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MLCAS2024

Information about the competition:
https://eval.ai/web/challenges/challenge-page/2332/overview

Setup data and environment

Clone the repository.

Download the data (https://iastate.box.com/s/p8nj1ukvwx3yo7off8y8yspdruc0mjna).

Put the unzipped folders train, validation, and test on the data folder.

I realized that depending on your OS, the folders might get unzipped differently.
Thus, make sure the data structure (after unzipping the folders) is as follows:

data/train/2022/DataPublication_final/...
data/train/2023/DataPublication_final/...
data/validation/2023/...
data/test/Test/...

Create a conda environment and install Python packages:

conda create -n mlcas2024 python=3.11
conda activate mlcas2024
conda install pandas rasterio tqdm

Preprocess the data

Create satellite features:

python -u src/process_satellite.py --data=train --year=2022
python -u src/process_satellite.py --data=train --year=2023
python -u src/process_satellite.py --data=validation --year=2023
python -u src/process_satellite.py --data=test --year=2023

Create datasets:

python -u src/create_datasets.py

Fit model

Install R packages:

install.packages("tidyverse")
install.packages("lme4")

Fit a Linear Mixed Model:

Rscript src/blup.R > output/results.txt

Note: if you can't run this code from your terminal, just run it from RStudio itself or from an RStudio terminal.

Predictions will be available at output/submission.csv. Log of the results will be at output/results.txt.

Tested on:

  • Windows 10
  • CentOS 7