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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 | """Reproducible, explicitly post-hoc analysis of completed campaign artifacts."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
from scipy import stats
from .artifacts import sha256_file, write_json_immutable
from .statistics import holm_adjust, paired_family_test
INTERNAL_METRICS = ["recall@50", "mrr", "map", "ndcg@50"]
def _aligned_edges(
first: pd.DataFrame, second: pd.DataFrame
) -> tuple[pd.DataFrame, pd.DataFrame]:
required = {"record_a", "record_b", "score", "structural_similarity"}
for name, frame in (("first", first), ("second", second)):
if missing := required.difference(frame.columns):
raise ValueError(f"{name} edge table is missing columns: {sorted(missing)}")
keys = ["record_a", "record_b"]
left = first.sort_values(keys).reset_index(drop=True)
right = second.sort_values(keys).reset_index(drop=True)
if not left[keys].equals(right[keys]):
raise ValueError("Paired methods must contain identical edges")
if not np.allclose(left["structural_similarity"], right["structural_similarity"]):
raise ValueError("Paired methods disagree on structural truth")
return left, right
def paired_correlation_difference_bootstrap(
first: pd.DataFrame,
second: pd.DataFrame,
samples: int = 10000,
confidence: float = 0.95,
seed: int = 0,
) -> dict[str, float | int]:
"""Paired two-endpoint BGC cluster bootstrap for a Spearman difference.
Ranks are fixed on the complete paired edge set. Each dyad contributes half
its sufficient-statistic weight to each endpoint block. Both methods use the
same resampled endpoint multiplicities in every replicate.
"""
if samples < 1:
raise ValueError("Bootstrap samples must be positive")
if not 0.0 < confidence < 1.0:
raise ValueError("Confidence must be between zero and one")
left, right = _aligned_edges(first, second)
if len(left) < 3:
raise ValueError("At least three paired edges are required")
score_first = stats.rankdata(left["score"].to_numpy(float), method="average")
score_second = stats.rankdata(right["score"].to_numpy(float), method="average")
truth = stats.rankdata(left["structural_similarity"].to_numpy(float), method="average")
identifiers = pd.Index(
sorted(set(left["record_a"].astype(str)) | set(left["record_b"].astype(str)))
)
index_left = identifiers.get_indexer(left["record_a"].astype(str))
index_right = identifiers.get_indexer(left["record_b"].astype(str))
def block_sum(values: np.ndarray) -> np.ndarray:
return 0.5 * (
np.bincount(index_left, weights=values, minlength=len(identifiers))
+ np.bincount(index_right, weights=values, minlength=len(identifiers))
)
weights = 0.5 * (
np.bincount(index_left, minlength=len(identifiers))
+ np.bincount(index_right, minlength=len(identifiers))
)
blocks = np.column_stack(
[
weights,
block_sum(score_first),
block_sum(score_second),
block_sum(truth),
block_sum(score_first**2),
block_sum(score_second**2),
block_sum(truth**2),
block_sum(score_first * truth),
block_sum(score_second * truth),
]
)
random_state = np.random.default_rng(seed)
differences: list[float] = []
for _ in range(samples):
selected = random_state.integers(0, len(blocks), size=len(blocks))
weight, sum_a, sum_b, sum_y, sum_aa, sum_bb, sum_yy, sum_ay, sum_by = (
blocks[selected].sum(axis=0)
)
variance_y = sum_yy - sum_y * sum_y / weight
def correlation(sum_x: float, sum_xx: float, sum_xy: float) -> float:
covariance = sum_xy - sum_x * sum_y / weight
variance_x = sum_xx - sum_x * sum_x / weight
denominator = np.sqrt(max(variance_x, 0.0) * max(variance_y, 0.0))
return covariance / denominator if denominator > 0.0 else float("nan")
difference = correlation(sum_a, sum_aa, sum_ay) - correlation(
sum_b, sum_bb, sum_by
)
if np.isfinite(difference):
differences.append(float(difference))
if not differences:
raise ValueError("No finite paired bootstrap differences were produced")
values = np.asarray(differences)
tail = (1.0 - confidence) / 2.0
lower, upper = np.quantile(values, [tail, 1.0 - tail])
first_rho = float(stats.spearmanr(left["score"], left["structural_similarity"]).statistic)
second_rho = float(stats.spearmanr(right["score"], right["structural_similarity"]).statistic)
sign_probability = 2.0 * min(
(np.count_nonzero(values <= 0.0) + 1) / (len(values) + 1),
(np.count_nonzero(values >= 0.0) + 1) / (len(values) + 1),
)
return {
"pairs": len(left),
"bgcs": len(identifiers),
"spearman_first": first_rho,
"spearman_second": second_rho,
"delta_spearman": first_rho - second_rho,
"ci_lower": float(lower),
"ci_upper": float(upper),
"bootstrap_two_sided_sign_probability": float(min(sign_probability, 1.0)),
"bootstrap_samples": len(values),
}
def internal_paired_comparisons(main: pd.DataFrame, no_phase1: pd.DataFrame) -> pd.DataFrame:
comparisons: list[dict[str, Any]] = []
ensemble_methods = sorted(
method
for method in main["method"].astype(str).unique()
if method.startswith("ensemble_validation_alpha_")
)
if len(ensemble_methods) != 1:
raise ValueError(
"Expected exactly one validation-selected ensemble method; "
f"found {ensemble_methods}"
)
ensemble_method = ensemble_methods[0]
families = [
("setnet", "raw_esm_mean", "main_vs_raw", main),
("setnet", "pfam_jaccard_max", "main_vs_pfam", main),
(
ensemble_method,
"pfam_jaccard_max",
"ensemble_vs_pfam",
main,
),
]
phase_ablation = pd.concat(
[
main[main["method"] == "setnet"].assign(method="setnet_phase1"),
no_phase1[no_phase1["method"] == "setnet"].assign(
method="setnet_no_phase1"
),
],
ignore_index=True,
)
families.append(
("setnet_phase1", "setnet_no_phase1", "phase1_ablation", phase_ablation)
)
for method, baseline, family, frame in families:
rows = [paired_family_test(frame, method, baseline, metric) for metric in INTERNAL_METRICS]
adjusted = holm_adjust(row["p_value"] for row in rows)
for row, corrected in zip(rows, adjusted):
row.update(family=family, p_value_holm=corrected, analysis_status="post_hoc")
comparisons.append(row)
return pd.DataFrame(comparisons)
def _methods(path: Path) -> dict[str, pd.DataFrame]:
frame = pd.read_csv(path)
return {
str(method): rows.drop(columns="method").reset_index(drop=True)
for method, rows in frame.groupby("method", sort=False)
}
def _training_summary(path: Path, metric: str, maximize: bool) -> dict[str, Any]:
with path.open("r", encoding="utf-8") as handle:
history = json.load(handle)
best = (max if maximize else min)(history, key=lambda row: row[metric])
return {"epochs": len(history), "best": best, "first": history[0], "last": history[-1]}
def analyze_campaign(
artifact_root: str | Path,
campaign_tag: str,
output_dir: str | Path,
bootstrap_samples: int = 10000,
confidence: float = 0.95,
seed: int = 20260810,
) -> Path:
root = Path(artifact_root)
output = Path(output_dir)
output.mkdir(parents=True, exist_ok=False)
main_internal = root / f"{campaign_tag}-main-evaluation/group_results.csv"
no_internal = root / f"{campaign_tag}-no-phase1-evaluation/group_results.csv"
main_external = root / f"{campaign_tag}-main-external/external_pair_scores.csv"
no_external = root / f"{campaign_tag}-no-phase1-external/external_pair_scores.csv"
required = [main_internal, no_internal, main_external, no_external]
if missing := [str(path) for path in required if not path.is_file()]:
raise FileNotFoundError(f"Campaign artifacts are missing: {missing}")
internal = internal_paired_comparisons(
pd.read_csv(main_internal), pd.read_csv(no_internal)
)
internal.to_csv(output / "internal_paired_comparisons.csv", index=False)
main_methods = _methods(main_external)
no_methods = _methods(no_external)
external_rows: list[dict[str, Any]] = []
comparisons = [
("main_setnet_vs_raw", main_methods["setnet"], main_methods["raw_esm_mean"]),
(
"main_setnet_vs_raw_bigscape_edges",
main_methods["setnet_on_bigscape_edges"],
main_methods["raw_esm_mean_on_bigscape_edges"],
),
("phase1_ablation", main_methods["setnet"], no_methods["setnet"]),
(
"phase1_ablation_bigscape_edges",
main_methods["setnet_on_bigscape_edges"],
no_methods["setnet_on_bigscape_edges"],
),
(
"bigscape_vs_main_setnet",
main_methods["bigscape"],
main_methods["setnet_on_bigscape_edges"],
),
]
for name, first, second in comparisons:
for subset_name, subset in (
("all", first),
("cross_genus", first[first["cross_genus"]]),
):
keys = set(zip(subset["record_a"], subset["record_b"]))
paired_second = second[
[pair in keys for pair in zip(second["record_a"], second["record_b"])]
]
result = paired_correlation_difference_bootstrap(
subset,
paired_second,
samples=bootstrap_samples,
confidence=confidence,
seed=seed,
)
external_rows.append(
{
"comparison": name,
"subset": subset_name,
**result,
"analysis_status": "post_hoc",
}
)
pd.DataFrame(external_rows).to_csv(
output / "external_paired_comparisons.csv", index=False
)
exact_rows: list[dict[str, Any]] = []
for run_name in (f"{campaign_tag}-main-external", f"{campaign_tag}-no-phase1-external"):
for method in ("setnet", "raw_esm_mean"):
path = root / run_name / f"{method}_exact_product_retrieval.csv"
frame = pd.read_csv(path)
exact_rows.append(
{
"run": run_name,
"method": method,
"references": len(frame),
"cross_genus_references": int(
(frame["cross_genus_positive_count"] > 0).sum()
),
**{metric: float(frame[metric].mean()) for metric in INTERNAL_METRICS},
"precision@50": float(frame["precision@50"].mean()),
}
)
pd.DataFrame(exact_rows).to_csv(output / "exact_product_summary.csv", index=False)
training = {
"phase1": _training_summary(
root / f"{campaign_tag}-phase1/phase1_history.json",
"validation_loss",
maximize=False,
),
"phase2_main": _training_summary(
root / f"{campaign_tag}-main/phase2_history.json",
"validation_recall@50",
maximize=True,
),
"phase2_no_phase1": _training_summary(
root / f"{campaign_tag}-no-phase1/phase2_history.json",
"validation_recall@50",
maximize=True,
),
}
write_json_immutable(output / "training_summary.json", training)
metadata = {
"schema_version": 1,
"campaign_tag": campaign_tag,
"analysis_status": "post_hoc_exploratory",
"bootstrap_samples": bootstrap_samples,
"confidence": confidence,
"seed": seed,
"pair_bootstrap": "paired_two_endpoint_bgc_cluster_fixed_ranks",
"input_sha256": {str(path.relative_to(root)): sha256_file(path) for path in required},
}
write_json_immutable(output / "analysis_metadata.json", metadata)
return output
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