Code of paper Challenges and opportunities in applying Neural Point Processes to large scale industry data

We present in this repository code to train, evaluate Neural Temporal Point Processes (TPPs), as described in our work: "Challenges and opportunities in applying Neural Point Processes to large scale industry data"

Contact

• Dominykas Šeputis dom.seputis@gmail.com

Data

We use three datasets:

• Simulated Hawkes process
• Stack Overflow users' activity
• Vinted platform members' actions. If needed to access Vinted data, request for the data via email provided in the contact section

Setup instructions

1. We use poetry as python dependency manager. To setup python virtual environment, first, follow the instructions to install poetry on your machine.
2. Run $ poetry install to setup virtual environment
3. Run $ source ./venv/bin/activate to activate the newly initiated virtual environment
4. Authenticate with wandb tool to track experiments
5. To prepare datasets for training and evaluation steps, process them by running $ sh ./runs/data_preparation.sh
6. To replicate experiments, run one of the experiments' scripts inside ./runs/.
7. Alternatively, run specific experiment by running $ python -m scripts.train --experiment <EXPERIMENT_NAME> --model-name <MODEL_NAME> --split-num <SPLITS_COUNT>

Structure of the repository

├── config <- Config files used for data processing and experimentation
│   ├── data
│   └── experiments <- Subdirectories of different experiments based on the dataset
│       ├── hawkes
│       ├── stack_overflow
│       └── vinted
├── data <- Place where raw and processed/generated data is stored
│   └── raw
│       └── stack_overflow
├── runs <- .sh files that run multiple python scripts
├── scripts <- Place where training and data processing python scripts are held
│   └── data
└── src <- Source files
    ├── datasets <- Datasets' implementations
    ├── models <- Models' implementations
    └── utils <- Various utility functions