Last updated on 2025-03-28 08:54:33 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.3 | 83.29 | 295.07 | 378.36 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.3 | 57.95 | 203.58 | 261.53 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.3 | 0.51 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.3 | 651.39 | OK | |||
| r-devel-macos-arm64 | 1.0.3 | 150.00 | OK | |||
| r-devel-macos-x86_64 | 1.0.3 | 270.00 | OK | |||
| r-devel-windows-x86_64 | 1.0.3 | 89.00 | 284.00 | 373.00 | OK | |
| r-patched-linux-x86_64 | 1.0.2 | 86.31 | 608.99 | 695.30 | ERROR | |
| r-release-linux-x86_64 | 1.0.2 | 80.47 | 591.66 | 672.13 | ERROR | |
| r-release-macos-arm64 | 1.0.3 | 182.00 | NOTE | |||
| r-release-macos-x86_64 | 1.0.3 | 276.00 | NOTE | |||
| r-release-windows-x86_64 | 1.0.3 | 84.00 | 290.00 | 374.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.0.3 | 136.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.0.3 | 275.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 1.0.3 | 77.00 | 307.00 | 384.00 | NOTE |
Version: 1.0.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [14m/32m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # CRAN OMP THREAD LIMIT
> Sys.setenv("OMP_THREAD_LIMIT" = 1)
>
> library(testthat)
> library(shapr)
Attaching package: 'shapr'
The following object is masked from 'package:testthat':
setup
>
> test_check("shapr")
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 128,
and is therefore set to 2^n_features = 128.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 7
* Number of observations to explain: 2
-- Main computation started --
i Using 128 of 128 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 64,
and is therefore set to 2^n_features = 64.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 6
* Number of observations to explain: 2
-- Main computation started --
i Using 64 of 64 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 2
-- Main computation started --
i Using 32 of 32 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 4,
and is therefore set to 2^n_groups = 4.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 4,
and is therefore set to 2^n_groups = 4.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 4,
and is therefore set to 2^n_groups = 4.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 32 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 10 of 32 coalitions, 2 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 14 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 16 of 32 coalitions, 2 new.
-- Iteration 7 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 8 -----------------------------------------------------------------
i Using 20 of 32 coalitions, 2 new.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 10 of 32 coalitions, 4 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 32,
and is therefore set to 2^n_groups = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: TRUE
* Number of group-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 10 of 32 coalitions, 4 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: independence
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: empirical
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: gaussian
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: ctree
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: empirical, ctree, gaussian, and ctree
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
The approximate 95% confidence intervals might be wide as they are only based on 3 observations.
OMP: Warning #96: Cannot form a team with 8 threads, using 1 instead.
OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set).
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 10 of 32 coalitions.
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 3
* Number of observations to explain: 3
-- Main computation started --
i Using 6 of 8 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
-- Starting `shapr::explain()` at 2025-03-26 19:37:55 --------------------------
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Computations (temporary) saved at:
'/tmp/Rtmpf7hV8P/working_dir/RtmpItuZcJ/shapr_obj_136c586d64068c.rds'
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian, gaussian, gaussian, and gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, independence, and empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, independence, and empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: vaeac
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: vaeac
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 32,
and is therefore set to 2^n_groups = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
[ FAIL 7 | WARN 0 | SKIP 135 | PASS 38 ]
══ Skipped tests (135) ═════════════════════════════════════════════════════════
• On CRAN (134): 'test-asymmetric-causal-output.R:16:3',
'test-asymmetric-causal-output.R:34:3',
'test-asymmetric-causal-output.R:52:3',
'test-asymmetric-causal-output.R:71:3',
'test-asymmetric-causal-output.R:89:3',
'test-asymmetric-causal-output.R:107:3',
'test-asymmetric-causal-output.R:125:3',
'test-asymmetric-causal-output.R:144:3',
'test-asymmetric-causal-output.R:162:3',
'test-asymmetric-causal-output.R:180:3',
'test-asymmetric-causal-output.R:199:3',
'test-asymmetric-causal-output.R:217:3',
'test-asymmetric-causal-output.R:235:3',
'test-asymmetric-causal-output.R:254:3',
'test-asymmetric-causal-output.R:272:3',
'test-asymmetric-causal-output.R:292:3',
'test-asymmetric-causal-output.R:312:3',
'test-asymmetric-causal-output.R:331:3',
'test-asymmetric-causal-output.R:357:3',
'test-asymmetric-causal-output.R:375:3',
'test-asymmetric-causal-output.R:394:3',
'test-asymmetric-causal-output.R:412:3',
'test-asymmetric-causal-output.R:431:3',
'test-asymmetric-causal-output.R:453:3',
'test-asymmetric-causal-output.R:474:3',
'test-asymmetric-causal-output.R:493:3',
'test-asymmetric-causal-setup.R:4:3', 'test-asymmetric-causal-setup.R:221:3',
'test-asymmetric-causal-setup.R:244:3',
'test-asymmetric-causal-setup.R:306:3', 'test-forecast-output.R:2:3',
'test-forecast-output.R:21:3', 'test-forecast-output.R:43:3',
'test-forecast-output.R:66:3', 'test-forecast-output.R:89:3',
'test-forecast-output.R:109:3', 'test-forecast-output.R:130:3',
'test-forecast-output.R:151:3', 'test-forecast-output.R:176:3',
'test-forecast-setup.R:5:3', 'test-forecast-setup.R:33:3',
'test-forecast-setup.R:108:3', 'test-forecast-setup.R:132:3',
'test-forecast-setup.R:158:3', 'test-forecast-setup.R:218:3',
'test-forecast-setup.R:289:3', 'test-forecast-setup.R:337:3',
'test-forecast-setup.R:429:3', 'test-forecast-setup.R:499:3',
'test-iterative-output.R:4:3', 'test-iterative-output.R:20:3',
'test-iterative-output.R:40:3', 'test-iterative-output.R:62:3',
'test-iterative-output.R:86:3', 'test-iterative-output.R:107:3',
'test-iterative-output.R:176:3', 'test-iterative-output.R:259:3',
'test-iterative-output.R:275:3', 'test-iterative-output.R:291:3',
'test-iterative-output.R:307:3', 'test-iterative-output.R:325:3',
'test-iterative-setup.R:76:3', 'test-plot.R:59:3', 'test-plot.R:83:3',
'test-plot.R:117:3', 'test-plot.R:141:3', 'test-plot.R:165:3',
'test-plot.R:191:3', 'test-plot.R:272:3', 'test-regression-output.R:3:3',
'test-regression-output.R:20:3', 'test-regression-output.R:36:3',
'test-regression-output.R:53:3', 'test-regression-output.R:69:3',
'test-regression-output.R:85:3', 'test-regression-output.R:104:3',
'test-regression-output.R:122:3', 'test-regression-output.R:163:3',
'test-regression-output.R:180:3', 'test-regression-output.R:196:3',
'test-regression-output.R:213:3', 'test-regression-output.R:231:3',
'test-regression-output.R:247:3', 'test-regression-output.R:263:3',
'test-regression-setup.R:4:3', 'test-regression-setup.R:40:3',
'test-regression-setup.R:161:3', 'test-regression-setup.R:216:3',
'test-regression-setup.R:275:3', 'test-regression-setup.R:314:3',
'test-regular-output.R:4:3', 'test-regular-output.R:19:3',
'test-regular-output.R:35:3', 'test-regular-output.R:50:3',
'test-regular-output.R:67:3', 'test-regular-output.R:84:3',
'test-regular-output.R:102:3', 'test-regular-output.R:119:3',
'test-regular-output.R:134:3', 'test-regular-output.R:149:3',
'test-regular-output.R:188:3', 'test-regular-output.R:227:3',
'test-regular-output.R:242:3', 'test-regular-output.R:257:3',
'test-regular-output.R:273:3', 'test-regular-output.R:288:3',
'test-regular-output.R:303:3', 'test-regular-output.R:321:3',
'test-regular-output.R:336:3', 'test-regular-output.R:376:3',
'test-regular-output.R:403:3', 'test-regular-output.R:430:3',
'test-regular-output.R:516:3', 'test-regular-output.R:531:3',
'test-regular-output.R:554:3', 'test-regular-output.R:575:3',
'test-regular-setup.R:5:3', 'test-regular-setup.R:37:3',
'test-regular-setup.R:116:3', 'test-regular-setup.R:232:3',
'test-regular-setup.R:250:3', 'test-regular-setup.R:305:3',
'test-regular-setup.R:378:3', 'test-regular-setup.R:531:3',
'test-regular-setup.R:648:3', 'test-regular-setup.R:758:3',
'test-regular-setup.R:779:3', 'test-regular-setup.R:834:3',
'test-regular-setup.R:889:3', 'test-regular-setup.R:990:3',
'test-regular-setup.R:1097:3', 'test-regular-setup.R:1165:3',
'test-regular-setup.R:1207:3', 'test-regular-setup.R:1343:3'
• empty test (1): 'test-regular-output.R:461:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-regression-output.R:143:3'): output_lm_mixed_xgboost_separate ──
Error in `fit_xy(spec, x = mold$predictors, y = mold$outcomes, case_weights = case_weights,
control = control_parsnip)`: Please install the xgboost package to use this engine.
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regression-output.R:143:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. └─shapr::compute_vS(internal, model, predict_model)
22. └─shapr:::future_compute_vS_batch(...)
23. └─future.apply::future_lapply(...)
24. └─future.apply:::future_xapply(...)
25. └─future::future(...)
26. ├─future::run(future)
27. └─future:::run.Future(future)
28. ├─future::run(future)
29. └─future:::run.UniprocessFuture(future)
30. └─base::eval(expr, envir = envir, enclos = baseenv())
31. └─base::eval(expr, envir = envir, enclos = baseenv())
32. ├─base::tryCatch(...)
33. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
34. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
35. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
36. ├─base::withCallingHandlers(...)
37. ├─base::withVisible(...)
38. ├─base::local(...)
39. │ └─base::eval.parent(substitute(eval(quote(expr), envir)))
40. │ └─base::eval(expr, p)
41. │ └─base::eval(expr, p)
42. └─base::eval(...)
43. └─base::eval(...)
44. ├─base::do.call(...)
45. └─shapr (local) `<fn>`(...)
46. └─base::lapply(...)
47. └─shapr (local) FUN(X[[i]], ...)
48. └─shapr (local) ...future.FUN(...future.X_jj, ...)
49. └─shapr:::batch_prepare_vS_regression(S = S, internal = internal)
50. ├─shapr::prepare_data(internal, index_features = S[S != max_id_coal])
51. └─shapr:::prepare_data.regression_separate(...)
52. └─shapr::regression.train_model(...)
53. ├─parsnip::fit(regression.workflow, data = x)
54. └─workflows:::fit.workflow(regression.workflow, data = x)
55. └─workflows::.fit_model(workflow, control)
56. ├─generics::fit(action_model, workflow = workflow, control = control)
57. └─workflows:::fit.action_model(...)
58. └─workflows:::fit_from_xy(spec, mold, case_weights, control_parsnip)
59. ├─generics::fit_xy(...)
60. └─parsnip::fit_xy.model_spec(...)
61. └─parsnip:::check_installs(object)
62. └─cli::cli_abort(...)
63. └─rlang::abort(...)
── Error ('test-regression-output.R:281:3'): output_lm_mixed_xgboost_surrogate ──
Error in `fit_xy(spec, x = mold$predictors, y = mold$outcomes, case_weights = case_weights,
control = control_parsnip)`: Please install the xgboost package to use this engine.
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regression-output.R:281:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.regression_surrogate(...)
23. └─shapr::regression.train_model(...)
24. ├─parsnip::fit(regression.workflow, data = x)
25. └─workflows:::fit.workflow(regression.workflow, data = x)
26. └─workflows::.fit_model(workflow, control)
27. ├─generics::fit(action_model, workflow = workflow, control = control)
28. └─workflows:::fit.action_model(...)
29. └─workflows:::fit_from_xy(spec, mold, case_weights, control_parsnip)
30. ├─generics::fit_xy(...)
31. └─parsnip::fit_xy.model_spec(...)
32. └─parsnip:::check_installs(object)
33. └─cli::cli_abort(...)
34. └─rlang::abort(...)
── Error ('test-regular-output.R:166:3'): output_lm_numeric_vaeac ──────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:166:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-output.R:205:3'): output_lm_categorical_vaeac ──────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:205:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-output.R:353:3'): output_lm_mixed_vaeac ────────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:353:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-setup.R:1553:3'): vaeac_set_seed_works ─────────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. └─shapr::explain(...) at test-regular-setup.R:1553:3
2. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
3. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
4. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
5. └─shapr (local) `<fn>`(...)
6. └─shapr:::vaeac(...)
7. └─torch (local) vaeac_tmp(...)
8. └─Module$new(...)
9. └─shapr (local) initialize(...)
10. ├─full_encoder_network$add_module(...)
11. │ └─self$register_module(name, module)
12. │ └─base::inherits(module, "nn_module")
13. └─shapr:::skip_connection(...)
14. └─torch (local) skip_connection_tmp(... = ...)
15. └─Module$new(...)
16. └─shapr (local) initialize(...)
17. └─torch::nn_sequential(...)
18. └─Module$new(...)
19. └─torch (local) initialize(...)
20. └─self$add_module(name = i - 1, module = modules[[i]])
21. └─self$register_module(name, module)
22. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
23. └─rlang::abort(...)
── Error ('test-regular-setup.R:1597:3'): vaeac_pretreained_vaeac_model ────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. └─shapr::explain(...) at test-regular-setup.R:1597:3
2. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
3. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
4. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
5. └─shapr (local) `<fn>`(...)
6. └─shapr:::vaeac(...)
7. └─torch (local) vaeac_tmp(...)
8. └─Module$new(...)
9. └─shapr (local) initialize(...)
10. ├─full_encoder_network$add_module(...)
11. │ └─self$register_module(name, module)
12. │ └─base::inherits(module, "nn_module")
13. └─shapr:::skip_connection(...)
14. └─torch (local) skip_connection_tmp(... = ...)
15. └─Module$new(...)
16. └─shapr (local) initialize(...)
17. └─torch::nn_sequential(...)
18. └─Module$new(...)
19. └─torch (local) initialize(...)
20. └─self$add_module(name = i - 1, module = modules[[i]])
21. └─self$register_module(name, module)
22. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
23. └─rlang::abort(...)
[ FAIL 7 | WARN 0 | SKIP 135 | PASS 38 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [9m/11m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # CRAN OMP THREAD LIMIT
> Sys.setenv("OMP_THREAD_LIMIT" = 1)
>
> library(testthat)
> library(shapr)
Attaching package: 'shapr'
The following object is masked from 'package:testthat':
setup
>
> test_check("shapr")
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 128,
and is therefore set to 2^n_features = 128.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 7
* Number of observations to explain: 2
-- Main computation started --
i Using 128 of 128 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 64,
and is therefore set to 2^n_features = 64.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 6
* Number of observations to explain: 2
-- Main computation started --
i Using 64 of 64 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 2
-- Main computation started --
i Using 32 of 32 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 4,
and is therefore set to 2^n_groups = 4.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 4,
and is therefore set to 2^n_groups = 4.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
Note: Feature names extracted from the model contains NA.
Consistency checks between model and data is therefore disabled.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 4,
and is therefore set to 2^n_groups = 4.
* Model class: <Arima>
* Approach: empirical
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 32 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 10 of 32 coalitions, 2 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 14 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 16 of 32 coalitions, 2 new.
-- Iteration 7 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 8 -----------------------------------------------------------------
i Using 20 of 32 coalitions, 2 new.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 10 of 32 coalitions, 4 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 32,
and is therefore set to 2^n_groups = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: TRUE
* Number of group-wise Shapley values: 5
* Number of observations to explain: 3
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 10 of 32 coalitions, 4 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: independence
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: empirical
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: gaussian
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: ctree
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
• Model class: <lm>
• Approach: empirical, ctree, gaussian, and ctree
• Iterative estimation: FALSE
• Number of feature-wise Shapley values: 5
• Number of observations to explain: 3
── Main computation started ──
ℹ Using 32 of 32 coalitions.
The approximate 95% confidence intervals might be wide as they are only based on 3 observations.
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 10 of 32 coalitions.
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 3
* Number of observations to explain: 3
-- Main computation started --
i Using 6 of 8 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
-- Starting `shapr::explain()` at 2025-03-26 05:49:29 --------------------------
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Computations (temporary) saved at:
'/home/hornik/tmp/scratch/RtmpQMmCEp/shapr_obj_a870e2aa886c2.rds'
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian, gaussian, gaussian, and gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, independence, and empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, independence, and empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: vaeac
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: vaeac
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 32,
and is therefore set to 2^n_groups = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
[ FAIL 5 | WARN 0 | SKIP 137 | PASS 39 ]
══ Skipped tests (137) ═════════════════════════════════════════════════════════
• On CRAN (137): 'test-asymmetric-causal-output.R:16:3',
'test-asymmetric-causal-output.R:34:3',
'test-asymmetric-causal-output.R:52:3',
'test-asymmetric-causal-output.R:71:3',
'test-asymmetric-causal-output.R:89:3',
'test-asymmetric-causal-output.R:107:3',
'test-asymmetric-causal-output.R:125:3',
'test-asymmetric-causal-output.R:144:3',
'test-asymmetric-causal-output.R:162:3',
'test-asymmetric-causal-output.R:180:3',
'test-asymmetric-causal-output.R:199:3',
'test-asymmetric-causal-output.R:217:3',
'test-asymmetric-causal-output.R:235:3',
'test-asymmetric-causal-output.R:254:3',
'test-asymmetric-causal-output.R:272:3',
'test-asymmetric-causal-output.R:292:3',
'test-asymmetric-causal-output.R:312:3',
'test-asymmetric-causal-output.R:331:3',
'test-asymmetric-causal-output.R:357:3',
'test-asymmetric-causal-output.R:375:3',
'test-asymmetric-causal-output.R:394:3',
'test-asymmetric-causal-output.R:412:3',
'test-asymmetric-causal-output.R:431:3',
'test-asymmetric-causal-output.R:453:3',
'test-asymmetric-causal-output.R:474:3',
'test-asymmetric-causal-output.R:493:3',
'test-asymmetric-causal-setup.R:4:3', 'test-asymmetric-causal-setup.R:221:3',
'test-asymmetric-causal-setup.R:244:3',
'test-asymmetric-causal-setup.R:306:3', 'test-forecast-output.R:2:3',
'test-forecast-output.R:21:3', 'test-forecast-output.R:43:3',
'test-forecast-output.R:66:3', 'test-forecast-output.R:89:3',
'test-forecast-output.R:109:3', 'test-forecast-output.R:130:3',
'test-forecast-output.R:151:3', 'test-forecast-output.R:176:3',
'test-forecast-setup.R:5:3', 'test-forecast-setup.R:33:3',
'test-forecast-setup.R:108:3', 'test-forecast-setup.R:132:3',
'test-forecast-setup.R:158:3', 'test-forecast-setup.R:218:3',
'test-forecast-setup.R:289:3', 'test-forecast-setup.R:337:3',
'test-forecast-setup.R:429:3', 'test-forecast-setup.R:499:3',
'test-iterative-output.R:4:3', 'test-iterative-output.R:20:3',
'test-iterative-output.R:40:3', 'test-iterative-output.R:62:3',
'test-iterative-output.R:86:3', 'test-iterative-output.R:107:3',
'test-iterative-output.R:176:3', 'test-iterative-output.R:259:3',
'test-iterative-output.R:275:3', 'test-iterative-output.R:291:3',
'test-iterative-output.R:307:3', 'test-iterative-output.R:325:3',
'test-iterative-setup.R:76:3', 'test-plot.R:59:3', 'test-plot.R:83:3',
'test-plot.R:117:3', 'test-plot.R:141:3', 'test-plot.R:165:3',
'test-plot.R:191:3', 'test-plot.R:272:3', 'test-regression-output.R:3:3',
'test-regression-output.R:20:3', 'test-regression-output.R:36:3',
'test-regression-output.R:53:3', 'test-regression-output.R:69:3',
'test-regression-output.R:85:3', 'test-regression-output.R:104:3',
'test-regression-output.R:122:3', 'test-regression-output.R:143:3',
'test-regression-output.R:163:3', 'test-regression-output.R:180:3',
'test-regression-output.R:196:3', 'test-regression-output.R:213:3',
'test-regression-output.R:231:3', 'test-regression-output.R:247:3',
'test-regression-output.R:263:3', 'test-regression-output.R:281:3',
'test-regression-setup.R:4:3', 'test-regression-setup.R:40:3',
'test-regression-setup.R:161:3', 'test-regression-setup.R:216:3',
'test-regression-setup.R:275:3', 'test-regression-setup.R:314:3',
'test-regular-output.R:4:3', 'test-regular-output.R:19:3',
'test-regular-output.R:35:3', 'test-regular-output.R:50:3',
'test-regular-output.R:67:3', 'test-regular-output.R:84:3',
'test-regular-output.R:102:3', 'test-regular-output.R:119:3',
'test-regular-output.R:134:3', 'test-regular-output.R:149:3',
'test-regular-output.R:188:3', 'test-regular-output.R:227:3',
'test-regular-output.R:242:3', 'test-regular-output.R:257:3',
'test-regular-output.R:273:3', 'test-regular-output.R:288:3',
'test-regular-output.R:303:3', 'test-regular-output.R:321:3',
'test-regular-output.R:336:3', 'test-regular-output.R:376:3',
'test-regular-output.R:403:3', 'test-regular-output.R:430:3',
'test-regular-output.R:492:5', 'test-regular-output.R:516:3',
'test-regular-output.R:531:3', 'test-regular-output.R:554:3',
'test-regular-output.R:575:3', 'test-regular-setup.R:5:3',
'test-regular-setup.R:37:3', 'test-regular-setup.R:116:3',
'test-regular-setup.R:232:3', 'test-regular-setup.R:250:3',
'test-regular-setup.R:305:3', 'test-regular-setup.R:378:3',
'test-regular-setup.R:531:3', 'test-regular-setup.R:648:3',
'test-regular-setup.R:758:3', 'test-regular-setup.R:779:3',
'test-regular-setup.R:834:3', 'test-regular-setup.R:889:3',
'test-regular-setup.R:990:3', 'test-regular-setup.R:1097:3',
'test-regular-setup.R:1165:3', 'test-regular-setup.R:1207:3',
'test-regular-setup.R:1343:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-regular-output.R:166:3'): output_lm_numeric_vaeac ──────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:166:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-output.R:205:3'): output_lm_categorical_vaeac ──────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:205:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-output.R:353:3'): output_lm_mixed_vaeac ────────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:353:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-setup.R:1553:3'): vaeac_set_seed_works ─────────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. └─shapr::explain(...) at test-regular-setup.R:1553:3
2. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
3. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
4. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
5. └─shapr (local) `<fn>`(...)
6. └─shapr:::vaeac(...)
7. └─torch (local) vaeac_tmp(...)
8. └─Module$new(...)
9. └─shapr (local) initialize(...)
10. ├─full_encoder_network$add_module(...)
11. │ └─self$register_module(name, module)
12. │ └─base::inherits(module, "nn_module")
13. └─shapr:::skip_connection(...)
14. └─torch (local) skip_connection_tmp(... = ...)
15. └─Module$new(...)
16. └─shapr (local) initialize(...)
17. └─torch::nn_sequential(...)
18. └─Module$new(...)
19. └─torch (local) initialize(...)
20. └─self$add_module(name = i - 1, module = modules[[i]])
21. └─self$register_module(name, module)
22. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
23. └─rlang::abort(...)
── Error ('test-regular-setup.R:1597:3'): vaeac_pretreained_vaeac_model ────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. └─shapr::explain(...) at test-regular-setup.R:1597:3
2. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
3. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
4. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
5. └─shapr (local) `<fn>`(...)
6. └─shapr:::vaeac(...)
7. └─torch (local) vaeac_tmp(...)
8. └─Module$new(...)
9. └─shapr (local) initialize(...)
10. ├─full_encoder_network$add_module(...)
11. │ └─self$register_module(name, module)
12. │ └─base::inherits(module, "nn_module")
13. └─shapr:::skip_connection(...)
14. └─torch (local) skip_connection_tmp(... = ...)
15. └─Module$new(...)
16. └─shapr (local) initialize(...)
17. └─torch::nn_sequential(...)
18. └─Module$new(...)
19. └─torch (local) initialize(...)
20. └─self$add_module(name = i - 1, module = modules[[i]])
21. └─self$register_module(name, module)
22. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
23. └─rlang::abort(...)
[ FAIL 5 | WARN 0 | SKIP 137 | PASS 39 ]
Error: Test failures
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.0.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [8m/10m]
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 2025-03-27 05:28:47 --------------------------
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Computations (temporary) saved at:
'/home/hornik/tmp/scratch/RtmpJofbPU/shapr_obj_b61bb3550eeb4.rds'
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, gaussian, and copula
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian, gaussian, gaussian, and gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, independence, and empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: independence, empirical, independence, and empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: vaeac
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: vaeac
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_features = 32,
and is therefore set to 2^n_features = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Success with message:
max_n_coalitions is NULL or larger than or 2^n_groups = 32,
and is therefore set to 2^n_groups = 32.
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
[ FAIL 5 | WARN 0 | SKIP 137 | PASS 39 ]
══ Skipped tests (137) ═════════════════════════════════════════════════════════
• On CRAN (137): 'test-asymmetric-causal-output.R:16:3',
'test-asymmetric-causal-output.R:34:3',
'test-asymmetric-causal-output.R:52:3',
'test-asymmetric-causal-output.R:71:3',
'test-asymmetric-causal-output.R:89:3',
'test-asymmetric-causal-output.R:107:3',
'test-asymmetric-causal-output.R:125:3',
'test-asymmetric-causal-output.R:144:3',
'test-asymmetric-causal-output.R:162:3',
'test-asymmetric-causal-output.R:180:3',
'test-asymmetric-causal-output.R:199:3',
'test-asymmetric-causal-output.R:217:3',
'test-asymmetric-causal-output.R:235:3',
'test-asymmetric-causal-output.R:254:3',
'test-asymmetric-causal-output.R:272:3',
'test-asymmetric-causal-output.R:292:3',
'test-asymmetric-causal-output.R:312:3',
'test-asymmetric-causal-output.R:331:3',
'test-asymmetric-causal-output.R:357:3',
'test-asymmetric-causal-output.R:375:3',
'test-asymmetric-causal-output.R:394:3',
'test-asymmetric-causal-output.R:412:3',
'test-asymmetric-causal-output.R:431:3',
'test-asymmetric-causal-output.R:453:3',
'test-asymmetric-causal-output.R:474:3',
'test-asymmetric-causal-output.R:493:3',
'test-asymmetric-causal-setup.R:4:3', 'test-asymmetric-causal-setup.R:221:3',
'test-asymmetric-causal-setup.R:244:3',
'test-asymmetric-causal-setup.R:306:3', 'test-forecast-output.R:2:3',
'test-forecast-output.R:21:3', 'test-forecast-output.R:43:3',
'test-forecast-output.R:66:3', 'test-forecast-output.R:89:3',
'test-forecast-output.R:109:3', 'test-forecast-output.R:130:3',
'test-forecast-output.R:151:3', 'test-forecast-output.R:176:3',
'test-forecast-setup.R:5:3', 'test-forecast-setup.R:33:3',
'test-forecast-setup.R:108:3', 'test-forecast-setup.R:132:3',
'test-forecast-setup.R:158:3', 'test-forecast-setup.R:218:3',
'test-forecast-setup.R:289:3', 'test-forecast-setup.R:337:3',
'test-forecast-setup.R:429:3', 'test-forecast-setup.R:499:3',
'test-iterative-output.R:4:3', 'test-iterative-output.R:20:3',
'test-iterative-output.R:40:3', 'test-iterative-output.R:62:3',
'test-iterative-output.R:86:3', 'test-iterative-output.R:107:3',
'test-iterative-output.R:176:3', 'test-iterative-output.R:259:3',
'test-iterative-output.R:275:3', 'test-iterative-output.R:291:3',
'test-iterative-output.R:307:3', 'test-iterative-output.R:325:3',
'test-iterative-setup.R:76:3', 'test-plot.R:59:3', 'test-plot.R:83:3',
'test-plot.R:117:3', 'test-plot.R:141:3', 'test-plot.R:165:3',
'test-plot.R:191:3', 'test-plot.R:272:3', 'test-regression-output.R:3:3',
'test-regression-output.R:20:3', 'test-regression-output.R:36:3',
'test-regression-output.R:53:3', 'test-regression-output.R:69:3',
'test-regression-output.R:85:3', 'test-regression-output.R:104:3',
'test-regression-output.R:122:3', 'test-regression-output.R:143:3',
'test-regression-output.R:163:3', 'test-regression-output.R:180:3',
'test-regression-output.R:196:3', 'test-regression-output.R:213:3',
'test-regression-output.R:231:3', 'test-regression-output.R:247:3',
'test-regression-output.R:263:3', 'test-regression-output.R:281:3',
'test-regression-setup.R:4:3', 'test-regression-setup.R:40:3',
'test-regression-setup.R:161:3', 'test-regression-setup.R:216:3',
'test-regression-setup.R:275:3', 'test-regression-setup.R:314:3',
'test-regular-output.R:4:3', 'test-regular-output.R:19:3',
'test-regular-output.R:35:3', 'test-regular-output.R:50:3',
'test-regular-output.R:67:3', 'test-regular-output.R:84:3',
'test-regular-output.R:102:3', 'test-regular-output.R:119:3',
'test-regular-output.R:134:3', 'test-regular-output.R:149:3',
'test-regular-output.R:188:3', 'test-regular-output.R:227:3',
'test-regular-output.R:242:3', 'test-regular-output.R:257:3',
'test-regular-output.R:273:3', 'test-regular-output.R:288:3',
'test-regular-output.R:303:3', 'test-regular-output.R:321:3',
'test-regular-output.R:336:3', 'test-regular-output.R:376:3',
'test-regular-output.R:403:3', 'test-regular-output.R:430:3',
'test-regular-output.R:492:5', 'test-regular-output.R:516:3',
'test-regular-output.R:531:3', 'test-regular-output.R:554:3',
'test-regular-output.R:575:3', 'test-regular-setup.R:5:3',
'test-regular-setup.R:37:3', 'test-regular-setup.R:116:3',
'test-regular-setup.R:232:3', 'test-regular-setup.R:250:3',
'test-regular-setup.R:305:3', 'test-regular-setup.R:378:3',
'test-regular-setup.R:531:3', 'test-regular-setup.R:648:3',
'test-regular-setup.R:758:3', 'test-regular-setup.R:779:3',
'test-regular-setup.R:834:3', 'test-regular-setup.R:889:3',
'test-regular-setup.R:990:3', 'test-regular-setup.R:1097:3',
'test-regular-setup.R:1165:3', 'test-regular-setup.R:1207:3',
'test-regular-setup.R:1343:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-regular-output.R:166:3'): output_lm_numeric_vaeac ──────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:166:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-output.R:205:3'): output_lm_categorical_vaeac ──────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:205:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-output.R:353:3'): output_lm_mixed_vaeac ────────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. ├─shapr:::expect_snapshot_rds(...) at test-regular-output.R:353:3
2. │ └─testthat::expect_snapshot((out <- code))
3. │ ├─testthat:::with_is_snapshotting(...)
4. │ └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay)
5. │ └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler)
6. │ ├─base::withRestarts(...)
7. │ │ └─base (local) withRestartList(expr, restarts)
8. │ │ ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
9. │ │ │ └─base (local) doWithOneRestart(return(expr), restart)
10. │ │ └─base (local) withRestartList(expr, restarts[-nr])
11. │ │ └─base (local) withOneRestart(expr, restarts[[1L]])
12. │ │ └─base (local) doWithOneRestart(return(expr), restart)
13. │ ├─evaluate:::with_handlers(...)
14. │ │ ├─base::eval(call)
15. │ │ │ └─base::eval(call)
16. │ │ └─base::withCallingHandlers(...)
17. │ ├─base::withVisible(eval(expr, envir))
18. │ └─base::eval(expr, envir)
19. │ └─base::eval(expr, envir)
20. └─shapr::explain(...)
21. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
22. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
23. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
24. └─shapr (local) `<fn>`(...)
25. └─shapr:::vaeac(...)
26. └─torch (local) vaeac_tmp(...)
27. └─Module$new(...)
28. └─shapr (local) initialize(...)
29. ├─full_encoder_network$add_module(...)
30. │ └─self$register_module(name, module)
31. │ └─base::inherits(module, "nn_module")
32. └─shapr:::skip_connection(...)
33. └─torch (local) skip_connection_tmp(... = ...)
34. └─Module$new(...)
35. └─shapr (local) initialize(...)
36. └─torch::nn_sequential(...)
37. └─Module$new(...)
38. └─torch (local) initialize(...)
39. └─self$add_module(name = i - 1, module = modules[[i]])
40. └─self$register_module(name, module)
41. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
42. └─rlang::abort(...)
── Error ('test-regular-setup.R:1553:3'): vaeac_set_seed_works ─────────────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. └─shapr::explain(...) at test-regular-setup.R:1553:3
2. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
3. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
4. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
5. └─shapr (local) `<fn>`(...)
6. └─shapr:::vaeac(...)
7. └─torch (local) vaeac_tmp(...)
8. └─Module$new(...)
9. └─shapr (local) initialize(...)
10. ├─full_encoder_network$add_module(...)
11. │ └─self$register_module(name, module)
12. │ └─base::inherits(module, "nn_module")
13. └─shapr:::skip_connection(...)
14. └─torch (local) skip_connection_tmp(... = ...)
15. └─Module$new(...)
16. └─shapr (local) initialize(...)
17. └─torch::nn_sequential(...)
18. └─Module$new(...)
19. └─torch (local) initialize(...)
20. └─self$add_module(name = i - 1, module = modules[[i]])
21. └─self$register_module(name, module)
22. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
23. └─rlang::abort(...)
── Error ('test-regular-setup.R:1597:3'): vaeac_pretreained_vaeac_model ────────
Error in `self$register_module(name, module)`: Expected <nn_module> but got object of type <NULL>
Backtrace:
▆
1. └─shapr::explain(...) at test-regular-setup.R:1597:3
2. ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
3. └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
4. ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
5. └─shapr (local) `<fn>`(...)
6. └─shapr:::vaeac(...)
7. └─torch (local) vaeac_tmp(...)
8. └─Module$new(...)
9. └─shapr (local) initialize(...)
10. ├─full_encoder_network$add_module(...)
11. │ └─self$register_module(name, module)
12. │ └─base::inherits(module, "nn_module")
13. └─shapr:::skip_connection(...)
14. └─torch (local) skip_connection_tmp(... = ...)
15. └─Module$new(...)
16. └─shapr (local) initialize(...)
17. └─torch::nn_sequential(...)
18. └─Module$new(...)
19. └─torch (local) initialize(...)
20. └─self$add_module(name = i - 1, module = modules[[i]])
21. └─self$register_module(name, module)
22. └─cli::cli_abort("Expected {.cls nn_module} but got object of type {.cls {class(module)}}")
23. └─rlang::abort(...)
[ FAIL 5 | WARN 0 | SKIP 137 | PASS 39 ]
Error: Test failures
Execution halted
Flavor: r-release-linux-x86_64
Version: 1.0.3
Check: installed package size
Result: NOTE
installed size is 9.5Mb
sub-directories of 1Mb or more:
doc 4.3Mb
libs 4.1Mb
Flavors: r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64