deepttc_params.txt

[Global_Params]
model_name = DeepTTC
cancer_id = improve_sample_id
drug_id = improve_chem_id
target_id = auc
sample_id = COSMIC_ID
save_data = True
predictions_output = output/results.tsv
default_data_url = https://ftp.mcs.anl.gov/pub/candle/public/improve/reproducability/DeepTTC/
benchmark_dir = ../IMP_data

[Preprocess]
train_split_file = CCLE_split_0_train.txt
val_split_file = CCLE_split_0_val.txt
test_split_file = CCLE_split_0_test.txt
data_format = .h5
y_data_files = [["response.tsv"]]
x_data_canc_files = [["cancer_gene_expression.tsv", ["Gene_Symbol"]]]
x_data_drug_files = [["drug_SMILES.tsv"]]
use_lincs = True
scaling = std
output_dir = ./ml_data
input_supp_data_dir = ./author_data

[Train]
data_format = .h5
model_file_name = model
model_file_format = .pt
epochs = 20
patience = 10
batch_size = 64
val_batch = 256
loss = mse
early_stop_metric = mse
ckpt_save_interval = 5
log_interval = 20
cuda_name = cuda:0
rng_seed=1
input_dim_drug = 2586
transformer_emb_size_drug = 128
dropout = 0.1
transformer_n_layer_drug = 8
transformer_intermediate_size_drug = 512
transformer_num_attention_heads_drug = 8
transformer_attention_probs_dropout = 0.1
transformer_hidden_dropout_rate = 0.1
learning_rate = 1e-5
input_dim_drug_classifier = 128
input_dim_gene_classifier = 256

[Infer]
input_data_dir = ./ml_data
output_dir = ./ml_data
data_format = .h5
test_batch = 256
input_model_dir = ./ml_data
cuda_name = cuda:0
input_dim_drug = 2586
transformer_emb_size_drug = 128
dropout = 0.1
transformer_n_layer_drug = 8
transformer_intermediate_size_drug = 512
transformer_num_attention_heads_drug = 8
transformer_attention_probs_dropout = 0.1
transformer_hidden_dropout_rate = 0.1
learning_rate = 1e-5
input_dim_drug_classifier = 128
input_dim_gene_classifier = 256