traceback() 37: solveiter(K, hess, (grad + alphag), solut) 36: .local(x, ...) 35: gausspr(x, y, scaled = scaled, ...) 34: gausspr(x, y, scaled = scaled, ...) 33: .local(x, ...) 32: kernlab::gausspr(f, data = getTaskData(.task, .subset), kpar = kpar, prob.model = pm, ...) 31: kernlab::gausspr(f, data = getTaskData(.task, .subset), kpar = kpar, prob.model = pm, ...) 30: trainLearner.classif.gausspr(.learner = list(id = "classif.gausspr", type = "classif", package = "kernlab", properties = c("twoclass", "multiclass", "numerics", "factors", "prob"), par.set = list( pars = list(scaled = list(id = "scaled", type = "logical", len = 1L, lower = NULL, upper = NULL, values = list( `TRUE` = TRUE, `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), kernel = list(id = "kernel", type = "discrete", len = 1L, lower = NULL, upper = NULL, values = list( vanilladot = "vanilladot", polydot = "polydot", rbfdot = "rbfdot", tanhdot = "tanhdot", laplacedot = "laplacedot", besseldot = "besseldot", anovadot = "anovadot", splinedot = "splinedot"), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "rbfdot", trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), sigma = list(id = "sigma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, trafo = NULL, requires = kernel %in% c("rbfdot", "anovadot", "besseldot", "laplacedot"), tunable = TRUE, special.vals = list(), when = "train"), degree = list(id = "degree", type = "integer", len = 1L, lower = 1L, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 3L, trafo = NULL, requires = kernel %in% c("polydot", "anovadot", "besseldot"), tunable = TRUE, special.vals = list(), when = "train"), scale = list(id = "scale", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1, trafo = NULL, requires = kernel %in% c("polydot", "tanhdot"), tunable = TRUE, special.vals = list(), when = "train"), offset = list(id = "offset", type = "numeric", len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1, trafo = NULL, requires = kernel %in% c("polydot", "tanhdot"), tunable = TRUE, special.vals = list(), when = "train"), order = list(id = "order", type = "integer", len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1L, trafo = NULL, requires = kernel == "besseldot", tunable = TRUE, special.vals = list(), when = "train"), tol = list(id = "tol", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.001, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), fit = list( id = "fit", type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train")), forbidden = NULL), par.vals = list(fit = FALSE, kernel = "polydot", degree = 4L, scale = 4.52147865854204, offset = 0.899868381844135), predict.type = "response", cache = FALSE, name = "Gaussian Processes", short.name = "gausspr", note = "Kernel parameters have to be passed directly and not by using the `kpar` list in `gausspr`.\n Note that `fit` has been set to `FALSE` by default for speed.", callees = "gausspr", help.list = list(sigma = "Argument of: kernlab::gausspr\n\nfor the ANOVA kernel \"anovadot\".", degree = "Argument of: kernlab::gausspr\n\nfor the ANOVA kernel \"anovadot\".", scale = "Argument of: kernlab::gausspr\n\nfor the Hyperbolic tangent kernel function \"tanhdot\"", offset = "Argument of: kernlab::gausspr\n\nfor the Hyperbolic tangent kernel function \"tanhdot\"", order = "Argument of: kernlab::gausspr\n\nfor the Bessel kernel \"besseldot\".", scaled = "Argument of: kernlab::gausspr\n\nA logical vector indicating the variables to be scaled. If scaled is of length 1, the value is recycled as many times as needed and all non-binary variables are scaled. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.", kernel = "Argument of: kernlab::gausspr\n\nthe kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:\n\nrbfdot Radial Basis kernel function \"Gaussian\"\n\npolydot Polynomial kernel function\n\nvanilladot Linear kernel function\n\ntanhdot Hyperbolic tangent kernel function\n\nlaplacedot Laplacian kernel function\n\nbesseldot Bessel kernel function\n\nanovadot ANOVA RBF kernel function\n\nsplinedot Spline kernel\n\nThe kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.", tol = "Argument of: kernlab::gausspr\n\ntolerance of termination criterion (default: 0.001)", fit = "Argument of: kernlab::gausspr\n\nindicates whether the fitted values should be computed and included in the model or not (default: 'TRUE')"), config = list(), fix.factors.prediction = FALSE), .task = list( type = "classif", env = , weights = NULL, blocking = NULL, coordinates = NULL, task.desc = list(id = "MLdatasetLabel", type = "classif", target = "LabelMLdataset", size = 800L, n.feat = c(numerics = 10L, factors = 0L, ordered = 0L, functionals = 0L), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE, has.coordinates = FALSE, class.levels = c("g", "p"), positive = "g", negative = "p", class.distribution = c(g = 399L, p = 401L))), .subset = NULL, fit = FALSE, kernel = "polydot", degree = 4L, scale = 4.52147865854204, offset = 0.899868381844135) 29: (function (.learner, .task, .subset, .weights = NULL, ...) { UseMethod("trainLearner") })(.learner = list(id = "classif.gausspr", type = "classif", package = "kernlab", properties = c("twoclass", "multiclass", "numerics", "factors", "prob"), par.set = list(pars = list( scaled = list(id = "scaled", type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), kernel = list(id = "kernel", type = "discrete", len = 1L, lower = NULL, upper = NULL, values = list( vanilladot = "vanilladot", polydot = "polydot", rbfdot = "rbfdot", tanhdot = "tanhdot", laplacedot = "laplacedot", besseldot = "besseldot", anovadot = "anovadot", splinedot = "splinedot"), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "rbfdot", trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), sigma = list(id = "sigma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, trafo = NULL, requires = kernel %in% c("rbfdot", "anovadot", "besseldot", "laplacedot"), tunable = TRUE, special.vals = list(), when = "train"), degree = list(id = "degree", type = "integer", len = 1L, lower = 1L, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 3L, trafo = NULL, requires = kernel %in% c("polydot", "anovadot", "besseldot"), tunable = TRUE, special.vals = list(), when = "train"), scale = list(id = "scale", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1, trafo = NULL, requires = kernel %in% c("polydot", "tanhdot"), tunable = TRUE, special.vals = list(), when = "train"), offset = list(id = "offset", type = "numeric", len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1, trafo = NULL, requires = kernel %in% c("polydot", "tanhdot"), tunable = TRUE, special.vals = list(), when = "train"), order = list(id = "order", type = "integer", len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1L, trafo = NULL, requires = kernel == "besseldot", tunable = TRUE, special.vals = list(), when = "train"), tol = list(id = "tol", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.001, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), fit = list( id = "fit", type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train")), forbidden = NULL), par.vals = list(fit = FALSE, kernel = "polydot", degree = 4L, scale = 4.52147865854204, offset = 0.899868381844135), predict.type = "response", cache = FALSE, name = "Gaussian Processes", short.name = "gausspr", note = "Kernel parameters have to be passed directly and not by using the `kpar` list in `gausspr`.\n Note that `fit` has been set to `FALSE` by default for speed.", callees = "gausspr", help.list = list(sigma = "Argument of: kernlab::gausspr\n\nfor the ANOVA kernel \"anovadot\".", degree = "Argument of: kernlab::gausspr\n\nfor the ANOVA kernel \"anovadot\".", scale = "Argument of: kernlab::gausspr\n\nfor the Hyperbolic tangent kernel function \"tanhdot\"", offset = "Argument of: kernlab::gausspr\n\nfor the Hyperbolic tangent kernel function \"tanhdot\"", order = "Argument of: kernlab::gausspr\n\nfor the Bessel kernel \"besseldot\".", scaled = "Argument of: kernlab::gausspr\n\nA logical vector indicating the variables to be scaled. If scaled is of length 1, the value is recycled as many times as needed and all non-binary variables are scaled. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.", kernel = "Argument of: kernlab::gausspr\n\nthe kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:\n\nrbfdot Radial Basis kernel function \"Gaussian\"\n\npolydot Polynomial kernel function\n\nvanilladot Linear kernel function\n\ntanhdot Hyperbolic tangent kernel function\n\nlaplacedot Laplacian kernel function\n\nbesseldot Bessel kernel function\n\nanovadot ANOVA RBF kernel function\n\nsplinedot Spline kernel\n\nThe kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.", tol = "Argument of: kernlab::gausspr\n\ntolerance of termination criterion (default: 0.001)", fit = "Argument of: kernlab::gausspr\n\nindicates whether the fitted values should be computed and included in the model or not (default: 'TRUE')"), config = list(), fix.factors.prediction = FALSE), .task = list( type = "classif", env = , weights = NULL, blocking = NULL, coordinates = NULL, task.desc = list(id = "MLdatasetLabel", type = "classif", target = "LabelMLdataset", size = 800L, n.feat = c(numerics = 10L, factors = 0L, ordered = 0L, functionals = 0L), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE, has.coordinates = FALSE, class.levels = c("g", "p"), positive = "g", negative = "p", class.distribution = c(g = 399L, p = 401L))), .subset = NULL, fit = FALSE, kernel = "polydot", degree = 4L, scale = 4.52147865854204, offset = 0.899868381844135) 28: do.call(trainLearner, pars) 27: fun3(do.call(trainLearner, pars)) 26: fun2(fun3(do.call(trainLearner, pars))) 25: fun1({ learner.model = fun2(fun3(do.call(trainLearner, pars))) }) 24: force(expr) 23: measureTime(fun1({ learner.model = fun2(fun3(do.call(trainLearner, pars))) })) 22: train(learner, task, subset = train.i, weights = weights[train.i]) 21: calculateResampleIterationResult(learner = learner, task = task, i = i, train.i = train.i, test.i = test.i, measures = measures, weights = weights, rdesc = rin$desc, model = model, extract = extract, show.info = show.info) 20: (function (learner, task, rin, i, measures, weights, model, extract, show.info) { setSlaveOptions() train.i = rin$train.inds[[i]] test.i = rin$test.inds[[i]] calculateResampleIterationResult(learner = learner, task = task, i = i, train.i = train.i, test.i = test.i, measures = measures, weights = weights, rdesc = rin$desc, model = model, extract = extract, show.info = show.info) })(dots[[1L]][[1L]], learner = list(id = "classif.gausspr", type = "classif", package = "kernlab", properties = c("twoclass", "multiclass", "numerics", "factors", "prob"), par.set = list(pars = list( scaled = list(id = "scaled", type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), kernel = list(id = "kernel", type = "discrete", len = 1L, lower = NULL, upper = NULL, values = list( vanilladot = "vanilladot", polydot = "polydot", rbfdot = "rbfdot", tanhdot = "tanhdot", laplacedot = "laplacedot", besseldot = "besseldot", anovadot = "anovadot", splinedot = "splinedot"), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = "rbfdot", trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), sigma = list(id = "sigma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, trafo = NULL, requires = kernel %in% c("rbfdot", "anovadot", "besseldot", "laplacedot"), tunable = TRUE, special.vals = list(), when = "train"), degree = list(id = "degree", type = "integer", len = 1L, lower = 1L, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 3L, trafo = NULL, requires = kernel %in% c("polydot", "anovadot", "besseldot"), tunable = TRUE, special.vals = list(), when = "train"), scale = list(id = "scale", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1, trafo = NULL, requires = kernel %in% c("polydot", "tanhdot"), tunable = TRUE, special.vals = list(), when = "train"), offset = list(id = "offset", type = "numeric", len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1, trafo = NULL, requires = kernel %in% c("polydot", "tanhdot"), tunable = TRUE, special.vals = list(), when = "train"), order = list(id = "order", type = "integer", len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 1L, trafo = NULL, requires = kernel == "besseldot", tunable = TRUE, special.vals = list(), when = "train"), tol = list(id = "tol", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0.001, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), fit = list( id = "fit", type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train")), forbidden = NULL), par.vals = list(fit = FALSE, kernel = "polydot", degree = 4L, scale = 4.52147865854204, offset = 0.899868381844135), predict.type = "response", cache = FALSE, name = "Gaussian Processes", short.name = "gausspr", note = "Kernel parameters have to be passed directly and not by using the `kpar` list in `gausspr`.\n Note that `fit` has been set to `FALSE` by default for speed.", callees = "gausspr", help.list = list(sigma = "Argument of: kernlab::gausspr\n\nfor the ANOVA kernel \"anovadot\".", degree = "Argument of: kernlab::gausspr\n\nfor the ANOVA kernel \"anovadot\".", scale = "Argument of: kernlab::gausspr\n\nfor the Hyperbolic tangent kernel function \"tanhdot\"", offset = "Argument of: kernlab::gausspr\n\nfor the Hyperbolic tangent kernel function \"tanhdot\"", order = "Argument of: kernlab::gausspr\n\nfor the Bessel kernel \"besseldot\".", scaled = "Argument of: kernlab::gausspr\n\nA logical vector indicating the variables to be scaled. If scaled is of length 1, the value is recycled as many times as needed and all non-binary variables are scaled. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.", kernel = "Argument of: kernlab::gausspr\n\nthe kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:\n\nrbfdot Radial Basis kernel function \"Gaussian\"\n\npolydot Polynomial kernel function\n\nvanilladot Linear kernel function\n\ntanhdot Hyperbolic tangent kernel function\n\nlaplacedot Laplacian kernel function\n\nbesseldot Bessel kernel function\n\nanovadot ANOVA RBF kernel function\n\nsplinedot Spline kernel\n\nThe kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.", tol = "Argument of: kernlab::gausspr\n\ntolerance of termination criterion (default: 0.001)", fit = "Argument of: kernlab::gausspr\n\nindicates whether the fitted values should be computed and included in the model or not (default: 'TRUE')"), config = list(), fix.factors.prediction = FALSE), task = list( type = "classif", env = , weights = NULL, blocking = NULL, coordinates = NULL, task.desc = list(id = "MLdatasetLabel", type = "classif", target = "LabelMLdataset", size = 1000L, n.feat = c(numerics = 10L, factors = 0L, ordered = 0L, functionals = 0L), has.missings = FALSE, has.weights = FALSE, has.blocking = FALSE, has.coordinates = FALSE, class.levels = c("g", "p"), positive = "g", negative = "p", class.distribution = c(g = 500L, p = 500L))), rin = list(desc = list(fixed = FALSE, blocking.cv = FALSE, id = "cross-validation", iters = 5L, predict = "test", stratify = FALSE), size = 1000L, train.inds = list(c(461L, 32L, 178L, 243L, 270L, 985L, 908L, 996L, 752L, 111L, 428L, 667L, 470L, 343L, 616L, 395L, 634L, 621L, 471L, 992L, 53L, 149L, 363L, 183L, 165L, 598L, 937L, 100L, 770L, 279L, 817L, 344L, 106L, 594L, 963L, 680L, 7L, 27L, 986L, 524L, 830L, 848L, 990L, 642L, 893L, 884L, 913L, 960L, 364L, 300L, 603L, 923L, 10L, 989L, 587L, 513L, 962L, 689L, 331L, 475L, 504L, 836L, 246L, 45L, 821L, 669L, 257L, 786L, 389L, 537L, 646L, 308L, 368L, 776L, 75L, 829L, 919L, 720L, 549L, 980L, 296L, 168L, 38L, 206L, 858L, 43L, 282L, 141L, 697L, 1000L, 768L, 112L, 142L, 322L, 196L, 625L, 589L, 77L, 784L, 522L, 407L, 221L, 533L, 610L, 640L, 724L, 321L, 319L, 719L, 197L, 386L, 161L, 20L, 303L, 41L, 742L, 745L, 408L, 690L, 943L, 747L, 13L, 385L, 496L, 80L, 302L, 540L, 16L, 612L, 563L, 21L, 619L, 927L, 785L, 626L, 189L, 288L, 320L, 737L, 415L, 796L, 89L, 265L, 68L, 490L, 469L, 33L, 867L, 427L, 815L, 615L, 545L, 361L, 811L, 872L, 445L, 82L, 760L, 205L, 340L, 360L, 683L, 865L, 28L, 988L, 284L, 897L, 330L, 838L, 763L, 234L, 133L, 19L, 861L, 286L, 804L, 698L, 120L, 766L, 847L, 525L, 315L, 281L, 434L, 925L, 866L, 278L, 164L, 262L, 137L, 268L, 601L, 541L, 840L, 202L, 424L, 949L, 957L, 487L, 920L, 125L, 873L, 984L, 436L, 559L, 338L, 967L, 61L, 704L, 228L, 150L, 362L, 780L, 816L, 716L, 787L, 946L, 416L, 6L, 547L, 666L, 880L, 159L, 134L, 255L, 406L, 449L, 618L, 171L, 29L, 104L, 938L, 225L, 955L, 528L, 391L, 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873L, 436L, 559L, 519L, 550L, 338L, 61L, 828L, 704L, 228L, 209L, 150L, 362L, 816L, 764L, 787L, 946L, 637L, 547L, 350L, 134L, 255L, 579L, 406L, 618L, 831L, 285L, 104L, 938L, 225L, 754L, 528L, 662L, 709L, 783L, 422L, 501L, 869L, 744L, 673L, 759L, 740L, 156L, 694L, 639L, 22L, 393L, 538L, 185L, 806L, 536L, 251L, 452L, 910L, 932L, 208L, 140L, 871L, 571L, 247L, 857L, 162L, 670L, 400L, 195L, 952L, 483L, 429L, 591L, 410L, 399L, 852L, 346L, 778L, 888L, 453L, 239L, 101L, 609L, 466L, 529L, 66L, 277L, 574L, 190L, 586L, 48L, 749L, 790L, 342L, 842L, 560L, 108L, 198L, 600L, 402L, 269L, 455L, 233L, 788L, 129L, 34L, 437L, 160L, 235L, 849L, 58L, 420L, 240L, 238L, 566L, 127L, 172L, 267L, 72L, 588L, 312L, 67L, 648L, 441L, 708L, 977L, 191L, 229L, 732L, 327L, 167L, 110L, 535L, 435L, 462L, 146L, 818L, 596L, 258L, 655L, 448L, 498L, 275L, 214L, 656L, 660L, 898L, 891L, 263L, 199L, 151L, 266L, 922L, 965L, 677L, 81L, 248L, 50L, 210L, 900L, 978L, 570L, 401L, 2L, 721L, 73L, 491L, 304L, 552L, 291L, 314L, 748L, 800L, 998L, 959L, 859L, 65L, 384L, 544L, 472L, 353L, 973L, 826L, 444L, 958L, 359L, 337L, 686L, 812L, 211L, 175L, 488L, 542L, 412L, 135L, 651L, 518L, 131L, 245L, 358L, 934L, 833L, 556L, 705L, 590L, 703L, 237L, 8L, 227L, 968L, 316L, 706L, 158L, 430L, 520L, 179L, 366L, 217L, 633L, 654L, 741L, 940L, 526L, 356L, 606L, 599L, 84L, 99L, 463L, 397L, 294L, 781L, 333L, 915L, 856L, 253L, 52L, 577L, 546L, 578L, 505L, 489L, 801L, 166L, 758L, 201L, 259L, 184L, 798L, 731L, 924L, 668L, 935L, 649L, 254L, 701L, 855L, 624L, 187L, 765L, 117L, 912L, 95L, 56L, 565L, 688L, 431L, 793L, 3L, 465L, 336L, 418L, 212L, 494L, 581L, 644L, 451L, 739L, 334L, 715L, 647L, 381L, 775L, 398L, 751L, 523L, 802L, 276L, 299L, 174L, 725L, 930L, 329L, 207L, 76L, 795L, 909L, 378L, 192L, 49L, 12L, 249L, 379L, 204L, 130L, 892L, 695L, 661L, 283L, 613L, 467L, 479L, 944L, 272L, 371L, 769L, 261L, 63L, 477L, 508L, 887L, 144L, 650L, 746L, 567L, 558L, 476L, 894L, 370L, 614L, 553L, 55L, 432L, 11L, 665L, 324L, 711L, 530L, 250L, 805L, 373L, 87L, 128L, 652L, 906L, 515L, 425L, 631L, 679L, 423L, 728L, 941L, 357L, 702L, 440L, 170L, 611L, 734L, 390L, 468L, 901L, 851L, 403L, 354L, 761L, 92L, 700L, 188L, 953L, 439L, 710L, 572L, 456L, 289L, 956L, 219L, 114L, 102L, 575L, 691L, 433L, 47L, 231L, 862L, 376L, 863L, 987L, 877L, 154L, 326L, 608L, 961L, 635L, 220L, 638L, 495L, 78L, 14L, 44L, 835L, 561L, 722L, 493L, 592L, 98L, 738L, 325L, 244L, 807L, 213L, 200L, 97L, 405L, 341L, 870L, 409L, 426L, 124L, 460L, 966L, 176L, 369L, 696L, 853L, 500L, 868L, 143L, 71L, 497L, 947L, 351L, 163L, 845L, 40L, 557L, 103L, 301L, 607L, 713L, 457L, 554L, 971L, 392L, 777L, 551L, 854L, 997L, 30L, 374L, 750L, 860L, 57L, 782L, 643L, 819L, 945L, 723L, 733L, 387L, 18L, 365L, 348L, 882L, 273L, 939L, 223L, 911L, 252L, 51L, 94L, 905L, 309L, 417L, 454L, 914L, 622L, 676L, 241L, 976L, 339L, 534L, 1L, 122L, 121L, 757L, 814L, 916L, 345L, 91L, 419L, 917L, 203L, 792L, 623L, 809L, 928L, 663L, 215L, 194L, 531L, 153L, 827L, 347L, 37L, 890L, 951L, 180L, 35L, 297L, 31L, 509L, 797L, 543L, 933L, 993L, 954L, 152L, 39L, 832L, 126L, 310L, 116L, 562L, 306L, 730L, 583L, 85L, 717L, 230L, 657L, 995L, 367L, 628L, 907L, 24L, 824L, 605L, 93L, 404L, 789L, 755L, 382L, 26L, 881L, 864L, 335L, 918L, 459L, 899L, 478L, 671L, 876L, 597L, 991L, 999L, 653L, 88L, 107L, 60L, 25L, 791L, 256L, 36L, 307L, 548L, 735L, 756L, 218L, 994L, 895L, 808L, 844L, 62L, 290L, 323L, 837L, 981L, 474L, 942L, 969L, 507L, 904L, 298L, 414L, 684L, 486L, 413L, 604L, 464L, 774L, 584L, 136L, 886L, 875L, 539L, 602L), c(178L, 243L, 270L, 985L, 908L, 996L, 752L, 726L, 667L, 169L, 470L, 343L, 687L, 105L, 616L, 395L, 634L, 621L, 471L, 992L, 53L, 149L, 363L, 183L, 810L, 672L, 598L, 937L, 903L, 100L, 90L, 260L, 279L, 564L, 817L, 594L, 963L, 680L, 7L, 27L, 986L, 524L, 896L, 380L, 990L, 893L, 193L, 960L, 300L, 603L, 923L, 10L, 989L, 587L, 513L, 113L, 962L, 689L, 331L, 475L, 504L, 836L, 246L, 821L, 669L, 678L, 421L, 786L, 389L, 537L, 511L, 646L, 936L, 576L, 308L, 573L, 776L, 75L, 829L, 499L, 919L, 720L, 549L, 168L, 206L, 282L, 141L, 697L, 328L, 264L, 664L, 96L, 768L, 142L, 322L, 196L, 625L, 589L, 784L, 522L, 407L, 221L, 636L, 533L, 640L, 321L, 719L, 197L, 823L, 386L, 161L, 20L, 699L, 303L, 41L, 742L, 745L, 408L, 690L, 943L, 747L, 921L, 115L, 385L, 496L, 302L, 540L, 16L, 612L, 132L, 506L, 727L, 785L, 626L, 189L, 288L, 46L, 658L, 480L, 737L, 796L, 89L, 450L, 265L, 295L, 68L, 843L, 490L, 469L, 33L, 874L, 867L, 685L, 427L, 815L, 615L, 485L, 361L, 811L, 879L, 872L, 82L, 760L, 205L, 340L, 865L, 28L, 988L, 284L, 674L, 897L, 330L, 838L, 236L, 763L, 234L, 799L, 19L, 736L, 286L, 822L, 804L, 698L, 120L, 766L, 847L, 525L, 315L, 281L, 293L, 925L, 866L, 712L, 164L, 262L, 137L, 123L, 601L, 541L, 840L, 202L, 396L, 424L, 949L, 957L, 487L, 920L, 125L, 873L, 984L, 519L, 550L, 338L, 967L, 828L, 209L, 150L, 780L, 764L, 716L, 946L, 637L, 416L, 6L, 547L, 350L, 666L, 880L, 159L, 134L, 579L, 449L, 831L, 171L, 29L, 285L, 104L, 754L, 955L, 391L, 662L, 709L, 422L, 869L, 744L, 982L, 740L, 694L, 22L, 393L, 23L, 224L, 902L, 538L, 185L, 806L, 536L, 251L, 452L, 910L, 932L, 208L, 140L, 871L, 571L, 247L, 857L, 803L, 681L, 162L, 670L, 400L, 195L, 532L, 483L, 429L, 591L, 292L, 410L, 177L, 399L, 852L, 346L, 778L, 888L, 239L, 595L, 609L, 66L, 277L, 574L, 190L, 586L, 48L, 749L, 790L, 342L, 842L, 560L, 600L, 627L, 269L, 455L, 233L, 788L, 129L, 34L, 743L, 849L, 58L, 242L, 948L, 420L, 238L, 566L, 771L, 127L, 820L, 267L, 72L, 312L, 773L, 648L, 708L, 970L, 977L, 926L, 794L, 191L, 229L, 983L, 119L, 167L, 110L, 535L, 375L, 435L, 462L, 146L, 675L, 818L, 516L, 596L, 258L, 655L, 349L, 448L, 271L, 498L, 275L, 214L, 656L, 660L, 898L, 411L, 263L, 199L, 151L, 266L, 922L, 965L, 677L, 81L, 248L, 50L, 210L, 377L, 900L, 978L, 155L, 570L, 401L, 721L, 693L, 73L, 447L, 304L, 552L, 291L, 314L, 748L, 332L, 998L, 959L, 384L, 15L, 544L, 472L, 826L, 145L, 444L, 958L, 359L, 337L, 812L, 729L, 582L, 569L, 175L, 488L, 442L, 510L, 767L, 59L, 245L, 358L, 5L, 394L, 934L, 109L, 833L, 556L, 705L, 590L, 703L, 237L, 8L, 227L, 352L, 968L, 682L, 147L, 482L, 520L, 355L, 217L, 216L, 633L, 654L, 940L, 885L, 526L, 356L, 599L, 84L, 397L, 294L, 781L, 762L, 9L, 950L, 718L, 915L, 856L, 253L, 52L, 577L, 546L, 514L, 489L, 801L, 758L, 201L, 259L, 184L, 173L, 659L, 731L, 924L, 974L, 83L, 668L, 254L, 855L, 624L, 187L, 765L, 834L, 186L, 95L, 56L, 565L, 878L, 688L, 793L, 3L, 465L, 336L, 418L, 503L, 212L, 494L, 555L, 492L, 451L, 334L, 715L, 647L, 775L, 398L, 751L, 523L, 802L, 276L, 299L, 174L, 725L, 329L, 207L, 795L, 909L, 192L, 12L, 249L, 379L, 972L, 204L, 892L, 695L, 661L, 283L, 613L, 479L, 311L, 979L, 944L, 272L, 371L, 769L, 261L, 477L, 887L, 144L, 650L, 383L, 502L, 746L, 567L, 69L, 558L, 370L, 55L, 517L, 432L, 11L, 324L, 711L, 530L, 250L, 805L, 373L, 87L, 128L, 652L, 568L, 906L, 515L, 631L, 679L, 423L, 728L, 629L, 941L, 357L, 702L, 440L, 170L, 734L, 390L, 481L, 468L, 901L, 851L, 403L, 446L, 92L, 700L, 188L, 585L, 710L, 527L, 572L, 456L, 846L, 645L, 222L, 289L, 219L, 114L, 102L, 575L, 691L, 433L, 47L, 231L, 54L, 931L, 862L, 376L, 863L, 593L, 987L, 877L, 154L, 64L, 326L, 961L, 226L, 74L, 220L, 484L, 638L, 495L, 438L, 825L, 78L, 14L, 44L, 182L, 835L, 561L, 714L, 722L, 493L, 592L, 443L, 738L, 839L, 244L, 807L, 213L, 97L, 405L, 341L, 139L, 870L, 409L, 42L, 753L, 426L, 124L, 460L, 975L, 4L, 176L, 369L, 696L, 500L, 143L, 497L, 138L, 947L, 351L, 163L, 845L, 40L, 557L, 103L, 301L, 607L, 713L, 457L, 630L, 305L, 554L, 971L, 458L, 392L, 777L, 772L, 854L, 617L, 779L, 997L, 30L, 374L, 883L, 181L, 388L, 841L, 750L, 860L, 782L, 643L, 819L, 945L, 70L, 521L, 723L, 733L, 387L, 18L, 365L, 17L, 273L, 223L, 252L, 51L, 94L, 632L, 309L, 692L, 417L, 287L, 929L, 372L, 317L, 676L, 318L, 473L, 976L, 534L, 1L, 118L, 757L, 814L, 916L, 345L, 917L, 623L, 809L, 663L, 620L, 194L, 531L, 153L, 580L, 890L, 148L, 180L, 964L, 35L, 31L, 797L, 543L, 933L, 993L, 954L, 152L, 39L, 832L, 126L, 86L, 79L, 280L, 116L, 232L, 562L, 306L, 717L, 230L, 995L, 512L, 274L, 628L, 907L, 24L, 824L, 605L, 404L, 789L, 813L, 382L, 881L, 864L, 335L, 889L, 459L, 478L, 671L, 876L, 597L, 850L, 999L, 653L, 88L, 60L, 25L, 791L, 256L, 313L, 36L, 307L, 548L, 735L, 707L, 756L, 218L, 895L, 62L, 290L, 323L, 837L, 981L, 474L, 942L, 969L, 157L, 904L, 298L, 414L, 684L, 486L, 604L, 464L, 774L, 584L, 641L, 136L, 602L )), test.inds = list(c(11L, 25L, 35L, 39L, 40L, 44L, 46L, 50L, 72L, 78L, 88L, 90L, 96L, 105L, 113L, 115L, 123L, 124L, 132L, 140L, 143L, 151L, 153L, 154L, 163L, 167L, 169L, 174L, 175L, 190L, 193L, 201L, 207L, 209L, 218L, 236L, 238L, 250L, 252L, 260L, 261L, 264L, 283L, 285L, 293L, 295L, 298L, 304L, 306L, 307L, 312L, 326L, 328L, 329L, 334L, 336L, 337L, 345L, 346L, 350L, 351L, 357L, 358L, 359L, 365L, 369L, 370L, 371L, 374L, 379L, 380L, 382L, 384L, 396L, 397L, 403L, 404L, 410L, 414L, 418L, 421L, 423L, 435L, 450L, 452L, 456L, 459L, 460L, 462L, 464L, 478L, 480L, 485L, 488L, 495L, 499L, 500L, 506L, 511L, 519L, 535L, 546L, 550L, 562L, 564L, 567L, 570L, 573L, 574L, 576L, 579L, 591L, 596L, 599L, 602L, 604L, 609L, 613L, 631L, 633L, 636L, 637L, 638L, 654L, 658L, 660L, 662L, 663L, 664L, 672L, 674L, 678L, 679L, 684L, 685L, 687L, 688L, 691L, 699L, 700L, 710L, 711L, 712L, 713L, 726L, 727L, 731L, 734L, 735L, 736L, 740L, 750L, 751L, 754L, 758L, 764L, 774L, 781L, 790L, 799L, 805L, 809L, 810L, 814L, 822L, 823L, 826L, 828L, 831L, 832L, 837L, 843L, 851L, 852L, 855L, 857L, 864L, 870L, 871L, 874L, 876L, 879L, 881L, 895L, 896L, 900L, 901L, 903L, 906L, 917L, 921L, 922L, 936L, 940L, 958L, 961L, 969L, 971L, 976L, 981L), c(1L, 3L, 14L, 16L, 18L, 19L, 22L, 28L, 30L, 31L, 34L, 41L, 47L, 48L, 52L, 55L, 56L, 58L, 73L, 75L, 81L, 84L, 95L, 100L, 104L, 114L, 128L, 129L, 136L, 137L, 141L, 144L, 146L, 150L, 152L, 170L, 176L, 178L, 180L, 183L, 184L, 188L, 196L, 199L, 205L, 212L, 221L, 223L, 229L, 237L, 239L, 246L, 247L, 248L, 249L, 251L, 254L, 256L, 259L, 265L, 267L, 272L, 277L, 281L, 282L, 284L, 286L, 288L, 289L, 290L, 291L, 300L, 301L, 321L, 322L, 324L, 335L, 340L, 356L, 373L, 386L, 390L, 392L, 393L, 398L, 399L, 401L, 407L, 417L, 424L, 432L, 433L, 444L, 448L, 455L, 457L, 477L, 483L, 486L, 493L, 494L, 497L, 498L, 515L, 520L, 524L, 530L, 531L, 536L, 544L, 549L, 554L, 557L, 561L, 566L, 571L, 575L, 584L, 587L, 592L, 597L, 621L, 625L, 634L, 643L, 652L, 653L, 655L, 656L, 667L, 669L, 694L, 697L, 705L, 708L, 723L, 737L, 738L, 746L, 749L, 752L, 757L, 763L, 765L, 775L, 776L, 778L, 786L, 788L, 789L, 791L, 793L, 797L, 801L, 807L, 817L, 833L, 835L, 840L, 842L, 854L, 856L, 862L, 866L, 867L, 869L, 873L, 888L, 890L, 893L, 898L, 904L, 907L, 908L, 916L, 919L, 924L, 932L, 934L, 943L, 945L, 946L, 947L, 949L, 954L, 959L, 960L, 963L, 965L, 968L, 977L, 978L, 986L, 988L, 990L, 993L, 995L, 997L, 998L, 999L), c(7L, 8L, 12L, 24L, 33L, 36L, 51L, 60L, 62L, 66L, 68L, 82L, 87L, 89L, 92L, 94L, 97L, 102L, 103L, 110L, 116L, 120L, 125L, 126L, 127L, 134L, 149L, 162L, 164L, 168L, 185L, 187L, 189L, 191L, 192L, 194L, 195L, 197L, 202L, 204L, 208L, 210L, 213L, 214L, 217L, 219L, 220L, 227L, 230L, 231L, 233L, 244L, 245L, 253L, 258L, 263L, 266L, 269L, 270L, 273L, 275L, 276L, 279L, 294L, 299L, 302L, 308L, 309L, 314L, 323L, 338L, 341L, 342L, 361L, 376L, 387L, 395L, 400L, 405L, 409L, 420L, 422L, 426L, 427L, 429L, 440L, 451L, 465L, 468L, 470L, 472L, 474L, 475L, 479L, 487L, 489L, 496L, 504L, 513L, 523L, 526L, 533L, 534L, 537L, 538L, 540L, 541L, 543L, 547L, 548L, 552L, 556L, 558L, 560L, 565L, 572L, 577L, 586L, 590L, 594L, 600L, 605L, 607L, 615L, 616L, 623L, 624L, 628L, 640L, 647L, 648L, 650L, 661L, 668L, 670L, 671L, 676L, 677L, 680L, 695L, 696L, 702L, 703L, 709L, 715L, 717L, 721L, 722L, 725L, 728L, 733L, 742L, 744L, 748L, 756L, 760L, 766L, 768L, 769L, 777L, 782L, 784L, 795L, 796L, 802L, 804L, 806L, 811L, 812L, 815L, 818L, 819L, 824L, 829L, 836L, 838L, 845L, 849L, 860L, 863L, 865L, 877L, 887L, 892L, 897L, 909L, 910L, 915L, 923L, 933L, 937L, 941L, 942L, 944L, 962L, 985L, 987L, 989L, 992L, 996L), c(4L, 5L, 6L, 9L, 10L, 15L, 17L, 20L, 23L, 27L, 29L, 42L, 53L, 54L, 59L, 64L, 69L, 70L, 74L, 79L, 83L, 86L, 109L, 118L, 119L, 138L, 139L, 142L, 145L, 147L, 148L, 155L, 157L, 159L, 161L, 171L, 173L, 177L, 181L, 182L, 186L, 206L, 216L, 222L, 224L, 226L, 232L, 234L, 242L, 243L, 262L, 271L, 274L, 280L, 287L, 292L, 303L, 305L, 311L, 313L, 315L, 317L, 318L, 330L, 331L, 332L, 343L, 349L, 352L, 355L, 363L, 372L, 375L, 377L, 383L, 385L, 388L, 389L, 391L, 394L, 408L, 411L, 416L, 438L, 442L, 443L, 446L, 447L, 449L, 458L, 469L, 471L, 473L, 481L, 482L, 484L, 490L, 492L, 502L, 503L, 510L, 512L, 514L, 516L, 517L, 521L, 522L, 525L, 527L, 532L, 555L, 568L, 569L, 580L, 582L, 585L, 589L, 593L, 595L, 598L, 601L, 603L, 612L, 617L, 620L, 626L, 627L, 629L, 630L, 632L, 641L, 645L, 646L, 659L, 666L, 675L, 681L, 682L, 689L, 690L, 692L, 693L, 698L, 707L, 714L, 716L, 718L, 719L, 720L, 729L, 743L, 745L, 747L, 753L, 762L, 767L, 771L, 772L, 773L, 779L, 780L, 785L, 794L, 803L, 813L, 820L, 821L, 825L, 834L, 839L, 841L, 846L, 847L, 850L, 872L, 878L, 880L, 883L, 885L, 889L, 902L, 920L, 925L, 926L, 929L, 931L, 948L, 950L, 955L, 957L, 964L, 967L, 970L, 972L, 974L, 975L, 979L, 982L, 983L, 984L), c(2L, 13L, 21L, 26L, 32L, 37L, 38L, 43L, 45L, 49L, 57L, 61L, 63L, 65L, 67L, 71L, 76L, 77L, 80L, 85L, 91L, 93L, 98L, 99L, 101L, 106L, 107L, 108L, 111L, 112L, 117L, 121L, 122L, 130L, 131L, 133L, 135L, 156L, 158L, 160L, 165L, 166L, 172L, 179L, 198L, 200L, 203L, 211L, 215L, 225L, 228L, 235L, 240L, 241L, 255L, 257L, 268L, 278L, 296L, 297L, 310L, 316L, 319L, 320L, 325L, 327L, 333L, 339L, 344L, 347L, 348L, 353L, 354L, 360L, 362L, 364L, 366L, 367L, 368L, 378L, 381L, 402L, 406L, 412L, 413L, 415L, 419L, 425L, 428L, 430L, 431L, 434L, 436L, 437L, 439L, 441L, 445L, 453L, 454L, 461L, 463L, 466L, 467L, 476L, 491L, 501L, 505L, 507L, 508L, 509L, 518L, 528L, 529L, 539L, 542L, 545L, 551L, 553L, 559L, 563L, 578L, 581L, 583L, 588L, 606L, 608L, 610L, 611L, 614L, 618L, 619L, 622L, 635L, 639L, 642L, 644L, 649L, 651L, 657L, 665L, 673L, 683L, 686L, 701L, 704L, 706L, 724L, 730L, 732L, 739L, 741L, 755L, 759L, 761L, 770L, 783L, 787L, 792L, 798L, 800L, 808L, 816L, 827L, 830L, 844L, 848L, 853L, 858L, 859L, 861L, 868L, 875L, 882L, 884L, 886L, 891L, 894L, 899L, 905L, 911L, 912L, 913L, 914L, 918L, 927L, 928L, 930L, 935L, 938L, 939L, 951L, 952L, 953L, 956L, 966L, 973L, 980L, 991L, 994L, 1000L)), group = integer(0)), weights = NULL, measures = list(list( id = "mmce", minimize = TRUE, properties = c("classif", "classif.multi", "req.pred", "req.truth"), fun = function (task, model, pred, feats, extra.args) { measureMMCE(pred$data$truth, pred$data$response) }, extra.args = list(), best = 0, worst = 1, name = "Mean misclassification error", note = "Defined as: mean(response != truth)", aggr = list( id = "test.mean", name = "Test mean", fun = function (task, perf.test, perf.train, measure, group, pred) mean(perf.test), properties = "req.test")), list(id = "mmce", minimize = TRUE, properties = c("classif", "classif.multi", "req.pred", "req.truth"), fun = function (task, model, pred, feats, extra.args) { measureMMCE(pred$data$truth, pred$data$response) }, extra.args = list(), best = 0, worst = 1, name = "Mean misclassification error", note = "Defined as: mean(response != truth)", aggr = list( id = "test.sd", name = "Test sd", fun = function (task, perf.test, perf.train, measure, group, pred) sd(perf.test), properties = "req.test"))), model = FALSE, extract = function (model) { }, show.info = FALSE) 19: mapply(fun2, ..., MoreArgs = more.args, SIMPLIFY = FALSE, USE.NAMES = FALSE) 18: parallelMap(doResampleIteration, seq_len(rin$desc$iters), level = "mlr.resample", more.args = more.args) 17: resample.fun(learner2, task, resampling, measures = measures, show.info = FALSE) 16: force(expr) 15: measureTime({ r = resample.fun(learner2, task, resampling, measures = measures, show.info = FALSE) }) 14: evalOptimizationState(learner, task, resampling, measures, par.set, NULL, ctrl, opt.path, show.info, dob, x, remove.nas, resample.fun) 13: tunerFitnFun(x, learner, task, resampling, measures, par.set, ctrl, opt.path, show.info, convertx, remove.nas, resample.fun, always.minimize = FALSE) 12: (function (x) { tunerFitnFun(x, learner, task, resampling, measures, par.set, ctrl, opt.path, show.info, convertx, remove.nas, resample.fun, always.minimize = FALSE) })(x = list(degree = 4L, scale = 4.52147865854204, offset = 0.899868381844135)) 11: do.call(getOptProblemFun(opt.problem), insert(list(x = x), getOptProblemMoreArgs(opt.problem))) 10: (function (x) { st = proc.time() y = do.call(getOptProblemFun(opt.problem), insert(list(x = x), getOptProblemMoreArgs(opt.problem))) user.extras = list() if (hasAttributes(y, "extras")) { user.extras = attr(y, "extras") y = setAttribute(y, "extras", NULL) } st = proc.time() - st list(y = y, time = st[3], user.extras = user.extras) })(dots[[1L]][[1L]]) 9: mapply(fun2, ..., MoreArgs = more.args, SIMPLIFY = FALSE, USE.NAMES = FALSE) 8: parallelMap(wrapFun, xs.trafo, level = "mlrMBO.feval", impute.error = if (is.null(imputeY)) NULL else identity) 7: evalTargetFun.OptState(opt.state, xs, extras) 6: evalMBODesign.OptState(opt.state) 5: mboTemplate.OptProblem(opt.problem) 4: mboTemplate(opt.problem) 3: mlrMBO::mbo(tff, design = control$mbo.design, learner = control$learner, control = mbo.control, show.info = FALSE) 2: sel.func(learner, task, resampling, measures, par.set, control, opt.path, show.info, resample.fun) 1: tuneParams(MLGPR, taskML, cv5, par.set = PSGPR, control = controlALL, measures = list(mmce, setAggregation(mmce, test.sd)))