The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

Related tags

Deep Learning data-science machine-learning scikit-learn active-learning
Overview

CircleCI Github Actions Codecov Documentation Status Pypi Version Black Python Versions DOI

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory.

Additionally, contributions at the algorithm level are available in the package mlresearch.

Installation

A Python distribution of version 3.8 or 3.9 is required to run this project. Due to the computational limitations of the free tiers in CI/CD platforms, currently we cannot ensure compatibility with earlier Python versions.

ML-Research requires:

  • numpy (>= 1.14.6)
  • pandas (>= 1.3.5)
  • sklearn (>= 1.0.0)
  • imblearn (>= 0.8.0)
  • rich (>= 10.16.1)
  • matplotlib (>= 2.2.3)
  • seaborn (>= 0.9.0)
  • rlearn (>= 0.2.1)
  • pytorch (>= 1.10.1)
  • torchvision (>= 0.11.2)
  • pytorch_lightning (>= 1.5.8)

User Installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip :

pip install -U ml-research

The documentation includes more detailed installation instructions.

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
pip install .[tests,docs]

Citing ML-Research

If you use ML-Research in a scientific publication, we would appreciate citations to the following paper:

@article{Fonseca2021,
  doi = {10.3390/RS13132619},
  url = {https://doi.org/10.3390/RS13132619},
  keywords = {SMOTE,active learning,artificial data generation,land use/land cover classification,oversampling},
  year = {2021},
  month = {jul},
  publisher = {Multidisciplinary Digital Publishing Institute},
  volume = {13},
  pages = {2619},
  author = {Fonseca, Joao and Douzas, Georgios and Bacao, Fernando},
  title = {{Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification}},
  journal = {Remote Sensing}
}
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Comments
  • Consider modifying default BYOL hyper-parameters for smaller batch sizes

    Consider modifying default BYOL hyper-parameters for smaller batch sizes

    Applicable to both BYOL and SimSiam: Some hyperparameters might need to be added. Some are hard-coded to the default values.

    Taken from the BYOL paper: Screenshot from 2022-03-18 17-54-43

    opened by joaopfonseca 1
  • Remove computer vision models, augmentations and datasets

    Remove computer vision models, augmentations and datasets

    They will be removed in the next release since:

    1. I'm not going to used these methods anytime soon and I don't have the time to test them properly
    2. They are out of scope of the library. It is meant to be used for machine learning techniques, focused on tabular data. In the feature it may be worth considering the development of another library for computer vision, for example.
    3. Setting Pytorch as a dependency for a reduced part of the library isn't particularly efficient.
    wontfix 
    opened by joaopfonseca 0
  • Host all raw data from datasets submodule elsewhere

    Host all raw data from datasets submodule elsewhere

    With Python 3.11, downloading some datasets returns an SSL error (when unsafe legacy renegotiation disabled). It happens when the server doesn't support "RFC 5746 secure renegotiation" and the client is using OpenSSL 3, which enforces that standard by default (source).

    Hosting the raw data elsewhere should fix this issue.

    bug 
    opened by joaopfonseca 0
  • Review and add examples to documentation

    Review and add examples to documentation

    The readthedocs page is getting a bit outdated:

    • [x] Add support for Python 3.10
    • [ ] Add support for Python 3.11
    • [ ] Check for missing, deleted or renamed functions and objects
    • [ ] Review content as a whole
    • [ ] Add examples to documentation
    • [ ] Add dependency groups to documentation
    • [ ] README contains dependencies that will no longer be used
    documentation 
    opened by joaopfonseca 0
Releases(v0.4a2)

  • v0.4a2(Jan 2, 2023)

    NOTE: This pre-release contains implementations of algorithms for Self-supervised learning (BYOL and SimSiam). This release also contains objects to download image data from Pytorch and general definitions for image augmentations. They will be removed in the next release since:

    1. I'm not going to used these methods anytime soon and I don't have the time to test them properly
    2. They are out of scope of the library. It is meant to be used for machine learning techniques, focused on tabular data. In the feature it may be worth considering the development of another library for computer vision, for example.
    3. Setting Pytorch as a dependency for a reduced part of the library isn't particularly efficient.

    Full Changelog: https://github.com/joaopfonseca/ml-research/compare/v0.4a1...v0.4a2

    Source code(tar.gz)
    Source code(zip)

  • v0.4a1(Apr 14, 2022)

  • v0.3.4(Feb 14, 2022)

  • v0.3.3(Feb 14, 2022)

  • v0.3.2(Feb 14, 2022)

  • v0.3.1(Feb 14, 2022)

  • v0.3.0(Feb 14, 2022)

  • v0.2.1(Feb 14, 2022)

  • v0.2.0(Feb 14, 2022)

  • 0.1.0(Feb 14, 2022)

Owner
João Fonseca
PhD student | Researcher | Invited lecturer @ NOVA Information Management School
João Fonseca
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