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c87881a | 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 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
from pathlib import Path
import pandas as pd
from bgc_retrieval.artifacts import sha256_file, write_json_immutable
from bgc_retrieval.statistics import holm_adjust, paired_family_test
METRICS = ["recall@50", "mrr", "map", "ndcg@50"]
def normalized_method(method: str) -> str:
if method.startswith("residual_pfam_validation_"):
return "residual_pfam_validation_selected"
if method.startswith("residual_validation_alpha_"):
return "residual_validation_selected"
return method
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--artifact-root", default="artifacts")
parser.add_argument("--seed", action="append", type=int, required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--ensemble-run")
args = parser.parse_args()
root = Path(args.artifact_root)
output = Path(args.output_dir)
output.mkdir(parents=True, exist_ok=False)
seed_rows = []
paired_rows = []
inputs: list[Path] = []
for seed in args.seed:
training_dir = root / f"paper-v2-residual-seed-{seed}"
evaluation_dir = root / f"paper-v2-residual-seed-{seed}-evaluation"
history_path = training_dir / "residual_history.json"
metadata_path = evaluation_dir / "metadata.json"
summary_path = evaluation_dir / "summary.csv"
groups_path = evaluation_dir / "group_results.csv"
inputs.extend([history_path, metadata_path, summary_path, groups_path])
history = json.loads(history_path.read_text(encoding="utf-8"))
best = max(history, key=lambda row: row["validation_recall@50"])
metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
summary = pd.read_csv(summary_path)
for record in summary.to_dict("records"):
seed_rows.append(
{
"training_seed": seed,
"method": normalized_method(str(record["method"])),
"metric": str(record["metric"]),
"value": float(record["mean"]),
"selected_residual_alpha": metadata["selected_residual_alpha"],
"selected_hybrid_alpha": metadata["selected_hybrid_alpha"],
"selected_pfam_beta": metadata["selected_pfam_beta"],
"best_epoch": int(best["epoch"]),
"best_validation_recall@50": float(best["validation_recall@50"]),
"epochs": len(history),
}
)
groups = pd.read_csv(groups_path)
actual = {
normalized_method(str(method)): str(method)
for method in groups["method"].unique()
}
comparisons = [
("weighted_vs_raw", actual["weighted_gene_esm"], "raw_esm_mean"),
(
"residual_vs_raw",
actual["residual_validation_selected"],
"raw_esm_mean",
),
(
"hybrid_vs_pfam",
actual["residual_pfam_validation_selected"],
"pfam_jaccard_max",
),
]
for family, method, baseline in comparisons:
results = [
paired_family_test(groups, method, baseline, metric)
for metric in METRICS
]
adjusted = holm_adjust(row["p_value"] for row in results)
for row, corrected in zip(results, adjusted):
paired_rows.append(
{
"training_seed": seed,
"comparison": family,
**row,
"p_value_holm": corrected,
}
)
seed_frame = pd.DataFrame(seed_rows).sort_values(
["method", "metric", "training_seed"]
)
seed_frame.to_csv(output / "seed_level_summary.csv", index=False)
paired_frame = pd.DataFrame(paired_rows).sort_values(
["comparison", "metric", "training_seed"]
)
paired_frame.to_csv(output / "paired_comparisons.csv", index=False)
aggregate = (
seed_frame.groupby(["method", "metric"], as_index=False)["value"]
.agg(
n="count",
mean="mean",
sample_std="std",
minimum="min",
maximum="max",
)
.sort_values(["method", "metric"])
)
aggregate.to_csv(output / "aggregate_summary.csv", index=False)
ensemble_record = None
if args.ensemble_run:
ensemble_dir = root / args.ensemble_run
ensemble_summary = ensemble_dir / "summary.csv"
ensemble_metadata = ensemble_dir / "metadata.json"
inputs.extend([ensemble_summary, ensemble_metadata])
pd.read_csv(ensemble_summary).to_csv(
output / "ensemble_summary.csv", index=False
)
ensemble_record = json.loads(
ensemble_metadata.read_text(encoding="utf-8")
)
write_json_immutable(
output / "analysis_metadata.json",
{
"schema_version": 1,
"organism_scope": "Streptomyces griseus",
"analysis_status": "post_hoc_redesign_pilot",
"seeds": args.seed,
"ensemble_metadata": ensemble_record,
"input_sha256": {
str(path.relative_to(root)): sha256_file(path)
for path in sorted(inputs)
},
},
)
print(output)
if __name__ == "__main__":
main()
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