tests/testthat/_snaps/error-handling.md

In yardstick: Tidy Characterizations of Model Performance

bad args

Code
  sens(pathology, truth = "patholosgy", estimate = "scan")
Condition
  Error in `sens()`:
  ! Can't select columns that don't exist.
  x Column `patholosgy` doesn't exist.

truth should be factor

Code
  sens(df, truth, estimate)
Condition
  Error in `sens()`:
  ! `truth` should be a factor, not a a number.

At least 2 levels in truth

Code
  sens(df, truth, estimate)
Condition
  Error in `sens()`:
  ! `estimator` is binary, only two class `truth` factors are allowed. A factor with 1 levels was provided.

Single character values are caught with correct errors

Code
  sens(pathology, "a", scan)
Condition
  Error in `sens()`:
  ! Can't select columns that don't exist.
  x Column `a` doesn't exist.

Bad unquoted input is caught

Code
  sens(pathology, !!bad, scan)
Condition
  Error in `sens()`:
  ! Can't select columns that don't exist.
  x Column `a` doesn't exist.

Non-allowed estimator

Code
  sens(pathology, pathology, scan, estimator = "blah")
Condition
  Error in `sens()`:
  ! `estimator` must be one of "binary", "macro", "micro", or "macro_weighted", not "blah".

Bad estimator + truth combination

Code
  sens(hpc_cv, obs, pred, estimator = "binary")
Condition
  Error in `sens()`:
  ! `estimator` is binary, only two class `truth` factors are allowed. A factor with 4 levels was provided.

Bad estimator type

Code
  sens(hpc_cv, obs, pred, estimator = 1)
Condition
  Error in `sens()`:
  ! `estimator` must be a character vector, not the number 1.

Code
  sens(hpc_cv, obs, pred, estimator = c("1", "2"))
Condition
  Error in `sens()`:
  ! `estimator` must be one of "binary", "macro", "micro", or "macro_weighted", not "1".

Numeric matrix in numeric metric

Code
  rmse(solubility_test, a, prediction)
Condition
  Error in `rmse()`:
  ! `truth` should be a numeric vector, not a numeric matrix.

Code
  rmse(solubility_test, solubility, a)
Condition
  Error in `rmse()`:
  ! `estimate` should be a numeric vector, not a numeric matrix.

Factors with non identical levels

Code
  sens(df, x, y)
Condition
  Error in `sens()`:
  x `truth` and `estimate` levels must be equivalent.
  * `truth`: a, b, and c.
  * `estimate`: a and b.

Multiple estimate columns for a binary metric

Code
  roc_auc(two_class_example, truth, Class1:Class2)
Condition
  Error in `roc_auc()`:
  ! You are using a binary metric but have passed multiple columns to `...`.

1 estimate column for a multiclass metric

Code
  roc_auc(hpc_cv, obs, VF)
Condition
  Error in `roc_auc()`:
  ! The number of levels in `truth` (4) must match the number of columns supplied in `...` (1).

truth and estimate of different lengths

Code
  rmse_vec(1:5, 1:6)
Condition
  Error in `rmse_vec()`:
  ! `truth` (5) and `estimate` (6) must be the same length.

Missing arguments

Code
  sens(two_class_example)
Condition
  Error in `sens()`:
  ! Must select at least one item.

Code
  sens(two_class_example, truth)
Condition
  Error in `sens()`:
  ! Must select at least one item.

Table with bad format

Code
  sens(as.table(matrix(1:6, 2)))
Condition
  Error in `sens()`:
  ! `x` must have equal dimensions. `x` has 3 columns and 2 rows.

Code
  sens(as.table(matrix(1:4, 2, dimnames = list(c("A", "B"), c("A", "C")))))
Condition
  Error in `sens()`:
  ! The table must the same groups in the same order.

yardstick documentation built on April 4, 2025, 12:27 a.m.