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6f1e670 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | from model.splitters import *
from model.featurizers import *
from model.predictors import *
from model.proposers import *
from model.utils import *
from pathlib import Path
# dataset directory
project_root = Path(__file__).resolve().parents[3]
main_dir = project_root / 'data' / 'benchmark'
seq_dir = main_dir / 'sequences'
datasets_dir = main_dir / 'datasets'
summary = pd.read_csv(main_dir / 'dataset_summary.csv')
for index, row in summary.iterrows():
# default variables
dataset_name, dataset_fname, sequence = receive_dataset_vars(row) # get dataset vars
wt_file = retrieve_wt_file(dataset_name, seq_dir, sequence) # generate fasta file of sequence
working_df_head, working_df_head_valid = preprocess_dataset(dataset_fname, datasets_dir, stringency='singles')
# variables for training models
protein_name = os.path.join(f"benchmark/", dataset_name)
train_df = working_df_head_valid[['mutant','DMS_score','DMS_score_bin']].copy()
# get feature
feature = select_feature('onehot', protein_name)
featurizers = [feature]
# get splitters
# generate split based on mutational load and do k-fold cross-validation
splitters = []
for max_train_mut_load in range(1,4,1):
splitter = MutLoadProteinSplitter(protein_name, train_df, wt_file, use_cache=True, y_scaling=True, val_split=0.15)
splitter.split_data(max_train_muts=max_train_mut_load, min_test_muts=4, k_folds=5)
splitters = splitters + splitter.folds
models = [Fcn]
run_nn_model_experiments(splitters,
featurizers,
models,
experiment_name=dataset_name,
use_cache=True,
sweep_depth='custom',
search_method='grid',
count=1
)
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