Function plot.aggregated_profiles_explainer plots partial dependence plot or accumulated effect plot. It works in a similar way to plot.ceteris_paribus, but instead of individual profiles show average profiles for each variable listed in the variables vector.

# S3 method for aggregated_profiles_explainer
plot(
  x,
  ...,
  size = 1,
  alpha = 1,
  color = "_label_",
  facet_ncol = NULL,
  facet_scales = "free_x",
  variables = NULL,
  title = NULL,
  subtitle = NULL
)

Arguments

x

a ceteris paribus explainer produced with function aggregate_profiles()

...

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 hex code for a color, or _label_ if models shall be colored, or _ids_ if instances shall be colored

facet_ncol

number of columns for the facet_wrap

facet_scales

a character value for the facet_wrap. Default is "free_x".

variables

if not NULL then only variables will be presented

title

a character. Partial and accumulated dependence explainers have deafult value.

subtitle

a character. If NULL value will be dependent on model usage.

Value

a ggplot2 object

References

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

Examples

library("DALEX") library("ingredients") model_titanic_glm <- glm(survived ~ gender + age + fare, data = titanic_imputed, family = "binomial") explain_titanic_glm <- explain(model_titanic_glm, data = titanic_imputed[,-8], y = titanic_imputed[,8], verbose = FALSE) pdp_rf_p <- partial_dependence(explain_titanic_glm, N = 50) pdp_rf_p$`_label_` <- "RF_partial" pdp_rf_l <- conditional_dependence(explain_titanic_glm, N = 50) pdp_rf_l$`_label_` <- "RF_local" pdp_rf_a<- accumulated_dependence(explain_titanic_glm, N = 50) pdp_rf_a$`_label_` <- "RF_accumulated" head(pdp_rf_p)
#> Top profiles : #> _vname_ _label_ _x_ _yhat_ _ids_ #> 1 fare RF_partial 0.0000000 0.2848672 0 #> 2 parch RF_partial 0.0000000 0.3221967 0 #> 3 sibsp RF_partial 0.0000000 0.3221967 0 #> 4 age RF_partial 0.1666667 0.3632136 0 #> 5 parch RF_partial 1.0000000 0.3221967 0 #> 6 sibsp RF_partial 1.0000000 0.3221967 0
plot(pdp_rf_p, pdp_rf_l, pdp_rf_a, color = "_label_")
# \donttest{ library("ranger") model_titanic_rf <- ranger(survived ~., data = titanic_imputed, probability = TRUE) explain_titanic_rf <- explain(model_titanic_rf, 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(explain_titanic_rf, selected_passangers) cp_rf
#> Top profiles : #> gender age class embarked fare sibsp parch _yhat_ #> 515 female 45 2nd Southampton 10.1000 0 0 0.8193970 #> 515.1 male 45 2nd Southampton 10.1000 0 0 0.1225410 #> 604 female 17 3rd Southampton 7.1701 1 0 0.4562376 #> 604.1 male 17 3rd Southampton 7.1701 1 0 0.1100723 #> 1430 female 25 engineering crew Southampton 0.0000 0 0 0.7582714 #> 1430.1 male 25 engineering crew Southampton 0.0000 0 0 0.2340968 #> _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 0.1225410 #> 604 male 17 3rd Southampton 7.1701 1 0 0.1100723 #> 1430 male 25 engineering crew Southampton 0.0000 0 0 0.2340968 #> 865 male 20 3rd Cherbourg 7.0406 0 0 0.1103113 #> 452 female 17 3rd Queenstown 7.1408 0 0 0.6707266 #> 1534 male 38 victualling crew Southampton 0.0000 0 0 0.1745903 #> _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
pdp_rf_p <- aggregate_profiles(cp_rf, variables = "age", type = "partial") pdp_rf_p$`_label_` <- "RF_partial" pdp_rf_c <- aggregate_profiles(cp_rf, variables = "age", type = "conditional") pdp_rf_c$`_label_` <- "RF_conditional" pdp_rf_a <- aggregate_profiles(cp_rf, variables = "age", type = "accumulated") pdp_rf_a$`_label_` <- "RF_accumulated" head(pdp_rf_p)
#> Top profiles : #> _vname_ _label_ _x_ _yhat_ _ids_ #> 1 age RF_partial 0.1666667 0.5167099 0 #> 2 age RF_partial 2.0000000 0.5412926 0 #> 3 age RF_partial 4.0000000 0.5523954 0 #> 4 age RF_partial 7.0000000 0.5049700 0 #> 5 age RF_partial 9.0000000 0.4885480 0 #> 6 age RF_partial 13.0000000 0.4229736 0
plot(pdp_rf_p)
plot(pdp_rf_p, pdp_rf_c, pdp_rf_a)
plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + show_rugs(cp_rf, variables = "age", color = "red") + show_aggregated_profiles(pdp_rf_p, size = 2)
# }