GitHub - MI2DataLab/pyBreakDown: Python implementation of R package breakDown (original) (raw)
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
from sklearn import datasets
x = datasets.load_boston()
feature_names = x.feature_names
Prepare model
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 imports from pyBreakDown.explainer import Explainer from pyBreakDown.explanation import Explanation
#make explainer object exp = Explainer(clf=model, data=train_data, colnames=feature_names)
#make explanation object that contains all information explanation = exp.explain(observation=data[302,:],direction="up")
Text form of explanations
#get information in text form explanation.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 form explanation.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
#customize height, width and dpi of plot explanation.visualize(figsize=(8,5),dpi=100)
#for different baselines than zero explanation = exp.explain(observation=data[302,:],direction="up",useIntercept=True) # baseline==intercept explanation.visualize(figsize=(8,5),dpi=100)
