Update RF.py

RF.py

@@ -179,8 +179,6 @@ def main(sm):
         from tensorflow.keras import backend as K
         
         load_model = pickle.load(open(r"predict.dat","rb"))
-
-     #   model = load_model('C:/Users/sunjinyu/Desktop/FingerID Reference/drug-likeness/CNN/single_model.h5')
         Y_predict = load_model.predict(K.cast_to_floatx(X_test).reshape((np.size(X_test,0),np.size(X_test,1)*np.size(X_test,2))))
          #Y_predict = model.predict(X_test) 
         x = list(Y_test)
@@ -188,22 +186,5 @@ def main(sm):
        
         return Y_predict
         
-def edit_dataset(drug,non_drug,task):
-  #  np.random.seed(111)  # fix the seed for shuffle.
-
-#    np.random.shuffle(non_drug)
-    non_drug=non_drug[0:len(drug)]
-       
-
-      #  np.random.shuffle(non_drug)
-   # np.random.shuffle(drug)
-    dataset_train_drug, dataset_test_drug = split_dataset(drug, 0.9)
-   # dataset_train_drug,dataset_dev_drug =  split_dataset(dataset_train_drug, 0.9)
-    dataset_train_no, dataset_test_no = split_dataset(non_drug, 0.9)
-   # dataset_train_no,dataset_dev_no =  split_dataset(dataset_train_no, 0.9)
-    dataset_train =  pd.concat([dataset_train_drug,dataset_train_no], axis=0)
-    dataset_test=pd.concat([ dataset_test_drug,dataset_test_no], axis=0)
-  #  dataset_dev = dataset_dev_drug+dataset_dev_no
-    return dataset_train, dataset_test
 if __name__ == "__main__":
     x = main("CCCCCCC1=CC=C(C2(C3=CC=C(CCCCCC)C=C3)C3=CC4=C(C=C3C3=C2C=C(/C=C2\SC(=S)N(CC)C2=O)S3)C(C2=CC=C(CCCCCC)C=C2)(C2=CC=C(CCCCCC)C=C2)C2=C4SC(/C=C3\SC(=S)N(CC)C3=O)=C2)C=C1")