{"id":14970678,"url":"https://github.com/joaopfonseca/ml-research","last_synced_at":"2025-10-26T13:31:10.060Z","repository":{"id":42081538,"uuid":"459017274","full_name":"joaopfonseca/ml-research","owner":"joaopfonseca","description":"A Python library with utilities for Machine Learning research and algorithm 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align=\"center\"\u003e\n\u003cimg src=\"docs/_static/logo.png\" width=\"400px\"\u003e\n\u003c/div\u003e\n\n______________________________________________________________________\n\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/joaopfonseca/ml-research/actions/workflows/ci.yml\"\u003e\u003cimg alt=\"Github Actions\" src=\"https://github.com/joaopfonseca/ml-research/actions/workflows/ci.yml/badge.svg\"\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/joaopfonseca/ml-research\"\u003e\u003cimg alt=\"Codecov\" src=\"https://codecov.io/gh/joaopfonseca/ml-research/branch/master/graph/badge.svg?token=J2EBA4YTMN\"\u003e\u003c/a\u003e\n\u003ca href=\"https://mlresearch.readthedocs.io/en/latest/?badge=latest\"\u003e\u003cimg alt=\"Documentation Status\" src=\"https://readthedocs.org/projects/mlresearch/badge/?version=latest\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/psf/black\"\u003e\u003cimg alt=\"Black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"\u003e\u003c/a\u003e\n\u003ca href=\"https://img.shields.io/badge/python-3.10%20|%203.11%20|%203.12%20|%203.13-blue\"\u003e\u003cimg alt=\"Python Versions\" src=\"https://img.shields.io/badge/python-3.10%20|%203.11%20|%203.12%20|%203.13-blue\"\u003e\u003c/a\u003e\n\u003ca href=\"https://doi.org/10.1155/2023/7941878\"\u003e\u003cimg alt=\"DOI\" src=\"https://zenodo.org/badge/DOI/10.1155/2023/7941878.svg\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ctable align=\"center\"\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\n      \u003cb\u003ePyPI\u003c/b\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n      \u003ca href=\"https://badge.fury.io/py/ml-research\"\u003e\u003cimg alt=\"Pypi Version\" src=\"https://badge.fury.io/py/ml-research.svg\"\u003e\u003c/a\u003e\n      \u003ca href=\"https://pepy.tech/project/ml-research\"\u003e\u003cimg alt=\"Downloads\" src=\"https://static.pepy.tech/personalized-badge/ml-research?period=total\u0026units=international_system\u0026left_color=grey\u0026right_color=brightgreen\u0026left_text=downloads\"\u003e\u003c/a\u003e\n    \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\n      \u003cb\u003eAnaconda\u003c/b\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n      \u003ca href=\"https://anaconda.org/conda-forge/ml-research\"\u003e\u003cimg alt=\"Conda Version\" src=\"https://img.shields.io/conda/vn/conda-forge/ml-research.svg\"\u003e\u003c/a\u003e\n      \u003ca href=\"https://anaconda.org/conda-forge/ml-research\"\u003e\u003cimg alt=\"Conda Downloads\" src=\"https://img.shields.io/conda/dn/conda-forge/ml-research.svg\"\u003e\u003c/a\u003e\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n``ML-Research`` is an open source library for machine learning research. It\ncontains several utilities for Machine Learning research and algorithm\nimplementations. Specifically, it contains ``scikit-learn`` compatible\nimplementations for Active Learning and synthetic data generation, as well as\ndatasets and various utilities to assist in experiment design and results\nreporting.\n\nThe LaTeX and Python code for generating the paper, experiments' results and\nvisualizations shown in each paper is available (whenever possible) in the\n[publications repo](https://github.com/joaopfonseca/publications).\n\n## Installation\n\nA Python distribution of version \u003e= 3.10 is required to run this\nproject. Earlier Python versions might work in most cases, but they are not\ntested. ``ML-Research`` requires:\n\n- numpy (\u003e= 1.20.0)\n- pandas (\u003e= 2.1.0)\n- sklearn (\u003e= 1.2.0)\n- imblearn (\u003e= 0.8.0)\n\nSome functions in the ``mlresearch.utils`` submodule (the ones in the script\n``_visualization.py``) require Matplotlib (\u003e= 2.2.3). The\n``mlresearch.datasets`` submodule and ``mlresearch.utils.parallel_apply``\nfunction require tqdm (\u003e= 4.46.0) to display progress bars.\n\n### User Installation\n\nThe easiest way to install ml-research is using ``pip`` :\n\n    pip install -U ml-research\n\nOr ``conda`` :\n\n    conda install -c conda-forge ml-research\n\nThe documentation includes more detailed [installation\ninstructions](https://mlresearch.readthedocs.io/en/latest/getting-started.html).\n\n### Installing from source\n\nThe following commands should allow you to setup the development version of the\nproject with minimal effort:\n\n    # Clone the project.\n    git clone https://github.com/joaopfonseca/ml-research.git\n    cd ml-research\n\n    # Create and activate an environment \n    make environment \n    conda activate mlresearch # Adapt this line accordingly if you're not running conda\n\n    # Install project requirements and the research package. Dependecy group\n    # \"all\" will also install the dependency groups shown below.\n    pip install .[optional,tests,docs] \n\n## Citing ML-Research\n\nIf you use ML-Research in a scientific publication, we would appreciate\ncitations to the following paper:\n\n    @article{fonseca2023improving,\n      title={Improving Active Learning Performance through the Use of Data Augmentation},\n      author={Fonseca, Joao and Bacao, Fernando and others},\n      journal={International Journal of Intelligent Systems},\n      volume={2023},\n      year={2023},\n      publisher={Hindawi}\n    }\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoaopfonseca%2Fml-research","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoaopfonseca%2Fml-research","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoaopfonseca%2Fml-research/lists"}