Update Preprocessing Script.py

Preprocessing Script.py

@@ -75,6 +75,8 @@ from scipy.cluster import hierarchy
 
 #7. Split the dataset into test and train
 
+#7. Split the dataset into test and train
+
 class MolecularFingerprint:
     def __init__(self, fingerprint):
         self.fingerprint = fingerprint
@@ -140,7 +142,7 @@ def butina_cluster(fingerprints, num_points, distance_threshold, reordering=Fals
 
 def hierarchal_cluster(fingerprints):
 
-      num_finger_prints = len(fingerprints)
+      num_fingerprints = len(fingerprints)
 
       av_cluster_size = 8
       dists = []
@@ -175,11 +177,9 @@ def cluster_fingerprints(fingerprints, method="Auto"):
         print("Butina clustering is selected. Dataset size is:", num_fingerprints)
         clusters = butina_cluster(fingerprints, num_fingerprints, cutoff)
 
-        return clusters
-
     elif method == "Hierarchy":
         print("Hierarchical clustering is selected. Dataset size is:", num_fingerprints)
-        clusters = hierarchal_cluster(fingerprints, num_fingerprints, 0.56)
+        clusters = hierarchal_cluster(fingerprints)
 
     return clusters
 
@@ -255,11 +255,13 @@ def split_df_into_train_and_test_sets(df):
     test = df[df['group'] == 'testing']
     return train, test
 
+# 8. Test and train datasets have been made
+
 smiles_index = 0  # Because smiles is in the first column
 realistic = realistic_split(newAA.copy(), smiles_index, 0.8, split_for_exact_frac=True, cluster_method="Auto")
 realistic_train, realistic_test = split_df_into_train_and_test_sets(realistic)
 
-#8. Test and train datasets have been made
+#9. Select columns and name the datasets
 
 selected_columns = realistic_train[['new SMILES', 'substance_id', 'aggref_index', 'logP', 'reference']]
 selected_columns.to_csv("AggregatorAdvisor_train.csv", index=False)