pyBreakDown

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()

data = x.data

feature_names = x.feature_names

y = x.target

Prepare model

import numpy as np

from sklearn import tree

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

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)