reduce making fastsynergydataset time

predictor/model/datasets.py

 import random
 
 from torch.utils.data import Dataset
-from .utils import read_map, get_index_by_name
+from .utils import read_map, read_files, get_index_by_name
 
 class FastTensorDataLoader:
     """
         self.train = train
         valid_drugs = set(self.drug2id.keys())
         valid_cells = set(self.cell2id.keys())
+        files_dict = read_files()
+
         with open(synergy_score_file, 'r') as f:
             f.readline()
             for line in f:
                 drug1, drug2, cellname, score, fold = line.rstrip().split('\t')
                 if drug1 in valid_drugs and drug2 in valid_drugs and cellname in valid_cells:
                     if int(fold) in use_folds:
-                        drug1_id = get_index_by_name(drug1)
-                        drug2_id = get_index_by_name(drug2)
+                        drug1_id = get_index_by_name(drug1,files_dict)
+                        drug2_id = get_index_by_name(drug2,files_dict)
                         sample = [
                             # TODO: specify drug_feat 
                             # drug1_feat + drug2_feat + cell_feat + score

predictor/model/utils.py

 import pandas as pd
 import numpy as np
 
+from const import SYNERGY_FILE, CELL_FEAT_FILE, CELL2ID_FILE, OUTPUT_DIR, DRUGNAME_2_DRUGBANKID_FILE, DRUGN2ID_FILE
+
 def calc_stat(numbers):
     mu = sum(numbers) / len(numbers)
     sigma = (sum([(x - mu) ** 2 for x in numbers]) / len(numbers)) ** 0.5
     return train_indices, test_indices
 
 # --------------------------------------------------------------------- Our Part:
+def read_files():
+    drug_name2drugbank_id_df = pd.read_csv(DRUGNAME_2_DRUGBANKID_FILE, sep='\s+')
+    drug2id_df = pd.read_csv(DRUGN2ID_FILE , sep='\t')
+
+    return {'drug_name2drugbank_id_df': drug_name2drugbank_id_df, 'drug2id_df': drug2id_df}
 
-def get_index_by_name(drug_name):
-    project_path = os.path.dirname(os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..')))
+def get_index_by_name(drug_name, files_dict = None):
     
-    drugname2drugbankid_file = os.path.join(project_path, 'drug/data/drugname2drugbankid.tsv')
-    drug_name2drugbank_id_df = pd.read_csv(drugname2drugbankid_file, sep='\s+')
+    if files_dict == None:
+        files_dict = read_files()
+    drug_name2drugbank_id_df = files_dict['drug_name2drugbank_id_df']
+    drug2id_df = files_dict['drug2id_df']
 
     drug_bank_id = drug_name2drugbank_id_df[drug_name2drugbank_id_df['drug_name'] == drug_name].drug_bank_id.item()
 
     negative_index = list(drug_name2drugbank_id_df['drug_name']).index(drug_name)
 
-    drug2id_file = os.path.join(project_path, 'drug/data/DDI/DrugBank/raw/', 'drug2id.tsv')
-    drug2id_df = pd.read_csv(drug2id_file , sep='\t')
-    
     row = drug2id_df[drug2id_df['DrugBank_id'] == drug_bank_id]
     if row.empty:
         drug_index = - negative_index