lux-explainer 1.2.0

Universal Local Rule-based Explainer

LUX (Local Universal Rule-based Explainer)

Main features

• Model-agnostic, rule-based and visual local explanations of black-box ML models
• Integrated counterfactual explanations
• Rule-based explanations (that are executable at the same time)
• Oblique trees backbone, which allows to explain more reliable linear decision boundaries
• Integration with Shapley values or Lime importances (or any other explainer that produces importances) that help in generating high quality rules

About

The workflow for LUX looks as follows:

• You train an arbitrary selected machine learning model on your train dataset. The only requirements is that the model is able to output probabilities.

• Next, you generate neighbourhood of an instance you wish to explain and you feed this neighbourhood to your model.

• You obtain a decision stump, which locally explains the model and is executable by HeaRTDroid inference engine

• You can obtain explanation for a selected instance (the number after # represents confidence of an explanation):

['IF x2  < 0.01 AND  THEN class = 1 # 0.9229009792453621']

Installation

pip install lux-explainer

If you want to use LUX with JupyterLab install it and run:

pip installta jupyterlab
jupyter lab

Caution: If you want to use LUX with categorical data, it is advised to use multiprocessing gower distance package (due to high computational complexity of the problem).

Usage

• For complete usage see lux_usage_example.ipynb
• Fos usage example with Shap integration see lux_usage_example_shap.ipynb

Simple example on Iris dataset

from lux.lux import LUX
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm
import numpy as np
import pandas as pd
# import some data to play with
iris = datasets.load_iris()
features = ['sepal_length','sepal_width','petal_length','petal_width']
target = 'class'

#create daatframe with columns names as strings (LUX accepts only DataFrames withj string columns names)
df_iris = pd.DataFrame(iris.data,columns=features)
df_iris[target] = iris.target

#train classifier
train, test = train_test_split(df_iris)
clf = svm.SVC(probability=True)
clf.fit(train[features],train[target])
clf.score(test[features],test[target])

#pick some instance from datasetr
iris_instance = train[features].sample(1).values
iris_instance

#train lux on neighbourhood equal 20 instances
lux = LUX(predict_proba = clf.predict_proba, neighborhood_size=20,max_depth=2,  node_size_limit = 1, grow_confidence_threshold = 0 )
lux.fit(train[features], train[target], instance_to_explain=iris_instance,class_names=[0,1,2])

#see the justification of the instance being classified for a given class
lux.justify(np.array(iris_instance))

The above code should give you the answer as follows:

['IF petal_length >= 5.15 THEN class = 2 # 0.9833409059468439\n']

Alternatively one can get counterfactual explanation for a given instance by calling:

cf = lux.counterfactual(np.array(iris_instance), train[features], counterfactual_representative='nearest', topn=1)[0]
print(f"Counterfactual for {iris_instance} to change from class {lux.predict(np.array(iris_instance))[0]} to class {cf['prediction']}: \n{cf['counterfactual']}")

The result from the above query should look as follows:

Counterfactual for [[7.7 2.6 6.9 2.3]] to change from class 2 to class 1: 
sepal_length    6.9
sepal_width     3.1
petal_length    5.1
petal_width     2.3

Rule-based model for local uncertain explanations

You can obtain a whole rule-based model for the local uncertain explanation that was generated by LUX for given instance by running following code

#have a look at the entire rule-based model that can be executed with https:://heartdroid.re
print(lux.to_HMR())

This will generate model which can later be executed by HeaRTDroid

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TYPES DEFINITIONS %%%%%%%%%%%%%%%%%%%%%%%%%%

xtype [
 name: petal_length, 
base:numeric,
domain : [-100000 to 100000]].
xtype [
 name: class, 
base:symbolic,
 domain : [1,0,2]].

%%%%%%%%%%%%%%%%%%%%%%%%% ATTRIBUTES DEFINITIONS %%%%%%%%%%%%%%%%%%%%%%%%%%
xattr [ name: petal_length,
 type:petal_length,
 class:simple,
 comm:out ].
xattr [ name: class,
 type:class,
 class:simple,
 comm:out ].

%%%%%%%%%%%%%%%%%%%%%%%% TABLE SCHEMAS DEFINITIONS %%%%%%%%%%%%%%%%%%%%%%%%
 xschm tree : [petal_length]==> [class].
xrule tree/0:
[petal_length  lt 3.05] ==> [class set 0]. # 0.9579256691362875
xrule tree/1:
[petal_length  gte 3.05, petal_length  lt 5.15] ==> [class set 1]. # 0.8398308552545226
xrule tree/2:
[petal_length  gte 3.05, petal_length  gte 5.15] ==> [class set 2]. # 0.9833409059468439

Visualization of the local uncertain explanation

Similarly you can obtain visualization of the rule-based model in a form of decision tree by executing following code.

import graphviz
from graphviz import Source
from IPython.display import SVG, Image
lux.uid3.tree.save_dot('tree.dot',fmt='.2f',visual=True, background_data=train)
gvz=graphviz.Source.from_file('tree.dot')
!dot -Tpng tree.dot > tree.png
Image('tree.png')

The code should yield something like that (depending on the instance that was selected):

Cite this work

@misc{bobek2023local,
      title={Local Universal Rule-based Explanations}, 
      author={Szymon Bobek and Grzegorz J. Nalepa},
      year={2023},
      eprint={2310.14894},
      archivePrefix={arXiv},
      primaryClass={cs.AI}

}