app domain assess and assess_batch. Add threshold check for compatability

epdb/models.py

@@ -2694,7 +2694,7 @@ class MLRelativeReasoning(PackageBasedModel):
         X, y = ds.X(na_replacement=np.nan), ds.y(na_replacement=np.nan)
 
         model = EnsembleClassifierChain(**s.DEFAULT_MODEL_PARAMS)
-        model.fit(X, y)
+        model.fit(X.to_numpy(), y.to_numpy())
         return model
 
     def _model_args(self):
@@ -2797,16 +2797,19 @@ class ApplicabilityDomain(EnviPathModel):
         joblib.dump(ad, f)
 
     def assess(self, structure: Union[str, "CompoundStructure"]):
+        return self.assess_batch([structure])[0]
+
+    def assess_batch(self, structures: List["CompoundStructure | str"]):
         ds = self.model.load_dataset()
 
-        if isinstance(structure, CompoundStructure):
-            smiles = structure.smiles
-        else:
-            smiles = structure
+        smiles = []
+        for struct in structures:
+            if isinstance(struct, CompoundStructure):
+                smiles.append(structures.smiles)
+            else:
+                smiles.append(structures)
 
-        assessment_ds, assessment_prods = ds.classification_dataset(
-            [structure], self.model.applicable_rules
-        )
+        assessment_ds, assessment_prods = ds.classification_dataset(structures, self.model.applicable_rules)
 
         # qualified_neighbours_per_rule is a nested dictionary structured as:
         #   {
@@ -2837,7 +2840,6 @@ class ApplicabilityDomain(EnviPathModel):
         )
 
         assessments = list()
-
         # loop through our assessment dataset
         for i, instance in enumerate(assessment_ds[:, assessment_ds.struct_features()]):
             rule_reliabilities = dict()
@@ -2871,7 +2873,7 @@ class ApplicabilityDomain(EnviPathModel):
                     "local_compatibility_threshold": self.local_compatibilty_threshold,
                 },
                 "assessment": {
-                    "smiles": smiles,
+                    "smiles": smiles[i],
                     "inside_app_domain": self.pca.is_applicable(assessment_ds[i])[0],
                 },
             }
@@ -2930,13 +2932,11 @@ class ApplicabilityDomain(EnviPathModel):
         distances = [tanimoto_distance(classify_instance, train) for train in train_instances]
         return distances
 
-    @staticmethod
-    def _compute_compatibility(rule_idx: int, neighbours: "RuleBasedDataset"):
+    def _compute_compatibility(self, rule_idx: int, neighbours: "RuleBasedDataset"):
         accuracy = 0.0
         import polars as pl
-        # TODO: Use a threshold to convert prob to boolean, or pass boolean in
         obs_pred = neighbours.select(obs=pl.col(f"obs_{rule_idx}").cast(pl.Boolean),
-                                     pred=pl.col(f"prob_{rule_idx}").cast(pl.Boolean))
+                                     pred=pl.col(f"prob_{rule_idx}") >= self.model.threshold)
         # Compute tp, tn, fp, fn using polars expressions
         tp = obs_pred.filter((pl.col("obs")) & (pl.col("pred"))).height
         tn = obs_pred.filter((~pl.col("obs")) & (~pl.col("pred"))).height