params_cs.txt

[Global_Params]
model_name = "DualGCN"

[Preprocess]
train_split_file = "gCSI_split_0_train.txt"
val_split_file = "gCSI_split_0_val.txt"
test_split_file = "CCLE_all.txt"
ml_data_outdir = "./ml_data/gCSI-CCLE/split_0"
y_data_files = [["response.tsv"]]
x_data_canc_files = [["cancer_gene_expression.tsv", ["Gene_Symbol"]], ["cancer_copy_number.tsv", ["Gene_Symbol"]]]
x_data_drug_files = [["drug_SMILES.tsv"]]
use_lincs = True
scaling = "std"

[Train]
train_ml_data_dir = "./ml_data/gCSI-gCSI/split_0"
val_ml_data_dir = "./ml_data/gCSI-gCSI/split_0"
model_file_name = "model"
model_file_format = ".h5"
epochs = 2
batch_size = 16
val_batch = 8
early_stop_metric = "mse"
patience = 20
ckpt_save_interval = 5
n_fold = 100
learning_rate = 0.0001
model_arch = "GCN"
log_interval = 20
cuda_name = "cuda:7"
# Model definition
Max_atoms = 230
drug_path = "./ml_data/gCSI-gCSI/split_0/drug_features/"
ppi_path = "./ml_data/gCSI-gCSI/split_0_PPI/PPI_network_new.txt"
omics_path = "./ml_data/gCSI-gCSI/split_0_omics_data/"
metrics = ['mse']
use_gexpr = True
use_cnv = True
regression = True

drug_gcn_units_list = [256, 128]
cell_feature_fc_units_list = [32, 128]
dense = [256, 128, 10]
cell_line_gcn_units_list = [256, 256, 256, 256]
universal_dropout = 0.1
fc_layers_dropout = [0.3, 0.2, 0]

# Checkpoints and logs
ckpt_directory = "./out_model/gCSI/split_0"
ckpt_save_best = True
ckpt_save_weights_only = False
ckpt_save_best_metric = "val_loss"
log_dir = "./logs/"

set = 'mixed'
test_batch = 256
test_data ='test_data'
train_data = 'train_data'
val_data = 'val_data'

[Infer]
test_ml_data_dir = "./ml_data/gCSI-gCSI/split_0"
model_dir = "./out_model/gCSI/split_0"
infer_outdir = "./out_model/gCSI/split_0"
test_batch = 256
cuda_name = "cuda:7"