Create dedicated environment and install dependencies:
conda env create -f environment.yml
conda activate dfe-cnnTo use the code:
cd modules
python main.py --helpOutput should look like this:
Usage: main.py [OPTIONS] COMMAND [ARGS]...
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --install-completion Install completion for the current shell. │
│ --show-completion Show completion for the current shell, to copy it or customize the installation. │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ haperize │
│ partition │
│ process │
│ scramble │
│ simulate │
│ train │
│ validate │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯simulate command is currently empty. Refer to old commits for simulate.py script, or use simulated example data.
Command for scrambling data
python main.py scramble ../data/processed_data/const_demog/test/tensors ../data/processed_data/const_demog/test/tensors_scramble
python main.py scramble ../data/processed_data/const_demog/test/tensors ../data/processed_data/const_demog/test/tensors_scramble_free --free-scrambleCommand for partitioning by row
python main.py partition ../data/processed_data/const_demog/test/tensors ../data/processed_data/const_demog/test/tensors_partition_by_row --by-rowCommand for processing data
# from tree simulated data dict from two epoch demography
python main.py process ../data/trees/test_data_1B08_varied_scale_2.pickle ../data/processed_data/two_epoch/test/
# process train and test data to "AFS-like tensor" (entries are frequency spectrum values)
python main.py process ../data/trees/test_data.pickle ../data/processed_data/const_demog/test/ afs --afs
python main.py process ../data/trees/train_data.pickle ../data/processed_data/const_demog/train/ afs --afsCommand for haperizing (I made this up xD) the tensor (haperize means convert to just one haplotype, all non-syn or syn only)
python main.py haperize ../data/processed_data/const_demog/test/tensors ../data/processed_data/const_demog/test/tensors_haps_back
python main.py haperize ../data/processed_data/const_demog/test/tensors ../data/processed_data/const_demog/test/tensors_haps_front --pad-frontCommand to train CNN
python main.py train ../data/processed_data/const_demog/train/tensors_haps_pad_back ../data/processed_data/const_demog/train/labels ../results/const_demog_trained_300_haps_pad_backCommand to validate the trained CNN (test on simulated test data)
python main.py validate ../data/processed_data/const_demog/test/tensors_haps_pad_back ../data/processed_data/const_demog/test/labels ../results/const_demog_trained_300_haps_pad_back ../results/plots/test_const_demog_haps_pad_back
# validate using swapped haps data
python main.py validate ../data/processed_data/const_demog/test/tensors_haps_pad_front ../data/processed_data/const_demog/test/labels ../results/const_demog_trained_300_haps_pad_back ../results/plots/test_const_demog_haps_pad_back_test_on_haps_pad_front
# validate using free scramble haps
python main.py validate ../data/processed_data/const_demog/test/tensors_haps_pad_back_scramble_free ../data/processed_data/const_demog/test/labels ../results/const_demog_trained_300_haps_pad_back ../results/plots/test_const_demog_haps_pad_back_test_on_haps_pad_back_scramble_free
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