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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| { | ||
| "notebooks": {} | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,90 +1,90 @@ | ||
| from setuptools import setup, find_packages | ||
|
|
||
| setup( | ||
| name='explainerdashboard', | ||
| version='0.3.3.1', | ||
| description='Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.', | ||
| long_description=""" | ||
| This package makes it convenient to quickly deploy a dashboard web app | ||
| that explains the workings of a (scikit-learn compatible) fitted machine | ||
| learning model. The dashboard provides interactive plots on model performance, | ||
| feature importances, feature contributions to individual predictions, | ||
| partial dependence plots, SHAP (interaction) values, visualisation of individual | ||
| decision trees, etc. | ||
| The goal is manyfold: | ||
| - Make it easy for data scientists to quickly inspect the inner workings and | ||
| performance of their model with just a few lines of code | ||
| - Make it possible for non data scientist stakeholders such as co-workers, | ||
| managers, directors, internal and external watchdogs to interactively | ||
| inspect the inner workings of the model without having to depend | ||
| on a data scientist to generate every plot and table | ||
| - Make it easy to build a custom application that explains individual | ||
| predictions of your model for customers that ask for an explanation | ||
| - Explain the inner workings of the model to the people working with | ||
| model in a human-in-the-loop deployment so that they gain understanding | ||
| what the model does do and does not do. | ||
| This is important so that they can gain an intuition for when the | ||
| model is likely missing information and may have to be overruled. | ||
| The dashboard includes: | ||
| - SHAP values (i.e. what is the contribution of each feature to each | ||
| individual prediction?) | ||
| - Permutation importances (how much does the model metric deteriorate | ||
| when you shuffle a feature?) | ||
| - Partial dependence plots (how does the model prediction change when | ||
| you vary a single feature? | ||
| - Shap interaction values (decompose the shap value into a direct effect | ||
| an interaction effects) | ||
| - For Random Forests and xgboost models: visualization of individual trees | ||
| in the ensemble. | ||
| - Plus for classifiers: precision plots, confusion matrix, ROC AUC plot, | ||
| PR AUC plot, etc | ||
| - For regression models: goodness-of-fit plots, residual plots, etc. | ||
| The library is designed to be modular so that it is easy to design your | ||
| own custom dashboards so that you can focus on the layout and project specific | ||
| textual explanations of the dashboard. (i.e. design it so that it will be | ||
| interpretable for business users in your organization, not just data scientists) | ||
| A deployed example can be found at http://titanicexplainer.herokuapp.com | ||
| """, | ||
| license='MIT', | ||
| packages=find_packages(), | ||
| package_dir={'explainerdashboard': 'explainerdashboard'}, # the one line where all the magic happens | ||
| package_data={ | ||
| 'explainerdashboard': ['assets/*', 'datasets/*', 'static/*'], | ||
| }, | ||
| classifiers=[ | ||
| "Development Status :: 3 - Alpha", | ||
| "Environment :: Web Environment", | ||
| "Framework :: Dash", | ||
| "Framework :: Flask", | ||
| "Intended Audience :: Developers", | ||
| "Intended Audience :: Education", | ||
| "Topic :: Scientific/Engineering :: Artificial Intelligence"], | ||
| install_requires=['dash>=1.19', 'dash-bootstrap-components', 'jupyter_dash', 'dash-auth', | ||
| 'dtreeviz>=1.1.4', 'numpy', 'pandas>=1.1', 'scikit-learn', | ||
| 'shap>=0.37', 'joblib', 'oyaml', 'click', 'waitress', | ||
| 'flask_simplelogin'], | ||
| python_requires='>=3.6', | ||
| entry_points={ | ||
| 'console_scripts': [ | ||
| 'explainerdashboard = explainerdashboard.cli:explainerdashboard_cli', | ||
| 'explainerhub = explainerdashboard.cli:explainerhub_cli', | ||
| ], | ||
| }, | ||
| author='Oege Dijk', | ||
| author_email='oegedijk@gmail.com', | ||
| keywords=['machine learning', 'explainability', 'shap', 'feature importances', 'dash'], | ||
| url='https://github.com/oegedijk/explainerdashboard', | ||
| project_urls={ | ||
| "Github page": "https://github.com/oegedijk/explainerdashboard/", | ||
| "Documentation": "https://explainerdashboard.readthedocs.io/", | ||
| }, | ||
| ) | ||
| from setuptools import setup, find_packages | ||
|
|
||
| setup( | ||
| name='explainerdashboard', | ||
| version='0.3.4', | ||
| description='Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.', | ||
| long_description=""" | ||
| This package makes it convenient to quickly deploy a dashboard web app | ||
| that explains the workings of a (scikit-learn compatible) fitted machine | ||
| learning model. The dashboard provides interactive plots on model performance, | ||
| feature importances, feature contributions to individual predictions, | ||
| partial dependence plots, SHAP (interaction) values, visualisation of individual | ||
| decision trees, etc. | ||
| The goal is manyfold: | ||
| - Make it easy for data scientists to quickly inspect the inner workings and | ||
| performance of their model with just a few lines of code | ||
| - Make it possible for non data scientist stakeholders such as co-workers, | ||
| managers, directors, internal and external watchdogs to interactively | ||
| inspect the inner workings of the model without having to depend | ||
| on a data scientist to generate every plot and table | ||
| - Make it easy to build a custom application that explains individual | ||
| predictions of your model for customers that ask for an explanation | ||
| - Explain the inner workings of the model to the people working with | ||
| model in a human-in-the-loop deployment so that they gain understanding | ||
| what the model does do and does not do. | ||
| This is important so that they can gain an intuition for when the | ||
| model is likely missing information and may have to be overruled. | ||
| The dashboard includes: | ||
| - SHAP values (i.e. what is the contribution of each feature to each | ||
| individual prediction?) | ||
| - Permutation importances (how much does the model metric deteriorate | ||
| when you shuffle a feature?) | ||
| - Partial dependence plots (how does the model prediction change when | ||
| you vary a single feature? | ||
| - Shap interaction values (decompose the shap value into a direct effect | ||
| an interaction effects) | ||
| - For Random Forests and xgboost models: visualization of individual trees | ||
| in the ensemble. | ||
| - Plus for classifiers: precision plots, confusion matrix, ROC AUC plot, | ||
| PR AUC plot, etc | ||
| - For regression models: goodness-of-fit plots, residual plots, etc. | ||
| The library is designed to be modular so that it is easy to design your | ||
| own custom dashboards so that you can focus on the layout and project specific | ||
| textual explanations of the dashboard. (i.e. design it so that it will be | ||
| interpretable for business users in your organization, not just data scientists) | ||
| A deployed example can be found at http://titanicexplainer.herokuapp.com | ||
| """, | ||
| license='MIT', | ||
| packages=find_packages(), | ||
| package_dir={'explainerdashboard': 'explainerdashboard'}, # the one line where all the magic happens | ||
| package_data={ | ||
| 'explainerdashboard': ['assets/*', 'datasets/*', 'static/*'], | ||
| }, | ||
| classifiers=[ | ||
| "Development Status :: 3 - Alpha", | ||
| "Environment :: Web Environment", | ||
| "Framework :: Dash", | ||
| "Framework :: Flask", | ||
| "Intended Audience :: Developers", | ||
| "Intended Audience :: Education", | ||
| "Topic :: Scientific/Engineering :: Artificial Intelligence"], | ||
| install_requires=['dash>=1.19', 'dash-bootstrap-components', 'jupyter_dash', 'dash-auth', | ||
| 'dtreeviz>=1.3', 'numpy', 'pandas>=1.1', 'scikit-learn', | ||
| 'shap>=0.37', 'joblib', 'oyaml', 'click', 'waitress', | ||
| 'flask_simplelogin'], | ||
| python_requires='>=3.6', | ||
| entry_points={ | ||
| 'console_scripts': [ | ||
| 'explainerdashboard = explainerdashboard.cli:explainerdashboard_cli', | ||
| 'explainerhub = explainerdashboard.cli:explainerhub_cli', | ||
| ], | ||
| }, | ||
| author='Oege Dijk', | ||
| author_email='oegedijk@gmail.com', | ||
| keywords=['machine learning', 'explainability', 'shap', 'feature importances', 'dash'], | ||
| url='https://github.com/oegedijk/explainerdashboard', | ||
| project_urls={ | ||
| "Github page": "https://github.com/oegedijk/explainerdashboard/", | ||
| "Documentation": "https://explainerdashboard.readthedocs.io/", | ||
| }, | ||
| ) |