evaluation/eval_criteria.md

# GIS-Bench Evaluation Criteria

## Overview

GIS-Bench uses a **VLM-based automated evaluation** pipeline where Claude Sonnet acts as a judge model to score GIS task outputs (HTML maps, PNG, JPG screenshots).

- **Judge Model:** `claude-sonnet-4-6`
- **Evaluation Repetitions:** N = 3 per task (averaged for robustness)
- **Raw Score Scale:** 130 points
- **Normalized Score Scale:** 100 points
- **Pass Threshold:** 60 points (normalized)

---

## Scoring Criteria

### Category 1: Basic Quality (100 pts)

| Criterion | Max Score | Scoring Rule |
|-----------|-----------|-------------|
| `exists` | 20 | **0 or 20** — Meaningful visualization exists (not blank/error) |
| `accuracy` | 30 | **0–30** — Requested region and dataset are correctly used |
| `requirement` | 30 | **0–30** — All conditions met (color, filter, buffer, etc.) |
| `completeness` | 20 | **0–20** — Visual completeness: legend, title, labels present |

### Category 2: Spatial Accuracy (30 pts)

Visual judgment from screenshot.

| Criterion | Max Score | Scoring Rule |
|-----------|-----------|-------------|
| `spatial_location` | 15 | **15** = correct location / **8** = minor offset / **0** = coordinate error or wrong area |
| `geometry_validity` | 5 | **5** = normal / **2** = minor distortion / **0** = obvious distortion |
| `numeric_match` | 10 | **10** = fully reflected / **5** = partially reflected / **0** = not reflected |

---

## Score Normalization

```
total_raw        = exists + accuracy + requirement + completeness
                   + spatial_location + geometry_validity + numeric_match

total_normalized = round(total_raw / 130 * 100)

pass             = (total_normalized >= 60)
```

---

## VLM Judge Prompt

The following prompt is passed to the judge model along with the task screenshot (base64 image):

```
GIS 결과물 평가 전문가로서 아래 태스크의 출력 이미지를 채점하세요.

[태스크 Level {level}]
{prompt}

[채점 기준 — 130점 만점]
기본(100점):
  exists(0/20): 의미있는 시각화 존재 여부
  accuracy(0-30): 요청 지역·데이터셋 정확성
  requirement(0-30): 색상·필터·버퍼 등 조건 충족
  completeness(0-20): 범례·제목·라벨 완성도

공간정확성(30점, 스크린샷 시각 판단):
  spatial_location(0/8/15): 데이터가 올바른 지역에 표시되는지
    15=정상, 8=경미한 이상, 0=좌표오류·엉뚱한위치
  geometry_validity(0/2/5): 폴리곤·버퍼 형태 이상 여부
    5=정상, 2=경미한이상, 0=명백한왜곡
  numeric_match(0/5/10): 수치·조건이 결과에 반영되었는지
    10=일치, 5=부분반영, 0=미반영

total_raw = 7개 항목 합계
total_normalized = round(total_raw / 130 * 100)

[응답] JSON만 출력, 다른 텍스트 금지:
{"exists":정수,"accuracy":정수,"requirement":정수,"completeness":정수,
 "spatial_location":정수,"geometry_validity":정수,"numeric_match":정수,
 "total_raw":정수,"total_normalized":정수,"pass":불리언,"reason":"한줄이유"}
```

---

## Evaluation Flow

```
Output File
    │
    ├── .html / .htm  →  Playwright rendering (3s wait)  →  Screenshot (PNG)
    │                                                              │
    └── .png / .jpg   ────────────────────────────────────────────┘
                                                                   │
                                                          Base64 encode
                                                                   │
                                                     Claude VLM Judge × 3
                                                                   │
                                                        Average scores
                                                                   │
                                                     Pass / Fail determination
```

---

## Output Fields

Each evaluated task produces the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `task_no` | int | Task ID |
| `level` | str | `Level1` / `Level2` / `Level3` |
| `model` | str | Model identifier |
| `file_type` | str | `html_screenshot` / `png` / `jpg` |
| `score` | float | Average normalized score (0–100) |
| `score_raw` | float | Average raw score (0–130) |
| `spatial_location` | float | Average spatial location score |
| `geometry_validity` | float | Average geometry validity score |
| `numeric_match` | float | Average numeric match score |
| `pass` | bool | `True` if score ≥ 60 |
| `reason` | str | VLM reason summary |
| `scores_3x` | str | Individual normalized scores from 3 runs |
| `scores_3x_raw` | str | Individual raw scores from 3 runs |

---

## Evaluation Script

The full evaluation pipeline is available at:
[`evaluation/run_evaluation_vlm_v3.py`](run_evaluation_vlm_v3.py)

### Requirements

```
anthropic
playwright
openpyxl
pandas
```

### Usage

```bash
# Set API key
set ANTHROPIC_API_KEY=your_key_here   # Windows
export ANTHROPIC_API_KEY=your_key_here  # Linux/Mac

# Install Playwright browser
playwright install chromium

# Run evaluation
python run_evaluation_vlm_v3.py
```

### Directory Structure Expected

```
C:\Users\A\Desktop\대학원 졸업논문\
├── GIS_Bench_문항.xlsx          # Task prompts (Excel)
├── claude_mcp\                  # Model output files
│   ├── 1.html
│   ├── 2.png
│   └── ...
├── claude_baseline\
└── gpt_5.2\
```

> **Note:** Update `BASE_DIR` in the script to match your local environment.

---

## Reliability & Validity

- **Inter-run consistency:** Each task evaluated 3 times; average used to reduce VLM variance
- **Prompt robustness:** JSON-only output enforced; fallback parsing with regex extraction
- **Rate limit handling:** 1.5s sleep between API calls
- **Token safety:** `max_tokens=1024` to prevent JSON truncation