Autoencoder Trainer Overview

• This project includes a Python implementation of an autoencoder model to reduce dimensionality of hyperspectral leaf reflectance data.
• The model is designed to preprocess the data, build an autoencoder model, and train it with a sample dataset.
• Cite: Tross MC, Grzybowski MW, Jubery TZ, Grove RJ, Nishimwe AV, Torres-Rodriguez JV, Guangchao S, Ganapathysubramanian B, Yufeng G, Schnable JC (2023) Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel. bioRxiv doi: 10.1101/2023.12.15.571950

Getting Started Prerequisites

• Python 3.x
• Pip package manager

Installation

1. Clone the Repository

   • Run the following commands:

     git clone https://github.com/mtross2/autoencoder_hyperspec_ref.git

     cd autoencoder_hyperspec_ref

2. Set Up a Virtual Environment (Optional but recommended)

   • For Windows:

     python -m venv venv

     .\venv\Scripts\activate

   • For Unix or MacOS:

     python3 -m venv venv

     source venv/bin/activate

3. Install Required Packages

   • Execute the command:

     pip install -r requirements.txt

4. Install Your Package (Optional if you want to use it as a package)

   • Use this command:

     python setup.py install

Training the Model

* Prepare Your Dataset
    * Place your dataset in the sample_data folder.
    * Ensure it is in the correct format as expected by the AutoencoderTrainer.

* Run the Training Script
    * Run the training script with default parameters or provide custom values.
        python scripts/train_autoencoder.py --dataset sample_data/sampledData_maize2020.csv.gz

* To see all available command-line options:
python scripts/train_autoencoder.py --help

Testing

* Run the test suite to ensure everything is set up correctly:
    python -m unittest