Visualizing openML flows

This repository provides an initial attempt at visualizing flows. Generally, this should work in three steps:

1. Retrieve the pipeline/model from the openML flow.
2. Convert the model to onnx format.
3. Load the onnx model through netron.

The current trial version is restricted to visualizing scikit-learn flows.

Usage

1. Setting up your environment

   cd .
   pip install -r requirements.txt
   

2. implementation.csv provides an overview of all distinct configurations found among openML flows. Run retrieve_sklearn_version.py in order to generate a docker for every distinct sklearn configuration found. Dockerfiles are stored in ./dockers (59 distinct configurations found).
3. Running a docker will call sk_onnx.py or sk_onnx_old.py, depending on the sklearn version used. This script will convert all flows that are built using that specific sklearn version to onnx files. Files will be stored in ./onnx_models.
4. Once you have the onnx file stored, you can visualize it using netron. A function to do so is provided in sk_onnx.visualize_onnx.

Keep in mind that there are currently multiple bugs in the code that need fixing. There are also multiple to-do's (such as integrating netron in the openML website). Find elaborate documentation on limitations and future work in Documentation.pdf.

App

A small Flask app is provided to show how netron can be used to visualize onnx models. This can be done by either loading a stored file or directly via url. Running the Flask app will call sk_onnx_test.py which allows you to visualize flow 19433:

cd .
flask run

Contact

Feel free to reach out to me ([email protected]) in case of questions!