WhatIfRegr {counterfactuals}

WhatIf for Regression Tasks

Description

WhatIf returns the n_counterfactual most similar observations to x_interest from observations in predictor$data$X whose prediction is in the desired_outcome interval.

Details

Only observations whose features values lie between the corresponding values in lower and upper are considered counterfactual candidates.

Super classes

counterfactuals::CounterfactualMethod -> counterfactuals::CounterfactualMethodRegr -> WhatIfRegr

Methods

Public methods

• WhatIfRegr$new()

• WhatIfRegr$clone()

Inherited methods

• counterfactuals::CounterfactualMethod$print()
• counterfactuals::CounterfactualMethodRegr$find_counterfactuals()

Method new()

Create a new WhatIfRegr object.

Usage

WhatIfRegr$new(
  predictor,
  n_counterfactuals = 1L,
  lower = NULL,
  upper = NULL,
  distance_function = "gower"
)

Arguments

predictor

(Predictor)
The object (created with iml::Predictor$new()) holding the machine learning model and the data.

n_counterfactuals

(integerish(1))
The number of counterfactuals to return Default is 1L.

lower

(numeric() | NULL)
Vector of minimum values for numeric features. If NULL (default), the element for each numeric feature in lower is taken as its minimum value in predictor$data$X. If not NULL, it should be named with the corresponding feature names.

upper

(numeric() | NULL)
Vector of maximum values for numeric features. If NULL (default), the element for each numeric feature in upper is taken as its maximum value in predictor$data$X. If not NULL, it should be named with the corresponding feature names.

distance_function

(⁠function()⁠ | 'gower' | 'gower_c')
The distance function used to compute the distances between x_interest and the training data points for finding x_nn. Either the name of an already implemented distance function ('gower' or 'gower_c') or a function. If set to 'gower' (default), then Gower's distance (Gower 1971) is used; if set to 'gower_c', a C-based more efficient version of Gower's distance is used. A function must have three arguments x, y, and data and should return a double matrix with nrow(x) rows and maximum nrow(y) columns.

Method clone()

The objects of this class are cloneable with this method.

Usage

WhatIfRegr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Gower, J. C. (1971), "A general coefficient of similarity and some of its properties". Biometrics, 27, 623–637.

Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F., & Wilson, J. (2019). The what-if tool: Interactive probing of machine learning models. IEEE transactions on visualization and computer graphics, 26(1), 56–65.

Examples

if (require("randomForest")) {
  set.seed(123456)
  # Train a model
  rf = randomForest(mpg ~ ., data = mtcars)
  # Create a predictor object
  predictor = iml::Predictor$new(rf)
  # Find counterfactuals for x_interest
  wi_regr = WhatIfRegr$new(predictor, n_counterfactuals = 5L)
  cfactuals = wi_regr$find_counterfactuals(
    x_interest = mtcars[1L, ], desired_outcome = c(22, 26)
  )
  # Print the results
  cfactuals
}