--- a
+++ b/AICare-baselines/test.py
@@ -0,0 +1,88 @@
+import os
+
+import lightning as L
+import pandas as pd
+
+from configs.exp import hparams
+from datasets.loader.datamodule import EhrDataModule
+from datasets.loader.load_los_info import get_los_info
+from pipelines import DlPipeline, MlPipeline
+
+def get_latest_file(path):
+    # Get list of all files in the directory
+    files = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+    
+    # Get the file with the latest modification time
+    latest_file = max(files, key=os.path.getctime)
+    
+    return latest_file
+
+def run_ml_experiment(config):
+    los_config = get_los_info(f'datasets/{config["dataset"]}/processed/fold_{config["fold"]}')
+    config.update({"los_info": los_config})
+    # data
+    dm = EhrDataModule(f'datasets/{config["dataset"]}/processed/fold_{config["fold"]}', batch_size=config["batch_size"])
+    # train/val/test
+    pipeline = MlPipeline(config)
+    trainer = L.Trainer(accelerator="cpu", max_epochs=1, logger=False, num_sanity_val_steps=0)
+    trainer.test(pipeline, dm)
+    perf = pipeline.test_performance
+    return perf
+
+def run_dl_experiment(config):
+    los_config = get_los_info(f'datasets/{config["dataset"]}/processed/fold_{config["fold"]}')
+    config.update({"los_info": los_config})
+
+    # data
+    dm = EhrDataModule(f'datasets/{config["dataset"]}/processed/fold_{config["fold"]}', batch_size=config["batch_size"])
+    # checkpoint
+    # checkpoint_path = f'logs/train/{config["dataset"]}/{config["task"]}/{config["model"]}-fold{config["fold"]}-seed{config["seed"]}/checkpoints/best.ckpt'
+
+    checkpoint_path = get_latest_file(f'logs/train/{config["dataset"]}/{config["task"]}/{config["model"]}-fold{config["fold"]}-seed{config["seed"]}/checkpoints')
+
+    print("checkpoint_path: ", checkpoint_path)
+
+    if "time_aware" in config and config["time_aware"] == True:
+        checkpoint_path = f'logs/train/{config["dataset"]}/{config["task"]}/{config["model"]}-fold{config["fold"]}-seed{config["seed"]}-ta/checkpoints/best.ckpt'
+
+    # train/val/test
+    pipeline = DlPipeline(config)
+    trainer = L.Trainer(accelerator="cpu", max_epochs=1, logger=False, num_sanity_val_steps=0)
+    trainer.test(pipeline, dm, ckpt_path=checkpoint_path)
+    perf = pipeline.test_performance
+    return perf
+
+if __name__ == "__main__":
+    best_hparams = hparams # [TO-SPECIFY]
+    performance_table = {'dataset':[], 'task': [], 'model': [], 'fold': [], 'seed': [], 'accuracy': [], 'auroc': [], 'auprc': [], 'f1': [], 'minpse': []}
+    for i in range(0, len(best_hparams)):
+    # for i in range(0, 1):
+        config = best_hparams[i]
+        print(f"Testing... {i}/{len(best_hparams)}")
+        run_func = run_ml_experiment if config["model"] in ["RF", "DT", "GBDT", "XGBoost", "CatBoost", "LR", "LightGBM"] else run_dl_experiment
+        seeds = [0] # [0,1,2,3,4]
+        folds = ['nshot']
+        for fold in folds:
+            config["fold"] = fold
+            for seed in seeds:
+                config["seed"] = seed
+                perf = run_func(config)
+                print(f"{config}, Test Performance: {perf}")
+
+                if "time_aware" in config and config["time_aware"] == True:
+                    model_name = config['model']+"_ta"
+                else:
+                    model_name = config['model']
+
+                performance_table['dataset'].append(config['dataset'])
+                performance_table['task'].append(config['task'])
+                performance_table['model'].append(model_name)
+                performance_table['fold'].append(config['fold'])
+                performance_table['seed'].append(config['seed'])
+                if config['task'] == 'outcome':
+                    performance_table['accuracy'].append(perf['accuracy'])
+                    performance_table['auroc'].append(perf['auroc'])
+                    performance_table['auprc'].append(perf['auprc'])
+                    performance_table['f1'].append(perf['f1'])
+                    performance_table['minpse'].append(perf['minpse'])
+    pd.DataFrame(performance_table).to_csv('ijcai24_ml_baselines_20240108.csv', index=False) # [TO-SPECIFY]