https://github.com/jettify/ibreakdown
ibreakdown is model agnostic predictions explainer with interactions support, library can show contribution of each feature of your prediction value
https://github.com/jettify/ibreakdown
explainability explainable-ai xai xai-library
Last synced: about 1 month ago
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ibreakdown is model agnostic predictions explainer with interactions support, library can show contribution of each feature of your prediction value
- Host: GitHub
- URL: https://github.com/jettify/ibreakdown
- Owner: jettify
- License: apache-2.0
- Created: 2019-09-25T03:36:10.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T22:21:18.000Z (almost 2 years ago)
- Last Synced: 2025-03-18T22:09:09.401Z (about 1 month ago)
- Topics: explainability, explainable-ai, xai, xai-library
- Language: Jupyter Notebook
- Homepage:
- Size: 186 KB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 13
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES.txt
- Contributing: CONTRIBUTING.rst
- License: LICENSE
Awesome Lists containing this project
README
ibreakdown
==========
.. image:: https://travis-ci.com/jettify/ibreakdown.svg?branch=master
:target: https://travis-ci.com/jettify/ibreakdown
.. image:: https://codecov.io/gh/jettify/ibreakdown/branch/master/graph/badge.svg
:target: https://codecov.io/gh/jettify/ibreakdown
.. image:: https://img.shields.io/pypi/pyversions/ibreakdown.svg
:target: https://pypi.org/project/ibreakdown
.. image:: https://img.shields.io/pypi/v/ibreakdown.svg
:target: https://pypi.python.org/pypi/ibreakdown
**ibreakdown** is model agnostic predictions explainer with uncertainty
and experimental interactions support.
SHAP_ or LIME_ consider only local additive feature attributions and
do not provide uncertainty, when **ibreakdown** also evaluates local feature
interactions.
Features
========
* Supports predictions explanations for classification and regression
* Easy to use API.
* Works with ``pandas`` and ``numpy``
* Speed is linear with respect to number of features
* Sum of contribution plus intercept/baseline equals predicted value
* Uncertainty support
* Experimental interactions support with O(n^2) time complexity where *n* is
number of features.
Algorithm
=========
Algorithm is based on ideas describe in paper *"iBreakDown: Uncertainty of Model
Explanations for Non-additive Predictive Models"* https://arxiv.org/abs/1903.11420 and
reference implementation in **R** (iBreakDown_)
Explanation With Uncertainty Example
------------------------------------
.. code:: python
from ibreakdown import URegressionExplainer
# model = GradientBoostingRegressor(...)
explainer = URegressionExplainer(model.predict)
explainer.fit(X_train, columns)
exp = explainer.explain(observation)
exp.print()
exp.plot()
.. code::
+----+----------------+-----------------+----------------+--------------------+
| | Feature Name | Feature Value | Contribution | Contribution STD |
|----+----------------+-----------------+----------------+--------------------|
| 0 | CRIM | 0.06724 | 0.0135305 | 0.131137 |
| 1 | ZN | 0 | 0 | 0.00165973 |
| 2 | INDUS | 3.24 | 0.0895993 | 0.0407877 |
| 3 | CHAS | 0 | -0.0322645 | 0.0073065 |
| 4 | NOX | 0.46 | 0.229041 | 0.273706 |
| 5 | RM | 6.333 | -1.60699 | 0.130217 |
| 6 | AGE | 17.2 | 0.877219 | 0.456707 |
| 7 | DIS | 5.2146 | -1.33008 | 0.405814 |
| 8 | RAD | 4 | -0.143538 | 0.043008 |
| 9 | TAX | 430 | -0.496451 | 0.0521049 |
| 10 | PTRATIO | 16.9 | 0.545435 | 0.273792 |
| 11 | B | 375.21 | 0.133995 | 0.0943484 |
| 12 | LSTAT | 7.34 | 3.61802 | 0.68494 |
+----+----------------+-----------------+----------------+--------------------+
.. image:: https://raw.githubusercontent.com/jettify/ibreakdown/master/docs/boston_housing_uncertenty.png
:alt: feature contributions for RF trained on boston housing dataset
Explanation With Interactions Example
-------------------------------------
.. code:: python
from ibreakdown import IClassificationExplainer
# model = RandomForestClassifier(...)
explainer = IClassificationExplainer(model.predict_proba)
classes = ['Deceased', 'Survived']
explainer.fit(X_train, columns, classes)
exp = explainer.explain(observation)
exp.print()
Please check full Titanic example here: https://github.com/jettify/ibreakdown/blob/master/examples/titanic.py
.. code::
+------------------------------------+-----------------+--------------------+--------------------+
| Feature Name | Feature Value | Contrib:Deceased | Contrib:Survived |
+------------------------------------+-----------------+--------------------+--------------------|
| intercept | | 0.613286 | 0.386714 |
| Sex | female | -0.305838 | 0.305838 |
| Pclass | 3 | 0.242448 | -0.242448 |
| Fare | 7.7375 | -0.119392 | 0.119392 |
| Siblings/Spouses Aboard | 0 | -0.0372811 | 0.0372811 |
| ('Age', 'Parents/Children Aboard') | [28.0 0] | 0.0122196 | -0.0122196 |
| PREDICTION | | 0.405443 | 0.594557 |
+------------------------------------+-----------------+--------------------+--------------------+
Installation
------------
Installation process is simple, just::
$ pip install ibreakdown
Requirements
------------
* Python_ 3.6+
* numpy_
.. _Python: https://www.python.org
.. _numpy: http://www.numpy.org/
.. _iBreakDown: https://github.com/ModelOriented/iBreakDown
.. _Shapley: https://en.wikipedia.org/wiki/Shapley_value
.. _SHAP: https://github.com/slundberg/shap
.. _LIME: https://github.com/marcotcr/lime