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+/shapash-2.3.0.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-shapash
+Version:	2.3.0
+Release:	1
+Summary:	Shapash is a Python library which aims to make machine learning interpretable and understandable by everyone.
+License:	Apache Software License 2.0
+URL:		https://github.com/MAIF/shapash
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d0/32/7258852e772286c39e9f610833f869a5a1acafe457fffff834a9048dea12/shapash-2.3.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-plotly
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-shap
+Requires:	python3-dash
+Requires:	python3-dash-bootstrap-components
+Requires:	python3-dash-core-components
+Requires:	python3-dash-daq
+Requires:	python3-dash-html-components
+Requires:	python3-dash-renderer
+Requires:	python3-dash-table
+Requires:	python3-nbformat
+Requires:	python3-numba
+Requires:	python3-scikit-learn
+Requires:	python3-category-encoders
+Requires:	python3-scipy
+Requires:	python3-acv-exp
+Requires:	python3-catboost
+Requires:	python3-lightgbm
+Requires:	python3-lime
+Requires:	python3-nbconvert
+Requires:	python3-papermill
+Requires:	python3-jupyter-client
+Requires:	python3-seaborn
+Requires:	python3-notebook
+Requires:	python3-Jinja2
+Requires:	python3-phik
+Requires:	python3-xgboost
+
+%description
+<p align="center">
+<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-resize.png" width="300" title="shapash-logo">
+</p>
+
+
+<p align="center">
+  <!-- Tests -->
+  <a href="https://github.com/MAIF/shapash/workflows/Build%20%26%20Test/badge.svg">
+    <img src="https://github.com/MAIF/shapash/workflows/Build%20%26%20Test/badge.svg" alt="tests">
+  </a>
+  <!-- PyPi -->
+  <a href="https://img.shields.io/pypi/v/shapash">
+    <img src="https://img.shields.io/pypi/v/shapash" alt="pypi">
+  </a>
+  <!-- Downloads -->
+  <a href="https://static.pepy.tech/personalized-badge/shapash?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads">
+    <img src="https://static.pepy.tech/personalized-badge/shapash?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads" alt="downloads">
+  </a>
+  <!-- Python Version -->
+  <a href="https://img.shields.io/pypi/pyversions/shapash">
+    <img src="https://img.shields.io/pypi/pyversions/shapash" alt="pyversion">
+  </a>
+  <!-- License -->
+  <a href="https://img.shields.io/pypi/l/shapash">
+    <img src="https://img.shields.io/pypi/l/shapash" alt="license">
+  </a>
+  <!-- Doc -->
+  <a href="https://shapash.readthedocs.io/en/latest/">
+    <img src="https://readthedocs.org/projects/shapash/badge/?version=latest" alt="doc">
+  </a>
+</p>
+
+## 🎉 What's new ?
+
+
+| Version       | New Feature                                                                           | Description                                                                                                                            | Tutorial |
+|:-------------:|:-------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
+| 2.3.x         |  Additional dataset columns <br> (Demo coming soon)                                                                | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options            |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/add_column_icon.png" width="50" title="add_column">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.3.x         |  Identity card <br> (Demo coming soon)                                                                  | In Webapp: New identity card to summarize the information of the selected sample                  |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/identity_card.png" width="50" title="identity">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.2.x         |  Picking samples <br> [New demo](https://shapash-demo.ossbymaif.fr/)                                                                | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values"            |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/picking.png" width="50" title="picking">](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb)
+| 2.2.x         |  Dataset Filter <br> [New demo](https://shapash-demo.ossbymaif.fr/)                                                                  | New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/webapp.png" width="50" title="webapp">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.0.x         |  Refactoring Shapash <br>                                                                   | Refactoring attributes of compile methods and init. Refactoring implementation for new backends                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/modular.png" width="50" title="modular">](https://github.com/MAIF/shapash/blob/master/tutorial/backend/tuto-backend-01.ipynb)
+| 1.7.x         |  Variabilize Colors <br>                                                                   | Giving possibility to have your own colour palette for outputs adapted to your design                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/variabilize-colors.png" width="50" title="variabilize-colors">](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common02-colors.ipynb)
+| 1.6.x         |  Explainability Quality Metrics <br> [article](https://towardsdatascience.com/building-confidence-on-explainability-methods-66b9ee575514)                                                                   | To help increase confidence in explainability methods, you can evaluate the relevance of your explainability using 3 metrics: **Stability**, **Consistency** and **Compacity**                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/quality-metrics.png" width="50" title="quality-metrics">](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) 
+| 1.5.x         |  ACV Backend <br>                                                                     | A new way of estimating Shapley values using ACV. [More info about ACV here](https://towardsdatascience.com/the-right-way-to-compute-your-shapley-values-cfea30509254).                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/wheel.png" width="50" title="wheel-acv-backend">](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb)    |
+| 1.4.x         |  Groups of features <br> [demo](https://shapash-demo2.ossbymaif.fr/)                  | You can now regroup features that share common properties together. <br>This option can be useful if your model has a lot of features. |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/groups_features.gif" width="120" title="groups-features">](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb)    | 
+| 1.3.x         |  Shapash Report <br> [demo](https://shapash.readthedocs.io/en/latest/report.html)     | A standalone HTML report that constitutes a basis of an audit document.                                                                |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/report-icon.png" width="50" title="shapash-report">](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb)    | 
+
+
+## 🔍 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. 
+
+- Readthedocs: [![documentation badge](https://readthedocs.org/projects/shapash/badge/?version=latest)](https://shapash.readthedocs.io/en/latest/)
+- [Presentation video for french speakers](https://www.youtube.com/watch?v=r1R_A9B9apk)
+- Medium:
+  - [Understand your model with Shapash - Towards AI](https://pub.towardsai.net/shapash-making-ml-models-understandable-by-everyone-8f96ad469eb3) 
+  - [Model auditability - Towards DS](https://towardsdatascience.com/shapash-1-3-2-announcing-new-features-for-more-auditable-ai-64a6db71c919)
+  - [Group of features - Towards AI](https://pub.towardsai.net/machine-learning-6011d5d9a444)
+  - [Building confidence on explainability - Towards DS](https://towardsdatascience.com/building-confidence-on-explainability-methods-66b9ee575514)
+  - [Picking Examples to Understand Machine Learning Model](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html)
+
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash_global.gif" width="800">
+</p>
+
+## 🤝 Contributors
+
+<div align="left">
+  <div style="display: flex; align-items: flex-start;">
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_maif.png" width="18%"/>
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_quantmetry.png" width="18%" />
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_societe_generale.png" width="18%" /> 
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_groupe_vyv.png" width="18%" /> 
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_SixfoisSept.png" width="18%" /> 
+  </div>
+</div>
+
+
+## 🏆 Awards
+
+<a href="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/awards-argus-or.png">
+  <img align="left" src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/awards-argus-or.png" width="180" />
+</a>
+
+<a href="https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html">
+  <img src="https://www.kdnuggets.com/images/tkb-2104-g.png?raw=true" width="65" />
+</a>  
+
+
+## 🔥 Features
+
+- Display clear and understandable results: plots and outputs use **explicit labels** for each feature and its values
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-02.png?raw=true" width="28%"/>
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-06.png?raw=true" width="28%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-04.png?raw=true" width="28%" /> 
+</p>
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-01.png?raw=true" width="28%" />
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-resize.png?raw=true" width="18%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-13.png?raw=true" width="28%" /> 
+</p>
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-12.png?raw=true" width="33%" />
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-03.png?raw=true" width="28%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-10.png?raw=true" width="25%" /> 
+</p>
+
+
+- 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](https://shapash-demo.ossbymaif.fr/)
+
+- **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](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html)
+
+- 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](https://shapash.readthedocs.io/en/latest/report.html) 
+>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...).
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-report-demo.gif" width="800">
+</p>
+
+## ⚙️ 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
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-diagram.png" width="700" title="diagram">
+</p>
+
+**Shapash** works for Regression, Binary Classification or Multiclass problem. <br />
+It is compatible with many models: *Catboost*, *Xgboost*, *LightGBM*, *Sklearn Ensemble*, *Linear models*, *SVM*. <br />
+Shapash can use category-encoders object, sklearn ColumnTransformer or simply features dictionary. <br />
+- Category_encoder: *OneHotEncoder*, *OrdinalEncoder*, *BaseNEncoder*, *BinaryEncoder*, *TargetEncoder*
+- Sklearn ColumnTransformer: *OneHotEncoder*, *OrdinalEncoder*, *StandardScaler*, *QuantileTransformer*, *PowerTransformer*
+
+## 🛠 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](https://shapash.readthedocs.io/en/latest/installation-instructions/index.html).
+
+## 🕐 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](https://shapash-demo.ossbymaif.fr/)
+
+- 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](https://shapash.readthedocs.io/en/latest/report.html)
+
+- 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.
+
+
+<details><summary><b>Overview</b> </summary>
+
+- [Launch the webapp with a concrete use case](tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+- [Jupyter Overviews - The main outputs and methods available with the SmartExplainer object](tutorial/tutorial02-Shapash-overview-in-Jupyter.ipynb)
+- [Shapash in production: From model training to deployment (API or Batch Mode)](tutorial/tutorial03-Shapash-overview-model-in-production.ipynb)
+- [Use groups of features](tutorial/common/tuto-common01-groups_of_features.ipynb)
+- [Deploy local explainability in production with SmartPredictor](tutorial/predictor/tuto-smartpredictor-introduction-to-SmartPredictor.ipynb)
+
+</details>
+
+<details><summary><b>Charts and plots</b> </summary>
+
+- [**Shapash** Features Importance](tutorial/plot/tuto-plot03-features-importance.ipynb)
+- [Contribution plot to understand how one feature affects a prediction](tutorial/plot/tuto-plot02-contribution_plot.ipynb)
+- [Summarize, display and export local contribution using filter and local_plot method](tutorial/plot/tuto-plot01-local_plot-and-to_pandas.ipynb)
+- [Contributions Comparing plot to understand why predictions on several individuals are different](tutorial/plot/tuto-plot04-compare_plot.ipynb)
+- [Visualize interactions between couple of variables](tutorial/plot/tuto-plot05-interactions-plot.ipynb)
+- [Customize colors in Webapp, plots and report](tutorial/common/tuto-common02-colors.ipynb)
+
+</details>
+
+<details><summary><b>Different ways to use Encoders and Dictionaries</b> </summary>
+
+- [Use Category_Encoder & inverse transformation](tutorial/encoder/tuto-encoder01-using-category_encoder.ipynb)
+- [Use ColumnTransformers](tutorial/encoder/tuto-encoder02-using-columntransformer.ipynb)
+- [Use Simple Python Dictionnaries](tutorial/encoder/tuto-encoder03-using-dict.ipynb)
+
+</details>
+
+<details><summary><b>Displaying data with postprocessing</b> </summary>
+
+[Using postprocessing parameter in compile method](tutorial/postprocess/tuto-postprocess01.ipynb)
+
+</details>
+
+<details><summary><b>Using different backends</b> </summary>
+
+- [Compute Shapley Contributions using **Shap**](tutorial/explainer/tuto-expl01-Shapash-Viz-using-Shap-contributions.ipynb)
+- [Use **Lime** to compute local explanation, Summarize-it with **Shapash**](tutorial/explainer/tuto-expl02-Shapash-Viz-using-Lime-contributions.ipynb)
+- [Use **ACV backend** to compute Active Shapley Values and SDP global importance](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb)
+- [Compile faster Lime and consistency of contributions](tutorial/explainer/tuto-expl04-Shapash-compute-Lime-faster.ipynb)
+
+</details>
+
+<details><summary><b>Evaluating the quality of your explainability</b> </summary>
+
+- [Building confidence on explainability methods using **Stability**, **Consistency** and **Compacity** metrics](tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb)
+
+</details>
+
+<details><summary><b>Generate a report of your project</b> </summary>
+
+- [Generate a standalone HTML report of your project with generate_report](tutorial/report/tuto-shapash-report01.ipynb)
+
+</details>
+
+
+
+
+%package -n python3-shapash
+Summary:	Shapash is a Python library which aims to make machine learning interpretable and understandable by everyone.
+Provides:	python-shapash
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-shapash
+<p align="center">
+<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-resize.png" width="300" title="shapash-logo">
+</p>
+
+
+<p align="center">
+  <!-- Tests -->
+  <a href="https://github.com/MAIF/shapash/workflows/Build%20%26%20Test/badge.svg">
+    <img src="https://github.com/MAIF/shapash/workflows/Build%20%26%20Test/badge.svg" alt="tests">
+  </a>
+  <!-- PyPi -->
+  <a href="https://img.shields.io/pypi/v/shapash">
+    <img src="https://img.shields.io/pypi/v/shapash" alt="pypi">
+  </a>
+  <!-- Downloads -->
+  <a href="https://static.pepy.tech/personalized-badge/shapash?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads">
+    <img src="https://static.pepy.tech/personalized-badge/shapash?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads" alt="downloads">
+  </a>
+  <!-- Python Version -->
+  <a href="https://img.shields.io/pypi/pyversions/shapash">
+    <img src="https://img.shields.io/pypi/pyversions/shapash" alt="pyversion">
+  </a>
+  <!-- License -->
+  <a href="https://img.shields.io/pypi/l/shapash">
+    <img src="https://img.shields.io/pypi/l/shapash" alt="license">
+  </a>
+  <!-- Doc -->
+  <a href="https://shapash.readthedocs.io/en/latest/">
+    <img src="https://readthedocs.org/projects/shapash/badge/?version=latest" alt="doc">
+  </a>
+</p>
+
+## 🎉 What's new ?
+
+
+| Version       | New Feature                                                                           | Description                                                                                                                            | Tutorial |
+|:-------------:|:-------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
+| 2.3.x         |  Additional dataset columns <br> (Demo coming soon)                                                                | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options            |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/add_column_icon.png" width="50" title="add_column">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.3.x         |  Identity card <br> (Demo coming soon)                                                                  | In Webapp: New identity card to summarize the information of the selected sample                  |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/identity_card.png" width="50" title="identity">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.2.x         |  Picking samples <br> [New demo](https://shapash-demo.ossbymaif.fr/)                                                                | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values"            |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/picking.png" width="50" title="picking">](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb)
+| 2.2.x         |  Dataset Filter <br> [New demo](https://shapash-demo.ossbymaif.fr/)                                                                  | New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/webapp.png" width="50" title="webapp">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.0.x         |  Refactoring Shapash <br>                                                                   | Refactoring attributes of compile methods and init. Refactoring implementation for new backends                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/modular.png" width="50" title="modular">](https://github.com/MAIF/shapash/blob/master/tutorial/backend/tuto-backend-01.ipynb)
+| 1.7.x         |  Variabilize Colors <br>                                                                   | Giving possibility to have your own colour palette for outputs adapted to your design                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/variabilize-colors.png" width="50" title="variabilize-colors">](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common02-colors.ipynb)
+| 1.6.x         |  Explainability Quality Metrics <br> [article](https://towardsdatascience.com/building-confidence-on-explainability-methods-66b9ee575514)                                                                   | To help increase confidence in explainability methods, you can evaluate the relevance of your explainability using 3 metrics: **Stability**, **Consistency** and **Compacity**                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/quality-metrics.png" width="50" title="quality-metrics">](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) 
+| 1.5.x         |  ACV Backend <br>                                                                     | A new way of estimating Shapley values using ACV. [More info about ACV here](https://towardsdatascience.com/the-right-way-to-compute-your-shapley-values-cfea30509254).                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/wheel.png" width="50" title="wheel-acv-backend">](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb)    |
+| 1.4.x         |  Groups of features <br> [demo](https://shapash-demo2.ossbymaif.fr/)                  | You can now regroup features that share common properties together. <br>This option can be useful if your model has a lot of features. |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/groups_features.gif" width="120" title="groups-features">](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb)    | 
+| 1.3.x         |  Shapash Report <br> [demo](https://shapash.readthedocs.io/en/latest/report.html)     | A standalone HTML report that constitutes a basis of an audit document.                                                                |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/report-icon.png" width="50" title="shapash-report">](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb)    | 
+
+
+## 🔍 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. 
+
+- Readthedocs: [![documentation badge](https://readthedocs.org/projects/shapash/badge/?version=latest)](https://shapash.readthedocs.io/en/latest/)
+- [Presentation video for french speakers](https://www.youtube.com/watch?v=r1R_A9B9apk)
+- Medium:
+  - [Understand your model with Shapash - Towards AI](https://pub.towardsai.net/shapash-making-ml-models-understandable-by-everyone-8f96ad469eb3) 
+  - [Model auditability - Towards DS](https://towardsdatascience.com/shapash-1-3-2-announcing-new-features-for-more-auditable-ai-64a6db71c919)
+  - [Group of features - Towards AI](https://pub.towardsai.net/machine-learning-6011d5d9a444)
+  - [Building confidence on explainability - Towards DS](https://towardsdatascience.com/building-confidence-on-explainability-methods-66b9ee575514)
+  - [Picking Examples to Understand Machine Learning Model](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html)
+
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash_global.gif" width="800">
+</p>
+
+## 🤝 Contributors
+
+<div align="left">
+  <div style="display: flex; align-items: flex-start;">
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_maif.png" width="18%"/>
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_quantmetry.png" width="18%" />
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_societe_generale.png" width="18%" /> 
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_groupe_vyv.png" width="18%" /> 
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_SixfoisSept.png" width="18%" /> 
+  </div>
+</div>
+
+
+## 🏆 Awards
+
+<a href="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/awards-argus-or.png">
+  <img align="left" src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/awards-argus-or.png" width="180" />
+</a>
+
+<a href="https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html">
+  <img src="https://www.kdnuggets.com/images/tkb-2104-g.png?raw=true" width="65" />
+</a>  
+
+
+## 🔥 Features
+
+- Display clear and understandable results: plots and outputs use **explicit labels** for each feature and its values
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-02.png?raw=true" width="28%"/>
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-06.png?raw=true" width="28%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-04.png?raw=true" width="28%" /> 
+</p>
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-01.png?raw=true" width="28%" />
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-resize.png?raw=true" width="18%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-13.png?raw=true" width="28%" /> 
+</p>
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-12.png?raw=true" width="33%" />
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-03.png?raw=true" width="28%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-10.png?raw=true" width="25%" /> 
+</p>
+
+
+- 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](https://shapash-demo.ossbymaif.fr/)
+
+- **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](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html)
+
+- 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](https://shapash.readthedocs.io/en/latest/report.html) 
+>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...).
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-report-demo.gif" width="800">
+</p>
+
+## ⚙️ 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
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-diagram.png" width="700" title="diagram">
+</p>
+
+**Shapash** works for Regression, Binary Classification or Multiclass problem. <br />
+It is compatible with many models: *Catboost*, *Xgboost*, *LightGBM*, *Sklearn Ensemble*, *Linear models*, *SVM*. <br />
+Shapash can use category-encoders object, sklearn ColumnTransformer or simply features dictionary. <br />
+- Category_encoder: *OneHotEncoder*, *OrdinalEncoder*, *BaseNEncoder*, *BinaryEncoder*, *TargetEncoder*
+- Sklearn ColumnTransformer: *OneHotEncoder*, *OrdinalEncoder*, *StandardScaler*, *QuantileTransformer*, *PowerTransformer*
+
+## 🛠 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](https://shapash.readthedocs.io/en/latest/installation-instructions/index.html).
+
+## 🕐 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](https://shapash-demo.ossbymaif.fr/)
+
+- 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](https://shapash.readthedocs.io/en/latest/report.html)
+
+- 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.
+
+
+<details><summary><b>Overview</b> </summary>
+
+- [Launch the webapp with a concrete use case](tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+- [Jupyter Overviews - The main outputs and methods available with the SmartExplainer object](tutorial/tutorial02-Shapash-overview-in-Jupyter.ipynb)
+- [Shapash in production: From model training to deployment (API or Batch Mode)](tutorial/tutorial03-Shapash-overview-model-in-production.ipynb)
+- [Use groups of features](tutorial/common/tuto-common01-groups_of_features.ipynb)
+- [Deploy local explainability in production with SmartPredictor](tutorial/predictor/tuto-smartpredictor-introduction-to-SmartPredictor.ipynb)
+
+</details>
+
+<details><summary><b>Charts and plots</b> </summary>
+
+- [**Shapash** Features Importance](tutorial/plot/tuto-plot03-features-importance.ipynb)
+- [Contribution plot to understand how one feature affects a prediction](tutorial/plot/tuto-plot02-contribution_plot.ipynb)
+- [Summarize, display and export local contribution using filter and local_plot method](tutorial/plot/tuto-plot01-local_plot-and-to_pandas.ipynb)
+- [Contributions Comparing plot to understand why predictions on several individuals are different](tutorial/plot/tuto-plot04-compare_plot.ipynb)
+- [Visualize interactions between couple of variables](tutorial/plot/tuto-plot05-interactions-plot.ipynb)
+- [Customize colors in Webapp, plots and report](tutorial/common/tuto-common02-colors.ipynb)
+
+</details>
+
+<details><summary><b>Different ways to use Encoders and Dictionaries</b> </summary>
+
+- [Use Category_Encoder & inverse transformation](tutorial/encoder/tuto-encoder01-using-category_encoder.ipynb)
+- [Use ColumnTransformers](tutorial/encoder/tuto-encoder02-using-columntransformer.ipynb)
+- [Use Simple Python Dictionnaries](tutorial/encoder/tuto-encoder03-using-dict.ipynb)
+
+</details>
+
+<details><summary><b>Displaying data with postprocessing</b> </summary>
+
+[Using postprocessing parameter in compile method](tutorial/postprocess/tuto-postprocess01.ipynb)
+
+</details>
+
+<details><summary><b>Using different backends</b> </summary>
+
+- [Compute Shapley Contributions using **Shap**](tutorial/explainer/tuto-expl01-Shapash-Viz-using-Shap-contributions.ipynb)
+- [Use **Lime** to compute local explanation, Summarize-it with **Shapash**](tutorial/explainer/tuto-expl02-Shapash-Viz-using-Lime-contributions.ipynb)
+- [Use **ACV backend** to compute Active Shapley Values and SDP global importance](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb)
+- [Compile faster Lime and consistency of contributions](tutorial/explainer/tuto-expl04-Shapash-compute-Lime-faster.ipynb)
+
+</details>
+
+<details><summary><b>Evaluating the quality of your explainability</b> </summary>
+
+- [Building confidence on explainability methods using **Stability**, **Consistency** and **Compacity** metrics](tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb)
+
+</details>
+
+<details><summary><b>Generate a report of your project</b> </summary>
+
+- [Generate a standalone HTML report of your project with generate_report](tutorial/report/tuto-shapash-report01.ipynb)
+
+</details>
+
+
+
+
+%package help
+Summary:	Development documents and examples for shapash
+Provides:	python3-shapash-doc
+%description help
+<p align="center">
+<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-resize.png" width="300" title="shapash-logo">
+</p>
+
+
+<p align="center">
+  <!-- Tests -->
+  <a href="https://github.com/MAIF/shapash/workflows/Build%20%26%20Test/badge.svg">
+    <img src="https://github.com/MAIF/shapash/workflows/Build%20%26%20Test/badge.svg" alt="tests">
+  </a>
+  <!-- PyPi -->
+  <a href="https://img.shields.io/pypi/v/shapash">
+    <img src="https://img.shields.io/pypi/v/shapash" alt="pypi">
+  </a>
+  <!-- Downloads -->
+  <a href="https://static.pepy.tech/personalized-badge/shapash?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads">
+    <img src="https://static.pepy.tech/personalized-badge/shapash?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads" alt="downloads">
+  </a>
+  <!-- Python Version -->
+  <a href="https://img.shields.io/pypi/pyversions/shapash">
+    <img src="https://img.shields.io/pypi/pyversions/shapash" alt="pyversion">
+  </a>
+  <!-- License -->
+  <a href="https://img.shields.io/pypi/l/shapash">
+    <img src="https://img.shields.io/pypi/l/shapash" alt="license">
+  </a>
+  <!-- Doc -->
+  <a href="https://shapash.readthedocs.io/en/latest/">
+    <img src="https://readthedocs.org/projects/shapash/badge/?version=latest" alt="doc">
+  </a>
+</p>
+
+## 🎉 What's new ?
+
+
+| Version       | New Feature                                                                           | Description                                                                                                                            | Tutorial |
+|:-------------:|:-------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
+| 2.3.x         |  Additional dataset columns <br> (Demo coming soon)                                                                | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options            |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/add_column_icon.png" width="50" title="add_column">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.3.x         |  Identity card <br> (Demo coming soon)                                                                  | In Webapp: New identity card to summarize the information of the selected sample                  |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/identity_card.png" width="50" title="identity">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.2.x         |  Picking samples <br> [New demo](https://shapash-demo.ossbymaif.fr/)                                                                | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values"            |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/picking.png" width="50" title="picking">](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb)
+| 2.2.x         |  Dataset Filter <br> [New demo](https://shapash-demo.ossbymaif.fr/)                                                                  | New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/webapp.png" width="50" title="webapp">](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+| 2.0.x         |  Refactoring Shapash <br>                                                                   | Refactoring attributes of compile methods and init. Refactoring implementation for new backends                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/modular.png" width="50" title="modular">](https://github.com/MAIF/shapash/blob/master/tutorial/backend/tuto-backend-01.ipynb)
+| 1.7.x         |  Variabilize Colors <br>                                                                   | Giving possibility to have your own colour palette for outputs adapted to your design                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/variabilize-colors.png" width="50" title="variabilize-colors">](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common02-colors.ipynb)
+| 1.6.x         |  Explainability Quality Metrics <br> [article](https://towardsdatascience.com/building-confidence-on-explainability-methods-66b9ee575514)                                                                   | To help increase confidence in explainability methods, you can evaluate the relevance of your explainability using 3 metrics: **Stability**, **Consistency** and **Compacity**                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/quality-metrics.png" width="50" title="quality-metrics">](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) 
+| 1.5.x         |  ACV Backend <br>                                                                     | A new way of estimating Shapley values using ACV. [More info about ACV here](https://towardsdatascience.com/the-right-way-to-compute-your-shapley-values-cfea30509254).                   |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/wheel.png" width="50" title="wheel-acv-backend">](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb)    |
+| 1.4.x         |  Groups of features <br> [demo](https://shapash-demo2.ossbymaif.fr/)                  | You can now regroup features that share common properties together. <br>This option can be useful if your model has a lot of features. |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/groups_features.gif" width="120" title="groups-features">](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb)    | 
+| 1.3.x         |  Shapash Report <br> [demo](https://shapash.readthedocs.io/en/latest/report.html)     | A standalone HTML report that constitutes a basis of an audit document.                                                                |  [<img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/report-icon.png" width="50" title="shapash-report">](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb)    | 
+
+
+## 🔍 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. 
+
+- Readthedocs: [![documentation badge](https://readthedocs.org/projects/shapash/badge/?version=latest)](https://shapash.readthedocs.io/en/latest/)
+- [Presentation video for french speakers](https://www.youtube.com/watch?v=r1R_A9B9apk)
+- Medium:
+  - [Understand your model with Shapash - Towards AI](https://pub.towardsai.net/shapash-making-ml-models-understandable-by-everyone-8f96ad469eb3) 
+  - [Model auditability - Towards DS](https://towardsdatascience.com/shapash-1-3-2-announcing-new-features-for-more-auditable-ai-64a6db71c919)
+  - [Group of features - Towards AI](https://pub.towardsai.net/machine-learning-6011d5d9a444)
+  - [Building confidence on explainability - Towards DS](https://towardsdatascience.com/building-confidence-on-explainability-methods-66b9ee575514)
+  - [Picking Examples to Understand Machine Learning Model](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html)
+
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash_global.gif" width="800">
+</p>
+
+## 🤝 Contributors
+
+<div align="left">
+  <div style="display: flex; align-items: flex-start;">
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_maif.png" width="18%"/>
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_quantmetry.png" width="18%" />
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_societe_generale.png" width="18%" /> 
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_groupe_vyv.png" width="18%" /> 
+    <img align=middle src="https://github.com/MAIF/shapash/blob/master/docs/_static/logo_SixfoisSept.png" width="18%" /> 
+  </div>
+</div>
+
+
+## 🏆 Awards
+
+<a href="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/awards-argus-or.png">
+  <img align="left" src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/awards-argus-or.png" width="180" />
+</a>
+
+<a href="https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html">
+  <img src="https://www.kdnuggets.com/images/tkb-2104-g.png?raw=true" width="65" />
+</a>  
+
+
+## 🔥 Features
+
+- Display clear and understandable results: plots and outputs use **explicit labels** for each feature and its values
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-02.png?raw=true" width="28%"/>
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-06.png?raw=true" width="28%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-04.png?raw=true" width="28%" /> 
+</p>
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-01.png?raw=true" width="28%" />
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-resize.png?raw=true" width="18%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-13.png?raw=true" width="28%" /> 
+</p>
+
+<p align="center">
+  <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-12.png?raw=true" width="33%" />
+  <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-03.png?raw=true" width="28%" />
+  <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-10.png?raw=true" width="25%" /> 
+</p>
+
+
+- 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](https://shapash-demo.ossbymaif.fr/)
+
+- **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](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html)
+
+- 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](https://shapash.readthedocs.io/en/latest/report.html) 
+>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...).
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-report-demo.gif" width="800">
+</p>
+
+## ⚙️ 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
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash-diagram.png" width="700" title="diagram">
+</p>
+
+**Shapash** works for Regression, Binary Classification or Multiclass problem. <br />
+It is compatible with many models: *Catboost*, *Xgboost*, *LightGBM*, *Sklearn Ensemble*, *Linear models*, *SVM*. <br />
+Shapash can use category-encoders object, sklearn ColumnTransformer or simply features dictionary. <br />
+- Category_encoder: *OneHotEncoder*, *OrdinalEncoder*, *BaseNEncoder*, *BinaryEncoder*, *TargetEncoder*
+- Sklearn ColumnTransformer: *OneHotEncoder*, *OrdinalEncoder*, *StandardScaler*, *QuantileTransformer*, *PowerTransformer*
+
+## 🛠 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](https://shapash.readthedocs.io/en/latest/installation-instructions/index.html).
+
+## 🕐 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](https://shapash-demo.ossbymaif.fr/)
+
+- 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](https://shapash.readthedocs.io/en/latest/report.html)
+
+- 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.
+
+
+<details><summary><b>Overview</b> </summary>
+
+- [Launch the webapp with a concrete use case](tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb)
+- [Jupyter Overviews - The main outputs and methods available with the SmartExplainer object](tutorial/tutorial02-Shapash-overview-in-Jupyter.ipynb)
+- [Shapash in production: From model training to deployment (API or Batch Mode)](tutorial/tutorial03-Shapash-overview-model-in-production.ipynb)
+- [Use groups of features](tutorial/common/tuto-common01-groups_of_features.ipynb)
+- [Deploy local explainability in production with SmartPredictor](tutorial/predictor/tuto-smartpredictor-introduction-to-SmartPredictor.ipynb)
+
+</details>
+
+<details><summary><b>Charts and plots</b> </summary>
+
+- [**Shapash** Features Importance](tutorial/plot/tuto-plot03-features-importance.ipynb)
+- [Contribution plot to understand how one feature affects a prediction](tutorial/plot/tuto-plot02-contribution_plot.ipynb)
+- [Summarize, display and export local contribution using filter and local_plot method](tutorial/plot/tuto-plot01-local_plot-and-to_pandas.ipynb)
+- [Contributions Comparing plot to understand why predictions on several individuals are different](tutorial/plot/tuto-plot04-compare_plot.ipynb)
+- [Visualize interactions between couple of variables](tutorial/plot/tuto-plot05-interactions-plot.ipynb)
+- [Customize colors in Webapp, plots and report](tutorial/common/tuto-common02-colors.ipynb)
+
+</details>
+
+<details><summary><b>Different ways to use Encoders and Dictionaries</b> </summary>
+
+- [Use Category_Encoder & inverse transformation](tutorial/encoder/tuto-encoder01-using-category_encoder.ipynb)
+- [Use ColumnTransformers](tutorial/encoder/tuto-encoder02-using-columntransformer.ipynb)
+- [Use Simple Python Dictionnaries](tutorial/encoder/tuto-encoder03-using-dict.ipynb)
+
+</details>
+
+<details><summary><b>Displaying data with postprocessing</b> </summary>
+
+[Using postprocessing parameter in compile method](tutorial/postprocess/tuto-postprocess01.ipynb)
+
+</details>
+
+<details><summary><b>Using different backends</b> </summary>
+
+- [Compute Shapley Contributions using **Shap**](tutorial/explainer/tuto-expl01-Shapash-Viz-using-Shap-contributions.ipynb)
+- [Use **Lime** to compute local explanation, Summarize-it with **Shapash**](tutorial/explainer/tuto-expl02-Shapash-Viz-using-Lime-contributions.ipynb)
+- [Use **ACV backend** to compute Active Shapley Values and SDP global importance](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb)
+- [Compile faster Lime and consistency of contributions](tutorial/explainer/tuto-expl04-Shapash-compute-Lime-faster.ipynb)
+
+</details>
+
+<details><summary><b>Evaluating the quality of your explainability</b> </summary>
+
+- [Building confidence on explainability methods using **Stability**, **Consistency** and **Compacity** metrics](tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb)
+
+</details>
+
+<details><summary><b>Generate a report of your project</b> </summary>
+
+- [Generate a standalone HTML report of your project with generate_report](tutorial/report/tuto-shapash-report01.ipynb)
+
+</details>
+
+
+
+
+%prep
+%autosetup -n shapash-2.3.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-shapash -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 2.3.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..0d901d8
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+cc3ca82be59056d0bdd811bdb172478b  shapash-2.3.0.tar.gz