CRAN Package Check Results for Package CORElearn

Last updated on 2014-11-24 19:50:20.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9.43 18.12 24.16 42.28 OK
r-devel-linux-x86_64-debian-gcc 0.9.43 23.77 23.62 47.39 OK
r-devel-linux-x86_64-fedora-clang 0.9.43 84.62 OK
r-devel-linux-x86_64-fedora-gcc 0.9.43 86.24 OK
r-devel-osx-x86_64-clang 0.9.43 68.51 OK
r-devel-windows-ix86+x86_64 0.9.43 106.00 48.00 154.00 OK
r-patched-linux-x86_64 0.9.43 23.89 23.39 47.28 OK
r-patched-solaris-sparc 0.9.43 409.70 ERROR
r-patched-solaris-x86 0.9.43 152.90 ERROR
r-release-linux-ix86 0.9.43 31.18 31.88 63.05 OK
r-release-linux-x86_64 0.9.43 23.80 22.96 46.76 OK
r-release-osx-x86_64-mavericks 0.9.43 OK
r-release-osx-x86_64-snowleopard 0.9.43 OK
r-release-windows-ix86+x86_64 0.9.43 110.00 52.00 162.00 OK
r-oldrel-windows-ix86+x86_64 0.9.43 112.00 54.00 166.00 OK

Memtest notes: UBSAN UBSAN-gcc valgrind

Check Details

Version: 0.9.43
Check: examples
Result: ERROR
    Running examples in ‘CORElearn-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: CORElearn-package
    > ### Title: R port of CORElearn
    > ### Aliases: CORElearn-package CORElearn
    > ### Keywords: package datasets models regression nonlinear tree
    > ### multivariate loess classif
    >
    > ### ** Examples
    >
    > # load the package
    > library(CORElearn)
    > cat(versionCore(),"\n")
    CORElearn, Linux R version 0.9.43, built on Jul 27 2014 at 15:05:41 with OpenMP support
    >
    > # use iris data set
    >
    > # build random forests model with certain parameters
    > model <- CoreModel(Species ~ ., iris, model="rf",
    + selectionEstimator="MDL",minNodeWeightRF=5,rfNoTrees=100)
    > print(model)
    $modelID
    [1] 0
    
    $terms
    Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width
    attr(,"variables")
    list(Species, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)
    attr(,"factors")
     Sepal.Length Sepal.Width Petal.Length Petal.Width
    Species 0 0 0 0
    Sepal.Length 1 0 0 0
    Sepal.Width 0 1 0 0
    Petal.Length 0 0 1 0
    Petal.Width 0 0 0 1
    attr(,"term.labels")
    [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
    attr(,"order")
    [1] 1 1 1 1
    attr(,"response")
    [1] 1
    attr(,".Environment")
    <environment: 123b9e4>
    
    $class.lev
    [1] "setosa" "versicolor" "virginica"
    
    $model
    [1] "rf"
    
    $formula
    Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width
    <environment: 123b9e4>
    
    $noClasses
    [1] 3
    
    $priorClassProb
    [1] 0.3333333 0.3333333 0.3333333
    
    $avgTrainPrediction
    [1] 0
    
    $noNumeric
    [1] 4
    
    $noDiscrete
    [1] 1
    
    $discAttrNames
    [1] "Species"
    
    $discValNames
    $discValNames[[1]]
    [1] "setosa" "versicolor" "virginica"
    
    
    $numAttrNames
    [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
    
    $discmap
    [1] 1
    
    $nummap
    [1] 2 3 4 5
    
    $skipmap
    integer(0)
    
    attr(,"class")
    [1] "CoreModel"
    > plot(model, iris, graphType="prototypes")
    >
    > # prediction with node distribution
    > pred <- predict(model, iris, rfPredictClass=FALSE)
    > print(pred)
    $class
     [1] setosa setosa setosa setosa setosa setosa
     [7] setosa setosa setosa setosa setosa setosa
     [13] setosa setosa setosa setosa setosa setosa
     [19] setosa setosa setosa setosa setosa setosa
     [25] setosa setosa setosa setosa setosa setosa
     [31] setosa setosa setosa setosa setosa setosa
     [37] setosa setosa setosa setosa setosa setosa
     [43] setosa setosa setosa setosa setosa setosa
     [49] setosa setosa versicolor versicolor versicolor versicolor
     [55] versicolor versicolor versicolor versicolor versicolor versicolor
     [61] versicolor versicolor versicolor versicolor versicolor versicolor
     [67] versicolor versicolor versicolor versicolor virginica versicolor
     [73] versicolor versicolor versicolor versicolor versicolor virginica
     [79] versicolor versicolor versicolor versicolor versicolor versicolor
     [85] versicolor versicolor versicolor versicolor versicolor versicolor
     [91] versicolor versicolor versicolor versicolor versicolor versicolor
     [97] versicolor versicolor versicolor versicolor virginica virginica
    [103] virginica virginica virginica virginica versicolor virginica
    [109] virginica virginica virginica virginica virginica virginica
    [115] virginica virginica virginica virginica virginica versicolor
    [121] virginica virginica virginica virginica virginica virginica
    [127] virginica virginica virginica virginica virginica virginica
    [133] virginica versicolor versicolor virginica virginica virginica
    [139] virginica virginica virginica virginica virginica virginica
    [145] virginica virginica virginica virginica virginica virginica
    Levels: setosa versicolor virginica
    
    $probabilities
     setosa versicolor virginica
     [1,] 0.9992307692 0.0005769231 0.0001923077
     [2,] 0.9992307692 0.0005769231 0.0001923077
     [3,] 0.9992307692 0.0005769231 0.0001923077
     [4,] 0.9992307692 0.0005769231 0.0001923077
     [5,] 0.9992307692 0.0005769231 0.0001923077
     [6,] 0.9992307692 0.0005769231 0.0001923077
     [7,] 0.9992307692 0.0005769231 0.0001923077
     [8,] 0.9992307692 0.0005769231 0.0001923077
     [9,] 0.9992307692 0.0005769231 0.0001923077
     [10,] 0.9992307692 0.0005769231 0.0001923077
     [11,] 0.9992307692 0.0005769231 0.0001923077
     [12,] 0.9992307692 0.0005769231 0.0001923077
     [13,] 0.9992307692 0.0005769231 0.0001923077
     [14,] 0.9992307692 0.0005769231 0.0001923077
     [15,] 0.8707996297 0.1149900456 0.0142103247
     [16,] 0.9098187684 0.0829709068 0.0072103247
     [17,] 0.9992307692 0.0005769231 0.0001923077
     [18,] 0.9992307692 0.0005769231 0.0001923077
     [19,] 0.9098187684 0.0829709068 0.0072103247
     [20,] 0.9992307692 0.0005769231 0.0001923077
     [21,] 0.9992307692 0.0005769231 0.0001923077
     [22,] 0.9992307692 0.0005769231 0.0001923077
     [23,] 0.9992307692 0.0005769231 0.0001923077
     [24,] 0.9992307692 0.0005769231 0.0001923077
     [25,] 0.9992307692 0.0005769231 0.0001923077
     [26,] 0.9992307692 0.0005769231 0.0001923077
     [27,] 0.9992307692 0.0005769231 0.0001923077
     [28,] 0.9992307692 0.0005769231 0.0001923077
     [29,] 0.9992307692 0.0005769231 0.0001923077
     [30,] 0.9992307692 0.0005769231 0.0001923077
     [31,] 0.9992307692 0.0005769231 0.0001923077
     [32,] 0.9992307692 0.0005769231 0.0001923077
     [33,] 0.9992307692 0.0005769231 0.0001923077
     [34,] 0.9673191919 0.0280296296 0.0046511785
     [35,] 0.9992307692 0.0005769231 0.0001923077
     [36,] 0.9992307692 0.0005769231 0.0001923077
     [37,] 0.9673191919 0.0280296296 0.0046511785
     [38,] 0.9992307692 0.0005769231 0.0001923077
     [39,] 0.9992307692 0.0005769231 0.0001923077
     [40,] 0.9992307692 0.0005769231 0.0001923077
     [41,] 0.9992307692 0.0005769231 0.0001923077
     [42,] 0.9678021978 0.0285206460 0.0036771562
     [43,] 0.9992307692 0.0005769231 0.0001923077
     [44,] 0.9992307692 0.0005769231 0.0001923077
     [45,] 0.9992307692 0.0005769231 0.0001923077
     [46,] 0.9992307692 0.0005769231 0.0001923077
     [47,] 0.9992307692 0.0005769231 0.0001923077
     [48,] 0.9992307692 0.0005769231 0.0001923077
     [49,] 0.9992307692 0.0005769231 0.0001923077
     [50,] 0.9992307692 0.0005769231 0.0001923077
     [51,] 0.0044359933 0.9370048424 0.0585591643
     [52,] 0.0044359933 0.9468084138 0.0487555929
     [53,] 0.0037314479 0.7651157792 0.2311527730
     [54,] 0.0009555556 0.9640509116 0.0349935328
     [55,] 0.0044359933 0.9361000805 0.0594639262
     [56,] 0.0034551321 0.9644810056 0.0320638624
     [57,] 0.0044359933 0.8847340470 0.1108299597
     [58,] 0.0178021978 0.9450267765 0.0371710257
     [59,] 0.0044359933 0.9485256757 0.0470383309
     [60,] 0.0178021978 0.9367281654 0.0454696368
     [61,] 0.0178021978 0.9322911298 0.0499066724
     [62,] 0.0044359933 0.9646125195 0.0309514872
     [63,] 0.0044359933 0.9527746902 0.0427893165
     [64,] 0.0044359933 0.9550232337 0.0405407730
     [65,] 0.0034551321 0.9667131484 0.0298317195
     [66,] 0.0044359933 0.9480465091 0.0475174976
     [67,] 0.0034551321 0.9591387437 0.0374061243
     [68,] 0.0044359933 0.9693297814 0.0262342253
     [69,] 0.0044359933 0.9344943941 0.0610696126
     [70,] 0.0034551321 0.9647687040 0.0317761639
     [71,] 0.0000000000 0.2110933225 0.7889066775
     [72,] 0.0044359933 0.9615024004 0.0340616063
     [73,] 0.0037314479 0.7706424232 0.2256261290
     [74,] 0.0044359933 0.9578357337 0.0377282730
     [75,] 0.0044359933 0.9485256757 0.0470383309
     [76,] 0.0044359933 0.9457131757 0.0498508309
     [77,] 0.0037314479 0.8153793641 0.1808891880
     [78,] 0.0018862915 0.4812353638 0.5168783447
     [79,] 0.0044359933 0.9627375195 0.0328264872
     [80,] 0.0034551321 0.9653937040 0.0311511639
     [81,] 0.0009555556 0.9640509116 0.0349935328
     [82,] 0.0009555556 0.9640509116 0.0349935328
     [83,] 0.0044359933 0.9693297814 0.0262342253
     [84,] 0.0018862915 0.5084237205 0.4896899880
     [85,] 0.0392307692 0.9214530932 0.0393161375
     [86,] 0.0044359933 0.8976631526 0.0979008541
     [87,] 0.0044359933 0.9444750805 0.0510889262
     [88,] 0.0044359933 0.9565812313 0.0389827754
     [89,] 0.0034551321 0.9667131484 0.0298317195
     [90,] 0.0009555556 0.9640509116 0.0349935328
     [91,] 0.0009555556 0.9640509116 0.0349935328
     [92,] 0.0044359933 0.9586899004 0.0368741063
     [93,] 0.0044359933 0.9673853369 0.0281786698
     [94,] 0.0178021978 0.9450267765 0.0371710257
     [95,] 0.0034551321 0.9667131484 0.0298317195
     [96,] 0.0034551321 0.9673381484 0.0292067195
     [97,] 0.0034551321 0.9673381484 0.0292067195
     [98,] 0.0044359933 0.9616000805 0.0339639262
     [99,] 0.0178021978 0.9450267765 0.0371710257
    [100,] 0.0034551321 0.9673381484 0.0292067195
    [101,] 0.0000000000 0.0219424873 0.9780575127
    [102,] 0.0000000000 0.0453141990 0.9546858010
    [103,] 0.0000000000 0.0112671210 0.9887328790
    [104,] 0.0000000000 0.0147373591 0.9852626409
    [105,] 0.0000000000 0.0099754544 0.9900245456
    [106,] 0.0000000000 0.0112671210 0.9887328790
    [107,] 0.0178021978 0.7082743911 0.2739234111
    [108,] 0.0000000000 0.0160290258 0.9839709742
    [109,] 0.0000000000 0.0213989238 0.9786010762
    [110,] 0.0063636364 0.0179960588 0.9756403049
    [111,] 0.0000000000 0.0249944354 0.9750055646
    [112,] 0.0000000000 0.0141659306 0.9858340694
    [113,] 0.0000000000 0.0112671210 0.9887328790
    [114,] 0.0000000000 0.1324084080 0.8675915920
    [115,] 0.0000000000 0.0411237228 0.9588762772
    [116,] 0.0000000000 0.0219424873 0.9780575127
    [117,] 0.0000000000 0.0147373591 0.9852626409
    [118,] 0.0063636364 0.0179960588 0.9756403049
    [119,] 0.0000000000 0.0141242639 0.9858757361
    [120,] 0.0018862915 0.6206249591 0.3774887494
    [121,] 0.0000000000 0.0142341540 0.9857658460
    [122,] 0.0000000000 0.1758655129 0.8241344871
    [123,] 0.0000000000 0.0112671210 0.9887328790
    [124,] 0.0000000000 0.1159274593 0.8840725407
    [125,] 0.0000000000 0.0166091540 0.9833908460
    [126,] 0.0000000000 0.0189960588 0.9810039412
    [127,] 0.0000000000 0.1673676315 0.8326323685
    [128,] 0.0000000000 0.1047651216 0.8952348784
    [129,] 0.0000000000 0.0099754544 0.9900245456
    [130,] 0.0018862915 0.4292972484 0.5688164601
    [131,] 0.0000000000 0.0126004544 0.9873995456
    [132,] 0.0063636364 0.0179960588 0.9756403049
    [133,] 0.0000000000 0.0099754544 0.9900245456
    [134,] 0.0018862915 0.5349845661 0.4631291424
    [135,] 0.0018862915 0.5082363768 0.4898773317
    [136,] 0.0000000000 0.0112671210 0.9887328790
    [137,] 0.0000000000 0.0259424873 0.9740575127
    [138,] 0.0000000000 0.0147373591 0.9852626409
    [139,] 0.0000000000 0.1879358134 0.8120641866
    [140,] 0.0000000000 0.0112671210 0.9887328790
    [141,] 0.0000000000 0.0106421210 0.9893578790
    [142,] 0.0000000000 0.0143190691 0.9856809309
    [143,] 0.0000000000 0.0453141990 0.9546858010
    [144,] 0.0000000000 0.0142341540 0.9857658460
    [145,] 0.0000000000 0.0166091540 0.9833908460
    [146,] 0.0000000000 0.0106421210 0.9893578790
    [147,] 0.0000000000 0.0636915083 0.9363084917
    [148,] 0.0000000000 0.0099754544 0.9900245456
    [149,] 0.0000000000 0.0266091540 0.9733908460
    [150,] 0.0000000000 0.0587061404 0.9412938596
    
    >
    > # Model evaluation
    > mEval <- modelEval(model, iris[["Species"]], pred$class, pred$prob)
    > print(mEval)
    $accuracy
    [1] 0.96
    
    $averageCost
    [1] 0.04
    
    $informationScore
    [1] 1.469656
    
    $AUC
    [1] 0.9984
    
    $predictionMatrix
     setosa versicolor virginica
    setosa 50 0 0
    versicolor 0 48 2
    virginica 0 4 46
    
    $sensitivity
    [1] 0
    
    $specificity
    [1] 0
    
    $brierScore
    [1] 0.04262617
    
    $kappa
    [1] 0.94
    
    $precision
    [1] 0
    
    $recall
    [1] 0
    
    $Fmeasure
    [1] 0
    
    $Gmean
    [1] 0
    
    $KS
    [1] 0
    
    $TPR
    [1] 0
    
    $FPR
    [1] 0
    
    >
    > # Clean up. otherwise the memory is still taken
    > destroyModels(model) # clean up
    >
    >
    > # evaluate features in given data set with selected method
    > estReliefF <- attrEval(Species ~ ., iris,
    + estimator="ReliefFexpRank", ReliefIterations=30)
    
     *** caught segfault ***
    address 0, cause 'memory not mapped'
    
     *** caught segfault ***
    address 1c00, cause 'memory not mapped'
    
    Traceback:
     1: .C("estimateCore", noInst = aux$noInst, noDiscrete = ncol(discdata), noDiscreteValues = as.integer(discnumvalues), discreteData = as.integer(discdata), noNumeric = ncol(numdata), numericData = as.double(numdata), costs = as.double(costMatrix), discAttrNames = as.character(discAttrNames), discValNames = as.character(discValCompressed), numAttrNames = as.character(numAttrNames), numOptions = length(options), optionsName = names(options),
    Traceback:
     optionsVal = options, selEst = estIndex, estDisc = double(ncol(discdata)), 1: estNum = double(ncol(numdata)), NAOK = TRUE, PACKAGE = "CORElearn").C("estimateCore", noInst = aux$noInst, noDiscrete = ncol(discdata),
     noDiscreteValues = as.integer(discnumvalues), discreteData = as.integer(discdata), 2: noNumeric = ncol(numdata), numericData = as.double(numdata), attrEval(Species ~ ., iris, estimator = "ReliefFexpRank", ReliefIterations = 30) costs = as.double(costMatrix), discAttrNames = as.character(discAttrNames),
     discValNames = as.character(discValCompressed), numAttrNames = as.character(numAttrNames), aborting ...
     numOptions = length(options), optionsName = names(options), optionsVal = options, selEst = estIndex, estDisc = double(ncol(discdata)), estNum = double(ncol(numdata)), NAOK = TRUE, PACKAGE = "CORElearn")
     2: attrEval(Species ~ ., iris, estimator = "ReliefFexpRank", ReliefIterations = 30)
    aborting ...
    rm: Unable to remove directory /tmp/RtmpHTayiZ: No such file or directory
Flavor: r-patched-solaris-sparc

Version: 0.9.43
Check: examples
Result: ERROR
    Running examples in ‘CORElearn-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: testCore
    > ### Title: Verification of the CORElearn installation
    > ### Aliases: testCore testCoreClass testCoreAttrEval testCoreReg
    > ### testCoreOrdEval testCoreNA testCoreRPORT testCoreRand allTests
    > ### Keywords: classif
    >
    > ### ** Examples
    >
    > allTests() # run all tests and generate an error, if any of the tests fails
    testCoreClass() : OK
    Comparison FAILED for testCoreAttrEval/estReliefF/stored
    stored
     [1] 0.074131090 0.085205400 0.050185750 0.026567790 0.065219700
     [6] 0.030826570 -0.008773201 0.100277400 0.082634870 -0.004818440
    difference
     a1 a2 a3 a4 a5 a6 a7 x1
    2.271002 1.679700 2.541132 1.474514 1.449294 2.834697 2.525920 9.554145
     x2 x3
    8.444995 8.808747
    estReliefF0
     a1 a2 a3 a4 a5 a6 a7 x1
    2.345133 1.764905 2.591318 1.501082 1.514514 2.865524 2.517147 9.654422
     x2 x3
    8.527630 8.803928
    comparison FAILED
    testCoreAttrEval() : FAIL
    testCoreReg() : OK
    testCoreOrdEval() : OK
    testCoreNA() : OK
    testCoreRPORT() : OK
    testCoreRand() : OK
    Error in outputResult("allTests", result, "", continue = FALSE) :
     Test FAILED: allTests
    Calls: allTests -> outputResult
    Execution halted
Flavor: r-patched-solaris-x86