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8f4ed7a | 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 | """Aggregate the six frozen reviewer-requested outer runs."""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Any
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[2]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from revision.scripts.reanalysis_core import ensure_new_output_dir
from revision.scripts.reanalysis_pipeline import _write_sha256_manifest
STRATEGIES = ("canonical_grouped", "scaffold_aware")
SEEDS = (123456, 123457, 123458)
METRIC_COLUMNS = (
"r2",
"mae",
"rmse",
"bias",
"calibration_slope",
"calibration_intercept",
)
def _load_json(path: Path) -> dict[str, Any]:
return json.loads(path.read_text(encoding="utf-8"))
def _metric_rows(
strategy: str,
seed: int,
payload: dict[str, Any],
) -> list[dict[str, Any]]:
return [
{
"strategy": strategy,
"seed": seed,
"primary_model_predeclared": payload["primary_model_predeclared"],
"model": model,
**metrics,
}
for model, metrics in payload["metrics"].items()
]
def _aggregate_metrics(frame: pd.DataFrame) -> pd.DataFrame:
metric_columns = [column for column in METRIC_COLUMNS if column in frame.columns]
aggregate = frame.groupby(["strategy", "model"])[metric_columns].agg(["mean", "std"])
aggregate.columns = [
f"{metric}_{'sd' if statistic == 'std' else statistic}"
for metric, statistic in aggregate.columns
]
return aggregate.reset_index()
def _aggregate_runtime(frame: pd.DataFrame) -> pd.DataFrame:
aggregate = (
frame.groupby(["strategy", "model"], sort=True)
.agg(
parameter_count=("parameter_count", "first"),
parameter_count_min=("parameter_count", "min"),
parameter_count_max=("parameter_count", "max"),
training_seconds_mean=("training_seconds", "mean"),
training_seconds_sd=("training_seconds", "std"),
)
.reset_index()
)
if not (
aggregate["parameter_count"] == aggregate["parameter_count_min"]
).all() or not (
aggregate["parameter_count"] == aggregate["parameter_count_max"]
).all():
raise ValueError("Parameter counts changed across frozen outer runs.")
return aggregate.drop(columns=["parameter_count_min", "parameter_count_max"])
def main() -> int:
parser = argparse.ArgumentParser(
description="Aggregate all six completed frozen reviewer reanalysis runs."
)
parser.add_argument(
"--artifacts-root",
default=str(
PROJECT_ROOT
/ "revision"
/ "artifacts"
/ "reviewer_requested_reanalysis_v4"
),
)
artifacts_root = Path(parser.parse_args().artifacts_root).resolve()
summary_dir = ensure_new_output_dir(artifacts_root / "summary")
classical_rows: list[dict[str, Any]] = []
neural_rows: list[dict[str, Any]] = []
paired_rows: list[dict[str, Any]] = []
domain_rows: list[dict[str, Any]] = []
per_lab_frames: list[pd.DataFrame] = []
runtime_rows: list[dict[str, Any]] = []
for strategy in STRATEGIES:
for seed in SEEDS:
split_dir = artifacts_root / strategy / f"seed_{seed}"
neural_dir = split_dir / "neural_stack"
neural_metrics_path = neural_dir / "metrics.json"
if not neural_metrics_path.is_file():
raise FileNotFoundError(f"Incomplete neural matrix: {neural_metrics_path}")
classical_rows.extend(
_metric_rows(strategy, seed, _load_json(split_dir / "classical_metrics.json"))
)
neural_rows.extend(
_metric_rows(strategy, seed, _load_json(neural_metrics_path))
)
for reference_model, metric_payload in _load_json(
neural_dir / "paired_group_bootstrap.json"
).items():
for metric, values in metric_payload.items():
paired_rows.append(
{
"strategy": strategy,
"seed": seed,
"candidate_model": "stack_all_plus_descriptors",
"reference_model": reference_model,
"metric": metric,
**values,
}
)
domain = _load_json(neural_dir / "prospective_domain_diagnostics.json")
for threshold_payload in domain["threshold_sensitivity"]:
domain_rows.append(
{
"strategy": strategy,
"seed": seed,
"spearman_similarity_vs_absolute_error": domain[
"spearman_similarity_vs_absolute_error"
],
"spearman_model_spread_vs_absolute_error": domain[
"spearman_model_spread_vs_absolute_error"
],
**threshold_payload,
}
)
per_lab = pd.read_csv(neural_dir / "per_lab_metrics.csv")
per_lab.insert(0, "seed", seed)
per_lab.insert(0, "strategy", strategy)
per_lab_frames.append(per_lab)
runtime = _load_json(neural_dir / "RUN_METADATA.json")
for model, training_seconds in runtime["training_seconds_by_model"].items():
runtime_rows.append(
{
"strategy": strategy,
"seed": seed,
"model": model,
"parameter_count": int(runtime["parameter_counts"][model]),
"training_seconds": float(training_seconds),
"total_run_seconds": float(runtime["total_run_seconds"]),
"device": runtime["device"],
"gpu_name": runtime.get("gpu_name"),
}
)
classical = pd.DataFrame(classical_rows)
neural = pd.DataFrame(neural_rows)
classical.to_csv(summary_dir / "classical_metrics_by_run.csv", index=False)
neural.to_csv(summary_dir / "neural_metrics_by_run.csv", index=False)
_aggregate_metrics(classical).to_csv(
summary_dir / "classical_metrics_aggregate.csv", index=False
)
_aggregate_metrics(neural).to_csv(
summary_dir / "neural_metrics_aggregate.csv", index=False
)
pd.DataFrame(paired_rows).to_csv(
summary_dir / "paired_bootstrap_by_run.csv", index=False
)
pd.DataFrame(domain_rows).to_csv(
summary_dir / "prospective_domain_by_run.csv", index=False
)
pd.concat(per_lab_frames, ignore_index=True).to_csv(
summary_dir / "neural_per_lab_by_run.csv", index=False
)
runtime_frame = pd.DataFrame(runtime_rows)
runtime_frame.to_csv(summary_dir / "neural_runtime_by_run.csv", index=False)
_aggregate_runtime(runtime_frame).to_csv(
summary_dir / "neural_runtime_aggregate.csv", index=False
)
_write_sha256_manifest(artifacts_root)
print(f"Wrote frozen aggregate tables to: {summary_dir}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
|