A Counterfactuals object should be created by the $find_counterfactuals method of CounterfactualMethodRegr
or CounterfactualMethodClassif.
It contains the counterfactuals and has several methods for their evaluation and visualization.
Gower, J. C. (1971), "A general coefficient of similarity and some of its properties". Biometrics, 27, 623–637.
desired(list(1) | list(2))
A list with the desired properties of the counterfactuals.
For regression tasks it has one element desired_outcome (CounterfactualMethodRegr) and for
classification tasks two elements desired_class and desired_prob (CounterfactualMethodClassif).
data(data.table)
The counterfactuals for x_interest.
x_interest(data.table(1))
A single row with the observation of interest.
distance_function(function())
The distance function used in the second and fourth evaluation measure.
The function must have three arguments:
x, y, and data and return a numeric matrix. If set to NULL (default), then Gower distance (Gower 1971) is used.
method(character)
A single row with the observation of interest.
new()Creates a new Counterfactuals object.
This method should only be called by the $find_counterfactuals methods of CounterfactualMethodRegr
and CounterfactualMethodClassif.
Counterfactuals$new(
cfactuals,
predictor,
x_interest,
param_set,
desired,
method = NULL
)cfactuals(data.table)
The counterfactuals. Must have the same column names and types as predictor$data$X.
predictor(Predictor)
The object (created with iml::Predictor$new()) holding the machine learning model and the data.
x_interest(data.table(1) | data.frame(1))
A single row with the observation of interest.
param_set(ParamSet)
A ParamSet based on the features of predictor$data$X.
desired(list(1) | list(2))
A list with the desired properties of the counterfactuals. It should have one element desired_outcome for
regression tasks (CounterfactualMethodRegr) and two elements desired_class and desired_prob
for classification tasks (CounterfactualMethodClassif).
method(character)
Name of the method with which counterfactuals were generated. Default is
NULL which means that no name is provided.
evaluate()Evaluates the counterfactuals. It returns the counterfactuals together with the evaluation measures.
Counterfactuals$evaluate(
measures = c("dist_x_interest", "dist_target", "no_changed", "dist_train",
"minimality"),
show_diff = FALSE,
k = 1L,
weights = NULL
)measures(character)
The name of one or more evaluation measures.
The following measures are available:
dist_x_interest: The distance of a counterfactual to x_interest measured by Gower's
dissimilarity measure (Gower 1971).
dist_target: The absolute distance of the prediction for a counterfactual to the interval desired_outcome
(regression tasks) or desired_prob (classification tasks).
no_changed: The number of feature changes w.r.t. x_interest.
dist_train: The (weighted) distance to the k nearest training data points measured by Gower's
dissimilarity measure (Gower 1971).
minimality: The number of changed features that each could be set to the
value of x_interest while keeping the desired prediction value.
show_diff(logical(1))
Should the counterfactuals be displayed as their differences to x_interest? Default is FALSE.
If set to TRUE, positive values for numeric features indicate an increase compared to the feature value in
x_interest, negative values indicate a decrease. For factors, the feature value is displayed if it differs
from x_interest; NA means "no difference" in both cases.
k(integerish(1))
How many nearest training points should be considered for computing the dist_train measure? Default is 1L.
weights(numeric(k) | NULL)
How should the k nearest training points be weighted when computing the dist_train measure? If NULL
(default) then all k points are weighted equally. If a numeric vector of length k is given, the i-th element
specifies the weight of the i-th closest data point.
evaluate_set()Evaluates a set of counterfactuals. It returns the evaluation measures.
Counterfactuals$evaluate_set(
measures = c("diversity", "no_nondom", "frac_nondom", "hypervolume"),
nadir = NULL
)measures(character)
The name of one or more evaluation measures.
The following measures are available:
diversity: Diversity of returned counterfactuals in the feature space
no_nondom: Number of counterfactuals that are not dominated by other
counterfactuals.
frac_nondom: Fraction of counterfactuals that are not dominated by
other counterfactuals
hypervolume: Hypervolume of the induced Pareto front
nadir(numeric)
Max objective values to calculate dominated hypervolume.
Only considered, if hypervolume is one of the measures.
May be a scalar, in which case it is used for all four objectives,
or a vector of length 4.
Default is NULL, meaning the nadir point by Dandl et al. (2020) is used:
(min distance between prediction of x_interest to desired_prob/_outcome,
1, number of features, 1).
subset_to_valid()Subset data to those meeting the desired prediction,
Process could be reverted using revert_subset_to_valid().
revert_subset_to_valid()Subset data to those meeting the desired prediction,
Process could be reverted using revert_subset_to_valid().
plot_parallel()Plots a parallel plot that connects the (scaled) feature values of each counterfactual and highlights
x_interest in blue.
feature_names(character | NULL)
The names of the (numeric) features to display. If NULL (default) all features are displayed.
row_ids(integerish | NULL)
The row ids of the counterfactuals to display. If NULL (default) all counterfactuals are displayed.
digits_min_maxMaximum number of digits for the minimum and maximum features values. Default is 2L.
plot_freq_of_feature_changes()Plots a bar chart with the frequency of feature changes across all counterfactuals.
get_freq_of_feature_changes()Returns the frequency of feature changes across all counterfactuals.
plot_surface()Creates a surface plot for two features. x_interest is represented as a white dot and
all counterfactuals that differ from x_interest only in the two selected features are represented as black dots.
The tick marks next to the axes show the marginal distribution of the observed data (predictor$data$X).
The exact plot type depends on the selected feature types and number of features:
2 numeric features: surface plot
2 non-numeric features: heatmap
1 numeric or non-numeric feature: line graph