root_scripts/eval_judge.py

#!/usr/bin/env python3
"""
Phase 2: Score open-ended inference results.

Two-stage scoring (adapted from eval_dual.py + MetaPhyX DeepSeek judge):
  Stage 1: Rule-based (boxed extraction + normalization + numeric tolerance)
           - If CORRECT → done, count as correct
           - If WRONG or UNCERTAIN → go to Stage 2
  Stage 2: Gemini 2.5 Flash LLM-as-Judge
           - Sends model's full response + ground truth to Gemini
           - Gemini determines [[YES]] or [[NO]] equivalence

Usage:
    python eval_openended_judge.py [--results_dir PATH] [--api_key KEY]

Inputs:
    inference_results_base.jsonl
    inference_results_sft.jsonl

Outputs:
    scored_results_base.jsonl
    scored_results_sft.jsonl
    comparison_report.json
"""
import json, os, re, time, sys, argparse
from collections import defaultdict, Counter

# ===================== CONFIG =====================
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY", "AIzaSyCXQ9gjVmRhoB1OVSqElnTB6p83GLX4W4w")
GEMINI_MODEL = "gemini-2.5-flash"
MAX_RETRIES = 3
RATE_LIMIT_DELAY = 0.5  # seconds between Gemini calls

# ===================== RULE-BASED SCORING =====================
# Adapted from eval_dual.py (verl/utils/reward_score/utils/utils.py approach)

def _strip_string(string):
    """Normalize math string: remove LaTeX formatting, units, whitespace."""
    string = string.replace("\n", "")
    string = string.replace("\\!", "")
    string = string.replace("\\\\", "\\")
    string = string.replace("tfrac", "frac")
    string = string.replace("dfrac", "frac")
    string = string.replace("\\left", "")
    string = string.replace("\\right", "")
    string = string.replace("^{\\circ}", "")
    string = string.replace("^\\circ", "")
    string = string.replace("\\$", "")
    if "\\text{ " in string:
        splits = string.split("\\text{ ")
        if len(splits) == 2:
            string = splits[0]
    string = string.replace("\\%", "")
    string = string.replace(" .", " 0.")
    string = string.replace("{.", "{0.")
    if len(string) == 0:
        return string
    if string[0] == ".":
        string = "0" + string
    if len(string.split("=")) == 2:
        if len(string.split("=")[0]) <= 2:
            string = string.split("=")[1]
    string = string.replace(" ", "")
    return string


def _normalize(expr):
    """Normalize answer expression for comparison."""
    if expr is None:
        return None
    m = re.search("^\\\\text\\{(?P<text>.+?)\\}$", expr)
    if m is not None:
        expr = m.group("text")
    expr = expr.replace("\\%", "%")
    expr = expr.replace("\\$", "$")
    expr = expr.replace("$", "")
    expr = expr.replace("%", "")
    expr = expr.replace(" or ", " , ")
    expr = expr.replace(" and ", " , ")
    for unit in ["degree", "cm", "centimeter", "meter", "mile", "second", "minute",
                 "hour", "day", "week", "month", "year", "foot", "feet", "inch", "yard",
                 "newton", "joule", "watt", "ampere", "volt", "ohm", "hertz",
                 "kilogram", "gram", "liter", "mole", "kelvin", "pascal",
                 "m/s", "km/h", "rad/s", "N", "J", "W", "A", "V", "Hz", "Pa", "kg", "mol"]:
        expr = re.sub(f"\\s*{re.escape(unit)}(es)?(s)?\\s*(\\^[0-9]+)?", "", expr, flags=re.IGNORECASE)
    if len(expr) > 0 and expr[0] == "{" and expr[-1] == "}":
        expr = expr[1:-1]
    try:
        if "." in expr:
            val = float(expr)
            if abs(val - int(round(val))) <= 1e-7:
                expr = str(int(round(val)))
    except:
        pass
    expr = re.sub("- *", "-", expr)
    expr = expr.replace(" ", "")
    expr = expr.replace("{", "")
    expr = expr.replace("}", "")
    expr = expr.lower()
    return expr


def extract_boxed_answer(text):
    """Extract the last \\boxed{} content from text."""
    idx = text.rfind("\\boxed")
    if idx < 0:
        idx = text.rfind("\\fbox")
        if idx < 0:
            return None
    i = idx
    num_left = 0
    right_idx = None
    while i < len(text):
        if text[i] == "{":
            num_left += 1
        if text[i] == "}":
            num_left -= 1
            if num_left == 0:
                right_idx = i
                break
        i += 1
    if right_idx is None:
        return None
    boxed = text[idx:right_idx + 1]
    left = "\\boxed{"
    if boxed.startswith(left) and boxed.endswith("}"):
        return boxed[len(left):-1]
    return None


def extract_answer_from_text(text):
    """Try to extract answer: first from \\boxed{}, then from common patterns."""
    # Handle <think>...</think>
    if '<think>' in text and '</think>' in text:
        text = text.split('</think>')[-1]

    # Priority 1: \boxed{}
    boxed = extract_boxed_answer(text)
    if boxed:
        return boxed

    # Priority 2: Common answer patterns
    patterns = [
        r'(?:the answer is|answer is|答案是|答案为)[:\s]*(.+?)(?:\.|$)',
        r'(?:therefore|thus|so|hence)[,\s]+(?:the answer is\s+)?(.+?)(?:\.|$)',
    ]
    for p in patterns:
        m = re.search(p, text, re.IGNORECASE)
        if m:
            ans = m.group(1).strip()
            if len(ans) < 100:
                return ans

    return None


def rule_based_score(prediction, ground_truth):
    """
    Rule-based scoring: extract answer + normalize + compare.
    Returns: (is_correct: bool, reason: str)
    """
    model_answer = extract_answer_from_text(prediction)
    if model_answer is None:
        return False, "no_answer_extracted"

    gt_norm = _normalize(ground_truth)
    pred_norm = _normalize(model_answer)

    if gt_norm is None or pred_norm is None:
        return False, "normalize_failed"

    # Direct match after normalization
    if gt_norm == pred_norm:
        return True, "exact_match"

    # Numeric comparison (1% tolerance)
    try:
        gt_float = float(gt_norm.replace(",", ""))
        pred_float = float(pred_norm.replace(",", ""))
        if abs(gt_float - pred_float) < 1e-6:
            return True, "numeric_match"
        if gt_float != 0 and abs((gt_float - pred_float) / gt_float) < 0.01:
            return True, "numeric_close"
    except:
        pass

    # Short answer containment (e.g., "III", "decreasing")
    if len(ground_truth.strip()) <= 10:
        gt_clean = ground_truth.strip()
        if re.search(r'\b' + re.escape(gt_clean) + r'\b', prediction, re.IGNORECASE):
            return True, "containment_match"

    return False, f"no_match(pred={pred_norm[:30]},gt={gt_norm[:30]})"


# ===================== GEMINI LLM-AS-JUDGE =====================
# Adapted from eval_dual.py + MetaPhyX deepscaler ORM prompt

ORM_PROMPT = """You are an expert in verifying if two physics answers are the same.
Your input is a physics question prompt and two answers:
- Answer 1: the model's prediction  
- Answer 2: the ground truth answer

Determine if they are equivalent.

Guidelines for equivalence:
- Different forms of the same number (0.5 = 1/2 = 50%)
- Same physical quantity with different units or notation (7.55N = 7.55 N = 7.55 newtons)
- Semantically equivalent descriptions ("point III" and "III", "decreasing" and "the velocity is decreasing")
- Algebraically equivalent expressions (x+1)^2 = x^2+2x+1
- Same choice letter or option name
- Correct numerical value even if formatting differs
- Minor rounding differences within 2% are acceptable

Your output must follow this format:
1) Brief explanation for why the answers are equivalent or not.
2) Final answer: [[YES]] or [[NO]]
"""


def call_gemini(prompt, api_key):
    """Call Gemini API using urllib (no external deps)."""
    import urllib.request, urllib.error

    url = f"https://generativelanguage.googleapis.com/v1beta/models/{GEMINI_MODEL}:generateContent?key={api_key}"
    payload = json.dumps({
        "contents": [{"parts": [{"text": prompt}]}],
        "generationConfig": {
            "temperature": 0.0,
            "maxOutputTokens": 512,
        }
    }).encode('utf-8')

    req = urllib.request.Request(
        url, data=payload,
        headers={"Content-Type": "application/json"},
        method="POST",
    )

    for attempt in range(MAX_RETRIES):
        try:
            with urllib.request.urlopen(req, timeout=30) as resp:
                result = json.loads(resp.read().decode('utf-8'))
                text = result['candidates'][0]['content']['parts'][0]['text']
                return text.strip()
        except urllib.error.HTTPError as e:
            if e.code == 429:
                wait = (attempt + 1) * 5
                print(f"    Rate limited, waiting {wait}s...")
                time.sleep(wait)
            else:
                print(f"    HTTP error {e.code}")
                if attempt == MAX_RETRIES - 1:
                    return None
                time.sleep(2)
        except Exception as e:
            print(f"    Error: {e}")
            if attempt == MAX_RETRIES - 1:
                return None
            time.sleep(2)
    return None


def gemini_judge(prediction, ground_truth, api_key):
    """Use Gemini to judge if model's prediction matches ground truth."""
    user_msg = f"""
Model's full response (contains reasoning and answer):
{prediction[:2000]}

Ground truth answer: {ground_truth}
"""
    response = call_gemini(ORM_PROMPT + "\n\n" + user_msg, api_key)

    if response is None:
        return False, "api_error"

    if "[[YES]]" in response:
        return True, response[:200]
    elif "[[NO]]" in response:
        return False, response[:200]
    else:
        lower = response.lower()
        if "yes" in lower and "no" not in lower:
            return True, response[:200]
        return False, response[:200]


# ===================== MAIN EVALUATION =====================

def score_model(results, model_name, api_key, output_file):
    """
    Score all results using two-stage approach:
      1. Rule-based first → if correct, DONE
      2. If rule-based says wrong/uncertain → Gemini fallback
    """
    print(f"\n{'='*60}")
    print(f"  Scoring: {model_name} ({len(results)} samples)")
    print(f"{'='*60}")

    rule_correct = 0
    rule_wrong_gemini_correct = 0
    rule_wrong_gemini_wrong = 0
    gemini_errors = 0
    total = len(results)

    cat_stats = defaultdict(lambda: {'total': 0, 'rule_correct': 0, 'gemini_correct': 0, 'final_correct': 0})

    for i, r in enumerate(results):
        cat = r.get('category', 'Unknown')
        pred = r.get('model_output', '')
        gt = r.get('ground_truth_value', '')
        cat_stats[cat]['total'] += 1

        # === Stage 1: Rule-based ===
        rule_match, rule_reason = rule_based_score(pred, gt)
        r['rule_match'] = rule_match
        r['rule_reason'] = rule_reason

        if rule_match:
            # Rule says CORRECT → done
            rule_correct += 1
            cat_stats[cat]['rule_correct'] += 1
            cat_stats[cat]['final_correct'] += 1
            r['final_correct'] = True
            r['final_method'] = f"rule:{rule_reason}"
            r['gemini_called'] = False
        else:
            # Rule says WRONG → Gemini fallback
            r['gemini_called'] = True
            gemini_match, gemini_reason = gemini_judge(pred, gt, api_key)
            r['gemini_match'] = gemini_match
            r['gemini_reason'] = gemini_reason

            if gemini_match:
                rule_wrong_gemini_correct += 1
                cat_stats[cat]['gemini_correct'] += 1
                cat_stats[cat]['final_correct'] += 1
                r['final_correct'] = True
                r['final_method'] = "gemini_override"
            else:
                rule_wrong_gemini_wrong += 1
                r['final_correct'] = False
                r['final_method'] = f"wrong:{rule_reason}"

            time.sleep(RATE_LIMIT_DELAY)

        # Progress
        final_correct_so_far = rule_correct + rule_wrong_gemini_correct
        if (i + 1) % 10 == 0 or (i + 1) == total:
            acc_so_far = final_correct_so_far / (i + 1)
            print(f"  [{i+1}/{total}] acc={acc_so_far:.1%} "
                  f"(rule✓={rule_correct} gemini✓={rule_wrong_gemini_correct} ✗={rule_wrong_gemini_wrong})",
                  flush=True)

    # Save scored results
    with open(output_file, 'w', encoding='utf-8') as f:
        for r in results:
            f.write(json.dumps(r, ensure_ascii=False) + '\n')

    # Summary
    final_correct = rule_correct + rule_wrong_gemini_correct
    final_acc = final_correct / total if total > 0 else 0

    print(f"\n{'─'*60}")
    print(f"  {model_name} — RESULTS")
    print(f"{'─'*60}")
    print(f"  Rule-based correct : {rule_correct}/{total} ({100*rule_correct/total:.1f}%)")
    print(f"  Gemini rescued     : {rule_wrong_gemini_correct} (rule wrong → Gemini correct)")
    print(f"  Final accuracy     : {final_correct}/{total} ({100*final_acc:.1f}%)")
    print(f"  Gemini calls made  : {rule_wrong_gemini_correct + rule_wrong_gemini_wrong}")
    print(f"\n  Per-category:")
    for cat, s in sorted(cat_stats.items()):
        acc = s['final_correct'] / s['total'] if s['total'] > 0 else 0
        print(f"    {cat:25s}: {s['final_correct']}/{s['total']} ({acc:.1%})"
              f"  [rule={s['rule_correct']}, gemini+={s['gemini_correct']}]")

    return {
        'model': model_name,
        'total': total,
        'rule_correct': rule_correct,
        'gemini_rescued': rule_wrong_gemini_correct,
        'final_correct': final_correct,
        'final_acc': round(100 * final_acc, 2),
        'category_stats': {cat: dict(s) for cat, s in cat_stats.items()},
    }


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--results_dir', type=str, default=None)
    parser.add_argument('--api_key', type=str, default=None)
    args = parser.parse_args()

    api_key = args.api_key or GEMINI_API_KEY

    # Find results directory
    results_dir = args.results_dir
    if results_dir is None:
        for d in [os.path.dirname(os.path.abspath(__file__)),
                  '/workspace/rl4phyx/RL4Phyx/SFT/sft_eval_footprint/']:
            if os.path.exists(os.path.join(d, 'inference_results_base.jsonl')):
                results_dir = d
                break
        if results_dir is None:
            print("ERROR: Cannot find inference results. Use --results_dir")
            sys.exit(1)

    print("=" * 60)
    print("  OPEN-ENDED EVAL: Rule-based + Gemini 2.5 Flash Judge")
    print(f"  Results dir: {results_dir}")
    print("=" * 60)

    # Load test data for context
    test_file = os.path.join(results_dir, 'test_1533_openended.jsonl')
    if os.path.exists(test_file):
        with open(test_file, 'r') as f:
            test_data = {json.loads(l)['index']: json.loads(l) for l in f if l.strip()}
        print(f"Test data loaded: {len(test_data)} samples")

    # Load and score base model
    base_file = os.path.join(results_dir, 'inference_results_base.jsonl')
    with open(base_file, 'r') as f:
        base_results = [json.loads(l) for l in f if l.strip()]
    base_scored_file = os.path.join(results_dir, 'scored_results_base.jsonl')
    base_stats = score_model(base_results, "Qwen2.5-VL-3B (Base)", api_key, base_scored_file)

    # Load and score SFT model
    sft_file = os.path.join(results_dir, 'inference_results_sft.jsonl')
    with open(sft_file, 'r') as f:
        sft_results = [json.loads(l) for l in f if l.strip()]
    sft_scored_file = os.path.join(results_dir, 'scored_results_sft.jsonl')
    sft_stats = score_model(sft_results, "Qwen2.5-VL-3B (SFT)", api_key, sft_scored_file)

    # Comparison
    delta = sft_stats['final_acc'] - base_stats['final_acc']
    report = {
        'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'),
        'scoring_method': 'rule-based + Gemini 2.5 Flash judge (fallback)',
        'base': base_stats,
        'sft': sft_stats,
        'improvement': f"{delta:+.2f}%",
    }

    report_file = os.path.join(results_dir, 'comparison_report.json')
    with open(report_file, 'w', encoding='utf-8') as f:
        json.dump(report, f, indent=2, ensure_ascii=False)

    print(f"\n{'='*60}")
    print(f"  FINAL COMPARISON")
    print(f"{'='*60}")
    print(f"  Base accuracy:  {base_stats['final_acc']}% ({base_stats['final_correct']}/{base_stats['total']})")
    print(f"  SFT accuracy:   {sft_stats['final_acc']}% ({sft_stats['final_correct']}/{sft_stats['total']})")
    print(f"  Improvement:    {delta:+.2f}%")
    print(f"\n  Per-category:")
    all_cats = sorted(set(list(base_stats['category_stats'].keys()) + list(sft_stats['category_stats'].keys())))
    for cat in all_cats:
        b = base_stats['category_stats'].get(cat, {'final_correct': 0, 'total': 0})
        s = sft_stats['category_stats'].get(cat, {'final_correct': 0, 'total': 0})
        b_acc = b['final_correct'] / b['total'] if b['total'] > 0 else 0
        s_acc = s['final_correct'] / s['total'] if s['total'] > 0 else 0
        print(f"    {cat:25s}  Base: {b_acc:.1%}  SFT: {s_acc:.1%}  Δ: {(s_acc-b_acc)*100:+.1f}%")
    print(f"\n  Report: {report_file}")
    print(f"{'='*60}")


if __name__ == '__main__':
    main()