Running ‘testthat.R’ [400s/508s]
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")
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.
  
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 128, 
  and is therefore set to 2^n_features = 128.
  
  * Model class: <Arima>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 7
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 128 of 128 coalitions. 
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.
  
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 64, 
  and is therefore set to 2^n_features = 64.
  
  * Model class: <Arima>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 6
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 64 of 64 coalitions. 
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.
  
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <Arima>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.
  
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_groups = 4, 
  and is therefore set to 2^n_groups = 4.
  
  * Model class: <Arima>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 2
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 4 of 4 coalitions. 
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.
  
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_groups = 4, 
  and is therefore set to 2^n_groups = 4.
  
  * Model class: <Arima>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 2
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 4 of 4 coalitions. 
  Note: Feature names extracted from the model contains NA.
  Consistency checks between model and data is therefore disabled.
  
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_groups = 4, 
  and is therefore set to 2^n_groups = 4.
  
  * Model class: <Arima>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 2
  * Number of observations to explain: 2
  
  -- Main computation started --
  
  i Using 4 of 4 coalitions. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: independence
  * 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 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: 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 10 of 32 coalitions, 4 new. 
  
  -- Iteration 3 -----------------------------------------------------------------
  i Using 12 of 32 coalitions, 2 new. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_groups = 32, 
  and is therefore set to 2^n_groups = 32.
  
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: TRUE
  * Number of group-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 10 of 32 coalitions, 4 new. 
  
  -- Iteration 3 -----------------------------------------------------------------
  i Using 12 of 32 coalitions, 2 new. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: ctree
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: ctree
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  • Model class: <lm>
  • Approach: 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  • Model class: <lm>
  • Approach: empirical
  • 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  • Model class: <lm>
  • Approach: gaussian
  • Iterative estimation: FALSE
  • Number of feature-wise Shapley values: 5
  • Number of observations to explain: 3
  
  ── Main computation started ──
  
  ℹ Using 32 of 32 coalitions. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  • Model class: <lm>
  • Approach: ctree
  • 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  • Model class: <lm>
  • Approach: empirical, ctree, gaussian, and ctree
  • 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. 
  The approximate 95% confidence intervals might be wide as they are only based on 3 observations.
  * Model class: <lm>
  * Approach: ctree
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 10 of 32 coalitions. 
  * Model class: <lm>
  * Approach: ctree
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 3
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 6 of 8 coalitions. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  
  -- Starting `shapr::explain()` at 2025-03-25 17:57:05 --------------------------
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  * Computations (temporary) saved at:
  '/tmp/RtmpFkvgaH/shapr_obj_39bec971e48784.rds'
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: independence, empirical, gaussian, and copula
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: independence, empirical, gaussian, and copula
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: independence, empirical, gaussian, and copula
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: gaussian, gaussian, gaussian, and 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: independence, empirical, independence, and empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: independence, empirical, independence, and empirical
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: vaeac
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: vaeac
  * 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. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_features = 32, 
  and is therefore set to 2^n_features = 32.
  
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  Success with message:
  max_n_coalitions is NULL or larger than or 2^n_groups = 32, 
  and is therefore set to 2^n_groups = 32.
  
  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 5
  * Number of observations to explain: 3
  
  -- Main computation started --
  
  i Using 32 of 32 coalitions. 
  [ FAIL 5 | WARN 0 | SKIP 137 | PASS 39 ]
  
  ══ Skipped tests (137) ═════════════════════════════════════════════════════════
  • On CRAN (137): 'test-asymmetric-causal-output.R:16:3',
    'test-asymmetric-causal-output.R:34:3',
    'test-asymmetric-causal-output.R:52:3',
    'test-asymmetric-causal-output.R:71:3',
    'test-asymmetric-causal-output.R:89:3',
    'test-asymmetric-causal-output.R:107:3',
    'test-asymmetric-causal-output.R:125:3',
    'test-asymmetric-causal-output.R:144:3',
    'test-asymmetric-causal-output.R:162:3',
    'test-asymmetric-causal-output.R:180:3',
    'test-asymmetric-causal-output.R:199:3',
    'test-asymmetric-causal-output.R:217:3',
    'test-asymmetric-causal-output.R:235:3',
    'test-asymmetric-causal-output.R:254:3',
    'test-asymmetric-causal-output.R:272:3',
    'test-asymmetric-causal-output.R:292:3',
    'test-asymmetric-causal-output.R:312:3',
    'test-asymmetric-causal-output.R:331:3',
    'test-asymmetric-causal-output.R:357:3',
    'test-asymmetric-causal-output.R:375:3',
    'test-asymmetric-causal-output.R:394:3',
    'test-asymmetric-causal-output.R:412:3',
    'test-asymmetric-causal-output.R:431:3',
    'test-asymmetric-causal-output.R:453:3',
    'test-asymmetric-causal-output.R:474:3',
    'test-asymmetric-causal-output.R:493:3',
    'test-asymmetric-causal-setup.R:4:3', 'test-asymmetric-causal-setup.R:221:3',
    'test-asymmetric-causal-setup.R:244:3',
    'test-asymmetric-causal-setup.R:306:3', 'test-forecast-output.R:2:3',
    'test-forecast-output.R:21:3', 'test-forecast-output.R:43:3',
    'test-forecast-output.R:66:3', 'test-forecast-output.R:89:3',
    'test-forecast-output.R:109:3', 'test-forecast-output.R:130:3',
    'test-forecast-output.R:151:3', 'test-forecast-output.R:176:3',
    'test-forecast-setup.R:5:3', 'test-forecast-setup.R:33:3',
    'test-forecast-setup.R:108:3', 'test-forecast-setup.R:132:3',
    'test-forecast-setup.R:158:3', 'test-forecast-setup.R:218:3',
    'test-forecast-setup.R:289:3', 'test-forecast-setup.R:337:3',
    'test-forecast-setup.R:429:3', 'test-forecast-setup.R:499:3',
    'test-iterative-output.R:4:3', 'test-iterative-output.R:20:3',
    'test-iterative-output.R:40:3', 'test-iterative-output.R:62:3',
    'test-iterative-output.R:86:3', 'test-iterative-output.R:107:3',
    'test-iterative-output.R:176:3', 'test-iterative-output.R:259:3',
    'test-iterative-output.R:275:3', 'test-iterative-output.R:291:3',
    'test-iterative-output.R:307:3', 'test-iterative-output.R:325:3',
    'test-iterative-setup.R:76:3', 'test-plot.R:59:3', 'test-plot.R:83:3',
    'test-plot.R:117:3', 'test-plot.R:141:3', 'test-plot.R:165:3',
    'test-plot.R:191:3', 'test-plot.R:272:3', 'test-regression-output.R:3:3',
    'test-regression-output.R:20:3', 'test-regression-output.R:36:3',
    'test-regression-output.R:53:3', 'test-regression-output.R:69:3',
    'test-regression-output.R:85:3', 'test-regression-output.R:104:3',
    'test-regression-output.R:122:3', 'test-regression-output.R:143:3',
    'test-regression-output.R:163:3', 'test-regression-output.R:180:3',
    'test-regression-output.R:196:3', 'test-regression-output.R:213:3',
    'test-regression-output.R:231:3', 'test-regression-output.R:247:3',
    'test-regression-output.R:263:3', 'test-regression-output.R:281:3',
    'test-regression-setup.R:4:3', 'test-regression-setup.R:40:3',
    'test-regression-setup.R:161:3', 'test-regression-setup.R:216:3',
    'test-regression-setup.R:275:3', 'test-regression-setup.R:314:3',
    'test-regular-output.R:4:3', 'test-regular-output.R:19:3',
    'test-regular-output.R:35:3', 'test-regular-output.R:50:3',
    'test-regular-output.R:67:3', 'test-regular-output.R:84:3',
    'test-regular-output.R:102:3', 'test-regular-output.R:119:3',
    'test-regular-output.R:134:3', 'test-regular-output.R:149:3',
    'test-regular-output.R:188:3', 'test-regular-output.R:227:3',
    'test-regular-output.R:242:3', 'test-regular-output.R:257:3',
    'test-regular-output.R:273:3', 'test-regular-output.R:288:3',
    'test-regular-output.R:303:3', 'test-regular-output.R:321:3',
    'test-regular-output.R:336:3', 'test-regular-output.R:376:3',
    'test-regular-output.R:403:3', 'test-regular-output.R:430:3',
    'test-regular-output.R:492:5', 'test-regular-output.R:516:3',
    'test-regular-output.R:531:3', 'test-regular-output.R:554:3',
    'test-regular-output.R:575:3', 'test-regular-setup.R:5:3',
    'test-regular-setup.R:37:3', 'test-regular-setup.R:116:3',
    'test-regular-setup.R:232:3', 'test-regular-setup.R:250:3',
    'test-regular-setup.R:305:3', 'test-regular-setup.R:378:3',
    'test-regular-setup.R:531:3', 'test-regular-setup.R:648:3',
    'test-regular-setup.R:758:3', 'test-regular-setup.R:779:3',
    'test-regular-setup.R:834:3', 'test-regular-setup.R:889:3',
    'test-regular-setup.R:990:3', 'test-regular-setup.R:1097:3',
    'test-regular-setup.R:1165:3', 'test-regular-setup.R:1207:3',
    'test-regular-setup.R:1343:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-regular-output.R:166:3'): output_lm_numeric_vaeac ──────────────
  Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
  Backtrace:
       ▆
    1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:166:3
    2. │ └─testthat::expect_snapshot((out <- code))
    3. │   ├─testthat:::with_is_snapshotting(...)
    4. │   └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
    5. │     └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
    6. │       ├─base::withRestarts(...)
    7. │       │ └─base (local) withRestartList(expr, restarts)
    8. │       │   ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
    9. │       │   │ └─base (local) doWithOneRestart(return(expr), restart)
   10. │       │   └─base (local) withRestartList(expr, restarts[-nr])
   11. │       │     └─base (local) withOneRestart(expr, restarts[[1L]])
   12. │       │       └─base (local) doWithOneRestart(return(expr), restart)
   13. │       ├─evaluate:::with_handlers(...)
   14. │       │ ├─base::eval(call)
   15. │       │ │ └─base::eval(call)
   16. │       │ └─base::withCallingHandlers(...)
   17. │       ├─base::withVisible(eval(expr, envir))
   18. │       └─base::eval(expr, envir)
   19. │         └─base::eval(expr, envir)
   20. └─shapr::explain(...)
   21.   ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
   22.   └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
   23.     ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
   24.     └─shapr (local) `<fn>`(...)
   25.       └─shapr:::vaeac(...)
   26.         └─torch (local) vaeac_tmp(...)
   27.           └─Module$new(...)
   28.             └─shapr (local) initialize(...)
   29.               ├─full_encoder_network$add_module(...)
   30.               │ └─self$register_module(name, module)
   31.               │   └─base::inherits(module, "nn_module")
   32.               └─shapr:::skip_connection(...)
   33.                 └─torch (local) skip_connection_tmp(... = ...)
   34.                   └─Module$new(...)
   35.                     └─shapr (local) initialize(...)
   36.                       └─torch::nn_sequential(...)
   37.                         └─Module$new(...)
   38.                           └─torch (local) initialize(...)
   39.                             └─self$add_module(name = i - 1, module = modules[[i]])
   40.                               └─self$register_module(name, module)
   41.                                 └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
   42.                                   └─rlang::abort(...)
  ── Error ('test-regular-output.R:205:3'): output_lm_categorical_vaeac ──────────
  Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
  Backtrace:
       ▆
    1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:205:3
    2. │ └─testthat::expect_snapshot((out <- code))
    3. │   ├─testthat:::with_is_snapshotting(...)
    4. │   └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
    5. │     └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
    6. │       ├─base::withRestarts(...)
    7. │       │ └─base (local) withRestartList(expr, restarts)
    8. │       │   ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
    9. │       │   │ └─base (local) doWithOneRestart(return(expr), restart)
   10. │       │   └─base (local) withRestartList(expr, restarts[-nr])
   11. │       │     └─base (local) withOneRestart(expr, restarts[[1L]])
   12. │       │       └─base (local) doWithOneRestart(return(expr), restart)
   13. │       ├─evaluate:::with_handlers(...)
   14. │       │ ├─base::eval(call)
   15. │       │ │ └─base::eval(call)
   16. │       │ └─base::withCallingHandlers(...)
   17. │       ├─base::withVisible(eval(expr, envir))
   18. │       └─base::eval(expr, envir)
   19. │         └─base::eval(expr, envir)
   20. └─shapr::explain(...)
   21.   ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
   22.   └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
   23.     ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
   24.     └─shapr (local) `<fn>`(...)
   25.       └─shapr:::vaeac(...)
   26.         └─torch (local) vaeac_tmp(...)
   27.           └─Module$new(...)
   28.             └─shapr (local) initialize(...)
   29.               ├─full_encoder_network$add_module(...)
   30.               │ └─self$register_module(name, module)
   31.               │   └─base::inherits(module, "nn_module")
   32.               └─shapr:::skip_connection(...)
   33.                 └─torch (local) skip_connection_tmp(... = ...)
   34.                   └─Module$new(...)
   35.                     └─shapr (local) initialize(...)
   36.                       └─torch::nn_sequential(...)
   37.                         └─Module$new(...)
   38.                           └─torch (local) initialize(...)
   39.                             └─self$add_module(name = i - 1, module = modules[[i]])
   40.                               └─self$register_module(name, module)
   41.                                 └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
   42.                                   └─rlang::abort(...)
  ── Error ('test-regular-output.R:353:3'): output_lm_mixed_vaeac ────────────────
  Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
  Backtrace:
       ▆
    1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:353:3
    2. │ └─testthat::expect_snapshot((out <- code))
    3. │   ├─testthat:::with_is_snapshotting(...)
    4. │   └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
    5. │     └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
    6. │       ├─base::withRestarts(...)
    7. │       │ └─base (local) withRestartList(expr, restarts)
    8. │       │   ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
    9. │       │   │ └─base (local) doWithOneRestart(return(expr), restart)
   10. │       │   └─base (local) withRestartList(expr, restarts[-nr])
   11. │       │     └─base (local) withOneRestart(expr, restarts[[1L]])
   12. │       │       └─base (local) doWithOneRestart(return(expr), restart)
   13. │       ├─evaluate:::with_handlers(...)
   14. │       │ ├─base::eval(call)
   15. │       │ │ └─base::eval(call)
   16. │       │ └─base::withCallingHandlers(...)
   17. │       ├─base::withVisible(eval(expr, envir))
   18. │       └─base::eval(expr, envir)
   19. │         └─base::eval(expr, envir)
   20. └─shapr::explain(...)
   21.   ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
   22.   └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
   23.     ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
   24.     └─shapr (local) `<fn>`(...)
   25.       └─shapr:::vaeac(...)
   26.         └─torch (local) vaeac_tmp(...)
   27.           └─Module$new(...)
   28.             └─shapr (local) initialize(...)
   29.               ├─full_encoder_network$add_module(...)
   30.               │ └─self$register_module(name, module)
   31.               │   └─base::inherits(module, "nn_module")
   32.               └─shapr:::skip_connection(...)
   33.                 └─torch (local) skip_connection_tmp(... = ...)
   34.                   └─Module$new(...)
   35.                     └─shapr (local) initialize(...)
   36.                       └─torch::nn_sequential(...)
   37.                         └─Module$new(...)
   38.                           └─torch (local) initialize(...)
   39.                             └─self$add_module(name = i - 1, module = modules[[i]])
   40.                               └─self$register_module(name, module)
   41.                                 └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
   42.                                   └─rlang::abort(...)
  ── Error ('test-regular-setup.R:1553:3'): vaeac_set_seed_works ─────────────────
  Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
  Backtrace:
       ▆
    1. └─shapr::explain(...) at test-regular-setup.R:1553:3
    2.   ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
    3.   └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
    4.     ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
    5.     └─shapr (local) `<fn>`(...)
    6.       └─shapr:::vaeac(...)
    7.         └─torch (local) vaeac_tmp(...)
    8.           └─Module$new(...)
    9.             └─shapr (local) initialize(...)
   10.               ├─full_encoder_network$add_module(...)
   11.               │ └─self$register_module(name, module)
   12.               │   └─base::inherits(module, "nn_module")
   13.               └─shapr:::skip_connection(...)
   14.                 └─torch (local) skip_connection_tmp(... = ...)
   15.                   └─Module$new(...)
   16.                     └─shapr (local) initialize(...)
   17.                       └─torch::nn_sequential(...)
   18.                         └─Module$new(...)
   19.                           └─torch (local) initialize(...)
   20.                             └─self$add_module(name = i - 1, module = modules[[i]])
   21.                               └─self$register_module(name, module)
   22.                                 └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
   23.                                   └─rlang::abort(...)
  ── Error ('test-regular-setup.R:1597:3'): vaeac_pretreained_vaeac_model ────────
  Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
  Backtrace:
       ▆
    1. └─shapr::explain(...) at test-regular-setup.R:1597:3
    2.   ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
    3.   └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
    4.     ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
    5.     └─shapr (local) `<fn>`(...)
    6.       └─shapr:::vaeac(...)
    7.         └─torch (local) vaeac_tmp(...)
    8.           └─Module$new(...)
    9.             └─shapr (local) initialize(...)
   10.               ├─full_encoder_network$add_module(...)
   11.               │ └─self$register_module(name, module)
   12.               │   └─base::inherits(module, "nn_module")
   13.               └─shapr:::skip_connection(...)
   14.                 └─torch (local) skip_connection_tmp(... = ...)
   15.                   └─Module$new(...)
   16.                     └─shapr (local) initialize(...)
   17.                       └─torch::nn_sequential(...)
   18.                         └─Module$new(...)
   19.                           └─torch (local) initialize(...)
   20.                             └─self$add_module(name = i - 1, module = modules[[i]])
   21.                               └─self$register_module(name, module)
   22.                                 └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
   23.                                   └─rlang::abort(...)
  
  [ FAIL 5 | WARN 0 | SKIP 137 | PASS 39 ]
  Error: Test failures
  Execution halted