{"id":26046844,"url":"https://github.com/sigilbyte/choquet-classifier","last_synced_at":"2026-05-05T10:33:03.406Z","repository":{"id":62822558,"uuid":"510484114","full_name":"sigilbyte/choquet-classifier","owner":"sigilbyte","description":"Implementation of the Choquet classifier using the scikit-learn API design.","archived":false,"fork":false,"pushed_at":"2023-11-25T16:29:14.000Z","size":352,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-24T15:48:54.718Z","etag":null,"topics":["machine-learning","regression","regression-models","scikit-learn","scikitlearn-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","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/sigilbyte.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":"2022-07-04T19:42:44.000Z","updated_at":"2023-11-27T14:20:01.000Z","dependencies_parsed_at":"2024-11-15T10:45:38.680Z","dependency_job_id":"13988b43-eacc-43cd-a205-46e8b5595516","html_url":"https://github.com/sigilbyte/choquet-classifier","commit_stats":{"total_commits":89,"total_committers":2,"mean_commits":44.5,"dds":0.1573033707865169,"last_synced_commit":"9f76fdd162d133619471a79fe78b4aaa06f4bc54"},"previous_names":["sigilbyte/choquet-classifier"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sigilbyte%2Fchoquet-classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sigilbyte%2Fchoquet-classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sigilbyte%2Fchoquet-classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sigilbyte%2Fchoquet-classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sigilbyte","download_url":"https://codeload.github.com/sigilbyte/choquet-classifier/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242467570,"owners_count":20133114,"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":["machine-learning","regression","regression-models","scikit-learn","scikitlearn-machine-learning"],"created_at":"2025-03-07T21:48:54.872Z","updated_at":"2026-05-05T10:33:03.360Z","avatar_url":"https://github.com/sigilbyte.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Choquet-classifier\n\nImplementation of the Choquet classifier. This model was presented in \"Learning monotone nonlinear models using the Choquet integral\" [[1]](#1)\n\n## Installation\n\n### Dependencies\n- Python (\u003e=3.9)\n- NumPy (\u003e=1.23.3)\n- SciPy (\u003e=1.9.1)\n- scikit-learn (\u003e=1.1.1)\n\n### User installation\n- clone this repository or\n- Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the Choquet classifier.\n\n```bash\npip install choquet-classifier-glenscalai\n```\n\n# Usage\nThe application is compatible to [scikit-learn](https://scikit-learn.org/stable/) and can be used like every other classifier from the scikit-learn library. In order to use the Choquet classifier, import the class **ChoquetClassifier** from the module **choquet_classifier** from the package **classifier**. Some examples are shown below:\n\n## Default example\n\nUse the constructor and the function **fit** to initialize the Choquet classifier for a given dataset.\n\n```python\n\u003e\u003e\u003e from choquet_classifier_glenscalai.choquet_classifier import ChoquetClassifier\n\u003e\u003e\u003e X = [[1, 3, 2],\n...      [1, 0, 3]]\n\u003e\u003e\u003e y = [1, 0]\n\u003e\u003e\u003e cc = ChoquetClassifier()\n\u003e\u003e\u003e cc.fit(X, y)\n```\nUse the function **predict** to classify samples.\n\n```python\n\u003e\u003e\u003e Z = [[1, 1, 2],\n...      [2, 1, 3]]\n\u003e\u003e\u003e cc.predict(Z)\narray([0, 0])\n```\n\n## Examples with hyper-parameters\n\n```python\n\u003e\u003e\u003e from choquet_classifier_glenscalai.choquet_classifier import ChoquetClassifier\n\u003e\u003e\u003e X = [[1, 3, 2],\n...      [1, 0, 3]]\n\u003e\u003e\u003e y = [1, 0]\n\u003e\u003e\u003e cc = ChoquetClassifier(additivity=3, regularization=1)\n\u003e\u003e\u003e cc.fit(X, y)\n```\nAgain, the function **predict** can be used to classify samples. Note the different output compared to the first example.\n\n```python\n\u003e\u003e\u003e Z = [[1, 1, 2],\n...      [2, 1, 3]]\n\u003e\u003e\u003e sc.predict(Z)\narray([0, 1])\n```\n\n## Example with different class labels\n\nThe classes do not have to be labeled with 0 and 1; any integer numbers or strings may be used instead. The smaller label in terms of the relation or lexicographic ordering is given to the negative class, whereas the other label is assigned to the positive class.\n\nThe first example contains the class labels 2 and 1. Label 2 is assigned to the positive class and label 1 is assigned to the negative class since 2\u003e1.\n\n```python\n\u003e\u003e\u003e from choquet_classifier_glenscalai.choquet_classifier import ChoquetClassifier\n\u003e\u003e\u003e X = [[1, 3, 2],\n...      [1, 0, 3]]\n\u003e\u003e\u003e y = [2, 1]\n\u003e\u003e\u003e cc = ChoquetClassifier()\n\u003e\u003e\u003e cc.fit(X, y)\n\u003e\u003e\u003e Z = [[1, 1, 2],\n...      [2, 1, 3]]\n\u003e\u003e\u003e sc.predict(Z)\narray([1, 1])\n```\n\nThe second example contains the class labels 'one' and 'two'. Label 'one' is assigned to the negative class and label 'two' is assigned to the positive class because 'one' comes lexicographically first.\n\n```python\n\u003e\u003e\u003e from choquet_classifier_glenscalai.choquet_classifier import ChoquetClassifier\n\u003e\u003e\u003e X = [[1, 3, 2],\n...      [1, 0, 3]]\n\u003e\u003e\u003e y = ['two', 'one']\n\u003e\u003e\u003e cc = ChoquetClassifier()\n\u003e\u003e\u003e cc.fit(X, y)\n\u003e\u003e\u003e Z = [[1, 1, 2],\n...      [2, 1, 3]]\n\u003e\u003e\u003e sc.predict(Z)\narray(['one', 'one'])\n```\n\n# Licence\n[MIT](https://choosealicense.com/licenses/mit/)\n\n# Reference\n[1] Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski and Eyke Hüllermeier. Learning monotone nonlinear models using the Choquet integral. 2012.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsigilbyte%2Fchoquet-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsigilbyte%2Fchoquet-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsigilbyte%2Fchoquet-classifier/lists"}