Merge pull request #2099 from RTXteam/issue2022

Returning Lookup results also when request with knowledge_type=Inferred is called

code/ARAX/ARAXQuery/ARAX_expander.py

@@ -388,7 +388,10 @@ def apply(self, response, input_parameters, mode: str = "ARAX"):
                         infer_input_parameters = {"action": "drug_treatment_graph_expansion",'node_curie': object_curie, 'qedge_id': inferred_qedge_key}
                         inferer = ARAXInfer()
                         infer_response = inferer.apply(response, infer_input_parameters)
-                        return infer_response
+                        # return infer_response
+                        response = infer_response
+                        overarching_kg = eu.convert_standard_kg_to_qg_organized_kg(message.knowledge_graph)
+
                     elif set(['biolink:regulates']).intersection(set(qedge.predicates)): # Figure out if this is a "regulates" query, then use call XCRG models
                         # Call XCRG models and simply return whatever it returns
                         # Get the subject and object of this edge
@@ -425,7 +428,8 @@ def apply(self, response, input_parameters, mode: str = "ARAX"):
                             infer_input_parameters = {"action": "chemical_gene_regulation_graph_expansion", 'object_qnode_id' : qedge.object, 'object_curie': object_curie, 'qedge_id': inferred_qedge_key, 'regulation_type': regulation_type}
                         inferer = ARAXInfer()
                         infer_response = inferer.apply(response, infer_input_parameters)
-                        return infer_response
+                        response = infer_response
+                        overarching_kg = eu.convert_standard_kg_to_qg_organized_kg(message.knowledge_graph)
                     else:
                         log.info(f"Qedge {inferred_qedge_key} has knowledge_type == inferred, but the query is not "
                                  f"DTD-related (e.g., 'biolink:ameliorates', 'biolink:treats') or CRG-related ('biolink:regulates') according to the specified predicate. Will answer using the normal 'fill' strategy (not creative mode).")
@@ -434,10 +438,12 @@ def apply(self, response, input_parameters, mode: str = "ARAX"):
                                 f"the qedges has knowledge_type == inferred. Will answer using the normal 'fill' strategy "
                                 f"(not creative mode).")
 
-
         # Expand any specified edges
         if qedge_keys_to_expand:
             query_sub_graph = self._extract_query_subgraph(qedge_keys_to_expand, query_graph, log)
+            if inferred_qedge_keys and len(query_graph.edges) == 1:
+                for edge in query_sub_graph.edges.keys():
+                    query_sub_graph.edges[edge].knowledge_type = 'lookup'
             if log.status != 'OK':
                 return response
             log.debug(f"Query graph for this Expand() call is: {query_sub_graph.to_dict()}")
@@ -473,7 +479,9 @@ def apply(self, response, input_parameters, mode: str = "ARAX"):
 
                 # Create a query graph for this edge (that uses curies found in prior steps)
                 one_hop_qg = self._get_query_graph_for_edge(qedge_key, query_graph, overarching_kg, log)
-
+                if inferred_qedge_keys and len(query_graph.edges) == 1:
+                    for edge in one_hop_qg.edges.keys():
+                        one_hop_qg.edges[edge].knowledge_type = 'lookup'
                 # Figure out the prune threshold (use what user provided or otherwise do something intelligent)
                 if parameters.get("prune_threshold"):
                     pre_prune_threshold = parameters["prune_threshold"]
@@ -486,7 +494,10 @@ def apply(self, response, input_parameters, mode: str = "ARAX"):
                     for qnode_key in fulfilled_qnode_keys:
                         num_kg_nodes = len(overarching_kg.nodes_by_qg_id[qnode_key])
                         if num_kg_nodes > pre_prune_threshold:
-                            overarching_kg = self._prune_kg(qnode_key, pre_prune_threshold, overarching_kg, query_graph, log)
+                            if inferred_qedge_keys and len(inferred_qedge_keys) == 1:
+                                overarching_kg = self._prune_kg(qnode_key, pre_prune_threshold, overarching_kg, message.query_graph, log)
+                            else:
+                                overarching_kg = self._prune_kg(qnode_key, pre_prune_threshold, overarching_kg, query_graph, log)
                             # Re-formulate the QG for this edge now that the KG has been slimmed down
                             one_hop_qg = self._get_query_graph_for_edge(qedge_key, query_graph, overarching_kg, log)
                 if log.status != 'OK':
@@ -650,7 +661,10 @@ def apply(self, response, input_parameters, mode: str = "ARAX"):
                     self._apply_any_kryptonite_edges(overarching_kg, message.query_graph,
                                                      message.encountered_kryptonite_edges_info, response)
                     # Remove any paths that are now dead-ends
-                    overarching_kg = self._remove_dead_end_paths(query_graph, overarching_kg, response)
+                    if inferred_qedge_keys and len(inferred_qedge_keys) == 1:
+                        overarching_kg = self._remove_dead_end_paths(message.query_graph, overarching_kg, response)
+                    else:
+                        overarching_kg = self._remove_dead_end_paths(query_graph, overarching_kg, response)
                     if response.status != 'OK':
                         return response
 

code/ARAX/ARAXQuery/ARAX_ranker.py

@@ -625,8 +625,29 @@ def aggregate_scores_dmk(self, response):
         #print(float(len(ranks_list)))
         result_scores = sum(ranks_list)/float(len(ranks_list))
         #print(result_scores)
+
+        # Replace Inferred Results Score with Probability score calculated by xDTD model
+        inferred_qedge_keys = [qedge_key for qedge_key, qedge in message.query_graph.edges.items() 
+                               if qedge.knowledge_type == "inferred"]
         for result, score in zip(results, result_scores):
             result.analyses[0].score = score  # For now we only ever have one Analysis per Result
+            if inferred_qedge_keys:
+                inferred_qedge_key = inferred_qedge_keys[0]
+                edge_bindings =  result.analyses[0].edge_bindings
+                inferred_edge_bindings = []
+                if edge_bindings:
+                    inferred_edge_bindings = edge_bindings.get(inferred_qedge_key,[])
+                for edge_name in inferred_edge_bindings:
+                    edge_id = edge_name.id
+                    edge_attributes = message.knowledge_graph.edges[edge_id].attributes
+                    if edge_attributes is not None:
+                        for edge_attribute in edge_attributes:
+                            if edge_attribute.original_attribute_name == 'probability_treats' and edge_attribute.value is not None:
+                                result.analyses[0].score = float(edge_attribute.value)
+
+
+
+
 
         # for result in message.results:
         #     self.result_confidence_maker(result)

code/ARAX/test/test_ARAX_expand.py

@@ -1114,12 +1114,18 @@ def test_xdtd_expand():
     nodes_by_qg_id, edges_by_qg_id, message = _run_query_and_do_standard_testing(json_query=query, return_message=True)
     assert message.auxiliary_graphs
     for edge in edges_by_qg_id["t_edge"].values():
-        assert edge.attributes
-        support_graph_attributes = [attribute for attribute in edge.attributes if attribute.attribute_type_id == "biolink:support_graphs"]
-        assert support_graph_attributes
-        assert len(support_graph_attributes) == 1
-        support_graph_attribute = support_graph_attributes[0]
-        assert support_graph_attribute.value[0] in message.auxiliary_graphs
+        inferred_edge = False
+        for source in edge.sources:
+            if source.resource_role == "primary_knowledge_source" and source.resource_id == "infores:arax":
+                inferred_edge = True
+        # Perform Tests only for inferred edges
+        if inferred_edge:
+            assert edge.attributes
+            support_graph_attributes = [attribute for attribute in edge.attributes if attribute.attribute_type_id == "biolink:support_graphs"]
+            assert support_graph_attributes
+            assert len(support_graph_attributes) == 1
+            support_graph_attribute = support_graph_attributes[0]
+            assert support_graph_attribute.value[0] in message.auxiliary_graphs
 
 
 @pytest.mark.slow