Explain the Machine Learning Model in SQLFlow

    This design doc introduces how to support the in SQLFlow with SHAP as the backend and display the visualization image to the user.

    Train SQL:

    1. SELECT * FROM train_table
    2. WITH
    3.   plots = force

    where:

    • train_table is the table of training data.
    • my_model is the trained model.
    • is the explain type.
    • Enhance the SQLFlow parser to support the Explain keyword.
    • Implement the codegen_shap.go to generate a SHAP Python program. The Python program would be executed by SQLFlow Executor module and prints the visualization image in HTML format to stdout. The stdout will be captured by the Go program using .
    • For each Explain SQL request from the SQLFlow magic command, the SQLFlow server would response the HTML text as a single message, and then display the visualization image on Jupyter Notebook
    • For the current milestone, SQLFlow only supports DeepExplainer for the Keras Model, and TreeExplainer for XGBoost, more abundant Explainer and Model type will be supported in the future.