{"id":27618041,"url":"https://github.com/ant-research/easytemporalpointprocess","last_synced_at":"2025-05-15T16:01:48.569Z","repository":{"id":170640927,"uuid":"646671531","full_name":"ant-research/EasyTemporalPointProcess","owner":"ant-research","description":"EasyTPP: Towards Open Benchmarking Temporal Point 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EasyTPP [ICLR 2024]\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"PyVersion\"\u003e\n    \u003cimg alt=\"Python Version\" src=\"https://img.shields.io/badge/python-3.9+-blue.svg\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"LICENSE-CODE\"\u003e\n    \u003cimg alt=\"Code License\" src=\"https://img.shields.io/badge/license-Apache-000000.svg?\u0026color=f5de53\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"commit\"\u003e\n    \u003cimg alt=\"Last Commit\" src=\"https://img.shields.io/github/last-commit/ant-research/EasyTemporalPointProcess\"\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n\u003ca href=\"https://pypi.python.org/pypi/easy-tpp/\"\u003e \n  \u003cimg alt=\"PyPI version\" src=\"https://img.shields.io/pypi/v/easy-tpp.svg?style=flat-square\u0026color=b7534\" /\u003e\n\u003c/a\u003e\n\u003ca href=\"https://static.pepy.tech/personalized-badge/easy-tpp\"\u003e \n  \u003cimg alt=\"Downloads\" src=\"https://static.pepy.tech/personalized-badge/easy-tpp?period=total\u0026units=international_system\u0026left_color=black\u0026right_color=blue\u0026left_text=Downloads\" /\u003e\n\u003c/a\u003e\n\u003ca href=\"https://huggingface.co/easytpp\" target=\"_blank\"\u003e\n    \u003cimg alt=\"Hugging Face\" src=\"https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-EasyTPP-ffc107?color=ffc107\u0026logoColor=white\" /\u003e\n\u003c/a\u003e\n\u003ca href=\"https://github.com/ant-research/EasyTemporalPointProcess/issues\"\u003e\n  \u003cimg alt=\"Open Issues\" src=\"https://img.shields.io/github/issues-raw/ant-research/EasyTemporalPointProcess\" /\u003e\n\u003c/a\u003e\n\u003c/div\u003e\n\n`EasyTPP` is an easy-to-use development and application toolkit for [Temporal Point Process](https://mathworld.wolfram.com/TemporalPointProcess.html) (TPP), with key features in configurability, compatibility and reproducibility. We hope this project could benefit both researchers and practitioners with the goal of easily customized development and open benchmarking in TPP.\n\u003cspan id='top'/\u003e\n\n\n\n| \u003ca href='#features'\u003eFeatures\u003c/a\u003e  | \u003ca href='#model-list'\u003eModel List\u003c/a\u003e | \u003ca href='#dataset'\u003eDataset\u003c/a\u003e  | \u003ca href='#quick-start'\u003eQuick Start\u003c/a\u003e | \u003ca href='#benchmark'\u003eBenchmark\u003c/a\u003e |\u003ca href='#doc'\u003eDocumentation\u003c/a\u003e |\u003ca href='#todo'\u003eTodo List\u003c/a\u003e | \u003ca href='#citation'\u003eCitation\u003c/a\u003e |\u003ca href='#acknowledgment'\u003eAcknowledgement\u003c/a\u003e | \u003ca href='#star-history'\u003eStar History\u003c/a\u003e | \n\n## News\n\u003cspan id='news'/\u003e\n\n- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [02-17-2024] EasyTPP supports HuggingFace dataset API: all datasets have been published in [HuggingFace Repo](https://huggingface.co/easytpp) and see [tutorial notebook](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/notebooks/easytpp_1_dataset.ipynb) for an example of usage.\n- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [01-16-2024] Our paper [EasyTPP: Towards Open Benchmarking Temporal Point Process](https://arxiv.org/abs/2307.08097) is accepted by ICLR'2024! \n- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [09-30-2023] We published two textual event sequence datasets [GDELT](https://drive.google.com/drive/folders/1Ms-ATMMFf6v4eesfJndyuPLGtX58fCnk) and [Amazon-text-review](https://drive.google.com/drive/folders/1-SLYyrl7ucEG7NpSIF0eSoG9zcbZagZw) that are used in our paper [LAMP](https://arxiv.org/abs/2305.16646), where LLM can be applied for event prediction! See [Documentation](https://ant-research.github.io/EasyTemporalPointProcess/user_guide/dataset.html#preprocessed-datasets) for more details.\n- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [09-30-2023] Two of our papers [Language Model Can Improve Event Prediction by Few-Shot Abductive Reasoning](https://arxiv.org/abs/2305.16646) (LAMP) and [Prompt-augmented Temporal Point Process for Streaming Event Sequence](https://arxiv.org/abs/2310.04993) (PromptTPP) are accepted by NeurIPS'2023!\n\u003cdetails\u003e\n  \u003csummary\u003eClick to see previous news\u003c/summary\u003e\n  \u003cp\u003e\n- [09-02-2023] We published two non-anthropogenic datasets [earthquake](https://drive.google.com/drive/folders/1ubeIz_CCNjHyuu6-XXD0T-gdOLm12rf4) and [volcano eruption](https://drive.google.com/drive/folders/1KSWbNi8LUwC-dxz1T5sOnd9zwAot95Tp?usp=drive_link)! See \u003ca href='#dataset'\u003eDataset\u003c/a\u003e for details.\n- [05-29-2023] We released ``EasyTPP`` v0.0.1!\n- [12-27-2022] Our paper [Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes](https://arxiv.org/abs/2201.12569) was accepted by AAAI'2023!\n- [10-01-2022] Our paper [HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences](https://arxiv.org/abs/2210.01753) was accepted by NeurIPS'2022!\n- [05-01-2022] We started to develop `EasyTPP`.\u003c/p\u003e\n\u003c/details\u003e\n\n\n## Features \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='features'/\u003e\n\n- **Configurable and customizable**: models are modularized and configurable，with abstract classes to support developing customized\n  TPP models.\n- **Compatible with both Tensorflow and PyTorch framework**: `EasyTPP` implements two equivalent sets of models, which can\n  be run under Tensorflow (both Tensorflow 1.13.1 and Tensorflow 2.0) and PyTorch 1.7.0+ respectively. While the PyTorch models are more popular among researchers, the compatibility with Tensorflow is important for industrial practitioners.\n- **Reproducible**: all the benchmarks can be easily reproduced.\n- **Hyper-parameter optimization**: a pipeline of [optuna](https://github.com/optuna/optuna)-based HPO is provided.\n\n\n## Model List \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='model-list'/\u003e\n\nWe provide reference implementations of various state-of-the-art TPP papers:\n\n| No  | Publication |     Model     | Paper                                                                                                                                    | Implementation                                                                                                             |\n|:---:|:-----------:|:-------------:|:-----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|\n|  1  |   KDD'16    |     RMTPP     | [Recurrent Marked Temporal Point Processes: Embedding Event History to Vector](https://www.kdd.org/kdd2016/papers/files/rpp1081-duA.pdf) | [Tensorflow](easy_tpp/model/tf_model/tf_rmtpp.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_rmtpp.py)                   |\n|  2  | NeurIPS'17  |      NHP      | [The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process](https://arxiv.org/abs/1612.09328)                     | [Tensorflow](easy_tpp/model/tf_model/tf_nhp.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_nhp.py)                       |\n|  3  | NeurIPS'19  |    FullyNN    | [Fully Neural Network based Model for General Temporal Point Processes](https://arxiv.org/abs/1905.09690)                                | [Tensorflow](easy_tpp/model/tf_model/tf_fullnn.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_fullynn.py)                |\n|  4  |   ICML'20   |     SAHP      | [Self-Attentive Hawkes process](https://arxiv.org/abs/1907.07561)                                                                        | [Tensorflow](easy_tpp/model/tf_model/tf_sahp.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_sahp.py)                     |\n|  5  |   ICML'20   |      THP      | [Transformer Hawkes process](https://arxiv.org/abs/2002.09291)                                                                           | [Tensorflow](easy_tpp/model/tf_model/tf_thp.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_thp.py)                       |\n|  6  |   ICLR'20   | IntensityFree | [Intensity-Free Learning of Temporal Point Processes](https://arxiv.org/abs/1909.12127)                                                  | [Tensorflow](easy_tpp/model/tf_model/tf_intensity_free.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_intensity_free.py) |\n|  7  |   ICLR'21   |    ODETPP     | [Neural Spatio-Temporal Point Processes (simplified)](https://arxiv.org/abs/2011.04583)                                                  | [Tensorflow](easy_tpp/model/tf_model/tf_ode_tpp.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_ode_tpp.py)               |\n|  8  |   ICLR'22   |    AttNHP     | [Transformer Embeddings of Irregularly Spaced Events and Their Participants](https://arxiv.org/abs/2201.00044)                           | [Tensorflow](easy_tpp/model/tf_model/tf_attnhp.py)\u003cbr/\u003e[Torch](easy_tpp/model/torch_model/torch_attnhp.py)                 |\n\n\n\n## Dataset \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='dataset'/\u003e\n\nWe preprocessed one synthetic and five real world datasets from widely-cited works that contain diverse characteristics in terms of their application domains and temporal statistics:\n- Synthetic: a univariate Hawkes process simulated by [Tick](https://github.com/X-DataInitiative/tick) library.\n- Retweet ([Zhou, 2013](http://proceedings.mlr.press/v28/zhou13.pdf)): timestamped user retweet events.\n- Taxi ([Whong, 2014](https://chriswhong.com/open-data/foil_nyc_taxi/)): timestamped taxi pick-up events.\n- StackOverflow ([Leskovec, 2014](https://snap.stanford.edu/data/)): timestamped user badge reward events in StackOverflow.\n- Taobao ([Xue et al, 2022](https://arxiv.org/abs/2210.01753)): timestamped user online shopping behavior events in Taobao platform.\n- Amazon ([Xue et al, 2022](https://arxiv.org/abs/2210.01753)): timestamped user online shopping behavior events in Amazon platform.\n\nPer users' request, we processed two non-anthropogenic datasets \n- [Earthquake](https://drive.google.com/drive/folders/1ubeIz_CCNjHyuu6-XXD0T-gdOLm12rf4): timestamped earthquake events over the Conterminous U.S from 1996 to 2023, processed from [USGS](https://www.usgs.gov/programs/earthquake-hazards/science/earthquake-data).\n- [Volcano eruption](https://drive.google.com/drive/folders/1KSWbNi8LUwC-dxz1T5sOnd9zwAot95Tp?usp=drive_link): timestamped volcano eruption events over the world in recent hundreds of years, processed from [The Smithsonian Institution](https://volcano.si.edu/).\n\n\n  All datasets are preprocess to the `Gatech` format dataset widely used for TPP researchers, and saved at [Google Drive](https://drive.google.com/drive/u/0/folders/1f8k82-NL6KFKuNMsUwozmbzDSFycYvz7) with a public access.\n\n## Quick Start \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='quick-start'/\u003e\n\n\n### Colab Tutorials\n\nExplore the following tutorials that can be opened directly in Google Colab:\n\n- [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ant-research/EasyTemporalPointProcess/blob/main/notebooks/easytpp_1_dataset.ipynb) Tutorial 1: Dataset in EasyTPP.\n- [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ant-research/EasyTemporalPointProcess/blob/main/notebooks/easytpp_2_tfb_wb.ipynb) Tutorial 2: Tensorboard in EasyTPP.\n- [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ant-research/EasyTemporalPointProcess/blob/main/notebooks/easytpp_3_train_eval.ipynb) Tutorial 3: Training and Evaluation of TPPs.\n\n### End-to-end Example\n\nWe provide an end-to-end example for users to run a standard TPP model with `EasyTPP`.\n\n\n### Step 1. Installation\n\nFirst of all, we can install the package either by using pip or from the source code on Github.\n\nTo install the latest stable version:\n```bash\npip install easy-tpp\n```\n\nTo install the latest on GitHub:\n```bash\ngit clone https://github.com/ant-research/EasyTemporalPointProcess.git\ncd EasyTemporalPointProcess\npython setup.py install\n```\n\n\n### Step 2. Prepare datasets \n\nWe need to put the datasets in a local directory before running a model and the datasets should follow a certain format. See [OnlineDoc - Datasets](https://ant-research.github.io/EasyTemporalPointProcess/user_guide/dataset.html) for more details.\n\nSuppose we use the [taxi dataset](https://chriswhong.com/open-data/foil_nyc_taxi/) in the example.\n\n### Step 3. Train the model\n\n\nBefore start training, we need to set up the config file for the pipeline. We provide a preset config file in [Example Config](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/configs/experiment_config.yaml). The details of the configuration can be found in [OnlineDoc - Training Pipeline](https://ant-research.github.io/EasyTemporalPointProcess/user_guide/run_train_pipeline.html).\n\nAfter the setup of data and config, the directory structure is as follows:\n\n```bash\n\n    data\n     |______taxi\n             |____ train.pkl\n             |____ dev.pkl\n             |____ test.pkl\n\n    configs\n     |______experiment_config.yaml\n\n```\n\n\nThen we start the training by simply running the script \n\n```python\n\nimport argparse\nfrom easy_tpp.config_factory import Config\nfrom easy_tpp.runner import Runner\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--config_dir', type=str, required=False, default='configs/experiment_config.yaml',\n                        help='Dir of configuration yaml to train and evaluate the model.')\n\n    parser.add_argument('--experiment_id', type=str, required=False, default='NHP_train',\n                        help='Experiment id in the config file.')\n\n    args = parser.parse_args()\n\n    config = Config.build_from_yaml_file(args.config_dir, experiment_id=args.experiment_id)\n\n    model_runner = Runner.build_from_config(config)\n\n    model_runner.run()\n\n\nif __name__ == '__main__':\n    main()\n\n```\n\nA more detailed example can be found at [OnlineDoc - QuickStart](https://ant-research.github.io/EasyTemporalPointProcess/get_started/quick_start.html).\n\n\n## Documentation \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='doc'/\u003e\n\nThe classes and methods of `EasyTPP` have been well documented so that users can generate the documentation by:\n\n```shell\ncd doc\npip install -r requirements.txt\nmake html\n```\nNOTE:\n* The `doc/requirements.txt` is only for documentation by Sphinx, which can be automatically generated by Github actions `.github/workflows/docs.yml`. (Trigger by pull request.)\n\nThe full documentation is available on the [website](https://ant-research.github.io/EasyTemporalPointProcess/).\n \n## Benchmark \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='benchmark'/\u003e\n\nIn the [examples](https://github.com/ant-research/EasyTemporalPointProcess/tree/main/examples) folder, we provide a [script](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/benchmark_script.py) to benchmark the TPPs, with Taxi dataset as the input. \n\nTo run the script, one should download the Taxi data following the above instructions. The [config](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/configs/experiment_config.yaml) file is readily setup up. Then run\n\n\n```shell\ncd examples\npython run_retweet.py\n```\n\n\n## License \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\nThis project is licensed under the [Apache License (Version 2.0)](https://github.com/alibaba/EasyNLP/blob/master/LICENSE). This toolkit also contains some code modified from other repos under other open-source licenses. See the [NOTICE](https://github.com/ant-research/EasyTPP/blob/master/NOTICE) file for more information.\n\n\n## Todo List \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='todo'/\u003e\n\n- [x] New dataset:\n  - [x] Earthquake: the source data is available in [USGS](https://www.usgs.gov/programs/earthquake-hazards/science/earthquake-data).\n  - [x] Volcano eruption: the source data is available in [NCEI](https://www.ngdc.noaa.gov/hazard/volcano.shtml).\n- [ ] New model:\n  - [ ] Meta Temporal Point Process, ICLR 2023.\n  - [ ] Model-based RL via TPP, AAAI 2022. \n\n## Citation \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\n\u003cspan id='citation'/\u003e\n\nIf you find `EasyTPP` useful for your research or development, please cite the following \u003ca href=\"https://arxiv.org/abs/2307.08097\" target=\"_blank\"\u003epaper\u003c/a\u003e:\n```\n@inproceedings{xue2024easytpp,\n      title={EasyTPP: Towards Open Benchmarking Temporal Point Processes}, \n      author={Siqiao Xue and Xiaoming Shi and Zhixuan Chu and Yan Wang and Hongyan Hao and Fan Zhou and Caigao Jiang and Chen Pan and James Y. Zhang and Qingsong Wen and Jun Zhou and Hongyuan Mei},\n      booktitle = {International Conference on Learning Representations (ICLR)},\n      year = {2024},\n      url ={https://arxiv.org/abs/2307.08097}\n}\n```\n\n## Acknowledgment \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='acknowledgment'/\u003e\n\nThe project is jointly initiated by Machine Intelligence Group, Alipay and DAMO Academy, Alibaba. \n\nThe following repositories are used in `EasyTPP`, either in close to original form or as an inspiration:\n\n- [EasyRec](https://github.com/alibaba/EasyRec)\n- [EasyNLP](https://github.com/alibaba/EasyNLP)\n- [FuxiCTR](https://github.com/xue-pai/FuxiCTR)\n- [Neural Hawkes Process](https://github.com/hongyuanmei/neurawkes)\n- [Neural Hawkes Particle Smoothing](https://github.com/hongyuanmei/neural-hawkes-particle-smoothing)\n- [Attentive Neural Hawkes Process](https://github.com/yangalan123/anhp-andtt)\n- [Huggingface - transformers](https://github.com/huggingface/transformers)\n\n\n## Star History \u003ca href='#top'\u003e[Back to Top]\u003c/a\u003e\n\u003cspan id='star-history'/\u003e\n\n![Star History Chart](https://api.star-history.com/svg?repos=ant-research/EasyTemporalPointProcess\u0026type=Date)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fant-research%2Feasytemporalpointprocess","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fant-research%2Feasytemporalpointprocess","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fant-research%2Feasytemporalpointprocess/lists"}