File size: 2,386 Bytes
e45abdb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import sys
import numpy as np
import yaml
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from sparsewake.data import load_h5
from sparsewake.evaluate import evaluate_predictions, predict
from sparsewake.features import build_design_matrix
from sparsewake.splits import pose_holdout_split
from sparsewake.train import standardize_train_val_test, train_temporal_mlp
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True)
parser.add_argument("--data", default=None)
parser.add_argument("--quick", action="store_true")
parser.add_argument("--out", default="tables/quick_train_metrics.json")
args = parser.parse_args()
cfg = yaml.safe_load(Path(args.config).read_text())
data_path = Path(args.data) if args.data else ROOT / cfg["data"]
data = load_h5(data_path, input_key=cfg.get("input_key", "X_raw"))
history = 4 if args.quick else int(cfg.get("history", 24))
x, idx = build_design_matrix(data, feature_set=cfg.get("feature_set", "raw_norm"), history=history)
y = data["target"][idx]
pose_id = data["pose_id"][idx]
train_idx, val_idx, test_idx = pose_holdout_split(pose_id, seed=int(cfg.get("seed", 1)))
if args.quick:
train_idx = train_idx[: min(len(train_idx), 512)]
val_idx = val_idx[: min(len(val_idx), 128)]
test_idx = test_idx[: min(len(test_idx), 128)]
x, _, _ = standardize_train_val_test(x, train_idx)
output_dim = 3 if cfg.get("target", "location") == "location_theta" else 2
model = train_temporal_mlp(
x,
y,
train_idx,
val_idx,
output_dim=output_dim,
epochs=3 if args.quick else int(cfg.get("epochs", 50)),
batch_size=int(cfg.get("batch_size", 1024)),
seed=int(cfg.get("seed", 1)),
)
pred = predict(model, x[test_idx])
metrics = evaluate_predictions(y[test_idx, :output_dim], pred)
metrics["quick_mode"] = bool(args.quick)
metrics["n_train"] = int(len(train_idx))
metrics["n_test"] = int(len(test_idx))
out = ROOT / args.out
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(json.dumps(metrics, indent=2) + "\n")
print(json.dumps(metrics, indent=2))
if __name__ == "__main__":
main()
|