Small fixes

icu_benchmarks/models/train.py

@@ -35,7 +35,7 @@ def train_common(
     test_on: str = Split.test,
     use_wandb: bool = True,
     cpu: bool = False,
-    num_workers: int = min(len(os.sched_getaffinity(0)), torch.cuda.device_count() * 4 * int(torch.cuda.is_available()), 16),
+    num_workers: int = min(torch.cuda.device_count() * 4 * int(torch.cuda.is_available()), 16),
 ):
     """Common wrapper to train all benchmarked models.
 
@@ -44,13 +44,11 @@ def train_common(
         log_dir: Path to directory where model output should be saved.
         load_weights: If set to true, skip training and load weights from source_dir instead.
         source_dir: If set to load weights, path to directory containing trained weights.
-        seed: Common seed used for any random operation.
         reproducible: If set to true, set torch to run reproducibly.
         mode: Mode of the model. Can be one of the values of RunMode.
         model: Model to be trained.
         weight: Weight to be used for the loss function.
         optimizer: Optimizer to be used for training.
-        do_test: If set to true, evaluate the model on the test set.
         batch_size: Batch size to be used for training.
         epochs: Number of epochs to train for.
         patience: Number of epochs to wait before early stopping.

icu_benchmarks/run.py

@@ -4,7 +4,6 @@
 import gin
 import logging
 import sys
-import torch
 from pathlib import Path
 
 import importlib.util
@@ -37,7 +36,7 @@ def main(my_args=tuple(sys.argv[1:])):
 
     log_fmt = "%(asctime)s - %(levelname)s: %(message)s"
     logging.basicConfig(format=log_fmt)
-    logging.getLogger().setLevel(logging.INFO)
+    logging.getLogger().setLevel(logging.DEBUG)
 
     load_weights = args.command == "evaluate"
     args.data_dir = Path(args.data_dir)
@@ -141,6 +140,6 @@ def main(my_args=tuple(sys.argv[1:])):
         plot_aggregated_results(run_dir, "aggregated_test_metrics.json")
 
 
-"""Main module."""
-if __name__ == "__main__":
-    main()
+# """Main module."""
+# if __name__ == "__main__":
+#     main()

icu_benchmarks/run_utils.py

@@ -169,6 +169,8 @@ def log_full_line(msg: str, level: int = logging.INFO, char: str = "-", num_newl
         "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, width=terminal_size.columns - reserved_chars),
     )
 
+# aggregate_results(Path(r"C:\Users\Robin\Downloads\2023-02-12T15-46-31"))
+
 def load_pretrained_imputation_model(use_pretrained_imputation):
     if use_pretrained_imputation is not None and not Path(use_pretrained_imputation).exists():
         logging.warning("the specified pretrained imputation model does not exist")

scripts/evaluate_results/aggregrate_experiment.py

@@ -83,3 +83,4 @@ def aggregate_results(
     # Exclude datasets
     results = results[results["Model"].isin(models)]
     return results
+print(aggregate_results(Path(r"C:\Users\Robin\Downloads\2023-02-12T15-46-31"), decimals=1))