tests/testthat/_snaps/setup.md

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

error with custom model without providing predict_model

Code
  model_custom_lm_mixed <- model_lm_mixed
  class(model_custom_lm_mixed) <- "whatever"
  explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
    approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
Message
  Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
  Consistency checks between model and data is therefore disabled.

Condition
  Error in `get_predict_model()`:
  ! You passed a model to explain() which is not natively supported, and did not supply the 'predict_model' function to explain().
  See ?shapr::explain or the vignette for more information on how to run shapr with custom models.

messages with missing detail in get_model_specs

Code
  explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
    approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
    get_model_specs = NA, n_batches = 1, timing = FALSE)
Message
  Note: You passed a model to explain() which is not natively supported, and did not supply a 'get_model_specs' function to explain().
  Consistency checks between model and data is therefore disabled.

Output
      none Solar.R    Wind   Temp    Day Month_factor
     <num>   <num>   <num>  <num>  <num>        <num>
  1: 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2: 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3: 42.44   3.857 -17.469 -1.466  1.099        3.379

Code
  custom_get_model_specs_no_lab <- (function(x) {
    feature_specs <- list(labels = NA, classes = NA, factor_levels = NA)
  })
  explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
    approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
    get_model_specs = custom_get_model_specs_no_lab, n_batches = 1, timing = FALSE)
Message
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.

Output
      none Solar.R    Wind   Temp    Day Month_factor
     <num>   <num>   <num>  <num>  <num>        <num>
  1: 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2: 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3: 42.44   3.857 -17.469 -1.466  1.099        3.379

Code
  custom_gms_no_classes <- (function(x) {
    feature_specs <- list(labels = labels(x$terms), classes = NA, factor_levels = NA)
  })
  explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
    approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
    get_model_specs = custom_gms_no_classes, n_batches = 1, timing = FALSE)
Message
  Note: Feature classes extracted from the model contains NA.
  Assuming feature classes from the data are correct.

Output
      none Solar.R    Wind   Temp    Day Month_factor
     <num>   <num>   <num>  <num>  <num>        <num>
  1: 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2: 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3: 42.44   3.857 -17.469 -1.466  1.099        3.379

Code
  custom_gms_no_factor_levels <- (function(x) {
    feature_specs <- list(labels = labels(x$terms), classes = attr(x$terms,
    "dataClasses")[-1], factor_levels = NA)
  })
  explain(model = model_custom_lm_mixed, x_train = x_train_mixed, x_explain = x_explain_mixed,
    approach = "independence", prediction_zero = p0, predict_model = custom_predict_model,
    get_model_specs = custom_gms_no_factor_levels, n_batches = 1, timing = FALSE)
Message
  Note: Feature factor levels extracted from the model contains NA.
  Assuming feature factor levels from the data are correct.

Output
      none Solar.R    Wind   Temp    Day Month_factor
     <num>   <num>   <num>  <num>  <num>        <num>
  1: 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2: 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3: 42.44   3.857 -17.469 -1.466  1.099        3.379

erroneous input: x_train/x_explain

Code
  x_train_wrong_format <- c(a = 1, b = 2)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_wrong_format,
    approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
Condition
  Error in `get_data()`:
  ! x_train should be a matrix or a data.frame/data.table.

Code
  x_explain_wrong_format <- c(a = 1, b = 2)
  explain(model = model_lm_numeric, x_explain = x_explain_wrong_format, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
Condition
  Error in `get_data()`:
  ! x_explain should be a matrix or a data.frame/data.table.

Code
  x_train_wrong_format <- c(a = 1, b = 2)
  x_explain_wrong_format <- c(a = 3, b = 4)
  explain(model = model_lm_numeric, x_explain = x_explain_wrong_format, x_train = x_train_wrong_format,
    approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
Condition
  Error in `get_data()`:
  ! x_train should be a matrix or a data.frame/data.table.
  x_explain should be a matrix or a data.frame/data.table.

Code
  x_train_no_column_names <- as.data.frame(x_train_numeric)
  names(x_train_no_column_names) <- NULL
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_no_column_names,
    approach = "independence", prediction_zero = p0, n_batches = 1, timing = FALSE)
Condition
  Error in `get_data()`:
  ! x_train misses column names.

Code
  x_explain_no_column_names <- as.data.frame(x_explain_numeric)
  names(x_explain_no_column_names) <- NULL
  explain(model = model_lm_numeric, x_explain = x_explain_no_column_names,
    x_train = x_train_numeric, approach = "independence", prediction_zero = p0,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_data()`:
  ! x_explain misses column names.

Code
  x_train_no_column_names <- as.data.frame(x_train_numeric)
  x_explain_no_column_names <- as.data.frame(x_explain_numeric)
  names(x_explain_no_column_names) <- NULL
  explain(model = model_lm_numeric, x_explain = x_explain_no_column_names,
    x_train = x_train_no_column_names, approach = "independence",
    prediction_zero = p0, n_batches = 1, timing = FALSE)
Condition
  Error in `get_data()`:
  ! x_explain misses column names.

erroneous input: model

Code
  explain(x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "independence",
    prediction_zero = p0, n_batches = 1, timing = FALSE)
Condition
  Error in `explain()`:
  ! argument "model" is missing, with no default

erroneous input: approach

Code
  approach_non_character <- 1
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = approach_non_character, prediction_zero = p0, n_batches = 1,
    timing = FALSE)
Condition
  Error in `check_approach()`:
  ! `approach` must be one of the following: 'categorical', 'copula', 'ctree', 'empirical', 'gaussian', 'independence', 'regression_separate', 'regression_surrogate', 'timeseries', 'vaeac'.
  These can also be combined (except 'regression_surrogate' and 'regression_separate') by passing a vector of length one less than the number of features (4).

Code
  approach_incorrect_length <- c("empirical", "gaussian")
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = approach_incorrect_length, prediction_zero = p0, n_batches = 1,
    timing = FALSE)
Condition
  Error in `check_approach()`:
  ! `approach` must be one of the following: 'categorical', 'copula', 'ctree', 'empirical', 'gaussian', 'independence', 'regression_separate', 'regression_surrogate', 'timeseries', 'vaeac'.
  These can also be combined (except 'regression_surrogate' and 'regression_separate') by passing a vector of length one less than the number of features (4).

Code
  approach_incorrect_character <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = approach_incorrect_character, prediction_zero = p0, n_batches = 1,
    timing = FALSE)
Condition
  Error in `check_approach()`:
  ! `approach` must be one of the following: 'categorical', 'copula', 'ctree', 'empirical', 'gaussian', 'independence', 'regression_separate', 'regression_surrogate', 'timeseries', 'vaeac'.
  These can also be combined (except 'regression_surrogate' and 'regression_separate') by passing a vector of length one less than the number of features (4).

erroneous input: prediction_zero

Code
  p0_non_numeric_1 <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0_non_numeric_1, n_batches = 1,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `prediction_zero` (bla) must be numeric and match the output size of the model (1).

Code
  p0_non_numeric_2 <- NULL
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0_non_numeric_2, n_batches = 1,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `prediction_zero` () must be numeric and match the output size of the model (1).

Code
  p0_too_long <- c(1, 2)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0_too_long, n_batches = 1,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `prediction_zero` (1, 2) must be numeric and match the output size of the model (1).

Code
  p0_is_NA <- as.numeric(NA)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0_is_NA, n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `prediction_zero` (NA) must be numeric and match the output size of the model (1).

erroneous input: n_combinations

Code
  n_combinations_non_numeric_1 <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_numeric_1,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_combinations` must be NULL or a single positive integer.

Code
  n_combinations_non_numeric_2 <- TRUE
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_numeric_2,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_combinations` must be NULL or a single positive integer.

Code
  n_combinations_non_integer <- 10.5
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_integer,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_combinations` must be NULL or a single positive integer.

Code
  n_combinations_too_long <- c(1, 2)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_combinations = n_combinations_too_long,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_combinations` must be NULL or a single positive integer.

Code
  n_combinations_is_NA <- as.numeric(NA)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_combinations = n_combinations_is_NA,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_combinations` must be NULL or a single positive integer.

Code
  n_combinations_non_positive <- 0
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_combinations = n_combinations_non_positive,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_combinations` must be NULL or a single positive integer.

Code
  n_combinations <- ncol(x_explain_numeric) - 1
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, approach = "gaussian", n_combinations = n_combinations,
    n_batches = 1, timing = FALSE)
Condition
  Error in `check_n_combinations()`:
  ! `n_combinations` has to be greater than the number of features.

Code
  groups <- list(A = c("Solar.R", "Wind"), B = c("Temp", "Month"), C = "Day")
  n_combinations <- length(groups) - 1
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    prediction_zero = p0, approach = "gaussian", group = groups, n_combinations = n_combinations,
    n_batches = 1, timing = FALSE)
Condition
  Error in `check_n_combinations()`:
  ! `n_combinations` has to be greater than the number of groups.

erroneous input: group

Code
  group_non_list <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, group = group_non_list,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `group` must be NULL or a list

Code
  group_with_non_characters <- list(A = 1, B = 2)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, group = group_with_non_characters,
    n_batches = 1, timing = FALSE)
Condition
  Error in `check_groups()`:
  ! All components of group should be a character.

Code
  group_with_non_data_features <- list(A = c("Solar.R", "Wind",
    "not_a_data_feature"), B = c("Temp", "Month", "Day"))
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, group = group_with_non_data_features,
    n_batches = 1, timing = FALSE)
Condition
  Error in `check_groups()`:
  ! The group feature(s) not_a_data_feature are not
  among the features in the data: Solar.R, Wind, Temp, Month, Day. Delete from group.

Code
  group_missing_data_features <- list(A = c("Solar.R"), B = c("Temp", "Month",
    "Day"))
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, group = group_missing_data_features,
    n_batches = 1, timing = FALSE)
Condition
  Error in `check_groups()`:
  ! The data feature(s) Wind do not
  belong to one of the groups. Add to a group.

Code
  group_dup_data_features <- list(A = c("Solar.R", "Solar.R", "Wind"), B = c(
    "Temp", "Month", "Day"))
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, group = group_dup_data_features,
    n_batches = 1, timing = FALSE)
Condition
  Error in `check_groups()`:
  ! Feature(s) Solar.R are found in more than one group or multiple times per group.
  Make sure each feature is only represented in one group, and only once.

Code
  single_group <- list(A = c("Solar.R", "Wind", "Temp", "Month", "Day"))
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, group = single_group,
    n_batches = 1, timing = FALSE)
Condition
  Error in `check_groups()`:
  ! You have specified only a single group named A, containing the features: Solar.R, Wind, Temp, Month, Day.
   The predictions must be decomposed in at least two groups to be meaningful.

erroneous input: n_samples

Code
  n_samples_non_numeric_1 <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_samples = n_samples_non_numeric_1,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_samples` must be a single positive integer.

Code
  n_samples_non_numeric_2 <- TRUE
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_samples = n_samples_non_numeric_2,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_samples` must be a single positive integer.

Code
  n_samples_non_integer <- 10.5
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_samples = n_samples_non_integer,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_samples` must be a single positive integer.

Code
  n_samples_too_long <- c(1, 2)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_samples = n_samples_too_long,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_samples` must be a single positive integer.

Code
  n_samples_is_NA <- as.numeric(NA)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_samples = n_samples_is_NA,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_samples` must be a single positive integer.

Code
  n_samples_non_positive <- 0
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_samples = n_samples_non_positive,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_samples` must be a single positive integer.

erroneous input: n_batches

Code
  n_batches_non_numeric_1 <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = n_batches_non_numeric_1,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_batches` must be NULL or a single positive integer.

Code
  n_batches_non_numeric_2 <- TRUE
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = n_batches_non_numeric_2,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_batches` must be NULL or a single positive integer.

Code
  n_batches_non_integer <- 10.5
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = n_batches_non_integer,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_batches` must be NULL or a single positive integer.

Code
  n_batches_too_long <- c(1, 2)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = n_batches_too_long,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_batches` must be NULL or a single positive integer.

Code
  n_batches_is_NA <- as.numeric(NA)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = n_batches_is_NA,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_batches` must be NULL or a single positive integer.

Code
  n_batches_non_positive <- 0
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = n_batches_non_positive,
    timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `n_batches` must be NULL or a single positive integer.

Code
  n_combinations <- 10
  n_batches_too_large <- 11
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_combinations = n_combinations,
    n_batches = n_batches_too_large, timing = FALSE)
Condition
  Error in `check_n_batches()`:
  ! `n_batches` (11) must be smaller than the number of feature combinations/`n_combinations` (10)

Code
  n_batches_too_large_2 <- 32
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, n_batches = n_batches_too_large_2,
    timing = FALSE)
Condition
  Error in `check_n_batches()`:
  ! `n_batches` (32) must be smaller than the number of feature combinations/`n_combinations` (32)

erroneous input: seed

Code
  seed_not_integer_interpretable <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, seed = seed_not_integer_interpretable,
    n_batches = 1, timing = FALSE)
Condition
  Warning in `set.seed()`:
  NAs introduced by coercion
  Error in `set.seed()`:
  ! supplied seed is not a valid integer

erroneous input: keep_samp_for_vS

Code
  keep_samp_for_vS_non_logical_1 <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, keep_samp_for_vS = keep_samp_for_vS_non_logical_1,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `keep_samp_for_vS` must be single logical.

Code
  keep_samp_for_vS_non_logical_2 <- NULL
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, keep_samp_for_vS = keep_samp_for_vS_non_logical_2,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `keep_samp_for_vS` must be single logical.

Code
  keep_samp_for_vS_too_long <- c(TRUE, FALSE)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, keep_samp_for_vS = keep_samp_for_vS_too_long,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `keep_samp_for_vS` must be single logical.

erroneous input: MSEv_uniform_comb_weights

Code
  MSEv_uniform_comb_weights_nl_1 <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `MSEv_uniform_comb_weights` must be single logical.

Code
  MSEv_uniform_comb_weights_nl_2 <- NULL
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `MSEv_uniform_comb_weights` must be single logical.

Code
  MSEv_uniform_comb_weights_long <- c(TRUE, FALSE)
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_parameters()`:
  ! `MSEv_uniform_comb_weights` must be single logical.

erroneous input: predict_model

Code
  predict_model_nonfunction <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, predict_model = predict_model_nonfunction,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_predict_model()`:
  ! `predict_model` must be NULL or a function.

Code
  predict_model_non_num_output <- (function(model, x) {
    "bla"
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, predict_model = predict_model_non_num_output,
    n_batches = 1, timing = FALSE)
Condition
  Error in `test_predict_model()`:
  ! The predict_model function of class `lm` does not return a numeric output of the desired length
  for single output models or a data.table of the correct
  dimensions for a multiple output model.
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')

  for more information on running shapr with custom models.

Code
  predict_model_wrong_output_len <- (function(model, x) {
    rep(1, nrow(x) + 1)
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, predict_model = predict_model_wrong_output_len,
    n_batches = 1, timing = FALSE)
Condition
  Error in `test_predict_model()`:
  ! The predict_model function of class `lm` does not return a numeric output of the desired length
  for single output models or a data.table of the correct
  dimensions for a multiple output model.
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')

  for more information on running shapr with custom models.

Code
  predict_model_invalid_argument <- (function(model) {
    rep(1, nrow(x))
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, predict_model = predict_model_invalid_argument,
    n_batches = 1, timing = FALSE)
Condition
  Error in `test_predict_model()`:
  ! The predict_model function of class `lm` is invalid.
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')
  for more information on running shapr with custom models.
  A basic function test threw the following error:
  Error in predict_model(model, x_test): unused argument (x_test)

Code
  predict_model_error <- (function(model, x) {
    1 + "bla"
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, predict_model = predict_model_error,
    n_batches = 1, timing = FALSE)
Condition
  Error in `test_predict_model()`:
  ! The predict_model function of class `lm` is invalid.
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')
  for more information on running shapr with custom models.
  A basic function test threw the following error:
  Error in 1 + "bla": non-numeric argument to binary operator

erroneous input: get_model_specs

Code
  get_model_specs_nonfunction <- "bla"
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, get_model_specs = get_model_specs_nonfunction,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_feature_specs()`:
  ! `get_model_specs` must be NULL, NA or a function.

Code
  get_ms_output_not_list <- (function(x) {
    "bla"
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, get_model_specs = get_ms_output_not_list,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_feature_specs()`:
  ! The `get_model_specs` function of class `lm` does not return a list of length 3 with elements "labels","classes","factor_levels".
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')
  for more information on running shapr with custom models and the required output format of get_model_specs.

Code
  get_ms_output_too_long <- (function(x) {
    list(1, 2, 3, 4)
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, get_model_specs = get_ms_output_too_long,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_feature_specs()`:
  ! The `get_model_specs` function of class `lm` does not return a list of length 3 with elements "labels","classes","factor_levels".
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')
  for more information on running shapr with custom models and the required output format of get_model_specs.

Code
  get_ms_output_wrong_names <- (function(x) {
    list(labels = 1, classes = 2, not_a_name = 3)
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, get_model_specs = get_ms_output_wrong_names,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_feature_specs()`:
  ! The `get_model_specs` function of class `lm` does not return a list of length 3 with elements "labels","classes","factor_levels".
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')
  for more information on running shapr with custom models and the required output format of get_model_specs.

Code
  get_model_specs_error <- (function(x) {
    1 + "bla"
  })
  explain(model = model_lm_numeric, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", prediction_zero = p0, get_model_specs = get_model_specs_error,
    n_batches = 1, timing = FALSE)
Condition
  Error in `get_feature_specs()`:
  ! The get_model_specs function of class `lm` is invalid.
  See the 'Advanced usage' section of the vignette:
  vignette('understanding_shapr', package = 'shapr')
  for more information on running shapr with custom models.
  Note that `get_model_specs` is not required (can be set to NULL)
  unless you require consistency checks between model and data.
  A basic function test threw the following error:
  Error in 1 + "bla": non-numeric argument to binary operator

incompatible input: data/approach

Code
  non_factor_approach_1 <- "gaussian"
  explain(model = model_lm_mixed, x_explain = x_explain_mixed, x_train = x_explain_mixed,
    approach = non_factor_approach_1, prediction_zero = p0, n_batches = 1,
    timing = FALSE)
Condition
  Error in `setup_approach.gaussian()`:
  ! The following feature(s) are factor(s): Month_factor.
  approach = 'gaussian' does not support factor features.
  Please change approach to one of 'independence' (not recommended), 'ctree', 'vaeac', 'categorical'.

Code
  non_factor_approach_2 <- "empirical"
  explain(model = model_lm_mixed, x_explain = x_explain_mixed, x_train = x_explain_mixed,
    approach = non_factor_approach_2, prediction_zero = p0, n_batches = 1,
    timing = FALSE)
Condition
  Error in `setup_approach.empirical()`:
  ! The following feature(s) are factor(s): Month_factor.
  approach = 'empirical' does not support factor features.
  Please change approach to one of 'independence' (not recommended), 'ctree', 'vaeac', 'categorical'.

Code
  non_factor_approach_3 <- "copula"
  explain(model = model_lm_mixed, x_explain = x_explain_mixed, x_train = x_explain_mixed,
    approach = non_factor_approach_3, prediction_zero = p0, n_batches = 1,
    timing = FALSE)
Condition
  Error in `setup_approach.copula()`:
  ! The following feature(s) are factor(s): Month_factor.
  approach = 'copula' does not support factor features.
  Please change approach to one of 'independence' (not recommended), 'ctree', 'vaeac', 'categorical'.

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