Improving config generation

explaining_framework/utils/config_gen.py

@@ -2,9 +2,10 @@ import os
 
 from explaining_framework.utils.io import write_yaml
 from torch_geometric.data.makedirs import makedirs
+from torch_geometric.graphgym.loader import load_pyg_dataset
 
 
-def divide_chunks(l, n):
+def chunkizing_list(l, n):
     for i in range(0, len(l), n):
         yield l[i : i + n]
 
@@ -19,7 +20,60 @@ if "__main__" == __name__:
     explainer_folder = os.path.join(config_folder, "explaining")
     makedirs(explainer_folder)
 
-    DATASET = ["CIFAR10"]
+    DATASET = [
+        "CIFAR10",
+        "TRIANGLES",
+        "COLORS-3",
+        "REDDIT-BINARY",
+        "REDDIT-MULTI-5K",
+        "REDDIT-MULTI-12K",
+        "COLLAB",
+        "DBLP_v1",
+        "COIL-DEL",
+        "COIL-RAG",
+        "Fingerprint",
+        "Letter-high",
+        "Letter-low",
+        "Letter-med",
+        "MSRC_9",
+        "MSRC_21",
+        "MSRC_21C",
+        "DD",
+        "ENZYMES",
+        "PROTEINS",
+        "QM9",
+        "MUTAG",
+        "Mutagenicity",
+        "AIDS",
+        "PATTERN",
+        "CLUSTER",
+        "MNIST",
+        "CIFAR10",
+        "TSP",
+        "CSL",
+        "KarateClub",
+        "CS",
+        "Physics",
+        "BBBP",
+        "Tox21",
+        "HIV",
+        "PCBA",
+        "MUV",
+        "BACE",
+        "SIDER",
+        "ClinTox",
+        "AIFB",
+        "AM",
+        "MUTAG",
+        "BGS",
+        "FAUST",
+        "DynamicFAUST",
+        "ShapeNet",
+        "ModelNet10",
+        "ModelNet40",
+        "PascalVOC-SP",
+        "COCO-SP",
+    ]
     EXPLAINER = [
         "CAM",
         "GradCAM",
@@ -27,7 +81,7 @@ if "__main__" == __name__:
         "GradExplainer",
         "GuidedBackPropagation",
         "IntegratedGradients",
-        "PGExplainer",
+        # "PGExplainer",
         "PGMExplainer",
         "RandomExplainer",
         "SubgraphX",
@@ -38,11 +92,11 @@ if "__main__" == __name__:
     ]
 
     for dataset_name in DATASET:
-        for chunk in divide_chunks(list(range(10000)), 500):
+        dataset = load_pyg_dataset(name=dataset_name, dataset_dir="/tmp/")
+        for chunk in chunkizing_list(list(range(len(dataset))), 300):
             for model_kind in ["best", "worst"]:
                 for explainer_name in EXPLAINER:
                     explaining_cfg = {}
-
                     # explaining_cfg['adjust']['strategy']= 'rpns'
                     # explaining_cfg['attack']['name']= 'all'
                     explaining_cfg[