Debug

evalNE_script.py

@@ -59,8 +59,8 @@ methods = ['random_prediction',
     "preferential_attachment",
     "resource_allocation_index",
     "stochastic_block_model",
-    "stochastic_block_model_edge_probs",
-    "stochastic_block_model_degree_corrected"
+    "stochastic_block_model_degree_corrected",
+    "spatial_link_prediction"
            ]
 
 # Evaluate baselines

generate_theoric_random_graph.py

@@ -17,8 +17,9 @@ parser.add_argument("output_dir")
 args = parser.parse_args()
 
 
-GRAPH_SIZE = [100,150,200]
-EDGE_SIZE = [300,500]
+GRAPH_SIZE = [80,800]
+EDGE_SIZE = [2,3]
+
 OUTPUT_DIR = args.output_dir
 if not os.path.exists(OUTPUT_DIR):
     raise FileExistsError("Output directory does not exists !")
@@ -28,7 +29,7 @@ parameters = {
     "stochastic_block_model_graph": {
         "nb_nodes":GRAPH_SIZE,
         "nb_edges":EDGE_SIZE,
-        "nb_com" :[2,5],
+        "nb_com" :[2,5,8,16],
         "percentage_edge_betw":[0.1,0.01]
     },
     "ER_graph": {
@@ -38,10 +39,11 @@ parameters = {
     "powerlaw_graph": {  # configuration_model
         "nb_nodes":GRAPH_SIZE,
         "nb_edges":EDGE_SIZE,
-        "exponent":[2,3]
+        "exponent":[2,3],
+        "tries":[100]
     },
     "spatial_graph":{
-        "nb_nodes":[100,150],
+        "nb_nodes":GRAPH_SIZE,
         "nb_edges":EDGE_SIZE,
         "coords":["random","country"],
     }
@@ -63,11 +65,14 @@ for method,args in pbar:
     list_of_params = get_params(parameters[method])
     func = getattr(ra,method)
     for ix,params in enumerate(list_of_params):
-        # try:
-        G = func(**params)
-        G.graph.update(params)
-        nx.write_gml(G, OUTPUT_DIR+"/graph_{method}_{ix}.gml".format(method=method,ix=ix),stringizer=str)
-        # except Exception as e:
-        #     print(e)
+        params["nb_edges"] = params["nb_edges"]*params["nb_nodes"]
+        print(params)
+        try:
+            G = func(**params)
+            G.graph.update(params)
+            nx.write_gml(G, OUTPUT_DIR+"/graph_{method}_{ix}.gml".format(method=method,ix=ix),stringizer=str)
+        except Exception as e:
+            print(e)
+            print("Can't generate graphs using these parameters")
 
 

lib/random.py

@@ -101,7 +101,7 @@ def _conf_model(degree_seq):
     return G
 
 
-def powerlaw_graph(nb_nodes, nb_edges, exponent=2, tries=1000, min_deg=1):
+def powerlaw_graph(nb_nodes, nb_edges, exponent=2, tries=1000, min_deg=0):
     """
     Generate a graph with a defined number of vertices, edges, and a degree distribution that fit the power law.
     Using the Molloy-Reed algorithm to
@@ -186,7 +186,7 @@ def spatial_graph(nb_nodes, nb_edges, coords="country", dist_func=lambda a, b: n
         for j in range(nb_nodes):
             if i == j and not self_link:
                 continue
-            data.append([i, j, dist_func(coords[i], coords[j])])
+            data.append([i, j, 1/(1+(dist_func(coords[i], coords[j])**2))])
     df = pd.DataFrame(data, columns="src tar weight".split()).astype({"src": int, "tar": int})
     df["hash"] = df.apply(lambda x: "_".join(sorted([str(int(x.src)), str(int(x.tar))])), axis=1)
     df = df.drop_duplicates(subset="hash")
@@ -259,7 +259,7 @@ def stochastic_block_model_graph(nb_nodes, nb_edges, nb_com, percentage_edge_bet
 
     percentage_edge_within = 1 - percentage_edge_betw
 
-    G = nx.planted_partition_graph(nb_com, int(np.round(nb_nodes / nb_com)), 1, 1)
+    G = nx.planted_partition_graph(nb_com, nb_nodes//nb_com, 1, 1)
     if verbose:
         print(G.size())
 

run_eval_par.py

@@ -21,7 +21,7 @@ args = parser.parse_args()
 fns = sorted(glob.glob(args.dataset_dir + "/*." + args.format))
 
 def run_eval(fn):
-    command = "python evalNE_script.py {0} -f {1} -n".format(fn, args.format).split()
+    command = "python evalNE_script.py {0} -f {1}".format(fn, args.format).split()
     output = subprocess.run(command)
     if not output.returncode == 0:
         print("Error! for the command :", " ".join(command))