feat: much better architecture
I understand at least some of this.
This commit is contained in:
+67
-163
@@ -3,6 +3,7 @@
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import sys
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from argparse import ArgumentParser
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from argparse import Namespace
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from collections import Counter
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from collections.abc import Sequence
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from pathlib import Path
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@@ -17,125 +18,39 @@ from torch.optim import SGD
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from sleep_detection.sleep_epoch_classifer import SleepEpochClassifier
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SLEEP_STAGE_LABELS = [0, 1, 2]
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def normalize_sleep_stage(stage) -> int:
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"""Map raw sleep stages to the 3-class target space.
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Raw labels:
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0 -> 0
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1, 2, 3, 4 -> 1
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5, 6 -> 2
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Other values are treated as unknown.
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"""
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if pandas.isna(stage):
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return int(numpy.nan)
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value = int(stage)
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if value == 0:
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return 0
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if 1 <= value <= 4:
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return 1
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if 5 <= value <= 6:
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return 2
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return int(numpy.nan)
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@logger.catch
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def main() -> None:
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"""The Main entrypoint function."""
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cli = parse_args()
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logger.info("welcome")
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load_session(cli.a, cli.H, cli.l)
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raise ValueError(f"Unknown command: {cli.command}")
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session = load_session(cli.a, cli.H, cli.l)
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inputs = torch.tensor(session[["timestamp", "heartrate", "motion-avg"]].to_numpy(), dtype=torch.float32)
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mean = inputs.mean(dim=0, keepdim=True) # shape (1, n_features)
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std = inputs.std(dim=0, keepdim=True) + 1e-8
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normalized_inputs = (inputs - mean) / std
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logger.info(f"inputs={normalized_inputs}")
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targets = torch.tensor(session["stage"].to_numpy(), dtype=torch.long)
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logger.info(f"targets={targets}")
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net = SleepEpochClassifier(n_features=3, hidden_size=16, n_classes=3)
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train(normalized_inputs, targets, net)
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torch.save(net.state_dict(), cli.output)
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def parse_args() -> Namespace:
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"""Parse commandline arguments."""
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parser = ArgumentParser(description="Training program.")
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parser.add_argument("-v", "--verbose", help="Increase verbosity.", action="store_true")
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parser.add_argument("-V", "--version", action="store_true", help="Show version")
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parser.add_argument("-a", type=Path, required=True, help="Acceleration data file(s).")
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parser.add_argument("-H", type=Path, required=True, help="Heartrate data file(s).")
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parser.add_argument("-l", type=Path, required=True, help="Labels data file(s).")
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parser.add_argument("-o", "--output", type=Path, required=True, help="Output .pt file.")
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return parser.parse_args()
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def featureize(data: pandas.DataFrame) -> tuple[pandas.DataFrame, Tensor, Tensor, list]:
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"""Convert loaded data into input and target tensors.
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Args:
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data: Loaded data, should contain the keys: [timestamp, heartrate, motion, x, y, z, stage]
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"""
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logger.info(f"Featurizing the data...")
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epochs = build_epochs(data)
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epochs["hr_std"] = epochs["hr_std"].fillna(0.0)
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epochs = epochs.dropna(subset=["hr_mean", "motion_mean", "motion_max", "stage"])
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epochs["seconds_since_start"] = epochs.index.total_seconds()
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CONTEXT = 2 # epochs each side -> 5 epochs total = 25 min window
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FEATURE_COLS = ["hr_mean", "hr_std", "motion_mean", "motion_max", "seconds_since_start"]
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features = epochs[FEATURE_COLS].to_numpy(dtype=numpy.float32)
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labels = epochs["stage"].astype(numpy.int64).to_numpy()
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X, y = [], []
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for i in range(CONTEXT, len(epochs) - CONTEXT):
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window = features[i - CONTEXT : i + CONTEXT + 1] # shape: (2*CONTEXT+1, n_feats)
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X.append(window.flatten()) # -> 1D vector
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y.append(labels[i])
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if not X:
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raise ValueError("Not enough complete epochs to build a training set.")
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X = torch.tensor(numpy.stack(X), dtype=torch.float32) # shape: (n_epochs, (2*CONTEXT+1)*n_feats)
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y = torch.tensor(y, dtype=torch.long) # shape: (n_epochs,)
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return epochs, X, y, SLEEP_STAGE_LABELS
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def featureize_sessions(dataframes: Sequence[pandas.DataFrame]) -> tuple[pandas.DataFrame, Tensor, Tensor, list]:
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"""Convert multiple loaded sessions into input and target tensors."""
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epochs_per_session = []
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for data in dataframes:
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epochs = build_epochs(data)
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epochs["hr_std"] = epochs["hr_std"].fillna(0.0)
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epochs = epochs.dropna(subset=["hr_mean", "motion_mean", "motion_max", "stage"])
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epochs["seconds_since_start"] = epochs.index.total_seconds()
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epochs_per_session.append(epochs)
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if not epochs_per_session:
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raise ValueError("No sessions provided.")
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combined_epochs = pandas.concat(epochs_per_session)
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CONTEXT = 2 # epochs each side -> 5 epochs total = 25 min window
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FEATURE_COLS = ["hr_mean", "hr_std", "motion_mean", "motion_max", "seconds_since_start"]
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X, y = [], []
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for epochs in epochs_per_session:
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features = epochs[FEATURE_COLS].to_numpy(dtype=numpy.float32)
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labels = epochs["stage"].astype(numpy.int64).to_numpy()
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for i in range(CONTEXT, len(epochs) - CONTEXT):
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window = features[i - CONTEXT : i + CONTEXT + 1] # shape: (2*CONTEXT+1, n_feats)
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X.append(window.flatten()) # -> 1D vector
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y.append(labels[i])
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if not X:
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raise ValueError("Not enough complete epochs to build a training set.")
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X = torch.tensor(numpy.stack(X), dtype=torch.float32) # shape: (n_epochs, (2*CONTEXT+1)*n_feats)
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y = torch.tensor(y, dtype=torch.long) # shape: (n_epochs,)
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return combined_epochs, X, y, SLEEP_STAGE_LABELS
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def load_session(acceleration_file: Path, heartrate_file: Path, label_file: Path) -> pandas.DataFrame:
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"""Load a session triplet."""
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adf = load_acceleration_data(acceleration_file)
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@@ -152,48 +67,10 @@ def load_session(acceleration_file: Path, heartrate_file: Path, label_file: Path
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# Label the heartrate dataset.
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ldf.index = ldf.index.astype(float)
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hdf = pandas.merge_asof(hdf, ldf, on="timestamp", direction="backward")
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with pandas.option_context('display.max_rows', None, 'display.max_columns', None):
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print(f"{hdf}")
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logger.info(f"\n{hdf}")
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return hdf
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def build_epochs(data: pandas.DataFrame) -> pandas.DataFrame:
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"""Aggregate raw signals into 5-minute epochs."""
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EPOCH_LEN = pandas.Timedelta(minutes=5)
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return data.resample(EPOCH_LEN).agg(
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hr_mean=("hr", "mean"),
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hr_std=("hr", "std"),
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motion_mean=("motion", "mean"),
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motion_max=("motion", "max"),
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stage=("stage", lambda s: s.dropna().mode().iloc[0] if len(s.dropna()) else numpy.nan),
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)
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def execute(epochs: pandas.DataFrame, input: Tensor, target: Tensor, classifier: Module, label_mapping: list) -> None:
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"""Run the model and emit per-epoch predictions."""
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classifier.eval()
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with torch.no_grad():
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output = classifier(input)
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predicted = output.argmax(dim=1)
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decoded_predicted = [label_mapping[index] for index in predicted.tolist()]
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decoded_target = [label_mapping[index] for index in target.tolist()]
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report = pandas.DataFrame(
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{
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"timestamp": epochs.index[2 : len(epochs) - 2].astype("timedelta64[s]") / numpy.timedelta64(1, "s"),
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"predicted": decoded_predicted,
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"actual": decoded_target,
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}
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)
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report.to_csv(sys.stdout, index=False)
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accuracy = (predicted == target).float().mean().item()
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logger.info(f"accuracy={accuracy:.3f}")
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def load_acceleration_data(data_file: Path) -> pandas.DataFrame:
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"""Load acceleration data file."""
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logger.info(f"Loading acceleration data file {data_file}")
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@@ -219,49 +96,76 @@ def load_labels_data(data_file: Path) -> pandas.DataFrame:
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"""Load label data file."""
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logger.info(f"Loading label date file {data_file}")
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df = pandas.read_csv(data_file, sep=r"\s+", names=["timestamp", "stage"], header=None)
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# df["stage"] = pandas.to_numeric(df["stage"], errors="coerce").map(normalize_sleep_stage)
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df["stage"] = pandas.to_numeric(df["stage"], errors="coerce").map(normalize_sleep_stage)
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df = df.drop(df[df["timestamp"] < 0].index).drop_duplicates()
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df = df.set_index("timestamp")
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logger.info(f"({len(df)})\n{df}")
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return df
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def train(input: Tensor, target: Tensor, classifier: Module):
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classifier.train()
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optimizer = SGD(classifier.parameters(), lr=0.01)
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criterion = CrossEntropyLoss()
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def normalize_sleep_stage(stage) -> int:
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"""Map raw sleep stages to the 3-class target space."""
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if pandas.isna(stage):
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return int(numpy.nan)
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value = int(stage)
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if value == 0:
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return 0
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if 1 <= value <= 4:
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return 1
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if 5 <= value <= 6:
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return 2
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return int(numpy.nan)
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def train(input: Tensor, target: Tensor, net: Module) -> None:
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net.train()
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optimizer = SGD(net.parameters(), lr=0.001, momentum=0.9)
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# 20/80 split the input so we can get an accurate `accuracy` log.
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n_samples = input.shape[0]
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n_train = max(1, int(n_samples * 0.8))
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permutation = torch.randperm(n_samples)
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train_idx = permutation[:n_train]
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val_idx = permutation[n_train:]
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train_idx, val_idx = permutation[:n_train], permutation[n_train:]
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train_input, train_target = input[train_idx], target[train_idx]
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val_input, val_target = input[val_idx], target[val_idx]
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train_input = input[train_idx]
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train_target = target[train_idx]
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val_input = input[val_idx] if len(val_idx) else None
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val_target = target[val_idx] if len(val_idx) else None
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# Tweak the loss function.
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class_counts = torch.bincount(train_target)
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class_weights = 1.0 / torch.sqrt(class_counts.float())
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class_weights = class_weights / class_weights.sum() # normalize, not strictly required but keeps scale sane
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criterion = CrossEntropyLoss(weight=class_weights)
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# Train for a bunch of epochs.
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for epoch in range(100):
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optimizer.zero_grad()
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output = classifier(train_input)
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output = net(train_input)
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loss = criterion(output, train_target)
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loss.backward()
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optimizer.step()
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# Every 10th epoch, we log the current status.
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if epoch % 10 == 0:
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message = f"epoch={epoch} train_loss={loss.item():.4f}"
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if val_input is not None and val_target is not None:
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classifier.eval()
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train_accuracy = (output.argmax(dim=1) == train_target).float().mean().item()
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net.eval()
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with torch.no_grad():
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val_output = classifier(val_input)
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val_loss = criterion(val_output, val_target)
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val_output = net(val_input)
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val_accuracy = (val_output.argmax(dim=1) == val_target).float().mean().item()
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classifier.train()
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message += f" val_loss={val_loss.item():.4f} val_acc={val_accuracy:.3f}"
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logger.info(message)
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logger.info(
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f"epoch={epoch} loss={loss.item():.4f} train_acc={train_accuracy:.2f} val_acc={val_accuracy:.2f}"
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)
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def save_network(output_file: Path, network: Module, label_mapping: list) -> None:
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logger.info(f"Saving network {network} to {output_file}")
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torch.save({"model_state_dict": network.state_dict(), "label_mapping": label_mapping}, output_file)
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logger.info(f"last output={output}")
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# Log the confusion matrix - so we get some better insights into how the training went.
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net.eval()
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with torch.no_grad():
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predictions = net(val_input).argmax(dim=1)
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pred_counts = Counter(predictions.tolist())
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logger.info(f"prediction distribution: {pred_counts}")
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for true_class in sorted(set(val_target.tolist())):
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mask = val_target == true_class
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class_predictions = predictions[mask]
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class_counts = Counter(class_predictions.tolist())
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logger.info(f"true={true_class} (n={mask.sum().item()}) -> predicted: {class_counts}")
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