shapash

Overview

Shapash is a Python library designed to make machine learning interpretable and comprehensible for everyone. It offers various visualizations with clear and explicit labels that are easily understood by all.

With Shapash, you can generate a Webapp that simplifies the comprehension of interactions between the model's features, and allows seamless navigation between local and global explainability. This Webapp enables Data Scientists to effortlessly understand their models and share their results with both data scientists and non-data experts.

Additionally, Shapash contributes to data science auditing by presenting valuable information about any model and data in a comprehensive report.

Shapash is suitable for Regression, Binary Classification and Multiclass problems. It is compatible with numerous models, including Catboost, Xgboost, LightGBM, Sklearn Ensemble, Linear models, and SVM. For other models, solutions to integrate Shapash are available; more details can be found here.

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Shapash App Demo

Documentation and resources

• Readthedocs:
• Video presentation for french speakers
• Medium:
  • Understand your model with Shapash - Towards AI
  • Model auditability - Towards DS
  • Group of features - Towards AI
  • Building confidence on explainability - Towards DS
  • Picking Examples to Understand Machine Learning Model
  • Enhancing Webapp Built-In Features for Comprehensive Machine Learning Model Interpretation

What's new ?

Features

• Display clear and understandable results: plots and outputs use explicit labels for each feature and its values

• Allow Data Scientists to quickly understand their models using a webapp to easily navigate between global and local explainability, and understand how the different features contribute: Live Demo Shapash-Monitor

• Summarize and export local explanation

Shapash provides concise and clear local explanations, It allows each user, enabling users of any Data background to understand a local prediction of a supervised model through a summarized and explicit explanation

• Evaluate the quality of your explainability with various metrics

• Effortlessly share and discuss results with non-Data users

• Select subsets for in-depth analysis of explainability by filtering based on explanatory and additional features, as well as correct or wrong predictions. Picking Examples to Understand Machine Learning Model

• Deploy interpretability part of your project: From model training to deployment (API or Batch Mode)

• Contribute to the auditability of your model by generating a standalone HTML report of your projects. Report Example

We believe that this report will offer valuable support for auditing models and data, leading to improved AI governance. Data Scientists can now provide anyone interested in their project with a document that captures various aspects of their work as the foundation for an audit report. This document can be easily shared among teams (internal audit, DPO, risk, compliance...).

How Shapash works

Shapash is an overlay package for libraries focused on model interpretability. It uses Shap or Lime backend to compute contributions. Shapash builds upon the various steps required to create a machine learning model, making the results more understandable.

Shapash is suitable for Regression, Binary Classification or Multiclass problem. It is compatible with numerous models: Catboost, Xgboost, LightGBM, Sklearn Ensemble, Linear models, SVM.

If your model is not in the list of compatible models, it is possible to provide Shapash with local contributions calculated with shap or another method. Here's an example of how to provide contributions to Shapash. An issue has been created to enhance this use case.

Shapash can use category-encoders object, sklearn ColumnTransformer or simply features dictionary.

• Category_encoder: OneHotEncoder, OrdinalEncoder, BaseNEncoder, BinaryEncoder, TargetEncoder
• Sklearn ColumnTransformer: OneHotEncoder, OrdinalEncoder, StandardScaler, QuantileTransformer, PowerTransformer

Installation

Shapash is intended to work with Python versions 3.9 to 3.12. Installation can be done with pip:

pip install shapash

In order to generate the Shapash Report some extra requirements are needed. You can install these using the following command :

pip install shapash[report]

If you encounter compatibility issues you may check the corresponding section in the Shapash documentation here.

Quickstart

The 4 steps to display results:

• Step 1: Declare SmartExplainer Object

  There 1 mandatory parameter in compile method: Model You can declare features dict here to specify the labels to display

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
)

• Step 2: Compile Dataset, ...

  There 1 mandatory parameter in compile method: Dataset

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
)

• Step 3: Display output

  There are several outputs and plots available. for example, you can launch the web app:

app = xpl.run_app()

Live Demo Shapash-Monitor

• Step 4: Generate the Shapash Report

  This step allows to generate a standalone html report of your project using the different splits of your dataset and also the metrics you used:

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": "sklearn.metrics.mean_squared_error"}],
)

Report Example

• Step 5: From training to deployment : SmartPredictor Object

  Shapash provides a SmartPredictor object to deploy the summary of local explanation for the operational needs. It is an object dedicated to deployment, lighter than SmartExplainer with additional consistency checks. SmartPredictor can be used with an API or in batch mode. It provides predictions, detailed or summarized local explainability using appropriate wording.

predictor = xpl.to_smartpredictor()

See the tutorial part to know how to use the SmartPredictor object

Tutorials

This github repository offers many tutorials to allow you to easily get started with Shapash.

• Launch the webapp with a concrete use case
• Jupyter Overviews - The main outputs and methods available with the SmartExplainer object
• Shapash in production: From model training to deployment (API or Batch Mode)
• Use groups of features
• Deploy local explainability in production with SmartPredictor

• Shapash Features Importance
• Contribution plot to understand how one feature affects a prediction
• Summarize, display and export local contribution using filter and local_plot method
• Contributions Comparing plot to understand why predictions on several individuals are different
• Visualize interactions between couple of variables
• Display True Values Vs Predicted Values
• Customize colors in Webapp, plots and report

• Use Category_Encoder & inverse transformation
• Use ColumnTransformers
• Use Simple Python Dictionnaries

Using postprocessing parameter in compile method

• Compute Shapley Contributions using Shap
• Use Lime to compute local explanation, Summarize-it with Shapash
• Compile faster Lime and consistency of contributions
• Use FastTreeSHAP or add contributions from another backend
• Use Class Shapash Backend

• Building confidence on explainability methods using Stability, Consistency and Compacity metrics

• Generate a standalone HTML report of your project with generate_report

• Add features outside of the model for more exploration options

Contributors

Awards