Analyze the Machine Learning Model in SQLFlow


Although the machine learning model is widely used in many fields, it remains mostly a black box. SHAP is widely used by data scientists to explain the output of any machine learning model.

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

User Interface

Users usually use a TO TRAIN SQL to train a model and then analyze the model using an TO EXPLAIN SQL, the simple pipeline like:

Train SQL:

SELECT * FROM train_table
TO TRAIN xgboost.Estimator
    train.objective = "reg:linear"
INTO my_model;

Analyze SQL:

SELECT * FROM train_table
TO EXPLAIN my_model
  plots = force 
USING TreeExplainer


  • train_table is the table of training data.
  • my_model is the trained model.
  • force and summary is the visualized method.
  • TreeExplainer is the explain type.

The Analyze SQL would display the visualization image on Jupyter like:

Implement Details

  • Enhance the SQLFlow parser to support the Analyze 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 CombinedOutput.
  • For each Analyze 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 Kerase Model, and TreeExaplainer for the XGboost, more abundant Explainer and Model type will be supported in the future.
  • We don’t use the more relevant keyword Explain just because Explain is used throughout various SQL databases.