{"id":18560856,"url":"https://github.com/harry24k/mida-pytorch","last_synced_at":"2025-04-10T02:31:13.532Z","repository":{"id":104396961,"uuid":"173681202","full_name":"Harry24k/MIDA-pytorch","owner":"Harry24k","description":"PyTorch implementation of \"MIDA: Multiple Imputation using Denoising Autoencoders\"","archived":false,"fork":false,"pushed_at":"2019-03-05T14:34:53.000Z","size":40,"stargazers_count":28,"open_issues_count":2,"forks_count":8,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-24T15:42:00.665Z","etag":null,"topics":["autoencoder","deep-learning","imputation","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Harry24k.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2019-03-04T05:46:31.000Z","updated_at":"2024-12-19T09:59:05.000Z","dependencies_parsed_at":null,"dependency_job_id":"4d1376d4-e867-4beb-8f07-f54ef6c7f215","html_url":"https://github.com/Harry24k/MIDA-pytorch","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FMIDA-pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FMIDA-pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FMIDA-pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FMIDA-pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Harry24k","download_url":"https://codeload.github.com/Harry24k/MIDA-pytorch/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248144194,"owners_count":21054881,"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":["autoencoder","deep-learning","imputation","pytorch"],"created_at":"2024-11-06T22:04:53.787Z","updated_at":"2025-04-10T02:31:13.521Z","avatar_url":"https://github.com/Harry24k.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MIDA-pytorch\n**A pytorch implementation of \"[MIDA: Multiple Imputation using Denoising Autoencoders](https://arxiv.org/abs/1705.02737)\"**\n\n## Summary\n1. Doing imputation with Overcomplete AutoEncoder for missing data\n2. Using complete data for training\n3. Dropout is used to generate artificial missings in the training session\n4. Experimenting with two missing methods(MCAR/MNAR)\n5. Simple but good\n\n## Requirements\n* python==3.6   \n* numpy==1.14.2   \n* pandas==0.22.0   \n* scikit-learn==0.19.1   \n* pytorch==1.0.0   \n\n## Data\nIn the paper, 15 publicly available datasets used.   \nIn this code, only 'Boston Housing' data is used among 15.   \nhttp://math.furman.edu/~dcs/courses/math47/R/library/mlbench/html/BostonHousing.html\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharry24k%2Fmida-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharry24k%2Fmida-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharry24k%2Fmida-pytorch/lists"}