Update MutagenLou2023 Preprocessing.py

MutagenLou2023 Preprocessing.py

@@ -67,7 +67,7 @@ for id, alert in problems:
 #5. Select columns and rename the dataset
 
 Lou2023.rename(columns={'X': 'new SMILES'}, inplace=True)
-Lou2023[['new SMILES', 'ID', 'endpoint', 'MW']].to_csv('Lou2023.csv', index=False)
+newLou2023 = Lou2023[['new SMILES', 'ID', 'endpoint', 'MW']]
 
 #6. Import modules to split the dataset
 
@@ -99,7 +99,7 @@ def tanimoto_distances_yield(fingerprints, num_fingerprints):
     for i in range(1, num_fingerprints):
         yield [1 - x for x in DataStructs.BulkTanimotoSimilarity(fingerprints[i], fingerprints[:i])]
 
-def cluster_data(fingerprints, num_points, distance_threshold, reordering=False):
+def butina_cluster(fingerprints, num_points, distance_threshold, reordering=False):
     nbr_lists = [None] * num_points
     for i in range(num_points):
         nbr_lists[i] = []
@@ -141,6 +141,32 @@ def cluster_data(fingerprints, num_points, distance_threshold, reordering=False)
         res.append(tuple(t_res))
     return tuple(res)
 
+def hierarchal_cluster(fingerprints):
+
+      num_finger_prints = len(fingerprints)
+
+      av_cluster_size = 8
+      dists = []
+
+      for i in range(0, num_fingerprints):
+          sims = DataStructs.BulkTanimotoSimilarity(fingerprints[i], fingerprints)
+          dists.append([1 - x for x in sims])
+
+      dis_array = ssd.squareform(dists)
+      Z = hierarchy.linkage(dis_array)
+      average_cluster_size = av_cluster_size
+      cluster_amount = int(num_fingerprints / average_cluster_size)
+      clusters = hierarchy.cut_tree(Z, n_clusters=cluster_amount)
+
+      clusters = list(clusters.transpose()[0])
+      cs = []
+      for i in range(max(clusters) + 1):
+          cs.append([])
+
+      for i in range(len(clusters)):
+          cs[clusters[i]].append(i)
+      return cs
+
 def cluster_fingerprints(fingerprints, method="Auto"):
     num_fingerprints = len(fingerprints)
 
@@ -150,40 +176,19 @@ def cluster_fingerprints(fingerprints, method="Auto"):
     if method == "TB":
         cutoff = 0.56
         print("Butina clustering is selected. Dataset size is:", num_fingerprints)
-        clusters = cluster_data(fingerprints, num_fingerprints, cutoff)
+        clusters = butina_cluster(fingerprints, num_fingerprints, cutoff)
 
-    elif method == "Hierarchy":
-        import scipy.spatial.distance as ssd
-        from scipy.cluster import hierarchy
+        return clusters
 
+    elif method == "Hierarchy":
         print("Hierarchical clustering is selected. Dataset size is:", num_fingerprints)
+        clusters = hierarchal_cluster(fingerprints, num_fingerprints, 0.56)
 
-        av_cluster_size = 8
-        dists = []
-
-        for i in range(0, num_fingerprints):
-            sims = DataStructs.BulkTanimotoSimilarity(fingerprints[i], fingerprints)
-            dists.append([1 - x for x in sims])
-
-        dis_array = ssd.squareform(dists)
-        Z = hierarchy.linkage(dis_array)
-        average_cluster_size = av_cluster_size
-        cluster_amount = int(num_fingerprints / average_cluster_size)
-        clusters = hierarchy.cut_tree(Z, n_clusters=cluster_amount)
-
-        clusters = list(clusters.transpose()[0])
-        cs = []
-        for i in range(max(clusters) + 1):
-            cs.append([])
-
-        for i in range(len(clusters)):
-            cs[clusters[i]].append(i)
-        return cs
+    return clusters
 
 def split_dataframe(dataframe, smiles_col_index, fraction_to_train, split_for_exact_fraction=True, cluster_method="Auto"):
     try:
         import math
-
         smiles_column_name = dataframe.columns[smiles_col_index]
         molecule = 'molecule'
         fingerprint = 'fingerprint'
@@ -253,9 +258,9 @@ 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
 
-Mutagen = pd.read_csv('Lou2023.csv')
 smiles_index = 0
 realistic = realistic_split(Mutagen.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)