debug

eval_mixed_model.py

@@ -49,7 +49,7 @@ NB_COM = args.nb_com
 NB_ITERATION = args.nb_iterations
 VERBOSE = args.verbose
 FEATURES = set(args.features.split(","))
-
+TIMEOUT = 60
 dist = lambda a,b : np.linalg.norm(a-b)**2
 hash_func = lambda x:"_".join(sorted([str(x[0]),str(x[1])]))
 
@@ -58,9 +58,12 @@ def get_aucs(G):
     traintest_split = LPEvalSplit()
     traintest_split.compute_splits(H, split_alg="spanning_tree", train_frac=0.90, fe_ratio=1)
     nee = LPEvaluator(traintest_split)
-
-    auc_spatial = nee.evaluate_baseline(method="spatial_link_prediction").test_scores.auroc()
-    auc_sbm = nee.evaluate_baseline(method="stochastic_block_model").test_scores.auroc()
+    auc_spatial, auc_sbm = 0, 0
+    try:
+        auc_spatial = nee.evaluate_baseline(method="spatial_link_prediction",timeout=TIMEOUT).test_scores.auroc()
+        auc_sbm = nee.evaluate_baseline(method="stochastic_block_model",timeout=TIMEOUT).test_scores.auroc()
+    except:
+        print("Could not compuyte AUC ! ")
     return auc_sbm,auc_spatial
 
 dist = lambda a,b : np.linalg.norm(a-b)
@@ -82,7 +85,6 @@ block_assign = nx.get_node_attributes(G,"block")
 H = G.copy()
 float_epsilon = np.finfo(float).eps
 df_data["p_0"] = df_data.apply(lambda x:1 if G.has_edge(x.u,x.v) else 0,axis =1)
-print(df_data)
 for i in range(1,NB_ITERATION+1):
     old_probs = dict(df_data["hash_ p_{0}".format(i-1).split()].values)
     auc_sbm,auc_spatial = get_aucs(H)
@@ -116,7 +118,7 @@ for i in range(1,NB_ITERATION+1):
         G2.nodes[n]["pos"] = pos[n]
     H=G2.copy()
 
-
+if VERBOSE:print(df_data)
 edge_feature= {hash_func([int(row.u),int(row.v)]):[row["p_{0}".format(i)] for i in range(1,NB_ITERATION+1)] for ix,row in df_data.iterrows()}
 
 G, _ = pp.prep_graph(G,maincc=True)