Update tool/deepdonor/sm.py

tool/deepdonor/sm.py

@@ -1,83 +1,83 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Sep  4 10:38:59 2023
-
-@author: BM109X32G-10GPU-02
-"""
-
-
-from sklearn.metrics import confusion_matrix
-import matplotlib.pyplot as plt
-import numpy as np
-from rdkit.Chem import AllChem
-from sklearn.datasets import make_blobs
-import json
-import numpy as np
-import math
-import pickle
-from scipy import sparse
-from sklearn.metrics import median_absolute_error,r2_score, mean_absolute_error,mean_squared_error
- 
-from tqdm import tqdm
-
-import pandas as pd
-import matplotlib.pyplot as plt
-from rdkit import Chem
-
-from sklearn.ensemble import RandomForestRegressor
-
-def split_dataset(dataset, ratio):
-    """Shuffle and split a dataset."""
-   # np.random.seed(111)  # fix the seed for shuffle.
-    #np.random.shuffle(dataset)
-    n = int(ratio * len(dataset))
-    return dataset[:n], dataset[n:]
-
-def split_string(string):
-    
-    result = []
-   
-    for char in string:
-      
-        result.append(char)
- 
-    return result
- 
-def main(sm):
- 
-        inchis = list([sm])
-        rts = list([0])
-        
-        smiles, targets,features = [], [],[]
-        for i, inc in enumerate(tqdm(inchis)):
-            mol = Chem.MolFromSmiles(inc)
-            if mol is None:
-                continue
-            else:
-                smi =AllChem. GetMorganFingerprintAsBitVect(mol,3,2048)
-                smi = smi.ToBitString()
-                a = split_string(smi)
-                a = np.array(a)
-                #smi = Chem.MolToSmiles(mol)
-                features.append(a)
-                targets.append(rts[i])
-                
-       
-
-        features = np.asarray(features)
-        targets = np.asarray(targets)
-        X_test=features
-        Y_test=targets
-        n_features=10
-        
-        model = RandomForestRegressor(n_estimators=100)
-        load_model = pickle.load(open(r"tool/deepdonor/sm.pkl", 'rb'))
-
-     #   model = load_model('C:/Users/sunjinyu/Desktop/FingerID Reference/drug-likeness/CNN/single_model.h5')
-        Y_predict = load_model.predict((X_test))
-         #Y_predict = model.predict(X_test) 
-        x = list(Y_test)
-        y = list(Y_predict)
-       
-        return Y_predict
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep  4 10:38:59 2023
+
+@author: BM109X32G-10GPU-02
+"""
+
+
+from sklearn.metrics import confusion_matrix
+ 
+import numpy as np
+from rdkit.Chem import AllChem
+from sklearn.datasets import make_blobs
+import json
+import numpy as np
+import math
+import pickle
+from scipy import sparse
+from sklearn.metrics import median_absolute_error,r2_score, mean_absolute_error,mean_squared_error
+ 
+from tqdm import tqdm
+
+import pandas as pd
+ 
+from rdkit import Chem
+
+from sklearn.ensemble import RandomForestRegressor
+
+def split_dataset(dataset, ratio):
+    """Shuffle and split a dataset."""
+   # np.random.seed(111)  # fix the seed for shuffle.
+    #np.random.shuffle(dataset)
+    n = int(ratio * len(dataset))
+    return dataset[:n], dataset[n:]
+
+def split_string(string):
+    
+    result = []
+   
+    for char in string:
+      
+        result.append(char)
+ 
+    return result
+ 
+def main(sm):
+ 
+        inchis = list([sm])
+        rts = list([0])
+        
+        smiles, targets,features = [], [],[]
+        for i, inc in enumerate(tqdm(inchis)):
+            mol = Chem.MolFromSmiles(inc)
+            if mol is None:
+                continue
+            else:
+                smi =AllChem. GetMorganFingerprintAsBitVect(mol,3,2048)
+                smi = smi.ToBitString()
+                a = split_string(smi)
+                a = np.array(a)
+                #smi = Chem.MolToSmiles(mol)
+                features.append(a)
+                targets.append(rts[i])
+                
+       
+
+        features = np.asarray(features)
+        targets = np.asarray(targets)
+        X_test=features
+        Y_test=targets
+        n_features=10
+        
+        model = RandomForestRegressor(n_estimators=100)
+        load_model = pickle.load(open(r"tool/deepdonor/sm.pkl", 'rb'))
+
+     #   model = load_model('C:/Users/sunjinyu/Desktop/FingerID Reference/drug-likeness/CNN/single_model.h5')
+        Y_predict = load_model.predict((X_test))
+         #Y_predict = model.predict(X_test) 
+        x = list(Y_test)
+        y = list(Y_predict)
+       
+        return Y_predict