alpfore

Name	alpfore JSON
Version	0.1.6 JSON
	download
home_page	https://github.com/nherringer/ALPineFOREst
Summary	Active Learning Pipeline For Optimal Ranking Estimation
upload_time	2025-08-31 03:10:14
maintainer	None
docs_url	None
author	Nicholas Herringer
requires_python	<4.0,>=3.7
license	MIT
keywords	active learning molecular simulation machine learning
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # ALPineFOREst

**A**ctive **L**earning **Pipel**ine **For** **Optima**l **Ranking Estimation**

[![PyPI version](https://badge.fury.io/py/alpfore.svg)](https://pypi.org/project/alpfore/)

ALPineFOREst is a flexible, modular framework for conducting large-scale active learning campaigns in scientific and materials research. It supports molecular dynamics (MD)-based evaluations, customizable models (e.g., Gaussian Processes), and popular Bayesian optimization strategies like Thompson Sampling — all within a high-throughput, reproducible pipeline.

---

## Installation

Install via PyPI:
```
pip install alpfore
```
Or to install from source:
```
git clone https://github.com/nherringer/ALPineFOREst.git
cd ALPineFOREst
pip install -e .
```


            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/nherringer/ALPineFOREst",
    "name": "alpfore",
    "maintainer": null,
    "docs_url": null,
    "requires_python": "<4.0,>=3.7",
    "maintainer_email": null,
    "keywords": "active learning, molecular simulation, machine learning",
    "author": "Nicholas Herringer",
    "author_email": "nherringer@uchicago.edu",
    "download_url": "https://files.pythonhosted.org/packages/b0/fb/972cfaa24ff03fb3516ed442b03f800a9f13427ad217865a5dbb589a296a/alpfore-0.1.6.tar.gz",
    "platform": null,
    "description": "# ALPineFOREst\n\n**A**ctive **L**earning **Pipel**ine **For** **Optima**l **Ranking Estimation**\n\n[![PyPI version](https://badge.fury.io/py/alpfore.svg)](https://pypi.org/project/alpfore/)\n\nALPineFOREst is a flexible, modular framework for conducting large-scale active learning campaigns in scientific and materials research. It supports molecular dynamics (MD)-based evaluations, customizable models (e.g., Gaussian Processes), and popular Bayesian optimization strategies like Thompson Sampling \u2014 all within a high-throughput, reproducible pipeline.\n\n---\n\n## Installation\n\nInstall via PyPI:\n```\npip install alpfore\n```\nOr to install from source:\n```\ngit clone https://github.com/nherringer/ALPineFOREst.git\ncd ALPineFOREst\npip install -e .\n```\n\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "Active Learning Pipeline For Optimal Ranking Estimation",
    "version": "0.1.6",
    "project_urls": {
        "Homepage": "https://github.com/nherringer/ALPineFOREst",
        "Repository": "https://github.com/nherringer/ALPineFOREst"
    },
    "split_keywords": [
        "active learning",
        " molecular simulation",
        " machine learning"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "d6a8fb26dcc68913806123ced2673dc6174e77b196ee865869218a4a667cf064",
                "md5": "50e1f4557eac8241ed37e4be8430942d",
                "sha256": "103d89865c5f3030959454d9af60bc296801bde7c46e6a3f779e68943a7c3348"
            },
            "downloads": -1,
            "filename": "alpfore-0.1.6-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "50e1f4557eac8241ed37e4be8430942d",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": "<4.0,>=3.7",
            "size": 30448,
            "upload_time": "2025-08-31T03:10:13",
            "upload_time_iso_8601": "2025-08-31T03:10:13.522774Z",
            "url": "https://files.pythonhosted.org/packages/d6/a8/fb26dcc68913806123ced2673dc6174e77b196ee865869218a4a667cf064/alpfore-0.1.6-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "b0fb972cfaa24ff03fb3516ed442b03f800a9f13427ad217865a5dbb589a296a",
                "md5": "ba3797059ae523e2b42f52a8f0f7b284",
                "sha256": "b9c195279e39357fbd3e599ba5d03274b366236782916852c3f146b74d491dfe"
            },
            "downloads": -1,
            "filename": "alpfore-0.1.6.tar.gz",
            "has_sig": false,
            "md5_digest": "ba3797059ae523e2b42f52a8f0f7b284",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": "<4.0,>=3.7",
            "size": 22309,
            "upload_time": "2025-08-31T03:10:14",
            "upload_time_iso_8601": "2025-08-31T03:10:14.721792Z",
            "url": "https://files.pythonhosted.org/packages/b0/fb/972cfaa24ff03fb3516ed442b03f800a9f13427ad217865a5dbb589a296a/alpfore-0.1.6.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-08-31 03:10:14",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "nherringer",
    "github_project": "ALPineFOREst",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "lcname": "alpfore"
}