Welcome to Shapash’s documentation !

Shapash is a Python library designed to make machine learning interpretable and accessible to everyone. It offers various visualization types with clear and explicit labels that are easy to understand. This enables Data Scientists to better comprehend their models and share their findings, while end users can grasp the decisions made by a model through a summary of the most influential factors. Shapash was developed by MAIF Data Scientists.

Company	GitHub Account	Website
	MAIF GitHub	MAIF

The objectives of Shapash:

• Provide clear and understandable results: Plots and outputs use explicit labels for each feature and its modalities:

• Enable Data Scientists to quickly comprehend their models using a web app for seamless navigation between global and local explainability, and to understand how different features contribute: Live Demo shapash-monitor

• Summarize and export local explanations: Shapash offers concise and transparent local explanations, allowing users of any data background to understand a local prediction of a supervised model through a simplified and straightforward explanation.

summary_df.head()

pred	proba	feature_1	value_1	contribution_1	feature_2	value_2	contribution_2	feature_3	value_3	contribution_3	feature_4	value_4	contribution_4	feature_5	value_5	contribution_5	feature_6	value_6	contribution_6	feature_7	value_7	contribution_7
Moderately Expensive	0.9949	Ground living area square feet	1792	0.3093	Interior finish of the garage?	Rough Finished	0.2755	Size of garage in square feet	564	0.2077	Full bathrooms above grade	2	0.1827	Physical locations within Ames city limits	College Creek	0.1709	Overall material and finish of the house	7	0.1640	Height of the basement	Good (90-99 inches)	0.1396
Moderately Expensive	0.8769	Second floor square feet	720	0.1833	Full bathrooms above grade	2	0.1551	Ground living area square feet	2192	0.1519	Remodel date	1997	0.1431	Type 1 finished square feet	378	0.1424	First Floor square feet	1052	0.1278	Half baths above grade	1	0.1277
Cheap	0.9973	Ground living area square feet	900	0.8189	Size of garage in square feet	280	0.5616	Total square feet of basement area	882	0.4091	Remodel date	1967	0.3490	Full bathrooms above grade	1	0.3248	Overall material and finish of the house	5	0.3180	First Floor square feet	900	0.2478
Cheap	0.9987	Ground living area square feet	630	0.8164	Size of garage in square feet	0	0.5877	Total square feet of basement area	630	0.4312	Remodel date	1970	0.3557	Overall material and finish of the house	4	0.3175	Full bathrooms above grade	1	0.3130	General zoning classification	Residential Medium Density	0.1784
Cheap	0.8524	Ground living area square feet	1188	0.9421	Remodel date	1959	0.4234	Overall material and finish of the house	5	0.3785	Full bathrooms above grade	1	0.3738	Number of fireplaces	0	0.1687	Rating of basement finished area	Average Rec Room	0.1302	Wood deck area in square feet	0	0.1225

• Establish a foundation for audit reports by freezing various aspects of a data science project.

• To discuss results: Shapash allows Data Scientists to easily share and discuss their results with non-Data users

Shapash features:

• Compatibility with Shap & Lime

• Uses Shap backend to display results in a few lines of code

• Encoders objects and features dictionaries used for clear results

• Compatibility with category_encoders & Sklearn ColumnTransformer

• Global and local explainability visualizations

• Web app for easy navigation from global to local

• Subset selection for in-depth explainability analysis by filtering explanatory and additional features, as well as correct or wrong predictions

• Local explanation summarization

• Offers several parameters in order to sum up in the most suitable way for your use case

• Exports your local summaries to Pandas DataFrames

• Applicable for Regression, Binary Classification or Multiclass

• Compatible with most of sklearn, lightgbm, catboost, xgboost models

• Suitable for exploration and also deployment (through an API or in Batch mode) for operational use

• Freezes various aspects of a data science project as a basis for audit reports

Shapash is easy to install and use, offering a SmartExplainer class to understand your model and summarize explanations with simple syntax.

High adaptability: Although only a few arguments are needed to display results, the more effort you put into cleaning and documenting the data, the clearer the results will be for the end user.

• Overview
  • Installation
  • How does shapash work?
  • User Manual
  • Getting Started : 3 minutes to Shapash
  • From data mining to deployment
• Installation instructions
  • Installing
  • Jupyter
    • Jupyter Notebook
    • Jupyter Lab 1 or 2
    • Jupyter Lab 3
  • Check your installation
  • Compatibility issues
• Tutorials
  • overview
    • Start the Shapash Web App on a sample dataset
    • Shapash in Jupyter - Overview
    • Shapash model in production - Overview
  • common
    • Groups of features
    • Use groups of features in production with SmartPredictor object
    • Shapash with custom colors
  • explainability_quality
    • Building confidence on explainability methods
  • explainer_and_backend
    • Compute Contributions with Shap - Summarize Them With Shapash
    • Using Shapash with Lime explainer - Titanic
    • Compile faster Lime and consistency of contributions
    • Using Shapash with FastTreeSHAP explainer
    • Tutorial Shapash Backend
  • generate_report
    • Shapash Report
  • generate_webapp
    • Add features outside of the model for more exploration options
  • plots_and_charts
    • How to use filter and local_plot methods
    • Contributions plot
    • Features importance
    • Contributions comparing plot
    • Interactions plot
    • scatter_plot_prediction
  • postprocess
    • Postprocessing parameter in compile method
    • Compiling without postprocessing parameter
    • Compiling with postprocessing parameter
  • predictor_to_production
    • From model training to deployment - an introduction to the SmartPredictor object
  • use_encoders
    • Category_encoder tutorial
    • ColumnTransformer tutorial
    • Dictionnary Encoding tutorial
• Documentation
  • SmartExplainer Object
    • SmartExplainer declaration and data preparation
    • The Plot Methods
  • SmartPredictor Object
  • SmartPredictor Loader Function
  • Data Loader

License is Apache Software License 2.0