File size: 3,729 Bytes
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 58 59 60 61 62 63 64 65 | """Fit the formal 3686-case experiment on a SLURM compute node."""
import argparse
from datetime import datetime, timezone
import importlib.metadata
import os
from pathlib import Path
import platform
import time
import numpy as np
from common import (load_config, ensure_compute_node, load_fixed_dataset, metrics_for,
print_metrics, prediction_frame, write_json, write_metrics, sha256, WavyChannelRF)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", required=True)
args = parser.parse_args()
config = load_config(args.config)
runtime = ensure_compute_node(config)
directory = Path(config["output"]["results_dir"])
directory.mkdir(parents=True, exist_ok=True)
started = time.perf_counter()
data, data_manifest = load_fixed_dataset(config)
split_col = config["data"]["split_column"]
train, test = (data.loc[data[split_col] == split] for split in ["train", "test"])
features, targets = config["data"]["features"], config["data"]["targets"]
versions = {p: importlib.metadata.version(p) for p in ["numpy", "pandas", "scikit-learn", "joblib", "PyYAML", "matplotlib"]}
print("[train] Formal full fit: two independent Random Forests, 800 trees per target; no epochs.", flush=True)
model = WavyChannelRF(config["model"]["rf_params"])
model.fit(train[features], train[targets])
predictions = model.predict(test[features])
train_predictions = model.predict(train[features])
metrics = metrics_for(test[targets], predictions, targets)
train_metrics = metrics_for(train[targets], train_predictions, targets)
print_metrics("test", metrics, len(test))
print_metrics("train", train_metrics, len(train))
manifest = {"experiment": config["experiment"], "utc_time": datetime.now(timezone.utc).isoformat(),
"runtime": runtime, "python": platform.python_version(), "versions": versions,
"data": data_manifest, "fit_seconds": model.fit_seconds_,
"fitted_train_rows": model.n_train_,
"trees_per_target": {t: len(model.models_[t].estimators_) for t in targets},
"actual_rf_parameters": {t: model.models_[t].get_params() for t in targets},
"checkpoint_semantics": "sole fully fitted formal model; no validation/test model selection",
"serialization": "joblib bundle containing two fitted sklearn forests; .pth is requested filename",
"sources": config["reference"], "training_script_sha256": sha256(__file__),
"model_source_sha256": sha256(Path(__file__).resolve().parents[1] / "model/wavy_channel_rf.py")}
checkpoint = Path(config["output"]["checkpoint"])
model.save(checkpoint, manifest)
print(f"[save] Reloadable joblib checkpoint: {checkpoint} ({checkpoint.stat().st_size} bytes)", flush=True)
write_metrics(directory, metrics)
write_json(directory / "train_metrics.json", train_metrics)
prediction_frame(test, predictions, config).to_csv(directory / "test_predictions.csv", index=False)
model.feature_importances().to_csv(directory / "feature_importances.csv", index=False)
write_json(directory / "data_manifest.json", data_manifest)
write_json(directory / "config_snapshot.json", config)
manifest.update({"test_metrics": metrics, "checkpoint": str(checkpoint), "checkpoint_sha256": sha256(checkpoint),
"total_seconds": time.perf_counter() - started, "execution_state": "completed"})
write_json(directory / "run_manifest.json", manifest)
print(f"[train] Completed formal experiment in {manifest['total_seconds']:.3f} s", flush=True)
if __name__ == "__main__":
main()
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