Calculate triplot that sums up automatic aspect/feature importance grouping

This function shows:

• plot for the importance of single variables,

• tree that shows importance for every newly expanded group of variables,

• clustering tree.

calculate_triplot(x, ...)

# S3 method for explainer
calculate_triplot(
  x,
  type = c("predict", "model"),
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

# S3 method for default
calculate_triplot(
  x,
  data,
  y = NULL,
  predict_function = predict,
  label = class(x)[1],
  type = c("predict", "model"),
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

# S3 method for triplot
print(x, ...)

model_triplot(x, ...)

predict_triplot(x, ...)

Arguments

x	an explainer created with the DALEX::explain() function or a model to be explained.
...	other parameters
type	if predict then aspect_importance is used, if model than feature_importance is calculated
new_observation	selected observation with columns that corresponds to variables used in the model, should be without target variable
N	number of rows to be sampled from data NOTE: Small N may cause unstable results.
loss_function	a function that will be used to assess variable importance, if type = model
B	integer, number of permutation rounds to perform on each variable in feature importance calculation, if type = model
fi_type	character, type of transformation that should be applied for dropout loss, if type = model. "raw" results raw drop losses, "ratio" returns drop_loss/drop_loss_full_model.
clust_method	the agglomeration method to be used, see hclust methods
cor_method	the correlation method to be used see cor methods
data	dataset, it will be extracted from x if it's an explainer NOTE: Target variable shouldn't be present in the data
y	true labels for data, will be extracted from x if it's an explainer
predict_function	predict function, it 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

triplot object

Examples

library(DALEX)
set.seed(123)
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model <- lm(m2.price ~ ., data = apartments_num)
apartments_num_new_observation <- apartments_num[30, ]
explainer_apartments <- explain(model = apartments_num_lm_model,
                                data = apartments_num[,-1],
                                y = apartments_num[, 1],
                                verbose = FALSE)
apartments_tri <- calculate_triplot(x = explainer_apartments,
                                    new_observation =
                                      apartments_num_new_observation[-1])
apartments_tri