wip

src/sleep_detection/main.py

@@ -23,15 +23,15 @@ from sleep_detection.sleep_epoch_classifer import SleepEpochClassifier
 def main() -> None:
     """The Main entrypoint function."""
     cli = parse_args()
-    session = load_session(cli.a, cli.H, cli.l)
+    sessions = load_sessions(cli.a, cli.H, cli.l)
 
-    inputs = torch.tensor(session[["timestamp", "heartrate", "motion-avg"]].to_numpy(), dtype=torch.float32)
+    inputs = torch.tensor(sessions[["timestamp", "heartrate", "motion-avg"]].to_numpy(), dtype=torch.float32)
     mean = inputs.mean(dim=0, keepdim=True)  # shape (1, n_features)
     std = inputs.std(dim=0, keepdim=True) + 1e-8
     normalized_inputs = (inputs - mean) / std
     logger.info(f"inputs={normalized_inputs}")
 
-    targets = torch.tensor(session["stage"].to_numpy(), dtype=torch.long)
+    targets = torch.tensor(sessions["stage"].to_numpy(), dtype=torch.long)
     logger.info(f"targets={targets}")
 
     net = SleepEpochClassifier(n_features=3, hidden_size=16, n_classes=3)
@@ -44,13 +44,25 @@ def parse_args() -> Namespace:
     parser = ArgumentParser(description="Training program.")
     parser.add_argument("-v", "--verbose", help="Increase verbosity.", action="store_true")
     parser.add_argument("-V", "--version", action="store_true", help="Show version")
-    parser.add_argument("-a", type=Path, required=True, help="Acceleration data file(s).")
-    parser.add_argument("-H", type=Path, required=True, help="Heartrate data file(s).")
-    parser.add_argument("-l", type=Path, required=True, help="Labels data file(s).")
+    parser.add_argument("-a", type=Path, nargs="+", required=True, help="Acceleration data file(s).")
+    parser.add_argument("-H", type=Path, nargs="+", required=True, help="Heartrate data file(s).")
+    parser.add_argument("-l", type=Path, nargs="+", required=True, help="Labels data file(s).")
     parser.add_argument("-o", "--output", type=Path, required=True, help="Output .pt file.")
     return parser.parse_args()
 
 
+def load_sessions(
+    acceleration_files: list[Path], heartrate_files: list[Path], label_files: list[Path]
+) -> pandas.DataFrame:
+    if len(acceleration_files) != len(heartrate_files) != len(label_files):
+        raise RuntimeError("unmatched session file sets.")
+    result = pandas.DataFrame()
+    for i in range(len(acceleration_files)):
+        result = pandas.concat([result, load_session(acceleration_files[i], heartrate_files[i], label_files[i])])
+    logger.info(f"sessions={result}")
+    return result
+
+
 def load_session(acceleration_file: Path, heartrate_file: Path, label_file: Path) -> pandas.DataFrame:
     """Load a session triplet."""
     adf = load_acceleration_data(acceleration_file)
@@ -108,13 +120,13 @@ def normalize_sleep_stage(stage) -> int:
     if pandas.isna(stage):
         return int(numpy.nan)
     value = int(stage)
-    if value == 0:
+    if value <= 0:
         return 0
     if 1 <= value <= 4:
         return 1
     if 5 <= value <= 6:
         return 2
-    return int(numpy.nan)
+    raise ValueError(stage)
 
 
 def train(input: Tensor, target: Tensor, net: Module) -> None: