shapper (version 0.1.3)

plot.individual_variable_effect: Plots Attributions for Variables of Individual Prediction

Description

Function 'plot.individual_variable_effect' plots variables effects plots.

Usage

# S3 method for 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

# NOT RUN {
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.')
}
# }