tests/testthat/_snaps/asymmetric-causal-setup.md

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

asymmetric erroneous input: causal_ordering

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      1:6), confounding = NULL, approach = "gaussian", iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all feature names or indices exactly once.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      1:5, 5), confounding = NULL, approach = "gaussian", iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all feature names or indices exactly once.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      2:5, 5), confounding = NULL, approach = "gaussian", iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all feature names or indices exactly once.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      1:2, 4), confounding = NULL, approach = "gaussian", iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all feature names or indices exactly once.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      "Solar.R", "Wind", "Temp", "Month", "Day", "Invalid feature name"),
    confounding = NULL, approach = "gaussian", iterative = FALSE)
Condition
  Error in `convert_feature_name_to_idx()`:
  ! `causal_ordering` contains feature names (`Invalid feature name`) that are not in the data (`Solar.R`, `Wind`, `Temp`, `Month`, `Day`).

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      "Solar.R", "Wind", "Temp", "Month", "Day", "Day"), confounding = NULL,
    approach = "gaussian", iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all feature names or indices exactly once.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      "Solar.R", "Wind", "Temp", "Day", "Day"), confounding = NULL, approach = "gaussian",
    iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all feature names or indices exactly once.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      "Solar.R", "Wind"), confounding = NULL, approach = "gaussian", iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all feature names or indices exactly once.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      c("Solar.R", "Wind", "Temp", "Month"), "Day"), confounding = NULL,
    approach = "gaussian", group = list(A = c("Solar.R", "Wind"), B = "Temp", C = c(
      "Month", "Day")), iterative = FALSE)
Condition
  Error in `convert_feature_name_to_idx()`:
  ! `causal_ordering` contains group names (`Solar.R`, `Wind`, `Temp`, `Month`, `Day`) that are not in the data (`A`, `B`, `C`).

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      c("A", "C"), "Wrong name"), confounding = NULL, approach = "gaussian",
    group = list(A = c("Solar.R", "Wind"), B = "Temp", C = c("Month", "Day")),
    iterative = FALSE)
Condition
  Error in `convert_feature_name_to_idx()`:
  ! `causal_ordering` contains group names (`Wrong name`) that are not in the data (`A`, `B`, `C`).

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = TRUE, causal_ordering = list(
      c("A"), "B"), confounding = NULL, approach = "gaussian", group = list(A = c(
      "Solar.R", "Wind"), B = "Temp", C = c("Month", "Day")), iterative = FALSE)
Condition
  Error in `check_and_set_causal_ordering()`:
  ! `causal_ordering` is incomplete/incorrect. It must contain all group names or indices exactly once.

asymmetric erroneous input: approach

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = FALSE, causal_ordering = list(
      1:2, 3:4, 5), confounding = TRUE, approach = c("gaussian", "independence",
      "empirical", "gaussian"), iterative = FALSE)
Condition
  Error in `check_and_set_causal_sampling()`:
  ! Causal Shapley values is not applicable for combined approaches.

asymmetric erroneous input: asymmetric

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = c(FALSE, FALSE),
    causal_ordering = list(1:2, 3:4, 5), confounding = TRUE, approach = "gaussian",
    iterative = FALSE)
Condition
  Error in `get_parameters()`:
  ! `asymmetric` must be a single logical.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = "Must be a single logical",
    causal_ordering = list(1:2, 3:4, 5), confounding = TRUE, approach = "gaussian",
    iterative = FALSE)
Condition
  Error in `get_parameters()`:
  ! `asymmetric` must be a single logical.

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = 1L, causal_ordering = list(
      1:2, 3:4, 5), confounding = TRUE, approach = "gaussian", iterative = FALSE)
Condition
  Error in `get_parameters()`:
  ! `asymmetric` must be a single logical.

asymmetric erroneous input: confounding

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = FALSE, causal_ordering = list(
      1:2, 3:4, 5), confounding = c("A", "B", "C"), approach = "gaussian",
    iterative = FALSE)
Condition
  Error in `get_parameters()`:
  ! `confounding` must be a logical (vector).

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, asymmetric = FALSE, causal_ordering = list(
      1:2, 3:4, 5), confounding = c(TRUE, FALSE), approach = "gaussian",
    iterative = FALSE)
Condition
  Error in `check_and_set_confounding()`:
  ! `confounding` must either be a single logical or a vector of logicals of the same length as the number of components in `causal_ordering` (3).

NorskRegnesentral/shapr documentation built on Feb. 11, 2025, 6:41 a.m.