In the following, we explain the `counterfactuals`

workflow for both a classification and a regression task using concrete
use cases.

```
library("counterfactuals")
library("iml")
library("randomForest")
library("mlr3")
library("mlr3learners")
```

To illustrate the `counterfactuals`

workflow for
classification tasks, we search for counterfactuals with
`MOC`

(Dandl et al. 2020) for a bank customer, whose credit
application is rejected.

As training data, we use the German Credit Data from the
`rchallenge`

package. The data set contains 1000 observations
with 21 features and the binary target variable
`credit_risk`

. For illustrative purposes, we only consider 8
of the 21 features in the following:

```
data(german, package = "rchallenge")
credit = german[, c("duration", "amount", "purpose", "age",
"employment_duration", "housing", "number_credits", "credit_risk")]
```

Variable | Description |
---|---|

duration | Credit duration |

amount | Credit amount (DM) |

purpose | Purpose of credit |

age | Age (years) |

savings | Amount in savings account |

employment_duration | Years in present employment |

housing | Type of housing |

number_credits | Number of credits |

credit_risk | Class variable (credit worthiness) |

First, we train a model to predict whether a credit is
`good`

or `bad`

, omitting one observation from the
training data, which is `x_interest`

.

An `iml::Predictor`

object serves as a wrapper for different model types. It contains the
model and the data for its analysis.

```
predictor = iml::Predictor$new(rf, type = "prob")
x_interest = credit[998L, ]
predictor$predict(x_interest)
#> bad good
#> 1 0.618 0.382
```

For `x_interest`

, the model predicts a probability of
being a bad credit risk of 0.38.

Now, we examine which factors need to be changed to incrase the
predicted probability of being a good credit risk to more than
60%.

Since we want to apply MOC to a classification model, we initialize a
`MOCClassif`

object. Individuals whose prediction is farther
away from the desired prediction than `epsilon`

can be
penalized. Here, we set `epsilon = 0`

, penalizing all
individuals whose prediction is outside the desired interval. With the
`fixed_features`

argument, we can fix non-actionable
features, here `age`

and
`employment_duration`

.

```
moc_classif = MOCClassif$new(
predictor, epsilon = 0, fixed_features = c("age", "employment_duration"),
quiet = TRUE, termination_crit = "genstag", n_generations = 10L
)
```

Then, we use the `find_counterfactuals()`

method to find
counterfactuals for `x_interest`

. As we aim to find
counterfactuals with a predicted probability of being a good credit risk
of at least 60%, we set the `desired_class`

to
`"good"`

and the `desired_prob`

to
`c(0.6, 1)`

.

The resulting `Counterfactuals`

object holds the
counterfactuals in the `data`

field and possesses several
methods for their evaluation and visualization. Printing a
`Counterfactuals`

object, gives an overview of the
results.

```
print(cfactuals)
#> 82 Counterfactual(s)
#>
#> Desired class: good
#> Desired predicted probability range: [0.6, 1]
#>
#> Head:
#> duration amount purpose age employment_duration housing number_credits
#> <int> <int> <fctr> <int> <fctr> <fctr> <ord>
#> 1: 21 7460 others 30 >= 7 yrs own 1
#> 2: 21 7054 others 30 >= 7 yrs own 1
#> 3: 21 6435 others 30 >= 7 yrs own 1
```

The `predict()`

method returns the predictions for the
counterfactuals.

The `evaluate()`

method returns the counterfactuals along
with the evaluation measures `dist_x_interest`

,
`dist_target`

, `no_changed`

, and
`dist_train`

.

Setting the `show_diff`

argument to `TRUE`

displays the counterfactuals as their difference to
`x_interest`

: for a numeric feature, positive values indicate
an increase compared to the feature value in `x_interest`

and
negative values indicate a decrease; for factors, the counterfactual
feature value is displayed if it differs from `x_interest.`

;
`NA`

means “no difference” in both cases.

```
head(cfactuals$evaluate(show_diff = TRUE,
measures = c("dist_x_interest", "dist_target", "no_changed", "dist_train")), 3L)
#> duration amount purpose age employment_duration housing number_credits dist_x_interest no_changed dist_train dist_target
#> <num> <num> <char> <num> <char> <char> <char> <num> <int> <num> <num>
#> 1: NA -5220 <NA> NA <NA> <NA> <NA> 0.04103193 1 0.04215022 0
#> 2: NA -5626 <NA> NA <NA> <NA> <NA> 0.04422330 1 0.03895885 0
#> 3: NA -6245 <NA> NA <NA> <NA> <NA> 0.04908897 1 0.03409318 0
```

By design, not all counterfactuals generated with MOC have a
prediction equal to the desired prediction. We can use
`subset_to_valid()`

to omit all counterfactuals that do not
achieve the desired prediction. This step can be reverted with
`revert_subset_to_valid()`

.

The `plot_freq_of_feature_changes()`

method plots the
frequency of feature changes across all remaining counterfactuals.
Setting `subset_zero = TRUE`

removes all unchanged features
from the plot.

The parallel plot connects the (scaled) feature values of each
counterfactual and highlights `x_interest`

in blue. We
specify `feature_names`

to order the features according to
their frequency of changes.

```
cfactuals$plot_parallel(feature_names = names(
cfactuals$get_freq_of_feature_changes()), digits_min_max = 2L)
```

In the following surface plot, the white dot represents
`x_interest`

. All counterfactuals that differ from
`x_interest`

only in the selected features are displayed as
black dots. The tick marks next to the axes indicate the marginal
distribution of the counterfactuals.

Additional diagnostic tools for MOC are available as part of the
MOCClassif and MOCRegr class. For example, the hypervolume indicator
(Zitzler and Thiele 1998) given a reference point (that represents the
maximal values of the objectives) could be computed. The evolution of
the hypervolume indicator can be plotted together with the evolution of
mean and minimum objective values using the
`plot_statistics()`

method.

[[1]] [[2]] [[3]]

Ideally, one would like the mean value of each objective to decrease
over the generations, leading to an increase of the hypervolume. We
could visualize the objective values of the emerging candidates
throughout the generations via the `plot_search`

method for
pairs of objectives.

Finding counterfactuals for regression models is analogous to
classification models. In this example, we use `NICE`

(Brughmans et al. (2002)) to search for counterfactuals for plasma .
Brughmans et al. introduced `NICE`

only for the
classification setting but for this package the method was extended to
also work for regression tasks by allowing prediction functions to
return real-valued outcomes instead of classification scores.

As training data, we use the plasma dataset from the
`gamlss.data`

package. The dataset contains 315 observations
with 13 features and the (continuous) target variable
`retplasma`

, the plasma retinol concentration in ng/ml. The
plasma retinol concentration is interesting because low concentrations
are associated with an increased risk for some types of cancer (Harrell
2022).

Variable | Description |
---|---|

age | Age (years) |

sex | Sex (1 = male, 2 = female) |

smokstat | Smoking status (1 = never, 2 = former, 3 = current smoker) |

bmi | Body mass index (weight/(height^2)) |

vituse | Vitamin use (1 = yes, fairly often, 2 = yes, not often, 3 = no) |

calories | Number of calories consumed per day |

fat | Grams of fat consumed per day |

fiber | Grams of fiber consumed per day |

alcohol | Number of alcoholic drinks consumed per week |

cholesterol | Cholesterol consumed (mg per day) |

betadiet | Dietary beta-carotene consumed (mcg per day) |

retdiet | Dietary retinol consumed (mcg per day) |

betaplasma | Plasma beta-carotene (ng/ml) |

retplasma | Plasma retinol (ng/ml) |

First, we train a model to predict `plasma_retinol`

, again
omitting `x_interest`

from the training data. This time we
use a regression tree trained with the `mlr3`

and
`rpart`

package.

Then, we initialize an `iml::Predictor`

object.

```
predictor = iml::Predictor$new(model, data = plasma, y = "retplasma")
x_interest = plasma[100L, ]
predictor$predict(x_interest)
#> .prediction
#> 1 342.9231
```

For `x_interest`

, the model predicts a plasma
concentration of 342.92.

Since we want to apply `NICE`

to a regression model, we
initialize a `NICERegr`

object. For regression models, we
define a correctly predicted datapoint when its prediction is less than
a user-specified value away. Here we allow for a deviation of
`margin_correct = 0.5`

. In this example, we aim for proximal
counterfactuals in additional to sparse ones, such that we set
`optimization = "proximity"`

.

```
nice_regr = NICERegr$new(predictor, optimization = "proximity",
margin_correct = 0.5, return_multiple = FALSE)
```

Then, we use the `find_counterfactuals()`

method to find
counterfactuals for `x_interest`

with a predicted plasma
concentration in the interval [500, Inf).

As a result, we obtain a `Counterfactuals`

object, just
like for the classification task.

```
cfactuals
#> 1 Counterfactual(s)
#>
#> Desired outcome range: [500, Inf]
#>
#> Head:
#> age sex smokstat bmi vituse calories fat fiber alcohol cholesterol betadiet retdiet betaplasma
#> <int> <fctr> <fctr> <num> <fctr> <num> <num> <num> <num> <num> <int> <int> <int>
#> 1: 46 1 3 35.25969 3 2667.5 131.6 10.1 0 550.5 1210 1291 218
```

To inspect the counterfactual, we can use the same tools as before. For example, in the surface plot, we see that increasing betaplasma helps while changing the age alone has no impact on the prediction.

At the beginning, `NICE`

calculates the distance of
`x_interest`

to each of the training samples. By default,
Gower’s distance measures this but users could also specify their own
distance functions in the `distance_function`

argument. For
example, the Gower distance can be replaces by the L_0 norm.

```
l0_norm = function(x, y, data) {
res = matrix(NA, nrow = nrow(x), ncol = nrow(y))
for (i in seq_len(nrow(x))) {
for (j in seq_len(nrow(y))) {
res[i, j] = sum(x[i,] != y[j,])
}
}
res
}
```

A short example illustrates the functionality of
`l0_norm()`

.

```
xt = data.frame(a = c(0.5), b = c("a"))
yt = data.frame(a = c(0.5, 3.2, 0.1), b = c("a", "b", "a"))
l0_norm(xt, yt, data = NULL)
#> [,1] [,2] [,3]
#> [1,] 0 2 1
```

Replacing the distance function is fairly easy:

```
nice_regr = NICERegr$new(predictor, optimization = "proximity",
margin_correct = 0.5, return_multiple = FALSE,
distance_function = l0_norm)
cfactuals = nice_regr$find_counterfactuals(x_interest,
desired_outcome = c(500, 1000))
cfactuals
#> 1 Counterfactual(s)
#>
#> Desired outcome range: [500, 1000]
#>
#> Head:
#> age sex smokstat bmi vituse calories fat fiber alcohol cholesterol betadiet retdiet betaplasma
#> <int> <fctr> <fctr> <num> <fctr> <num> <num> <num> <num> <num> <int> <int> <int>
#> 1: 46 1 3 35.25969 3 2667.5 131.6 10.1 0 550.5 1210 1291 218
```

Dandl, Susanne, Christoph Molnar, Martin Binder, and Bernd Bischl. 2020. “Multi-Objective Counterfactual Explanations.” In Parallel Problem Solving from Nature – PPSN XVI, edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann, 448–469. Cham: Springer International Publishing. .

Brughmans D, Martens D (2022). “NICE: An Algorithm for Nearest Instance Counterfactual Explanations.” Technical report, <arXiv:2104.07411> v2.

Zitzler, Eckart, and Lothar Thiele. 1998. “Multiobjective Optimization Using Evolutionary Algorithms—a Comparative Case Study.” In International Conference on Parallel Problem Solving from Nature, 292–301. Springer.

Harrell, F. E. 2002. “Plasma Retinol and Beta-Carotene Dataset“. https://hbiostat.org/data/repo/plasma.html.