added training scheme for dataset with hydrophobics-acids feature

trainer.py

@@ -15,29 +15,14 @@
 
 import pickle
 import os
-
-def dataset_loader(filepath):
- data = []
- try:
- with open(filepath, 'rb') as fr:
- try:
- while True:
- data.append(pickle.load(fr))
- except EOFError:
- pass 
- except FileNotFoundError:
- print('File is not found')
- saved_ids = [d['id'] for d in data]
- return data
-
-
-
+import data_checker
 
 if __name__ == '__main__':
  '''
  load and split the dataset
  '''
- dataset = dataset_loader(os.getcwd()+'/Data/dataset_alpha_121019.pkl')
+ filename = os.getcwd()+'/Data/dataset_ha_alpha_122319.pkl'
+ dataset = data_checker.data_load(filename)
 
  features = np.array([data['x_vector'] for data in dataset])
  labels = np.array([data['y'] for data in dataset])
@@ -57,7 +42,7 @@ def dataset_loader(filepath):
 # '''
 # model saver
 # '''
-# with open(os.getcwd()+"/Model/rf_pp_alpha.pkl", "wb") as f:
+# with open(os.getcwd()+"/Model/rf_pp_ha_alpha.pkl", "wb") as f:
 # pickle.dump(rf, f)
 # 
 # 
@@ -147,7 +132,7 @@ def dataset_loader(filepath):
  kfolds = sorted(kfolds, key=lambda k: k['pcc_test'], reverse=True) 
  print(kfolds[0]['k'], kfolds[0]['pcc_test'])
  #save best model
- with open(os.getcwd()+"/Model/rf_pp_a_"+str(n)+"fold_best.pkl", "wb") as f:
+ with open(os.getcwd()+"/Model/rf_pp_ha_a_"+str(n)+"fold_best.pkl", "wb") as f:
  pickle.dump(kfolds[0], f)
 
 # '''