Title: Wrapper of Python Library 'shap'
Version: 0.1.3
Description: Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <doi:10.48550/arXiv.1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
License: GPL-2 | GPL-3 [expanded from: GPL]
Encoding: UTF-8
LazyData: true
URL: https://github.com/ModelOriented/shapper
BugReports: https://github.com/ModelOriented/shapper/issues
RoxygenNote: 7.1.1
Imports: reticulate, DALEX, ggplot2
Suggests: covr, knitr, randomForest, rpart, testthat, markdown, qpdf
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-28 08:34:06 UTC; 01131304
Author: Szymon Maksymiuk [aut, cre], Alicja Gosiewska [aut], Przemyslaw Biecek [aut], Mateusz Staniak [ctb], Michal Burdukiewicz [ctb]
Maintainer: Szymon Maksymiuk <sz.maksymiuk@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-28 09:00:03 UTC

Individual Variable Effect

Description

Individual Variable Effect

Usage

individual_variable_effect(x, ...)

## S3 method for class 'explainer'
individual_variable_effect(
  x,
  new_observation,
  method = "KernelSHAP",
  nsamples = "auto",
  ...
)

## Default S3 method:
individual_variable_effect(
  x,
  data,
  predict_function = predict,
  new_observation,
  label = tail(class(x), 1),
  method = "KernelSHAP",
  nsamples = "auto",
  ...
)

shap(x, ...)

Arguments

x

a model to be explained, or an explainer created with function explain.

...

other parameters.

new_observation

an observation/observations to be explained. Required for local/instance level explainers. Columns in should correspond to columns in the data argument. Data set should not contain any additional columns.

method

an estimation method of SHAP values. Currently the only availible is 'KernelSHAP'.

nsamples

number of samples or "auto". Note that number must be as integer. Use 'as.integer()'.

data

validation dataset. Used to determine univariate distributions, calculation of quantiles, correlations and so on. It will be extracted from 'x' if it’s an explainer.

predict_function

predict function that operates on the model 'x'. Since the model is a black box, the 'predict_function' is the only interface to access values from the model. It should be a function that takes at least a model 'x' and data and returns vector of predictions. If model response has more than a single number (like multiclass models) then this function should return a marix/data.frame of the size 'm' x 'd', where 'm' is the number of observations while 'd' is the dimensionality of model response. It will be extracted from 'x' if it’s an explainer.

label

name of the model. By default it’s extracted from the class attribute of the model

Value

an object of class individual_variable_effect with shap values of each variable for each new observation. Columns:

In order to use shapper with other python virtual environment following R command are required to execute reticulate::use_virtualenv("path_to_your_env") or for conda reticulate::use_conda("name_of_conda_env") before attaching shapper.

Examples

have_shap <- reticulate::py_module_available("shap")

if(have_shap){
  library("shapper")
  library("DALEX")
  library("randomForest")
  Y_train <- HR$status
  x_train <- HR[ , -6]
  set.seed(123)
  model_rf <- randomForest(x = x_train, y = Y_train, ntree= 50)
  p_function <- function(model, data) predict(model, newdata = data, type = "prob")

  ive_rf <- individual_variable_effect(model_rf, data = x_train, predict_function = p_function,
                                     new_observation = x_train[1:2,], nsamples = 50)
  ive_rf
} else{
    print('Python testing environment is required.')
}

Install shap Python library

Description

Install shap Python library

Usage

install_shap(method = "auto", conda = "auto", envname = NULL)

Arguments

method

Installation method. By default, "auto". It is passed to the py_install function from package 'reticulate'.

conda

Path to conda executable. It is passed to the py_install function from package 'reticulate'.

envname

Name of environment to install shapp package into. If NULL it will install into default It is passed to the py_install function from package 'reticulate'.

To use conda installation execute install_shap(method = "conda", envname = nameofenv) Please keep in mind that winodws accepts only conda instalations

Examples

## Not run: 
  install_shap((method = "auto", conda = "auto")

## End(Not run)

Plots Attributions for Variables of Individual Prediction

Description

Function 'plot.individual_variable_effect' plots variables effects plots.

Usage

## S3 method for class 'individual_variable_effect'
plot(
  x,
  ...,
  id = 1,
  digits = 2,
  rounding_function = round,
  show_predicted = TRUE,
  show_attributions = TRUE,
  cols = c("label", "id"),
  rows = "ylevel",
  selected = NULL,
  bar_width = 8,
  vcolors = c(`-` = "#f05a71", `0` = "#371ea3", `+` = "#8bdcbe", X = "#371ea3", pred =
    "#371ea3")
)

Arguments

x

an individual variable effect explainer produced with function 'individual_variable_effect()'

...

other explainers that shall be plotted together

id

of observation. By default first observation is taken.

digits

number of decimal places (round) or significant digits (signif) to be used. See the rounding_function argument.

rounding_function

function that is to used for rounding numbers. It may be signif() which keeps a specified number of significant digits. Or the default round() to have the same precision for all components

show_predicted

show arrows for predicted values.

show_attributions

show attributions values.

cols

A vector of characters defining faceting groups on columns dimension. Possible values: 'label', 'id', 'ylevel'.

rows

A vector of characters defining faceting groups on rows dimension. Possible values: 'label', 'id', 'ylevel'.

selected

A vector of characters. If specified, then only selected classes are presented

bar_width

width of bars. By default 8

vcolors

named vector with colors

Value

a ggplot2 object

Examples

have_shap <- reticulate::py_module_available("shap")

if(have_shap){
  library("shapper")
  library("DALEX")
  library("randomForest")
  Y_train <- HR$status
  x_train <- HR[ , -6]
  set.seed(123)
  model_rf <- randomForest(x = x_train, y = Y_train, ntree = 50)
  p_function <- function(model, data) predict(model, newdata = data, type = "prob")

  ive_rf <- individual_variable_effect(model_rf, data = x_train, predict_function = p_function,
                                     new_observation = x_train[1:2,], nsamples = 50)
  pl1 <- plot(ive_rf, bar_width = 4)
  pl2 <- plot(ive_rf, bar_width = 4, show_predicted = FALSE)
  pl3 <- plot(ive_rf, bar_width = 4, show_predicted = FALSE,
              cols = c("id","ylevel"), rows = "label")
  print(pl1)
  print(pl2)
  print(pl3)
} else {
    print('Python testing environment is required.')
}

Print Individual Variable Effects

Description

Print Individual Variable Effects

Usage

## S3 method for class 'individual_variable_effect'
print(x, ...)

Arguments

x

an individual variable importance explainer created with the individual_variable_effect function.

...

further arguments passed to or from other methods.

Examples

have_shap <- reticulate::py_module_available("shap")

if(have_shap){
  library("shapper")
  library("DALEX")
  library("randomForest")
  Y_train <- HR$status
  x_train <- HR[ , -6]
  set.seed(123)
  model_rf <- randomForest(x = x_train, y = Y_train, ntree= 50)
  p_function <- function(model, data) predict(model, newdata = data, type = "prob")

  ive_rf <- individual_variable_effect(model_rf, data = x_train, predict_function = p_function,
                                     new_observation = x_train[1:2,], nsamples = 50)
  print(ive_rf)
}else{
    print('Python testing environment is required.')
}

DrWhy Theme for ggplot objects

Description

DrWhy Theme for ggplot objects

Usage

theme_drwhy_colors(n = 2)

Arguments

n

number of colors for color palette

Value

theme for ggplot2 objects