🍁 Shapash

pip install shapash

pip install shapash[report]

from shapash import SmartExplainer
xpl = SmartExplainer(
  model=regressor,
  features_dict=house_dict,  # Optional parameter
  preprocessing=encoder, # Optional: compile step can use inverse_transform method
  postprocessing=postprocess, # Optional: see tutorial postprocessing  
)

xpl.compile(
    x=xtest,    
    y_pred=y_pred, # Optional: for your own prediction (by default: model.predict)
    y_target=yTest, # Optional: allows to display True Values vs Predicted Values
    additional_data=xadditional, # Optional: additional dataset of features for Webapp
    additional_features_dict=features_dict_additional, # Optional: dict additional data    
)

app = xpl.run_app()

xpl.generate_report(
    output_file='path/to/output/report.html',
    project_info_file='path/to/project_info.yml',
    x_train=xtrain,
    y_train=ytrain,
    y_test=ytest,
    title_story="House prices report",
    title_description="""This document is a data science report of the kaggle house prices tutorial project.
        It was generated using the Shapash library.""",
    metrics=[{'name': 'MSE', 'path': '🔟 scikit-learn.metrics.mean_squared_error'}]
)

predictor = xpl.to_smartpredictor()

...
- with open(output_file, "w") as file:
+ with open(output_file, "w", encoding='utf-8') as file: