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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.
Nothing fancy, just python 3.5.2+ and pip.
Install directly from github
git clone https://github.com/bondyra/pyBreakDown
cd ./pyBreakDown
python3 setup.py install # (or use pip install . instead)
from sklearn import datasetsx = datasets.load_boston()data = x.datafeature_names = x.feature_namesy = x.targetimport numpy as npfrom sklearn import treemodel = tree.DecisionTreeRegressor()train_data = data[1:300,:]
train_labels=y[1:300]model = model.fit(train_data,y=train_labels)#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")#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
explanation.visualize()
#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)