ingredients: aggregate_profiles – R documentation

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aggregate_profiles

Aggregates Ceteris Paribus Profiles

Description

The function aggregate_profiles() calculates an aggregate of ceteris paribus profiles. It can be: Partial Dependence Profile (average across Ceteris Paribus Profiles), Conditional Dependence Profile (local weighted average across Ceteris Paribus Profiles) or Accumulated Local Dependence Profile (cummulated average local changes in Ceteris Paribus Profiles).

Usage

aggregate_profiles(
  x,
  ...,
  variable_type = "numerical",
  groups = NULL,
  type = "partial",
  variables = NULL,
  span = 0.25,
  center = FALSE
)

Arguments

`x`	a ceteris paribus explainer produced with function `ceteris_paribus()`
`...`	other explainers that shall be calculated together
`variable_type`	a character. If `numerical` then only numerical variables will be calculated. If `categorical` then only categorical variables will be calculated.
`groups`	a variable name that will be used for grouping. By default `NULL` which means that no groups shall be calculated
`type`	either `partial/conditional/accumulated` for partial dependence, conditional profiles of accumulated local effects
`variables`	if not `NULL` then aggregate only for selected `variables` will be calculated
`span`	smoothing coefficient, by default `0.25`. It's the sd for gaussian kernel
`center`	by default accumulated profiles start at 0. If `center=TRUE`, then they are centered around mean prediction, which is calculated on the observations used in `ceteris_paribus`.

Value

an object of the class aggregated_profiles_explainer

References

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

Examples

library("DALEX")
library("ingredients")
library("ranger")
head(titanic_imputed)

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)
head(cp_rf)

# continuous variable
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"

plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_")

pdp_rf <- aggregate_profiles(cp_rf, variables = "age",
                             groups = "gender")

head(pdp_rf)
plot(cp_rf, variables = "age") +
  show_observations(cp_rf, variables = "age") +
  show_rugs(cp_rf, variables = "age", color = "red") +
  show_aggregated_profiles(pdp_rf, size = 3, color = "_label_")

# categorical variable
pdp_rf_p <- aggregate_profiles(cp_rf, variables = "class",
                               variable_type = "categorical",  type = "partial")
pdp_rf_p$`_label_` <- "RF_partial"
pdp_rf_c <- aggregate_profiles(cp_rf, variables = "class",
                               variable_type = "categorical", type = "conditional")
pdp_rf_c$`_label_` <- "RF_conditional"
pdp_rf_a <- aggregate_profiles(cp_rf, variables = "class",
                               variable_type = "categorical", type = "accumulated")
pdp_rf_a$`_label_` <- "RF_accumulated"
plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_")

# or maybe flipped?
library(ggplot2)
plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_") + coord_flip()

pdp_rf <- aggregate_profiles(cp_rf, variables = "class", variable_type = "categorical",
                             groups = "gender")
head(pdp_rf)
plot(pdp_rf, variables = "class")
# or maybe flipped?
plot(pdp_rf, variables = "class") + coord_flip()

ingredients

Effects and Importances of Model Ingredients

v2.2.0

GPL-3

Authors

Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>), Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>), Adam Izdebski [ctb]

Initial release