{"id":20696459,"url":"https://github.com/andreazoccatelli/light_permanova","last_synced_at":"2026-05-08T02:15:38.348Z","repository":{"id":254164549,"uuid":"839451732","full_name":"AndreaZoccatelli/light_permanova","owner":"AndreaZoccatelli","description":"A lightweight implementation of PERMANOVA based on Euclidean distance from centroid","archived":false,"fork":false,"pushed_at":"2024-09-19T21:46:05.000Z","size":2757,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-11T02:47:30.345Z","etag":null,"topics":["computervision","numpy","permanova","pytorch","statistics","tabular-data"],"latest_commit_sha":null,"homepage":"https://light-permanova.readthedocs.io","language":"Jupyter Notebook","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/AndreaZoccatelli.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-07T16:27:33.000Z","updated_at":"2024-09-19T21:46:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"98d0ef3d-f335-418b-88c0-4155e0c35021","html_url":"https://github.com/AndreaZoccatelli/light_permanova","commit_stats":null,"previous_names":["andreazoccatelli/light_permanova"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AndreaZoccatelli/light_permanova","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AndreaZoccatelli%2Flight_permanova","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AndreaZoccatelli%2Flight_permanova/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AndreaZoccatelli%2Flight_permanova/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AndreaZoccatelli%2Flight_permanova/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AndreaZoccatelli","download_url":"https://codeload.github.com/AndreaZoccatelli/light_permanova/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AndreaZoccatelli%2Flight_permanova/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":27992997,"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","status":"online","status_checked_at":"2025-12-24T02:00:07.193Z","response_time":83,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["computervision","numpy","permanova","pytorch","statistics","tabular-data"],"created_at":"2024-11-17T00:14:03.748Z","updated_at":"2025-12-24T02:11:51.297Z","avatar_url":"https://github.com/AndreaZoccatelli.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# LightPERMANOVA\nA lightweight implementation of PERMANOVA based on Euclidean distance from centroid.\n\n## Overview\nOne known problem of machine learning models in production that affects their predictive ability is covariate shift. It is defined as a change in the distribution of one or more independent variables used to train the model.\n\nANOVA is often adopted to assess if two samples are from the same population by comparing the variance of their means (H0: all $$\\mu$$’s are equal; H1: at least one pair of $$\\mu$$’s are\nnot equal). This test relies, however, on the normality assumption of the samples, which makes it a non-viable solution to effectively monitor batches of data.\n\nPERMANOVA is a multivariate version of ANOVA based on the pseudo-F statistic, which makes use of permutations, allowing for a non-parametric estimation.\n\nIn the case of covariates shift monitoring, the test compares the original sample $$s_0$$ used at time $$t_0$$ to train the model with a new, unseen sample $$s_1$$ on which the model made predictions at time $$t_1$$.\n\n\n## Useful links\nRead the docs [here](https://light-permanova.readthedocs.io/en/latest/index.html).\n\nThis project is part of \"[Root.](https://andrea-zoccatelli.gitbook.io/me/v/root.)\".\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreazoccatelli%2Flight_permanova","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandreazoccatelli%2Flight_permanova","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreazoccatelli%2Flight_permanova/lists"}