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"""Build a locked development report from schema-v2 token predictions."""
from __future__ import annotations
import argparse
from collections import defaultdict
from dataclasses import replace
import json
from pathlib import Path
import sys
import numpy as np
REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from baselines import TokenPredictionRow # noqa: E402
from evaluation.provenance import ( # noqa: E402
git_provenance,
validate_prediction_provenance,
)
from evaluation.metrics import binary_metrics # noqa: E402
from evaluation.statistics import ( # noqa: E402
assert_common_support,
descriptive_metrics,
observations_from_predictions,
paired_cluster_bootstrap,
)
def parse_run_spec(value: str) -> tuple[str, int, Path]:
try:
method, seed_text, path_text = value.split(":", 2)
return method, int(seed_text), Path(path_text)
except Exception as exc:
raise argparse.ArgumentTypeError(
"run must be METHOD:SEED:PREDICTIONS_JSONL"
) from exc
def parse_args(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument("--run", action="append", type=parse_run_spec, required=True)
parser.add_argument(
"--canonical-metadata-run",
type=parse_run_spec,
required=True,
help=(
"METHOD:SEED:PREDICTIONS_JSONL run whose complete source and "
"split-group metadata is applied to the common token support"
),
)
parser.add_argument("--primary-method", required=True)
parser.add_argument("--comparator-method", required=True)
parser.add_argument("--bootstrap-draws", type=int, default=10_000)
parser.add_argument("--bootstrap-seed", type=int, default=20260806)
parser.add_argument("--expected-seeds", nargs="+", type=int, required=True)
parser.add_argument("--expected-model-revision", required=True)
parser.add_argument("--expected-input-sha256", required=True)
parser.add_argument("--expected-split-sha256", required=True)
parser.add_argument("--expected-git-commit", required=True)
parser.add_argument("--expected-operating-fpr", type=float, required=True)
parser.add_argument(
"--expected-primary-protocol", default="latticespike_causal_token_v2"
)
parser.add_argument(
"--expected-comparator-protocol", default="causal_token_v1"
)
parser.add_argument("--output-json", type=Path, required=True)
parser.add_argument("--output-md", type=Path, required=True)
return parser.parse_args(argv)
def load_prediction_rows(path: Path, method: str) -> list[TokenPredictionRow]:
rows = []
with path.open(encoding="utf-8") as file:
for line_number, line in enumerate(file, start=1):
if not line.strip():
continue
try:
row = TokenPredictionRow.from_json_dict(json.loads(line))
except Exception as exc:
raise ValueError(f"{path}:{line_number} is not a prediction row") from exc
if row.method != method:
continue
split = str(row.metadata.get("split", "")).lower()
if split and split not in {"eval", "test"}:
continue
rows.append(row)
if not rows:
raise ValueError(f"{path} contains no held-out rows for method {method}")
validate_prediction_provenance(rows)
return rows
def operating_point_metrics(
predictions: list[TokenPredictionRow], observations
) -> dict:
thresholds = {
float(row.metadata["operating_point_threshold"])
for row in predictions
if row.metadata.get("operating_point_threshold") is not None
}
if len(thresholds) != 1:
raise ValueError(
"each run must record one validation-locked operating-point threshold"
)
threshold = next(iter(thresholds))
result = binary_metrics(
[observation.score for observation in observations],
[observation.label for observation in observations],
threshold=threshold,
)
return result.to_json_dict()
def validate_run_contract(args, method: str, seed: int, predictions) -> None:
expected_protocol = (
args.expected_primary_protocol
if method == args.primary_method
else args.expected_comparator_protocol
)
errors = set()
for row in predictions:
metadata = row.metadata
protocol = metadata.get("evaluation_protocol") or {}
if protocol.get("suite") != "causal_matched":
errors.add("suite")
if protocol.get("protocol_id") != expected_protocol:
errors.add("protocol_id")
if int(metadata.get("training_seed", -1)) != int(seed):
errors.add("training_seed")
if metadata.get("git_commit") != args.expected_git_commit:
errors.add("git_commit")
if metadata.get("input_sha256") != args.expected_input_sha256:
errors.add("input_sha256")
if metadata.get("split_sha256") != args.expected_split_sha256:
errors.add("split_sha256")
if (
metadata.get("resolved_scoring_model_revision")
!= args.expected_model_revision
):
errors.add("model_revision")
if metadata.get("threshold_selection_split") != "validation":
errors.add("threshold_selection_split")
if metadata.get("threshold_selection_policy") != "validation_target_fpr":
errors.add("threshold_selection_policy")
target = metadata.get("target_false_positive_rate")
if target is None or not np.isclose(
float(target), float(args.expected_operating_fpr)
):
errors.add("target_false_positive_rate")
if errors:
raise ValueError(
f"run contract mismatch for {method} seed {seed}: {sorted(errors)}"
)
def bind_canonical_metadata(observations, canonical_observations):
"""Bind method-independent source/group fields by exact token support."""
assert_common_support(
{"candidate": observations, "canonical": canonical_observations}
)
metadata_by_support = {}
for observation in canonical_observations:
metadata = (observation.source, observation.split_group)
previous = metadata_by_support.setdefault(observation.support_key, metadata)
if previous != metadata:
raise ValueError("canonical metadata conflicts within token support")
if observation.source == "unknown":
raise ValueError("canonical metadata contains unknown source")
if observation.split_group.startswith("row:"):
raise ValueError("canonical metadata is missing dataset split_group")
return [
replace(
observation,
source=metadata_by_support[observation.support_key][0],
split_group=metadata_by_support[observation.support_key][1],
)
for observation in observations
]
def build_report(args) -> dict:
canonical_spec = args.canonical_metadata_run
if canonical_spec not in args.run:
raise ValueError("--canonical-metadata-run must also appear in --run")
canonical_method, canonical_seed, canonical_path = canonical_spec
canonical_predictions = load_prediction_rows(canonical_path, canonical_method)
validate_run_contract(
args, canonical_method, canonical_seed, canonical_predictions
)
canonical_observations = observations_from_predictions(canonical_predictions)
bind_canonical_metadata(canonical_observations, canonical_observations)
method_runs = defaultdict(list)
run_rows = []
for method, seed, path in args.run:
predictions = load_prediction_rows(path, method)
validate_run_contract(args, method, seed, predictions)
observations = bind_canonical_metadata(
observations_from_predictions(predictions), canonical_observations
)
metrics = descriptive_metrics(observations)
metrics["operating_point"] = operating_point_metrics(
predictions, observations
)
method_runs[method].append(observations)
run_rows.append(
{
"method": method,
"seed": seed,
"predictions": str(path),
"provenance": validate_prediction_provenance(predictions),
"metrics": metrics,
}
)
if args.primary_method not in method_runs or args.comparator_method not in method_runs:
raise ValueError("primary and comparator methods must both have run specs")
seen_specs = [(method, seed) for method, seed, _ in args.run]
if len(seen_specs) != len(set(seen_specs)):
raise ValueError("duplicate method/seed run specs")
for method in (args.primary_method, args.comparator_method):
observed = sorted(seed for candidate, seed in seen_specs if candidate == method)
if observed != sorted(args.expected_seeds):
raise ValueError(
f"{method} seeds {observed} do not match {sorted(args.expected_seeds)}"
)
aggregate = {}
for method in sorted(method_runs):
rows = [row for row in run_rows if row["method"] == method]
metric_names = (
"micro_average_precision",
"micro_auroc",
"answer_balanced_average_precision",
"source_macro_average_precision",
)
metric_summary = {}
for metric_name in metric_names:
values = [float(row["metrics"][metric_name]) for row in rows]
metric_summary[f"{metric_name}_mean"] = float(np.mean(values))
metric_summary[f"{metric_name}_sd"] = (
float(np.std(values, ddof=1)) if len(values) > 1 else 0.0
)
for metric_name in (
"threshold",
"precision",
"recall",
"f1",
"false_positive_rate",
):
values = [
float(row["metrics"]["operating_point"][metric_name])
for row in rows
]
metric_summary[f"operating_{metric_name}_mean"] = float(
np.mean(values)
)
metric_summary[f"operating_{metric_name}_sd"] = (
float(np.std(values, ddof=1)) if len(values) > 1 else 0.0
)
aggregate[method] = {
"n_seeds": len(rows),
**metric_summary,
"runs": rows,
}
bootstrap = paired_cluster_bootstrap(
method_runs,
primary_method=args.primary_method,
comparator_method=args.comparator_method,
draws=args.bootstrap_draws,
seed=args.bootstrap_seed,
)
return {
"status": "v5.3 development result; not untouched confirmatory evidence",
"prediction_schema_version": 2,
"report_git": git_provenance(REPO_ROOT, require_clean=True),
"canonical_metadata": {
"method": canonical_method,
"seed": canonical_seed,
"predictions": str(canonical_path),
"sources": sorted(
{observation.source for observation in canonical_observations}
),
"split_groups": len(
{observation.split_group for observation in canonical_observations}
),
"provenance": validate_prediction_provenance(
canonical_predictions
),
},
"primary_estimand": bootstrap["estimand"],
"methods": aggregate,
"paired_source_stratified_group_bootstrap": bootstrap,
}
def write_markdown(path: Path, report: dict) -> None:
lines = [
"# Paper-ready v5.3 development evaluation",
"",
"> v5.3 is development data with judge-derived labels; this is not an untouched confirmatory result.",
"",
f"- Reporting commit: `{report['report_git']['commit']}`",
f"- Canonical metadata run: `{report['canonical_metadata']['method']}` seed {report['canonical_metadata']['seed']}",
f"- Canonical sources/groups: {len(report['canonical_metadata']['sources'])} / {report['canonical_metadata']['split_groups']}",
"",
"| Method | Seeds | Micro AP | Micro AUROC | Answer-balanced AP | Source-macro AP |",
"|---|---:|---:|---:|---:|---:|",
]
for method, values in report["methods"].items():
lines.append(
f"| {method} | {values['n_seeds']} | "
f"{values['micro_average_precision_mean']:.6f} ± {values['micro_average_precision_sd']:.6f} | "
f"{values['micro_auroc_mean']:.6f} ± {values['micro_auroc_sd']:.6f} | "
f"{values['answer_balanced_average_precision_mean']:.6f} ± {values['answer_balanced_average_precision_sd']:.6f} | "
f"{values['source_macro_average_precision_mean']:.6f} ± {values['source_macro_average_precision_sd']:.6f} |"
)
lines.extend(
[
"",
"## Validation-locked operating point on test",
"",
"| Method | Threshold | Precision | Recall | F1 | Actual FPR |",
"|---|---:|---:|---:|---:|---:|",
]
)
for method, values in report["methods"].items():
lines.append(
f"| {method} | "
f"{values['operating_threshold_mean']:.6f} ± {values['operating_threshold_sd']:.6f} | "
f"{values['operating_precision_mean']:.6f} ± {values['operating_precision_sd']:.6f} | "
f"{values['operating_recall_mean']:.6f} ± {values['operating_recall_sd']:.6f} | "
f"{values['operating_f1_mean']:.6f} ± {values['operating_f1_sd']:.6f} | "
f"{values['operating_false_positive_rate_mean']:.6f} ± {values['operating_false_positive_rate_sd']:.6f} |"
)
bootstrap = report["paired_source_stratified_group_bootstrap"]
low, high = bootstrap["ci_95_percentile"]
lines.extend(
[
"",
"## Primary paired comparison",
"",
f"- Estimand: {bootstrap['estimand']}",
f"- Mean AP delta: {bootstrap['delta_mean']:.6f}",
f"- 95% cluster-bootstrap CI: [{low:.6f}, {high:.6f}]",
f"- Bootstrap draws used: {bootstrap['draws_used']:,}",
"- Resampling unit: complete duplicate split groups, sampled within source.",
"- Seed variation is reported separately; seed and token observations are not pooled.",
]
)
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text("\n".join(lines) + "\n", encoding="utf-8")
def main(argv=None):
args = parse_args(argv)
report = build_report(args)
args.output_json.parent.mkdir(parents=True, exist_ok=True)
args.output_json.write_text(
json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
write_markdown(args.output_md, report)
print(args.output_json)
print(args.output_md)
if __name__ == "__main__":
main()
|