图神经网络 - Python实现LSTM-GNN时间序列预测

Training the ML models ================================== Before proceeding to training the ML models, do the following. 1) Define data_dir, graph_dir, log_path, and ray_dir in `paths.json` to convenient locations. 2) Run the following to unpack the processed eICU data into mmap files for easy loading during training. The mmap files will be saved in `data_dir`. ``` python3 -m src.dataloader.convert ``` The following commands train and evaluate the models introduced in our paper. N.B. - The models are structured using pytorch-lightning. Graph neural networks and neighbourhood sampling are implemented using pytorch-geometric. - Our models assume a default graph which is made with k=3 under a k-closest scheme. If you wish to use other graphs, refer to `read_graph_edge_list` in `src/dataloader/pyg_reader.py` to add a reference handle to `version2filename` for your graph. - The default task is **In-House-Mortality Prediction (ihm)**, add `--task los` to the command to perform the **Length-of-Stay Prediction (los)** task instead. - These commands use the best set of hyperparameters; To use other hyperparameters, remove `--read_best` from the command and refer to `src/args.py`. ### a. LSTM-GNN The following runs the training and evaluation for LSTM-GNN models. `--gnn_name` can be set as `gat`, `sage`, or `mpnn`. When `mpnn` is used, add `--ns_sizes 10` to the command. ``` python3 -m train_ns_lstmgnn --bilstm --ts_mask --add_flat --class_weights --gnn_name gat --add_diag --read_best ``` The following runs a hyperparameter search. ``` python3 -m src.hyperparameters.lstmgnn_search --bilstm --ts_mask --add_flat --class_weights --gnn_name gat --add_diag ``` ### b. Dynamic LSTM-GNN The following runs the training & evaluation for dynamic LSTM-GNN models. `--gnn_name` can be set as `gcn`, `gat`, or `mpnn`. ``` python3 -m train_dynamic --bilstm --random_g --ts_mask --add_flat --class_weights --gnn_name mpnn --read_best ``` The following runs a hyperparameter search. ``` python3 -m src.hyperparameters.dynamic_lstmgnn_search --bilstm --random_g --ts_mask --add_flat --class_weights --gnn_name mpnn ``` ### c. GNN The following runs the GNN models (with neighbourhood sampling). `--gnn_name` can be set as `gat`, `sage`, or `mpnn`. When `mpnn` is used, add `--ns_sizes 10` to the command. ``` python3 -m train_ns_gnn --ts_mask --add_flat --class_weights --gnn_name gat --add_diag --read_best ``` The following runs a hyperparameter search. ``` python3 -m src.hyperparameters.ns_gnn_search --ts_mask --add_flat --class_weights --gnn_name gat --add_diag ``` ### d. LSTM (Baselines) The following runs the baseline bi-LSTMs. To remove diagnoses from the input vector, remove `--add_diag` from the command. ``` python3 -m train_ns_lstm --bilstm --ts_mask --add_flat --class_weights --num_workers 0 --add_diag --read_best ``` The following runs a hyperparameter search. ``` python3 -m src.hyperparameters.lstm_search --bilstm --ts_mask --add_flat --class_weights --num_workers 0 --add_diag ```