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HeaderGen

HeaderGen is a tool-based approach to enhance the comprehension and navigation of undocumented Python based Jupyter notebooks by automatically creating a narrative structure in the notebook.

Data scientists build an ML-based solution notebook by first preparing the data, then extracting key features, and then creating and training the model. HeaderGen leverages the implicit narrative structure of an ML notebook to add structural headers as annotations to the notebook.

Preview

Install HeaderGen

pip install headergen

Features

  • Automated Markdown Header Insertion: Through a taxonomy for machine-learning operations, HeaderGen annotates code cells with relevant markdown headers.

  • Function Call Taxonomy: Methodically classifies function calls based on a machine-learning operations taxonomy.

  • Advanced Call Graph Analysis: Enhances PyCG framework with flow-sensitivity and external library return-type resolution.

  • Precision in External Libraries: capability to accurately resolve function return types from external libraries using typestubs.

  • Syntax Pattern Matching: Employs type data for pattern matching.

CLI Usage

generate Command:

Generate the HeaderGen annotated notebook in the current directory. Note that the caches will be created the first time HeaderGen is run.

headergen generate -i /path/to/input.ipynb

Generate a JSON metadata file that includes various analysis information, use the --json_output or -j flag.

headergen generate -i /path/to/input.ipynb -o /path/to/output/ -j

types Command:

Run type inference on the file and fetch type information.

headergen types -i /path/to/input.ipynb

Generate a JSON file with type information, use the --json_output or -j flag.

headergen types -i /path/to/input.ipynb -o /path/to/output/ -j

server Command:

Starting the server is straightforward:

headergen server

This will start the Uvicorn server listening on host 0.0.0.0 and port 54068.

get_analysis_notebook Endpoint:

This endpoint returns the analysis of the specified notebook or python script as a JSON response containing analysis data like cell_callsites and block_mapping.

Example using curl:

curl "http://0.0.0.0:54068/get_analysis_notebook?file_path=/absolute/path/to/your/file.ipynb"

get_types Endpoint:

This endpoint returns type information of the specified notebook or python script as a JSON response.

Example using curl:

curl "http://0.0.0.0:54068/get_types?file_path=/absolute/path/to/your/file.ipynb"

generate_annotated_notebook Endpoint:

This endpoint returns the annotated notebook based on the analysis. The response will be a file download.

Example using curl:

curl "http://0.0.0.0:54068/generate_annotated_notebook?file_path=/absolute/path/to/your/file.ipynb" --output annotated_file.ipynb

Folder Structure

  • callsites-jupyternb-micro-benchmark: Micro benchmark
  • callsites-jupyternb-real-world-benchmark: Real-world benchmark
  • evaluation: Contains manual header annotation and user study results
  • framework_models: Function calls to ML Taxonomy mapping
  • typestub-database: Type-stbs for ML libraries
  • headergen: Source code of HeaderGen
  • pycg_extended: Source code of extended PyCG
  • headergen-extension: Jupyter notebook plugin for HG
  • headergen_output: Folder where the generated notebooks from the docker container are stored

1. Build container

  • Get source files

    git clone --recursive
git submodule update --init --recursive
git pull --recurse-submodules

  • Linux

    docker build -t headergen .
docker run -v {$PWD}/headergen_output:/headergen_output -it headergen bash

  • Windows

    docker build -t headergen .
docker run -v "%cd%"/headergen_output:/headergen_output -it headergen bash

2. Run HeaderGen benchmarks from inside contatiner

Output generated from the following commands, such as annotated notebooks, reports, callsites, headers, etc, are stored in the local folder headergen_output after the following commands are done executing.

  • Micro Benchmark (generates a csv file with results)

    make ROOT_PATH=/app/HeaderGen microbench

  • Real-world Benchmark (generates annotated notebooks and csv file that reproduce table 2)

    make ROOT_PATH=/app/HeaderGen realworldbench

  • Both Benchmarks

    make ROOT_PATH=/app/HeaderGen all

  • Clean generated output

    make clean

Building from Source

  • Get source files

    git clone --recursive
git submodule update --init --recursive
git pull --recurse-submodules

  • Clear cache if exists

    rm framework_models/models_cache.pickle
rm pycg_extended/machinery/pytd_cache.pickle

  • Setup venv and dependencies with setup.sh script

    ./setup.sh -i

  • Micro Benchmark (generates a csv file with results)

    make ROOT_PATH=<path to repo root> microbench

  • Real-world Benchmark (generates annotated notebooks and csv file that reproduce table 2)

    make ROOT_PATH=<path to repo root> realworldbench

  • Both Benchmarks

    make ROOT_PATH=<path to repo root> all

  • Clean generated output

    make clean

This repo contains code for the paper "Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis" published at the SANER Conference 2023.