Merge pull request #10 from KeishiS/modify_pkg_ver

Pinned Python and its package versions & Modified some function arguments due to version update

experiments/.python-version

@@ -0,0 +1 @@
+3.8

experiments/README.md

@@ -6,6 +6,7 @@ This script reproduces the experiment presented in the paper for 2 datasets.
 Before launching the script, ensure to ahve the following dependencies installed:
 - `numpy`
 - `scikit-learn`
+- `pandas`
 - `POT` ([github](https://github.com/rflamary/POT))
 - `python-igraph`
 
@@ -32,4 +33,4 @@ optional arguments:
 
 # For example:
 python main.py -d ENZYMES --gridsearch --crossvalidation --h 7
-```
+```

experiments/main.py

@@ -53,7 +53,9 @@ def main():
 
     # Save embeddings to output folder
     out_name = f'{dataset}_wl_{embedding_type}_embeddings_h{h}.npy'
-    np.save(os.path.join(output_path, out_name), label_sequences)
+    np.save(
+            os.path.join(output_path, out_name),
+            np.array(label_sequences, dtype=object))
     print(f'Embeddings for {dataset} computed, saved to {os.path.join(output_path, out_name)}.')
     print()
 
@@ -177,4 +179,4 @@ def main():
         print('No results saved to file as --crossvalidation or --gridsearch were not selected.')
 
 if __name__ == '__main__':
-    main()
+    main()

experiments/requirements.txt

@@ -0,0 +1,16 @@
+Cython==3.0.11
+igraph==0.11.6
+joblib==1.4.2
+numpy==1.23.5
+pandas==2.0.3
+POT==0.9.4
+python-dateutil==2.9.0.post0
+python-igraph==0.11.6
+pytz==2024.1
+scikit-learn==0.23.2
+scipy==1.10.1
+six==1.16.0
+texttable==1.7.0
+threadpoolctl==3.5.0
+tzdata==2024.1
+wwl==0.1.2

experiments/utilities.py

@@ -70,7 +70,7 @@ def load_continuous_graphs(data_directory):
     # If none is present, keep degree or label as features.
     attribtues_filenames = os.path.join(data_directory, 'node_features.npy')
     if os.path.isfile(attribtues_filenames):
-        node_features = np.load(attribtues_filenames)
+        node_features = np.load(attribtues_filenames, allow_pickle=True)
 
     n_nodes = np.asarray(n_nodes)
     node_features = np.asarray(node_features)