Create a conda environment using usp_environment.yml file and activate it.
conda env create -f usp_environment.yml
conda activate usp
You will need to set the python path in the following way:
PYTHONPATH=<FULL_PATH_TO_REPO>/undersampling-project/scripts/
where FULL_PATH_TO_REPO is the full path to the repository on your machine.
To run an experiment, run the experiment.py script. Here is its usage:
usage: experiment.py [-h] -n NOISE_DATASET -s SIGNAL_DATASET [-i SNR_INT [SNR_INT ...]] [-f N_FOLDS] [-nn NUM_NOISE] [-v] [-cv]
options:
-h, --help show this help message and exit
-n NOISE_DATASET, --noise-dataset NOISE_DATASET [required]
noise dataset name (FBIRN, COBRE, VAR)
-s SIGNAL_DATASET, --signal-dataset SIGNAL_DATASET [required]
signal dataset name (OULU, HCP)
-i SNR_INT [SNR_INT ...], --snr-int SNR_INT [SNR_INT ...]
upper, lower, step of SNR interval
-f N_FOLDS, --n-folds N_FOLDS
number of folds for cross-validation
-nn NUM_NOISE, --num_noise NUM_NOISE
number of noise iterations
-v, --verbose turn on debug logging
-cv, --cov-mat use covariance matrix
For results, see the plot.ipynb notebook. You can modify this script to plot results of your classification experiments.