NeuroSeqRet

Artifacts related to the AAAI 2022 paper "Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences".

Requirements

Use a python-3.7 environment and install PyTorch v1.6.0, TorchVision v0.4.2, and Rank-Eval v0.0.1. More details are given in requirements.txt file.

Execution Instructions

Dataset Format

We have also provided a dataset for your reference. For any new dataset, you need to structure it to different files containing the continuous-time event sequences (CTES) as follows:

test_query_ev test_query_ti train_corpus_ev train_corpus_ti train_query_ev train_query_ti

File Details

Here, we provide the description of the files that are given in the dataset:

• train_query_ev.txt = The types of the events in the query CTES.
• train_query_ti.txt = The times of the events in the query CTES.

Similarly, we have the exact files for the corpus data and the test CTES.

Running the Code

Use the following command to run NeuroSeqRet on the dataset. For example, to run the model on a dataset, use the command:

python main.py --dataset <dataset_name>

Once you run the command, the following procedure steps into action:

• NeuroSeqRet trains its retrieval system using the cross/self attention model.
• Later, it creates a dump for all CTES in the dataset. This dump is available in the 'Hash' folder with the name of the dataset followed by 'Embs'. This is a time-consuming process as these embeddings are calculated for all CTES in the corpus.
• Lastly, it will automatically initiate the evaluation process and will return results in therm of MPA and NDCG@10.

For calculating the hash-codes for all CTES data, you can run the following command

python hashing.py --dataset <dataset_name>

Once you run the above command, NeuroSeqRet loads the embedding dumps and creates the hash-codes and the collision files. In detail, it creates two files in 'Hash' folder:

• 'Codes' = The hash codes for all corpus CTES.
• 'Hashed' = The corpus sequences that were indexed for every test query, i.e., the collisions for each query. This is a list of the same size as the total number of test CTES.

Citing

If you use this code in your research, please cite:

@inproceedings{aaai22,
 author = {Vinayak Gupta and Abir De and Srikanta Bedathur},
 booktitle = {Proc. of the 36th AAAI Conference on Artificial Intelligence (AAAI)},
 title = {Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences},
 year = {2022}
}

Contact

In case of any issues, please send a mail to guptavinayak51 (at) gmail (dot) com