--- a
+++ b/HINT/sponsor_inference.py
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+## 1. import 
+## 2. input & hyperparameter
+## 3. pretrain 
+## 4. 'dataloader, model build, train, inference'
+################################################
+
+
+## 1. import 
+import torch, os, sys
+torch.manual_seed(0) 
+sys.path.append('.')
+from HINT.dataloader import csv_three_feature_2_dataloader, generate_admet_dataloader_lst, csv_three_feature_2_complete_dataloader
+from HINT.molecule_encode import MPNN, ADMET 
+from HINT.icdcode_encode import GRAM, build_icdcode2ancestor_dict
+from HINT.protocol_encode import Protocol_Embedding
+from HINT.model import Interaction, HINT_nograph, HINTModel
+device = torch.device("cpu")
+if not os.path.exists("figure"):
+	os.makedirs("figure")
+
+
+
+
+## 2. data
+base_name = 'phase_III' 
+
+for base_name in ['phase_III', 'phase_II', 'phase_I']: 
+	datafolder = "data"
+	train_file = os.path.join(datafolder, base_name + '_train.csv')
+	valid_file = os.path.join(datafolder, base_name + '_valid.csv')
+	test_file = os.path.join(datafolder, base_name + '_test.csv')
+	ongoing_file = "data/ongoing_"+base_name+".csv" 
+
+
+	## 4. dataloader, model build, train, inference
+	train_loader = csv_three_feature_2_dataloader(train_file, shuffle=True, batch_size=32) 
+	valid_loader = csv_three_feature_2_dataloader(valid_file, shuffle=False, batch_size=32) 
+	test_loader = csv_three_feature_2_dataloader(test_file, shuffle=False, batch_size=32) 
+	ongoing_loader = csv_three_feature_2_dataloader(ongoing_file, shuffle=False, batch_size=32) 
+
+	icdcode2ancestor_dict = build_icdcode2ancestor_dict()
+	gram_model = GRAM(embedding_dim = 50, icdcode2ancestor = icdcode2ancestor_dict, device = device)
+	protocol_model = Protocol_Embedding(output_dim = 50, highway_num=3, device = device)
+
+
+	hint_model_path = "save_model/" + base_name + ".ckpt"
+	# if not os.path.exists(hint_model_path):
+	if True:
+		## 3. pretrain 
+		mpnn_model = MPNN(mpnn_hidden_size = 50, mpnn_depth=3, device = device)
+		admet_model_path = "save_model/admet_model.ckpt"
+		if not os.path.exists(admet_model_path):
+			admet_dataloader_lst = generate_admet_dataloader_lst(batch_size=32)
+			admet_trainloader_lst = [i[0] for i in admet_dataloader_lst]
+			admet_testloader_lst = [i[1] for i in admet_dataloader_lst]
+			admet_model = ADMET(molecule_encoder = mpnn_model, 
+								highway_num=2, 
+								device = device, 
+								epoch=3, 
+								lr=5e-4, 
+								weight_decay=0, 
+								save_name = 'admet_')
+			admet_model.train(admet_trainloader_lst, admet_testloader_lst)
+			torch.save(admet_model, admet_model_path)
+		else:
+			admet_model = torch.load(admet_model_path)
+			admet_model = admet_model.to(device)
+			admet_model.set_device(device)
+
+
+
+
+		model = HINTModel(molecule_encoder = mpnn_model, 
+				 disease_encoder = gram_model, 
+				 protocol_encoder = protocol_model,
+				 device = device, 
+				 global_embed_size = 50, 
+				 highway_num_layer = 1,
+				 prefix_name = base_name, 
+				 gnn_hidden_size = 50,  
+				 epoch = 2,
+				 lr = 1e-3, 
+				 weight_decay = 0, 
+				)
+		model.init_pretrain(admet_model)
+		model.learn(train_loader, valid_loader, test_loader)
+
+
+
+	nctid_lst, predict_lst = model.ongoing_test(test_loader)
+	with open('data/test_predict_' + base_name + '.txt', 'w') as fout:
+		for nctid, predict in zip(nctid_lst, predict_lst):
+			fout.write(nctid+'\t'+str(predict)+'\n')
+
+
+	nctid_lst, predict_lst = model.ongoing_test(ongoing_loader)
+	with open('data/ongoing_predict_' + base_name + '.txt', 'w') as fout:
+		for nctid, predict in zip(nctid_lst, predict_lst):
+			fout.write(nctid+'\t'+str(predict)+'\n')
+
+
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