{"id":18401687,"url":"https://github.com/borealisai/meta-tpp","last_synced_at":"2025-06-21T16:37:16.619Z","repository":{"id":238053191,"uuid":"600144736","full_name":"BorealisAI/meta-tpp","owner":"BorealisAI","description":"PyTorch-Lightning implementation of Meta Temporal Point Processes","archived":false,"fork":false,"pushed_at":"2024-05-04T00:19:47.000Z","size":3528,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-12T18:19:21.207Z","etag":null,"topics":["asynchronous-time-series","meta-learning","temporal-point-process","time-series"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BorealisAI.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-10T17:26:17.000Z","updated_at":"2025-03-15T03:59:13.000Z","dependencies_parsed_at":"2024-05-04T03:29:13.671Z","dependency_job_id":"54eaf5d5-1e92-43ed-b88a-941c8fee7357","html_url":"https://github.com/BorealisAI/meta-tpp","commit_stats":null,"previous_names":["borealisai/meta-tpp"],"tags_count":13,"template":false,"template_full_name":null,"purl":"pkg:github/BorealisAI/meta-tpp","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorealisAI%2Fmeta-tpp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorealisAI%2Fmeta-tpp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorealisAI%2Fmeta-tpp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorealisAI%2Fmeta-tpp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BorealisAI","download_url":"https://codeload.github.com/BorealisAI/meta-tpp/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorealisAI%2Fmeta-tpp/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261159670,"owners_count":23118064,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["asynchronous-time-series","meta-learning","temporal-point-process","time-series"],"created_at":"2024-11-06T02:39:38.577Z","updated_at":"2025-06-21T16:37:11.603Z","avatar_url":"https://github.com/BorealisAI.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Meta Temporal Point Processes\n[[Paper](https://openreview.net/pdf?id=QZfdDpTX1uM)][[Poster](https://iclr.cc/media/PosterPDFs/ICLR%202023/11395.png?t=1682361273.0520558)][[OpenReview](https://openreview.net/forum?id=QZfdDpTX1uM)]\n\n## Datasets\nWe provide the compressed datasets: Stack Overflow, Mooc, Reddit, Wiki, Sin, Uber, NYC Taxi, in this [link](https://drive.google.com/file/d/1pL1wDG1elgtUa0CPv4GP21xGII-Ymk0x/view?usp=drive_link).\nUnzip the compressed file and locate it in the `$ROOT` directory.\n\n\n## Setup\nSetup the pipeline by installing dependencies using the following command.\npretrained models and utils.\n```bash\npip install -r requirements.txt\n```\nFor nfe pacakge, install the package in [neural flows repo](https://github.com/mbilos/neural-flows-experiments) using\n```bash\npip install -e .\n```\n\n\n## Pre-trained models\nWe also provide the checkpoints for Intensity free, THP+ and Attentive TPP on all the datasets.\nPlease download the compress file in this [link](https://drive.google.com/file/d/1frnaUoToJIMh9BnQaqz4zy3HNtaoKe35/view?usp=drive_link), unzip it and locate it in the `$ROOT` directory.\n\n\n\n## Train\nA model can be trained using the following command.\n```bash\npython src/train.py data/datasets=$DATASET model=$MODEL\n```\n`$DATASET` can be chosen from `{so_fold1, mooc, reddit, wiki, sin, uber_drop, taxi_times_jan_feb}` and `$MODEL` can be chosen from `{intensity_free,thp_mix,attn_lnp}`.\nOther configurations can be also easily modified using hydra syntax. Please refer to [hydra](https://hydra.cc/docs/intro/) for further details.\n\n\n## Eval\nA model can be evaluated on test datasets using the following command.\n```bash\npython src/eval.py data/datasets=$DATASET model=$MODEL\n```\nHere, the default checkpoint paths are set to the ones in `checkpoints` directory we provided above.\nTo use different checkpoints, please chagne `ckpt_path` argument in `configs/eval.yaml`.\n\n\n## Modifications\nWe made some modifications during code refactorization after ICLR 2023.\nFor the NLL metric, we took out L2 norm of model params, which we had to include for implementation purpose using the internal pipeline.\nNote that since our proposed model contains stricly more number of parameters, this change is on favor of our method.\nFor the RMSE metric, we divide MSE by the number events for which we previously included the first events but not anymore.\nWe made this change because we do not make predictions on the first events.\nNote that it is applied the same way for every model.\nThese changes do not change the rank of models nor the narrative of the paper.\n\n\n## Citation\nIf you use this code or model for your research, please cite:\n\n    @inproceedings{bae2023meta,\n      title = {Meta Temporal Point Processes},\n      author = {Bae, Wonho and Ahmed, Mohamed Osama and Tung, Frederick and Oliveira, Gabriel L},\n      booktitle={The International Conference on Learning Representations (ICLR)},\n      year={2023}\n    }\n\n\n## Acknowledgment\nThe pipeline is built on [PyTorch-Lightning Hydra Template](https://github.com/ashleve/lightning-hydra-template).\nIntensity free is based on [the original implementation](https://github.com/shchur/ifl-tpp) and THP+ is based on (the corrected version of THP](https://github.com/yangalan123/anhp-andtt).\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborealisai%2Fmeta-tpp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fborealisai%2Fmeta-tpp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborealisai%2Fmeta-tpp/lists"}