# bake_check_class helper function gives expected output
Code
bake_check_class_core(x1, "character", "x1")
Condition
Error in `bake_check_class_core()`:
! x1 should have the class(es) character but has the class(es) numeric.
---
Code
bake_check_class_core(x2, c("POSIXct", "Julian"), "x2")
Condition
Error in `bake_check_class_core()`:
! x2 should have the class(es) POSIXct, Julian but has the class(es) POSIXct, POSIXt.
---
Code
bake_check_class_core(x2, "POSIXct", "x2")
Condition
Error in `bake_check_class_core()`:
! x2 has the class(es) POSIXct, POSIXt, but only the following is/are asked POSIXct, allow_additional is FALSE.
# check_class works when class is learned
Code
bake(rec1, x_newdata)
Condition
Error:
! x1 should have the class(es) numeric but has the class(es) character.
---
Code
bake(rec1, x_newdata_2)
Condition
Error:
! x2 has the class(es) POSIXct, POSIXt, Julian, but only the following is/are asked POSIXct, POSIXt, allow_additional is FALSE.
# check_class works when class is provided
Code
bake(rec2, x_newdata)
Condition
Error:
! x1 should have the class(es) numeric but has the class(es) character.
---
Code
bake(rec3, x_newdata_2)
Condition
Error:
! x2 has the class(es) POSIXct, POSIXt, Julian, but only the following is/are asked POSIXct, POSIXt, allow_additional is FALSE.
# characters are handled correctly
Code
bake(rec6_NULL, sacr_fac[11:20, ])
Condition
Error:
! city should have the class(es) factor but has the class(es) character.
---
Code
bake(rec6_man, sacr_fac[11:20, ])
Condition
Error:
! type should have the class(es) factor but has the class(es) character.
# printing
Code
print(rec7)
Output
Recipe
Inputs:
2 variables (no declared roles)
Operations:
Checking the class(es) for everything()
---
Code
prep(rec7)
Output
Recipe
Inputs:
2 variables (no declared roles)
Training data contained 3 data points and no missing data.
Operations:
Checking the class(es) for x1, x2 [trained]
# empty printing
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Operations:
Checking the class(es) for <none>
---
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Training data contained 32 data points and no missing data.
Operations:
Checking the class(es) for <none> [trained]
Generated by dwww version 1.15 on Sat May 18 04:20:46 CEST 2024.