Function show_rugs 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_rugs(
  x,
  ...,
  size = 0.5,
  alpha = 1,
  color = "#371ea3",
  variable_type = "numerical",
  sides = "b",
  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.

sides

a string containing any of "trbl", for top, right, bottom, and left. Passed to geom rug.

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") titanic_small <- select_sample(titanic_imputed, n = 500, seed = 1313) # build a model model_titanic_glm <- glm(survived ~ gender + age + fare, data = titanic_small, family = "binomial") explain_titanic_glm <- explain(model_titanic_glm, data = titanic_small[,-8], y = titanic_small[,8])
#> Preparation of a new explainer is initiated #> -> model label : lm ( default ) #> -> data : 500 rows 7 cols #> -> target variable : 500 values #> -> predict function : yhat.glm will be used ( default ) #> -> predicted values : No value for predict function target column. ( default ) #> -> model_info : package stats , ver. 4.1.1 , task classification ( default ) #> -> predicted values : numerical, min = 0.0795294 , mean = 0.302 , max = 0.9859411 #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -0.8204691 , mean = 8.796651e-12 , max = 0.8567173 #> A new explainer has been created!
cp_glm <- ceteris_paribus(explain_titanic_glm, titanic_small[1,]) cp_glm
#> Top profiles : #> gender age class embarked fare sibsp parch _yhat_ _vname_ _ids_ #> 515 female 45.00 2nd Southampton 10.1 0 0 0.5595687 gender 515 #> 515.1 male 45.00 2nd Southampton 10.1 0 0 0.1448038 gender 515 #> 5151 male 0.75 2nd Southampton 10.1 0 0 0.3135247 age 515 #> 515.110 male 2.99 2nd Southampton 10.1 0 0 0.3028164 age 515 #> 515.2 male 4.98 2nd Southampton 10.1 0 0 0.2934793 age 515 #> 515.3 male 7.00 2nd Southampton 10.1 0 0 0.2841757 age 515 #> _label_ #> 515 lm #> 515.1 lm #> 5151 lm #> 515.110 lm #> 515.2 lm #> 515.3 lm #> #> #> Top observations: #> gender age class embarked fare sibsp parch _yhat_ _label_ _ids_ #> 515 male 45 2nd Southampton 10.1 0 0 0.1448038 lm 1
# \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 0.8130768 #> 515.1 male 45 2nd Southampton 10.1000 0 0 0.1134421 #> 604 female 17 3rd Southampton 7.1701 1 0 0.4691766 #> 604.1 male 17 3rd Southampton 7.1701 1 0 0.1146410 #> 1430 female 25 engineering crew Southampton 0.0000 0 0 0.7580796 #> 1430.1 male 25 engineering crew Southampton 0.0000 0 0 0.2382367 #> _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 0.1134421 #> 604 male 17 3rd Southampton 7.1701 1 0 0.1146410 #> 1430 male 25 engineering crew Southampton 0.0000 0 0 0.2382367 #> 865 male 20 3rd Cherbourg 7.0406 0 0 0.1177139 #> 452 female 17 3rd Queenstown 7.1408 0 0 0.6675361 #> 1534 male 38 victualling crew Southampton 0.0000 0 0 0.1723540 #> _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")
# }