{"id":15140767,"url":"https://github.com/jpaillard/kernelchallenge","last_synced_at":"2026-01-19T22:01:50.065Z","repository":{"id":188513653,"uuid":"615088038","full_name":"jpaillard/KernelChallenge","owner":"jpaillard","description":"Data Challenge - Kernel methods","archived":false,"fork":false,"pushed_at":"2023-04-15T12:38:39.000Z","size":823,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-12T19:39:23.431Z","etag":null,"topics":["graphs","kernel-logistic-regression","kernel-methods","kernel-support-vector-machine","molecule","weisfeiler-lehman"],"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/jpaillard.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}},"created_at":"2023-03-16T23:37:13.000Z","updated_at":"2023-04-10T17:14:10.000Z","dependencies_parsed_at":"2023-08-15T17:54:41.355Z","dependency_job_id":null,"html_url":"https://github.com/jpaillard/KernelChallenge","commit_stats":null,"previous_names":["jpaillard/kernelchallenge"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpaillard%2FKernelChallenge","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpaillard%2FKernelChallenge/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpaillard%2FKernelChallenge/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jpaillard%2FKernelChallenge/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jpaillard","download_url":"https://codeload.github.com/jpaillard/KernelChallenge/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247487896,"owners_count":20946843,"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":["graphs","kernel-logistic-regression","kernel-methods","kernel-support-vector-machine","molecule","weisfeiler-lehman"],"created_at":"2024-09-26T08:40:57.537Z","updated_at":"2026-01-19T22:01:50.059Z","avatar_url":"https://github.com/jpaillard.png","language":"Python","readme":"# Data Challenge - Kernel methods\n[![codecov](https://codecov.io/github/jpaillard/KernelChallenge/branch/master/graph/badge.svg?token=TJZSQ80QCV)](https://codecov.io/github/jpaillard/KernelChallenge)\n\n[https://www.kaggle.com/competitions/data-challenge-kernel-methods-2022-2023](https://www.kaggle.com/competitions/data-challenge-kernel-methods-2022-2023)\n\nPlease refer to the `report.pdf` file to learn more about the project and the methods.\n\n\n## Install\n```\ngit clone \u003cssh/html repo\u003e\n```\nRequirements and conda env\n```\nconda env create -f environment.yml\n```\n\nInstall the package itself in developer mode\n```\npip install -e .\n```\n\n## Usage\n```\nusage: main.py [-h] [--data_path DATA_PATH] [--n N] [--h_iter H_ITER] [--c C] [--method METHOD] [--edges]\n               [--submit]\n\noptions:\n  -h, --help            show this help message and exit\n  --data_path DATA_PATH\n                        Path to folder that contains the dataset (.pkl files)\n  --n N                 Number of samples from the dataset to use for training\n  --h_iter H_ITER       Number of iterations (depth) for the WL algorithm\n  --c C                 Regularization parameter for the classifier\n  --method METHOD       Classifier to use (SVC or KLR)\n  --edges               Use edge embedding in the WL algorithm (see report for more details)\n  --submit              create submission file for the challenge\n```\n\n### The best results have been obtained using the following command line:\n` \npython main.py --method SVC --n 6000 --c 0.01 --submit --h_iter 1 --edges\n`\n\n## Description\n### Architecture\n```\n.\n├── KernelChallenge       # Main package\n│   ├── kernels.py        # WL kernel implementation\n│   ├── SVC.py            # SVC implementation\n|   ├── KLR.py            # KLR implementation\n|   ├── preprocessing.py  # preprocessing scripts before WL kernel\n│   └── ...\n├── tests                 # PyTest scripts\n│   └── ...\n├── environment.yml       # Conda environment\n├── format_output.py      # script to format the prediction for kaggle competition   \n├── main.py               # executable script\n├── report.pdf            # Report presenting the project\n└── ...\n```\n\nImplementation of the Weisfeiler-lehman kernel for graph classification. And two kernel methods classification algorithms: SVR and KLR. \n\n\n```BibTex\n@article{shervashidze2011weisfeiler,\n  title={Weisfeiler-lehman graph kernels.},\n  author={Shervashidze, Nino and Schweitzer, Pascal and Van Leeuwen, Erik Jan and Mehlhorn, Kurt and Borgwardt, Karsten M},\n  journal={Journal of Machine Learning Research},\n  year={2011}\n}\n```","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjpaillard%2Fkernelchallenge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjpaillard%2Fkernelchallenge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjpaillard%2Fkernelchallenge/lists"}