fixes

deconvolution/main/d2_path_evaluation.py

@@ -28,24 +28,23 @@ def load_graph(filename):
         print("Wrong file format. Require graphml or gefx format", file=sys.stderr)
         exit()
 
+""" return a random path in G starting in u and having n nodes """
 import random
-def findRandomPath(G,u,n):
+def findRandomPath(G,u,n,previous_path_nodes=set()):
     if n==0:
         return [u]
     path = [u]
-    poss_neigh = list(G.neighbors(u))
-    while u in path:
-        if len(poss_neigh) == 0: return None
-        neighbor = random.choice(poss_neigh)
-        poss_neigh.remove(neighbor)
-        path = findRandomPath(G,neighbor,n-1)
-        if path is None: return None
+    poss_neigh = list(set(G.neighbors(u)) - previous_path_nodes)
+    if len(poss_neigh) == 0: return None
+    neighbor = random.choice(poss_neigh)
+    new_previous_path_nodes = previous_path_nodes | set([u])
+    path = findRandomPath(G,neighbor,n-1, new_previous_path_nodes)
+    if path is None: return None
     return [u]+path
 
-
 import itertools
 
-def is_there_path(central_nodes,overlap_length):
+def is_there_path_acc(central_nodes,overlap_length):
     for mols in itertools.product(*central_nodes):
         #print(mols)
         last_end = None
@@ -78,7 +77,7 @@ def is_coherent_path(central_nodes, overlap_length):
     for node in central_nodes:
         cur_node_mols = central_node_to_molecules(node)
         mols += [cur_node_mols]
-    return is_there_path(mols,overlap_length)
+    return is_there_path_acc(mols,overlap_length)
 
 graph = None
 def evaluate_accuracy_paths(path_len,overlap_length=7000,max_paths_per_node=100):
@@ -87,9 +86,12 @@ def evaluate_accuracy_paths(path_len,overlap_length=7000,max_paths_per_node=100)
     nb_good_paths = 0
     for node in graph.nodes():
         nb_paths = 0
+        seen_paths = set()
         for _ in range(max_paths_per_node):
             path = findRandomPath(graph,node,path_len)
             if path is None: continue
+            if tuple(sorted(path)) in seen_paths: continue # avoids looking at the same path twice
+            seen_paths.add(tuple(sorted(path)))
             #print("path",path)
             central_nodes = [graph.nodes[x]['udg'].split()[0] for x in path]
             #print(path,central_nodes)
@@ -99,6 +101,8 @@ def evaluate_accuracy_paths(path_len,overlap_length=7000,max_paths_per_node=100)
                 nb_bad_paths += 1
     print("accuracy for l=%d:" % path_len,nb_good_paths / (nb_good_paths + nb_bad_paths))
 
+# ---- sensitivity evaluation
+
 def is_there_path(graph,molecules_to_nodes,sought_path):
     possible_central_nodes = []
     for mol in sought_path:
@@ -108,7 +112,7 @@ def is_there_path(graph,molecules_to_nodes,sought_path):
         if nx.is_connected(graph.subgraph(mols)):
             #print("found connected path",mols)
             return True
-    print("found no connected paths",sought_path)
+    #print("found no connected paths",sought_path)
     return False
 
 def evaluate_sensitivity_paths(path_len,overlap_length=7000):
@@ -140,8 +144,7 @@ def main():
     graph = load_graph(args.filename)
 
     p = Pool(4)
-    #p.map(evaluate_accuracy_paths, [1,2,3,4])
-
+    p.map(evaluate_accuracy_paths, [1,2,3,4])
     p.map(evaluate_sensitivity_paths, [1,2,3,4])
 
 if __name__ == "__main__":