Base class for Counterfactual Explanation Methods for Regression Tasks

Abstract base class for counterfactual explanation methods for regression tasks.

CounterfactualMethodRegr can only be initialized for regression tasks. Child classes inherit the (public) $find_counterfactuals() method, which calls a (private) $run() method. This $run() method should be implemented by the child classes and return the counterfactuals as a data.table (preferably) or a data.frame.

Inheritance

Child classes: MOCRegr, WhatIfRegr, NICERegr

Super class

counterfactuals::CounterfactualMethod -> CounterfactualMethodRegr

Methods

Public methods

• CounterfactualMethodRegr$new()

• CounterfactualMethodRegr$find_counterfactuals()

• CounterfactualMethodRegr$clone()

Inherited methods

• counterfactuals::CounterfactualMethod$print()

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Method new()

Creates a new CounterfactualMethodRegr object.

Usage

CounterfactualMethodRegr$new(
  predictor,
  lower = NULL,
  upper = NULL,
  distance_function = NULL
)

Arguments

predictor

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

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() | NULL)
A distance function that may be used by the leaf classes. If specified, the function must have three arguments: x, y, and data and return a double matrix with nrow(x) rows and nrow(y) columns.

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Method find_counterfactuals()

Runs the counterfactual method and returns the counterfactuals. It searches for counterfactuals that have a predicted outcome in the interval desired_outcome.

Usage

CounterfactualMethodRegr$find_counterfactuals(x_interest, desired_outcome)

Arguments

x_interest

(data.table(1) | data.frame(1))
A single row with the observation of interest.

desired_outcome

(numeric(1) | numeric(2))
The desired predicted outcome. It can be a numeric scalar or a vector with two numeric values that specify an outcome interval. A scalar is internally converted to an interval.

Returns

A Counterfactuals object containing the results.

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Method clone()

The objects of this class are cloneable with this method.

Usage

CounterfactualMethodRegr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.