Welcome to Shapash’s documentation !

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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

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MAIF GitHub

MAIF

The objectives of Shapash:

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

_images/shapash-contribution_plot-example.png
  • 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

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  • 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.

_images/shapash-local_plot-example.png
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.

_images/shapash-report-demo.gif
  • 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.

License is Apache Software License 2.0