Conditional Dependence Profiles

Conditional Dependence Profiles (aka Local Profiles) average localy Ceteris Paribus Profiles. Function 'conditional_dependence' calls 'ceteris_paribus' and then 'aggregate_profiles'.

conditional_dependence(x, ...)

# S3 method for explainer
conditional_dependence(
  x,
  variables = NULL,
  N = 500,
  variable_splits = NULL,
  grid_points = 101,
  ...,
  variable_type = "numerical"
)

# S3 method for default
conditional_dependence(
  x,
  data,
  predict_function = predict,
  label = class(x)[1],
  variables = NULL,
  N = 500,
  variable_splits = NULL,
  grid_points = 101,
  ...,
  variable_type = "numerical"
)

# S3 method for ceteris_paribus_explainer
conditional_dependence(x, ..., variables = NULL)

local_dependency(x, ...)

conditional_dependency(x, ...)

Arguments

x

an explainer created with function DALEX::explain(), an object of the class ceteris_paribus_explainer or a model to be explained.

...

other parameters

variables

names of variables for which profiles shall be calculated. Will be passed to calculate_variable_split. If NULL then all variables from the validation data will be used.

N

number of observations used for calculation of partial dependence profiles. By default 500.

variable_splits

named list of splits for variables, in most cases created with calculate_variable_split. If NULL then it will be calculated based on validation data avaliable in the explainer.

grid_points

number of points for profile. Will be passed to calculate_variable_split.

variable_type

a character. If "numerical" then only numerical variables will be calculated. If "categorical" then only categorical variables will be calculated.

data

validation dataset, will be extracted from x if it's an explainer NOTE: It is best when target variable is not present in the data

predict_function

predict function, will be extracted from x if it's an explainer

label

name of the model. By default it's extracted from the class attribute of the model

Value

an object of the class aggregated_profile_explainer

Details

Find more details in the Accumulated Local Dependence Chapter.

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)

cdp_glm <- conditional_dependence(explain_titanic_glm,
                                  N = 150, variables = c("age", "fare"))
head(cdp_glm)
#> Top profiles    : 
#>                     _vname_ _label_        _x_    _yhat_ _ids_
#> age.lm.0.1666666667     age      lm  0.1666667 0.3762917     0
#> age.lm.2                age      lm  2.0000000 0.3713934     0
#> age.lm.4                age      lm  4.0000000 0.3662976     0
#> age.lm.7                age      lm  7.0000000 0.3591476     0
#> age.lm.9                age      lm  9.0000000 0.3547151     0
#> age.lm.13               age      lm 13.0000000 0.3466645     0
plot(cdp_glm)


# \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)

cdp_rf <- conditional_dependence(explain_titanic_rf, N = 200, variable_type = "numerical")
plot(cdp_rf)


cdp_rf <- conditional_dependence(explain_titanic_rf, N = 200, variable_type = "categorical")
plotD3(cdp_rf, label_margin = 100, scale_plot = TRUE)

# }