[Fix] Canonicalize SMILES when checking overlaps in smiles_covered_by (#443)

Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#443

utilities/ml.py

@@ -238,9 +238,11 @@ class RuleBasedDataset(Dataset):
     ):
         if feat_funcs is None:
             feat_funcs = [FormatConverter.maccs]
-        _structures = set()  # Get all the structures
+
+        _structures = set()
         for r in reactions:
             _structures.update(r.educts.all())
+
             if not educts_only:
                 _structures.update(r.products.all())
 
@@ -282,17 +284,14 @@ class RuleBasedDataset(Dataset):
                     if key not in triggered:
                         continue
 
-                    # standardize products from reactions for comparison
-                    standardized_products = []
-                    for cs in r.products.all():
-                        smi = cs.smiles
-                        try:
-                            smi = FormatConverter.standardize(smi, remove_stereo=True)
-                        except Exception:
-                            logger.debug(f"Standardizing SMILES failed for {smi}")
-                        standardized_products.append(smi)
-                    if len(set(standardized_products).difference(triggered[key])) == 0:
+                    if FormatConverter.smiles_covered_by(
+                        [prod.smiles for prod in r.products.all()],
+                        list(triggered[key]),
+                        standardize=True,
+                        canonicalize_tautomers=True,
+                    ):
                         observed.add(key)
+
         feat_columns = []
         for feat_func in feat_funcs:
             if isinstance(feat_func, Descriptor):
@@ -301,6 +300,7 @@ class RuleBasedDataset(Dataset):
                 feats = feat_func(compounds[0].smiles)
             start_i = len(feat_columns)
             feat_columns.extend([f"feature_{start_i + i}" for i, _ in enumerate(feats)])
+
         ds_columns = (
             ["structure_id"]
             + feat_columns
@@ -334,7 +334,9 @@ class RuleBasedDataset(Dataset):
                     obs.append(None)
                 else:
                     obs.append(0)
+
             rows.append([str(comp.uuid)] + feats + trig + obs)
+
         ds = RuleBasedDataset(len(applicable_rules), ds_columns, data=rows)
         return ds
 
@@ -680,9 +682,13 @@ class RelativeReasoning:
 
     def predict(self, X):
         res = np.zeros((len(X), (self.end_index + 1 - self.start_index)))
+        immutable_res = np.zeros((len(X), (self.end_index + 1 - self.start_index)))
 
         # Loop through all instances
         for inst_idx, inst in enumerate(X):
+            for i, t in enumerate(inst[self.start_index : self.end_index + 1]):
+                immutable_res[inst_idx][i] = t
+
             # Loop through all "triggered" features
             for i, t in enumerate(inst[self.start_index : self.end_index + 1]):
                 # Set label
@@ -696,7 +702,7 @@ class RelativeReasoning:
                             if i2 in self.winmap.get(i, []):
                                 # if thatat rule also triggered, it dominated the current
                                 # set label to 0
-                                if X[inst_idx][i2]:
+                                if immutable_res[inst_idx][i2]:
                                     res[inst_idx][i] = 0
 
         return res