CRAN Package Check Results for Package shapr

Last updated on 2025-03-27 05:50:35 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	1.0.2	82.88	622.49	705.37	ERROR
r-devel-linux-x86_64-debian-gcc	1.0.2	56.42	480.53	536.95	ERROR
r-devel-linux-x86_64-fedora-clang	1.0.3				ERROR
r-devel-linux-x86_64-fedora-gcc	1.0.3			651.39	OK
r-devel-macos-arm64	1.0.3			150.00	OK
r-devel-macos-x86_64	1.0.3			270.00	OK
r-devel-windows-x86_64	1.0.3	89.00	284.00	373.00	OK
r-patched-linux-x86_64	1.0.2	86.31	608.99	695.30	ERROR
r-release-linux-x86_64	1.0.2	83.06	576.59	659.65	ERROR
r-release-macos-arm64	1.0.3			182.00	NOTE
r-release-macos-x86_64	1.0.3			276.00	NOTE
r-release-windows-x86_64	1.0.2	90.00	580.00	670.00	ERROR
r-oldrel-macos-arm64	1.0.3			136.00	NOTE
r-oldrel-macos-x86_64	1.0.3			275.00	NOTE
r-oldrel-windows-x86_64	1.0.2	110.00	826.00	936.00	ERROR

Check Details

Version: 1.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [9m/11m] 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. OMP: Warning #96: Cannot form a team with 3 threads, using 1 instead. OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set). * 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 06:05:12 -------------------------- * 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: '/home/hornik/tmp/scratch/RtmpUjSjNe/shapr_obj_d498e38c5fe4e.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 Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [412s/513s] 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 2025-03-26 17:57:06 -------------------------- * 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/RtmpGs1iwj/shapr_obj_3084fe7f81a709.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 Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [14m/32m] 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. OMP: Warning #96: Cannot form a team with 8 threads, using 1 instead. OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set). * 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 2025-03-26 19:37:55 -------------------------- * 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/Rtmpf7hV8P/working_dir/RtmpItuZcJ/shapr_obj_136c586d64068c.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 7 | WARN 0 | SKIP 135 | PASS 38 ] ══ Skipped tests (135) ═════════════════════════════════════════════════════════ • On CRAN (134): '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: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-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: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' • empty test (1): 'test-regular-output.R:461:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-regression-output.R:143:3'): output_lm_mixed_xgboost_separate ── Error in `fit_xy(spec, x = mold$predictors, y = mold$outcomes, case_weights = case_weights, control = control_parsnip)`: Please install the xgboost package to use this engine. Backtrace: ▆ 1. ├─shapr:::expect_snapshot_rds(...) at test-regression-output.R:143:3 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::compute_vS(internal, model, predict_model) 22. └─shapr:::future_compute_vS_batch(...) 23. └─future.apply::future_lapply(...) 24. └─future.apply:::future_xapply(...) 25. └─future::future(...) 26. ├─future::run(future) 27. └─future:::run.Future(future) 28. ├─future::run(future) 29. └─future:::run.UniprocessFuture(future) 30. └─base::eval(expr, envir = envir, enclos = baseenv()) 31. └─base::eval(expr, envir = envir, enclos = baseenv()) 32. ├─base::tryCatch(...) 33. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 34. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 35. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 36. ├─base::withCallingHandlers(...) 37. ├─base::withVisible(...) 38. ├─base::local(...) 39. │ └─base::eval.parent(substitute(eval(quote(expr), envir))) 40. │ └─base::eval(expr, p) 41. │ └─base::eval(expr, p) 42. └─base::eval(...) 43. └─base::eval(...) 44. ├─base::do.call(...) 45. └─shapr (local) `<fn>`(...) 46. └─base::lapply(...) 47. └─shapr (local) FUN(X[[i]], ...) 48. └─shapr (local) ...future.FUN(...future.X_jj, ...) 49. └─shapr:::batch_prepare_vS_regression(S = S, internal = internal) 50. ├─shapr::prepare_data(internal, index_features = S[S != max_id_coal]) 51. └─shapr:::prepare_data.regression_separate(...) 52. └─shapr::regression.train_model(...) 53. ├─parsnip::fit(regression.workflow, data = x) 54. └─workflows:::fit.workflow(regression.workflow, data = x) 55. └─workflows::.fit_model(workflow, control) 56. ├─generics::fit(action_model, workflow = workflow, control = control) 57. └─workflows:::fit.action_model(...) 58. └─workflows:::fit_from_xy(spec, mold, case_weights, control_parsnip) 59. ├─generics::fit_xy(...) 60. └─parsnip::fit_xy.model_spec(...) 61. └─parsnip:::check_installs(object) 62. └─cli::cli_abort(...) 63. └─rlang::abort(...) ── Error ('test-regression-output.R:281:3'): output_lm_mixed_xgboost_surrogate ── Error in `fit_xy(spec, x = mold$predictors, y = mold$outcomes, case_weights = case_weights, control = control_parsnip)`: Please install the xgboost package to use this engine. Backtrace: ▆ 1. ├─shapr:::expect_snapshot_rds(...) at test-regression-output.R:281:3 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.regression_surrogate(...) 23. └─shapr::regression.train_model(...) 24. ├─parsnip::fit(regression.workflow, data = x) 25. └─workflows:::fit.workflow(regression.workflow, data = x) 26. └─workflows::.fit_model(workflow, control) 27. ├─generics::fit(action_model, workflow = workflow, control = control) 28. └─workflows:::fit.action_model(...) 29. └─workflows:::fit_from_xy(spec, mold, case_weights, control_parsnip) 30. ├─generics::fit_xy(...) 31. └─parsnip::fit_xy.model_spec(...) 32. └─parsnip:::check_installs(object) 33. └─cli::cli_abort(...) 34. └─rlang::abort(...) ── 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 7 | WARN 0 | SKIP 135 | PASS 38 ] Error: Test failures Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [9m/11m] 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 2025-03-26 05:49:29 -------------------------- * 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: '/home/hornik/tmp/scratch/RtmpQMmCEp/shapr_obj_a870e2aa886c2.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 Flavor: r-patched-linux-x86_64

Version: 1.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [484s/590s] 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 2025-03-23 05:27:31 -------------------------- * 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: '/home/hornik/tmp/scratch/RtmplncSCj/shapr_obj_c92362d2c8526.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 Flavor: r-release-linux-x86_64

Version: 1.0.3
Check: installed package size
Result: NOTE installed size is 9.5Mb sub-directories of 1Mb or more: doc 4.3Mb libs 4.1Mb Flavors: r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64

Version: 1.0.2
Check: installed package size
Result: NOTE installed size is 6.8Mb sub-directories of 1Mb or more: doc 4.2Mb libs 1.3Mb Flavors: r-release-windows-x86_64, r-oldrel-windows-x86_64

Version: 1.0.2
Check: tests
Result: ERROR Running 'testthat.R' [432s] 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 2025-03-26 07:32:39 -------------------------- * 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: 'D:\temp\2025_03_26_01_50_00_5935\Rtmp8EifuO\shapr_obj_104b822db6dab.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 Flavor: r-release-windows-x86_64

Version: 1.0.2
Check: tests
Result: ERROR Running 'testthat.R' [11m] 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 2025-03-26 08:57:47 -------------------------- * 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: 'D:\temp\2025_03_26_01_50_00_5935\Rtmp2zXA0b\shapr_obj_18f18462295e.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 Flavor: r-oldrel-windows-x86_64