pyBreakDown

Please note that the Break Down method is moved to the dalex Python package which is actively maintained. If you will experience any problem with pyBreakDown please consider the dalex implementation at https://dalex.drwhy.ai/python/api/.

Python implementation of breakDown package (https://github.com/pbiecek/breakDown).

Docs: https://pybreakdown.readthedocs.io.

Requirements

Nothing fancy, just python 3.5.2+ and pip.

Installation

Install directly from github

 git clone https://github.com/bondyra/pyBreakDown
cd ./pyBreakDown
python3 setup.py install # (or use pip install . instead)

Basic usage

Load dataset

fromsklearnimportdatasets

x=datasets.load_boston()

data=x.data

feature_names=x.feature_names

y=x.target

Prepare model

importnumpyasnp

fromsklearnimporttree

model=tree.DecisionTreeRegressor()

Train model

train_data=data[1:300,:]
train_labels=y[1:300]

model=model.fit(train_data,y=train_labels)

Explain predictions on test data

#necessary importsfrompyBreakDown.explainerimportExplainerfrompyBreakDown.explanationimportExplanation

#make explainer objectexp=Explainer(clf=model, data=train_data, colnames=feature_names)

#make explanation object that contains all informationexplanation=exp.explain(observation=data[302,:],direction="up")

Text form of explanations

#get information in text formexplanation.text()

Feature Contribution Cumulative Intercept = 1 29.1 29.1 RM = 6.495 -1.98 27.12 TAX = 329.0 -0.2 26.92 B = 383.61 -0.12 26.79 CHAS = 0.0 -0.07 26.72 NOX = 0.433 -0.02 26.7 RAD = 7.0 0.0 26.7 INDUS = 6.09 0.01 26.71 DIS = 5.4917 -0.04 26.66 ZN = 34.0 0.01 26.67 PTRATIO = 16.1 0.04 26.71 AGE = 18.4 0.06 26.77 CRIM = 0.09266 1.33 28.11 LSTAT = 8.67 4.6 32.71 Final prediction 32.71 Baseline = 0

#customized text formexplanation.text(fwidth=40, contwidth=40, cumulwidth=40, digits=4)

Feature Contribution Cumulative Intercept = 1 29.1 29.1 RM = 6.495 -1.9826 27.1174 TAX = 329.0 -0.2 26.9174 B = 383.61 -0.1241 26.7933 CHAS = 0.0 -0.0686 26.7247 NOX = 0.433 -0.0241 26.7007 RAD = 7.0 0.0 26.7007 INDUS = 6.09 0.0074 26.708 DIS = 5.4917 -0.0438 26.6642 ZN = 34.0 0.0077 26.6719 PTRATIO = 16.1 0.0385 26.7104 AGE = 18.4 0.0619 26.7722 CRIM = 0.09266 1.3344 28.1067 LSTAT = 8.67 4.6037 32.7104 Final prediction 32.7104 Baseline = 0

Visual form of explanations

explanation.visualize()

#customize height, width and dpi of plotexplanation.visualize(figsize=(8,5),dpi=100)

#for different baselines than zeroexplanation=exp.explain(observation=data[302,:],direction="up",useIntercept=True) # baseline==interceptexplanation.visualize(figsize=(8,5),dpi=100)