feat: first training attempt
It still sucks. I'm sure the the feature embedding is terribly wrong.
This commit is contained in:
@@ -8,3 +8,7 @@ wheels/
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# Virtual environments
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.venv
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# Models
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**/*.pt
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*.pt
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+4
-1
@@ -8,7 +8,10 @@ authors = [
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]
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requires-python = ">=3.12"
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dependencies = [
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"torch"
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"torch",
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"numpy",
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"pandas",
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"loguru"
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]
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[project.scripts]
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Executable
+88
@@ -0,0 +1,88 @@
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#!/usr/bin/env bash
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set -euo pipefail
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ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
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DATA_DIR="${ROOT_DIR}/data"
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OUTPUT_FILE="${ROOT_DIR}/sleep-model.pt"
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MODEL_FILE="${OUTPUT_FILE}"
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if [[ ${1:-} == "-h" || ${1:-} == "--help" ]]; then
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cat <<'EOF'
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Usage: scripts/train_from_data.sh [-o OUTPUT_FILE]
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Trains the model on complete session triplets found under data/{motion,heart_rate,labels}/.
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EOF
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exit 0
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fi
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if [[ ${1:-} == "-o" || ${1:-} == "--output" ]]; then
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OUTPUT_FILE="${2:?Missing output file after -o/--output}"
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MODEL_FILE="${OUTPUT_FILE}"
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fi
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shopt -s nullglob
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checkpoint_class_count() {
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python - "$1" <<'PY'
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from pathlib import Path
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import sys
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import torch
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payload = torch.load(Path(sys.argv[1]), weights_only=True)
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state_dict = payload.get("model_state_dict", payload)
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print(state_dict["net.2.bias"].shape[0])
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PY
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}
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motions=("${DATA_DIR}/motion"/*_acceleration.txt)
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subject_ids=()
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for motion_file in "${motions[@]}"; do
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sample_id="$(basename "${motion_file}" _acceleration.txt)"
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heartrate_file="${DATA_DIR}/heart_rate/${sample_id}_heartrate.txt"
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label_file="${DATA_DIR}/labels/${sample_id}_labeled_sleep.txt"
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if [[ -f "${heartrate_file}" && -f "${label_file}" ]]; then
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subject_ids+=("${sample_id}")
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fi
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done
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if [[ ${#subject_ids[@]} -eq 0 ]]; then
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printf 'No complete data triplets found under %s\n' "${DATA_DIR}" >&2
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exit 1
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fi
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mapfile -t subject_ids < <(printf '%s\n' "${subject_ids[@]}" | sort -u)
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printf 'Training on %d subject(s)\n' "${#subject_ids[@]}"
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export PYTHONPATH="${ROOT_DIR}/src${PYTHONPATH:+:${PYTHONPATH}}"
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for subject_id in "${subject_ids[@]}"; do
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acceleration_file="${DATA_DIR}/motion/${subject_id}_acceleration.txt"
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heartrate_file="${DATA_DIR}/heart_rate/${subject_id}_heartrate.txt"
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label_file="${DATA_DIR}/labels/${subject_id}_labeled_sleep.txt"
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resume_args=()
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printf 'Training subject %s\n' "${subject_id}"
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if [[ -f "${MODEL_FILE}" ]]; then
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checkpoint_classes="$(checkpoint_class_count "${MODEL_FILE}")"
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if [[ "${checkpoint_classes}" == "3" ]]; then
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resume_args=(-i "${MODEL_FILE}")
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else
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printf 'Skipping checkpoint for subject %s: model classes=%s, expected=3\n' "${subject_id}" "${checkpoint_classes}"
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fi
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fi
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args=(python -m sleep_detection train -a "${acceleration_file}" -H "${heartrate_file}" -l "${label_file}" -o "${MODEL_FILE}")
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if [[ ${#resume_args[@]} -gt 0 ]]; then
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args+=("${resume_args[@]}")
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fi
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"${args[@]}"
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done
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printf 'Saved trained model to %s\n' "${MODEL_FILE}"
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+340
-2
@@ -1,6 +1,344 @@
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"""Main Entrypoint codebase."""
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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.abc import Sequence
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from pathlib import Path
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def main():
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import numpy
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import pandas
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import torch
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from loguru import logger
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from torch import Tensor
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from torch.nn import CrossEntropyLoss
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from torch.nn import Module
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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) -> float:
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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 numpy.nan
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value = int(stage)
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if value == 0:
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return 0.0
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if 1 <= value <= 4:
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return 1.0
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if 5 <= value <= 6:
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return 2.0
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return 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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print("Hello from sleep-detection!")
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cli = parse_args()
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logger.info("welcome")
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if cli.command == "train":
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sessions = load_sessions(cli.a, cli.H, cli.l)
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epochs, features, target, label_mapping = featureize_sessions(sessions)
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c = SleepEpochClassifier(
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n_features=features.shape[1],
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n_classes=len(SLEEP_STAGE_LABELS),
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hidden_size=8,
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)
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if cli.input is not None:
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c.load(cli.input)
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logger.info(f"Using a {c.__class__.__name__}")
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train(features, target, c)
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save_network(cli.output, c, label_mapping)
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return
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if cli.command == "execute":
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adf = load_acceleration_data(cli.a)
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hdf = load_heartrate_data(cli.H)
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ldf = load_labels_data(cli.l)
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df = adf.join(hdf, how="outer").join(ldf, how="outer").sort_index()
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epochs, features, target, label_mapping = featureize(df)
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c = SleepEpochClassifier(
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n_features=features.shape[1],
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n_classes=len(SLEEP_STAGE_LABELS),
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hidden_size=8,
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)
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loaded_label_mapping = c.load(cli.model)
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logger.info(f"Using a {c.__class__.__name__}")
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execute(epochs, features, target, c, loaded_label_mapping or label_mapping)
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return
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raise ValueError(f"Unknown command: {cli.command}")
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def parse_args() -> Namespace:
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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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command = parser.add_subparsers(dest="command")
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train = command.add_parser("train", help="Train the model.")
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train.add_argument("-a", type=Path, nargs="+", required=True, help="Acceleration data file(s).")
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train.add_argument("-H", type=Path, nargs="+", required=True, help="Heartrate data file(s).")
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train.add_argument("-l", type=Path, nargs="+", required=True, help="Labels data file(s).")
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train.add_argument("-o", "--output", type=Path, default=None, required=True, help="Output .pt file.")
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train.add_argument("-i", "--input", type=Path, default=None, required=False, help="Input .pt file.")
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execute = command.add_parser("execute", help="Execute the model.")
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execute.add_argument("-m", "--model", type=Path, default=None, required=True, help="Input .pr file to use.")
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execute.add_argument("-a", type=Path, default=None, required=True, help="Acceleration data file.")
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execute.add_argument("-H", type=Path, default=None, required=True, help="Heartrate data file.")
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execute.add_argument("-l", type=Path, default=None, required=True, help="Labels data 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_sessions(
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acceleration_files: Sequence[Path], heartrate_files: Sequence[Path], label_files: Sequence[Path]
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) -> list[pandas.DataFrame]:
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"""Load multiple session triplets."""
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if not (len(acceleration_files) == len(heartrate_files) == len(label_files)):
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raise ValueError("Acceleration, heartrate, and label file counts must match.")
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sessions = []
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for acceleration_file, heartrate_file, label_file in zip(acceleration_files, heartrate_files, label_files):
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adf = load_acceleration_data(acceleration_file)
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hdf = load_heartrate_data(heartrate_file)
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ldf = load_labels_data(label_file)
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sessions.append(adf.join(hdf, how="outer").join(ldf, how="outer").sort_index())
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return sessions
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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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Expected input format: <timestamp> <x> <y> <z>
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Args:
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data_file: The filepath for the file containing acceleration data.
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Returns:
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A pandas data frame with the loaded acceleration data.
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"""
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logger.info(f"Loading acceleration data file {data_file}")
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df = pandas.read_csv(data_file, sep=r"\s+", names=["timestamp", "x", "y", "z"], header=None)
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df["timestamp"] = pandas.to_numeric(df["timestamp"], errors="coerce")
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df = df.dropna(subset=["timestamp"])
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df = df.sort_values("timestamp").set_index(pandas.to_timedelta(df["timestamp"], unit="s"))
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df = df.drop(columns=["timestamp"])
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df.index.name = "timestamp"
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# df["motion"] = df[["x", "y", "z"]].pow(2).sum(axis=1).pow(0.5)
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df["motion"] = df[["x", "y", "z"]].diff().pow(2).sum(axis=1).pow(0.5)
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return df
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def load_heartrate_data(data_file: Path) -> pandas.DataFrame:
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"""Load heart rate data file.
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Expected input format: <timestamp>,<heartrate>
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Args:
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data_file: The filepath for the file containing heartrate data.
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Returns:
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A pandas data frame with the loaded heartrate data.
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"""
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logger.info(f"Loading heartrate date file {data_file}")
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df = pandas.read_csv(data_file, sep=",", names=["timestamp", "hr"], header=None)
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df["timestamp"] = pandas.to_numeric(df["timestamp"], errors="coerce")
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df = df.dropna(subset=["timestamp"])
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df = df.sort_values("timestamp").set_index(pandas.to_timedelta(df["timestamp"], unit="s"))
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df = df.drop(columns=["timestamp"])
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df.index.name = "timestamp"
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return df
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def load_labels_data(data_file: Path) -> pandas.DataFrame:
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"""Load label data file.
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Expected input format: <timestamp>,<label>
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Args:
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data_file: The filepath for the file containing label data.
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Returns:
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A pandas data frame with the loaded label data.
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"""
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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["timestamp"] = pandas.to_numeric(df["timestamp"], errors="coerce")
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df["stage"] = pandas.to_numeric(df["stage"], errors="coerce").map(normalize_sleep_stage)
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df = df.dropna(subset=["timestamp"])
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df = df.sort_values("timestamp").set_index(pandas.to_timedelta(df["timestamp"], unit="s"))
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df = df.drop(columns=["timestamp"])
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df.index.name = "timestamp"
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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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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_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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for epoch in range(100):
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optimizer.zero_grad()
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output = classifier(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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if epoch % 10 == 0:
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message = f"epoch={epoch} train_loss={loss.item():.4f}"
|
||||
if val_input is not None and val_target is not None:
|
||||
classifier.eval()
|
||||
with torch.no_grad():
|
||||
val_output = classifier(val_input)
|
||||
val_loss = criterion(val_output, val_target)
|
||||
val_accuracy = (val_output.argmax(dim=1) == val_target).float().mean().item()
|
||||
classifier.train()
|
||||
message += f" val_loss={val_loss.item():.4f} val_acc={val_accuracy:.3f}"
|
||||
logger.info(message)
|
||||
|
||||
|
||||
def save_network(output_file: Path, network: Module, label_mapping: list) -> None:
|
||||
logger.info(f"Saving network {network} to {output_file}")
|
||||
torch.save({"model_state_dict": network.state_dict(), "label_mapping": label_mapping}, output_file)
|
||||
|
||||
@@ -0,0 +1,40 @@
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from loguru import logger
|
||||
from torch import Tensor
|
||||
from torch.nn import Linear
|
||||
from torch.nn import Module
|
||||
from torch.nn import ReLU
|
||||
from torch.nn import Sequential
|
||||
|
||||
|
||||
class SleepEpochClassifier(Module):
|
||||
"""Per-epoch sleep stage classifier.
|
||||
|
||||
Input: a fixed-size feature vector summarizing one epoch's context window
|
||||
(e.g. motion mean/max, HR mean/variance, time-of-night, previous
|
||||
predicted label — whatever features you settle on).
|
||||
Output: logits over sleep stage classes for that single epoch.
|
||||
|
||||
No recurrence/attention — sequence smoothing (majority vote / HMM)
|
||||
happens as a separate post-processing step, not inside this network.
|
||||
"""
|
||||
|
||||
def __init__(self, n_features: int, n_classes: int, hidden_size: int = 16):
|
||||
super().__init__()
|
||||
self.net = Sequential(
|
||||
Linear(n_features, hidden_size),
|
||||
ReLU(),
|
||||
Linear(hidden_size, n_classes),
|
||||
)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
# x shape: (batch, n_features) -> returns (batch, n_classes) logits
|
||||
return self.net(x)
|
||||
|
||||
def load(self, input_file: Path) -> list | None:
|
||||
payload = torch.load(input_file, weights_only=True)
|
||||
state_dict = payload.get("model_state_dict", payload)
|
||||
self.load_state_dict(state_dict)
|
||||
return payload.get("label_mapping")
|
||||
Reference in New Issue
Block a user