--- a
+++ b/HINT/learn_multiple_aim.py
@@ -0,0 +1,123 @@
+## 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 HINTModel_multi
+device = torch.device("cpu")
+if not os.path.exists("figure"):
+	os.makedirs("figure")
+
+
+
+
+## 2. data
+base_name = 'phase_II' 
+base_name = "indication"
+# base_name = 'phase_III'
+# base_name = "toy" 
+datafolder = "auxiliary_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')
+
+
+
+
+
+## 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)
+
+
+
+
+## 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) 
+
+
+
+
+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_model2/" + base_name + ".ckpt"
+if not os.path.exists(hint_model_path):
+	model = HINTModel_multi(molecule_encoder = mpnn_model, 
+			 disease_encoder = gram_model, 
+			 protocol_encoder = protocol_model,
+			 device = device, 
+			 global_embed_size = 50, 
+			 highway_num_layer = 2,
+			 prefix_name = base_name, 
+			 # gnn_hidden_size = 50,  
+			 epoch = 5,
+			 lr = 1e-3, 
+			 weight_decay = 0, 
+			)
+	# model.init_pretrain(admet_model)
+	pred_all, label_all = model.learn(train_loader, valid_loader, test_loader)
+	# model.bootstrap_test(test_loader)
+	# torch.save(model, hint_model_path)
+else:
+	model = torch.load(hint_model_path)
+	model.bootstrap_test(test_loader)
+
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np 
+from sklearn.datasets import make_classification
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+lst = np.array([0,1,2,3])
+cm = confusion_matrix(label_all, pred_all, labels=lst)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=lst)
+disp.plot()
+plt.xlabel("predicted label", fontsize = 21)
+plt.ylabel("True label", fontsize = 20)
+plt.tight_layout()
+plt.savefig("figure/"+base_name + "_cm.png")
+
+
+
+
+
+
+
+
+
+
+