0,1,3,5 fixes

Author: mishadr

.gitignore

@@ -25,7 +25,7 @@ sphinx_docs
 *explainers_metrics*
 experiments/explainers_metrics/**/*_metrics.json
 
-data
+datasets
 explanations
 <base.ptg_datasets.PTGDataset object*
 models

metainfo/mi_attack_parameters.json

@@ -9,7 +9,7 @@
  "ShadowModelMIAttacker": {
   "shadow_data_ratio": ["Shadow data ratio", "float", 0.25, {"min": 0, "max": 1, "step":  0.01}, "Percentage of original dataset available for intruder"],
   "shadow_epochs": ["Shadow epochs", "int", 100, {"min":  1, "step": 1}, "Number of train epochs for shadow model"],
-  "classifier_type": ["Classifier type", "str", "svc", ["svc"], "Type of classifier for MI"]
+  "classifier_type": ["Classifier type", "string", "svc", ["svc"], "Type of classifier for MI"]
  }
 }
 

src/attacks/mi_attacks.py

@@ -116,6 +116,8 @@ def attack(
         mask_tensor: Union[str, List[bool], torch.Tensor],
     ):
         assert not isinstance(mask_tensor, str), "Input of original mask seems senseless"
+        if isinstance(mask_tensor, list):
+            mask_tensor = torch.tensor(mask_tensor)
 
         model.eval()
 
@@ -222,6 +224,8 @@ def attack(
             mask_tensor: Union[List[bool], torch.Tensor],
             **kwargs
     ):
+        if isinstance(mask_tensor, list):
+            mask_tensor = torch.tensor(mask_tensor)
         task_type = gen_dataset.is_multi()
         if task_type:
             self.model_name = 'gcn_gcn_linear'

src/models_builder/gnn_constructor.py

@@ -626,6 +626,8 @@ def get_predictions(
             *args,
             **kwargs
     ) -> torch.Tensor:
+        # FIXME Kirill. tmp fix of AttributeError: 'dict' object has no attribute 'softmax' for SubgraphX
+        # self._save_emb_flag = False
         return self(*args, **kwargs).softmax(dim=-1)
         # return self.forward(*args, **kwargs)
 

src/models_builder/gnn_models.py

@@ -1394,7 +1394,7 @@ def evaluate_model(
                 metrics_values[mask][metric.name] = metric.compute(y_pred, y_true)
                 # metrics_values[mask][metric.name] = MetricManager.compute(metric, y_pred, y_true)
         if self.mi_attacker and self.mi_attack_flag:
-            self.call_mi_attack(gen_dataset=gen_dataset, mask_tensor=mask, model=self.gnn)
+            self.call_mi_attack(gen_dataset=gen_dataset, mask=mask, model=self.gnn)
         return metrics_values
 
     def call_evasion_attack(
@@ -1423,9 +1423,20 @@ def call_mi_attack(
             self,
             gen_dataset: GeneralDataset,
             model: torch.nn.Module,
-            mask_tensor: Union[str, List[bool], torch.Tensor] = 'test'
+            mask: Union[str, List[bool], torch.Tensor] = 'test'
     ):
         if self.mi_attacker:
+            # FIXME misha this is tmp fix should be removed after merge with models stage 3
+            try:
+                mask_tensor = {
+                    'train': gen_dataset.train_mask.tolist(),
+                    'val': gen_dataset.val_mask.tolist(),
+                    'test': gen_dataset.test_mask.tolist(),
+                    'all': [True] * len(gen_dataset.labels),
+                }[mask]
+            except KeyError:
+                assert isinstance(mask, torch.Tensor)
+                mask_tensor = mask
             self.mi_attacker.attack(gen_dataset=gen_dataset, model=model, mask_tensor=mask_tensor)
 
     def compute_stats_data(

tutorials/01_evasion_attack_defense/evasion_attack.py

@@ -50,12 +50,13 @@ def evasion_attack():
     # the evasion attack. You can see the available evasion attack types and their default parameters in
     # ./metainfo/evasion_attack_parameters.json
     evasion_attack_config = ConfigPattern(
-        _class_name="FGSM",
+        # _class_name="FGSM",
+        _class_name="PGD",
         _import_path=EVASION_ATTACK_PARAMETERS_PATH,
         _config_class="EvasionAttackConfig",
         _config_kwargs={
-            "epsilon": 0.01,
-            "is_feature_attack": True,
+            # "epsilon": 0.01,
+            # "is_feature_attack": True,
         }
     )
     # Here we pass information to the model manager about the attack configuration, which will be enabled by default

web_interface/back_front/model_blocks.py

@@ -366,6 +366,8 @@ def _finalize(
     def _submit(
             self
     ) -> None:
+        self.metrics = [Metric(**m) for m in self._config.get('metrics')]
+
         self._object = [self.model_manager, self.metrics]
 
     def do(

web_interface/static/js/presentation/left_menu/model/menuModelTrainerView.js

@@ -32,6 +32,13 @@ class MenuModelTrainerView extends MenuView {
         await this.updateTrainerMenu()
     }
 
+    async _accept() {
+        let metrics = this.getMetrics()
+        // We should pass metrics if user presses 'accept' without training
+        await controller.blockRequest(
+            this.requestBlock, 'modify', {'metrics': metrics})
+    }
+
     onReceive(block, args) {
         // super.onReceive(block, args)
         if (block === 'mt') {