From e8d6a3cc6692d8e64ce09ec9f0c492b8e3cf8c2d Mon Sep 17 00:00:00 2001
From: AndiMajore <andi.majore@googlemail.com>
Date: Thu, 9 Feb 2023 16:07:27 +0100
Subject: [PATCH] fixed connector node definition
---
tasks/util/scores_to_results.py | 35 ++++++++++++++++++++++-----------
1 file changed, 24 insertions(+), 11 deletions(-)
diff --git a/tasks/util/scores_to_results.py b/tasks/util/scores_to_results.py
index b690db5..cb47496 100755
--- a/tasks/util/scores_to_results.py
+++ b/tasks/util/scores_to_results.py
@@ -12,14 +12,14 @@ def scores_to_results(
pdi_dataset,
filterPaths
):
-
r"""Transforms the scores to the required result format."""
node_name_attribute = "internal_id" # nodes in the input network which is created from RepoTrialDB have primaryDomainId as name attribute
if target == "drug":
candidates = [(node, scores[node]) for node in drug_ids if scores[node] > 0]
else:
- candidates = [(node, scores[node]) for node in range(g.num_vertices()) if scores[node] > 0 and node not in set(seed_ids)]
+ candidates = [(node, scores[node]) for node in range(g.num_vertices()) if
+ scores[node] > 0 and node not in set(seed_ids)]
best_candidates = [item[0] for item in sorted(candidates, key=lambda item: item[1], reverse=True)[:result_size]]
# Concatenate best result candidates with seeds and compute induced subgraph.
# since the result size filters out nodes, the result network is not complete anymore.
@@ -29,7 +29,7 @@ def scores_to_results(
intermediate_nodes = set()
returned_edges = set()
- returned_nodes = set(seed_ids) # return seed_ids in any case
+ returned_nodes = set(seed_ids) # return seed_ids in any case
# return only the path to a drug with the shortest distance
accepted_candidates = set()
@@ -44,11 +44,15 @@ def scores_to_results(
vertices, edges = gtt.shortest_path(g, candidate, seed_id)
drug_in_path = False
+ seed_in_path = False
for vertex in vertices:
if g.vertex_properties["type"][int(vertex)] == "drug" and vertex != candidate:
drug_in_path = True
break
- if drug_in_path:
+ if int(vertex) in seed_ids and int(vertex) != seed_id:
+ seed_in_path = True
+ break
+ if drug_in_path or seed_in_path:
continue
accepted_candidates.add(g.vertex_properties[node_name_attribute][int(candidate)])
for vertex in vertices:
@@ -58,7 +62,8 @@ def scores_to_results(
intermediate_nodes.add(g.vertex_properties[node_name_attribute][int(vertex)])
returned_nodes.add(int(vertex))
for edge in edges:
- if ((edge.source(), edge.target()) not in returned_edges) or ((edge.target(), edge.source()) not in returned_edges):
+ if (((edge.source(), edge.target()) not in returned_edges) or (
+ (edge.target(), edge.source()) not in returned_edges)) and int(edge.target()) in returned_nodes and int(edge.source()) in returned_nodes:
returned_edges.add((edge.source(), edge.target()))
else:
for candidate in best_candidates:
@@ -66,11 +71,15 @@ def scores_to_results(
vertices, edges = gtt.shortest_path(g, candidate, seed_id)
drug_in_path = False
+ seed_in_path = False
for vertex in vertices:
if g.vertex_properties["type"][int(vertex)] == "drug" and vertex != candidate:
drug_in_path = True
break
- if drug_in_path:
+ if int(vertex) in seed_ids and int(vertex) != seed_id:
+ seed_in_path = True
+ break
+ if drug_in_path or seed_in_path:
continue
accepted_candidates.add(g.vertex_properties[node_name_attribute][int(candidate)])
for vertex in vertices:
@@ -80,18 +89,22 @@ def scores_to_results(
intermediate_nodes.add(g.vertex_properties[node_name_attribute][int(vertex)])
returned_nodes.add(int(vertex))
for edge in edges:
- if ((edge.source(), edge.target()) not in returned_edges) or ((edge.target(), edge.source()) not in returned_edges):
+ if (((edge.source(), edge.target()) not in returned_edges) or (
+ (edge.target(), edge.source()) not in returned_edges)) and int(
+ edge.target()) in returned_nodes and int(edge.source()) in returned_nodes:
returned_edges.add((edge.source(), edge.target()))
for node in accepted_candidates:
if node in intermediate_nodes:
intermediate_nodes.remove(node)
subgraph = {
- "nodes":[g.vertex_properties[node_name_attribute][node] for node in returned_nodes],
- "edges": [{"from": g.vertex_properties[node_name_attribute][source], "to": g.vertex_properties[node_name_attribute][target]} for source, target in returned_edges],
- }
+ "nodes": [g.vertex_properties[node_name_attribute][node] for node in returned_nodes],
+ "edges": [{"from": g.vertex_properties[node_name_attribute][source],
+ "to": g.vertex_properties[node_name_attribute][target]} for source, target in returned_edges],
+ }
# Compute node attributes.
- node_types = {g.vertex_properties[node_name_attribute][node]: g.vertex_properties["type"][node] for node in returned_nodes}
+ node_types = {g.vertex_properties[node_name_attribute][node]: g.vertex_properties["type"][node] for node in
+ returned_nodes}
is_seed = {g.vertex_properties[node_name_attribute][node]: node in set(seed_ids) for node in returned_nodes}
returned_scores = {g.vertex_properties[node_name_attribute][node]: scores[node] for node in returned_nodes}
--
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