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e4b1ed6 | 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 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | """Train the calibrated supervised baseline for synthetic datacenter verification data."""
from __future__ import annotations
import argparse
import subprocess
import sys
from pathlib import Path
from typing import Any
import joblib
import numpy as np
from sklearn.calibration import CalibratedClassifierCV
from sklearn.ensemble import HistGradientBoostingClassifier
try:
from .common import (
DEFAULT_SEED,
determine_feature_columns,
derive_dataset_dir,
ensure_dir,
load_feature_table,
make_episode_split,
make_preprocessor,
model_input_frame,
utc_now_iso,
write_json,
)
from .evaluate_model import evaluate_model_run
except ImportError: # pragma: no cover - direct script execution
from common import (
DEFAULT_SEED,
determine_feature_columns,
derive_dataset_dir,
ensure_dir,
load_feature_table,
make_episode_split,
make_preprocessor,
model_input_frame,
utc_now_iso,
write_json,
)
from evaluate_model import evaluate_model_run
def repo_root() -> Path:
return Path(__file__).resolve().parents[2]
def run_dataset_validations(features_path: Path) -> list[dict[str, Any]]:
root = repo_root()
dataset_dir = derive_dataset_dir(features_path)
try:
dataset_arg = dataset_dir.relative_to(root)
except ValueError:
dataset_arg = dataset_dir
commands = [
{
"name": "synthetic_dataset_validator",
"command": [
sys.executable,
str(root / "src/datacenter_verification_synthetic/validate_synthetic_dataset.py"),
"--dataset",
str(dataset_arg),
],
},
{
"name": "public_dataset_validator",
"command": [
sys.executable,
"-m",
"src.datacenter_verification_validators",
"--dataset",
str(dataset_arg),
],
},
]
results: list[dict[str, Any]] = []
for item in commands:
completed = subprocess.run(
item["command"],
cwd=root,
check=False,
text=True,
capture_output=True,
)
result = {
"name": item["name"],
"command": " ".join(item["command"]),
"returncode": int(completed.returncode),
"stdout": completed.stdout.strip(),
"stderr": completed.stderr.strip(),
}
results.append(result)
if completed.returncode != 0:
raise RuntimeError(
f"dataset validation failed for {item['name']} with return code {completed.returncode}\n"
f"stdout:\n{completed.stdout}\n\nstderr:\n{completed.stderr}"
)
return results
def make_base_classifier(seed: int) -> HistGradientBoostingClassifier:
kwargs: dict[str, Any] = {
"learning_rate": 0.05,
"max_iter": 350,
"max_leaf_nodes": 31,
"l2_regularization": 0.03,
"early_stopping": True,
"validation_fraction": 0.15,
"random_state": seed,
}
try:
return HistGradientBoostingClassifier(class_weight="balanced", **kwargs)
except TypeError: # pragma: no cover - older scikit-learn fallback
return HistGradientBoostingClassifier(**kwargs)
def calibrate_prefit_model(base_model: Any, x_validation: np.ndarray, y_validation: np.ndarray) -> tuple[Any, str]:
try:
from sklearn.frozen import FrozenEstimator
calibrator = CalibratedClassifierCV(FrozenEstimator(base_model), method="sigmoid")
method = "sigmoid_on_validation_split_frozen_estimator"
except Exception: # pragma: no cover - older scikit-learn fallback
calibrator = CalibratedClassifierCV(base_model, method="sigmoid", cv="prefit")
method = "sigmoid_on_validation_split_prefit"
calibrator.fit(x_validation, y_validation)
return calibrator, method
def train_model(features_path: Path, output_dir: Path, seed: int = DEFAULT_SEED, skip_dataset_validation: bool = False) -> dict[str, Any]:
ensure_dir(output_dir)
validation_status = [] if skip_dataset_validation else run_dataset_validations(features_path)
df = load_feature_table(features_path)
split_df, split_manifest = make_episode_split(df, seed=seed)
feature_columns, excluded_columns = determine_feature_columns(split_df)
write_json(output_dir / "split_manifest.json", split_manifest)
write_json(output_dir / "feature_columns.json", feature_columns)
write_json(output_dir / "excluded_columns.json", excluded_columns)
train_df = split_df[split_df["split"] == "train"].copy()
validation_df = split_df[split_df["split"] == "validation"].copy()
if train_df.empty or validation_df.empty:
raise ValueError("train and validation splits must both be non-empty")
preprocessor = make_preprocessor(train_df, feature_columns)
x_train = preprocessor.fit_transform(model_input_frame(train_df, feature_columns))
y_train = train_df["label_0_to_4"].astype(int).to_numpy()
x_validation = preprocessor.transform(model_input_frame(validation_df, feature_columns))
y_validation = validation_df["label_0_to_4"].astype(int).to_numpy()
base_model = make_base_classifier(seed)
base_model.fit(x_train, y_train)
calibrated_model, calibration_method = calibrate_prefit_model(base_model, x_validation, y_validation)
joblib.dump(calibrated_model, output_dir / "model.joblib")
joblib.dump(preprocessor, output_dir / "preprocessing.joblib")
training_metadata = {
"trained_at": utc_now_iso(),
"seed": int(seed),
"features_path": str(features_path),
"model_type": "HistGradientBoostingClassifier",
"model_parameters": base_model.get_params(),
"calibration_method": calibration_method,
"train_rows": int(len(train_df)),
"validation_rows": int(len(validation_df)),
"train_episodes": int(train_df["episode_id"].nunique()),
"validation_episodes": int(validation_df["episode_id"].nunique()),
"feature_count": int(len(feature_columns)),
"dataset_validation_status": validation_status,
}
write_json(
output_dir / "manifest.json",
{
"model_run_id": output_dir.name,
"created_or_updated_at": utc_now_iso(),
"features_path": str(features_path),
"model_type": "CalibratedClassifierCV over HistGradientBoostingClassifier",
"calibration_method": calibration_method,
"training_metadata": training_metadata,
"validation_status": validation_status,
},
)
metrics = evaluate_model_run(
output_dir,
features_path,
validation_status=validation_status,
training_metadata=training_metadata,
)
return {
"metrics": metrics,
"split_summary": split_manifest["summary"],
"validation_status": validation_status,
"training_metadata": training_metadata,
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--features", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--seed", type=int, default=DEFAULT_SEED)
parser.add_argument(
"--skip-dataset-validation",
action="store_true",
help="Skip pre-training dataset validation. Intended only for development.",
)
args = parser.parse_args(argv)
result = train_model(args.features, args.output, seed=args.seed, skip_dataset_validation=args.skip_dataset_validation)
metrics = result["metrics"]
print(f"train_rows: {result['split_summary']['train']['rows']}")
print(f"validation_rows: {result['split_summary']['validation']['rows']}")
print(f"test_rows: {result['split_summary']['test']['rows']}")
print(f"accuracy: {metrics['model']['accuracy']:.4f}")
print(f"macro_f1: {metrics['model']['macro_f1']:.4f}")
print(f"label_3_4_precision: {metrics['governance']['label_3_4_predicted_label']['precision']:.4f}")
print(f"label_3_4_recall: {metrics['governance']['label_3_4_predicted_label']['recall']:.4f}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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