Merge pull request #14 from ispras/new_dir_structure

New dir structure

demo/back_explainers_demo.py

@@ -16,15 +16,13 @@ def explainers_test():
     dataset_mg_small, _, results_dataset_path_mg_small = DatasetManager.get_by_full_name(
         full_name=("multiple-graphs", "custom", "small",),
         features={'attr': {'a': 'as_is'}},
-        dataset_attack_type='original',
         labeling='binary',
         dataset_ver_ind=0
     )
 
     dataset_sg_example, _, results_dataset_path_sg_example = DatasetManager.get_by_full_name(
         full_name=("single-graph", "custom", "example",),
         features={'attr': {'a': 'as_is'}},
-        dataset_attack_type='original',
         labeling='binary',
         dataset_ver_ind=0
     )
@@ -36,7 +34,6 @@ def explainers_test():
             graph="small"),
         DatasetVarConfig(features={'attr': {'a': 'as_is'}},
                          labeling='binary',
-                         dataset_attack_type='original',
                          dataset_ver_ind=0)
     )
     gen_dataset_sg_example = DatasetManager.get_by_config(
@@ -46,7 +43,6 @@ def explainers_test():
             graph="example"),
         DatasetVarConfig(features={'attr': {'a': 'as_is'}},
                          labeling='binary',
-                         dataset_attack_type='original',
                          dataset_ver_ind=0)
     )
     gen_dataset_mg_small.train_test_split(percent_train_class=0.6, percent_test_class=0.4)

demo/backend_demo.py

@@ -14,17 +14,14 @@ def backend_demo():
     # Init datasets VK and Cora
     # dataset_cora, _, results_dataset_path_cora = DatasetManager.get_by_full_name(
     #     full_name=("single-graph", "Planetoid", 'Cora'),
-    #     dataset_attack_type='original',
     #     dataset_ver_ind=0)
     # dataset_comp, _, results_dataset_path_comp = DatasetManager.get_by_full_name(
     #     full_name=("single-graph", "Amazon", "Computers",),
-    #     dataset_attack_type='original',
     #     dataset_ver_ind=0
     # )
     # dataset_mg_example, _, results_dataset_path_mg_example = DatasetManager.get_by_full_name(
     #     full_name=("multiple-graphs", "custom", "example",),
     #     features={'attr': {'type': 'as_is'}},
-    #     dataset_attack_type='original',
     #     labeling='binary',
     #     dataset_ver_ind=0
     # )
@@ -53,7 +50,6 @@ def backend_demo():
             graph="example"),
         DatasetVarConfig(features={'attr': {'type': 'as_is'}},
                          labeling='binary',
-                         dataset_attack_type='original',
                          dataset_ver_ind=0)
     )
     gen_dataset_mg_example.train_test_split(percent_train_class=0.6, percent_test_class=0.4)

demo/user_exp_example.py

@@ -26,7 +26,6 @@ def test_Konst_model():
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=full_name,
         features={'attr': {'a': 'as_is', 'b': 'as_is'}},
-        dataset_attack_type='original',
         labeling='threeClasses',
         dataset_ver_ind=0
     )

experiments/GNNExplainerDIG_exp_example.py

@@ -17,40 +17,12 @@ def dig_GNNExplainer_test():
     my_device = device('cuda' if is_available() else 'cpu')
     my_device = device('cpu')
 
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "Planetoid", 'Cora'),
-    #     dataset_attack_type='original')
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "pytorch-geometric-other", 'KarateClub'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0)
-
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=("single-graph", "Planetoid", 'Cora'),
-        dataset_attack_type='original',
         dataset_ver_ind=0)
 
     # dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
     #     full_name=("multiple-graphs", "TUDataset", 'PROTEINS'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0
-    # )
-
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     # full_name=("single-graph", "vk_samples", "vk2-ff40-N100000-A.1612175945",),
-    #     full_name=("single-graph", "vk_samples", "vk2-ff20-N10000-A.1611943634",),
-    #     # full_name=("single-graph", "vk_samples", "vk2-ff20-N1000-U.1612273925",),
-    #     # features=('sex',),
-    #     features={'str_f': tuple(), 'str_g': None, 'attr': {
-    #         # "('personal', 'political')": 'one_hot',
-    #         # "('occupation', 'type')": 'one_hot', # Don't work now
-    #         # "('relation',)": 'one_hot',
-    #         # "('age',)": 'one_hot',
-    #         "('sex',)": 'one_hot',
-    #     }},
-    #     # features={'str_f': tuple(), 'str_g': None, 'attr': {'sex': 'one_hot', }},
-    #     labeling='sex1',
-    #     dataset_attack_type='original',
     #     dataset_ver_ind=0
     # )
 

experiments/GNNExplainerGEOM_exp_example.py

@@ -17,41 +17,9 @@ def geom_GNNExplainer_test():
     my_device = device('cuda' if is_available() else 'cpu')
     my_device = device('cpu')
 
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "Planetoid", 'Cora'),
-    #     dataset_attack_type='original')
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "pytorch-geometric-other", 'KarateClub'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0)
-
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=("single-graph", "Planetoid", 'Cora'),
-        dataset_attack_type='original',
         dataset_ver_ind=0)
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("multiple-graphs", "TUDataset", 'PROTEINS'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0
-    # )
-
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     # full_name=("single-graph", "vk_samples", "vk2-ff40-N100000-A.1612175945",),
-    #     full_name=("single-graph", "vk_samples", "vk2-ff20-N10000-A.1611943634",),
-    #     # full_name=("single-graph", "vk_samples", "vk2-ff20-N1000-U.1612273925",),
-    #     # features=('sex',),
-    #     features={'str_f': tuple(), 'str_g': None, 'attr': {
-    #         # "('personal', 'political')": 'one_hot',
-    #         # "('occupation', 'type')": 'one_hot', # Don't work now
-    #         # "('relation',)": 'one_hot',
-    #         # "('age',)": 'one_hot',
-    #         "('sex',)": 'one_hot',
-    #     }},
-    #     # features={'str_f': tuple(), 'str_g': None, 'attr': {'sex': 'one_hot', }},
-    #     labeling='sex1',
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0
-    # )
 
     gcn2 = model_configs_zoo(dataset=dataset, model_name='gcn_gcn')
 

experiments/GradCAM_exp_example.py

@@ -17,7 +17,6 @@ def GradCAM_test():
 
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=("single-graph", "Planetoid", 'Cora'),
-        dataset_attack_type='original',
         dataset_ver_ind=0)
 
     # print(dataset)

experiments/GraphMask_exp_example.py

@@ -32,41 +32,9 @@ def test_graph_mask(configuration):
     my_device = device('cuda' if is_available() else 'cpu')
     my_device = device('cpu')
 
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "Planetoid", 'Cora'),
-    #     dataset_attack_type='original')
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "pytorch-geometric-other", 'KarateClub'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0)
-
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=("single-graph", "Planetoid", 'Cora'),
-        dataset_attack_type='original',
         dataset_ver_ind=0)
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("multiple-graphs", "TUDataset", 'PROTEINS'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0
-    # )
-
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     # full_name=("single-graph", "vk_samples", "vk2-ff40-N100000-A.1612175945",),
-    #     full_name=("single-graph", "vk_samples", "vk2-ff20-N10000-A.1611943634",),
-    #     # full_name=("single-graph", "vk_samples", "vk2-ff20-N1000-U.1612273925",),
-    #     # features=('sex',),
-    #     features={'str_f': tuple(), 'str_g': None, 'attr': {
-    #         # "('personal', 'political')": 'one_hot',
-    #         # "('occupation', 'type')": 'one_hot', # Don't work now
-    #         # "('relation',)": 'one_hot',
-    #         # "('age',)": 'one_hot',
-    #         "('sex',)": 'one_hot',
-    #     }},
-    #     # features={'str_f': tuple(), 'str_g': None, 'attr': {'sex': 'one_hot', }},
-    #     labeling='sex1',
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0
-    # )
 
     gcn2 = model_configs_zoo(dataset=dataset, model_name='gcn_gcn')
 

experiments/NeuralAnalysis_our_exp_example.py

@@ -15,7 +15,6 @@ def test_neural_analysis(percent_train_class: float = 0.8, percent_test_class: f
     full_name = ("multiple-graphs", "TUDataset", 'MUTAG')
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=full_name,
-        dataset_attack_type='original',
         dataset_ver_ind=0
     )
 

experiments/ProtGNN_exp.py

@@ -34,7 +34,6 @@ def test_prot(i=None, conv=None, batch_size=24, seed=5,
     """
     dataset, data, results_dataset_path = Datasets.get_pytorch_geometric(
         full_name=("single-graph", "TUDataset", 'MUTAG'),
-        dataset_attack_type='original',
         dataset_ver_ind=0)
     """
 
@@ -50,7 +49,6 @@ def test_prot(i=None, conv=None, batch_size=24, seed=5,
     full_name = ("multiple-graphs", "TUDataset", 'MUTAG')
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=full_name,
-        dataset_attack_type='original',
         dataset_ver_ind=0
     )
 

experiments/attack_defense_test.py

@@ -4,15 +4,9 @@
 
 from torch import device
 
-from attacks.attack_base import RandomPoisonAttack
-from defense.defense_base import BadRandomPoisonDefender
-from models_builder.attack_defense_manager import AttackAndDefenseManager
-from src.aux.utils import OPTIMIZERS_PARAMETERS_PATH, EXPLAINERS_LOCAL_RUN_PARAMETERS_PATH, \
-    EXPLAINERS_INIT_PARAMETERS_PATH, POISON_ATTACK_PARAMETERS_PATH, POISON_DEFENSE_PARAMETERS_PATH
-from src.explainers.explainers_manager import FrameworkExplainersManager
+from src.aux.utils import POISON_ATTACK_PARAMETERS_PATH, POISON_DEFENSE_PARAMETERS_PATH
 from src.models_builder.gnn_models import FrameworkGNNModelManager, Metric
-from src.aux.configs import ModelManagerConfig, ModelModificationConfig, ExplainerInitConfig, ExplainerRunConfig, \
-    ConfigPattern, ExplainerModificationConfig
+from src.aux.configs import ModelModificationConfig, ConfigPattern
 from src.base.datasets_processing import DatasetManager
 from src.models_builder.models_zoo import model_configs_zoo
 
@@ -28,28 +22,14 @@ def test_attack_defense():
     full_name = ("single-graph", "Planetoid", 'Cora')
     # full_name = ("multiple-graphs", "TUDataset", 'PROTEINS')
 
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "Planetoid", 'Cora'),
-    #     dataset_attack_type='original')
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "pytorch-geometric-other", 'KarateClub'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0)
-
-    # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
-    #     full_name=("single-graph", "Planetoid", 'Cora'),
-    #     dataset_attack_type='original',
-    #     dataset_ver_ind=0)
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=full_name,
-        dataset_attack_type='original',
         dataset_ver_ind=0
     )
 
     # dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
     #     full_name=("single-graph", "custom", "example",),
     #     features={'attr': {'a': 'as_is', 'b': 'as_is'}},
-    #     dataset_attack_type='original',
     #     labeling='threeClasses',
     #     dataset_ver_ind=0
     # )
@@ -68,7 +48,6 @@ def test_attack_defense():
     #     }},
     #     # features={'str_f': tuple(), 'str_g': None, 'attr': {'sex': 'one_hot', }},
     #     labeling='sex1',
-    #     dataset_attack_type='original',
     #     dataset_ver_ind=0
     # )
 
@@ -115,8 +94,8 @@ def test_attack_defense():
         modification=ModelModificationConfig(model_ver_ind=0, epochs=steps_epochs)
     )
 
-    save_model_flag = False
-    # save_model_flag = True
+    # save_model_flag = False
+    save_model_flag = True
 
     # data.x = data.x.float()
     gnn_model_manager.gnn.to(my_device)
@@ -131,20 +110,24 @@ def test_attack_defense():
         }
     )
 
+    # poison_defense_config = ConfigPattern(
+    #     _class_name="BadRandomPoisonDefender",
+    #     _import_path=POISON_DEFENSE_PARAMETERS_PATH,
+    #     _config_class="PoisonDefenseConfig",
+    #     _config_kwargs={
+    #         "n_edges_percent": 0.1,
+    #     }
+    # )
     poison_defense_config = ConfigPattern(
-        _class_name="BadRandomPoisonDefender",
+        _class_name="EmptyPoisonDefender",
         _import_path=POISON_DEFENSE_PARAMETERS_PATH,
         _config_class="PoisonDefenseConfig",
         _config_kwargs={
-            "n_edges_percent": 0.1,
         }
     )
 
-    attack_defense_manager = AttackAndDefenseManager(gen_dataset=dataset, gnn_manager=gnn_model_manager)
-    attack_defense_manager.set_poison_attacker(poison_attack_config=poison_attack_config)
-    attack_defense_manager.set_poison_defender(poison_defense_config=poison_defense_config)
-
-    attack_defense_manager.conduct_experiment()
+    gnn_model_manager.set_poison_attacker(poison_attack_config=poison_attack_config)
+    gnn_model_manager.set_poison_defender(poison_defense_config=poison_defense_config)
 
     warnings.warn("Start training")
     dataset.train_test_split()
@@ -172,7 +155,6 @@ def test_attack_defense():
     print(metric_loc)
 
 
-
 if __name__ == '__main__':
     test_attack_defense()
 

experiments/computation_graph.py

@@ -677,15 +677,13 @@ def computation_graph_data(x, edge_index, inputs, result, tensors, ops, edges):
         full_name=("single-graph", "custom", 'example'),
         features={'attr': {'a': 'as_is'}},
         labeling='binary',
-        dataset_attack_type='original',
         dataset_ver_ind=0)
 
     # # multi
     # dataset, data, results_dataset_path = DatasetManager.get_pytorch_geometric(
     #     full_name=("multiple-graphs", "custom", 'example'),
     #     features={'attr': {'type': 'as_is'}},
     #     labeling='binary',
-    #     dataset_attack_type='original',
     #     dataset_ver_ind=0)
 
     sage1: torch.nn.Module = GNNStructure(