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tests pypi downloads pyversion license doc

πŸŽ‰ What's new ?

Version New Feature Description Tutorial
2.3.x Additional dataset columns
New demo
In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options
2.3.x Identity card
New demo
In Webapp: New identity card to summarize the information of the selected sample
2.2.x Picking samples
New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values"
2.2.x Dataset Filter
New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments
2.0.x Refactoring Shapash
Refactoring attributes of compile methods and init. Refactoring implementation for new backends
1.7.x Variabilize Colors
Giving possibility to have your own colour palette for outputs adapted to your design
1.6.x Explainability Quality Metrics
article
To help increase confidence in explainability methods, you can evaluate the relevance of your explainability using 3 metrics: Stability, Consistency and Compacity
1.5.x ACV Backend
A new way of estimating Shapley values using ACV. More info about ACV here.
1.4.x Groups of features
demo
You can now regroup features that share common properties together.
This option can be useful if your model has a lot of features.
1.3.x Shapash Report
demo
A standalone HTML report that constitutes a basis of an audit document.

πŸ” Overview

Shapash is a Python library which aims to make machine learning interpretable and understandable by everyone. It provides several types of visualization that display explicit labels that everyone can understand.

Data Scientists can understand their models easily and share their results. End users can understand the decision proposed by a model using a summary of the most influential criteria.

Shapash also contributes to data science auditing by displaying usefull information about any model and data in a unique report.

🀝 Contributors

πŸ† Awards

πŸ”₯ 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 by 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 the local explanation

Shapash proposes a short and clear local explanation. It allows each user, whatever their Data background, to understand a local prediction of a supervised model thanks to a summarized and explicit explanation

  • Evaluate the quality of your explainability using different metrics

  • Easily share and discuss results with non-Data users

  • Select subsets for further analysis of explainability by filtering on explanatory and additional features, 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 hope that this report will bring a valuable support to auditing models and data related to a better AI governance. Data Scientists can now deliver to anyone who is interested in their project a document that freezes different aspects of their work as a basis of an audit report. This document can be easily shared across teams (internal audit, DPO, risk, compliance...).

βš™οΈ How Shapash works

Shapash is an overlay package for libraries dedicated to the interpretability of models. It uses Shap or Lime backend to compute contributions. Shapash builds on the different steps necessary to build a machine learning model to make the results understandable

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

πŸ›  Installation

Shapash is intended to work with Python versions 3.8 to 3.10. 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.

πŸ• 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=X_additional, # 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=[{β€˜name’: β€˜MSE’, β€˜path’: β€˜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

πŸ“– Tutorials

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

Overview
Charts and plots
Different ways to use Encoders and Dictionaries
Displaying data with postprocessing

Using postprocessing parameter in compile method

Using different backends
Evaluating the quality of your explainability
Generate a report of your project
Analysing your model via Shapash WebApp