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Open Source Science

https://github.com/thomaswong2022/thor-public

AutoML tools for solving Time-Varying High-Dimensional Ordinal Regression Problems

https://github.com/thomaswong2022/thor-public

Science Score: 20.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.8%) to scientific vocabulary

Keywords

deep-learning gradient-boosting incremental-learning machine-learning reinforcement-learning statistical-analysis time-series time-series-analysis
Last synced: 5 months ago · JSON representation

Repository

AutoML tools for solving Time-Varying High-Dimensional Ordinal Regression Problems

Basic Info
Statistics
  • Stars: 16
  • Watchers: 6
  • Forks: 3
  • Open Issues: 0
  • Releases: 1
Topics
deep-learning gradient-boosting incremental-learning machine-learning reinforcement-learning statistical-analysis time-series time-series-analysis
Created almost 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

THOR: Time-Varying High-dimensional Ordinal Regression

Downloads

THOR is a new autoML tool for temporal tabular datasets and time series. It handles high dimensional datasets with distribution shifts better than other tools. It makes use of the latest research results from incremental learning to improve robustness of machine learning methods.

Docker

As this packages used various machine learning and CUDA libaries for GPU support, we recommend to use docker to manage the dependencies.

The image is now uploaded on Docker Hub.

The following Docker images contains all the dependencies used in this tool.

```bash docker pull thomaswong2023/thor-public:deps docker run --gpus device=all -it -d --rm --name thor-public-example thomaswong2023/thor:public:deps bash

```

PyPI

This project is also on PyPI.

Install the package with the following command. Dependencies are not installed with the package

```bash pip install thorml -r requirements.txt

```

Citation

If you are using this package in your scientific work, we would appreciate citations to the following preprint on arxiv.

Dynamic Feature Projection and model selection methods for temporal tabular datasets with regime changes

Bibtex entry: @misc{wong2023dynamic, title={Dynamic Feature Engineering and model selection methods for temporal tabular datasets with regime changes}, author={Thomas Wong and Mauricio Barahona}, year={2023}, eprint={2301.00790}, archivePrefix={arXiv}, primaryClass={q-fin.CP} }

Owner

  • Name: Thomas Wong
  • Login: ThomasWong2022
  • Kind: user

Machine Learning, Quantitative Finance and Data Engineering.

GitHub Events

Total
  • Watch event: 1
  • Fork event: 1
Last Year
  • Watch event: 1
  • Fork event: 1

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 24
  • Total Committers: 3
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.583
Past Year
  • Commits: 24
  • Committers: 3
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.583
Top Committers
Name Email Commits
ThomasWong2022 m****5@i****k 10
John Doe j****e@e****m 10
Thomas Wong 3****2 4
Committer Domains (Top 20 + Academic)
ic.ac.uk: 1

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 202 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 0
    (may contain duplicates)
  • Total versions: 3
  • Total maintainers: 1
pypi.org: thor-public

AutoML tools for Tabular Dataset

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 116 Last month
Rankings
Dependent packages count: 6.9%
Downloads: 17.4%
Average: 18.3%
Dependent repos count: 30.6%
Maintainers (1)
ThomasW2046
Last synced: over 1 year ago
pypi.org: thorml

AutoML tools for Tabular Datasets

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 86 Last month
Rankings
Dependent packages count: 7.0%
Average: 26.9%
Dependent repos count: 30.5%
Forks count: 30.6%
Stargazers count: 39.5%
Maintainers (1)
ThomasW2046
Last synced: about 1 year ago

Dependencies

.github/workflows/release.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/test.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
Dockerfile docker
  • gcr.io/kaggle-gpu-images/python latest build
pyproject.toml pypi
requirements.txt pypi
  • catboost *
  • cuml *
  • cupy *
  • joblib *
  • lightgbm *
  • numpy *
  • optuna *
  • pandas *
  • scikit-learn *
  • scipy *
  • setuptools *
  • signatory *
  • torch *
  • xgboost *
setup.py pypi
  • Pillow *