Merge remote-tracking branch 'origin/graphhdv2' into graphhdv2

# Conflicts: # GraphHD_v2/graphhd_experiment_1.py # GraphHD_v2/graphhd_experiment_2.py # GraphHD_v2/graphhd_experiment_3.py
hyperdimensional-computing · Nov 7, 2023 · 764e79c · 764e79c
2 parents c8dc7a4 + bcd600e
commit 764e79c
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Showing 3 changed files with 53 additions and 65 deletions.
diff --git a/GraphHD_v2/graphhd_experiment_33.py b/GraphHD_v2/graphhd_experiment_33.py
@@ -15,7 +15,9 @@
 import csv
 
 import time
-csv_file = 'experiment_3/result'+str(time.time())+'.csv'
+
+csv_file = "experiment_3/result" + str(time.time()) + ".csv"
+
 
 def experiment(randomness=0, dataset="MUTAG"):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -31,7 +33,6 @@ def experiment(randomness=0, dataset="MUTAG"):
     test_size = len(graphs) - train_size
     train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
 
-
     def sparse_stochastic_graph(G):
         """
         Returns a sparse adjacency matrix of the graph G.
@@ -45,7 +46,6 @@ def sparse_stochastic_graph(G):
         size = (G.num_nodes, G.num_nodes)
         return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
 
-
     def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
         N = G.num_nodes
         M = sparse_stochastic_graph(G) * alpha
@@ -60,7 +60,6 @@ def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
                 return v
         return v
 
-
     def to_undirected(edge_index):
         """
         Returns the undirected edge_index
@@ -70,7 +69,6 @@ def to_undirected(edge_index):
         edge_index = torch.unique(edge_index, dim=1)
         return edge_index
 
-
     def min_max_graph_size(graph_dataset):
         if len(graph_dataset) == 0:
             return None, None
@@ -85,9 +83,6 @@ def min_max_graph_size(graph_dataset):
 
         return min_num_nodes, max_num_nodes
 
-
-
-
     class Encoder(nn.Module):
         def __init__(self, out_features, size):
             super(Encoder, self).__init__()
@@ -116,7 +111,6 @@ def forward(self, x):
             hvs = torchhd.bind(node_id_hvs_2[row], node_id_hvs_2[col])
             return torchhd.multiset(hvs)
 
-
     min_graph_size, max_graph_size = min_max_graph_size(graphs)
     encode = Encoder(DIMENSIONS, max_graph_size)
     encode = encode.to(device)
@@ -133,8 +127,10 @@ def forward(self, x):
             model.add(samples_hv, samples.y)
 
     accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
-    f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
-    #f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
+    f1 = torchmetrics.F1Score(
+        num_classes=graphs.num_classes, average="macro", multiclass=True
+    )
+    # f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
 
     with torch.no_grad():
         model.normalize()
@@ -147,14 +143,13 @@ def forward(self, x):
             accuracy.update(outputs.cpu(), samples.y)
             f1.update(outputs.cpu(), samples.y)
 
-    acc = (accuracy.compute().item() * 100)
-    f = (f1.compute().item() * 100)
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
     return acc, f
 
 
-
 REPETITIONS = 5
-DATASET = ['MUTAG', 'ENZYMES', 'PROTEINS']
+DATASET = ["MUTAG", "ENZYMES", "PROTEINS"]
 
 for d in DATASET:
     acc_final = []
@@ -165,13 +160,11 @@ def forward(self, x):
         acc, f1 = experiment(100, d)
         acc_aux.append(acc)
         f1_aux.append(f1)
-    acc_final.append(round(sum(acc_aux)/REPETITIONS, 2))
-    f1_final.append(round(sum(f1_aux)/REPETITIONS,2))
+    acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+    f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
 
-    with open(csv_file, mode='a', newline='') as file:
+    with open(csv_file, mode="a", newline="") as file:
         writer = csv.writer(file)
-        writer.writerow([d] +['RANDOM'])
+        writer.writerow([d] + ["RANDOM"])
         writer.writerows([acc_final])
         writer.writerows([f1_final])
-
-
diff --git a/GraphHD_v2/graphhd_experiment_4.py b/GraphHD_v2/graphhd_experiment_4.py
@@ -15,7 +15,9 @@
 import csv
 
 import time
-csv_file = 'experiment_4/result'+str(time.time())+'.csv'
+
+csv_file = "experiment_4/result" + str(time.time()) + ".csv"
+
 
 def experiment(randomness=0, dataset="MUTAG"):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -31,7 +33,6 @@ def experiment(randomness=0, dataset="MUTAG"):
     test_size = len(graphs) - train_size
     train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
 
-
     def sparse_stochastic_graph(G):
         """
         Returns a sparse adjacency matrix of the graph G.
@@ -45,7 +46,6 @@ def sparse_stochastic_graph(G):
         size = (G.num_nodes, G.num_nodes)
         return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
 
-
     def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
         N = G.num_nodes
         M = sparse_stochastic_graph(G) * alpha
@@ -60,7 +60,6 @@ def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
                 return v
         return v
 
-
     def to_undirected(edge_index):
         """
         Returns the undirected edge_index
@@ -70,7 +69,6 @@ def to_undirected(edge_index):
         edge_index = torch.unique(edge_index, dim=1)
         return edge_index
 
-
     def min_max_graph_size(graph_dataset):
         if len(graph_dataset) == 0:
             return None, None
@@ -113,7 +111,9 @@ def local_centrality(self, x):
 
             for i in nodes:
                 adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
-                node_id_hvs[i] = torchhd.bind(self.node_ids.weight[i], self.levels.weight[len(adjacent_nodes)])
+                node_id_hvs[i] = torchhd.bind(
+                    self.node_ids.weight[i], self.levels.weight[len(adjacent_nodes)]
+                )
 
             row, col = to_undirected(x.edge_index)
             hvs = torchhd.bind(node_id_hvs[row], node_id_hvs[col])
@@ -127,7 +127,10 @@ def semi_local_centrality(self, x):
             for i in nodes:
                 adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
                 for j in adjacent_nodes:
-                    node_id_hvs[i] = torchhd.bundle(self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])], node_id_hvs[i])
+                    node_id_hvs[i] = torchhd.bundle(
+                        self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])],
+                        node_id_hvs[i],
+                    )
                 node_id_hvs[i] = torchhd.bind(node_id_hvs[i], (self.node_ids.weight[i]))
 
             row, col = to_undirected(x.edge_index)
@@ -136,7 +139,7 @@ def semi_local_centrality(self, x):
 
         def forward(self, x):
             return self.local_centrality(x)
-            '''
+            """
             nodes, _ = x.edge_index
             nodes = list(set(nodes))
             node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
@@ -157,7 +160,7 @@ def forward(self, x):
             row, col = to_undirected(x.edge_index)
             hvs = torchhd.bind(node_id_hvs_2[row], node_id_hvs_2[col])
             return torchhd.multiset(hvs)
-            '''
+            """
 
     min_graph_size, max_graph_size = min_max_graph_size(graphs)
     encode = Encoder(DIMENSIONS, max_graph_size)
@@ -175,7 +178,7 @@ def forward(self, x):
             model.add(samples_hv, samples.y)
 
     accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
-    #f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
     f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
 
     with torch.no_grad():
@@ -189,14 +192,13 @@ def forward(self, x):
             accuracy.update(outputs.cpu(), samples.y)
             f1.update(outputs.cpu(), samples.y)
 
-    acc = (accuracy.compute().item() * 100)
-    f = (f1.compute().item() * 100)
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
     return acc, f
 
 
-
 REPETITIONS = 1
-DATASET = ['MUTAG']
+DATASET = ["MUTAG"]
 
 for d in DATASET:
     acc_final = []
@@ -207,13 +209,11 @@ def forward(self, x):
         acc, f1 = experiment(100, d)
         acc_aux.append(acc)
         f1_aux.append(f1)
-    acc_final.append(round(sum(acc_aux)/REPETITIONS, 2))
-    f1_final.append(round(sum(f1_aux)/REPETITIONS,2))
+    acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+    f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
 
-    with open(csv_file, mode='a', newline='') as file:
+    with open(csv_file, mode="a", newline="") as file:
         writer = csv.writer(file)
-        writer.writerow([d] +['RANDOM'])
+        writer.writerow([d] + ["RANDOM"])
         writer.writerows([acc_final])
         writer.writerows([f1_final])
-
-
diff --git a/GraphHD_v2/graphhd_experiment_5.py b/GraphHD_v2/graphhd_experiment_5.py
@@ -15,7 +15,9 @@
 import csv
 
 import time
-csv_file = 'experiment_4/result'+str(time.time())+'.csv'
+
+csv_file = "experiment_4/result" + str(time.time()) + ".csv"
+
 
 def experiment(randomness=0, dataset="MUTAG"):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -31,7 +33,6 @@ def experiment(randomness=0, dataset="MUTAG"):
     test_size = len(graphs) - train_size
     train_ld, test_ld = torch.utils.data.random_split(graphs, [train_size, test_size])
 
-
     def sparse_stochastic_graph(G):
         """
         Returns a sparse adjacency matrix of the graph G.
@@ -45,7 +46,6 @@ def sparse_stochastic_graph(G):
         size = (G.num_nodes, G.num_nodes)
         return torch.sparse_coo_tensor(G.edge_index, values_per_node, size)
 
-
     def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
         N = G.num_nodes
         M = sparse_stochastic_graph(G) * alpha
@@ -60,7 +60,6 @@ def pagerank(G, alpha=0.85, max_iter=100, tol=1e-06):
                 return v
         return v
 
-
     def to_undirected(edge_index):
         """
         Returns the undirected edge_index
@@ -70,7 +69,6 @@ def to_undirected(edge_index):
         edge_index = torch.unique(edge_index, dim=1)
         return edge_index
 
-
     def min_max_graph_size(graph_dataset):
         if len(graph_dataset) == 0:
             return None, None
@@ -90,20 +88,17 @@ def __init__(self, out_features, size, node_features):
             super(Encoder, self).__init__()
             self.out_features = out_features
 
-
             self.node_ids = embeddings.Random(size, out_features)
             self.node_attr = embeddings.Density(node_features, out_features)
 
-
-
             self.levels = embeddings.Circular(size, out_features)
 
         def local_centrality(self, x):
             nodes, _ = x.edge_index
             nodes = list(set(nodes))
             node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
 
-            #for i in nodes:
+            # for i in nodes:
             #    node_id_hvs[i] = torchhd.bind(self.node_ids.weight[i], self.node_attr(x.x[i]))
 
             node_id_hvs = torchhd.bind(self.node_ids.weight, self.node_attr(x.x))
@@ -121,7 +116,10 @@ def semi_local_centrality(self, x):
             for i in nodes:
                 adjacent_nodes = x.edge_index[1][x.edge_index[0] == i]
                 for j in adjacent_nodes:
-                    node_id_hvs[i] = torchhd.bundle(self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])], node_id_hvs[i])
+                    node_id_hvs[i] = torchhd.bundle(
+                        self.levels.weight[len(x.edge_index[1][x.edge_index[0] == j])],
+                        node_id_hvs[i],
+                    )
                 node_id_hvs[i] = torchhd.bind(node_id_hvs[i], (self.node_ids.weight[i]))
 
             row, col = to_undirected(x.edge_index)
@@ -130,7 +128,7 @@ def semi_local_centrality(self, x):
 
         def forward(self, x):
             return self.local_centrality(x)
-            '''
+            """
             nodes, _ = x.edge_index
             nodes = list(set(nodes))
             node_id_hvs = torch.zeros((x.num_nodes, self.out_features), device=device)
@@ -151,10 +149,10 @@ def forward(self, x):
             row, col = to_undirected(x.edge_index)
             hvs = torchhd.bind(node_id_hvs_2[row], node_id_hvs_2[col])
             return torchhd.multiset(hvs)
-            '''
+            """
 
     min_graph_size, max_graph_size = min_max_graph_size(graphs)
-    encode = Encoder(DIMENSIONS, max_graph_size,len(graphs.x[0]))
+    encode = Encoder(DIMENSIONS, max_graph_size, len(graphs.x[0]))
     encode = encode.to(device)
 
     model = Centroid(DIMENSIONS, graphs.num_classes)
@@ -169,7 +167,7 @@ def forward(self, x):
             model.add(samples_hv, samples.y)
 
     accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
-    #f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
+    # f1 = torchmetrics.F1Score(num_classes=graphs.num_classes, average='macro', multiclass=True)
     f1 = torchmetrics.F1Score("multiclass", num_classes=graphs.num_classes)
 
     with torch.no_grad():
@@ -183,14 +181,13 @@ def forward(self, x):
             accuracy.update(outputs.cpu(), samples.y)
             f1.update(outputs.cpu(), samples.y)
 
-    acc = (accuracy.compute().item() * 100)
-    f = (f1.compute().item() * 100)
+    acc = accuracy.compute().item() * 100
+    f = f1.compute().item() * 100
     return acc, f
 
 
-
 REPETITIONS = 1
-DATASET = ['MUTAG', 'ENZYMES', 'PROTEINS']
+DATASET = ["MUTAG", "ENZYMES", "PROTEINS"]
 
 for d in DATASET:
     acc_final = []
@@ -201,13 +198,11 @@ def forward(self, x):
         acc, f1 = experiment(100, d)
         acc_aux.append(acc)
         f1_aux.append(f1)
-    acc_final.append(round(sum(acc_aux)/REPETITIONS, 2))
-    f1_final.append(round(sum(f1_aux)/REPETITIONS,2))
+    acc_final.append(round(sum(acc_aux) / REPETITIONS, 2))
+    f1_final.append(round(sum(f1_aux) / REPETITIONS, 2))
 
-    with open(csv_file, mode='a', newline='') as file:
+    with open(csv_file, mode="a", newline="") as file:
         writer = csv.writer(file)
-        writer.writerow([d] +['RANDOM'])
+        writer.writerow([d] + ["RANDOM"])
         writer.writerows([acc_final])
         writer.writerows([f1_final])
-
-