feat: add device

main.py

 
     # Step 2: Load training data
     train_data, train_drug_screen = RawDataLoader.load_data(data_modalities=DATA_MODALITIES,
-                                                            raw_file_directory=RAW_BOTH_DATA_FOLDER,
-                                                            screen_file_directory=BOTH_SCREENING_DATA_FOLDER,
+                                                            raw_file_directory=GDSC_RAW_DATA_FOLDER,
+                                                            screen_file_directory=GDSC_SCREENING_DATA_FOLDER,
                                                             sep="\t")
 
 
     if is_test:
         test_data, test_drug_screen = RawDataLoader.load_data(data_modalities=DATA_MODALITIES,
                                                               raw_file_directory=CCLE_RAW_DATA_FOLDER,
-                                                              screen_file_directory=CCLE_SCREENING_DATA_FOLDER,
+                                                              screen_file_directory=CTRP_SCREENING_DATA_FOLDER,
                                                               sep="\t")
         train_data, test_data = RawDataLoader.data_features_intersect(train_data, test_data)
 
     torch.manual_seed(RANDOM_SEED)
     random.seed(RANDOM_SEED)
     np.random.seed(RANDOM_SEED)
-    run(10, is_test=True)
+    run(30, is_test=False)

utils.py

 SAVE_MODEL = False  # Change it to True to save the trained model
 VARIATIONAL_AUTOENCODERS = False
 # DATA_MODALITIES=['cell_CN','cell_exp','cell_methy','cell_mut','drug_comp','drug_DT'] # Change this list to only consider specific data modalities
-DATA_MODALITIES = ['cell_CN','cell_exp','cell_mut', 'drug_desc','drug_finger']
+DATA_MODALITIES = ['cell_exp', 'drug_desc','drug_finger']
 RANDOM_SEED = 42  # Must be used wherever can be used