{"id":15469231,"url":"https://github.com/kulbachcedric/evoautoml","last_synced_at":"2025-10-27T21:07:43.049Z","repository":{"id":52284348,"uuid":"331562781","full_name":"kulbachcedric/EvOAutoML","owner":"kulbachcedric","description":null,"archived":false,"fork":false,"pushed_at":"2022-12-11T14:26:19.000Z","size":15143,"stargazers_count":17,"open_issues_count":5,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-03T08:08:53.226Z","etag":null,"topics":["automl","data-science","incremental-learning","machine-learning","online-learning","python"],"latest_commit_sha":null,"homepage":"https://kulbachcedric.github.io/EvOAutoML/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kulbachcedric.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}},"created_at":"2021-01-21T08:27:22.000Z","updated_at":"2024-07-11T00:33:49.000Z","dependencies_parsed_at":"2023-01-27T01:01:02.974Z","dependency_job_id":null,"html_url":"https://github.com/kulbachcedric/EvOAutoML","commit_stats":null,"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulbachcedric%2FEvOAutoML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulbachcedric%2FEvOAutoML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulbachcedric%2FEvOAutoML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulbachcedric%2FEvOAutoML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kulbachcedric","download_url":"https://codeload.github.com/kulbachcedric/EvOAutoML/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224391698,"owners_count":17303635,"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":["automl","data-science","incremental-learning","machine-learning","online-learning","python"],"created_at":"2024-10-02T01:56:35.066Z","updated_at":"2025-10-27T21:07:42.967Z","avatar_url":"https://github.com/kulbachcedric.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg height=\"150px\" src=\"docs/img/logo.png\" alt=\"incremental dl logo\"\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    EvO AutoML is a Python library for Evolution based Online AutoML.\n    EvO AutoML ambition is to enable hyperparameter optimization for \u003ca href=\"https://www.wikiwand.com/en/Online_machine_learning\"\u003eonline machine learning\u003c/a\u003e pipelines build on \u003ca href=\"https://riverml.xyz/latest/\"\u003eriver\u003c/a\u003e.\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n    \u003cimg alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/EvoAutoML\"\u003e\n    \u003ca href=\"https://codecov.io/gh/kulbachcedric/EvOAutoML\" \u003e\n        \u003cimg src=\"https://codecov.io/gh/kulbachcedric/EvOAutoML/branch/master/graph/badge.svg?token=7RIEXKNR6K\"/\u003e\n    \u003c/a\u003e\n    \u003cimg alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dw/EvOAutoML\"\u003e\n    \u003cimg alt=\"GitHub\" src=\"https://img.shields.io/github/license/kulbachcedric/EvoAutoML\"\u003e \n\n\u003c/p\u003e\n\n# EvO AutoML\n\nEvO AutoML is a Python library for Evolution based Online AutoML.\n\n## 💈 Installation\n\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install EvoAutoML.\n\n```bash\npip install evoautoml\n```\n\nYou can install the latest development version from GitHub as so:\n```shell\npip install https://github.com/kulbachcedric/EvOAutoML//archive/refs/heads/master.zip\n```\n## 🍫 Quickstart\n### Classification\n\n```python\n\n\u003e\u003e\u003e from river import datasets, ensemble, evaluate, metrics, compose, optim\n\u003e\u003e\u003e from river import preprocessing, neighbors, naive_bayes, tree, linear_model\n\u003e\u003e\u003e from EvOAutoML import classification, pipelinehelper\n\u003e\u003e\u003e dataset = datasets.Phishing()\n\u003e\u003e\u003e model_pipeline = compose.Pipeline(\n...     ('Scaler', pipelinehelper.PipelineHelperTransformer([\n...         ('StandardScaler', preprocessing.StandardScaler()),\n...         ('MinMaxScaler', preprocessing.MinMaxScaler()),\n...         ('MinAbsScaler', preprocessing.MaxAbsScaler()),\n...     ])),\n...     ('Classifier', pipelinehelper.PipelineHelperClassifier([\n...         ('HT', tree.HoeffdingTreeClassifier()),\n...         ('LR', linear_model.LogisticRegression()),\n...         ('GNB', naive_bayes.GaussianNB()),\n...         ('KNN', neighbors.KNNClassifier()),\n...     ])))\n\u003e\u003e\u003e model = classification.EvolutionaryBaggingClassifier(\n...     model=model_pipeline,\n...     param_grid={\n...         'Scaler': model_pipeline.steps['Scaler'].generate({}),\n...         'Classifier': model_pipeline.steps['Classifier'].generate({\n...             'HT__max_depth': [10, 30, 60, 10, 30, 60],\n...             'HT__grace_period': [10, 100, 200, 10, 100, 200],\n...             'HT__max_size': [5, 10],\n...             'LR__l2': [.0,.01,.001],\n...             'KNN__n_neighbors': [1, 5, 20],\n...             'KNN__window_size': [100, 500, 1000],\n...             'KNN__weighted': [True, False],\n...             'KNN__p': [1, 2],\n...         })\n...     },\n...     seed=42\n... )\n\u003e\u003e\u003e metric = metrics.F1()\n\u003e\u003e\u003e for x, y in dataset:\n...     y_pred = model.predict_one(x)  # make a prediction\n...     metric = metric.update(y, y_pred)  # update the metric\n...     model = model.learn_one(x,y)  # make the model learn\n\n```\n\n## 📚 Cite\n```\n@inproceedings{DBLP:conf/pakdd/KulbachMBHB22,\n  author    = {Cedric Kulbach and\n               Jacob Montiel and\n               Maroua Bahri and\n               Marco Heyden and\n               Albert Bifet},\n  editor    = {Jo{\\~{a}}o Gama and\n               Tianrui Li and\n               Yang Yu and\n               Enhong Chen and\n               Yu Zheng and\n               Fei Teng},\n  title     = {Evolution-Based Online Automated Machine Learning},\n  booktitle = {Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia\n               Conference, {PAKDD} 2022, Chengdu, China, May 16-19, 2022, Proceedings,\n               Part {I}},\n  series    = {Lecture Notes in Computer Science},\n  volume    = {13280},\n  pages     = {472--484},\n  publisher = {Springer},\n  year      = {2022},\n  url       = {https://doi.org/10.1007/978-3-031-05933-9\\_37},\n  doi       = {10.1007/978-3-031-05933-9\\_37},\n  timestamp = {Tue, 17 May 2022 15:53:17 +0200},\n  biburl    = {https://dblp.org/rec/conf/pakdd/KulbachMBHB22.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```\n## 🏫 Affiliations\n\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"https://upload.wikimedia.org/wikipedia/de/thumb/4/44/Fzi_logo.svg/1200px-Fzi_logo.svg.png?raw=true\" alt=\"FZI Logo\" height=\"200\"/\u003e\n\u003c/p\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkulbachcedric%2Fevoautoml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkulbachcedric%2Fevoautoml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkulbachcedric%2Fevoautoml/lists"}