Running ‘testthat.R’ [237s/249s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # CRAN OMP THREAD LIMIT
  > Sys.setenv("OMP_THREAD_LIMIT" = 1)
  > 
  > library(testthat)
  > library(shapr)
  
  Attaching package: 'shapr'
  
  The following object is masked from 'package:testthat':
  
      setup
  
  > 
  > test_check("shapr")
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 5
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 5
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 10 of 32 coalitions. 
     explain_id  none Solar.R   Wind  Temp Month   Day
          <int> <num>   <num>  <num> <num> <num> <num>
  1:          1  42.4   1.919  1.919 17.49 -5.61 -3.12
  2:          2  42.4  -0.495 -0.495 -5.26 -5.61 -2.03
  3:          3  42.4  -7.399 -7.399 -1.47 -2.57  1.26
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 10 of 32 coalitions. 
  
  -- Summary of Shapley value explanation ----------------------------------------
  * Computed with `shapr::explain()`
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of coalitions used: 10 (of total 32)
  * Number of observations to explain: 3
  
  -- Estimated Shapley values (sd in parentheses) 
     explain_id      none      Solar.R         Wind         Temp        Month
          <int>    <char>       <char>       <char>       <char>       <char>
  1:          1 42.44 (0)  1.92 (1.47)  1.92 (1.47) 17.49 (3.41) -5.61 (2.84)
  2:          2 42.44 (0) -0.50 (0.53) -0.50 (0.53) -5.26 (0.67) -5.61 (0.79)
  3:          3 42.44 (0) -7.40 (1.34) -7.40 (1.34) -1.47 (0.84) -2.57 (0.75)
              Day
           <char>
  1: -3.12 (1.56)
  2: -2.03 (0.47)
  3:  1.26 (2.68)
  
  -- Estimated MSEv 
  Estimated MSE of v(S) = 108 (with sd = 25)
  
  -- Starting `shapr::explain_forecast()` ----------------------------------------
  i Feature names extracted from the model contain `NA`.
    Consistency checks between model and data are therefore disabled.
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 128`, and is therefore set to `2^n_features = 128`.
  
  -- Explanation overview --
  
  * Model class: <Arima>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 7
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 128 of 128 coalitions. 
  
  -- Starting `shapr::explain_forecast()` ----------------------------------------
  i Feature names extracted from the model contain `NA`.
    Consistency checks between model and data are therefore disabled.
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 64`, and is therefore set to `2^n_features = 64`.
  
  -- Explanation overview --
  
  * Model class: <Arima>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 6
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 64 of 64 coalitions. 
  
  -- Starting `shapr::explain_forecast()` ----------------------------------------
  i Feature names extracted from the model contain `NA`.
    Consistency checks between model and data are therefore disabled.
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <Arima>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain_forecast()` ----------------------------------------
  i Feature names extracted from the model contain `NA`.
    Consistency checks between model and data are therefore disabled.
  i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
  
  -- Explanation overview --
  
  * Model class: <Arima>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of group-wise Shapley values: 2
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 4 of 4 coalitions. 
  
  -- Starting `shapr::explain_forecast()` ----------------------------------------
  i Feature names extracted from the model contain `NA`.
    Consistency checks between model and data are therefore disabled.
  i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
  
  -- Explanation overview --
  
  * Model class: <Arima>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of group-wise Shapley values: 2
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 4 of 4 coalitions. 
  
  -- Starting `shapr::explain_forecast()` ----------------------------------------
  i Feature names extracted from the model contain `NA`.
    Consistency checks between model and data are therefore disabled.
  i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
  
  -- Explanation overview --
  
  * Model class: <Arima>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of group-wise Shapley values: 2
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 4 of 4 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence
  * Procedure: Iterative
  * Number of Monte Carlo integration samples: 1000
  * 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 10 of 32 coalitions, 2 new. 
  
  -- Iteration 4 -----------------------------------------------------------------
  i Using 12 of 32 coalitions, 2 new. 
  
  -- Iteration 5 -----------------------------------------------------------------
  i Using 14 of 32 coalitions, 2 new. 
  
  -- Iteration 6 -----------------------------------------------------------------
  i Using 16 of 32 coalitions, 2 new. 
  
  -- Iteration 7 -----------------------------------------------------------------
  i Using 18 of 32 coalitions, 2 new. 
  
  -- Iteration 8 -----------------------------------------------------------------
  i Using 20 of 32 coalitions, 2 new. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Iterative
  * Number of Monte Carlo integration samples: 1000
  * 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 16 of 32 coalitions, 4 new. 
  
  -- Iteration 5 -----------------------------------------------------------------
  i Using 18 of 32 coalitions, 2 new. 
  
  -- Iteration 6 -----------------------------------------------------------------
  i Using 22 of 32 coalitions, 4 new. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 32`, and is therefore set to `2^n_groups = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of group-wise Shapley values: 5
  * Feature groups: Solar.R: {"Solar.R"}; Wind: {"Wind"}; Temp: {"Temp"}; Month:
  {"Month"}; Day: {"Day"}
  * 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 16 of 32 coalitions, 4 new. 
  
  -- Iteration 5 -----------------------------------------------------------------
  i Using 18 of 32 coalitions, 2 new. 
  
  -- Iteration 6 -----------------------------------------------------------------
  i Using 22 of 32 coalitions, 4 new. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: ctree
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: ctree
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: ctree
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 10 of 32 coalitions. 
  Saving _problems/test-regular-setup-1297.R
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: ctree
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of group-wise Shapley values: 3
  * Feature groups: A: {"Solar.R", "Wind"}; B: {"Temp", "Month_factor"}; C:
  {"Day"}
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 6 of 8 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: ctree
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: ctree
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` at 2026-06-07 16:12:44 --------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  * Computations (temporary) saved at:
  '/tmp/Rtmpf2lVR1/shapr_obj_218643142dd9af.rds'
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence, empirical, gaussian, and copula
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence, empirical, gaussian, and copula
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence, empirical, gaussian, and copula
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian, gaussian, gaussian, and gaussian
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence, empirical, independence, and empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence, empirical, independence, and empirical
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 32`, and is therefore set to `2^n_groups = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: gaussian
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of group-wise Shapley values: 5
  * Feature groups: Solar.R: {"Solar.R"}; Wind: {"Wind"}; Temp: {"Temp"}; Month:
  {"Month"}; Day: {"Day"}
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 1000
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: ctree
  * Procedure: Iterative
  * Number of Monte Carlo integration samples: 1000
  * 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. 
  
  -- Iteration 7 -----------------------------------------------------------------
  i Using 30 of 32 coalitions, 2 new. 
  
  -- Iteration 8 -----------------------------------------------------------------
  i Using 32 of 32 coalitions, 2 new. 
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 50
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 18 of 32 coalitions. 
  
  -- Convergence info 
  v Iterative Shapley value estimation stopped at 18 coalitions after 1 iterations, due to:
  Maximum number of iterations (1) reached!
  Maximum number of coalitions (18) reached!
  
  Final estimated Shapley values (sd)
     explain_id      none      Solar.R          Wind         Temp        Month
          <int>    <char>       <char>        <char>       <char>       <char>
  1:          1 42.44 (0) -3.39 (0.80)   7.95 (0.62) 14.86 (3.27) -4.63 (2.39)
  2:          2 42.44 (0)  3.08 (0.62)  -3.56 (0.36) -4.64 (0.97) -6.03 (1.03)
  3:          3 42.44 (0)  3.73 (0.60) -18.90 (0.68) -1.04 (1.40) -3.56 (1.36)
              Day
           <char>
  1: -2.20 (2.47)
  2: -2.74 (0.96)
  3:  2.20 (0.96)
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * v(S) estimation class: Monte Carlo integration
  * Approach: independence
  * Procedure: Non-iterative
  * Number of Monte Carlo integration samples: 50
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 20 of 32 coalitions. 
  
  -- Convergence info 
  v Iterative Shapley value estimation stopped at 20 coalitions after 1 iterations, due to:
  Maximum number of iterations (1) reached!
  Maximum number of coalitions (20) reached!
  
  Final estimated Shapley values (sd)
     explain_id      none      Solar.R          Wind         Temp        Month
          <int>    <char>       <char>        <char>       <char>       <char>
  1:          1 42.44 (0) -4.33 (0.59)   7.52 (0.79) 17.47 (0.29) -5.01 (0.72)
  2:          2 42.44 (0)  2.87 (0.55)  -4.41 (0.35) -4.71 (0.16) -4.97 (0.50)
  3:          3 42.44 (0)  3.35 (0.18) -18.35 (0.16) -1.83 (0.06) -2.82 (0.21)
              Day
           <char>
  1: -3.06 (0.29)
  2: -2.67 (0.16)
  3:  2.08 (0.06)
  [ FAIL 1 | WARN 0 | SKIP 53 | PASS 105 ]
  
  ══ Skipped tests (53) ══════════════════════════════════════════════════════════
  • On CRAN (49): 'test-asymmetric-causal-output.R:14:1',
    'test-asymmetric-causal-setup.R:1:1', 'test-asymmetric-causal-setup.R:229:1',
    'test-asymmetric-causal-setup.R:253:1',
    'test-asymmetric-causal-setup.R:318:1', 'test-forecast-output.R:2:1',
    'test-forecast-setup.R:3:1', 'test-forecast-setup.R:33:1',
    'test-forecast-setup.R:107:1', 'test-forecast-setup.R:136:1',
    'test-forecast-setup.R:163:1', 'test-forecast-setup.R:225:1',
    'test-forecast-setup.R:299:1', 'test-forecast-setup.R:349:1',
    'test-forecast-setup.R:445:1', 'test-forecast-setup.R:518:1',
    'test-iterative-output.R:1:1', 'test-iterative-setup.R:75:1',
    'test-iterative-setup.R:263:1', 'test-iterative-setup.R:397:1',
    'test-plot.R:1:1', 'test-regression-output.R:1:1',
    'test-regular-output.R:1:1', 'test-regular-setup.R:1:1',
    'test-regular-setup.R:26:1', 'test-regular-setup.R:118:1',
    'test-regular-setup.R:236:1', 'test-regular-setup.R:259:1',
    'test-regular-setup.R:317:1', 'test-regular-setup.R:394:1',
    'test-regular-setup.R:554:1', 'test-regular-setup.R:677:1',
    'test-regular-setup.R:792:1', 'test-regular-setup.R:813:1',
    'test-regular-setup.R:871:1', 'test-regular-setup.R:929:1',
    'test-regular-setup.R:1036:1', 'test-regular-setup.R:1148:1',
    'test-regular-setup.R:1221:1', 'test-regular-setup.R:1780:1',
    'test-regular-setup.R:1826:1', 'test-regular-setup.R:1847:1',
    'test-semi-deterministic-output.R:1:1',
    'test-semi-deterministic-setup.R:1:1',
    'test-semi-deterministic-setup.R:22:1',
    'test-semi-deterministic-setup.R:47:1',
    'test-semi-deterministic-setup.R:71:1',
    'test-semi-deterministic-setup.R:100:1', 'test-summary.R:1:1'
  • torch::torch_is_installed() is not TRUE (2): 'test-regular-setup.R:1628:3',
    'test-regular-setup.R:1671:3'
  • {parsnip} is not installed (1): 'test-regression-setup.R:1:1'
  • {workflows} is not installed (1): 'test-model-classes.R:369:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-regular-setup.R:1283:3'): Shapr with `max_n_coalitions` >= 2^m uses exact Shapley kernel weights ──
  Error in `grDevices::pdf(new, ...)`: cannot open file '/tmp/Rtmpf2lVR1/verify_exec_21864329a6d4f0'
  Backtrace:
      ▆
   1. └─testthat::expect_snapshot(...) at test-regular-setup.R:1283:3
   2.   └─testthat:::expect_snapshot_(...)
   3.     ├─testthat:::with_is_snapshotting(...)
   4.     └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
   5.       └─withr::local_pdf(device_path)
   6.         └─grDevices::pdf(new, ...)
  
  [ FAIL 1 | WARN 0 | SKIP 53 | PASS 105 ]
  Error:
  ! Test failures.
  Execution halted