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96d8696 | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | #!/usr/bin/env python
"""Stage 04 — evaluate.
Loads the best checkpoint from stage 03 and reports SMAPE/MAE on a held-out
split of the cached training embeddings. Writes reports/<run>/eval_report.json.
Usage:
python scripts/04_evaluate.py --config configs/base.yaml
"""
import argparse
import json
import sys
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, random_split
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from src.data.dataset import EmbeddingDataset
from src.evaluation.evaluate import evaluate_model
from src.models.price_model import PriceModel
from src.utils.config import load_config
from src.utils.exceptions import CheckpointError, PricePredictorError
from src.utils.logging import get_logger
from src.utils.seed import set_seed
logger = get_logger(__name__)
def run(config_path: str) -> dict:
config = load_config(config_path)
set_seed(config["seed"])
embeddings_dir = Path(config["data"]["embeddings_dir"])
prices = np.load(embeddings_dir / "train_price.npy")
full_dataset = EmbeddingDataset(
text_embeddings_path=str(embeddings_dir / "train_text.npy"),
image_embeddings_path=str(embeddings_dir / "train_image.npy"),
prices=prices,
)
val_split = config["training"].get("val_split", 0.15)
n_val = max(1, int(len(full_dataset) * val_split))
n_train = len(full_dataset) - n_val
generator = torch.Generator().manual_seed(config["seed"])
_, val_ds = random_split(full_dataset, [n_train, n_val], generator=generator)
val_loader = DataLoader(val_ds, batch_size=config["training"]["batch_size"], shuffle=False)
model = PriceModel.from_config(config)
checkpoint_path = Path(config["checkpoint_dir"]) / "best.pt"
if not checkpoint_path.exists():
raise CheckpointError(f"No checkpoint found at {checkpoint_path} — run scripts/03_train.py first")
checkpoint = torch.load(checkpoint_path, map_location="cpu")
model.load_state_dict(checkpoint["model_state_dict"])
device = "cuda" if torch.cuda.is_available() else "cpu"
metrics = evaluate_model(model, val_loader, device)
report_dir = Path("reports") / Path(config["checkpoint_dir"]).name
report_dir.mkdir(parents=True, exist_ok=True)
report_path = report_dir / "eval_report.json"
with report_path.open("w") as f:
json.dump(metrics, f, indent=2)
logger.info("Wrote evaluation report to %s", report_path)
return metrics
def main() -> None:
parser = argparse.ArgumentParser(description="Stage 04: evaluate a trained checkpoint")
parser.add_argument("--config", default="configs/base.yaml")
args = parser.parse_args()
try:
run(args.config)
except PricePredictorError as e:
logger.error("Evaluation failed: %s", e)
sys.exit(1)
except Exception as e:
logger.exception("Unexpected error during evaluation: %s", e)
sys.exit(1)
if __name__ == "__main__":
main()
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