FRDC-ML

Forest Recover Digital Companion Machine Learning Pipeline Repository

This repository contains all code regarding our models used. This is part of the entire E2E pipeline for our product.

graph LR
    A[Data Collection] --> B[FRDC-ML] --> C[FRDC-UI]

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Currently, it's heavily WIP.

Getting Started

I highly recommend reading our website documentation. There contains tutorials and docs on how to use our modules.

Dev Info

FRDC-ML/
    src/                    # All relevant code
        frdc/               # Package/Component Level code
            load/           # Image I/O
            preprocess/     # Image Preprocessing
            train/          # ML Training
            evaluate/       # Model Evaluation
            ...             # ...
        main.py             # Pipeline Entry Point

    tests/                  # PyTest Tests
        model-tests/        # Tests for each model
        integration-tests/  # Tests that run the entire pipeline
        unit-tests/         # Tests for each component

    poetry.lock             # Poetry managed environment file
    pyproject.toml          # Project-level information: requirements, settings, name, deployment info

    .github/                # GitHub Actions

Our Architecture

This is a classic, simple Python Package architecture, however, we HEAVILY EMPHASIZE encapsulation of each stage. That means, there should never be data that IMPLICITLY persists across stages.

To illustrate this, take a look at how tests/model_tests/chestnut_dec_may/train.py is written. It pulls in relevant modules from each stage and constructs a pipeline.

Contributing

Pre-commit Hooks

We use Black and Flake8 as our pre-commit hooks. To install them, run the following commands:

poetry install
pre-commit install

If you're using pip instead of poetry, run the following commands:

pip install pre-commit
pre-commit install

Alternatively, you can use Black configured with your own IDE.