tests/testthat/_snaps/regression-setup.md

In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values

regression erroneous input: approach

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = c(
      "regression_surrogate", "gaussian", "independence", "empirical"), )
Condition
  Error in `check_approach()`:
  ! The `regression_separate` and `regression_surrogate` approaches cannot be combined with other approaches.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = c(
      "regression_separate", "gaussian", "independence", "empirical"), )
Condition
  Error in `check_approach()`:
  ! The `regression_separate` and `regression_surrogate` approaches cannot be combined with other approaches.

regression erroneous input: regression.model

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = NULL)
Condition
  Error in `regression.get_tune()`:
  ! `regression.model` must be a tidymodels object with class 'model_spec'. See documentation.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = lm)
Condition
  Error in `regression.get_tune()`:
  ! `regression.model` must be a tidymodels object with class 'model_spec'. See documentation.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"))
Condition
  Error in `regression.get_tune()`:
  ! `regression.tune_values` must be provided when `regression.model` contains hyperparameters to tune.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"), regression.tune_values = data.frame(num_terms = c(1, 2,
      3)))
Condition
  Error in `regression.get_tune()`:
  ! The tunable parameters in `regression.model` ('tree_depth') and `regression.tune_values` ('num_terms') must match.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1, 2,
      3), num_terms = c(1, 2, 3)))
Condition
  Error in `regression.get_tune()`:
  ! The tunable parameters in `regression.model` ('tree_depth') and `regression.tune_values` ('tree_depth', 'num_terms') must match.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = 2, engine = "rpart",
      mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1,
      2, 3)))
Condition
  Error in `regression.get_tune()`:
  ! The tunable parameters in `regression.model` ('') and `regression.tune_values` ('tree_depth') must match.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_surrogate",
    regression.tune_values = data.frame(tree_depth = c(1, 2, 3)))
Condition
  Error in `regression.get_tune()`:
  ! The tunable parameters in `regression.model` ('') and `regression.tune_values` ('tree_depth') must match.

regression erroneous input: regression.tune_values

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = 2, engine = "rpart",
      mode = "regression"), regression.tune_values = as.matrix(data.frame(
      tree_depth = c(1, 2, 3))))
Condition
  Error in `regression.get_tune()`:
  ! `regression.tune_values` must be of either class `data.frame` or `function`. See documentation.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"), regression.tune_values = function(x) c(1, 2, 3))
Condition
  Error in `regression.get_tune()`:
  ! The output of the user provided `regression.tune_values` function must be of class `data.frame`.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"), regression.tune_values = function(x) data.frame(
      wrong_name = c(1, 2, 3)))
Condition
  Error in `regression.get_tune()`:
  ! The tunable parameters in `regression.model` ('tree_depth') and `regression.tune_values` ('wrong_name') must match.

regression erroneous input: regression.vfold_cv_para

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1, 2,
      3)), regression.vfold_cv_para = 10)
Condition
  Error in `regression.check_vfold_cv_para()`:
  ! `regression.vfold_cv_para` must be a named list. See documentation using '?shapr::explain()'.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1, 2,
      3)), regression.vfold_cv_para = list(10))
Condition
  Error in `regression.check_vfold_cv_para()`:
  ! `regression.vfold_cv_para` must be a named list. See documentation using '?shapr::explain()'.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.model = parsnip::decision_tree(tree_depth = tune(), engine = "rpart",
    mode = "regression"), regression.tune_values = data.frame(tree_depth = c(1, 2,
      3)), regression.vfold_cv_para = list(hey = 10))
Condition
  Error in `regression.check_vfold_cv_para()`:
  ! The following parameters in `regression.vfold_cv_para` are not supported by `rsample::vfold_cv()`: 'hey'.

regression erroneous input: regression.recipe_func

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_separate",
    regression.recipe_func = 3)
Condition
  Error in `regression.check_recipe_func()`:
  ! `regression.recipe_func` must be a function. See documentation.

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_surrogate",
    regression.recipe_func = function(x) {
      return(2)
    })
Condition
  Error in `regression.check_recipe_func()`:
  ! The output of the `regression.recipe_func` must be of class `recipe`.

regression erroneous input: regression.surrogate_n_comb

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_surrogate",
    regression.surrogate_n_comb = 2^ncol(x_explain_numeric) - 1)
Condition
  Error in `regression.check_sur_n_comb()`:
  ! `regression.surrogate_n_comb` (31) must be a positive integer less than or equal to `used_n_combinations` minus two (30).

Code
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, n_batches = 1, timing = FALSE, approach = "regression_surrogate",
    regression.surrogate_n_comb = 0)
Condition
  Error in `regression.check_sur_n_comb()`:
  ! `regression.surrogate_n_comb` (0) must be a positive integer less than or equal to `used_n_combinations` minus two (30).

NorskRegnesentral/shapr documentation built on April 19, 2024, 1:19 p.m.