MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series - a PyTorch Version [Paper] [Code]

Authors: Qianwen Meng, Hangwei Qian, Yong Liu, Lizhen Cui, Yonghui Xu, Zhiqi Shen
This work is accepted for publication in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023).

MHCCL Overview:

Abstract

Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for multivariate time series. Motivated by the observation that fine-grained clustering preserves higher purity while coarse-grained one reflects higher-level semantics, we propose a novel downward masking strategy to filter out fake negatives and supplement positives by incorporating the multi-granularity information from the clustering hierarchy. In addition, a novel upward masking strategy is designed in MHCCL to remove outliers of clusters at each partition to refine prototypes, which helps speed up the hierarchical clustering process and improves the clustering quality. We conduct experimental evaluations on seven widely-used multivariate time series datasets. The results demonstrate the superiority of MHCCL over the state-of-the-art approaches for unsupervised time series representation learning.

Requirements for this project:

Python ≥ 3.6
PyTorch ≥ 1.4

Required Packages:

numpy
sklearn
openpyxl
torchvision
random
copy
pandas
matplotlib
time
collections
scipy
pynndescent
builtins
math
shutil
os
sys
warnings
tqdm
argparse
tensorboard_logger

To perform unsupervised representation learning, please refer to the options below:

python main.py \
--dataset_name wisdm \
--lr 0.03 \
--batch_size 128 \
--mlp --cos \ 
--layers 3 \
--posi 2 \
--negi 100 \
--posp 3 \
--negp 4 \
--protoNCE_only \ #If --flag is not entered, the default value is False. The True value is triggered when --flag is entered
--mask_layer0 \ #If --flag is not entered, the default value is False. The True value is triggered when --flag is entered
--mask_others \ #If --flag is not entered, the default value is False. The True value is triggered when --flag is entered
--replace_centroids \ #If --flag is not entered, the default value is False. The True value is triggered when --flag is entered
--mask_mode 'mask_proportion' \ #choices['mask_farthest', 'mask_threshold'(if use, specify the dist_threshold), 'mask_proportion'(if use, specify the proportion)]
--proportion 0.5 \
--dist_threshold 0.7 \
--dist_url 'tcp://localhost:10001' --multiprocessing_distributed --world_size 1 --rank 0

To perform downstream classification task based on the learned representations, please refer to the options below:

python classifier.py \
--dataset_name wisdm \
--pretrained experiment_wisdm/checkpoint_0149.pth.tar \
--lr 5 --batch_size 128 \
--dist_url 'tcp://localhost:10002' --multiprocessing_distributed --world_size 1 --rank 0 \
--id wisdm_linear_0149

Results:

The experimental results will be saved in "experiment_{args.dataset_name}" directory by default
If you choose different partitions or masking strategies, the suffix will be added automatically such as "experiment_{args.dataset_name}_{args.mask_mode}layer0{args.dist_threshold}"

Citation:

If you find any of the codes helpful, kindly cite our paper.

@inproceedings{DBLP:conf/aaai/MengQLCX023,
  author       = {Qianwen Meng and
                  Hangwei Qian and
                  Yong Liu and
                  Lizhen Cui and
                  Yonghui Xu and
                  Zhiqi Shen},
  title        = {{MHCCL:} Masked Hierarchical Cluster-Wise Contrastive Learning for
                  Multivariate Time Series},
  booktitle    = {{AAAI}},
  pages        = {9153--9161},
  publisher    = {{AAAI} Press},
  year         = {2023}
}

References:

Part of the codes are referenced from

https://github.com/emadeldeen24/TS-TCC
https://github.com/salesforce/PCL
https://github.com/ssarfraz/FINCH-Clustering

tdg2088/MHCCL