R Under development (unstable) (2024-12-12 r87438 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(fuseMLR) > > test_check("fuseMLR") Class : Data name : geneexpr ind. id. : IDS n : 49 p : 132 Class: HashTable id: test ----------------- [1] key class <0 rows> (or 0-length row.names) Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger Training of base model on layer geneexpr started. Training of base model on layer geneexpr done. Class : Model Learner info. ----------------------- Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger Train data info. ----------------------- TrainData : geneexpr Layer : geneexpr ind. id. : IDS target : disease n : 50 Missing : 0 p : 131 TrainLayer : geneexpr Status : Not trained Empty layer. TrainData : methylation Layer : methylation ind. id. : IDS target : disease n : 50 Missing : 0 p : 367 Layer geneexpr ---------------- TrainLayer : geneexpr Status : Not trained Empty layer. ---------------- Object(s) on layer geneexpr Empty layer Layer geneexpr ---------------- TrainLayer : geneexpr Status : Not trained Nb. of objects stored : 3 ---------------- Object(s) on layer geneexpr ---------------- Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger ---------------- ---------------- VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta ---------------- ---------------- TrainData : geneexpr Layer : geneexpr Ind. id. : IDS Target : disease n : 50 Missing : 0 p : 131 ---------------- Training of base model on layer geneexpr started. Training of base model on layer geneexpr done. Layer geneexpr ---------------- TrainLayer : geneexpr Status : Trained Nb. of objects stored : 4 ---------------- Object(s) on layer geneexpr ---------------- Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger ---------------- ---------------- VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta ---------------- ---------------- TrainData : geneexpr Layer : geneexpr Ind. id. : IDS Target : disease n : 50 Missing : 0 p : 131 ---------------- TrainMetaLayer : meta Status : Not trained Empty layer. MetaLayer ---------------- TrainMetaLayer : meta Status : Not trained Empty layer. ---------------- Object(s) on MetaLayer Empty layer Training : training Problem type : classification Status : Not trained Number of layers: 0 Layers trained : 0 Variable selection on layer geneexpr started. Variable selection on layer geneexpr done. Layer variable 1 geneexpr BRAF 2 geneexpr PEA15 3 geneexpr SHC1 Creating fold predictions. | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% Training of base model on layer geneexpr started. Training of base model on layer geneexpr done. Creating fold predictions. | | | 0% | |=================================== | 50% | |======================================================================| 100% Training of base model on layer geneexpr started. Training of base model on layer geneexpr done. Training of base model on layer proteinexpr started. Training of base model on layer proteinexpr done. Training : training Problem type : regression Status : Trained Number of layers: 3 Layers trained : 3 Var. sel. used : Yes p : 3 | 160 | 2 n : 50 | 50 | 64 na.action : na.rm | na.rm | na.keep TrainLayer : geneexpr Status : Trained Nb. of objects stored : 4 ----------------------- key class 1 geneexpr TrainData 2 varsel_geneexpr VarSel 3 ranger Lrner 4 rangerMo Model TrainData : modality-specific prediction data Layer : meta_layer ind. id. : IDS target : disease n : 64 Missing : 28 p : 2 Model ----------------------- Individual(s) used : 0 Variable(s) used : 0 ----------------------- Training training ---------------- Training : training Problem type : regression Status : Trained Number of layers: 3 Layers trained : 3 Var. sel. used : Yes p : 3 | 160 | 2 n : 50 | 50 | 64 na.action : na.rm | na.rm | na.keep ---------------- Layer geneexpr ---------------- TrainLayer : geneexpr Status : Trained Nb. of objects stored : 4 ---------------- Object(s) on layer geneexpr ---------------- TrainData : geneexpr Layer : geneexpr Ind. id. : IDS Target : disease n : 50 Missing : 0 p : 3 ---------------- ---------------- VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta ---------------- ---------------- Learner : ranger TrainLayer : geneexpr Package : ranger Learn function : ranger ---------------- Layer proteinexpr ---------------- TrainLayer : proteinexpr Status : Trained Nb. of objects stored : 3 ---------------- Object(s) on layer proteinexpr ---------------- TrainData : proteinexpr Layer : proteinexpr Ind. id. : IDS Target : disease n : 50 Missing : 0 p : 160 ---------------- ---------------- Learner : ranger TrainLayer : proteinexpr Package : ranger Learn function : ranger ---------------- MetaLayer ---------------- TrainMetaLayer : meta_layer Status : Trained Nb. of objects stored : 3 ---------------- Object(s) on MetaLayer ---------------- Learner : weighted TrainLayer : meta_layer Learn function : weightedMeanLearner ---------------- ---------------- TrainData : modality-specific predictions Layer : meta_layer Ind. id. : IDS Target : disease n : 64 Missing : 28 p : 2 ---------------- Testing : testing Number of layers: 0 TestLayer : geneexpr Contains 0 object. Testing testing ---------------- Testing : testing Number of layers: 2 p : 131 | 160 n : 20 | 20 ---------------- Class : TestData name : geneexpr ind. id. : IDS n : 20 p : 132 Class : TestData name : proteinexpr ind. id. : IDS n : 20 p : 161 $predicting Predicting : testing Nb. layers : 3 $predicted_values IDS geneexpr proteinexpr meta_layer 1 participant100 NA 0.4104444 NA 2 participant24 0.6209206 0.4080000 NA 3 participant25 0.9000000 NA NA 4 participant27 0.7642222 NA NA 5 participant28 0.5809206 0.5012063 NA 6 participant3 0.1480000 0.4805397 NA 7 participant32 1.0000000 0.5196667 NA 8 participant34 0.2926667 0.5030635 NA 9 participant39 0.3538095 NA NA 10 participant42 0.3549206 0.6270952 NA 11 participant51 NA 0.5510000 NA 12 participant53 NA 0.7053333 NA 13 participant54 1.0000000 NA NA 14 participant55 0.6620317 NA NA 15 participant6 NA 0.6240952 NA 16 participant63 NA 0.6272381 NA 17 participant64 0.9580000 0.4481111 NA 18 participant68 0.4889206 NA NA 19 participant71 0.2818095 0.5307302 NA 20 participant75 NA 0.6336667 NA 21 participant77 0.9142222 0.5616667 NA 22 participant79 0.6342222 0.6071746 NA 23 participant81 0.9580000 0.5089841 NA 24 participant84 0.0340000 0.7011111 NA 25 participant86 0.3138095 NA NA 26 participant94 0.6120317 0.3607619 NA 27 participant98 NA 0.4221429 NA VarSel : varsel_geneexpr TrainLayer : geneexpr Package : Boruta Function : Boruta Tuning 'epsilon' via cross-validation with 5 folds. Optimal epsilon: 0.071. Tuning with 5 folds. Tuning 'alpha' and 'epsilon' via cross-validation with 5 folds. Optimal alpha: 1 (1 Learner(s)). Optimal epsilon: 0.313. Tuning with 5 folds. Using user-defined 'epsilon' = 0.1. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Tuning 'epsilon' via cross-validation with 10 folds. Optimal epsilon: 0.669. Tuning with 10 folds. Tuning 'alpha' and 'epsilon' via cross-validation with 10 folds. Optimal alpha: 1 (1 Learner(s)). Optimal epsilon: 0.217. Tuning with 10 folds. [ FAIL 0 | WARN 1 | SKIP 15 | PASS 121 ] ══ Skipped tests (15) ══════════════════════════════════════════════════════════ • On CRAN (2): 'test-TrainMetaLayer.R:60:5', 'test-VarSel.R:43:5' • empty test (13): 'test-Data.R:21:1', 'test-Data.R:26:1', 'test-Data.R:42:1', 'test-Data.R:46:1', 'test-Data.R:50:1', 'test-Data.R:56:1', 'test-Data.R:60:1', 'test-HashTable.R:8:1', 'test-HashTable.R:25:1', 'test-HashTable.R:34:1', 'test-HashTable.R:38:1', 'test-HashTable.R:43:1', 'test-mutli_omics.R:1:1' [ FAIL 0 | WARN 1 | SKIP 15 | PASS 121 ] > > proc.time() user system elapsed 6.73 0.62 7.20