Python implementation of R package breakDown

Related tags

Deep Learning Model Explanation pyBreakDown
Overview

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()

png

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

png

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

png

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Comments
  • Bump numpy from 1.14.2 to 1.22.0

    Bump numpy from 1.14.2 to 1.22.0

    Bumps numpy from 1.14.2 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Transfer pyBreakDown from MI2DataLab to ModelOriented

    Transfer pyBreakDown from MI2DataLab to ModelOriented

    Let's move the MI2DataLab/pyBreakDown package to ModelOriented/pyBreakDown. All DrWhy projects will be in one organization. Suggested date for the movement: August 21st

    opened by pbiecek 0
  • Making the Explainer work with both classification and regression models

    Making the Explainer work with both classification and regression models

    The explainer used model.predict() which for classification returns the predicted class label, which is then cast to 0 or 1. The correct approach is to use model.predict_proba() instead.

    This fix also adds some unit tests to ensure code quality. Minor PEP related refactoring has also been done.

    By Tomasz Rudny, Alistair Garfoot @ MindFoundry (http://mindfoundry.ai)

    opened by tomaszmf 2
  • clf.predict or clf.predict_proba ?

    clf.predict or clf.predict_proba ?

    The explainer._predict_down() method, in line 116 has the following:

    target_yhat = self.clf.predict(observation)

    But for classification this is always cast to 1.0 or 0.0 (for binary classification), thus making the contributions sum up to 0.0 or 1.0 and not to the predicted probability of the model.

    Should the aforementioned line not be changed into:

    target_yhat = self.clf.predict_proba(observation)

    ?

    thanks.

    opened by tomaszmf 1
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