eval/respace/debug_data_matching.py

#!/usr/bin/env python3
import json
from pathlib import Path

# 扫描 qwen7b_trainset，筛选 total_pbl_loss < 0.01
base_dir = Path("/home/v-meiszhang/amlt-project/respace/eval/viz/qwen7b_trainset")
good_samples = set()
bad_samples = set()

for subdir in sorted(base_dir.iterdir()):
    if not subdir.is_dir() or subdir.name.startswith('.'):
        continue
    metrics_file = subdir / "evaluation_metrics.json"
    if not metrics_file.exists():
        continue
    try:
        with open(metrics_file, 'r', encoding='utf-8') as f:
            metrics = json.load(f)
        total_pbl_loss = metrics.get('total_pbl_loss', float('inf'))
        if total_pbl_loss < 0.01:
            good_samples.add(subdir.name)
        else:
            bad_samples.add(subdir.name)
    except:
        pass

print(f"Good: {len(good_samples)}, Bad: {len(bad_samples)}")

# 读取训练数据
training_data_file = Path("/home/v-meiszhang/amlt-project/respace/training_data_with_content_v3.1.json")
with open(training_data_file, 'r', encoding='utf-8') as f:
    raw_data = json.load(f)

# 数据可能是 {metadata, data} 结构
if isinstance(raw_data, dict) and 'data' in raw_data:
    training_data = raw_data['data']
else:
    training_data = raw_data

print(f"Training data: {len(training_data)}")

# 检查结构：打印第一条数据的所有字段名
if training_data:
    first_item = training_data[0]
    print(f"\nFields in first item: {list(first_item.keys())}")
    print(f"\nFirst item sample:")
    for k, v in first_item.items():
        if isinstance(v, str):
            print(f"  {k}: {v[:80]}")
        elif isinstance(v, (list, dict)):
            print(f"  {k}: {type(v).__name__} (len={len(v)})")
        else:
            print(f"  {k}: {v}")

# 尝试匹配
print(f"\n{'='*60}")
print("Attempting to match samples...")
print(f"{'='*60}")

matched_count = 0
updated_count = 0
id_fields_found = {}

for i, data_item in enumerate(training_data):
    candidates = [
        ('sample_id', data_item.get('sample_id')),
        ('id', data_item.get('id')),
        ('uuid', data_item.get('uuid')),
        ('scene_id', data_item.get('scene_id')),
        ('jid', data_item.get('jid')),
    ]
    
    sample_id = None
    field_name = None
    for fname, fvalue in candidates:
        if fvalue is not None:
            sample_id = fvalue
            field_name = fname
            id_fields_found[fname] = id_fields_found.get(fname, 0) + 1
            break
    
    if sample_id is None:
        if i < 5:
            print(f"Item {i}: NO ID FOUND")
        continue
    
    if sample_id in good_samples or sample_id in bad_samples:
        matched_count += 1
        if sample_id in bad_samples:
            data_item['split'] = 'filtered'
            updated_count += 1
        if i < 5:
            print(f"Item {i}: MATCHED (field={field_name}, id={sample_id[:40]}...)")

print(f"\n{'='*60}")
print(f"Results:")
print(f"  Matched: {matched_count} / {len(training_data)}")
print(f"  Updated: {updated_count}")
print(f"\nID fields found in data:")
for fname, count in sorted(id_fields_found.items(), key=lambda x: -x[1]):
    print(f"  {fname}: {count}")

# 保存结果
if matched_count > 0:
    output_file = training_data_file.parent / "training_data_with_content_v3.1_filtered.json"
    # 保持原始结构
    if isinstance(raw_data, dict) and 'data' in raw_data:
        raw_data['data'] = training_data
        output_data = raw_data
    else:
        output_data = training_data
    with open(output_file, 'w', encoding='utf-8') as f:
        json.dump(output_data, f, ensure_ascii=False, indent=2)
    print(f"\n✓ Saved to {output_file.name}")
else:
    print(f"\n✗ No matches found - check ID field names above")
    print(f"\nSample from good_samples (first 5):")
    for sid in sorted(good_samples)[:5]:
        print(f"  {sid}")