docs/chartbench_e_rebuilt_README.md

# ChartBench-E (Rebuilt, Insight-Oriented)

This implementation rebuilds ChartBench-E and extends it into a more diagnostic benchmark for VLM chart reading.

## What it provides

- 20 data patterns grouped into 6 behavior families.
- Multiple chart morphologies: `line`, `bar_v`, `bar_h`, `stacked`.
- Annotation modes: `labeled`, `partial`, `unlabeled`.
- Multi-factor difficulty beyond point count:
  - visual perturbations (noise, blur, JPEG artifacts, partial occlusion)
  - semantic traps (truncated axis, log-scale reading, legend confusion)
  - event-level stressors (outlier spikes, near-tie values, regime shifts)
- Diagnostic metadata tags for capability slicing:
  - `event_tags`
  - `perceptual_tags`
  - `semantic_risk_tags`
- Reproducible generation with random seed.
- Evaluation metrics:
  - SPR (Success Parsing Rate)
  - OA (Overall Accuracy)
  - NRA (Numerical Reading Accuracy)
  - ARE (Average Relative Error)

## Install

```bash
cd chartbench_e
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

## Build benchmark-focused dataset

```bash
PYTHONPATH=. python scripts/build_chartbench_e.py \
  --out output/chartbench_e_benchmark_plus \
  --total-samples 20000 \
  --seed 42 \
  --task-mode benchmark \
  --challenge-level standard
```

Challenge levels:

- `standard`: light perturbation, mostly clean charts.
- `advanced`: balanced stress setting.
- `extreme`: heavy perturbation and denser hard cases.

## Make fixed evaluation subset

```bash
PYTHONPATH=. python scripts/make_eval_subset.py \
  --index output/chartbench_e_benchmark_plus/metadata/index.jsonl \
  --out output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --per-task 400
```

Risk-focused subset example:

```bash
PYTHONPATH=. python scripts/make_eval_subset.py \
  --index output/chartbench_e_benchmark_plus/metadata/index.jsonl \
  --out output/chartbench_e_benchmark_plus/metadata/eval_compositional.jsonl \
  --per-task 200 \
  --require-risk-tag compositional_reasoning
```

## Evaluate predictions

Prediction format (`jsonl`):

```json
{"sample_id": "cb_0000001", "values": [[...], [...]]}
```

Run:

```bash
PYTHONPATH=. python scripts/evaluate_predictions.py \
  --truth output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --pred your_predictions.jsonl \
  --out output/report.json \
  --tolerance 0.05
```

## Multi-model benchmark (new)

### Supported model registry

See `scripts/model_registry.json`.

Default enabled models:
- `gpt-5.2`
- `gpt-5.2-high`
- `gpt-5`
- `gpt-5-mini-high`
- `qwen3-vl-30b-a3b-thinking`
- `qwen2.5-vl-72b`
- `glm-4.6v`

Explicitly excluded in this round:
- `deepseek-vl2`
- `paddleocr-vl-1.5`

### Required env vars

OpenAI provider:
- `OPENAI_API_KEY`
- `OPENAI_BASE_URL` (optional)

SiliconFlow provider:
- `SILICONFLOW_API_KEY`
- `SILICONFLOW_BASE_URL` (optional, default `https://api.siliconflow.cn/v1`)

Fallback rule for SiliconFlow models:
- if `SILICONFLOW_*` is missing, script falls back to `OPENAI_*`

### Prompt behavior

- The benchmark runner defaults to `--prompt-mode type_specific`.
- In `type_specific` mode, prompts are loaded from `prompts/chart_types/<chart_type>.txt`.
- Each prompt instantiation now includes a shape-aware JSON example through `{json_example}`, in addition to the strict JSON output contract.
- `prompts/chart_reading_prompt.txt` remains available for `--prompt-mode unified`.

### Run benchmark

Single model + 3 repeats + evaluation:

```bash
PYTHONPATH=. python scripts/run_vlm_benchmark.py \
  --truth-index output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --dataset-root output/chartbench_e_benchmark_plus \
  --models gpt-5.2 \
  --repeats 3 \
  --max-workers 2 \
  --max-retries 2 \
  --timeout-seconds 120 \
  --run-eval \
  --tolerance 0.05
```

All enabled models:

```bash
PYTHONPATH=. python scripts/run_vlm_benchmark.py \
  --truth-index output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --dataset-root output/chartbench_e_benchmark_plus \
  --models all \
  --repeats 3 \
  --max-workers 4 \
  --max-retries 2 \
  --timeout-seconds 120 \
  --run-eval
```

Offline smoke test (no API call, sanity-only):

```bash
PYTHONPATH=. python scripts/run_vlm_benchmark.py \
  --truth-index output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --dataset-root output/chartbench_e_benchmark_plus \
  --models gpt-5.2 \
  --repeats 3 \
  --max-samples 20 \
  --run-eval \
  --mock-from-truth
```

### Prompt A/B pilot

This pilot samples a 54-chart subset (18 chart types x 3 difficulty tiers x 1 sample), runs baseline vs. updated chart-type prompts, and writes a comparison report:

```bash
PYTHONPATH=. python scripts/run_prompt_ab_pilot.py \
  --mock-from-truth
```

For real model runs, set provider credentials first and rerun without `--mock-from-truth`.

### Output structure

`output/bench_runs/{run_id}/`
- `run_config.json`
- `truth_subset.jsonl`
- `run_summary.json`
- `leaderboard.csv`
- `models/{model_key}/repeat_0x/pred.jsonl`
- `models/{model_key}/repeat_0x/report.json`
- `models/{model_key}/repeat_0x/failures.jsonl`
- `models/{model_key}/repeat_0x/raw_responses.jsonl`
- `models/{model_key}/repeat_0x/slice_report.json`
- `models/{model_key}/model_stability.json`
- `models/{model_key}/sample_stability.jsonl`
- `models/{model_key}/slice_summary.json`

Slicing coverage:
- `difficulty_tier` (L1-L4)
- `theme_domain`
- `axis_scale`
- `y_axis_truncated`

## Output structure (dataset)

- `images/*.png`: generated chart images.
- `metadata/index.jsonl`: full metadata with ground truth and challenge tags.
- `metadata/summary.json`: split and distribution summary.
- `metadata/eval_subset.jsonl`: fixed benchmark subset.