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a4019dd | 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 | from __future__ import annotations
import json
from dataclasses import asdict, dataclass
from pathlib import Path
import numpy as np
import torch
from .data import BinaryEvalRow, SourcePair
from .modeling import ModelBundle, build_prompt, compute_average_logprob, compute_token_logprobs, encode_response
@dataclass(slots=True)
class ScoredText:
text: str
label: int
pair_id: str
score: float
ai_avg_logp: float
human_avg_logp: float
def compute_auc(labels: list[int], scores: list[float]) -> float:
order = np.argsort(scores)
ranks = np.empty(len(scores), dtype=np.float64)
ranks[order] = np.arange(1, len(scores) + 1)
pos_count = sum(labels)
neg_count = len(labels) - pos_count
if pos_count == 0 or neg_count == 0:
return 0.5
pos_rank_sum = float(sum(rank for rank, label in zip(ranks, labels) if label == 1))
return (pos_rank_sum - pos_count * (pos_count + 1) / 2.0) / (pos_count * neg_count)
def dual_score(bundle: ModelBundle, text: str) -> tuple[float, float, float]:
ai_prompt = build_prompt(bundle, bundle.config.model.ai_token)
human_prompt = build_prompt(bundle, bundle.config.model.human_token)
ai_ids, ai_prompt_len = encode_response(bundle, ai_prompt, text)
human_ids, human_prompt_len = encode_response(bundle, human_prompt, text)
ai_logp = float(compute_average_logprob(bundle, ai_ids, ai_prompt_len).item())
human_logp = float(compute_average_logprob(bundle, human_ids, human_prompt_len).item())
score_mode = bundle.config.scoring.score_mode
if score_mode == "avg_margin":
score = ai_logp - human_logp
elif score_mode == "soft_token_sigmoid":
ai_token_logps = compute_token_logprobs(bundle, ai_ids, ai_prompt_len)
human_token_logps = compute_token_logprobs(bundle, human_ids, human_prompt_len)
if ai_token_logps.shape[0] != human_token_logps.shape[0]:
token_count = min(ai_token_logps.shape[0], human_token_logps.shape[0])
ai_token_logps = ai_token_logps[:token_count]
human_token_logps = human_token_logps[:token_count]
margins = ai_token_logps - human_token_logps
tau = max(bundle.config.scoring.token_sigmoid_tau, 1.0e-6)
score = float(torch.sigmoid(margins / tau).mean().item())
else:
raise ValueError(f"Unsupported scoring mode: {score_mode}")
return score, ai_logp, human_logp
def decision_threshold(bundle: ModelBundle) -> float:
if bundle.config.scoring.score_mode == "soft_token_sigmoid":
return 0.5
return 0.0
def evaluate_holdout(bundle: ModelBundle, holdout_pairs: list[SourcePair], output_dir: Path) -> dict:
threshold = decision_threshold(bundle)
rows: list[ScoredText] = []
for pair in holdout_pairs:
for label, text in ((1, pair.ai_text), (0, pair.human_text)):
score, ai_logp, human_logp = dual_score(bundle, text)
rows.append(
ScoredText(
text=text,
label=label,
pair_id=pair.pair_id,
score=score,
ai_avg_logp=ai_logp,
human_avg_logp=human_logp,
)
)
labels = [row.label for row in rows]
scores = [row.score for row in rows]
tp = sum(int(row.label == 1 and row.score > threshold) for row in rows)
fp = sum(int(row.label == 0 and row.score > threshold) for row in rows)
tn = sum(int(row.label == 0 and row.score <= threshold) for row in rows)
fn = sum(int(row.label == 1 and row.score <= threshold) for row in rows)
precision = tp / max(1, tp + fp)
recall = tp / max(1, tp + fn)
f1 = 0.0 if precision + recall == 0.0 else 2.0 * precision * recall / (precision + recall)
pairwise_wins = 0
by_pair: dict[str, dict[int, ScoredText]] = {}
for row in rows:
by_pair.setdefault(row.pair_id, {})[row.label] = row
for item in by_pair.values():
pairwise_wins += int(item[1].score > item[0].score)
summary = {
"num_holdout_texts": len(rows),
"score_mode": bundle.config.scoring.score_mode,
"decision_threshold": threshold,
"auroc": compute_auc(labels, scores),
"accuracy": (tp + tn) / len(rows),
"pairwise_rate": pairwise_wins / len(holdout_pairs),
"mean_score_ai": float(np.mean([row.score for row in rows if row.label == 1])),
"mean_score_human": float(np.mean([row.score for row in rows if row.label == 0])),
"tp": tp,
"fp": fp,
"tn": tn,
"fn": fn,
"precision": precision,
"recall": recall,
"f1": f1,
}
output_dir.mkdir(parents=True, exist_ok=True)
(output_dir / "dual_eval_summary.json").write_text(json.dumps(summary, indent=2), encoding="utf-8")
(output_dir / "dual_eval_details.json").write_text(
json.dumps([asdict(row) for row in rows], indent=2),
encoding="utf-8",
)
return summary
def evaluate_binary_rows(
bundle: ModelBundle,
rows: list[BinaryEvalRow],
output_path: Path | None = None,
) -> dict:
threshold = decision_threshold(bundle)
scores: list[float] = []
labels: list[int] = []
details = []
for row in rows:
score, ai_logp, human_logp = dual_score(bundle, row.text)
scores.append(score)
labels.append(row.label)
details.append(
{
"row_id": row.row_id,
"label": row.label,
"text_type": row.text_type,
"model": row.model,
"source_id": row.source_id,
"score": score,
"ai_avg_logp": ai_logp,
"human_avg_logp": human_logp,
}
)
labels_array = np.asarray(labels, dtype=np.int64)
scores_array = np.asarray(scores, dtype=np.float64)
predictions = (scores_array > threshold).astype(np.int64)
tp = int(((labels_array == 1) & (predictions == 1)).sum())
fp = int(((labels_array == 0) & (predictions == 1)).sum())
tn = int(((labels_array == 0) & (predictions == 0)).sum())
fn = int(((labels_array == 1) & (predictions == 0)).sum())
precision = tp / max(1, tp + fp)
recall = tp / max(1, tp + fn)
f1 = 0.0 if precision + recall == 0.0 else 2.0 * precision * recall / (precision + recall)
summary = {
"num_rows": len(rows),
"score_mode": bundle.config.scoring.score_mode,
"decision_threshold": threshold,
"positive_rows": int(labels_array.sum()),
"negative_rows": int((labels_array == 0).sum()),
"auroc": compute_auc(labels, scores),
"accuracy_at_zero": float((predictions == labels_array).mean()),
"tp": tp,
"fp": fp,
"tn": tn,
"fn": fn,
"precision": precision,
"recall": recall,
"f1": f1,
"mean_score_positive": float(scores_array[labels_array == 1].mean()),
"mean_score_negative": float(scores_array[labels_array == 0].mean()),
}
if output_path is not None:
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(
json.dumps({"summary": summary, "details": details}, indent=2),
encoding="utf-8",
)
return summary
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