From 5b42075c52dcd18d56d37f51463aefbcd143e32b Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 25 Apr 2023 05:17:24 +0000 Subject: automatic import of python-shapash --- .gitignore | 1 + python-shapash.spec | 65 ++++++++++++++++++++++++++++++++--------------------- sources | 2 +- 3 files changed, 42 insertions(+), 26 deletions(-) diff --git a/.gitignore b/.gitignore index b9e8014..e876da7 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ /shapash-2.3.0.tar.gz +/shapash-2.3.2.tar.gz diff --git a/python-shapash.spec b/python-shapash.spec index 0f9637f..5ab3e12 100644 --- a/python-shapash.spec +++ b/python-shapash.spec @@ -1,11 +1,11 @@ %global _empty_manifest_terminate_build 0 Name: python-shapash -Version: 2.3.0 +Version: 2.3.2 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 +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/9b/f0/6a638eb01670477ef85038500791ba23fdc9742b856fa79a182ebb2ae8e2/shapash-2.3.2.tar.gz BuildArch: noarch Requires: python3-plotly @@ -76,16 +76,16 @@ Requires: python3-xgboost | Version | New Feature | Description | Tutorial | |:-------------:|:-------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:| -| 2.3.x | Additional dataset columns
(Demo coming soon) | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) -| 2.3.x | Identity card
(Demo coming soon) | In Webapp: New identity card to summarize the information of the selected sample | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) -| 2.2.x | Picking samples
[New demo](https://shapash-demo.ossbymaif.fr/) | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values" | [](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb) -| 2.2.x | Dataset Filter
[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 | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) +| 2.3.x | Additional dataset columns
[New demo](https://shapash-demo.ossbymaif.fr/)
[Article](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb) | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options | [](https://github.com/MAIF/shapash/blob/master/tutorial/webapp/tuto-webapp01-additional-data.ipynb) +| 2.3.x | Identity card
[New demo](https://shapash-demo.ossbymaif.fr/)
[Article](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb) | In Webapp: New identity card to summarize the information of the selected sample | [](https://github.com/MAIF/shapash/blob/master/tutorial/webapp/tuto-webapp01-additional-data.ipynb) +| 2.2.x | Picking samples
[Article](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html) | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values" | [](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb) +| 2.2.x | Dataset Filter
| New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) | 2.0.x | Refactoring Shapash
| Refactoring attributes of compile methods and init. Refactoring implementation for new backends | [](https://github.com/MAIF/shapash/blob/master/tutorial/backend/tuto-backend-01.ipynb) | 1.7.x | Variabilize Colors
| Giving possibility to have your own colour palette for outputs adapted to your design | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common02-colors.ipynb) -| 1.6.x | Explainability Quality Metrics
[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** | [](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) +| 1.6.x | Explainability Quality Metrics
[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** | [](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) | 1.5.x | ACV Backend
| 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). | [](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb) | -| 1.4.x | Groups of features
[demo](https://shapash-demo2.ossbymaif.fr/) | You can now regroup features that share common properties together.
This option can be useful if your model has a lot of features. | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb) | -| 1.3.x | Shapash Report
[demo](https://shapash.readthedocs.io/en/latest/report.html) | A standalone HTML report that constitutes a basis of an audit document. | [](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb) | +| 1.4.x | Groups of features
[Demo](https://shapash-demo2.ossbymaif.fr/) | You can now regroup features that share common properties together.
This option can be useful if your model has a lot of features. | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb) | +| 1.3.x | Shapash Report
[Demo](https://shapash.readthedocs.io/en/latest/report.html) | A standalone HTML report that constitutes a basis of an audit document. | [](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb) | ## 🔍 Overview @@ -105,6 +105,7 @@ Shapash also contributes to data science auditing by displaying usefull informat - [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) + - [Enhancing Webapp Built-In Features for Comprehensive Machine Learning Model Interpretation](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb)

@@ -343,7 +344,11 @@ This github repository offers many tutorials to allow you to easily get started +

Analysing your model via Shapash WebApp +- [Add features outside of the model for more exploration options](tutorial/webapp/tuto-webapp01-additional-data.ipynb) + +
%package -n python3-shapash @@ -390,16 +395,16 @@ BuildRequires: python3-pip | Version | New Feature | Description | Tutorial | |:-------------:|:-------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:| -| 2.3.x | Additional dataset columns
(Demo coming soon) | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) -| 2.3.x | Identity card
(Demo coming soon) | In Webapp: New identity card to summarize the information of the selected sample | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) -| 2.2.x | Picking samples
[New demo](https://shapash-demo.ossbymaif.fr/) | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values" | [](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb) -| 2.2.x | Dataset Filter
[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 | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) +| 2.3.x | Additional dataset columns
[New demo](https://shapash-demo.ossbymaif.fr/)
[Article](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb) | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options | [](https://github.com/MAIF/shapash/blob/master/tutorial/webapp/tuto-webapp01-additional-data.ipynb) +| 2.3.x | Identity card
[New demo](https://shapash-demo.ossbymaif.fr/)
[Article](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb) | In Webapp: New identity card to summarize the information of the selected sample | [](https://github.com/MAIF/shapash/blob/master/tutorial/webapp/tuto-webapp01-additional-data.ipynb) +| 2.2.x | Picking samples
[Article](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html) | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values" | [](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb) +| 2.2.x | Dataset Filter
| New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) | 2.0.x | Refactoring Shapash
| Refactoring attributes of compile methods and init. Refactoring implementation for new backends | [](https://github.com/MAIF/shapash/blob/master/tutorial/backend/tuto-backend-01.ipynb) | 1.7.x | Variabilize Colors
| Giving possibility to have your own colour palette for outputs adapted to your design | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common02-colors.ipynb) -| 1.6.x | Explainability Quality Metrics
[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** | [](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) +| 1.6.x | Explainability Quality Metrics
[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** | [](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) | 1.5.x | ACV Backend
| 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). | [](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb) | -| 1.4.x | Groups of features
[demo](https://shapash-demo2.ossbymaif.fr/) | You can now regroup features that share common properties together.
This option can be useful if your model has a lot of features. | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb) | -| 1.3.x | Shapash Report
[demo](https://shapash.readthedocs.io/en/latest/report.html) | A standalone HTML report that constitutes a basis of an audit document. | [](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb) | +| 1.4.x | Groups of features
[Demo](https://shapash-demo2.ossbymaif.fr/) | You can now regroup features that share common properties together.
This option can be useful if your model has a lot of features. | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb) | +| 1.3.x | Shapash Report
[Demo](https://shapash.readthedocs.io/en/latest/report.html) | A standalone HTML report that constitutes a basis of an audit document. | [](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb) | ## 🔍 Overview @@ -419,6 +424,7 @@ Shapash also contributes to data science auditing by displaying usefull informat - [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) + - [Enhancing Webapp Built-In Features for Comprehensive Machine Learning Model Interpretation](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb)

@@ -657,7 +663,11 @@ This github repository offers many tutorials to allow you to easily get started +

Analysing your model via Shapash WebApp + +- [Add features outside of the model for more exploration options](tutorial/webapp/tuto-webapp01-additional-data.ipynb) +
%package help @@ -701,16 +711,16 @@ Provides: python3-shapash-doc | Version | New Feature | Description | Tutorial | |:-------------:|:-------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:| -| 2.3.x | Additional dataset columns
(Demo coming soon) | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) -| 2.3.x | Identity card
(Demo coming soon) | In Webapp: New identity card to summarize the information of the selected sample | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) -| 2.2.x | Picking samples
[New demo](https://shapash-demo.ossbymaif.fr/) | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values" | [](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb) -| 2.2.x | Dataset Filter
[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 | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) +| 2.3.x | Additional dataset columns
[New demo](https://shapash-demo.ossbymaif.fr/)
[Article](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb) | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options | [](https://github.com/MAIF/shapash/blob/master/tutorial/webapp/tuto-webapp01-additional-data.ipynb) +| 2.3.x | Identity card
[New demo](https://shapash-demo.ossbymaif.fr/)
[Article](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb) | In Webapp: New identity card to summarize the information of the selected sample | [](https://github.com/MAIF/shapash/blob/master/tutorial/webapp/tuto-webapp01-additional-data.ipynb) +| 2.2.x | Picking samples
[Article](https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html) | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values" | [](https://github.com/MAIF/shapash/blob/master/tutorial/plot/tuto-plot06-prediction_plot.ipynb) +| 2.2.x | Dataset Filter
| New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments | [](https://github.com/MAIF/shapash/blob/master/tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb) | 2.0.x | Refactoring Shapash
| Refactoring attributes of compile methods and init. Refactoring implementation for new backends | [](https://github.com/MAIF/shapash/blob/master/tutorial/backend/tuto-backend-01.ipynb) | 1.7.x | Variabilize Colors
| Giving possibility to have your own colour palette for outputs adapted to your design | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common02-colors.ipynb) -| 1.6.x | Explainability Quality Metrics
[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** | [](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) +| 1.6.x | Explainability Quality Metrics
[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** | [](https://github.com/MAIF/shapash/blob/master/tutorial/explainability_quality/tuto-quality01-Builing-confidence-explainability.ipynb) | 1.5.x | ACV Backend
| 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). | [](tutorial/explainer/tuto-expl03-Shapash-acv-backend.ipynb) | -| 1.4.x | Groups of features
[demo](https://shapash-demo2.ossbymaif.fr/) | You can now regroup features that share common properties together.
This option can be useful if your model has a lot of features. | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb) | -| 1.3.x | Shapash Report
[demo](https://shapash.readthedocs.io/en/latest/report.html) | A standalone HTML report that constitutes a basis of an audit document. | [](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb) | +| 1.4.x | Groups of features
[Demo](https://shapash-demo2.ossbymaif.fr/) | You can now regroup features that share common properties together.
This option can be useful if your model has a lot of features. | [](https://github.com/MAIF/shapash/blob/master/tutorial/common/tuto-common01-groups_of_features.ipynb) | +| 1.3.x | Shapash Report
[Demo](https://shapash.readthedocs.io/en/latest/report.html) | A standalone HTML report that constitutes a basis of an audit document. | [](https://github.com/MAIF/shapash/blob/master/tutorial/report/tuto-shapash-report01.ipynb) | ## 🔍 Overview @@ -730,6 +740,7 @@ Shapash also contributes to data science auditing by displaying usefull informat - [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) + - [Enhancing Webapp Built-In Features for Comprehensive Machine Learning Model Interpretation](https://pub.towardsai.net/shapash-2-3-0-comprehensive-model-interpretation-40b50157c2fb)

@@ -968,11 +979,15 @@ This github repository offers many tutorials to allow you to easily get started +

Analysing your model via Shapash WebApp + +- [Add features outside of the model for more exploration options](tutorial/webapp/tuto-webapp01-additional-data.ipynb) +
%prep -%autosetup -n shapash-2.3.0 +%autosetup -n shapash-2.3.2 %build %py3_build @@ -1012,5 +1027,5 @@ mv %{buildroot}/doclist.lst . %{_docdir}/* %changelog -* Tue Apr 11 2023 Python_Bot - 2.3.0-1 +* Tue Apr 25 2023 Python_Bot - 2.3.2-1 - Package Spec generated diff --git a/sources b/sources index 0d901d8..3ccc57f 100644 --- a/sources +++ b/sources @@ -1 +1 @@ -cc3ca82be59056d0bdd811bdb172478b shapash-2.3.0.tar.gz +84c18f3cecd7768e4895919f9f6da73d shapash-2.3.2.tar.gz -- cgit v1.2.3