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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i You passed a model to `shapr::explain()` which is not natively supported, and did not supply a `get_model_specs` function to `shapr::explain()`.
Consistency checks between model and data is therefore disabled.
i `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 `shapr::explain()` which is not natively supported, and did not supply the 'predict_model' function to `shapr::explain()`. See the documentation of `shapr::explain()` or the `vignette(shapr::general_usage)` 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, seed = 1,
predict_model = custom_predict_model, get_model_specs = NA)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i You passed a model to `shapr::explain()` which is not natively supported, and did not supply a `get_model_specs` function to `shapr::explain()`.
Consistency checks between model and data is therefore disabled.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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, seed = 1,
predict_model = custom_predict_model, get_model_specs = custom_get_model_specs_no_lab)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i Feature names extracted from the model contains `NA`.
Consistency checks between model and data is therefore disabled.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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$terms), classes = NA, factor_levels = NA)
})
explain(testing = TRUE, model = model_custom_lm_mixed, x_train = x_train_mixed,
x_explain = x_explain_mixed, approach = "independence", phi0 = p0, seed = 1,
predict_model = custom_predict_model, get_model_specs = custom_gms_no_classes)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i Feature classes extracted from the model contains `NA`.
Assuming feature classes from the data are correct.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <whatever>
* Approach: independence
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -4.730 7.750 17.753 -2.601 -7.588
2: 2 42.44 2.338 -3.147 -5.310 -1.676 -7.588
3: 3 42.44 3.857 -17.469 -1.466 1.099 3.379
Code
custom_gms_no_factor_levels <- (function(x) {
feature_specs <- list(labels = labels(x$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, seed = 1,
predict_model = custom_predict_model, get_model_specs = custom_gms_no_factor_levels)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i Feature factor levels extracted from the model contains `NA`.
Assuming feature factor levels from the data are correct.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_comb_non_numeric_1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_comb_non_numeric_2)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_coalitions_non_integer)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_coalitions_too_long)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_coalitions_is_NA)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_comb_non_positive)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1, approach = "gaussian",
max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is smaller than `max(10, n_features + 1 = 6)`, which will result in unreliable results.
It is therefore set to 10.
-- Explanation overview --
* 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, seed = 1, approach = "gaussian", group = groups,
max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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`.
-- Explanation overview --
* 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, seed = 1,
group = group_non_list)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_with_non_characters)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_with_non_data_features)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_missing_data_features)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_dup_data_features)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = single_group)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_numeric_1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_numeric_2)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_integer)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_too_long)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_is_NA)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_positive)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_1))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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, seed = 1,
output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_2))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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$keep_samp_for_vS` must be single logical.
Code
keep_samp_for_vS_too_long <- c(TRUE, FALSE)
explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
x_train = x_train_numeric, approach = "independence", phi0 = p0, seed = 1,
output_args = list(keep_samp_for_vS = keep_samp_for_vS_too_long))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
Condition
Error in `check_output_args()`:
! `output_args$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, seed = 1,
output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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$MSEv_uniform_comb_weights` must be single logical.
Code
MSEv_uniform_comb_weights_nl_2 <- NULL
explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
x_train = x_train_numeric, approach = "independence", phi0 = p0, seed = 1,
output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
Condition
Error in `check_output_args()`:
! `output_args$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, seed = 1,
output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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, seed = 1,
predict_model = predict_model_nonfunction)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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, seed = 1,
predict_model = predict_model_non_num_output)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
predict_model = predict_model_wrong_output_len)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
predict_model = predict_model_invalid_argument)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
predict_model = predict_model_error)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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()`:
! 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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()`:
! 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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()`:
! 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, seed = 1, approach = "gaussian",
max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is smaller than `max(10, n_features + 1 = 6)`, which will result in unreliable results.
It is therefore set to 10.
-- Explanation overview --
* 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, seed = 1, approach = "gaussian",
group = groups, max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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`.
-- Explanation overview --
* 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
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* 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
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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.
output_custom_lm_numeric_independence_2
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i You passed a model to `shapr::explain()` which is not natively supported, and did not supply a `get_model_specs` function to `shapr::explain()`.
Consistency checks between model and data is therefore disabled.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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 Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -4.537 8.269 17.517 -5.581 -3.066
2: 2 42.44 2.250 -3.345 -5.232 -5.581 -1.971
3: 3 42.44 3.708 -18.610 -1.440 -2.541 1.316
output_lm_numeric_independence_keep_samp_for_vS
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* 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 Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -4.537 8.269 17.517 -5.581 -3.066
2: 2 42.44 2.250 -3.345 -5.232 -5.581 -1.971
3: 3 42.44 3.708 -18.610 -1.440 -2.541 1.316
output_lm_numeric_vS_batching_method
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 32 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 4 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 6 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 24 of 32 coalitions, 6 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 28 of 32 coalitions, 4 new.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -8.187 8.808 16.651 -1.355 -3.315
2: 2 42.44 5.551 -6.142 -8.670 -2.622 -1.997
3: 3 42.44 7.009 -21.407 -4.878 0.418 1.290
NorskRegnesentral/shapr documentation built on June 15, 2025, 6:18 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i You passed a model to `shapr::explain()` which is not natively supported, and did not supply a `get_model_specs` function to `shapr::explain()`.
Consistency checks between model and data is therefore disabled.
i `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 `shapr::explain()` which is not natively supported, and did not supply the 'predict_model' function to `shapr::explain()`. See the documentation of `shapr::explain()` or the `vignette(shapr::general_usage)` 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, seed = 1,
predict_model = custom_predict_model, get_model_specs = NA)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i You passed a model to `shapr::explain()` which is not natively supported, and did not supply a `get_model_specs` function to `shapr::explain()`.
Consistency checks between model and data is therefore disabled.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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, seed = 1,
predict_model = custom_predict_model, get_model_specs = custom_get_model_specs_no_lab)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i Feature names extracted from the model contains `NA`.
Consistency checks between model and data is therefore disabled.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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, seed = 1,
predict_model = custom_predict_model, get_model_specs = custom_gms_no_classes)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i Feature classes extracted from the model contains `NA`.
Assuming feature classes from the data are correct.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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, seed = 1,
predict_model = custom_predict_model, get_model_specs = custom_gms_no_factor_levels)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i Feature factor levels extracted from the model contains `NA`.
Assuming feature factor levels from the data are correct.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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,
seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_comb_non_numeric_1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_comb_non_numeric_2)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_coalitions_non_integer)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_coalitions_too_long)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_coalitions_is_NA)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
max_n_coalitions = max_n_comb_non_positive)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1, approach = "gaussian",
max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is smaller than `max(10, n_features + 1 = 6)`, which will result in unreliable results.
It is therefore set to 10.
-- Explanation overview --
* 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, seed = 1, approach = "gaussian", group = groups,
max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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`.
-- Explanation overview --
* 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, seed = 1,
group = group_non_list)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_with_non_characters)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_with_non_data_features)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_missing_data_features)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = group_dup_data_features)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
group = single_group)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_numeric_1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_numeric_2)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_integer)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_too_long)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_is_NA)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
n_MC_samples = n_samples_non_positive)
Message
-- Starting `shapr::explain()` -------------------------------------------------
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, seed = 1,
output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_1))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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$keep_samp_for_vS` must be single logical.
Code
keep_samp_for_vS_non_logical_2 <- NULL
explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
x_train = x_train_numeric, approach = "independence", phi0 = p0, seed = 1,
output_args = list(keep_samp_for_vS = keep_samp_for_vS_non_logical_2))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
Condition
Error in `check_output_args()`:
! `output_args$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, seed = 1,
output_args = list(keep_samp_for_vS = keep_samp_for_vS_too_long))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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$keep_samp_for_vS` must be single logical.
erroneous input: MSEv_uniform_comb_weights
Code
MSEv_uniform_comb_weights_nl_1 <- "bla"
explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
x_train = x_train_numeric, approach = "independence", phi0 = p0, seed = 1,
output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_1))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
Condition
Error in `check_output_args()`:
! `output_args$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, seed = 1,
output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_nl_2))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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$MSEv_uniform_comb_weights` must be single logical.
Code
MSEv_uniform_comb_weights_long <- c(TRUE, FALSE)
explain(testing = TRUE, model = model_lm_numeric, x_explain = x_explain_numeric,
x_train = x_train_numeric, approach = "independence", phi0 = p0, seed = 1,
output_args = list(MSEv_uniform_comb_weights = MSEv_uniform_comb_weights_long))
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
Condition
Error in `check_output_args()`:
! `output_args$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, seed = 1,
predict_model = predict_model_nonfunction)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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, seed = 1,
predict_model = predict_model_non_num_output)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
predict_model = predict_model_wrong_output_len)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
predict_model = predict_model_invalid_argument)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
predict_model = predict_model_error)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1,
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 `vignette(shapr::general_usage)` vignette 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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()`:
! 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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()`:
! 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, seed = 1)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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()`:
! 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, seed = 1, approach = "gaussian",
max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is smaller than `max(10, n_features + 1 = 6)`, which will result in unreliable results.
It is therefore set to 10.
-- Explanation overview --
* 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, seed = 1, approach = "gaussian",
group = groups, max_n_coalitions = max_n_coalitions)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `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`.
-- Explanation overview --
* 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
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* 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
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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.
output_custom_lm_numeric_independence_2
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i You passed a model to `shapr::explain()` which is not natively supported, and did not supply a `get_model_specs` function to `shapr::explain()`.
Consistency checks between model and data is therefore disabled.
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* 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 Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -4.537 8.269 17.517 -5.581 -3.066
2: 2 42.44 2.250 -3.345 -5.232 -5.581 -1.971
3: 3 42.44 3.708 -18.610 -1.440 -2.541 1.316
output_lm_numeric_independence_keep_samp_for_vS
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* 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 Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -4.537 8.269 17.517 -5.581 -3.066
2: 2 42.44 2.250 -3.345 -5.232 -5.581 -1.971
3: 3 42.44 3.708 -18.610 -1.440 -2.541 1.316
output_lm_numeric_vS_batching_method
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 32 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 4 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 6 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 24 of 32 coalitions, 6 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 28 of 32 coalitions, 4 new.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -8.187 8.808 16.651 -1.355 -3.315
2: 2 42.44 5.551 -6.142 -8.670 -2.622 -1.997
3: 3 42.44 7.009 -21.407 -4.878 0.418 1.290
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.