{"id":18435716,"url":"https://github.com/ibis-project/ibis-ml","last_synced_at":"2025-04-12T22:29:12.547Z","repository":{"id":74766751,"uuid":"588674493","full_name":"ibis-project/ibis-ml","owner":"ibis-project","description":"IbisML is a library for building scalable ML pipelines using Ibis.","archived":false,"fork":false,"pushed_at":"2024-12-25T15:19:37.000Z","size":2741,"stargazers_count":108,"open_issues_count":29,"forks_count":15,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-04-05T21:40:47.575Z","etag":null,"topics":["feature-engineering","ibis","machine-learning","sql"],"latest_commit_sha":null,"homepage":"https://ibis-project.github.io/ibis-ml/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ibis-project.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":"2023-01-13T17:51:53.000Z","updated_at":"2025-03-23T06:35:41.000Z","dependencies_parsed_at":"2024-01-12T19:08:20.196Z","dependency_job_id":"c483d268-09fa-489a-a3d8-d8e58d500d11","html_url":"https://github.com/ibis-project/ibis-ml","commit_stats":null,"previous_names":["ibis-project/ibisml","ibis-project/ibis-ml"],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ibis-project%2Fibis-ml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ibis-project%2Fibis-ml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ibis-project%2Fibis-ml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ibis-project%2Fibis-ml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ibis-project","download_url":"https://codeload.github.com/ibis-project/ibis-ml/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248639382,"owners_count":21137838,"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":["feature-engineering","ibis","machine-learning","sql"],"created_at":"2024-11-06T06:09:08.962Z","updated_at":"2025-04-12T22:29:12.528Z","avatar_url":"https://github.com/ibis-project.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# IbisML\n\n[![Build status](https://github.com/ibis-project/ibis-ml/actions/workflows/ci.yml/badge.svg)](https://github.com/ibis-project/ibis-ml/actions/workflows/ci.yml)\n[![Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://ibis-project.github.io/ibis-ml/)\n[![License](https://img.shields.io/github/license/ibis-project/ibis-ml.svg)](https://github.com/ibis-project/ibis-ml/blob/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/ibis-ml.svg)](https://pypi.org/project/ibis-ml/)\n\n## What is IbisML?\n\nIbisML is a library for building scalable ML pipelines using Ibis:\n\n- Preprocess your data at scale on any [Ibis](https://ibis-project.org/)-supported\n  backend.\n- Compose [`Recipe`](/reference/core.html#ibis_ml.Recipe)s with other scikit-learn\n  estimators using\n  [`Pipeline`](https://scikit-learn.org/stable/modules/compose.html#pipeline-chaining-estimators)s.\n- Seamlessly integrate with [scikit-learn](https://scikit-learn.org/stable/),\n  [XGBoost](https://xgboost.readthedocs.io/en/stable/python/sklearn_estimator.html), and\n  [PyTorch](https://skorch.readthedocs.io/en/stable/) models.\n\n## How do I install IbisML?\n\n```bash\npip install ibis-ml\n```\n\n## How do I use IbisML?\n\nWith recipes, you can define sequences of feature engineering steps to get your data\nready for modeling. For example, create a recipe to replace missing values using the\nmean of each numeric column and then normalize numeric data to have a standard deviation\nof one and a mean of zero.\n\n```python\nimport ibis_ml as ml\n\nimputer = ml.ImputeMean(ml.numeric())\nscaler = ml.ScaleStandard(ml.numeric())\nrec = ml.Recipe(imputer, scaler)\n```\n\nA recipe can be chained in a\n[`Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)\nlike any other\n[transformer](https://scikit-learn.org/stable/glossary.html#term-transformer).\n\n```python\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.svm import SVC\n\npipe = Pipeline([(\"rec\", rec), (\"svc\", SVC())])\n```\n\nThe pipeline can be used as any other estimator and avoids leaking the test set into the\ntrain set.\n\n```python\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\n\nX, y = make_classification(random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\npipe.fit(X_train, y_train).score(X_test, y_test)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fibis-project%2Fibis-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fibis-project%2Fibis-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fibis-project%2Fibis-ml/lists"}