CRAN Package Check Results for Package SuperLearner

Last updated on 2019-01-23 09:50:42 CET.

Check Details

Version: 2.0-24
Check: examples
Result: ERROR
    Running examples in ‘SuperLearner-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: SL.dbarts
    > ### Title: Discrete bayesian additive regression tree sampler
    > ### Aliases: SL.dbarts
    >
    > ### ** Examples
    >
    >
    > data(Boston, package = "MASS")
    > Y = Boston$medv
    > # Remove outcome from covariate dataframe.
    > X = Boston[, -14]
    >
    > set.seed(1)
    >
    > # Sample rows to speed up example.
    > row_subset = sample(nrow(X), 30)
    >
    > sl = SuperLearner(Y[row_subset], X[row_subset, ], family = gaussian(),
    + cvControl = list(V = 2), SL.library = c("SL.mean", "SL.dbarts"))
    Loading required package: dbarts
    
     *** caught segfault ***
    address 3ff0c692, cause 'memory not mapped'
    
    Traceback:
     1: .Call(C_dbarts_run, ptr, as.integer(numBurnIn), as.integer(numSamples))
     2: sampler$run(0L, control@n.burn, FALSE)
     3: dbarts::bart(x.train = X, y.train = Y, x.test = newX, sigest = sigest, sigdf = sigdf, sigquant = sigquant, k = k, power = power, base = base, binaryOffset = binaryOffset, weights = obsWeights, ntree = ntree, ndpost = ndpost, nskip = nskip, printevery = printevery, keepevery = keepevery, keeptrainfits = keeptrainfits, usequants = usequants, numcut = numcut, printcutoffs = printcutoffs, nthread = nthread, keepcall = keepcall, verbose = verbose)
     4: (function (Y, X, newX, family, obsWeights, id, sigest = NA, sigdf = 3, sigquant = 0.9, k = 2, power = 2, base = 0.95, binaryOffset = 0, ntree = 200, ndpost = 1000, nskip = 100, printevery = 100, keepevery = 1, keeptrainfits = TRUE, usequants = FALSE, numcut = 100, printcutoffs = 0, nthread = 1, keepcall = TRUE, verbose = FALSE, ...) { .SL.require("dbarts") model = dbarts::bart(x.train = X, y.train = Y, x.test = newX, sigest = sigest, sigdf = sigdf, sigquant = sigquant, k = k, power = power, base = base, binaryOffset = binaryOffset, weights = obsWeights, ntree = ntree, ndpost = ndpost, nskip = nskip, printevery = printevery, keepevery = keepevery, keeptrainfits = keeptrainfits, usequants = usequants, numcut = numcut, printcutoffs = printcutoffs, nthread = nthread, keepcall = keepcall, verbose = verbose) if (family$family == "gaussian") { pred = model$yhat.test.mean } if (family$family == "binomial") { pred = colMeans(stats::pnorm(model$yhat.test)) } fit = list(object = model) class(fit) = c("SL.dbarts") out = list(pred = pred, fit = fit) return(out)})(Y = c(32, 22.3, 13.4, 23.2, 22.6, 21.6, 12.7, 18.6, 22.2, 19, 24.1, 21.2, 18.7, 22.9, 7.2), X = list(crim = c(0.07875, 0.0459, 6.71772, 3.56868, 0.06724, 0.26938, 1.13081, 0.22876, 0.07151, 0.05497, 0.0795, 3.67367, 0.14932, 0.08829, 18.0846), zn = c(45, 52.5, 0, 0, 0, 0, 0, 0, 0, 0, 60, 0, 25, 12.5, 0), indus = c(3.44, 5.32, 18.1, 18.1, 3.24, 9.9, 8.14, 8.56, 4.49, 5.19, 1.69, 18.1, 5.13, 7.87, 18.1), chas = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), nox = c(0.437, 0.405, 0.713, 0.58, 0.46, 0.544, 0.538, 0.52, 0.449, 0.515, 0.411, 0.583, 0.453, 0.524, 0.679), rm = c(6.782, 6.315, 6.749, 6.437, 6.333, 6.266, 5.713, 6.405, 6.121, 5.985, 6.579, 6.312, 5.741, 6.012, 6.434), age = c(41.1, 45.6, 92.6, 75, 17.2, 82.8, 94.1, 85.4, 56.8, 45.4, 35.9, 51.9, 66.2, 66.6, 100), dis = c(3.7886, 7.3172, 2.3236, 2.8965, 5.2146, 3.2628, 4.233, 2.7147, 3.7476, 4.8122, 10.7103, 3.9917, 7.2254, 5.5605, 1.8347), rad = c(5L, 6L, 24L, 24L, 4L, 4L, 4L, 5L, 3L, 5L, 4L, 24L, 8L, 5L, 24L), tax = c(398, 293, 666, 666, 430, 304, 307, 384, 247, 224, 411, 666, 284, 311, 666), ptratio = c(15.2, 16.6, 20.2, 20.2, 16.9, 18.4, 21, 20.9, 18.5, 20.2, 18.3, 20.2, 19.7, 15.2, 20.2), black = c(393.87, 396.9, 0.32, 393.37, 375.21, 393.39, 360.17, 70.8, 395.15, 396.9, 370.78, 388.62, 395.11, 395.6, 27.25), lstat = c(6.68, 7.6, 17.44, 14.36, 7.34, 7.9, 22.6, 10.63, 8.44, 9.74, 5.49, 10.58, 13.15, 12.43, 29.05)), newX = list(crim = c(23.6482, 0.0837, 0.17899, 0.10574, 0.20608, 0.32543, 1.83377, 9.51363, 0.97617, 4.83567, 0.11432, 4.66883, 0.10008, 0.06047, 0.25356), zn = c(0, 45, 0, 0, 22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), indus = c(18.1, 3.44, 9.69, 27.74, 5.86, 21.89, 19.58, 18.1, 21.89, 18.1, 8.56, 18.1, 2.46, 2.46, 9.9), chas = c(0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), nox = c(0.671, 0.437, 0.585, 0.609, 0.431, 0.624, 0.605, 0.713, 0.624, 0.583, 0.52, 0.713, 0.488, 0.488, 0.544), rm = c(6.38, 7.185, 5.67, 5.983, 5.593, 6.431, 7.802, 6.728, 5.757, 5.905, 6.781, 5.976, 6.563, 6.153, 5.705), age = c(96.2, 38.9, 28.8, 98.8, 76.5, 98.8, 98.2, 94.1, 98.4, 53.2, 71.3, 87.9, 95.6, 68.8, 77.7), dis = c(1.3861, 4.5667, 2.7986, 1.8681, 7.9549, 1.8125, 2.0407, 2.4961, 2.346, 3.1523, 2.8561, 2.5806, 2.847, 3.2797, 3.945), rad = c(24L, 5L, 6L, 4L, 7L, 4L, 5L, 24L, 4L, 24L, 5L, 24L, 3L, 3L, 4L), tax = c(666, 398, 391, 711, 330, 437, 403, 666, 437, 666, 384, 666, 193, 193, 304), ptratio = c(20.2, 15.2, 19.2, 20.1, 19.1, 21.2, 14.7, 20.2, 21.2, 20.2, 20.9, 20.2, 17.8, 17.8, 18.4), black = c(396.9, 396.9, 393.29, 390.11, 372.49, 396.9, 389.61, 6.68, 262.76, 388.22, 395.58, 10.48, 396.9, 387.11, 396.42), lstat = c(23.69, 5.39, 17.6, 18.07, 12.5, 15.39, 1.92, 18.71, 17.31, 11.45, 7.67, 19.01, 5.68, 13.15, 11.5)), family = list(family = "gaussian", link = "identity", linkfun = function (mu) mu, linkinv = function (eta) eta, variance = function (mu) rep.int(1, length(mu)), dev.resids = function (y, mu, wt) wt * ((y - mu)^2), aic = function (y, n, mu, wt, dev) { nobs <- length(y) nobs * (log(dev/nobs * 2 * pi) + 1) + 2 - sum(log(wt)) }, mu.eta = function (eta) rep.int(1, length(eta)), initialize = expression({ n <- rep.int(1, nobs) if (is.null(etastart) && is.null(start) && is.null(mustart) && ((family$link == "inverse" && any(y == 0)) || (family$link == "log" && any(y <= 0)))) stop("cannot find valid starting values: please specify some") mustart <- y }), validmu = function (mu) TRUE, valideta = function (eta) TRUE), id = c(2L, 3L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 17L, 18L, 24L, 27L, 29L), obsWeights = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
     5: do.call(pred_fn, list(Y = tempOutcome, X = subset(tempLearn, select = tempWhichScreen[library$library$rowScreen[s], ], drop = FALSE), newX = subset(tempValid, select = tempWhichScreen[library$library$rowScreen[s], ], drop = FALSE), family = family, id = tempId, obsWeights = tempObsWeights))
     6: doTryCatch(return(expr), name, parentenv, handler)
     7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     8: tryCatchList(expr, classes, parentenv, handlers)
     9: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
    10: try(do.call(pred_fn, list(Y = tempOutcome, X = subset(tempLearn, select = tempWhichScreen[library$library$rowScreen[s], ], drop = FALSE), newX = subset(tempValid, select = tempWhichScreen[library$library$rowScreen[s], ], drop = FALSE), family = family, id = tempId, obsWeights = tempObsWeights)))
    11: FUN(X[[i]], ...)
    12: lapply(validRows, FUN = .crossValFUN, Y = Y, dataX = X, id = id, obsWeights = obsWeights, library = library, kScreen = kScreen, k = k, p = p, libraryNames = libraryNames)
    13: do.call("rbind", lapply(validRows, FUN = .crossValFUN, Y = Y, dataX = X, id = id, obsWeights = obsWeights, library = library, kScreen = kScreen, k = k, p = p, libraryNames = libraryNames))
    14: SuperLearner(Y[row_subset], X[row_subset, ], family = gaussian(), cvControl = list(V = 2), SL.library = c("SL.mean", "SL.dbarts"))
    An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86

Version: 2.0-24
Check: tests
Result: ERROR
     Running ‘testthat.R’ [142s/251s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(SuperLearner)
     Loading required package: nnls
     Super Learner
     Version: 2.0-24
     Package created on 2018-08-10
    
     >
     > test_check("SuperLearner")
    
     Call:
     SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
     xgb_grid$names), cvControl = list(V = 2))
    
    
     Risk Coef
     SL.mean_All 1.114574 0.6304922
     SL.xgboost_All 1.326036 0.1298382
     SL.xgb.1_All 1.240151 0.0000000
     SL.xgb.2_All 1.140147 0.0000000
     SL.xgb.3_All 1.224808 0.0000000
     SL.xgb.4_All 1.135035 0.2396697
     SL.xgb.5_All 1.458607 0.0000000
     SL.xgb.6_All 1.424464 0.0000000
     SL.xgb.7_All 1.455419 0.0000000
     SL.xgb.8_All 1.420126 0.0000000
     SL.xgb.9_All 1.492676 0.0000000
     SL.xgb.10_All 1.488743 0.0000000
     SL.xgb.11_All 1.492265 0.0000000
     SL.xgb.12_All 1.487902 0.0000000
     lasso-penalized linear regression with n=506, p=13
     At minimum cross-validation error (lambda=0.0068):
     -------------------------------------------------
     Nonzero coefficients: 12
     Cross-validation error (deviance): 23.55
     R-squared: 0.72
     Signal-to-noise ratio: 2.58
     Scale estimate (sigma): 4.853
     lasso-penalized logistic regression with n=506, p=13
     At minimum cross-validation error (lambda=0.0024):
     -------------------------------------------------
     Nonzero coefficients: 12
     Cross-validation error (deviance): 0.65
     R-squared: 0.49
     Signal-to-noise ratio: 0.95
     Prediction error: 0.128
     lasso-penalized linear regression with n=506, p=13
     At minimum cross-validation error (lambda=0.0238):
     -------------------------------------------------
     Nonzero coefficients: 11
     Cross-validation error (deviance): 24.01
     R-squared: 0.72
     Signal-to-noise ratio: 2.52
     Scale estimate (sigma): 4.900
     lasso-penalized logistic regression with n=506, p=13
     At minimum cross-validation error (lambda=0.0026):
     -------------------------------------------------
     Nonzero coefficients: 12
     Cross-validation error (deviance): 0.67
     R-squared: 0.48
     Signal-to-noise ratio: 0.93
     Prediction error: 0.128
    
     Call:
     SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
     "SL.biglasso"), cvControl = list(V = 2))
    
    
     Risk Coef
     SL.mean_All 84.77675 0.00326959
     SL.biglasso_All 23.67154 0.99673041
    
     Call:
     SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
     "SL.biglasso"), cvControl = list(V = 2))
    
    
     Risk Coef
     SL.mean_All 0.2382946 0.02063139
     SL.biglasso_All 0.1024175 0.97936861
     Y
     0 1
     66 34
     $grid
     NULL
    
     $names
     [1] "SL.randomForest_1"
    
     $base_learner
     [1] "SL.randomForest"
    
     $params
     $params$ntree
     [1] 100
    
    
     [1] "SL.randomForest_1" "X" "Y"
     [4] "create_rf" "data"
    
     Call:
     SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
     cvControl = list(V = 2))
    
    
     Risk Coef
     SL.randomForest_1_All 0.050813 1
     $grid
     mtry
     1 1
     2 4
     3 20
    
     $names
     [1] "SL.randomForest_1" "SL.randomForest_2" "SL.randomForest_3"
    
     $base_learner
     [1] "SL.randomForest"
    
     $params
     list()
    
    
     Call:
     SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
     cvControl = list(V = 2))
    
    
     Risk Coef
     SL.randomForest_1_All 0.05805154 0.2008891
     SL.randomForest_2_All 0.04498379 0.6278342
     SL.randomForest_3_All 0.04960304 0.1712766
     $grid
     alpha
     1 0.00
     2 0.25
     3 0.50
     4 0.75
     5 1.00
    
     $names
     [1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
     [5] "SL.glmnet_1"
    
     $base_learner
     [1] "SL.glmnet"
    
     $params
     list()
    
     [1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
     [5] "SL.glmnet_1"
    
     Call:
     SuperLearner(Y = Y, X = X, family = binomial(), SL.library = ls(learners),
     cvControl = list(V = 2), env = learners)
    
    
     Risk Coef
     SL.glmnet_0_All 0.04764882 0.5990378
     SL.glmnet_0.25_All 0.05046096 0.0000000
     SL.glmnet_0.5_All 0.04818493 0.4009622
     SL.glmnet_0.75_All 0.05707002 0.0000000
     SL.glmnet_1_All 0.06232678 0.0000000
    
     Call:
     SuperLearner(Y = Y, X = X_clean, family = binomial(), SL.library = c("SL.mean",
     svm$names), cvControl = list(V = 3))
    
    
     Risk Coef
     SL.mean_All 0.2355795 0.1272078
     SL.svm_polynomial_All 0.1746948 0.0000000
     SL.svm_radial_All 0.1662340 0.1502054
     SL.svm_sigmoid_All 0.1587918 0.7225868
    
     *** caught segfault ***
     address 3fe2c50c, cause 'memory not mapped'
    
     Traceback:
     1: .Call(C_dbarts_run, ptr, as.integer(numBurnIn), as.integer(numSamples))
     2: sampler$run(0L, control@n.burn, FALSE)
     3: dbarts::bart(x.train = X, y.train = Y, x.test = newX, sigest = sigest, sigdf = sigdf, sigquant = sigquant, k = k, power = power, base = base, binaryOffset = binaryOffset, weights = obsWeights, ntree = ntree, ndpost = ndpost, nskip = nskip, printevery = printevery, keepevery = keepevery, keeptrainfits = keeptrainfits, usequants = usequants, numcut = numcut, printcutoffs = printcutoffs, nthread = nthread, keepcall = keepcall, verbose = verbose)
     4: SuperLearner::SL.dbarts(Y_gaus, X, X, family = gaussian(), obsWeights = rep(1, nrow(X)))
     5: eval(code, test_env)
     6: eval(code, test_env)
     7: withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error)
     8: doTryCatch(return(expr), name, parentenv, handler)
     9: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     10: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     11: doTryCatch(return(expr), name, parentenv, handler)
     12: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])
     13: tryCatchList(expr, classes, parentenv, handlers)
     14: tryCatch(withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error), error = handle_fatal, skip = function(e) { })
     15: test_code(NULL, exprs, env)
     16: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     17: force(code)
     18: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter, { lister$start_file(basename(path)) source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap) end_context() })
     19: FUN(X[[i]], ...)
     20: lapply(paths, test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     21: force(code)
     22: with_reporter(reporter = current_reporter, results <- lapply(paths, test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap))
     23: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, wrap = wrap)
     24: test_dir(path = test_path, reporter = reporter, env = env, filter = filter, ..., stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, wrap = wrap)
     25: test_package_dir(package = package, test_path = test_path, filter = filter, reporter = reporter, ..., stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, wrap = wrap)
     26: test_check("SuperLearner")
     An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86