README.md

Supervised Graph Neural Network (aka supGNN) paired with downstream XGBoost model

Introduction

Please refer to 0_train/2_supGNN/README.md to complete training Here is remain steps for inference

1. Convert tabular data to edge list for inference

Using both ./sharechat_recsys2023_data/train and ./sharechat_recsys2023_data/test folders, we create ./supGNN_graph_data/full_edges.csv.gz which will be used for supGNN inference later.

python3 1_inference/2_supGNN/convert_full_tabular_data_to_edge_list.py

2. Create CSVDataset from edge list

Convert full graph into full CSVDataset (for GNN inference)

python3 1_inference/2_supGNN/convert_full_edge_list_to_CSVDataset.py

Please see newly created files inside ./data/supGNN_graph_data/full_graph/recsys_graph folder.

3. Run supervised Graph Neural Network to generate node embeddings

Run inference on full graph using saved GNN model

python3 1_inference/2_supGNN/infer_supervised_graphsage.py

4. Map GNN embeddings to original features

python3 1_inference/2_supGNN/map_node_emb_to_edge_list.py

this script will map GNN-generated node embeddings to their respective edges and save GNN-boosted features for train and test split separately.

5. Merge supGNN data with Feature Engineered data

Merge test supGNN data with test FE data

python3 1_inference/2_supGNN/merge_test_FE_and_test_supgnn_data.py

6. Run XGBoost Inference

Infer XGB on merged test data

python3 1_inference/2_supGNN/infer_xgb.py

Finally, we get ./data/supGNN_graph_data/test_graph/submission.csv as our final submission file which we upload to the leaderboard for evaluation on test set.