fix issue in the text experiment

config.json

+{
+    "structured": [
+        {
+            "datasetName": "BA2Motif"
+        },
+        {
+            "datasetName": "BAMultiShape"
+        },
+        {
+            "datasetName": "MUTAG"
+        }
+    ],
+    "text": [
+        {
+            "datasetName": "dblp",
+            "grouped_keyword_dir": "rawData/dblp/groups",
+            "entity_name": "author"
+        },
+        {
+            "datasetName": "imdb",
+            "grouped_keyword_dir": "rawData/imdb/groups",
+            "entity_name": "movie"
+        }
+    ]
+}

text_based_datasets_experiment.py

@@ -185,9 +185,10 @@ def summarize_aggregated_results(aggregated_results, summary_filename):
 
     print(f"Summary results saved to {summary_filename}")
 
-def experiment(grouped_keyword_dir, dataset_name, entity_name, bag_of_words_size=1000, iterations=5):
+def experiment(grouped_keyword_dir, dataset_name, entity_name, bag_of_words_size=1000, iterations=5, num_groups_list=[0, 5, 10, 15, 20, 25], create_high_level_concepts_as_boolean=False):
     """
     Handles dataset loading and evaluation for experiments.
+    Manages the experiment based on specified number of groups and boolean concept creation settings.
     """
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     print(f"CUDA is {'available. Using GPU.' if device.type == 'cuda' else 'not available. Using CPU.'}")
@@ -204,30 +205,22 @@ def experiment(grouped_keyword_dir, dataset_name, entity_name, bag_of_words_size
         dataset = load_datasets(dataset_name=dataset_name, bag_of_words_size=bag_of_words_size)
         model = run_gnn(dataset, entity_name, dataset_name, run_timestamp)
 
-        grouped_keyword_files = [
-            os.path.join(grouped_keyword_dir, f)
-            for f in os.listdir(grouped_keyword_dir)
-            if f.startswith('groupedKeywords_') and f.endswith('.json')
-        ]
-        grouped_keyword_files.insert(0, "")  # Allow the possibility of no grouped keywords
-
         write_header = True
-        for create_high_level_concepts_as_boolean in [True, False]:
-            for group_keyword_file in sorted(grouped_keyword_files):
-                num_groups = 0 if group_keyword_file == "" else int(group_keyword_file.split('_')[1].split('.')[0])
+        for num_groups in num_groups_list:
+            group_keyword_file = "" if num_groups == 0 else os.path.join(grouped_keyword_dir, f'groupedKeywords_{num_groups}.json')
             owl_graph_path = f'./owlGraphs/{dataset_name}_{run_timestamp}_{num_groups}_groups_{"bool" if create_high_level_concepts_as_boolean else "data"}.owl'
 
             print("\n" + "=" * 50)
             print(f"Running experiment {run} with create_high_level_concepts_as_boolean={create_high_level_concepts_as_boolean} and num_groups={num_groups}")
             print("=" * 50)
 
-                high_level_concepts = fetch_high_level_concepts(dataset, num_groups, group_keyword_file) if num_groups != 0 else None
+            high_level_concepts = None if num_groups == 0 else fetch_high_level_concepts(dataset, num_groups, group_keyword_file)
 
             results = explain_and_evaluate(
                 model, dataset.dataset, entity_name, owl_graph_path, high_level_concepts, create_high_level_concepts_as_boolean
             )
 
-                append_to_csv_file(results, run_csv_filename, dataset_name, num_groups, write_header=write_header)
+            append_to_csv_file(results, run_csv_filename, dataset_name, num_groups, create_high_level_concepts_as_boolean, write_header=write_header)
 
             for label, data in results.items():
                 # Initialize aggregation for this label and number of groups if not yet present