Update inference.py

inference.py

@@ -114,7 +114,7 @@ class Inference(object):
 
     def decoder_load(self, dictionary_name):
         ''' Loading the atom and bond decoders'''
-        with open("DrugGEN/data/decoders/" + dictionary_name + "_" + self.dataset_name + '.pkl', 'rb') as f:
+        with open("data/decoders/" + dictionary_name + "_" + self.dataset_name + '.pkl', 'rb') as f:
             return pickle.load(f)
 
 
@@ -140,16 +140,16 @@ class Inference(object):
         self.restore_model(self.submodel, self.inference_model)
 
         # smiles data for metrics calculation.
-        chembl_smiles = [line for line in open("DrugGEN/data/chembl_train.smi", 'r').read().splitlines()]
-        chembl_test = [line for line in open("DrugGEN/data/chembl_test.smi", 'r').read().splitlines()]
-        drug_smiles = [line for line in open("DrugGEN/data/akt_inhibitors.smi", 'r').read().splitlines()]
+        chembl_smiles = [line for line in open("data/chembl_train.smi", 'r').read().splitlines()]
+        chembl_test = [line for line in open("data/chembl_test.smi", 'r').read().splitlines()]
+        drug_smiles = [line for line in open("data/akt_inhibitors.smi", 'r').read().splitlines()]
         drug_mols = [Chem.MolFromSmiles(smi) for smi in drug_smiles]
         drug_vecs = [AllChem.GetMorganFingerprintAsBitVect(x, 2, nBits=1024) for x in drug_mols if x is not None]
 
 
         # Make directories if not exist.
-        if not os.path.exists("DrugGEN/experiments/inference/{}".format(self.submodel)):
-            os.makedirs("DrugGEN/experiments/inference/{}".format(self.submodel))
+        if not os.path.exists("experiments/inference/{}".format(self.submodel)):
+            os.makedirs("experiments/inference/{}".format(self.submodel))
         if self.correct:
             correct = smi_correct(self.submodel, "DrugGEN_/experiments/inference/{}".format(self.submodel))
         search_res = pd.DataFrame(columns=["submodel", "validity",
@@ -166,7 +166,7 @@ class Inference(object):
         uniqueness_calc = []
         real_smiles_snn = []
         nodes_sample = torch.Tensor(size=[1,45,1]).to(self.device)
-        f = open("DrugGEN/experiments/inference/{}/inference_drugs.txt".format(self.submodel), "w")
+        f = open("experiments/inference/{}/inference_drugs.txt".format(self.submodel), "w")
         f.write("SMILES")
         f.write("\n")
         val_counter = 0
@@ -226,16 +226,16 @@ class Inference(object):
         f.close()
         print("Inference completed, starting metrics calculation.")
         if self.correct:
-            corrected = correct.correct("DrugGEN/experiments/inference/{}/inference_drugs.txt".format(self.submodel))
+            corrected = correct.correct("experiments/inference/{}/inference_drugs.txt".format(self.submodel))
             gen_smi = corrected["SMILES"].tolist()
             
         else:
-            gen_smi = pd.read_csv("DrugGEN/experiments/inference/{}/inference_drugs.txt".format(self.submodel))["SMILES"].tolist()
+            gen_smi = pd.read_csv("experiments/inference/{}/inference_drugs.txt".format(self.submodel))["SMILES"].tolist()
             
             
         et = time.time() - start_time
 
-        with open("DrugGEN/experiments/inference/{}/inference_drugs.txt".format(self.submodel), "w") as f:
+        with open("experiments/inference/{}/inference_drugs.txt".format(self.submodel), "w") as f:
             for i in gen_smi:
                 f.write(i)
                 f.write("\n")
@@ -265,9 +265,9 @@ if __name__=="__main__":
    
     # Data configuration.
     parser.add_argument('--inf_dataset_file', type=str, default='chembl45_test.pt')
-    parser.add_argument('--inf_raw_file', type=str, default='DrugGEN/data/chembl_test.smi')
+    parser.add_argument('--inf_raw_file', type=str, default='data/chembl_test.smi')
     parser.add_argument('--inf_batch_size', type=int, default=1, help='Batch size for inference')
-    parser.add_argument('--mol_data_dir', type=str, default='DrugGEN/data')
+    parser.add_argument('--mol_data_dir', type=str, default='data')
     parser.add_argument('--features', type=str2bool, default=False, help='features dimension for nodes')
 
     # Model configuration.