Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
R
 
 
man
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

ShapleyR

shapleyR is an R package that provides some functionality to use mlr tasks and models to generate shapley values. And thus analyze the effects of the features on the outcome of a model. shapleyR already supports the regression, classification, clustering and multilabel tasks from mlr. We plan to add the missing tasks from that package.

The package can be installed directly from github with devtools (see following section). Beside that we also plan to upload this package to CRAN as soon as it gets production ready.

Installation

install.packages("devtools")
devtools::install_github('redichh/shapleyR')
library(shapleyR)

Quickstart

As a quickstart we will calculate the shapley values for a regression task. For that we take a look at the Boston Housing dataset. This is alredy included in the mlr-package and can be called with bh.task from the R terminal. The Dataset looks as following:

> head(getTaskData(bh.task))
crim    zn indus chas   nox    rm  age    dis rad tax ptratio      b lstat medv
0.00632 18  2.31    0 0.538 6.575 65.2 4.0900   1 296    15.3 396.90  4.98 24.0
0.02731  0  7.07    0 0.469 6.421 78.9 4.9671   2 242    17.8 396.90  9.14 21.6
0.02729  0  7.07    0 0.469 7.185 61.1 4.9671   2 242    17.8 392.83  4.03 34.7
0.03237  0  2.18    0 0.458 6.998 45.8 6.0622   3 222    18.7 394.63  2.94 33.4
0.06905  0  2.18    0 0.458 7.147 54.2 6.0622   3 222    18.7 396.90  5.33 36.2
0.02985  0  2.18    0 0.458 6.430 58.7 6.0622   3 222    18.7 394.12  5.21 28.7

Wheras the first 13 features are the describing variables and the last one "medv" is the dependant variable. The prediction for the medv feature is:

> prediction = head(getPredictionResponse(predict(train("regr.lm", bh.task), newdata = getTaskData(bh.task))))
[1] 30.00384 25.02556 30.56760 28.60704 27.94352 25.25628

And the mean for medv over all observation available in the dataset is:

> data.mean = mean(getTaskData(bh.task)[,getTaskTargetNames(bh.task)])
[1] 22.53281

Now we can take a look at the shapley-function itself. Running following code shows the influence for every feature according to a specific observation:

> shap.values = getShapleyValues(shapley(1:6, task = bh.task, model = train("regr.lm", bh.task)))
  _Id _Class  crim     zn  indus   chas   nox     rm    age    dis    rad   tax ptratio     b lstat
1   1     NA 0.275  0.256 -0.185 -0.358 1.070  1.632 -0.002  0.065 -2.510 1.229   2.690 0.337 4.468
2   2     NA 0.503 -0.385 -0.078 -0.358 2.026 -0.014  0.005 -2.157 -1.969 2.419   0.407 0.455 2.233
3   3     NA 0.389 -0.244 -0.090 -0.269 1.542  3.346 -0.008 -1.740 -2.724 2.139   0.213 0.227 4.538
4   4     NA 0.363 -0.390 -0.152 -0.090 1.673  2.048 -0.014 -3.148 -1.938 1.746  -0.254 0.318 4.894
5   5     NA 0.427 -0.666 -0.167 -0.448 1.840  3.286 -0.012 -3.435 -3.060 2.852   0.079 0.450 4.004
6   6     NA 0.454 -0.430 -0.165  0.000 1.957  1.211 -0.010 -3.336 -1.367 2.455  -0.143 0.666 3.907

Taking the sum of all explaining features for every row results in the following:

> approximation = rowSums(shap.values[,getTaskFeatureNames(bh.task)])
[1] 8.967 3.087 7.319 5.056 5.150 5.199

And this is the approximated difference between the previously calculated prediction and data.mean. Assuming that the sum of all shapley values for one observation equals the difference between the prediction and the data mean the following calculation should be close to zero:

> prediction - data.mean - approximation
[1] -1.4959629 -0.5942439  0.7157904  1.0182302  0.2607179 -2.4755219

We see that this is not the case. But increasing the amount of iterations should lead to better results:

> shap.values.2 = getShapleyValues(shapley(1:6, task = bh.task, model = train("regr.lm", bh.task), iterations = 200))
> approximation.2 = rowSums(shap.values.2[,getTaskFeatureNames(bh.task)])
> prediction - data.mean - approximation.2
[1] -0.1209629  0.7367561 -0.3352096 -0.1327698 -0.1692821 -0.1395219

Related Work

tbd

More information

In our Vignette can be found further information about this package. There is also shown the usage of plots for the shapley values.

About

Package for a nice and smoothe usage of the shapley value for mlr

Topics

Resources

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •  

Languages