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README.rst
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@ -5,6 +5,10 @@ Python Deep Outlier/Anomaly Detection (DeepOD)
:target: https://github.com/xuhongzuo/DeepOD/actions/workflows/testing.yml
:alt: testing2
.. image:: https://readthedocs.org/projects/deepod/badge/?version=latest
:target: https://deepod.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://coveralls.io/repos/github/xuhongzuo/DeepOD/badge.svg?branch=main
:target: https://coveralls.io/github/xuhongzuo/DeepOD?branch=main
:alt: coveralls
@ -18,7 +22,7 @@ Python Deep Outlier/Anomaly Detection (DeepOD)
and `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_. ``DeepOD`` supports tabular anomaly detection and time-series anomaly detection.
DeepOD includes **26** deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm).
DeepOD includes **27** deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm).
More baseline algorithms will be included later.
@ -169,14 +173,14 @@ Implemented Models
RCA, IJCAI, 2021, unsupervised, RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection [#Liu2021RCA]_
GOAD, ICLR, 2020, unsupervised, Classification-Based Anomaly Detection for General Data [#Bergman2020GOAD]_
NeuTraL, ICML, 2021, unsupervised, Neural Transformation Learning for Deep Anomaly Detection Beyond Images [#Qiu2021Neutral]_
ICL, ICLR, 2022, unsupervised, Anomaly Detection for Tabular Data with Internal Contrastive Learning
DIF, TKDE, 2023, unsupervised, Deep Isolation Forest for Anomaly Detection
SLAD, ICML, 2023, unsupervised, Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
DevNet, KDD, 2019, weakly-supervised, Deep Anomaly Detection with Deviation Networks
PReNet, KDD, 2023, weakly-supervised, Deep Weakly-supervised Anomaly Detection
Deep SAD, ICLR, 2020, weakly-supervised, Deep Semi-Supervised Anomaly Detection
FeaWAD, TNNLS, 2021, weakly-supervised, Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
RoSAS, IP&M, 2023, weakly-supervised, RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision
ICL, ICLR, 2022, unsupervised, Anomaly Detection for Tabular Data with Internal Contrastive Learning [#Shenkar2022ICL]_
DIF, TKDE, 2023, unsupervised, Deep Isolation Forest for Anomaly Detection [#Xu2023DIF]_
SLAD, ICML, 2023, unsupervised, Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning [#Xu2023SLAD]_
DevNet, KDD, 2019, weakly-supervised, Deep Anomaly Detection with Deviation Networks [#Pang2019DevNet]_
PReNet, KDD, 2023, weakly-supervised, Deep Weakly-supervised Anomaly Detection [#Pang2023PreNet]_
Deep SAD, ICLR, 2020, weakly-supervised, Deep Semi-Supervised Anomaly Detection [#Ruff2020DSAD]_
FeaWAD, TNNLS, 2021, weakly-supervised, Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection [#Zhou2021FeaWAD]_
RoSAS, IP&M, 2023, weakly-supervised, RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision [#Xu2023RoSAS]_
**Time-series Anomaly Detection models:**
@ -187,15 +191,16 @@ Implemented Models
DCdetector, KDD, 2023, unsupervised, DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection [#Yang2023dcdetector]_
TimesNet, ICLR, 2023, unsupervised, TIMESNET: Temporal 2D-Variation Modeling for General Time Series Analysis [#Wu2023timesnet]_
AnomalyTransformer, ICLR, 2022, unsupervised, Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [#Xu2022transformer]_
TranAD, VLDB, 2022, unsupervised, TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
COUTA, arXiv, 2022, unsupervised, Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
NCAD, IJCAI, 2022, unsupervised, Neural Contextual Anomaly Detection for Time Series [#Carmona2022NCAD]_
TranAD, VLDB, 2022, unsupervised, TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data [#Tuli2022TranAD]_
COUTA, arXiv, 2022, unsupervised, Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection [#Xu2022COUTA]_
USAD, KDD, 2020, unsupervised, USAD: UnSupervised Anomaly Detection on Multivariate Time Series
DIF, TKDE, 2023, unsupervised, Deep Isolation Forest for Anomaly Detection
TcnED, TNNLS, 2021, unsupervised, An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
Deep SVDD (TS), ICML, 2018, unsupervised, Deep One-Class Classification
DevNet (TS), KDD, 2019, weakly-supervised, Deep Anomaly Detection with Deviation Networks
PReNet (TS), KDD, 2023, weakly-supervised, Deep Weakly-supervised Anomaly Detection
Deep SAD (TS), ICLR, 2020, weakly-supervised, Deep Semi-Supervised Anomaly Detection
DIF, TKDE, 2023, unsupervised, Deep Isolation Forest for Anomaly Detection [#Xu2023DIF]_
TcnED, TNNLS, 2021, unsupervised, An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series [#Garg2021Evaluation]_
Deep SVDD (TS), ICML, 2018, unsupervised, Deep One-Class Classification [#Ruff2018Deep]_
DevNet (TS), KDD, 2019, weakly-supervised, Deep Anomaly Detection with Deviation Networks [#Pang2019DevNet]_
PReNet (TS), KDD, 2023, weakly-supervised, Deep Weakly-supervised Anomaly Detection [#Pang2023PreNet]_
Deep SAD (TS), ICLR, 2020, weakly-supervised, Deep Semi-Supervised Anomaly Detection [#Ruff2020DSAD]_
NOTE:
@ -252,8 +257,32 @@ Reference
.. [#Qiu2021Neutral] Qiu, Chen, et al. "Neural Transformation Learning for Deep Anomaly Detection Beyond Images". ICML. 2021.
.. [#Xu2022transformer] Xu Jiehui, et al. "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy". ICLR, 2022.
.. [#Shenkar2022ICL] Shenkar, Tom, et al. "Anomaly Detection for Tabular Data with Internal Contrastive Learning". ICLR. 2022.
.. [#Wu2023timesnet] Wu Haixu, et al. "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis". ICLR. 2023.
.. [#Pang2019DevNet] Pang, Guansong, et al. "Deep Anomaly Detection with Deviation Networks". KDD. 2019.
.. [#Yang2023dcdetector] Yang Yiyuan et al. "DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection". KDD. 2023
.. [#Pang2023PreNet] Pang, Guansong, et al. "Deep Weakly-supervised Anomaly Detection". KDD. 2023.
.. [#Ruff2020DSAD] Ruff, Lukas, et al. "Deep Semi-Supervised Anomaly Detection". ICLR. 2020.
.. [#Zhou2021FeaWAD] Zhou, Yingjie, et al. "Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection". TNNLS. 2021.
.. [#Xu2022transformer] Xu, Jiehui, et al. "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy". ICLR, 2022.
.. [#Wu2023timesnet] Wu, Haixu, et al. "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis". ICLR. 2023.
.. [#Yang2023dcdetector] Yang, Yiyuan, et al. "DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection". KDD. 2023
.. [#Tuli2022TranAD] Tuli, Shreshth, et al. "TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data". VLDB. 2022.
.. [#Carmona2022NCAD] Carmona, Chris U., et al. "Neural Contextual Anomaly Detection for Time Series". IJCAI. 2022.
.. [#Garg2021Evaluation] Garg, Astha, et al. "An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series". TNNLS. 2021.
.. [#Xu2022COUTA] Xu, Hongzuo et al. "Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection". arXiv:2207.12201. 2022.
.. [#Xu2023DIF] Xu, Hongzuo et al. "Deep Isolation Forest for Anomaly Detection". TKDE. 2023.
.. [#Xu2023SLAD] Xu, Hongzuo et al. "Fascinating supervisory signals and where to find them: deep anomaly detection with scale learning". ICML. 2023.
.. [#Xu2023RoSAS] Xu, Hongzuo et al. "RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision". IP&M. 2023.

1
deepod/core/base_model.py
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@ -21,6 +21,7 @@ from functools import partial
from deepod.utils.utility import get_sub_seqs, get_sub_seqs_label
import pickle
class BaseDeepAD(metaclass=ABCMeta):
"""
Abstract class for deep outlier detection models

12
deepod/core/networks/__init__.py
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@ -0,0 +1,12 @@
from .base_networks import MLPnet
from .base_networks import MlpAE
from .base_networks import GRUNet
from .base_networks import LSTMNet
from .base_networks import ConvSeqEncoder
from .base_networks import ConvNet
from .ts_network_transformer import TSTransformerEncoder
from .ts_network_tcn import TCNnet
from .ts_network_tcn import TcnAE
__all__ = ['MLPnet', 'MlpAE', 'GRUNet', 'LSTMNet', 'ConvSeqEncoder',
'ConvNet', 'TSTransformerEncoder', 'TCNnet', 'TcnAE']

30
deepod/core/networks/base_networks.py
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@ -6,7 +6,7 @@ from deepod.core.networks.ts_network_dilated_conv import DilatedConvEncoder
from deepod.core.networks.ts_network_tcn import TCNnet, TcnAE
# from deepod.core.base_transformer_network_dev import TSTransformerEncoder
from deepod.core.networks.network_utility import _instantiate_class, _handle_n_hidden
import torch.nn.modules.activation
sequential_net_name = ['TCN', 'GRU', 'LSTM', 'Transformer', 'ConvSeq', 'DilatedConv']
@ -32,6 +32,26 @@ def get_network(network_name):
class ConvNet(torch.nn.Module):
"""Convolutional Network
Args:
n_features (int):
number of input data features
kernel_size (int):
kernel size (Default=1)
n_hidden (int):
number of hidden units in hidden layers (Default=8)
n_layers (int):
number of layers (Default=5)
activation (str):
name of activation layer,
activation should be implemented in torch.nn.module.activation
(Default='ReLU')
bias (bool):
use bias or not
(Default=False)
"""
def __init__(self, n_features, kernel_size=1, n_hidden=8, n_layers=5,
activation='ReLU', bias=False):
super(ConvNet, self).__init__()
@ -62,6 +82,7 @@ class ConvNet(torch.nn.Module):
class MlpAE(torch.nn.Module):
"""MLP-based AutoEncoder"""
def __init__(self, n_features, n_hidden='500,100', n_emb=20, activation='ReLU',
bias=False, batch_norm=False,
skip_connection=None, dropout=None
@ -105,6 +126,7 @@ class MlpAE(torch.nn.Module):
class MLPnet(torch.nn.Module):
"""MLP-based Representation Network"""
def __init__(self, n_features, n_hidden='500,100', n_output=20, mid_channels=None,
activation='ReLU', bias=False, batch_norm=False,
skip_connection=None, dropout=None):
@ -140,7 +162,6 @@ class MLPnet(torch.nn.Module):
]
self.network = torch.nn.Sequential(*self.layers)
def forward(self, x):
x = self.network(x)
return x
@ -158,6 +179,7 @@ class MLPnet(torch.nn.Module):
class LinearBlock(torch.nn.Module):
"""Linear Block"""
def __init__(self, in_channels, out_channels, mid_channels=None,
activation='Tanh', bias=False, batch_norm=False,
skip_connection=None, dropout=None):
@ -214,6 +236,7 @@ class LinearBlock(torch.nn.Module):
class GRUNet(torch.nn.Module):
"""GRU Network"""
def __init__(self, n_features, n_hidden='20', n_output=20,
bias=False, dropout=None, activation='ReLU'):
super(GRUNet, self).__init__()
@ -235,8 +258,8 @@ class GRUNet(torch.nn.Module):
return out
class LSTMNet(torch.nn.Module):
"""LSTM Network"""
def __init__(self, n_features, n_hidden='20', n_output=20,
bias=False, dropout=None, activation='ReLU'):
super(LSTMNet, self).__init__()
@ -309,6 +332,7 @@ class ConvSeqEncoder(torch.nn.Module):
class ConvResBlock(torch.nn.Module):
"""Convolutional Residual Block"""
def __init__(self, in_dim, out_dim, conv_param=None, down_sample=None,
batch_norm=False, bias=False, activation='ReLU'):
super(ConvResBlock, self).__init__()

5
deepod/core/networks/ts_network_tcn.py
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@ -1,9 +1,12 @@
# TCN is partially adapted from https://github.com/locuslab/TCN
import torch
from torch.nn.utils import weight_norm
from deepod.core.networks.network_utility import _instantiate_class, _handle_n_hidden
class TcnAE(torch.nn.Module):
"""Temporal Convolutional Network-based AutoEncoder"""
def __init__(self, n_features, n_hidden='500,100', n_emb=20, activation='ReLU', bias=False,
kernel_size=2, dropout=0.2):
super(TcnAE, self).__init__()
@ -60,7 +63,7 @@ class TcnAE(torch.nn.Module):
class TCNnet(torch.nn.Module):
"""TCN is adapted from https://github.com/locuslab/TCN"""
"""Temporal Convolutional Network (TCN) for encoding/representing input time series sequences"""
def __init__(self, n_features, n_hidden='8', n_output=20,
kernel_size=2, bias=False,
dropout=0.2, activation='ReLU'):

7
deepod/core/networks/ts_network_transformer.py
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@ -271,16 +271,13 @@ class TransformerBatchNormEncoderLayer(torch.nn.modules.Module):
return src
class TSTransformerEncoder(torch.nn.Module):
"""
Simplest classifier/regressor. Can be either regressor or classifier because the output does not include
softmax. Concatenates final layer embeddings and uses 0s to ignore padding embeddings in final output layer.
Transformer for encoding/representing input time series sequences
"""
def __init__(self, n_features, n_output=20, seq_len=100, d_model=128,
n_heads=8, n_hidden='128', dropout=0.1,
n_heads=8, n_hidden='512', dropout=0.1,
token_encoding='convolutional', pos_encoding='fixed', activation='GELU', bias=False,
attn='self_attn', norm='LayerNorm', freeze=False):
super(TSTransformerEncoder, self).__init__()

8
deepod/metrics/_tsad_adjustment.py
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@ -13,7 +13,13 @@ def point_adjustment(y_true, y_score):
data label, 0 indicates normal timestamp, and 1 is anomaly
y_score: np.array, required
anomaly score, higher score indicates higher likelihoods to be anomaly
predicted anomaly scores, higher score indicates higher likelihoods to be anomaly
Returns
-------
score: np.array
adjusted anomaly scores
"""
score = y_score.copy()
assert len(score) == len(y_true)

5
deepod/models/__init__.py
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@ -23,16 +23,17 @@ from deepod.models.time_series.dsvdd import DeepSVDDTS
from deepod.models.time_series.dcdetector import DCdetector
from deepod.models.time_series.timesnet import TimesNet
from deepod.models.time_series.anomalytransformer import AnomalyTransformer
from deepod.models.time_series.ncad import NCAD
from deepod.models.time_series.tranad import TranAD
from deepod.models.time_series.couta import COUTA
from deepod.models.time_series.usad import USAD
from deepod.models.time_series.tcned import TcnED
__all__ = [
'RCA', 'DeepSVDD', 'GOAD', 'NeuTraL', 'RDP', 'ICL', 'SLAD', 'DeepIsolationForest',
'DeepSAD', 'DevNet', 'PReNet', 'FeaWAD', 'REPEN', 'RoSAS',
'DCdetector', 'TimesNet', 'AnomalyTransformer', 'TranAD', 'COUTA', 'USAD', 'TcnED',
'DCdetector', 'TimesNet', 'AnomalyTransformer', 'NCAD',
'TranAD', 'COUTA', 'USAD', 'TcnED',
'DeepIsolationForestTS', 'DeepSVDDTS',
'PReNetTS', 'DeepSADTS', 'DevNetTS'
]

97
deepod/models/tabular/devnet.py
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@ -15,54 +15,47 @@ import numpy as np
class DevNet(BaseDeepAD):
"""
Parameters
----------
epochs: int, optional (default=100)
Number of training epochs
Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19)
:cite:`pang2019deep`
batch_size: int, optional (default=64)
Number of samples in a mini-batch
lr: float, optional (default=1e-3)
Learning rate
rep_dim: int, optional (default=128)
it is for consistency, unused in this model
hidden_dims: list, str or int, optional (default='100,50')
Number of neural units in hidden layers
- If list, each item is a layer
- If str, neural units of hidden layers are split by comma
- If int, number of neural units of single hidden layer
act: str, optional (default='ReLU')
activation layer name
Args:
epochs (int, optional):
number of training epochs (default: 100).
batch_size (int, optional):
number of samples in a mini-batch (default: 64)
lr (float, optional):
learning rate (default: 1e-3)
rep_dim (int, optional):
it is for consistency, unused in this model.
hidden_dims (list, str or int, optional):
number of neural units in hidden layers,
If list, each item is a layer;
If str, neural units of hidden layers are split by comma;
If int, number of neural units of single hidden layer
(default: '100,50')
act (str, optional):
activation layer name,
choice = ['ReLU', 'LeakyReLU', 'Sigmoid', 'Tanh']
bias: bool, optional (default=False)
Additive bias in linear layer
margin: float, optional (default=5.)
margin value used in the deviation loss function
l: int, optional (default=5000.)
the size of samples of the Gaussian distribution used in the deviation loss function
epoch_steps: int, optional (default=-1)
Maximum steps in an epoch
- If -1, all the batches will be processed
prt_steps: int, optional (default=10)
Number of epoch intervals per printing
device: str, optional (default='cuda')
torch device,
verbose: int, optional (default=1)
Verbosity mode
random_state： int, optional (default=42)
the seed used by the random
(default='ReLU')
bias (bool, optional):
Additive bias in linear layer (default=False)
margin (float, optional):
margin value used in the deviation loss function (default=5.)
l (int, optional):
the size of samples of the Gaussian distribution
used in the deviation loss function (default=5000.)
epoch_steps (int, optional):
Maximum steps in an epoch.
If -1, all the batches will be processed
(default=-1)
prt_steps (int, optional):
Number of epoch intervals per printing (default=10)
device (str, optional):
torch device (default='cuda').
verbose (int, optional):
Verbosity mode (default=1)
random_state (int, optional):
the seed used by the random (default=42)
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
network='MLP',
@ -87,6 +80,18 @@ class DevNet(BaseDeepAD):
return
def training_prepare(self, X, y):
"""
Args:
X (np.array): input data array
y (np.array): input data label
Returns:
train_loader (torch.DataLoader): data loader of training data
net (torch.nn.Module): neural network
criterion (torch.nn.Module): loss function
"""
# loader: balanced loader, a mini-batch contains a half of normal data and a half of anomalies
n_anom = np.where(y == 1)[0].shape[0]
n_norm = self.n_samples - n_anom

14
deepod/models/tabular/dif.py
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@ -17,9 +17,19 @@ import numpy as np
class DeepIsolationForest(BaseDeepAD):
"""
Deep Isolation Forest for Anomaly Detection
Args:
epochs (int):
number of training epochs (Default=100).
batch_size (int):
number of samples in a mini-batch (Default=64)
lr (float):
it is for consistency, unused in this model
"""
def __init__(self,
epochs=100, batch_size=1000, lr=1e-3,
seq_len=100, stride=1,
rep_dim=128, hidden_dims='100,50', act='ReLU', bias=False,
n_ensemble=50, n_estimators=6,
max_samples=256, n_jobs=1,
@ -28,7 +38,7 @@ class DeepIsolationForest(BaseDeepAD):
super(DeepIsolationForest, self).__init__(
model_name='DIF', data_type='tabular',
epochs=epochs, batch_size=batch_size, lr=lr,
network='MLP', seq_len=seq_len, stride=stride,
network='MLP',
epoch_steps=epoch_steps, prt_steps=prt_steps, device=device,
verbose=verbose, random_state=random_state
)

3
deepod/models/tabular/dsad.py
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@ -15,8 +15,7 @@ from collections import Counter
class DeepSAD(BaseDeepAD):
""" Deep Semi-supervised Anomaly Detection (Deep SAD)
See :cite:`ruff2020dsad` for details
""" Deep Semi-supervised Anomaly Detection (ICLR'20)
Parameters
----------

35
deepod/models/tabular/dsvdd.py
View File

@ -17,8 +17,9 @@ from functools import partial
class DeepSVDD(BaseDeepAD):
""" Deep One-class Classification (Deep SVDD) for anomaly detection
See :cite:`ruff2018deepsvdd` for details
"""
Deep One-class Classification for Anomaly Detection (ICML'18)
:cite:`ruff2018deepsvdd`
Parameters
----------
@ -63,7 +64,27 @@ class DeepSVDD(BaseDeepAD):
random_state： int, optional (default=42)
the seed used by the random
Attributes
----------
decision_scores_ : numpy array of shape (n_samples,)
The outlier scores of the training data.
The higher, the more abnormal. Outliers tend to have higher
scores. This value is available once the detector is
fitted.
threshold_ : float
The threshold is based on ``contamination``. It is the
``n_samples * contamination`` most abnormal samples in
``decision_scores_``. The threshold is calculated for generating
binary outlier labels.
labels_ : int, either 0 or 1
The binary labels of the training data. 0 stands for inliers
and 1 for outliers/anomalies. It is generated by applying
``threshold_`` on ``decision_scores_``.
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
rep_dim=128, hidden_dims='100,50', act='ReLU', bias=False,
epoch_steps=-1, prt_steps=10, device='cuda',
@ -96,7 +117,7 @@ class DeepSVDD(BaseDeepAD):
net = MLPnet(**network_params).to(self.device)
self.c = self._set_c(net, train_loader)
criterion = DSVDDLoss(c=self.c)
criterion = _DSVDDLoss(c=self.c)
if self.verbose >= 2:
print(net)
@ -107,7 +128,7 @@ class DeepSVDD(BaseDeepAD):
test_loader = DataLoader(X, batch_size=self.batch_size,
drop_last=False, shuffle=False)
assert self.c is not None
self.criterion = DSVDDLoss(c=self.c, reduction='none')
self.criterion = _DSVDDLoss(c=self.c, reduction='none')
return test_loader
def training_forward(self, batch_x, net, criterion):
@ -132,7 +153,7 @@ class DeepSVDD(BaseDeepAD):
self.net = self.set_tuned_net(config)
self.c = self._set_c(self.net, train_loader)
criterion = DSVDDLoss(c=self.c, reduction='mean')
criterion = _DSVDDLoss(c=self.c, reduction='mean')
optimizer = torch.optim.Adam(self.net.parameters(), lr=config['lr'], eps=1e-6)
@ -230,7 +251,7 @@ class DeepSVDD(BaseDeepAD):
return c
class DSVDDLoss(torch.nn.Module):
class _DSVDDLoss(torch.nn.Module):
"""
Parameters
@ -247,7 +268,7 @@ class DSVDDLoss(torch.nn.Module):
"""
def __init__(self, c, reduction='mean'):
super(DSVDDLoss, self).__init__()
super(_DSVDDLoss, self).__init__()
self.c = c
self.reduction = reduction

3
deepod/models/tabular/feawad.py
View File

@ -15,6 +15,9 @@ import numpy as np
class FeaWAD(BaseDeepAD):
"""
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
(TNNLS'21)
Parameters
----------
epochs: int, optional (default=100)

3
deepod/models/tabular/goad.py
View File

@ -15,6 +15,9 @@ import numpy as np
class GOAD(BaseDeepAD):
"""
Classification-Based Anomaly Detection for General Data (ICLR'20)
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
n_trans=256, trans_dim=32,
alpha=0.1, margin=1., eps=0,

6
deepod/models/tabular/icl.py
View File

@ -15,8 +15,10 @@ import numpy as np
class ICL(BaseDeepAD):
""" Anomaly Detection for Tabular Data with Internal Contrastive Learning (ICL for short)
See :cite:`shenkar2022internal` for details
"""
Anomaly Detection for Tabular Data with Internal Contrastive Learning
(ICLR'22)
:cite:`shenkar2022internal`
Parameters
----------

4
deepod/models/tabular/neutral.py
View File

@ -14,6 +14,10 @@ import numpy as np
class NeuTraL(BaseDeepAD):
"""
Neural Transformation Learning-based Anomaly Detection (ICML'21)
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
n_trans=11, trans_type='residual', temp=0.1,
rep_dim=128, hidden_dims='100,50', trans_hidden_dims=50,

3
deepod/models/tabular/prenet.py
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@ -11,6 +11,9 @@ import numpy as np
class PReNet(BaseDeepAD):
"""
Deep Weakly-supervised Anomaly Detection (KDD‘23)
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
rep_dim=128, hidden_dims='100,50', act='LeakyReLU', bias=False,
epoch_steps=-1, prt_steps=10, device='cuda',

3
deepod/models/tabular/rca.py
View File

@ -15,6 +15,9 @@ import numpy as np
class RCA(BaseDeepAD):
"""
A Deep Collaborative Autoencoder Approach for Anomaly Detection (IJCAI'21)
Args:
epochs: int, optional (default=100)
Number of training epochs

3
deepod/models/tabular/rdp.py
View File

@ -15,6 +15,9 @@ import copy
class RDP(BaseDeepAD):
"""
Unsupervised Representation Learning by Predicting Random Distances
(IJCAI'20)
Parameters
----------
epochs: int, optional (default=100)

4
deepod/models/tabular/repen.py
View File

@ -19,9 +19,9 @@ import numpy as np
class REPEN(BaseDeepAD):
"""
Pang et al.: Learning Representations of Ultrahigh-dimensional Data for Random
Learning Representations of Ultrahigh-dimensional Data for Random
Distance-based Outlier Detection (KDD'18)
See :cite:`pang2018repen` for details
:cite:`pang2018repen`
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,

4
deepod/models/tabular/rosas.py
View File

@ -7,6 +7,10 @@ from deepod.core.networks.base_networks import MLPnet
class RoSAS(BaseDeepAD):
"""
RoSAS: Deep semi-supervised anomaly detection with contamination-resilient
continuous supervision (IP&M'23)
"""
def __init__(self, epochs=100, batch_size=128, lr=0.005,
rep_dim=32, hidden_dims='32', act='LeakyReLU', bias=False,
margin=5., alpha=0.5, T=2, k=2,

4
deepod/models/tabular/slad.py
View File

@ -12,6 +12,10 @@ import torch
class SLAD(BaseDeepAD):
"""
Fascinating Supervisory Signals and Where to Find Them:
Deep Anomaly Detection with Scale Learning (ICML'23)
"""
def __init__(self, epochs=100, batch_size=128, lr=1e-3,
hidden_dims=100, act='LeakyReLU',
distribution_size=10, # the member size in a group, c in the paper

5
deepod/models/time_series/anomalytransformer.py
View File

@ -15,6 +15,11 @@ def my_kl_loss(p, q):
class AnomalyTransformer(BaseDeepAD):
"""
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
(ICLR'22)
"""
def __init__(self, seq_len=100, stride=1, lr=0.0001, epochs=10, batch_size=32,
epoch_steps=20, prt_steps=1, device='cuda',
k=3, verbose=2, random_state=42):

41
deepod/models/time_series/couta.py
View File

@ -1,6 +1,6 @@
"""
Calibrated One-class classifier for Unsupervised Time series Anomaly detection (COUTA)
@author: Hongzuo Xu (hongzuo.xu@gmail.com)
@author: Hongzuo Xu <hongzuoxu@126.com, xuhongzuo13@nudt.edu.cn>
"""
import numpy as np
@ -20,7 +20,8 @@ from deepod.metrics import ts_metrics, point_adjustment
class COUTA(BaseDeepAD):
"""
COUTA class for Calibrated One-class classifier for Unsupervised Time series Anomaly detection
Calibrated One-class classifier for Unsupervised Time series
Anomaly detection (arXiv'22)
Parameters
----------
@ -144,7 +145,7 @@ class COUTA(BaseDeepAD):
train_seqs = sequences
val_seqs = None
self.net = COUTANet(
self.net = _COUTANet(
input_dim=self.n_features,
hidden_dims=self.hidden_dims,
n_output=self.rep_dim,
@ -187,7 +188,7 @@ class COUTA(BaseDeepAD):
The anomaly score of the input samples.
"""
test_sub_seqs = get_sub_seqs(X, seq_len=self.seq_len, stride=1)
test_dataset = SubseqData(test_sub_seqs)
test_dataset = _SubseqData(test_sub_seqs)
dataloader = DataLoader(dataset=test_dataset, batch_size=self.batch_size, drop_last=False, shuffle=False)
representation_lst = []
@ -211,12 +212,12 @@ class COUTA(BaseDeepAD):
return dis_pad
def train(self, net, train_seqs, val_seqs=None):
val_loader = DataLoader(dataset=SubseqData(val_seqs),
val_loader = DataLoader(dataset=_SubseqData(val_seqs),
batch_size=self.batch_size,
drop_last=False, shuffle=False) if val_seqs is not None else None
optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
criterion_oc_umc = DSVDDUncLoss(c=self.c, reduction='mean')
criterion_oc_umc = _DSVDDUncLoss(c=self.c, reduction='mean')
criterion_mse = torch.nn.MSELoss(reduction='mean')
y0 = -1 * torch.ones(self.batch_size).float().to(self.device)
@ -229,7 +230,7 @@ class COUTA(BaseDeepAD):
copy_times += 1
train_seqs = np.concatenate([train_seqs for _ in range(copy_times)])
train_loader = DataLoader(dataset=SubseqData(train_seqs),
train_loader = DataLoader(dataset=_SubseqData(train_seqs),
batch_size=self.batch_size,
drop_last=True, pin_memory=True, shuffle=True)
@ -246,7 +247,7 @@ class COUTA(BaseDeepAD):
loss_oc = criterion_oc_umc(rep_x0, rep_x0_dup)
neg_cand_idx = RandomState(epoch_seed[ii]).randint(0, self.batch_size, self.neg_batch_size)
x1, y1 = create_batch_neg(batch_seqs=x0[neg_cand_idx],
x1, y1 = self.create_batch_neg(batch_seqs=x0[neg_cand_idx],
max_cut_ratio=self.max_cut_ratio,
seed=epoch_seed[ii],
return_mul_label=False,
@ -300,14 +301,14 @@ class COUTA(BaseDeepAD):
train_data = self.train_data[:int(0.8 * len(self.train_data))]
val_data = self.train_data[int(0.8 * len(self.train_data)):]
train_loader = DataLoader(dataset=SubseqData(train_data), batch_size=self.batch_size,
train_loader = DataLoader(dataset=_SubseqData(train_data), batch_size=self.batch_size,
drop_last=True, pin_memory=True, shuffle=True)
val_loader = DataLoader(dataset=SubseqData(val_data), batch_size=self.batch_size,
val_loader = DataLoader(dataset=_SubseqData(val_data), batch_size=self.batch_size,
drop_last=True, pin_memory=True, shuffle=True)
self.net = self.set_tuned_net(config)
self.c = self._set_c(self.net, train_data)
criterion_oc_umc = DSVDDUncLoss(c=self.c, reduction='mean')
criterion_oc_umc = _DSVDDUncLoss(c=self.c, reduction='mean')
criterion_mse = torch.nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(self.net.parameters(), lr=config['lr'], eps=1e-6)
@ -331,7 +332,7 @@ class COUTA(BaseDeepAD):
neg_batch_size = int(config['neg_batch_ratio'] * self.batch_size)
neg_candidate_idx = tmp_rng.randint(0, self.batch_size, neg_batch_size)
x1, y1 = create_batch_neg(
x1, y1 = self.create_batch_neg(
batch_seqs=x0[neg_candidate_idx],
max_cut_ratio=self.max_cut_ratio,
seed=epoch_seed[ii],
@ -413,7 +414,7 @@ class COUTA(BaseDeepAD):
return config
def set_tuned_net(self, config):
net = COUTANet(
net = _COUTANet(
input_dim=self.n_features,
hidden_dims=config['hidden_dims'],
n_output=config['rep_dim'],
@ -436,7 +437,7 @@ class COUTA(BaseDeepAD):
def _set_c(self, net, seqs, eps=0.1):
"""Initializing the center for the hypersphere"""
dataloader = DataLoader(dataset=SubseqData(seqs), batch_size=self.batch_size,
dataloader = DataLoader(dataset=_SubseqData(seqs), batch_size=self.batch_size,
drop_last=False, pin_memory=True, shuffle=True)
z_ = []
net.eval()
@ -468,8 +469,8 @@ class COUTA(BaseDeepAD):
"""define test_loader"""
return
def create_batch_neg(batch_seqs, max_cut_ratio=0.5, seed=0, return_mul_label=False, ss_type='FULL'):
@staticmethod
def create_batch_neg(batch_seqs, max_cut_ratio=0.5, seed=0, return_mul_label=False, ss_type='FULL'):
rng = np.random.RandomState(seed=seed)
batch_size, l, dim = batch_seqs.shape
@ -536,11 +537,11 @@ def create_batch_neg(batch_seqs, max_cut_ratio=0.5, seed=0, return_mul_label=Fal
return batch_neg, neg_labels
class COUTANet(torch.nn.Module):
class _COUTANet(torch.nn.Module):
def __init__(self, input_dim, hidden_dims=32, rep_hidden=32, pretext_hidden=16,
n_output=10, kernel_size=2, dropout=0.2, out_dim=2,
bias=True, dup=True, pretext=True):
super(COUTANet, self).__init__()
super(_COUTANet, self).__init__()
self.layers = []
@ -598,7 +599,7 @@ class COUTANet(torch.nn.Module):
return rep
class SubseqData(Dataset):
class _SubseqData(Dataset):
def __init__(self, x, y=None, w1=None, w2=None):
self.sub_seqs = x
self.label = y
@ -624,7 +625,7 @@ class SubseqData(Dataset):
return self.sub_seqs[idx]
class DSVDDUncLoss(torch.nn.Module):
class _DSVDDUncLoss(torch.nn.Module):
def __init__(self, c, reduction='mean'):
super(DSVDDUncLoss, self).__init__()
self.c = c

26
deepod/models/time_series/dcdetector.py
View File

@ -17,6 +17,10 @@ def my_kl_loss(p, q):
class DCdetector(BaseDeepAD):
"""
DCdetector: Dual Attention Contrastive Representation Learning
for Time Series Anomaly Detection (KDD'23)
"""
def __init__(self, seq_len=100, stride=1, lr=0.0001, epochs=5, batch_size=128,
epoch_steps=20, prt_steps=1, device='cuda',
n_heads=1, d_model=256, e_layers=3, patch_size=None,
@ -156,6 +160,28 @@ class DCdetector(BaseDeepAD):
return test_energy, preds # (n,d)
def predict(self, X, return_confidence=False):
## self.threshold
self.threshold_ = None
# ------------------------------ #
pred_score = self.decision_function(X)
prediction = (pred_score > self.threshold_).astype('int').ravel()
if return_confidence:
confidence = self._predict_confidence(pred_score)
return prediction, confidence
return prediction
def training_forward(self, batch_x, net, criterion):
"""define forward step in training"""
return

3
deepod/models/time_series/devnet.py
View File

@ -16,6 +16,9 @@ import numpy as np
class DevNetTS(BaseDeepAD):
"""
Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19)
:cite:`pang2019deep`
Parameters
----------
epochs: int, optional (default=100)

4
deepod/models/time_series/dif.py
View File

@ -17,6 +17,10 @@ import numpy as np
class DeepIsolationForestTS(BaseDeepAD):
"""
Deep isolation forest for anomaly detection (TKDE'23)
"""
def __init__(self,
epochs=100, batch_size=1000, lr=1e-3,
seq_len=100, stride=1,

4
deepod/models/time_series/dsad.py
View File

@ -16,8 +16,8 @@ from collections import Counter
class DeepSADTS(BaseDeepAD):
""" Deep Semi-supervised Anomaly Detection (Deep SAD)
See :cite:`ruff2020dsad` for details
""" Deep Semi-supervised Anomaly Detection (ICLR'20)
:cite:`ruff2020dsad`
Parameters
----------

8
deepod/models/time_series/dsvdd.py
View File

@ -11,14 +11,12 @@ import torch
class DeepSVDDTS(BaseDeepAD):
""" Deep One-class Classification (Deep SVDD) for anomaly detection
See :cite:`ruff2018deepsvdd` for details
"""
Deep One-class Classification for Anomaly Detection (ICML'18)
:cite:`ruff2018deepsvdd`
Parameters
----------
data_type: str, optional (default='tabular')
Data type, choice=['tabular', 'ts']
epochs: int, optional (default=100)
Number of training epochs

344
deepod/models/time_series/ncad.py Normal file
View File

@ -0,0 +1,344 @@
# -*- coding: utf-8 -*-
"""
Neural Contextual Anomaly Detection for Time Series (NCAD)
@Author: Hongzuo Xu <hongzuoxu@126.com, xuhongzuo13@nudt.edu.cn>
"""
from deepod.core.base_model import BaseDeepAD
from deepod.core.networks.ts_network_tcn import TCNnet
from torch.utils.data import DataLoader, TensorDataset
import torch
import numpy as np
import torch.nn.functional as F
class NCAD(BaseDeepAD):
"""
Neural Contextual Anomaly Detection for Time Series (IJCAI'22)
Parameters
----------
epochs: int, optional (default=100)
Number of training epochs
batch_size: int, optional (default=64)
Number of samples in a mini-batch
lr: float, optional (default=1e-3)
Learning rate
rep_dim: int, optional (default=128)
Dimensionality of the representation space
hidden_dims: list, str or int, optional (default='100,50')
Number of neural units in hidden layers
- If list, each item is a layer
- If str, neural units of hidden layers are split by comma
- If int, number of neural units of single hidden layer
act: str, optional (default='ReLU')
activation layer name
choice = ['ReLU', 'LeakyReLU', 'Sigmoid', 'Tanh']
bias: bool, optional (default=False)
Additive bias in linear layer
epoch_steps: int, optional (default=-1)
Maximum steps in an epoch
- If -1, all the batches will be processed
prt_steps: int, optional (default=10)
Number of epoch intervals per printing
device: str, optional (default='cuda')
torch device,
verbose: int, optional (default=1)
Verbosity mode
random_state： int, optional (default=42)
the seed used by the random
"""
def __init__(self, epochs=100, batch_size=64, lr=3e-4,
seq_len=100, stride=1,
suspect_win_len=10, coe_rate=0.5, mixup_rate=2.0,
hidden_dims='32,32,32,32', rep_dim=128,
act='ReLU', bias=False,
kernel_size=5, dropout=0.0,
epoch_steps=-1, prt_steps=10, device='cuda',
verbose=2, random_state=42):
super(NCAD, self).__init__(
model_name='NCAD', data_type='ts', epochs=epochs, batch_size=batch_size, lr=lr,
seq_len=seq_len, stride=stride,
epoch_steps=epoch_steps, prt_steps=prt_steps, device=device,
verbose=verbose, random_state=random_state
)
self.suspect_win_len = suspect_win_len
self.coe_rate = coe_rate
self.mixup_rate = mixup_rate
self.hidden_dims = hidden_dims
self.rep_dim = rep_dim
self.act = act
self.bias = bias
self.dropout = dropout
self.kernel_size = kernel_size
return
def training_prepare(self, X, y):
y_train = np.zeros(len(X))
train_dataset = TensorDataset(torch.from_numpy(X).float(),
torch.from_numpy(y_train).long())
train_loader = DataLoader(train_dataset, batch_size=self.batch_size,
drop_last=True, pin_memory=True, shuffle=True)
net = NCADNet(
n_features=self.n_features,
n_hidden=self.hidden_dims,
n_output=self.rep_dim,
kernel_size=self.kernel_size,
bias=True,
eps=1e-10,
dropout=0.2,
activation=self.act,
).to(self.device)
criterion = torch.nn.BCELoss()
return train_loader, net, criterion
def training_forward(self, batch_x, net, criterion):
x0, y0 = batch_x
if self.coe_rate > 0:
x_oe, y_oe = self.coe_batch(
x=x0.transpose(2, 1),
y=y0,
coe_rate=self.coe_rate,
suspect_window_length=self.suspect_win_len,
random_start_end=True,
)
# Add COE to training batch
x0 = torch.cat((x0, x_oe.transpose(2, 1)), dim=0)
y0 = torch.cat((y0, y_oe), dim=0)
if self.mixup_rate > 0.0:
x_mixup, y_mixup = self.mixup_batch(
x=x0.transpose(2, 1),
y=y0,
mixup_rate=self.mixup_rate,
)
# Add Mixup to training batch
x0 = torch.cat((x0, x_mixup.transpose(2, 1)), dim=0)
y0 = torch.cat((y0, y_mixup), dim=0)
x0 = x0.float().to(self.device)
y0 = y0.float().to(self.device)
x_context = x0[:, :-self.suspect_win_len]
logits_anomaly = net(x0, x_context)
probs_anomaly = torch.sigmoid(logits_anomaly.squeeze())
# Calculate Loss
loss = criterion(probs_anomaly, y0)
return loss
def inference_forward(self, batch_x, net, criterion):
ts = batch_x.float().to(self.device)
# ts = ts.transpose(2, 1)
# stride = self.suspect_win_len
# unfold_layer = torch.nn.Unfold(
# kernel_size=(self.n_features, self.win_len),
# stride=stride
# )
# ts_windows = unfold_layer(ts.unsqueeze(1))
#
# num_windows = int(1 + (self.seq_len - self.win_len) / stride)
# assert ts_windows.shape == (
# batch_x.shape[0],
# self.n_features * self.win_len,
# num_windows,
# )
# ts_windows = ts_windows.transpose(1, 2)
# ts_windows = ts_windows.reshape(
# batch_x.shape[0], num_windows,
# self.n_features, self.win_len
# )
# x0 = ts_windows.flatten(start_dim=0, end_dim=1)
# x0 = x0.transpose(2, 1)
x0 = ts
x_context = x0[:, :-self.suspect_win_len]
logits_anomaly = net(x0, x_context)
logits_anomaly = logits_anomaly.squeeze()
return batch_x, logits_anomaly
def inference_prepare(self, X):
test_loader = DataLoader(X, batch_size=self.batch_size,
drop_last=False, shuffle=False)
self.criterion.reduction = 'none'
return test_loader
@staticmethod
def coe_batch(x: torch.Tensor, y: torch.Tensor, coe_rate: float, suspect_window_length: int,
random_start_end: bool = True):
"""Contextual Outlier Exposure.
Args:
x : Tensor of shape (batch, ts channels, time)
y : Tensor of shape (batch, )
coe_rate : Number of generated anomalies as proportion of the batch size.
random_start_end : If True, a random subset within the suspect segment is permuted between time series;
if False, the whole suspect segment is randomly permuted.
"""
if coe_rate == 0:
raise ValueError(f"coe_rate must be > 0.")
batch_size = x.shape[0]
ts_channels = x.shape[1]
oe_size = int(batch_size * coe_rate)
# Select indices
idx_1 = torch.arange(oe_size)
idx_2 = torch.arange(oe_size)
while torch.any(idx_1 == idx_2):
idx_1 = torch.randint(low=0, high=batch_size, size=(oe_size,)).type_as(x).long()
idx_2 = torch.randint(low=0, high=batch_size, size=(oe_size,)).type_as(x).long()
if ts_channels > 3:
numb_dim_to_swap = np.random.randint(low=3, high=ts_channels, size=(oe_size))
# print(numb_dim_to_swap)
else:
numb_dim_to_swap = np.ones(oe_size) * ts_channels
x_oe = x[idx_1].clone() # .detach()
oe_time_start_end = np.random.randint(
low=x.shape[-1] - suspect_window_length, high=x.shape[-1] + 1, size=(oe_size, 2)
)
oe_time_start_end.sort(axis=1)
# for start, end in oe_time_start_end:
for i in range(len(idx_2)):
# obtain the dimensons to swap
numb_dim_to_swap_here = int(numb_dim_to_swap[i])
dims_to_swap_here = np.random.choice(
range(ts_channels), size=numb_dim_to_swap_here, replace=False
)
# obtain start and end of swap
start, end = oe_time_start_end[i]
# swap
x_oe[i, dims_to_swap_here, start:end] = x[idx_2[i], dims_to_swap_here, start:end]
# Label as positive anomalies
y_oe = torch.ones(oe_size).type_as(y)
return x_oe, y_oe
@staticmethod
def mixup_batch(x: torch.Tensor, y: torch.Tensor, mixup_rate: float):
"""
Args:
x : Tensor of shape (batch, ts channels, time)
y : Tensor of shape (batch, )
mixup_rate : Number of generated anomalies as proportion of the batch size.
"""
if mixup_rate == 0:
raise ValueError(f"mixup_rate must be > 0.")
batch_size = x.shape[0]
mixup_size = int(batch_size * mixup_rate) #
# Select indices
idx_1 = torch.arange(mixup_size)
idx_2 = torch.arange(mixup_size)
while torch.any(idx_1 == idx_2):
idx_1 = torch.randint(low=0, high=batch_size, size=(mixup_size,)).type_as(x).long()
idx_2 = torch.randint(low=0, high=batch_size, size=(mixup_size,)).type_as(x).long()
# sample mixing weights:
beta_param = float(0.05)
beta_distr = torch.distributions.beta.Beta(
torch.tensor([beta_param]), torch.tensor([beta_param])
)
weights = torch.from_numpy(np.random.beta(beta_param, beta_param, (mixup_size,))).type_as(x)
oppose_weights = 1.0 - weights
# Create contamination
x_mix_1 = x[idx_1].clone()
x_mix_2 = x[idx_1].clone()
x_mixup = (
x_mix_1 * weights[:, None, None] + x_mix_2 * oppose_weights[:, None, None]
) # .detach()
# Label as positive anomalies
y_mixup = y[idx_1].clone() * weights + y[idx_2].clone() * oppose_weights
return x_mixup, y_mixup
class NCADNet(torch.nn.Module):
def __init__(self, n_features, n_hidden=32, n_output=128,
kernel_size=2, bias=True,
eps=1e-10, dropout=0.2, activation='ReLU',
):
super(NCADNet, self).__init__()
self.network = TCNnet(
n_features=n_features,
n_hidden=n_hidden,
n_output=n_output,
kernel_size=kernel_size,
bias=bias,
dropout=dropout,
activation=activation
)
self.distance_metric = CosineDistance()
self.eps = eps
def forward(self, x, x_c):
x_whole_embedding = self.network(x)
x_context_embedding = self.network(x_c)
dists = self.distance_metric(x_whole_embedding, x_context_embedding)
# Probability of the two embeddings being equal: exp(-dist)
log_prob_equal = -dists
# Computation of log_prob_different
prob_different = torch.clamp(1 - torch.exp(log_prob_equal), self.eps, 1)
log_prob_different = torch.log(prob_different)
logits_different = log_prob_different - log_prob_equal
return logits_different
class CosineDistance(torch.nn.Module):
r"""Returns the cosine distance between :math:`x_1` and :math:`x_2`, computed along dim."""
def __init__( self, dim=1, keepdim=True):
super().__init__()
self.dim = int(dim)
self.keepdim = bool(keepdim)
self.eps = 1e-10
def forward(self, x1, x2):
# Cosine of angle between x1 and x2
cos_sim = F.cosine_similarity(x1, x2, dim=self.dim, eps=self.eps)
dist = -torch.log((1 + cos_sim) / 2)
if self.keepdim:
dist = dist.unsqueeze(dim=self.dim)
return dist

3
deepod/models/time_series/prenet.py
View File

@ -12,6 +12,9 @@ import numpy as np
class PReNetTS(BaseDeepAD):
"""
Deep Weakly-supervised Anomaly Detection (KDD‘23)
"""
def __init__(self, epochs=100, batch_size=64, lr=1e-3,
network='Transformer', seq_len=30, stride=1,
rep_dim=128, hidden_dims='512', act='GELU', bias=False,

3
deepod/models/time_series/tcned.py
View File

@ -15,6 +15,9 @@ from ray.air import session, Checkpoint
class TcnED(BaseDeepAD):
"""
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series (TNNLS'21)
"""
def __init__(self, seq_len=100, stride=1, epochs=10, batch_size=32, lr=1e-4,
rep_dim=32, hidden_dims=32, kernel_size=3, act='ReLU', bias=True, dropout=0.2,
epoch_steps=-1, prt_steps=1, device='cuda',

4
deepod/models/time_series/timesnet.py
View File

@ -10,6 +10,10 @@ from deepod.core.base_model import BaseDeepAD
class TimesNet(BaseDeepAD):
"""
TIMESNET: Temporal 2D-Variation Modeling for General Time Series Analysis (ICLR'23)
"""
def __init__(self, seq_len=100, stride=1, lr=0.0001, epochs=10, batch_size=32,
epoch_steps=20, prt_steps=1, device='cuda',
pred_len=0, e_layers=2, d_model=64, d_ff=64, dropout=0.1, top_k=5, num_kernels=6,

4
deepod/models/time_series/tranad.py
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@ -10,6 +10,10 @@ from deepod.core.base_model import BaseDeepAD
class TranAD(BaseDeepAD):
"""
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (VLDB'22)
"""
def __init__(self, seq_len=100, stride=1, lr=0.001, epochs=5, batch_size=128,
epoch_steps=20, prt_steps=1, device='cuda',
verbose=2, random_state=42):

3
deepod/models/time_series/usad.py
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@ -9,6 +9,9 @@ from deepod.core.base_model import BaseDeepAD
class USAD(BaseDeepAD):
"""
"""
def __init__(self, seq_len=100, stride=1, hidden_dims=100, rep_dim=128,
epochs=100, batch_size=128, lr=1e-3,
es=1, train_val_pc=0.2,

1
deepod/utils/data.py
View File

@ -72,7 +72,6 @@ def _generate_data(n_inliers, n_outliers, n_features, coef, offset,
return X, y
def generate_data(n_train=1000, n_test=500, n_features=2, contamination=0.1,
train_only=False, offset=10,
random_state=None, n_nan=0, n_inf=0):

3
docs/.debug.yml
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@ -1,3 +0,0 @@
remote_theme: false
theme: jekyll-rtd-theme

14
docs/_config.yml
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@ -1,14 +0,0 @@
title: Your project name
lang: en
description: a catchy description for your project
remote_theme: rundocs/jekyll-rtd-theme
readme_index:
with_frontmatter: true
exclude:
- Makefile
- CNAME
- Gemfile
- Gemfile.lock

22
docs/_templates/class.rst vendored Normal file
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@ -0,0 +1,22 @@
{{ fullname }}
{{ underline }}
.. currentmodule:: {{ module }}
.. autoclass:: {{ objname }}
{% block methods %}
{% if methods %}
.. rubric:: Methods
.. autosummary::
{% for item in methods %}
~{{ name }}.{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
{% block attributes %}
{% if attributes %}
.. rubric:: Attributes
.. autosummary::
{% for item in attributes %}
~{{ name }}.{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}

35
docs/additional.contributing.rst Normal file
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@ -0,0 +1,35 @@
Contributing
=============
Everyone are very welcome to contribute.
We share the same values of the `scikit-learn <https://scikit-learn.org/stable/developers/contributing.html>`_ community
.. note::
We are a community based on openness and friendly, didactic, discussions.
We aspire to treat everybody equally, and value their contributions. We are particularly seeking people
from underrepresented backgrounds in Open Source Software and scikit-learn in particular to participate
and contribute their expertise and experience.
Decisions are made based on technical merit and consensus.
Code is not the only way to help the project. Reviewing pull requests,
answering questions to help others on mailing lists or issues, organizing and teaching tutorials,
working on the website, improving the documentation, are all priceless contributions.
We abide by the principles of openness, respect, and consideration of others of the Python
Software Foundation: https://www.python.org/psf/codeofconduct/
In case you experience issues using this package, do not hesitate to submit a ticket to the GitHub issue tracker.
You are also welcome to post feature requests or pull requests.
For any questions, you may open issue on Github or drop me an email at hongzuoxu(at)126.com.
TODO list
---------
We attach a TODO list below, we are very pleased if you can contribute anything on this list.

33
docs/additional.license.rst Normal file
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@ -0,0 +1,33 @@
License
=======
This project is licensed under the BSD 2-Clause License.
.. code-block::
BSD 2-Clause License
Copyright (c) 2023, Hongzuo Xu All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

9
docs/additional.star_history.rst Normal file
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@ -0,0 +1,9 @@
Star History on Github
======================
.. image:: https://api.star-history.com/svg?repos=xuhongzuo/DeepOD&type=Date
:target: https://star-history.com/#xuhongzuo/DeepOD&Date
:align: center

28
docs/api_cc.rst Normal file
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@ -0,0 +1,28 @@
API CheatSheet
==============
The following APIs are applicable for all detector models for easy use.
* :func:`deepod.core.base_model.BaseDeepAD.fit`: Fit detector. y is ignored in unsupervised methods.
* :func:`deepod.core.base_model.BaseDeepAD.decision_function`: Predict raw anomaly score of X using the fitted detector.
* :func:`deepod.core.base_model.BaseDeepAD.predict`: Predict if a particular sample is an outlier or not using the fitted detector.
Key Attributes of a fitted model:
* :attr:`deepod.core.base_model.BaseDeepAD.decision_scores_`: The outlier scores of the training data. The higher, the more abnormal.
Outliers tend to have higher scores.
* :attr:`deepod.core.base_model.BaseDeepAD.labels_`: The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies.
See base class definition below:
deepod.core.base_model module
-----------------------
.. automodule:: deepod.core.base_model
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

23
docs/api_reference.base_networks.rst Normal file
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@ -0,0 +1,23 @@
Network Architectures
------------------------------------
.. currentmodule:: deepod
.. autosummary::
:nosignatures:
:template: class.rst
:toctree: generated
core.networks.MLPnet
core.networks.MlpAE
core.networks.GRUNet
core.networks.LSTMNet
core.networks.ConvSeqEncoder
core.networks.ConvNet
core.networks.TcnAE
core.networks.TCNnet
core.networks.TSTransformerEncoder

17
docs/api_reference.metrics.rst Normal file
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@ -0,0 +1,17 @@
Evaluation Metrics
===================
.. automodule:: deepod.metrics
:members:
:undoc-members:
:show-inheritance:
:inherited-members:
.. rubric:: References
.. bibliography::
:cited:
:labelprefix: B

17
docs/api_reference.rst Normal file
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@ -0,0 +1,17 @@
API Reference
-------------
This is the API documentation for ``DeepOD``.
.. toctree::
api_reference.tabular
api_reference.time_series
api_reference.base_networks
api_reference.metrics

53
docs/api_reference.tabular.rst Normal file
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@ -0,0 +1,53 @@
Models for Tabular Data
------------------------------------------
.. automodule:: deepod.models.tabular
:no-members:
:no-inherited-members:
.. currentmodule:: deepod
Unsupervised Models
^^^^^^^^^^^^^^^^^^^^^
implemented unsupervised anomaly detection models
.. autosummary::
:nosignatures:
:template: class.rst
:toctree: generated
models.DeepSVDD
models.RCA
models.DevNet
models.DeepIsolationForest
models.REPEN
models.SLAD
models.ICL
models.RDP
models.GOAD
models.NeuTraL
Weakly-supervised Models
^^^^^^^^^^^^^^^^^^^^^^^^^^
implemented weakly-sueprvised anomaly detection models
.. autosummary::
:nosignatures:
:template: class.rst
:toctree: generated
models.DevNet
models.DeepSAD
models.FeaWAD
models.RoSAS
models.PReNet
.. rubric:: References
.. bibliography::
:cited:
:labelprefix: B

40
docs/api_reference.time_series.rst Normal file
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@ -0,0 +1,40 @@
Models for Time Series
========================================
.. automodule:: deepod.models.time_series
:no-members:
:no-inherited-members:
.. currentmodule:: deepod
implemented unsupervised anomaly detection models for time series data.
.. autosummary::
:nosignatures:
:template: class.rst
:toctree: generated
models.TimesNet
models.DCdetector
models.AnomalyTransformer
models.NCAD
models.TranAD
models.COUTA
models.TcnED
models.DeepIsolationForestTS
models.DeepSVDDTS
models.DeepSADTS
models.DevNetTS
models.PReNetTS
.. rubric:: References
.. bibliography::
:cited:
:labelprefix: B

36
docs/conf.py
View File

@ -24,7 +24,7 @@ version_path = os.path.join(deepod_dir, 'deepod', 'version.py')
exec(open(version_path).read())
# -- Project information -----------------------------------------------------
project = 'deepod'
project = 'DeepOD'
copyright = '2023, Hongzuo Xu'
author = 'Hongzuo Xu'
@ -45,9 +45,11 @@ extensions = [
'sphinx.ext.coverage',
'sphinx.ext.imgmath',
'sphinx.ext.viewcode',
'sphinx.ext.autosummary',
'sphinxcontrib.bibtex',
# 'sphinx.ext.napoleon',
# 'sphinx_rtd_theme',
'sphinx_rtd_theme',
'sphinx.ext.napoleon'
]
bibtex_bibfiles = ['zreferences.bib']
@ -68,7 +70,7 @@ master_doc = 'index'
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
language = 'en'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
@ -84,19 +86,43 @@ pygments_style = 'sphinx'
# a list of builtin themes.
# https://www.sphinx-doc.org/en/master/usage/theming.html#themes#Themes
# html_theme = 'default'
html_theme = "alabaster"
# html_theme = "alabaster"
# html_theme = 'sphinxawesome_theme'
html_theme = 'furo'
# html_theme = 'sphinx_rtd_theme'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# html_theme_options = {
# 'canonical_url': '',
# 'logo_only': False,
# 'display_version': True,
# 'prev_next_buttons_location': 'bottom',
# 'style_external_links': False,
# #'vcs_pageview_mode': '',
# #'style_nav_header_background': 'white',
# # Toc options
# 'collapse_navigation': True,
# 'sticky_navigation': True,
# 'navigation_depth': 7,
# 'includehidden': True,
# 'titles_only': False,
# }
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
autosummary_generate = True
autodoc_default_options = {'members': True,
'inherited-members': True,
}
autodoc_typehints = "none"
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#

37
docs/index.rst
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@ -13,6 +13,10 @@ Welcome to DeepOD documentation!
:target: https://github.com/xuhongzuo/DeepOD/actions/workflows/testing.yml
:alt: testing2
.. image:: https://readthedocs.org/projects/deepod/badge/?version=latest
:target: https://deepod.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://coveralls.io/repos/github/xuhongzuo/DeepOD/badge.svg?branch=main
:target: https://coveralls.io/github/xuhongzuo/DeepOD?branch=main
:alt: coveralls
@ -22,6 +26,8 @@ Welcome to DeepOD documentation!
:alt: downloads
``DeepOD`` is an open-source python library for Deep Learning-based `Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
and `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_. ``DeepOD`` supports tabular anomaly detection and time-series anomaly detection.
@ -45,6 +51,9 @@ If you are interested in our project, we are pleased to have your stars and fork
Citation
~~~~~~~~~~~~~~~~~
If you use this library in your work, please cite this paper:
@ -68,15 +77,6 @@ You can also use the BibTex entry below for citation.
}
Star History
~~~~~~~~~~~~~~~~~
.. image:: https://api.star-history.com/svg?repos=xuhongzuo/DeepOD&type=Date
:target: https://star-history.com/#xuhongzuo/DeepOD&Date
:align: center
----
@ -87,6 +87,23 @@ Star History
:hidden:
:caption: Getting Started
start.install
start.examples
start.model_save
install
.. toctree::
:maxdepth: 2
:hidden:
:caption: Documentation
api_reference
api_cc
.. toctree::
:maxdepth: 2
:hidden:
:caption: Additional Information
additional.contributing
additional.license
additional.star_history

34
docs/install.rst
View File

@ -1,34 +0,0 @@
Installation
============
It is recommended to use **pip** for installation. Please make sure
**the latest version** is installed, as DeepOD is updated frequently:
.. code-block:: bash
pip install deepod # normal install
pip install --upgrade deepod # or update if needed
Alternatively, you could clone and run setup.py file:
.. code-block:: bash
git clone https://github.com/xuhongzuo/deepod.git
cd pyod
pip install .
**Required Dependencies**\ :
* Python 3.7+
* numpy>=1.19
* scipy>=1.5.1
* scikit_learn>=0.20.0
* pandas>=1.0.0
* torch>1.10.0,<1.13.1
* ray==2.6.1
* pyarrow>=11.0.0
* einops

5
docs/requirements.txt
View File

@ -1,2 +1,3 @@
sphinx-rtd-theme
sphinxcontrib-bibtex
sphinx-rtd-theme==1.3.0
sphinxawesome-theme==4.1.0
sphinxcontrib-bibtex==2.5.0

17
docs/zreferences.bib
View File

@ -29,3 +29,20 @@
pages={1-14},
doi={10.1109/TKDE.2023.3270293}
}
@article{ruff2018deepsvdd,
title={Deep One-Class Classification},
author={Ruff, Lukas and Vandermeulen, Robert and Görnitz, Nico and Deecke, Lucas and Siddiqui, Shoaib and Binder, Alexander and Müller, Emmanuel and Kloft, Marius},
journal={International conference on machine learning},
year={2018}
}
@inproceedings{pang2019deep,
title={Deep anomaly detection with deviation networks},
author={Pang, Guansong and Shen, Chunhua and van den Hengel, Anton},
booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
pages={353--362},
year={2019}
}

7
testbed/testbed_unsupervised_tsad.py
View File

@ -23,14 +23,17 @@ parser.add_argument("--runs", type=int, default=5,
"obtain the average performance")
parser.add_argument("--output_dir", type=str, default='@records/',
help="the output file path")
parser.add_argument("--dataset", type=str, default='ASD',
parser.add_argument("--dataset", type=str,
default='ASD',
# default='SMD,MSL,SMAP,SWaT_cut,EP,DASADS',
help='dataset name or a list of names split by comma')
parser.add_argument("--entities", type=str,
# default='omi-1',
default='FULL',
help='FULL represents all the csv file in the folder, '
'or a list of entity names split by comma')
parser.add_argument("--entity_combined", type=int, default=1)
parser.add_argument("--model", type=str, default='DCdetector', help="")
parser.add_argument("--model", type=str, default='NCAD', help="")
parser.add_argument("--auto_hyper", default=False, action='store_true', help="")
parser.add_argument('--silent_header', action='store_true')