device))
print('data: ',args.data,' filename: ',args.filename,
' f-beta (no compensation): ', f_beta.max().item(),' beta: ',args.beta)
if args.compensate:
precision, recall, f_beta = get_precision_recall(args, score, num_samples=1000, beta=args.beta,
label=TimeseriesData.testLabel.to(args.device),
predicted_score=predicted_score)
print('data: ',args.data,' filename: ',args.filename,
' f-beta (compensation): ', f_beta.max().item(),' beta: ',args.beta)
target = preprocess_data.reconstruct(test_dataset.cpu()[:, 0, channel_idx],
TimeseriesData.mean[channel_idx],
TimeseriesData.std[channel_idx]).numpy()
mean_prediction = preprocess_data.reconstruct(sorted_prediction.mean(dim=1).cpu(),
TimeseriesData.mean[channel_idx],
TimeseriesData.std[channel_idx]).numpy()
oneStep_prediction = preprocess_data.reconstruct(sorted_prediction[:, -1].cpu(),
TimeseriesData.mean[channel_idx],
TimeseriesData.std[channel_idx]).numpy()
Nstep_prediction = preprocess_data.reconstruct(sorted_prediction[:, 0].cpu(),
TimeseriesData.mean[channel_idx],
TimeseriesData.std[channel_idx]).numpy()
sorted_errors_mean = sorted_error.abs().mean(dim=1).cpu()
sorted_errors_mean *= TimeseriesData.std[channel_idx]
sorted_errors_mean = sorted_errors_mean.numpy()
score = score.cpu()
scores.append(score), targets.append(targets), predicted_scores.append(predicted_score)
mean_predictions.append(mean_prediction), oneStep_predictions.append(oneStep_prediction)
Nstep_predictions.append(Nstep_prediction)
precisions.append(precision), recalls.append(recall), f_betas.append(f_beta)
if args.save_fig:
save_dir = Path('result',args.data,args.filename).with_suffix('').joinpath('fig_detection')
save_dir.mkdir(parents=True,exist_ok=True)
plt.plot(precision.cpu().numpy(),label='precision')
plt.plot(recall.cpu().numpy(),label='recall')
plt.plot(f_beta.cpu().numpy(), label='f1')
plt.legend()
plt.xlabel('Threshold (log scale)')
plt.ylabel('Value')
plt.title('Anomaly Detection on ' + args.data + ' Dataset', fontsize=18, fontweight='bold')
plt.savefig(str(save_dir.joinpath('fig_f_beta_channel'+str(channel_idx)).with_suffix('.png')))
plt.close()
fig, ax1 = plt.subplots(figsize=(15,5))
ax1.plot(target,label='Target',
color='black', marker='.', linestyle='--', markersize=1, linewidth=0.5)
ax1.plot(mean_prediction, label='Mean predictions',
color='purple', marker='.', linestyle='--', markersize=1, linewidth=0.5)
ax1.plot(oneStep_prediction, label='1-step predictions',
color='green', marker='.', linestyle='--', markersize=1, linewidth=0.5)
ax1.plot(Nstep_prediction, label=str(args.prediction_window_size) + '-step predictions',
color='blue', marker='.', linestyle='--', markersize=1, linewidth=0.5)
ax1.plot(sorted_errors_mean,label='Absolute mean prediction errors',
color='orange', marker='.', linestyle='--', markersize=1, linewidth=1.0)
ax1.legend(loc='upper left')
ax1.set_ylabel('Value',fontsize=15)
ax1.set_xlabel('Index',fontsize=15)
ax2 = ax1.twinx()
ax2.plot(score.numpy().reshape(-1, 1), label='Anomaly scores from \nmultivariate normal distribution',
color='red', marker='.', linestyle='--', markersize=1, linewidth=1)
if args.compensate:
ax2.plot(predicted_score, label='Predicted anomaly scores from SVR',
color='cyan', marker='.', linestyle='--', markersize=1, linewidth=1)
#ax2.plot(score.reshape(-1,1)/(predicted_score+1),label='Anomaly scores from \nmultivariate normal distribution',
# color='hotpink', marker='.', linestyle='--', markersize=1, linewidth=1)
ax2.legend(loc='upper right')
ax2.set_ylabel('anomaly score',fontsize=15)
#plt.axvspan(2830,2900 , color='yellow', alpha=0.3)
plt.title('Anomaly Detection on ' + args.data + ' Dataset', fontsize=18, fontweight='bold')
plt.tight_layout()
plt.xlim([0,len(test_dataset)])
plt.savefig(str(save_dir.joinpath('fig_scores_channel'+str(channel_idx)).with_suffix('.png')))
#plt.show()
plt.close()
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
print('=> saving the results as pickle extensions')
save_dir = Path('result', args.data, args.filename).with_suffix('')
save_dir.mkdir(parents=True, exist_ok=True)
pickle.dump(targets, open(str(save_dir.joinpath('target.pkl')),'wb'))
pickle.dump(mean_predictions, open(str(save_dir.joinpath('mean_predictions.pkl')),'wb'))
pickle.dump(oneStep_predictions, open(str(save_dir.joinpath('oneStep_predictions.pkl')),'wb'))
pickle.dump(Nstep_predictions, open(str(save_dir.joinpath('Nstep_predictions.pkl')),'wb'))
pickle.dump(scores, open(str(save_dir.joinpath('score.pkl')),'wb'))
pickle.dump(predicted_scores, open(str(save_dir.joinpath('predicted_scores.pkl')),'wb'))
pickle.dump(precisions, open(str(save_dir.joinpath('precision.pkl')),'wb'))
pickle.dump(recalls, open(str(save_dir.joinpath('recall.pkl')),'wb'))
pickle.dump(f_betas, open(str(save_dir.joinpath('f_beta.pkl')),'wb'))
print('-' * 89)