{"id":16651340,"url":"https://github.com/dkatz23238/randomforestadaptiveexperim","last_synced_at":"2026-04-08T21:31:48.162Z","repository":{"id":98244011,"uuid":"185485277","full_name":"dkatz23238/RandomForestAdaptiveExperim","owner":"dkatz23238","description":"Using Facebook Adaptive Experimentation platform to tune random forest regressors using docker","archived":false,"fork":false,"pushed_at":"2019-05-10T23:50:44.000Z","size":4714,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-10-31T11:16:34.041Z","etag":null,"topics":["adaptive-learning","docker","docker-compose","hyper-parameter-tuning","machine-learning","pytorch"],"latest_commit_sha":null,"homepage":null,"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/dkatz23238.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":"2019-05-07T22:13:21.000Z","updated_at":"2025-07-01T11:40:29.000Z","dependencies_parsed_at":"2023-05-18T22:30:40.179Z","dependency_job_id":null,"html_url":"https://github.com/dkatz23238/RandomForestAdaptiveExperim","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dkatz23238/RandomForestAdaptiveExperim","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dkatz23238%2FRandomForestAdaptiveExperim","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dkatz23238%2FRandomForestAdaptiveExperim/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dkatz23238%2FRandomForestAdaptiveExperim/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dkatz23238%2FRandomForestAdaptiveExperim/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dkatz23238","download_url":"https://codeload.github.com/dkatz23238/RandomForestAdaptiveExperim/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dkatz23238%2FRandomForestAdaptiveExperim/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31575451,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["adaptive-learning","docker","docker-compose","hyper-parameter-tuning","machine-learning","pytorch"],"created_at":"2024-10-12T09:24:55.083Z","updated_at":"2026-04-08T21:31:48.145Z","avatar_url":"https://github.com/dkatz23238.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ax-container\n\nDependens:\n\nPython 3.7+\n\nThis project does the following:\n- Loads a machine learning data set\n- Instantiates an Adaptive Experimentation service loop\n- Trains and tunes an xgboost.XGBRegressor model on a dataset. The dataset provided by default is house pricing data in buenos aires.\n\nNote: the data set must be ready to process by an sklearn or xgboost algorithm.\n\n## Quickstart\n\nReview the enviornment variables and if needed update the dataset.csv with custom data set. Remember to update the TARGET enviornment variable with the name of the target variable column.\n\nThe following enviornment variables can be used and modifed in docker-compose.yml:\n - N_TRIALS: the amount of trials to run by adaptive experimentation\n - DATASET_PATH: the path to the machine learning dataset\n - DATASET_TARGET_NAME: the name of the column that contains the target variable\n\n``` docker-compose up```\n\nor without docker:\n\ncd to app\n``` python -m pip install -r requirements.txt \u0026\u0026 python run.py```\n\nThe hyper-parameter tunining process will be printed to screen or loged to the docker compose logs.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdkatz23238%2Frandomforestadaptiveexperim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdkatz23238%2Frandomforestadaptiveexperim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdkatz23238%2Frandomforestadaptiveexperim/lists"}