https://github.com/joaopfonseca/ml-research

A Python library with utilities for Machine Learning research and algorithm implementations
https://github.com/joaopfonseca/ml-research

active-learning data-science machine-learning python scikit-learn

Last synced: 3 months ago
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A Python library with utilities for Machine Learning research and algorithm implementations

• Host: GitHub
• URL: https://github.com/joaopfonseca/ml-research
• Owner: joaopfonseca
• License: mit
• Created: 2022-02-14T04:38:16.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2024-09-19T13:56:04.000Z (3 months ago)
• Last Synced: 2024-09-28T13:22:59.668Z (3 months ago)
• Topics: active-learning, data-science, machine-learning, python, scikit-learn
• Language: Python
• Homepage:
• Size: 409 MB
• Stars: 5
• Watchers: 2
• Forks: 2
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        






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``ML-Research`` is an open source library for machine learning research. It

contains several utilities for Machine Learning research and algorithm

implementations. Specifically, it contains ``scikit-learn`` compatible

implementations for Active Learning and synthetic data generation, as well as

datasets and various utilities to assist in experiment design and results

reporting.

The LaTeX and Python code for generating the paper, experiments' results and

visualizations shown in each paper is available (whenever possible) in the

[publications repo](https://github.com/joaopfonseca/publications).

## Installation

A Python distribution of version >= 3.9 is required to run this

project. Earlier Python versions might work in most cases, but they are not

tested. ``ML-Research`` requires:

- numpy (>= 1.20.0)

- pandas (>= 2.1.0)

- sklearn (>= 1.2.0)

- imblearn (>= 0.8.0)

Some functions in the ``mlresearch.utils`` submodule (the ones in the script

``_visualization.py``) require Matplotlib (>= 2.2.3). The

``mlresearch.datasets`` submodule and ``mlresearch.utils.parallel_apply``

function require tqdm (>= 4.46.0) to display progress bars.

### User Installation

The easiest way to install ml-research is using ``pip`` :

    pip install -U ml-research

Or ``conda`` :

    conda install -c conda-forge ml-research

The documentation includes more detailed [installation

instructions](https://mlresearch.readthedocs.io/en/latest/getting-started.html).

### Installing from source

The following commands should allow you to setup the development version of the

project with minimal effort:

    # Clone the project.

    git clone https://github.com/joaopfonseca/ml-research.git

    cd ml-research

    # Create and activate an environment 

    make environment 

    conda activate mlresearch # Adapt this line accordingly if you're not running conda

    # Install project requirements and the research package. Dependecy group

    # "all" will also install the dependency groups shown below.

    pip install .[optional,tests,docs] 

## Citing ML-Research

If you use ML-Research in a scientific publication, we would appreciate

citations to the following paper:

    @article{fonseca2023improving,

      title={Improving Active Learning Performance through the Use of Data Augmentation},

      author={Fonseca, Joao and Bacao, Fernando and others},

      journal={International Journal of Intelligent Systems},

      volume={2023},

      year={2023},

      publisher={Hindawi}

    }