fix path

dataset.py

@@ -96,9 +96,6 @@ def seq2emb_encoder(input_seq, max_len, vocab):
         ids = np.array([0])
 
     l = len(ids)
-    with open('./utils/data_analyse_train.tsv', 'w') as f:
-        tsv = csv.writer(f)
-        tsv.writerow([ids, l, '\n'])
 
     if l < max_len:
         ids = np.pad(ids, (0, max_len - l), 'constant', constant_values=0)
@@ -158,17 +155,17 @@ if __name__ == "__main__":
     # vocab = load_vocab(vocab_file)
 
     # test train
-    df_train = {"sps": './IC50/SPS/train_sps',
-                "smile": './IC50/SPS/train_smile',
-                "affinity": './IC50/SPS/train_ic50',
-                "vocab_file": './ESPF/vocab.txt',
+    df_train = {"sps": './data/train_sps',
+                "smile": './data/train_smile',
+                "affinity": './data/train_ic50',
+                "vocab_file": './config/vocab.txt',
                 "begin_id": '[CLS]',
                 "separate_id": "[SEP]",
                 "max_len": 256
                 }
 
-    tokenizer_config = {"vocab_file": './ESPF/vocab.txt',
-                        "vocab_pair": './ESPF/drug_codes_chembl.txt'
+    tokenizer_config = {"vocab_file": './config/vocab.txt',
+                        "vocab_pair": './config/drug_codes_chembl.txt'
                         }
     params = {'batch_size': 5,
               'shuffle': False,
@@ -177,4 +174,4 @@ if __name__ == "__main__":
     trainset = Data_Encoder(df_train, tokenizer_config)
     training_generator = data.DataLoader(trainset)
     for i, (input, affinity) in tqdm(enumerate(training_generator)):
-        print('----------------')
+        print('')