R Under development (unstable) (2025-10-28 r88973 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 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(fastml) > > test_check("fastml") Attaching package: 'dplyr' The following object is masked from 'package:testthat': matches The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Preparation of a new explainer is initiated -> model label : ranger -> data : 80 rows 4 cols -> target variable : 80 values -> predict function : predict_function -> predicted values : No value for predict function target column. (  default  ) -> model_info : package , ver. , task classification -> predicted values : predict function returns multiple columns: 2 (  default  )  A new explainer has been created!  === DALEX Variable Importance (with Boxplots) === === DALEX Shapley Values (SHAP) === iter imp variable 1 1 ph.ecog ph.karno pat.karno meal.cal wt.loss 1 2 ph.ecog ph.karno pat.karno meal.cal wt.loss 1 3 ph.ecog ph.karno pat.karno meal.cal wt.loss 1 4 ph.ecog ph.karno pat.karno meal.cal wt.loss 1 5 ph.ecog ph.karno pat.karno meal.cal wt.loss 2 1 ph.ecog ph.karno pat.karno meal.cal wt.loss 2 2 ph.ecog ph.karno pat.karno meal.cal wt.loss 2 3 ph.ecog ph.karno pat.karno meal.cal wt.loss 2 4 ph.ecog ph.karno pat.karno meal.cal wt.loss 2 5 ph.ecog ph.karno pat.karno meal.cal wt.loss 3 1 ph.ecog ph.karno pat.karno meal.cal wt.loss 3 2 ph.ecog ph.karno pat.karno meal.cal wt.loss 3 3 ph.ecog ph.karno pat.karno meal.cal wt.loss 3 4 ph.ecog ph.karno pat.karno meal.cal wt.loss 3 5 ph.ecog ph.karno pat.karno meal.cal wt.loss 4 1 ph.ecog ph.karno pat.karno meal.cal wt.loss 4 2 ph.ecog ph.karno pat.karno meal.cal wt.loss 4 3 ph.ecog ph.karno pat.karno meal.cal wt.loss 4 4 ph.ecog ph.karno pat.karno meal.cal wt.loss 4 5 ph.ecog ph.karno pat.karno meal.cal wt.loss 5 1 ph.ecog ph.karno pat.karno meal.cal wt.loss 5 2 ph.ecog ph.karno pat.karno meal.cal wt.loss 5 3 ph.ecog ph.karno pat.karno meal.cal wt.loss 5 4 ph.ecog ph.karno pat.karno meal.cal wt.loss 5 5 ph.ecog ph.karno pat.karno meal.cal wt.loss iter imp variable 1 1 inst pat.karno meal.cal wt.loss 1 2 inst pat.karno meal.cal wt.loss 1 3 inst pat.karno meal.cal wt.loss 1 4 inst pat.karno meal.cal wt.loss 1 5 inst pat.karno meal.cal wt.loss 2 1 inst pat.karno meal.cal wt.loss 2 2 inst pat.karno meal.cal wt.loss 2 3 inst pat.karno meal.cal wt.loss 2 4 inst pat.karno meal.cal wt.loss 2 5 inst pat.karno meal.cal wt.loss 3 1 inst pat.karno meal.cal wt.loss 3 2 inst pat.karno meal.cal wt.loss 3 3 inst pat.karno meal.cal wt.loss 3 4 inst pat.karno meal.cal wt.loss 3 5 inst pat.karno meal.cal wt.loss 4 1 inst pat.karno meal.cal wt.loss 4 2 inst pat.karno meal.cal wt.loss 4 3 inst pat.karno meal.cal wt.loss 4 4 inst pat.karno meal.cal wt.loss 4 5 inst pat.karno meal.cal wt.loss 5 1 inst pat.karno meal.cal wt.loss 5 2 inst pat.karno meal.cal wt.loss 5 3 inst pat.karno meal.cal wt.loss 5 4 inst pat.karno meal.cal wt.loss 5 5 inst pat.karno meal.cal wt.loss Engine-level early stopping will be applied when supported ===== fastml Model Summary ===== Task: survival Number of Models Trained: 1 Best Model(s): cox_ph (survival) (ibs: 0.2490306) Performance Metrics (Sorted by ibs): ----------------------------------------------------------------------------------------------------------- Model Engine Harrell C-index Uno's C-index Integrated Brier Score RMST diff (t<=551) Brier(t=269) ----------------------------------------------------------------------------------------------------------- cox_ph* survival 0.441 0.536 0.249 16.357 0.309 ----------------------------------------------------------------------------------------------------------- (*Best model) Forming integrated rmst function... Forming integrated mean function... Forming integrated rmst function... Forming integrated mean function... [ FAIL 0 | WARN 1 | SKIP 0 | PASS 140 ] [ FAIL 0 | WARN 1 | SKIP 0 | PASS 140 ] > > proc.time() user system elapsed 298.20 12.14 286.37