Local Conditional Expectation Explainer

This explainer works for individual observations. For each observation it calculates Local Conditional Expectation (LCE) profiles for selected variables.

local_conditional_expectations(explainer, observations, y = NULL,
  variable_splits = NULL, variables = NULL, grid_points = 101)

Arguments

explainer	a model to be explained, preprocessed by function `DALEX::explain()`.
observations	set of observarvation for which profiles are to be calculated
y	true labels for `observations`. If specified then will be added to local conditional expectations plots.
variable_splits	named list of splits for variables, in most cases created with `calculate_variable_splits()`. If NULL then it will be calculated based on validation data avaliable in the `explainer`.
variables	names of variables for which profiles shall be calculated. Will be passed to `calculate_variable_splits()`. If NULL then all variables from the validation data will be used.
grid_points	number of points for profile. Will be passed to `calculate_variable_splits()`.

Value

An object of the class 'ceteris_paribus_explainer'. A data frame with calculated LCE profiles.

Examples

library("DALEX")
library("randomForest")
set.seed(59)

apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
      no.rooms + district, data = apartments)

explainer_rf <- explain(apartments_rf_model,
      data = apartments[,2:6], y = apartments$m2.price)

new_apartment <- apartments[1, ]

lce_rf <- local_conditional_expectations(explainer_rf, new_apartment)
lce_rf
#> Top profiles    : 
#>     m2.price construction.year  surface    floor  no.rooms    district   _yhat_
#> 1       5897              1920 21.84884 3.087808 0.9151128 Srodmiescie 5450.533
#> 1.1     5897              1921 21.94433 3.085148 0.9176852 Srodmiescie 5490.713
#> 1.2     5897              1922 22.03982 3.082487 0.9202575 Srodmiescie 5485.823
#> 1.3     5897              1923 22.13531 3.079826 0.9228298 Srodmiescie 5498.118
#> 1.4     5897              1924 22.23080 3.077165 0.9254022 Srodmiescie 5509.683
#> 1.5     5897              1925 22.32629 3.074504 0.9279745 Srodmiescie 5477.263
#>               _vname_ _ids_      _label_
#> 1   construction.year     1 randomForest
#> 1.1 construction.year     1 randomForest
#> 1.2 construction.year     1 randomForest
#> 1.3 construction.year     1 randomForest
#> 1.4 construction.year     1 randomForest
#> 1.5 construction.year     1 randomForest
#> 
#> 
#> Top observations:
#>   m2.price construction.year surface floor no.rooms    district   _yhat_
#> 1     5897              1953      25     3        1 Srodmiescie 5447.656
#>        _label_
#> 1 randomForest

lce_rf <- local_conditional_expectations(explainer_rf, new_apartment, y = new_apartment$m2.price)
lce_rf
#> Top profiles    : 
#>     m2.price construction.year  surface    floor  no.rooms    district   _yhat_
#> 1       5897              1920 21.84884 3.087808 0.9151128 Srodmiescie 5450.533
#> 1.1     5897              1921 21.94433 3.085148 0.9176852 Srodmiescie 5490.713
#> 1.2     5897              1922 22.03982 3.082487 0.9202575 Srodmiescie 5485.823
#> 1.3     5897              1923 22.13531 3.079826 0.9228298 Srodmiescie 5498.118
#> 1.4     5897              1924 22.23080 3.077165 0.9254022 Srodmiescie 5509.683
#> 1.5     5897              1925 22.32629 3.074504 0.9279745 Srodmiescie 5477.263
#>               _vname_ _ids_      _label_
#> 1   construction.year     1 randomForest
#> 1.1 construction.year     1 randomForest
#> 1.2 construction.year     1 randomForest
#> 1.3 construction.year     1 randomForest
#> 1.4 construction.year     1 randomForest
#> 1.5 construction.year     1 randomForest
#> 
#> 
#> Top observations:
#>   m2.price construction.year surface floor no.rooms    district   _yhat_  _y_
#> 1     5897              1953      25     3        1 Srodmiescie 5447.656 5897
#>        _label_
#> 1 randomForest