Repeated mincriterion should give identical results to one mincriterion

[JensWahl]

Repeating the same value for mincriterion gives different results when approach = "ctree"

#remotes::install_github("NorskRegnesentral/shapr")
library(shapr)
library(xgboost)
library(data.table)

data("Boston", package = "MASS")

x_var <- c("lstat", "rm", "dis", "indus")
y_var <- "medv"

x_train <- as.matrix(Boston[-1:-6, x_var])
y_train <- Boston[-1:-6, y_var]
x_test <- as.matrix(Boston[1:6, x_var])

# Fitting a basic xgboost model to the training data
model <- xgboost(
  data = x_train,
  label = y_train,
  nround = 20,
  verbose = FALSE
)
explainer <- shapr(x_train, model,n_combinations = 100)

p = mean(y_train)
ctree = explain(x_test, explainer, "ctree", mincriterion = 0.95, prediction_zero = p, n_samples = 10000, seed = 123)
ctree2 = explain(x_test, explainer, "ctree", mincriterion = rep(0.95, 4) , prediction_zero = p, n_samples = 10000, seed = 123)

ctree$dt
#>      none       lstat         rm        dis     indus
#> 1: 22.446  5.27101195 -2.2623617  0.7167850 5.1300151
#> 2: 22.446 -0.01718546 -0.7017281  0.6321774 0.8926739
#> 3: 22.446  6.24540411  4.0077874 -0.3611664 0.7774418
#> 4: 22.446  8.55715410 -0.4802524  0.4757659 2.4314768
#> 5: 22.446  1.14282068  4.9039389  0.6773156 2.5597634
#> 6: 22.446  2.40798688 -4.9300163  1.1378937 3.7881587
ctree2$dt
#>      none      lstat         rm        dis     indus
#> 1: 22.446 5.44940813 -2.2008549  0.5946036 5.0122935
#> 2: 22.446 0.00713506 -0.6693527  0.5442665 0.9238889
#> 3: 22.446 6.28454770  4.1246398 -0.5241423 0.7844218
#> 4: 22.446 8.42424493 -0.3048220  0.4379691 2.4267524
#> 5: 22.446 1.10024545  4.8669534  0.6718916 2.6447482
#> 6: 22.446 2.29351315 -4.9580286  1.2324759 3.8360624