Basic implementation of the life2vec model with the dummy data.

This repository contains basic code for the Using Sequences of Life-events to Predict Human Lives (life2vec) paper. The SocialComplexityLab/life2vec depends (in large) on the specific structure of the data and the version of the packages available.

Here, we provide a code with the simple dummy data (+ using the latest versions of Python packages) - the code contains only the backbone of the model. Thus, you can easily extend it for your specific use.

Open the simple_workflow.ipynb notebook to try a simple step-by-step workflow. The specifications for the Anaconda environment are located in Environment.yml; I run the code on the Windows Subsystem for Linux (WS2).

Compared to the original implementation of the life2vec model, this repository does not include the following:

1. Bookkeeping and logging functionality,
2. Data visualisation functionality,
3. Code for experiments, including robustness tests,
4. Implementation of loss and metric functions mentioned in the paper.

You can generate dummy data (that we use here as an example) using the Jupyter Notebook in misc/synthetic_data.ipynb:

1. Generate the dummy user database, aka users.csv,
2. Generate the dummy labor dataset, aka synth_labor.csv,
3. Move both to the data\rawdata folder.

• Add code for the pretraining
• More detailed annotation
• Add code with working data pipeline (by the 20th Feb)
• Add Data Example (misc/synthetic_data.ipynb)
• Create a Source file for the Synthetic Labor Data (src/sources/synth_labor.py)
• Add Logging support
• Add finetuning example with the specialised decoder

@misc{https://doi.org/10.5281/zenodo.10680995,
      doi = {10.5281/ZENODO.10680995},
      url = {https://zenodo.org/doi/10.5281/zenodo.10680995},
      author = {Germans Savcisens},
      title = {Github Repository for carlomarxdk/life2vec-light},
      publisher = {Zenodo},
      year = {2024},
      copyright = {Creative Commons Attribution 4.0 International}
}

If you want to cite a specific release, check the DOI number at the top of the README file (or see details of the release).

@article{savcisens2024using,
      author={Savcisens, Germans and Eliassi-Rad, Tina and Hansen, Lars Kai and Mortensen, Laust Hvas and Lilleholt, Lau and Rogers, Anna and Zettler, Ingo and Lehmann, Sune},
      title={Using sequences of life-events to predict human lives},
      journal={Nature Computational Science},
      year={2024},
      month={Jan},
      day={01},
      volume={4},
      number={1},
      pages={43-56},
      issn={2662-8457},
      doi={10.1038/s43588-023-00573-5},
      url={https://doi.org/10.1038/s43588-023-00573-5}
}

@misc{life2vec_code,
  author = {Germans Savcisens},
  title = {Official code for the "Using Sequences of Life-events to Predict Human Lives" paper},
  note = {GitHub: SocialComplexityLab/life2vec},
  year = {2023},
  howpublished = {\url{https://doi.org/10.5281/zenodo.10118621}},
}