Semi-supervised Learning Multiclass Anomaly Detection in Multivariate Timeseries

Author: Wooheon Hong, Minsoo Kim with Samsung Electronics

Date: 2021.05.10 ~ 2022. 04. 29

This is the pytorch implementation of MAD (Multiclass Anomaly Detection)

Train & Evaluation

CE

python main.py {data_name} {data_path} {output_dir} --window 10 --normal_class 0 --is_ood --labeled_normal_ratio 0.1 --labeled_anomaly_ratio 0.1 --model_name base_model --net_name wavenet --lr 0.0001 --n_epochs 150 --hidden_channels 128 --batch_size 128 

PseudoLabeling + ABC

python main.py {data_name} {data_path} {output_dir} --window 10 --normal_class 0 --is_ood --labeled_normal_ratio 0.1 --labeled_anomaly_ratio 0.1 --model_name PseudoLabeling --net_name wavenet --lr 0.0001 --n_epochs 150 --hidden_channels 128 --batch_size 128 --class_imbalance_name abc  

The detailed descriptions about the parameters are as following:

Parameter name	Description of parameter
data_name	File name of input .npy
data_path	Directory location with the train data and test data
output_dir	Directory location of the results, default log/
window	Length of sliding window, default 10
normal_class	Set which class is the normal class of the dataset (all other classes except ood class are considered anomaly), default 0
is_ood	Decide whether to experiment or not in the out-of-distribution setting
labeled_normal_ratio	Ratio of labeled normal training examples, default 0.01
labeled_anomaly_ratio	Ratio of labeled anomaly training examples, default 0.01
load_config	Config JSON-file path, default None
load_model	Saved model file path, default None
model_name	Set which model to use
margin_name	Set which margin to use or not, default None
net_name	Set which neural network to use for embedding
class_imbalance_name	Set which class imbalance loss to use or not, default None
eta	..
hidden_channels	Dimension size for latent representation
seed	Set seed.
n_epochs	Epoch size during training, default 100
lr	Initial learning rate of Adam optimizer, default 0.0001
lr_milestone	Lr scheduler milestones at which lr is multiplied by 0.1. Can be multiple and must be increasing.
weight_decay	Weight decay (L2 penalty) hyperparameter for ..
batch_size	Batch size for mini-batch training, default 128
device	Computation device to use ("cpu", "cuda", "cuda:2", etc.), default "cuda"
num_threads	Number of threads used for parallelizing CPU operations. 0 means that all resources are used, default 0
n_jobs_dataloader	Number of workers for data loading. 0 means that the data will be loaded in the main process, default 0

Models

The detailed descriptions about the models are as following:

Model name	Description of model
base_model	Cross entropy
DeepMCDD	DeepMCDD, OOD detection model
PseudoLabeling	PseudoLabeling, SSL model
MPUMAD	MPUMAD, MPU model

Code Structure

├── base
│   ├── base_dataset.py
│   ├── base_net.py
│   ├── base_trainer.py
│   ├── __init__.py
│   └── torchtimeseries_dataset.py
├── datasets
│   ├── __init__.py
│   ├── main.py
│   ├── nsl_kdd.py
│   ├── preprocessing.py
│   └── unsw_nb15.py
├── log
│   ├── config.json
│   ├── model.tar
│   └── results.json
├── mad.py
├── main.py
├── models
│   ├── base_model.py
│   ├── deepmcdd.py
│   ├── embedding
│   │   ├── mlp.py
│   │   └── network_data_wavenet.py
│   ├── __init__.py
│   ├── main.py
│   ├── mb.py
│   └── mdeepsad.py
├── optim
│   ├── deepmcdd_abc_trainer.py
│   ├── deepmcdd_trainer.py
│   ├── face_trainer.py
│   ├── __init__.py
│   ├── main.py
│   ├── margin
│   │   ├── ArcMarginProductAdaptive.py
│   │   ├── ArcMarginProduct.py
│   │   ├── CosineMarginProduct.py
│   │   ├── __init__.py
│   │   └── MultiMarginProduct.py
│   ├── mb_trainer.py
│   ├── mdeepsad_trainer.py
│   ├── pseudolabeling_abc_trainer.py
│   ├── pseudolabeling_trainer.py
│   ├── semi_deepmcdd_trainer.py
│   ├── supervised_abc_trainer.py
│   ├── supervised_focal_trainer.py
│   └── supervised_trainer.py
├── README.md
└── utils
    ├── config.py
    ├── focal.py
    ├── __init__.py
    ├── loss.py
    ├── utils_mpumad.py
    └── utils.py