feature_importance: Feature Importance

In jmaspons/MLTools: Pipes and tools for machine learning

Feature Importance

Description

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 class '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
)

## Default S3 method:
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.

Details

Find more details in the Feature Importance Chapter.

Value

an object of the class feature_importance

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])

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

## Not run: 

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)


## End(Not run)

jmaspons/MLTools documentation built on Jan. 27, 2024, 4:31 a.m.