Adds a Layer with Observations to a Profile Plot

Function show_observations adds a layer to a plot created with plot.ceteris_paribus_explainer for selected observations. Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs.

show_observations(
  x,
  ...,
  size = 2,
  alpha = 1,
  color = "#371ea3",
  variable_type = "numerical",
  variables = NULL
)

Arguments

x

a ceteris paribus explainer produced with function ceteris_paribus()

...

other explainers that shall be plotted together

size

a numeric. Size of lines to be plotted

alpha

a numeric between 0 and 1. Opacity of lines

color

a character. Either name of a color or name of a variable that should be used for coloring

variable_type

a character. If numerical then only numerical variables will be plotted. If categorical then only categorical variables will be plotted.

variables

if not NULL then only variables will be presented

Value

a ggplot2 layer

References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

library("DALEX")
library("ingredients")
# \donttest{
library("ranger")

rf_model <- ranger(survived ~., data = titanic_imputed, probability = TRUE)

explainer_rf <- explain(rf_model,
                        data = titanic_imputed[,-8],
                        y = titanic_imputed[,8],
                        label = "ranger forest",
                        verbose = FALSE)

selected_passangers <- select_sample(titanic_imputed, n = 100)
cp_rf <- ceteris_paribus(explainer_rf, selected_passangers)
cp_rf
#> Top profiles    : 
#>        gender age            class    embarked    fare sibsp parch    _yhat_
#> 515    female  45              2nd Southampton 10.1000     0     0 0.8168505
#> 515.1    male  45              2nd Southampton 10.1000     0     0 0.1059383
#> 604    female  17              3rd Southampton  7.1701     1     0 0.4675747
#> 604.1    male  17              3rd Southampton  7.1701     1     0 0.1167633
#> 1430   female  25 engineering crew Southampton  0.0000     0     0 0.7796172
#> 1430.1   male  25 engineering crew Southampton  0.0000     0     0 0.2402162
#>        _vname_ _ids_       _label_
#> 515     gender   515 ranger forest
#> 515.1   gender   515 ranger forest
#> 604     gender   604 ranger forest
#> 604.1   gender   604 ranger forest
#> 1430    gender  1430 ranger forest
#> 1430.1  gender  1430 ranger forest
#> 
#> 
#> Top observations:
#>      gender age            class    embarked    fare sibsp parch    _yhat_
#> 515    male  45              2nd Southampton 10.1000     0     0 0.1059383
#> 604    male  17              3rd Southampton  7.1701     1     0 0.1167633
#> 1430   male  25 engineering crew Southampton  0.0000     0     0 0.2402162
#> 865    male  20              3rd   Cherbourg  7.0406     0     0 0.1177738
#> 452  female  17              3rd  Queenstown  7.1408     0     0 0.6502409
#> 1534   male  38 victualling crew Southampton  0.0000     0     0 0.1697892
#>            _label_ _ids_
#> 515  ranger forest     1
#> 604  ranger forest     2
#> 1430 ranger forest     3
#> 865  ranger forest     4
#> 452  ranger forest     5
#> 1534 ranger forest     6

plot(cp_rf, variables = "age", color = "grey") +
show_observations(cp_rf, variables = "age", color = "black") +
  show_rugs(cp_rf, variables = "age", color = "red")

# }