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')
model = model.RNNPredictor(rnn_type = args.model, enc_inp_size=2, rnn_inp_size = args.emsize, rnn_hid_size = args.nhid,
dec_out_size=2,
nlayers = args.nlayers,)
model.load_state_dict(checkpoint['state_dict'])
del checkpoint
if args.cuda:
model.cuda()
# At any point you can hit Ctrl + C t
endPoint=3500
try:
mean, cov = fit_norm_distribution_param(args, model, train_dataset, endPoint,channel_idx=1)
train_scores, _, _, hiddens,_ = anomalyScore(args, model, train_dataset, mean, cov, 3000)
score_predictor = SVR(C=1.0,epsilon=0.2)
score_predictor.fit(torch.cat(hiddens,dim=0).numpy(),train_scores)
scores, sorted_predictions,sorted_errors, _, predicted_scores = anomalyScore(args, model, test_dataset, mean, cov, endPoint,score_predictor=score_predictor,channel_idx=1)
sorted_predictions = torch.cat(sorted_predictions, dim=0)
sorted_errors = torch.cat(sorted_errors,dim=0)
scores = np.array(scores)
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
target= preprocess_data.reconstruct(test_dataset.cpu()[:, 0,1].numpy(),
TimeseriesData.trainData['seqData2_mean'],
def main():
"""Run inference."""
from model import model
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')
parser.add_argument('--device',
type=str,
default='cuda',
help='cuda or cpu')
args_ = parser.parse_args()
print('-' * 89)
print("=> loading checkpoint ")
if args_.device == 'cpu':
checkpoint = torch.load(str(
Path('save', args_.data, 'checkpoint',
args_.filename).with_suffix('.pth')),
map_location=torch.device('cpu'))
else:
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
args.device = args_.device
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'])
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(
target), 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.legend(loc='upper right')
ax2.set_ylabel('anomaly score', fontsize=15)
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.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)
''' 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