Feature Importance — feature_importance • NNTools

This function calculates permutation based feature importance. For this reason it is also called the Variable Dropout Plot.

Usage

feature_importance(x, ...)

# S3 method for explainer
feature_importance(
  x,
  loss_function = DALEX::loss_root_mean_square,
  ...,
  type = c("raw", "ratio", "difference"),
  n_sample = NULL,
  B = 10,
  variables = NULL,
  variable_groups = NULL,
  N = n_sample,
  label = NULL
)

# S3 method for default
feature_importance(
  x,
  data,
  y,
  predict_function = predict,
  loss_function = DALEX::loss_root_mean_square,
  ...,
  label = class(x)[1],
  type = c("raw", "ratio", "difference"),
  n_sample = NULL,
  B = 10,
  variables = NULL,
  N = n_sample,
  variable_groups = NULL,
  perm_dim = NULL,
  comb_dims = FALSE
)

Arguments

x: an explainer created with function DALEX::explain(), or a model to be explained.
...: other parameters passed to predict_function.
loss_function: a function thet will be used to assess variable importance.
type: character, type of transformation that should be applied for dropout loss. "raw" results raw drop losses, "ratio" returns drop_loss / drop_loss_full_model while "difference" returns drop_loss - drop_loss_full_model.
n_sample: alias for N held for backwards compatibility. number of observations that should be sampled for calculation of variable importance.
B: integer, number of permutation rounds to perform on each variable. By default it's 10.
variables: vector of variables or a list of vectors for multiinput models. If NULL then variable importance will be tested for each variable from the data separately. By default NULL
variable_groups: list of variables names vectors or a list of vectors for multiinput models. This is for testing joint variable importance. If NULL then variable importance will be tested separately for variables. By default NULL. If specified then it will override variables, perm_dim and comb_dims.
N: number of observations that should be sampled for calculation of variable importance. If NULL then variable importance will be calculated on whole dataset (no sampling).
label: name of the model. By default it's extracted from the class attribute of the model.
data: validation dataset, will be extracted from x if it's an explainer. Can be a list of arrays for multiinput models. NOTE: It is safer when target variable is not present in the data.
y: true labels for data, will be extracted from x if it's an explainer.
predict_function: predict function, will be extracted from x if it's an explainer.
perm_dim: the dimensions to perform the permutations when data is a 3d array (e.g. [case, time, variable]). If perm_dim = 2:3, it calculates the importance for each variable in the 2nd and 3rd dimensions. For multiinput models, a list of dimensions in the same order than in data. If NULL, the default, take all dimensions except the first one (i.e. rows) which correspond to cases.
comb_dims: if TRUE, do the permutations for each combination of the levels of the variables from 2nd and 3rd dimensions for input data with 3 dimensions. By default, FALSE.

Value

an object of the class feature_importance

Details

Find more details in the Feature Importance Chapter.

References

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

Examples

library("DALEX")
#> Welcome to DALEX (version: 2.4.3).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
#> Additional features will be available after installation of: ggpubr.
#> Use 'install_dependencies()' to get all suggested dependencies
#> 
#> Attaching package: ‘DALEX’
#> The following object is masked from ‘package:NNTools’:
#> 
#>     feature_importance
library("ingredients")
#> 
#> Attaching package: ‘ingredients’
#> The following object is masked from ‘package:DALEX’:
#> 
#>     feature_importance
#> The following object is masked from ‘package:NNTools’:
#> 
#>     feature_importance

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])
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  2207  rows  7  cols 
#>   -> target variable   :  2207  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.3.2 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.1490412 , mean =  0.3221568 , max =  0.9878987  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.8898433 , mean =  4.165191e-13 , max =  0.8448637  
#>   A new explainer has been created!  

fi_glm <- feature_importance(explain_titanic_glm, B = 1)
plot(fi_glm)


if (FALSE) {

fi_glm_joint1 <- feature_importance(explain_titanic_glm,
                   variable_groups = list("demographics" = c("gender", "age"),
                   "ticket_type" = c("fare")),
                   label = "lm 2 groups")

plot(fi_glm_joint1)

fi_glm_joint2 <- feature_importance(explain_titanic_glm,
                   variable_groups = list("demographics" = c("gender", "age"),
                                          "wealth" = c("fare", "class"),
                                          "family" = c("sibsp", "parch"),
                                          "embarked" = "embarked"),
                   label = "lm 5 groups")

plot(fi_glm_joint2, fi_glm_joint1)

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)

fi_rf <- feature_importance(explain_titanic_rf)
plot(fi_rf)

fi_rf <- feature_importance(explain_titanic_rf, B = 6) # 6 replications
plot(fi_rf)

fi_rf_group <- feature_importance(explain_titanic_rf,
                   variable_groups = list("demographics" = c("gender", "age"),
                   "wealth" = c("fare", "class"),
                   "family" = c("sibsp", "parch"),
                   "embarked" = "embarked"),
                   label = "rf 4 groups")

plot(fi_rf_group, fi_rf)

HR_rf_model <- ranger(status ~., data = HR, probability = TRUE)

explainer_rf  <- explain(HR_rf_model, data = HR, y = HR$status,
                         model_info = list(type = 'multiclass'))

fi_rf <- feature_importance(explainer_rf, type = "raw",
                            loss_function = DALEX::loss_cross_entropy)
head(fi_rf)
plot(fi_rf)

HR_glm_model <- glm(status == "fired"~., data = HR, family = "binomial")
explainer_glm <- explain(HR_glm_model, data = HR, y = as.numeric(HR$status == "fired"))
fi_glm <- feature_importance(explainer_glm, type = "raw",
                             loss_function = DALEX::loss_root_mean_square)
head(fi_glm)
plot(fi_glm)

}