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Culinary Computation: Data Analysis for Novel Recipe Generation

Project Condition

Currently this project is in its early stages. The end goal is to provide tools to procedurally generate recipes or changes to recipes using statistical models and machine learning. So far working components include:

  • Scrapers to collect recipe data from the websites New York Times Cooking and AllRecipes
  • Command line tool to select recipes from the database given some predicates
  • Tools to build some data structures from the selected data like ingredient co-occurrence graphs and ingredient vector representations of recipes
  • Integration with tools that embed the ingredients into 3 dimensional space by applying a restrictive algebraic multigrid method known as the modularity functional to the co-occurrence graph

Usage

Unfortunately the project isn't well documented yet and would be difficult to clone and experiment with yourself. There are absolute file paths that are hard coded into the Rust code and you would need my database to access any of the data.

If you are interested in trying to run what I have so far, it should still be possible. To generate your own database you could run the scrapers, it takes about a day to scrape NYTC. If you would like a pg_dump of my postgres I can also provide that on request.

Dependencies not included in this repository include, rustup, Cargo, Docker and my fork of mtlynch's ingredient-phrase-tagger. The training file for the conditional random field is included in that repository so it is ready to run. The accuracy isn't great but it is good enough to get some interesting clustering results.

To build the project:

Clone the repo and initialize the submodules

git clone [email protected]:aujxn/research
git clone [email protected]:aujxn/ingredient-phrase-tagger
cd research
git submodule init
git submodule update

Build linalgcpp

cd linalgcpp
mkdir build && cd build && cmake .. && make
cd ..

Build graph-embed

cd graph-embed
mkdir build && cd build && cmake .. && make
cd ..

Create a python virtual environment in the project root and get the dependencies for graph-embed

python3 -m venv pyvenv
source pyvenv/bin/activate
pip install --upgrade pip
pip install -r graph-embed/requirements.txt

Build the recipe_analysis tools

cd recipe_analysis
cargo build --release

To run the program from the recipe_analysis module

cargo run --bin main -- --help

will provide what command line options are available. In the future I will remove the absolute path names and change the database connections to environment variables.