tests/testthat/_snaps/regular-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(testing = TRUE, model = model_custom_lm_mixed, x_train = x_train_mixed,
    x_explain = x_explain_mixed, approach = "independence", phi0 = p0)
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.

  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

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(testing = TRUE, model = model_custom_lm_mixed, x_train = x_train_mixed,
    x_explain = x_explain_mixed, approach = "independence", phi0 = p0,
    predict_model = custom_predict_model, get_model_specs = NA)
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.

  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

  * Model class: <whatever>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Day Month_factor
          <int> <num>   <num>   <num>  <num>  <num>        <num>
  1:          1 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2:          2 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3:          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(testing = TRUE, model = model_custom_lm_mixed, x_train = x_train_mixed,
    x_explain = x_explain_mixed, approach = "independence", phi0 = p0,
    predict_model = custom_predict_model, get_model_specs = custom_get_model_specs_no_lab)
Message
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.

  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

  * Model class: <whatever>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Day Month_factor
          <int> <num>   <num>   <num>  <num>  <num>        <num>
  1:          1 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2:          2 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3:          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(testing = TRUE, model = model_custom_lm_mixed, x_train = x_train_mixed,
    x_explain = x_explain_mixed, approach = "independence", phi0 = p0,
    predict_model = custom_predict_model, get_model_specs = custom_gms_no_classes)
Message
  Note: Feature classes extracted from the model contains NA.
  Assuming feature classes from the data are correct.

  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

  * Model class: <whatever>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Day Month_factor
          <int> <num>   <num>   <num>  <num>  <num>        <num>
  1:          1 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2:          2 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3:          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(testing = TRUE, model = model_custom_lm_mixed, x_train = x_train_mixed,
    x_explain = x_explain_mixed, approach = "independence", phi0 = p0,
    predict_model = custom_predict_model, get_model_specs = custom_gms_no_factor_levels)
Message
  Note: Feature factor levels extracted from the model contains NA.
  Assuming feature factor levels from the data are correct.

  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

  * Model class: <whatever>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Day Month_factor
          <int> <num>   <num>   <num>  <num>  <num>        <num>
  1:          1 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2:          2 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3:          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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_wrong_format, approach = "independence", phi0 = p0)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_wrong_format,
    x_train = x_train_numeric, approach = "independence", phi0 = p0)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_wrong_format,
    x_train = x_train_wrong_format, approach = "independence", phi0 = p0)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_no_column_names, approach = "independence", phi0 = p0)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_no_column_names,
    x_train = x_train_numeric, approach = "independence", phi0 = p0)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_no_column_names,
    x_train = x_train_no_column_names, approach = "independence", phi0 = p0)
Condition
  Error in `get_data()`:
  ! x_explain misses column names.

erroneous input: model

Code
  explain(testing = TRUE, x_explain = x_explain_numeric, x_train = x_train_numeric,
    approach = "independence", phi0 = p0)
Condition
  Error in `explain()`:
  ! argument "model" is missing, with no default

erroneous input: approach

Code
  approach_non_character <- 1
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = approach_non_character, phi0 = p0)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = approach_incorrect_length, phi0 = p0)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = approach_incorrect_character, phi0 = p0)
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: phi0

Code
  p0_non_numeric_1 <- "bla"
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0_non_numeric_1)
Condition
  Error in `get_parameters()`:
  ! `phi0` (bla) must be numeric and match the output size of the model (1).

Code
  p0_non_numeric_2 <- NULL
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0_non_numeric_2)
Condition
  Error in `get_parameters()`:
  ! `phi0` () must be numeric and match the output size of the model (1).

Code
  p0_too_long <- c(1, 2)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0_too_long)
Condition
  Error in `get_parameters()`:
  ! `phi0` (1, 2) must be numeric and match the output size of the model (1).

Code
  p0_is_NA <- as.numeric(NA)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0_is_NA)
Condition
  Error in `get_parameters()`:
  ! `phi0` (NA) must be numeric and match the output size of the model (1).

erroneous input: max_n_coalitions

Code
  max_n_comb_non_numeric_1 <- "bla"
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    max_n_coalitions = max_n_comb_non_numeric_1)
Condition
  Error in `get_parameters()`:
  ! `max_n_coalitions` must be NULL or a single positive integer.

Code
  max_n_comb_non_numeric_2 <- TRUE
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    max_n_coalitions = max_n_comb_non_numeric_2)
Condition
  Error in `get_parameters()`:
  ! `max_n_coalitions` must be NULL or a single positive integer.

Code
  max_n_coalitions_non_integer <- 10.5
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    max_n_coalitions = max_n_coalitions_non_integer)
Condition
  Error in `get_parameters()`:
  ! `max_n_coalitions` must be NULL or a single positive integer.

Code
  max_n_coalitions_too_long <- c(1, 2)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    max_n_coalitions = max_n_coalitions_too_long)
Condition
  Error in `get_parameters()`:
  ! `max_n_coalitions` must be NULL or a single positive integer.

Code
  max_n_coalitions_is_NA <- as.numeric(NA)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    max_n_coalitions = max_n_coalitions_is_NA)
Condition
  Error in `get_parameters()`:
  ! `max_n_coalitions` must be NULL or a single positive integer.

Code
  max_n_comb_non_positive <- 0
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    max_n_coalitions = max_n_comb_non_positive)
Condition
  Error in `get_parameters()`:
  ! `max_n_coalitions` must be NULL or a single positive integer.

Code
  max_n_coalitions <- ncol(x_explain_numeric) - 1
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, approach = "gaussian",
    max_n_coalitions = max_n_coalitions)
Message
  Success with message:
  max_n_coalitions is smaller than max(10, n_features + 1 = 6),which will result in unreliable results.
  It is therefore set to 10.

  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 6 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind     Temp   Month     Day
          <int> <num>   <num>   <num>    <num>   <num>   <num>
  1:          1 42.44 -0.4778 -0.4778  15.3062 -0.4778 -1.2708
  2:          2 42.44 -0.7900 -0.7900 -10.7058 -0.7900 -0.8036
  3:          3 42.44 -6.2514 -6.2514   0.2771 -6.2514  0.9088

Code
  groups <- list(A = c("Solar.R", "Wind"), B = c("Temp", "Month"), C = "Day")
  max_n_coalitions <- length(groups) - 1
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, phi0 = p0, approach = "gaussian", group = groups,
    max_n_coalitions = max_n_coalitions)
Message
  Success with message:
  n_groups is smaller than or equal to 3, meaning there are so few unique coalitions (8) that we should use all to get reliable results.
  max_n_coalitions is therefore set to 2^n_groups = 8.

  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 3
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 8 of 8 coalitions. 
Output
     explain_id  none        A        B       C
          <int> <num>    <num>    <num>   <num>
  1:          1 42.44   0.2636  13.7991 -1.4606
  2:          2 42.44   0.1788 -13.1512 -0.9071
  3:          3 42.44 -18.4998  -0.1635  1.0951

erroneous input: group

Code
  group_non_list <- "bla"
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, group = group_non_list)
Condition
  Error in `get_parameters()`:
  ! `group` must be NULL or a list

Code
  group_with_non_characters <- list(A = 1, B = 2)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, group = group_with_non_characters)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, group = group_with_non_data_features)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, group = group_missing_data_features)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, group = group_dup_data_features)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, group = single_group)
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_MC_samples

Code
  n_samples_non_numeric_1 <- "bla"
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    n_MC_samples = n_samples_non_numeric_1)
Condition
  Error in `get_parameters()`:
  ! `n_MC_samples` must be a single positive integer.

Code
  n_samples_non_numeric_2 <- TRUE
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    n_MC_samples = n_samples_non_numeric_2)
Condition
  Error in `get_parameters()`:
  ! `n_MC_samples` must be a single positive integer.

Code
  n_samples_non_integer <- 10.5
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    n_MC_samples = n_samples_non_integer)
Condition
  Error in `get_parameters()`:
  ! `n_MC_samples` must be a single positive integer.

Code
  n_samples_too_long <- c(1, 2)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    n_MC_samples = n_samples_too_long)
Condition
  Error in `get_parameters()`:
  ! `n_MC_samples` must be a single positive integer.

Code
  n_samples_is_NA <- as.numeric(NA)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    n_MC_samples = n_samples_is_NA)
Condition
  Error in `get_parameters()`:
  ! `n_MC_samples` must be a single positive integer.

Code
  n_samples_non_positive <- 0
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    n_MC_samples = n_samples_non_positive)
Condition
  Error in `get_parameters()`:
  ! `n_MC_samples` must be a single positive integer.

erroneous input: seed

Code
  seed_not_integer_interpretable <- "bla"
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, seed = seed_not_integer_interpretable)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, output_args = list(
      keep_samp_for_vS = keep_samp_for_vS_non_logical_1))
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `check_output_args()`:
  ! `output_args$keep_samp_for_vS` must be single logical.

Code
  keep_samp_for_vS_non_logical_2 <- NULL
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, output_args = list(
      keep_samp_for_vS = keep_samp_for_vS_non_logical_2))
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `check_output_args()`:
  ! `output_args$keep_samp_for_vS` must be single logical.

Code
  keep_samp_for_vS_too_long <- c(TRUE, FALSE)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, output_args = list(
      keep_samp_for_vS = keep_samp_for_vS_too_long))
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `check_output_args()`:
  ! `output_args$keep_samp_for_vS` must be single logical.

erroneous input: MSEv_uniform_comb_weights

Code
  MSEv_uniform_comb_weights_nl_1 <- "bla"
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, output_args = list(
      MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1))
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `check_output_args()`:
  ! `output_args$MSEv_uniform_comb_weights` must be single logical.

Code
  MSEv_uniform_comb_weights_nl_2 <- NULL
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, output_args = list(
      MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2))
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `check_output_args()`:
  ! `output_args$MSEv_uniform_comb_weights` must be single logical.

Code
  MSEv_uniform_comb_weights_long <- c(TRUE, FALSE)
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0, output_args = list(
      MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long))
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `check_output_args()`:
  ! `output_args$MSEv_uniform_comb_weights` must be single logical.

erroneous input: predict_model

Code
  predict_model_nonfunction <- "bla"
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    predict_model = predict_model_nonfunction)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    predict_model = predict_model_non_num_output)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

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('general_usage', 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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    predict_model = predict_model_wrong_output_len)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

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('general_usage', package = 'shapr')

  for more information on running shapr with custom models.

Code
  predict_model_invalid_argument <- (function(model) {
    rep(1, nrow(x))
  })
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    predict_model = predict_model_invalid_argument)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `test_predict_model()`:
  ! The predict_model function of class `lm` is invalid.
  See the 'Advanced usage' section of the vignette:
  vignette('general_usage', 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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    predict_model = predict_model_error)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

Condition
  Error in `test_predict_model()`:
  ! The predict_model function of class `lm` is invalid.
  See the 'Advanced usage' section of the vignette:
  vignette('general_usage', 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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    get_model_specs = get_model_specs_nonfunction)
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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    get_model_specs = get_ms_output_not_list)
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('general_usage', 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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    get_model_specs = get_ms_output_too_long)
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('general_usage', 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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    get_model_specs = get_ms_output_wrong_names)
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('general_usage', 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(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_train_numeric, approach = "independence", phi0 = p0,
    get_model_specs = get_model_specs_error)
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('general_usage', 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(testing = TRUE, model = model_lm_mixed, x_explain = x_explain_mixed,
    x_train = x_explain_mixed, approach = non_factor_approach_1, phi0 = p0)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

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', 'regression_separate', 'regression_surrogate'.

Code
  non_factor_approach_2 <- "empirical"
  explain(testing = TRUE, model = model_lm_mixed, x_explain = x_explain_mixed,
    x_train = x_explain_mixed, approach = non_factor_approach_2, phi0 = p0)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

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', 'regression_separate', 'regression_surrogate'.

Code
  non_factor_approach_3 <- "copula"
  explain(testing = TRUE, model = model_lm_mixed, x_explain = x_explain_mixed,
    x_train = x_explain_mixed, approach = non_factor_approach_3, phi0 = p0)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

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', 'regression_separate', 'regression_surrogate'.

Message with too low max_n_coalitions

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_explain_numeric, phi0 = p0, approach = "gaussian",
    max_n_coalitions = max_n_coalitions)
Message
  Success with message:
  max_n_coalitions is smaller than max(10, n_features + 1 = 6),which will result in unreliable results.
  It is therefore set to 10.

  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 6 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp   Month     Day
          <int> <num>   <num>   <num>  <num>   <num>   <num>
  1:          1 42.44  1.6821  1.6821  5.891  1.6821   1.665
  2:          2 42.44 -1.1944 -1.1944 -8.918 -1.1944  -1.378
  3:          3 42.44 -0.2038 -0.2038 -6.450 -0.2038 -10.507

Code
  explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
    x_train = x_explain_numeric, phi0 = p0, approach = "gaussian", group = groups,
    max_n_coalitions = max_n_coalitions)
Message
  Success with message:
  n_groups is smaller than or equal to 3, meaning there are so few unique coalitions (8) that we should use all to get reliable results.
  max_n_coalitions is therefore set to 2^n_groups = 8.

  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 3
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 8 of 8 coalitions. 
Output
     explain_id  none      A      B       C
          <int> <num>  <num>  <num>   <num>
  1:          1 42.44  5.589  5.591  1.4213
  2:          2 42.44 -6.637 -6.636 -0.6071
  3:          3 42.44 -5.439 -5.436 -6.6932

Shapr with max_n_coalitions >= 2^m uses exact Shapley kernel weights

Code
  explanation_exact <- explain(testing = TRUE, model = model_lm_numeric,
    x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian",
    phi0 = p0, n_MC_samples = 2, seed = 123, max_n_coalitions = NULL, iterative = FALSE)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions.

Code
  explanation_equal <- explain(testing = TRUE, model = model_lm_numeric,
    x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian",
    phi0 = p0, n_MC_samples = 2, seed = 123, extra_computation_args = list(
      compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric),
    iterative = FALSE)
Message
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions.

Code
  explanation_larger <- explain(testing = TRUE, model = model_lm_numeric,
    x_explain = x_explain_numeric, x_train = x_train_numeric, approach = "gaussian",
    phi0 = p0, n_MC_samples = 2, seed = 123, extra_computation_args = list(
      compute_sd = FALSE), max_n_coalitions = 2^ncol(x_explain_numeric) + 1,
    iterative = FALSE)
Message
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.

  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions.

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