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c023008 | 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 | """Reload the saved model in a fresh process and predict all 922 test cases."""
import argparse
from pathlib import Path
import time
import numpy as np
import pandas as pd
from common import (load_config, ensure_compute_node, load_fixed_dataset, WavyChannelRF,
metrics_for, print_metrics, prediction_frame, write_json, write_metrics, sha256)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", required=True)
parser.add_argument("--checkpoint", help="Optional explicit path to saved joblib .pth bundle")
args = parser.parse_args()
config = load_config(args.config)
runtime = ensure_compute_node(config)
directory = Path(config["output"]["results_dir"])
data, data_manifest = load_fixed_dataset(config)
test = data.loc[data[config["data"]["split_column"]] == "test"]
features, targets = config["data"]["features"], config["data"]["targets"]
checkpoint = Path(args.checkpoint or config["output"]["checkpoint"])
print(f"[inference] Loading {checkpoint}", flush=True)
model, metadata = WavyChannelRF.load(checkpoint)
if model.n_train_ != config["data"]["expected_counts"]["train"]:
raise ValueError("Checkpoint did not fit the required complete training set")
for name in ["dataset", "split_file"]:
if metadata["data"]["files"][name]["sha256"] != data_manifest["files"][name]["sha256"]:
raise ValueError(f"{name} changed since model fitting")
start = time.perf_counter()
predicted = model.predict(test[features])
seconds = time.perf_counter() - start
metrics = metrics_for(test[targets], predicted, targets)
print_metrics("inference-test", metrics, len(test))
output = prediction_frame(test, predicted, config)
output.to_csv(directory / "inference_predictions.csv", index=False)
write_metrics(directory, metrics, "inference_metrics")
reference = pd.read_csv(directory / "test_predictions.csv")
key = config["data"]["join_key"]
if reference[key].tolist() != output[key].tolist():
raise ValueError("Reloaded inference test case order differs from training evaluation")
pred_cols = [f"{t}_pred" for t in targets]
error = float(np.max(np.abs(reference[pred_cols].to_numpy() - output[pred_cols].to_numpy())))
np.testing.assert_allclose(reference[pred_cols], output[pred_cols], atol=1e-10, rtol=1e-12)
verification = {"status": "passed", "test_rows": len(test), "predictions_finite": True,
"independent_process_reload": True, "case_uids_equal": True,
"max_abs_prediction_difference": error, "tolerance": {"atol": 1e-10, "rtol": 1e-12},
"prediction_seconds": seconds, "runtime": runtime,
"checkpoint_sha256": sha256(checkpoint), "test_metrics": metrics}
write_json(directory / "reload_validation.json", verification)
print(f"[inference] Reload validation passed: n={len(test)}, max_abs_difference={error:.3g}", flush=True)
if __name__ == "__main__":
main()
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