import json from collections import Counter INPUT_FILE = "/root/test/weitiao/data_process_bq/data34/set1_extracted/B_Romance_3249_safeguard_set1.jsonl" # conversations 中带 _safety 的 gpt 轮：Unsafe 占比 **严格大于** 该比例时，样本级 conversation 记为 Unsafe UNSAFE_CONV_RATIO_THRESHOLD = 0.3 # ── 统计容器 ────────────────────────────────────────────── stats = { "total_samples": 0, "conv_labels": Counter(), "chosen_labels": Counter(), "rejected_labels": Counter(), "conv_categories": Counter(), "chosen_categories": Counter(), "rejected_categories": Counter(), "sample_any_unsafe": 0, "sample_any_controversial": 0, "sample_all_safe": 0, "total_conv_turns": 0, } unsafe_turn_count = Counter() controversial_turn_count = Counter() triple_counter = Counter() with open(INPUT_FILE, "r", encoding="utf-8") as f: for line in f: sample = json.loads(line) stats["total_samples"] += 1 sample_labels = [] # ── conversations gpt turn ──────────────────────── n_unsafe_conv = 0 n_controversial_conv = 0 conv_turn_labels = [] for turn in sample["conversations"]: if turn["from"] != "gpt" or "_safety" not in turn: continue safety = turn["_safety"] label = safety.get("label") stats["total_conv_turns"] += 1 stats["conv_labels"][label] += 1 for cat in safety.get("categories", []): stats["conv_categories"][cat] += 1 sample_labels.append(label) conv_turn_labels.append(label) if label == "Unsafe": n_unsafe_conv += 1 elif label == "Controversial": n_controversial_conv += 1 unsafe_turn_count[n_unsafe_conv] += 1 controversial_turn_count[n_controversial_conv] += 1 # ── chosen ──────────────────────────────────────── chosen_label = None if "_safety" in sample.get("chosen", {}): safety = sample["chosen"]["_safety"] chosen_label = safety.get("label") stats["chosen_labels"][chosen_label] += 1 for cat in safety.get("categories", []): stats["chosen_categories"][cat] += 1 sample_labels.append(chosen_label) # ── rejected ────────────────────────────────────── rejected_label = None if "_safety" in sample.get("rejected", {}): safety = sample["rejected"]["_safety"] rejected_label = safety.get("label") stats["rejected_labels"][rejected_label] += 1 for cat in safety.get("categories", []): stats["rejected_categories"][cat] += 1 sample_labels.append(rejected_label) # ── 样本级汇总 ──────────────────────────────────── if "Unsafe" in sample_labels: stats["sample_any_unsafe"] += 1 elif "Controversial" in sample_labels: stats["sample_any_controversial"] += 1 else: stats["sample_all_safe"] += 1 # ── 三元组（三分类）────────────────────────────── # 仅统计 conversations 里 from=gpt 且含 _safety 的轮；Unsafe 轮数 / 该总轮数 > 30% 则整条为 Unsafe n_conv_turns = len(conv_turn_labels) if n_conv_turns == 0: conv_status = "Safe" elif (n_unsafe_conv / n_conv_turns) >= UNSAFE_CONV_RATIO_THRESHOLD: conv_status = "Unsafe" elif any(l == "Controversial" for l in conv_turn_labels): conv_status = "Controversial" else: conv_status = "Safe" chosen_status = chosen_label if chosen_label in ("Safe", "Controversial", "Unsafe") else "Safe" rejected_status = rejected_label if rejected_label in ("Safe", "Controversial", "Unsafe") else "Safe" triple_counter[( f"conv:{conv_status}", f"chosen:{chosen_status}", f"rejected:{rejected_status}", )] += 1 # ── 打印报告 ────────────────────────────────────────────── total = stats["total_samples"] print(f"{'='*50}") print(f"总样本数: {total}") print( f"conversation 级 Unsafe 判定：gpt 且含 _safety 的轮中，Unsafe 轮占比 > " f"{UNSAFE_CONV_RATIO_THRESHOLD * 100:.0f}%" ) print(f"{'='*50}") # 样本级 print(f"\n── 样本级分布 ──────────────────────────────────") print(f" 全部 Safe : {stats['sample_all_safe']:>6} ({stats['sample_all_safe']/total*100:.1f}%)") print(f" 含 Controversial : {stats['sample_any_controversial']:>6} ({stats['sample_any_controversial']/total*100:.1f}%)") print(f" 含 Unsafe : {stats['sample_any_unsafe']:>6} ({stats['sample_any_unsafe']/total*100:.1f}%)") # 各位置 label / category 分布 for name, label_counter, cat_counter, turn_total in [ ("conversations gpt turn", stats["conv_labels"], stats["conv_categories"], stats["total_conv_turns"]), ("chosen", stats["chosen_labels"], stats["chosen_categories"], total), ("rejected", stats["rejected_labels"], stats["rejected_categories"], total), ]: print(f"\n── {name} (共 {turn_total} 条) ─────────────────────") print(" Label 分布:") for label, cnt in label_counter.most_common(): print(f" {label:<15}: {cnt:>6} ({cnt/turn_total*100:.1f}%)") print(" Category 分布 (不含 None):") for cat, cnt in cat_counter.most_common(): if cat == "None": continue print(f" {cat:<40}: {cnt:>6} ({cnt/turn_total*100:.1f}%)") # conversations unsafe/controversial turn 数量分布 print(f"\n── conversations 中 Unsafe turn 数量分布 ────────────") print(f" {'Unsafe turns':<15} {'样本数':>8} {'占比':>8} {'累计占比':>10}") cumulative = 0 for n in sorted(unsafe_turn_count): cnt = unsafe_turn_count[n] cumulative += cnt print(f" {n:<15} {cnt:>8} {cnt/total*100:>7.1f}% {cumulative/total*100:>9.1f}%") print(f"\n── conversations 中 Controversial turn 数量分布 ─────") print(f" {'Controversial':<15} {'样本数':>8} {'占比':>8} {'累计占比':>10}") cumulative = 0 for n in sorted(controversial_turn_count): cnt = controversial_turn_count[n] cumulative += cnt print(f" {n:<15} {cnt:>8} {cnt/total*100:>7.1f}% {cumulative/total*100:>9.1f}%") # 三元组分布（27 种组合，跳过空的） print(f"\n── 三元组分布 (conv / chosen / rejected) ───────────") print(f" {'conv':<16} {'chosen':<16} {'rejected':<16} {'样本数':>8} {'占比':>8}") print(f" {'-'*66}") all_keys = [ (c, ch, r) for c in ("conv:Safe", "conv:Controversial", "conv:Unsafe") for ch in ("chosen:Safe", "chosen:Controversial", "chosen:Unsafe") for r in ("rejected:Safe", "rejected:Controversial", "rejected:Unsafe") ] for key in all_keys: cnt = triple_counter.get(key, 0) if cnt == 0: continue c, ch, r = [k.split(":")[1] for k in key] print(f" {c:<16} {ch:<16} {r:<16} {cnt:>8} ({cnt/total*100:.1f}%)") print(f" {'-'*66}") print(f" {'合计':<48} {total:>8} (100.0%)")