CRAN Package Check Results for Package shapr

Last updated on 2025-03-19 08:52:56 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	1.0.2	83.03	621.15	704.18	OK
r-devel-linux-x86_64-debian-gcc	1.0.2	56.61	484.14	540.75	ERROR
r-devel-linux-x86_64-fedora-clang	1.0.2			1222.88	ERROR
r-devel-linux-x86_64-fedora-gcc	1.0.2			1202.72	ERROR
r-devel-macos-arm64	1.0.2			244.00	OK
r-devel-macos-x86_64	1.0.2			627.00	OK
r-devel-windows-x86_64	1.0.2	95.00	598.00	693.00	OK
r-patched-linux-x86_64	1.0.2	89.44	610.00	699.44	ERROR
r-release-linux-x86_64	1.0.2	77.90	582.17	660.07	OK
r-release-macos-arm64	1.0.2			275.00	NOTE
r-release-macos-x86_64	1.0.2			717.00	NOTE
r-release-windows-x86_64	1.0.2	88.00	558.00	646.00	NOTE
r-oldrel-macos-arm64	1.0.2			289.00	NOTE
r-oldrel-macos-x86_64	1.0.2			686.00	NOTE
r-oldrel-windows-x86_64	1.0.2	110.00	785.00	895.00	NOTE

Check Details

Version: 1.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [413s/469s] 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 2025-03-18 17:56:23 -------------------------- * 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/RtmpvJRoph/shapr_obj_36442a640091ee.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/19m] 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 2025-03-17 23:03:32 -------------------------- * 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/RtmptJexfr/working_dir/Rtmpyqjf74/shapr_obj_1687b55adc5007.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-fedora-clang

Version: 1.0.2
Check: tests
Result: ERROR Running ‘testthat.R’ [14m/31m] 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 2025-03-18 05:19:38 -------------------------- * 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/Rtmp6XxPh3/working_dir/RtmpWW1gH0/shapr_obj_530e013ed38a8.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-fedora-gcc

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 2025-03-19 05:50:36 -------------------------- * 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/RtmplzMXvd/shapr_obj_c0b2f2a170905.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: installed package size
Result: NOTE installed size is 9.5Mb sub-directories of 1Mb or more: doc 4.2Mb libs 4.1Mb Flavors: r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64