Potential Bugs in the hybrid_node.py Example Script

[vladislavalerievich]

Several possible issues have been observed in the example script examples/hybrid_node.py that may lead to unexpected behavior or unfair comparisons. The following points outline the bugs along with the problematic code lines and suggested improvements:

1. LightGBM Test Metrics Printing:
   The script prints outdated test metrics (e.g., using metrics from the GNN model when evaluating LightGBM predictions).
   Bug:

    pred = model.predict(tf_test).numpy()
    print(f"Test: {test_metrics}")

   Improvement:

    pred = pred.predict(tf_test).numpy()
    test_metrics = task.evaluate(test_pred)
    print(f"LightGBM Test metrics: {test_metrics}"

2. State Dict Path Formatting:
   Bug:
   The state dict file path is defined with placeholders in a plain string:

   STATE_DICT_PTH = "results/{args.dataset}_{args.task}_state_dict.pth"

   Improvement:
   Use an f-string so that the placeholders are replaced with actual values:

   STATE_DICT_PTH = f"results/{args.dataset}_{args.task}_state_dict.pth"

3. Sample Size Usage for GNN Model:
   Issue:
   The GNN model does not utilize the sample_size parameter when training. This means that while the LightGBM model is trained on a subsampled dataset (first sample_size rows), the GNN model is trained on the full training set. This discrepancy can lead to an unfair comparison between the models.

4. Entity Table Overwriting:
   Bug:
   The script reassigns the entity_table variable in a loop when creating loaders for each split. For example:

   for split in ["train", "val", "test"]:
       table = task.get_table(split)
       table_input = get_node_train_table_input(table=table, task=task)
       entity_table = table_input.nodes[0]  # This gets overwritten each iteration
       ...

   Improvement:
   Instead of overwriting, maintain a mapping for each split and reference the correct table. For instance:

   entity_table_mapping: Dict[str, str] = {}
   for split in ["train", "val", "test"]:
       table = task.get_table(split)
       table_input = get_node_train_table_input(table=table, task=task)
       entity_table_mapping[split] = table_input.nodes[0]
       ...
   # Later reference the appropriate entity table, e.g., using task.entity_table = entity_table_mapping["train"]

Addressing these issues may help improve the robustness of the example script and ensure a fair comparison between the GNN and LightGBM models.

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[rishabh-ranjan]

Thanks for pointing this out @vladislavalerievich ! Since you have proposed the improvements, can you make a quick PR with these changes? We will be happy to merge to main!