Goal: Explore predictability of various psychiatric diagnoses based on neuroimaging features in subjects from the ABCD study.

1. Copy the following files into data/raw/:

   From the baseline release of the ABCD study:

   abcd_ksad01.txt
   abcd_ksad501.txt
   acspsw03.txt
   btsv01.txt
   

   Additional files (contact repository creator for these files):

   abcd_freesurfer.csv
   sociodem_bl.csv
   

2. Run python src/runnable/make_dataset.py to process and combine these data into one dataframe. Use the following options:

   --select-one-child-per-family: Whether to randomly select only one child per family
   --seed: Random number seed for selecting one child per family
   

   For the paper, a seed of 77 was used.

1. To fit and obtain training, validation, and test set predictions by the OVR logistic regression, CCE logistic regression, and CCE Bayesian optimized XGBoost models on the processed dataset, run python src/runnable/run_unpermuted.py. Use the following options:

   --seed: Random number seed (int)
   --k: Number of cross validation folds (int, default 5)
   --n: Number of successive k-fold CV runs (int)
   

2. To fit and obtain predictions on random permutations of the processed dataset, run python src/runnable/run_permuted.py using the following options:

   --seed: Random number seed (int)
   --k: Number of cross validation folds (int, default 5)
   --n: Number of successive k-fold CV runs (int)
   --num_permutations: Number of random permutations (int)
   

Note: Running these experiments will take extended amounts of time (about 20 hours for a single repeat of 5-fold cross validation on a fast machine). Consider parallelizing computations on several machines by using different seeds.

All raw predictions are saved to results/.

Project based on the cookiecutter data science project template. #cookiecutterdatascience