This function executes all tests for drift between two datasets / models

Currently three checks are implemented, covariate drift, residual drift and model drift.

check_drift(model_old, model_new, data_old, data_new, y_old, y_new,
  predict_function = predict, max_obs = 100, bins = 20,
  scale = sd(y_new, na.rm = TRUE))

Arguments

model_old	model created on historical / `old`data
model_new	model created on current / `new`data
data_old	data frame with historical / `old` data
data_new	data frame with current / `new` data
y_old	true values of target variable for historical / `old` data
y_new	true values of target variable for current / `new` data
predict_function	function that takes two arguments: model and new data and returns numeric vector with predictions, by default it's `predict`
max_obs	if negative, them all observations are used for calculation of PDP, is positive, then only `max_obs` are used for calculation of PDP
bins	continuous variables are discretized to `bins` intervals of equal sizes
scale	scale parameter for calculation of scaled drift

Value

This function is executed for its side effects, all checks are being printed on the screen. Additionaly it returns list with particualr checks.

Examples

 library("DALEX")
 model_old <- lm(m2.price ~ ., data = apartments)
 model_new <- lm(m2.price ~ ., data = apartments_test[1:1000,])
 check_drift(model_old, model_new,
                  apartments, apartments_test,
                  apartments$m2.price, apartments_test$m2.price)
#>    -------------------------------------
#>                   Variable  Shift
#>   -------------------------------------
#>                   m2.price    4.9  
#>          construction.year    6.0  
#>                    surface    6.8  
#>                      floor    4.9  
#>                   no.rooms    2.8  
#>                   district    2.8  
#>    -------------------------------------
#>                   Variable  Shift
#>   -------------------------------------
#>                  Residuals    8.3  
#>    -----------------------------------------------
#>                   Variable    Shift  Scaled
#>   -----------------------------------------------
#>                      floor    22.10     2.5  
#>                   no.rooms    27.44     3.0  
#>                    surface    30.12     3.3  
#>                   m2.price    26.41     2.9  
#>          construction.year    29.49     3.3  
 
 library("ranger")
 predict_function <- function(m,x,...) predict(m, x, ...)$predictions
 model_old <- ranger(m2.price ~ ., data = apartments)
 model_new <- ranger(m2.price ~ ., data = apartments_test)
 check_drift(model_old, model_new,
                  apartments, apartments_test,
                  apartments$m2.price, apartments_test$m2.price,
                  predict_function = predict_function)
#>    -------------------------------------
#>                   Variable  Shift
#>   -------------------------------------
#>                   m2.price    4.9  
#>          construction.year    6.0  
#>                    surface    6.8  
#>                      floor    4.9  
#>                   no.rooms    2.8  
#>                   district    2.8  
#>    -------------------------------------
#>                   Variable  Shift
#>   -------------------------------------
#>                  Residuals   34.1  **
#>    -----------------------------------------------
#>                   Variable    Shift  Scaled
#>   -----------------------------------------------
#>                      floor    83.14     9.2  
#>                   no.rooms   160.79    17.9  .
#>                    surface   164.33    18.2  .
#>                   m2.price   166.15    18.5  .
#>          construction.year   201.95    22.4  *