HCL_TPP

AAAI 2023 Hierarchical Contrastive Learning for Temporal Point Processes

In this work, we develop a novel hierarchical contrastive (HCL) learning method for temporal point processes, which provides a new regularizer of maximum likelihood estimation. In principle, our HCL considers the noise contrastive estimation (NCE) problem at the event-level and that at the sequence-level jointly. Given a sequence, the event-level NCE maximizes the probability of each observed event given its history while penalizing the conditional probabilities of the unobserved events. At the same time, we generate positive and negative event sequences from the observed sequence and maximize the discrepancy between their likelihoods through the sequence-level NCE. Instead of using time-consuming simulation methods, we generate the positive and negative sequences via a simple but efficient model-guided thinning process.

Reference

If you use this code as part of any published research, please acknowledge the following paper:

  @article{wang2023hierarchical,
  title={Hierarchical Contrastive Learning for Temporal Point Processes},
  author={Wang, Qingmei and Cheng, Minjie and Yuan, Shen and Xu, Hongteng},
  year={2023}
}

Instructions

Here are the instructions to use the code base

Dependencies

This code is written in python. To use it you will need:

PyTorch == 1.10.0
Python == 3.9.0

Data

Downloadable from this Google Drive link

Training & Evaluation(take dataset'Hawkes' for example)

MLE+Reg.

python main.py -batch_size 4 -epoch 50 -model 'MLE' -save_label 'MLE + Reg' -data_folder 'tpp-data/data_hawkes' -w_mle 1 -w_dis 1 -w_cl1 0 -w_cl2 0 -seed 12

MLE+DA

python main.py -batch_size 4 -superpose -epoch 50 -model 'MLE' -save_label 'MLE + DA' -data_folder 'tpp-data/data_hawkes' -w_mle 1 -w_dis 1 -w_cl1 0 -w_cl2 0 -seed 12

Dis

python main.py  -batch_size 4  -model 'MLE' -save_label 'Dis' -data_folder 'tpp-data/data_hawkes' -epoch 50 -w_mle 0 -w_dis 1 -w_cl1 0 -w_cl2 0 -seed 12

HCL+MLE

python main.py  -batch_size 4  -num_neg 20 -ratio_remove 0.4 -model 'HCL' -save_label 'HCL+MLE ' -data_folder 'tpp-data/data_hawkes' -epoch 50 -w_mle 1 -w_dis 1 -w_cl1 1 -w_cl2 1 -seed 12

HCLeve+MLE

python main.py  -batch_size 4  -num_neg 20 -ratio_remove 0.4 -model 'HCL' -save_label 'HCL+MLE ' -data_folder 'tpp-data/data_hawkes' -epoch 50 -w_mle 1 -w_dis 1 -w_cl1 1 -w_cl2 0 -seed 12

HCLseq+MLE

python main.py  -batch_size 4  -num_neg 20 -ratio_remove 0.4 -model 'HCL' -save_label 'HCL+MLE ' -data_folder 'tpp-data/data_hawkes' -epoch 50 -w_mle 1 -w_dis 1 -w_cl1 0 -w_cl2 1 -seed 12

Parameters

tpp-data is the dataset.

Learning is the learning methods chosen for the training, including mle, hcl.

TPPSis the model chosen for the backbone of training.

num_neg is the number of negative sequence for contrastive learning. The default value of Hawkes dataset is 20.

wcl1 corresponds to the weight of event level contrastive learning loss. The default value is 1.

wcl2 corresponds to the weight of event level contrastive learning loss. The default value is 1.

ratio_remove corresponds to the ratio of removing events of per sequence when generate negative and positive sequence . The default value is 0.4.

qingmeiwangdaily / HCL_TPP