edaplot-vl 0.0.1

Plot your data with natural language

EDAplot (VegaChat)

This repository contains a snapshot of the code used for the paper "Generating and Evaluating Declarative Charts Using Large Language Models".

Usage

Set an OpenAI API key as an env variable and install uv first. If you're unfamiliar with it refer to the environment section.

Run the interactive Streamlit prototype locally with:

uv run python -m streamlit run frontend/app.py

To use the code as a library, look into api.py.

Evaluation

Setup

Download evaluation datasets:

• NLV Corpus is included
• chart-llm should be cloned into ./dataset/:

  cd dataset
  git clone https://github.com/hyungkwonko/chart-llm.git
  cd ..
  

Benchmarks

Example for running the NLV Corpus benchmark:

uv run python -m scripts.run_benchmark nlv_corpus --dataset_dir dataset/nlv_corpus --output_path out/benchmarks

Run the interactive results report with:

uv run python -m streamlit run benchmark/reports/vega_chat_benchmark_report.py out/benchmarks

where out is the path to the directory containing the saved outputs.

Evals

Our set of custom test cases (evals) are defined as yaml files. Each eval specifies the actions to take and the checks to perform after each action.

Run the evals with:

uv run python -m scripts.run_benchmark evals --output_path out/evals

Run the interactive results report with:

uv run python -m streamlit run benchmark/reports/evals_report.py out/evals

where out is the path to the directory containing the saved outputs.

Update existing results with new checks using:

uv run python -m scripts.run_eval_checks out/evals/

Request Analyzer

Run the request analyzer benchmark with:

uv run python -m scripts.run_request_analyzer_benchmark --dataset_dir dataset/chart-llm --take_n 180 --output_path out/request_analyzer_benchmark/ chart_llm_gold

View the results with:

uv run python -m streamlit run benchmark/reports/request_analyzer_benchmark_report.py out/request_analyzer_benchmark/

LLM as a judge

Vision Judge

The vision judge uses a multimodal LLM to compare the generated image to the reference image. It can be used to compare results from different plotting libraries (e.g., matplotlib and Vega-Lite).

To run the vision judge evaluation on existing outputs use:

uv run python -m scripts.run_vision_judge example.jsonl

or use the --vision_judge flag together with scripts/run_benchmark.py

Vision Judge Benchmark

To evaluate the vision judge, we use a separate benchmark.

Run it with:

uv run python -m scripts.run_vision_judge_benchmark

View the results with:

uv run python -m streamlit run benchmark/reports/vision_judge_benchmark_report.py out/vision_judge_benchmark/

Correlation with Human Judgments

To measure the correlation between the human judgments and different metrics requires running:

1. vision_judge_human_eval.py to generate an evaluation dataset
2. human_eval_db.py to store the evaluation dataset in a Postgres database
3. vision_judge_human_eval_app.py to run the interactive evaluation environment

LIDA Self-Evaluation

LIDA's self-evaluation can be run with:

uv run python -m scripts.run_lida_self_eval example.jsonl

Configuring dev environment

1. Install uv
2. Install dependencies:

   uv sync
   

3. Enable pre-commit:

   uv run pre-commit install
   

4. Add OpenAI API key to the env variable OPENAI_API_KEY

Run tests with:

uv run pytest tests

For some tests you need to first download the Evaluation datasets.

Docker

Build the image and run the container:

docker build -f frontend.Dockerfile -t edaplot .
docker run --rm -p 8501:8501 -e OPENAI_API_KEY -t edaplot