add fgsm for link pred + test

Author: mishabounty

gnn_aid/attacks/evasion_attacks.py

@@ -78,7 +78,7 @@ def __init__(
         self.epsilon = epsilon
         self.grad_aggr_type = 'mean'
         self.attack_diff = GraphModificationArtifact()
-        self.attack_res_misha = None
+        # self.attack_res_misha = None
 
     def attack(
             self,
@@ -100,7 +100,7 @@ def attack(
                 x.requires_grad = True
 
                 # TODO now support only one edge, mask_tensor?
-                edge_label_index = torch.tensor([[5], [6]]).to(device)
+                edge_label_index = torch.tensor(self.element_idx).unsqueeze(dim=1).to(device)
                 edge_label = ((data.edge_index == edge_label_index).all(dim=0).any()).float().unsqueeze(dim=0).to(device)
 
                 node_out = model(data.x, data.edge_index)
@@ -170,7 +170,7 @@ def attack(
                 y = gen_dataset.data.y
                 x = gen_dataset.data.x
 
-                edge_label_index = torch.tensor([[7], [6]]).to(device)
+                edge_label_index = torch.tensor(self.element_idx).unsqueeze(dim=1).to(device)
                 edge_label = ((edge_index == edge_label_index).all(dim=0).any()).float().unsqueeze(dim=0).to(device)
                 node_idx_1 = edge_label_index[0].item()
                 node_idx_2 = edge_label_index[1].item()
@@ -275,8 +275,8 @@ def attack(
                     perturbed_edges = torch.cat((perturbed_edges[:, :max_index], perturbed_edges[:, max_index + 1:]),
                                                 dim=1)
 
-                from torch_geometric.data import Data
-                self.attack_res_misha = Data(x=x, edge_index=perturbed_edges, y=y)
+                # from torch_geometric.data import Data
+                # self.attack_res_misha = Data(x=x, edge_index=perturbed_edges, y=y)
                 set_a = set(map(tuple, edge_index.T.tolist()))
                 set_b = set(map(tuple, perturbed_edges.T.tolist()))
 
@@ -348,7 +348,7 @@ def attack(
                 edges_to_keep = edge_index[:, ~edge_mask]
                 updated_edge_index = torch.cat([edges_to_keep, perturbed_edges], dim=1)
                 gen_dataset.data.edge_index = updated_edge_index
-                self.attack_res_misha = gen_dataset
+                # self.attack_res_misha = gen_dataset
 
                 set_a = set(map(tuple, edge_index.T.tolist()))
                 set_b = set(map(tuple, updated_edge_index.T.tolist()))

metainfo/evasion_attack_parameters.json

@@ -3,7 +3,7 @@
  }, 
  "FGSM": {
   "is_feature_attack": ["is feature attack", "bool", false, {}, "If true, applies perturbations to node features; otherwise, perturbs the graph structure (edges)"],
-  "element_idx": ["node", "int", 0, {"min": 0, "step": 1}, "Index of the element to attack"],
+  "element_idx": ["node", "int_or_tuple", 0, {"min": 0, "step": 1}, "Index of the element to attack"],
   "epsilon": ["epsilon", "float", 0.1, {"min": 0.0001, "step": 0.01}, "Magnitude of the perturbation — higher values result in stronger attacks"]
  },
  "Nettack": {

tests/attacks_test.py

@@ -11,7 +11,7 @@
     ConfigPattern, FeatureConfig, Task
 from gnn_aid.models_builder.models_zoo import model_configs_zoo
 from gnn_aid.aux.utils import POISON_ATTACK_PARAMETERS_PATH, EVASION_ATTACK_PARAMETERS_PATH, \
-    OPTIMIZERS_PARAMETERS_PATH, MI_ATTACK_PARAMETERS_PATH
+    OPTIMIZERS_PARAMETERS_PATH, MI_ATTACK_PARAMETERS_PATH, FUNCTIONS_PARAMETERS_PATH
 from .utils import monkey_patch_dirs, cleanup_patches
 
 
@@ -75,6 +75,15 @@ def setUp(self):
             }
         )
 
+        # Cora for link pred
+        dc = DatasetConfig((LibPTGDataset.data_folder, 'Homogeneous', 'Planetoid', 'Cora'))
+        dvc = LibPTGDataset.default_dataset_var_config.clone_with({"task": Task.EDGE_PREDICTION})
+
+        self.gen_dataset_sg_cora_link = DatasetManager.get_by_config(dc, dvc)
+        self.gen_dataset_sg_cora_link.train_test_split(percent_train_class=0.85, percent_test_class=0.1)
+        self.results_dataset_path_sg_cora_link = self.gen_dataset_sg_cora_link.prepared_dir
+        self.gen_dataset_sg_cora_link.data.to(self.my_device)
+
         monkey_patch_dirs()
 
     def tearDown(self):
@@ -368,6 +377,76 @@ def test_fgsm_SG(self):
                                              metrics=[Metric("Accuracy", mask='test')])['test']['Accuracy']
         # ---------- ----------------- ----------
 
+    def test_fgsm_LINK(self):
+        sage_cossim = model_configs_zoo(dataset=self.gen_dataset_sg_cora_link, model_name="sage_cossim")
+
+        manager_config = ConfigPattern(
+            _config_class="ModelManagerConfig",
+            _config_kwargs={
+                "batch": 64,
+                "mask_features": [],
+                "optimizer": {
+                    "_class_name": "Adam",
+                    "_config_kwargs": {},
+                },
+                "loss_function": {
+                    "_config_class": "Config",
+                    "_class_name": "CrossEntropyLoss",
+                    "_import_path": FUNCTIONS_PARAMETERS_PATH,
+                    "_class_import_info": ["torch.nn"],
+                    "_config_kwargs": {},
+                },
+            }
+        )
+
+        gnn_model_manager = FrameworkGNNModelManager(
+            gnn=sage_cossim,
+            dataset_path=self.gen_dataset_sg_cora_link.prepared_dir,
+            manager_config=manager_config,
+            modification=ModelModificationConfig(model_ver_ind=0, epochs=0)
+        )
+
+        gnn_model_manager.gnn.to(self.my_device)
+        gnn_model_manager.train_model(gen_dataset=self.gen_dataset_sg_cora_link, steps=10, save_model_flag=False)
+
+        # ---------- Attack on structure ----------
+        evasion_attack_config = ConfigPattern(
+            _class_name="FGSM",
+            _import_path=EVASION_ATTACK_PARAMETERS_PATH,
+            _config_class="EvasionAttackConfig",
+            _config_kwargs={
+                "is_feature_attack": False,
+                "element_idx": (1, 2),
+                "epsilon": 0.5,
+            }
+        )
+
+        gnn_model_manager.set_evasion_attacker(evasion_attack_config=evasion_attack_config)
+
+        # Attack
+        _ = gnn_model_manager.evaluate_model(gen_dataset=self.gen_dataset_sg_cora_link,
+                                             metrics=[Metric("Accuracy", mask='test')])['test']['Accuracy']
+        # ---------- ------------------- ----------
+
+        # ---------- Attack on feature ----------
+        evasion_attack_config = ConfigPattern(
+            _class_name="FGSM",
+            _import_path=EVASION_ATTACK_PARAMETERS_PATH,
+            _config_class="EvasionAttackConfig",
+            _config_kwargs={
+                "is_feature_attack": True,
+                "element_idx": (1, 2),
+                "epsilon": 0.5,
+            }
+        )
+
+        gnn_model_manager.set_evasion_attacker(evasion_attack_config=evasion_attack_config)
+
+        # Attack
+        _ = gnn_model_manager.evaluate_model(gen_dataset=self.gen_dataset_sg_cora_link,
+                                             metrics=[Metric("Accuracy", mask='test')])['test']['Accuracy']
+        # ---------- ----------------- ----------
+
     def test_fgsm_MG(self):
         gcn_gcn = model_configs_zoo(dataset=self.gen_dataset_mg_small, model_name='gin_gin_gin_lin_lin_con')
 

tests/explainers_test.py

@@ -226,7 +226,7 @@ def setUp(self) -> None:
             gnn=self.sage_cossim,
             dataset_path=self.gen_dataset_sg_cora_link.prepared_dir,
             manager_config=manager_config,
-            modification=ModelModificationConfig(model_ver_ind=0, epochs=10)
+            modification=ModelModificationConfig(model_ver_ind=0, epochs=0)
         )
         self.sage_cossim_mm.train_model(
             gen_dataset=self.gen_dataset_sg_cora_link, steps=10,