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Generalized linear models with elastic net regularization. Calls glmnet::glmnet() from package glmnet.

Initial parameter values

  • family is set to "cox" and cannot be changed.

Prediction types

This learner returns three prediction types:

  1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using glmnet::predict.coxnet().

  2. crank: same as lp.

  3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using glmnet::survfit.coxnet(). Parameters stype and ctype relate to how lp predictions are transformed into survival predictions and are described in survival::survfit.coxph(). By default the Breslow estimator is used for computing the baseline hazard.

Caution: This learner is different to learners calling glmnet::cv.glmnet() in that it does not use the internal optimization of parameter lambda. Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning). When lambda is tuned, the glmnet will be trained for each tuning iteration. While fitting the whole path of lambdas would be more efficient, as is done by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time (using parameter s) is currently not supported in mlr3 (at least not in efficient manner). Tuning the s parameter is, therefore, currently discouraged.

When the data are i.i.d. and efficiency is key, we recommend using the respective auto-tuning counterpart in mlr_learners_surv.cv_glmnet(). However, in some situations this is not applicable, usually when data are imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires custom resampling strategies (blocked design, stratification).

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in glmnet::glmnet(). During prediction, the offset column from the test set is used only if use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.coxnet(). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.glmnet")

Meta Information

  • Task type: “surv”

  • Predict Types: “crank”, “distr”, “lp”

  • Feature Types: “logical”, “integer”, “numeric”

  • Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet

Parameters

IdTypeDefaultLevelsRange
alignmentcharacterlambdalambda, fraction-
alphanumeric1\([0, 1]\)
bignumeric9.9e+35\((-\infty, \infty)\)
devmaxnumeric0.999\([0, 1]\)
dfmaxinteger-\([0, \infty)\)
epsnumeric1e-06\([0, 1]\)
epsnrnumeric1e-08\([0, 1]\)
exactlogicalFALSETRUE, FALSE-
excludeuntyped--
exmxnumeric250\((-\infty, \infty)\)
fdevnumeric1e-05\([0, 1]\)
gammauntyped--
groupedlogicalTRUETRUE, FALSE-
interceptlogicalTRUETRUE, FALSE-
keeplogicalFALSETRUE, FALSE-
lambdauntyped--
lambda.min.rationumeric-\([0, 1]\)
lower.limitsuntyped-Inf-
maxitinteger100000\([1, \infty)\)
mnlaminteger5\([1, \infty)\)
mxitinteger100\([1, \infty)\)
mxitnrinteger25\([1, \infty)\)
nlambdainteger100\([1, \infty)\)
use_pred_offsetlogicalTRUETRUE, FALSE-
parallellogicalFALSETRUE, FALSE-
penalty.factoruntyped--
pmaxinteger-\([0, \infty)\)
pminnumeric1e-09\([0, 1]\)
precnumeric1e-10\((-\infty, \infty)\)
predict.gammanumericgamma.1se\((-\infty, \infty)\)
relaxlogicalFALSETRUE, FALSE-
snumeric0.01\([0, \infty)\)
standardizelogicalTRUETRUE, FALSE-
threshnumeric1e-07\([0, \infty)\)
trace.itinteger0\([0, 1]\)
type.logisticcharacterNewtonNewton, modified.Newton-
type.multinomialcharacterungroupedungrouped, grouped-
upper.limitsuntypedInf-
stypeinteger2\([1, 2]\)
ctypeinteger-\([1, 2]\)

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01 .

See also

Author

be-marc

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGlmnet

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerSurvGlmnet$selected_features(lambda = NULL)

Arguments

lambda

(numeric(1))
Custom lambda, defaults to the active lambda depending on parameter set.

Returns

(character()) of feature names.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("surv.glmnet")
print(learner)
#> <LearnerSurvGlmnet:surv.glmnet>: Regularized Generalized Linear Model
#> * Model: -
#> * Parameters: use_pred_offset=TRUE
#> * Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
#> * Predict Types:  [crank], distr, lp
#> * Feature Types: logical, integer, numeric
#> * Properties: offset, selected_features, weights

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> $model
#> 
#> Call:  (if (cv) glmnet::cv.glmnet else glmnet::glmnet)(x = data, y = target,      family = "cox") 
#> 
#>    Df  %Dev   Lambda
#> 1   0  0.00 0.181200
#> 2   1  0.56 0.165100
#> 3   2  1.32 0.150500
#> 4   2  2.14 0.137100
#> 5   3  5.59 0.124900
#> 6   3  8.12 0.113800
#> 7   3 10.04 0.103700
#> 8   3 11.54 0.094500
#> 9   3 12.74 0.086100
#> 10  3 13.74 0.078450
#> 11  3 14.55 0.071480
#> 12  3 15.23 0.065130
#> 13  5 15.84 0.059350
#> 14  5 16.41 0.054070
#> 15  5 16.90 0.049270
#> 16  5 17.30 0.044890
#> 17  5 17.65 0.040910
#> 18  6 17.95 0.037270
#> 19  6 18.21 0.033960
#> 20  6 18.42 0.030940
#> 21  6 18.61 0.028190
#> 22  6 18.76 0.025690
#> 23  6 18.90 0.023410
#> 24  6 19.01 0.021330
#> 25  6 19.10 0.019430
#> 26  6 19.18 0.017710
#> 27  6 19.25 0.016130
#> 28  6 19.30 0.014700
#> 29  6 19.35 0.013390
#> 30  6 19.39 0.012200
#> 31  6 19.43 0.011120
#> 32  6 19.45 0.010130
#> 33  6 19.48 0.009232
#> 34  6 19.50 0.008412
#> 35  6 19.51 0.007665
#> 36  6 19.53 0.006984
#> 37  6 19.54 0.006364
#> 38  6 19.55 0.005798
#> 39  6 19.56 0.005283
#> 40  6 19.56 0.004814
#> 41  6 19.57 0.004386
#> 42  6 19.58 0.003997
#> 43  6 19.58 0.003641
#> 44  6 19.58 0.003318
#> 
#> $x
#>        age los revasc revascdays stchange sysbp
#>   [1,]  28   9      0        180        1   107
#>   [2,]  32   5      1          0        1   121
#>   [3,]  33   2      0          2        0   150
#>   [4,]  35  10      1          9        0   106
#>   [5,]  35   2      0        180        0   121
#>   [6,]  37   9      0        180        1   151
#>   [7,]  38   2      0        115        0   150
#>   [8,]  36   1      0        180        1   155
#>   [9,]  38  12      1          8        1   120
#>  [10,]  36   5      1          0        1   115
#>  [11,]  38  16      1         10        0   160
#>  [12,]  38  12      1         11        1    92
#>  [13,]  40  12      1          9        0   153
#>  [14,]  42   3      1          1        1   130
#>  [15,]  37   1      1          0        1   146
#>  [16,]  40   2      1          1        1   148
#>  [17,]  42   2      0        180        1   100
#>  [18,]  38   5      1          3        0   125
#>  [19,]  42   2      0          2        0   140
#>  [20,]  40   6      0        180        1   138
#>  [21,]  40  11      1         10        1   120
#>  [22,]  42   2      0        180        0   100
#>  [23,]  43   3      1          0        1   100
#>  [24,]  40   1      1          0        1   145
#>  [25,]  42   4      0        180        0   162
#>  [26,]  42  15      1         13        1   125
#>  [27,]  43   2      1          1        1   116
#>  [28,]  42   2      0        180        1   124
#>  [29,]  44   5      1          1        0   170
#>  [30,]  45   3      0        180        1   154
#>  [31,]  41  10      1          8        0   150
#>  [32,]  44   3      0        180        0   141
#>  [33,]  41  13      1          1        0   140
#>  [34,]  45   9      1          7        0   110
#>  [35,]  45   6      0        180        1   170
#>  [36,]  41   5      1          4        1   141
#>  [37,]  44   2      1          1        1   150
#>  [38,]  45   2      0        180        1   140
#>  [39,]  46  15      0        180        0   120
#>  [40,]  46   2      1          1        0   126
#>  [41,]  47   4      1          3        0   118
#>  [42,]  48  15      0        180        1   160
#>  [43,]  45   3      0        150        0   130
#>  [44,]  43  10      0        180        0   185
#>  [45,]  47   4      1          3        1   160
#>  [46,]  43   3      1          0        1   124
#>  [47,]  45   8      1          0        1   117
#>  [48,]  45   5      0          5        0   141
#>  [49,]  46   2      1          1        1   122
#>  [50,]  46   6      1          0        1   100
#>  [51,]  44   4      1          0        1   114
#>  [52,]  45   5      0        180        1   190
#>  [53,]  46   4      0        180        1   121
#>  [54,]  46  15      0        180        1   120
#>  [55,]  47   3      1          1        1   120
#>  [56,]  48  12      1         11        0   200
#>  [57,]  47   9      1          6        0   170
#>  [58,]  46   3      1          0        1   119
#>  [59,]  47  10      0         10        1   140
#>  [60,]  47   7      0        180        0   145
#>  [61,]  50   4      1          1        0   125
#>  [62,]  50   6      1          2        1   140
#>  [63,]  49   7      1          7        1   110
#>  [64,]  46   9      1          9        1   122
#>  [65,]  50   7      0        180        1   110
#>  [66,]  49   2      0          2        0   105
#>  [67,]  51   1      0          1        1   145
#>  [68,]  49  15      1         11        1   160
#>  [69,]  46   6      1          0        1   156
#>  [70,]  52   2      0        180        1   170
#>  [71,]  50   7      1          0        1    92
#>  [72,]  51   3      1          2        0   113
#>  [73,]  50   1      1          0        0   150
#>  [74,]  50   9      0        180        0   130
#>  [75,]  49   7      1          4        1    90
#>  [76,]  47   6      0        180        1   162
#>  [77,]  51   8      0        180        1   140
#>  [78,]  46   3      0        180        1   120
#>  [79,]  46   1      1          1        0   145
#>  [80,]  50   4      1          1        0   150
#>  [81,]  48  17      1         10        0   111
#>  [82,]  47   2      1          1        0   110
#>  [83,]  52   4      1          4        0   152
#>  [84,]  53   5      0        180        1   140
#>  [85,]  53   5      0         77        0   159
#>  [86,]  53   7      1          0        0   199
#>  [87,]  54   6      1          3        0   129
#>  [88,]  51   3      1          1        0   140
#>  [89,]  50  10      1          6        0   122
#>  [90,]  50  14      1         13        0   170
#>  [91,]  53   8      1          7        0   160
#>  [92,]  48   3      1          2        0   150
#>  [93,]  51  25      1          1        0   202
#>  [94,]  53   4      0          4        0   140
#>  [95,]  48   6      0        180        0   160
#>  [96,]  51  13      0         99        1   160
#>  [97,]  54   9      1          0        1   138
#>  [98,]  49  16      0         16        0   125
#>  [99,]  55   3      1          1        0   150
#> [100,]  54  23      1         10        0   131
#> [101,]  52   7      1          2        0   154
#> [102,]  55   6      1          2        1   114
#> [103,]  54   9      1          1        0   130
#> [104,]  55   4      1          2        0   150
#> [105,]  52   4      0        180        1   180
#> [106,]  51  13      1         11        0   145
#> [107,]  50   5      1          4        1   150
#> [108,]  52   4      0        180        0   183
#> [109,]  49   6      1          0        1   130
#> [110,]  49   1      0          1        1   110
#> [111,]  50   7      1          1        0   156
#> [112,]  53   9      0          9        1    95
#> [113,]  55   2      0          2        0   145
#> [114,]  54   1      0        180        0   162
#> [115,]  54   7      1          0        1   100
#> [116,]  56   3      0        180        1   193
#> [117,]  56   2      0        180        0   132
#> [118,]  55   5      1          4        1   120
#> [119,]  52   8      0        180        0   119
#> [120,]  53  18      1          9        1   150
#> [121,]  54   3      0        180        1   180
#> [122,]  55   6      0        180        0   170
#> [123,]  52  16      0         16        0   152
#> [124,]  53  10      1          9        0   172
#> [125,]  52  16      1         14        0   170
#> [126,]  53  15      0         15        1    90
#> [127,]  55   6      0        180        1   100
#> [128,]  55   6      1          5        1   138
#> [129,]  54  12      1          0        1   190
#> [130,]  55   1      0          2        0   130
#> [131,]  57   3      0          3        0   120
#> [132,]  52   9      1          3        0   170
#> [133,]  57   5      1          3        1   138
#> [134,]  57   1      0        180        1   156
#> [135,]  57   1      0          1        1   100
#> [136,]  56   4      1          0        1   140
#> [137,]  52   2      0        180        0   140
#> [138,]  55  11      1          7        0   104
#> [139,]  52  15      1         14        0   130
#> [140,]  56  14      1         11        0   130
#> [141,]  58   8      0          8        1   130
#> [142,]  55   3      1          1        1   156
#> [143,]  53  21      1         13        1   130
#> [144,]  53  15      1         10        1   130
#> [145,]  54  17      1          8        1   227
#> [146,]  55   9      1          2        1   147
#> [147,]  55  13      0        166        1   140
#> [148,]  54  23      1          8        0   120
#> [149,]  57   4      1          2        1   185
#> [150,]  53   4      0        147        1   145
#> [151,]  53   7      1          0        1   120
#> [152,]  57  11      1         10        1   129
#> [153,]  54   7      1          0        1   141
#> [154,]  56   4      0          4        0   164
#> [155,]  59  15      1         10        0   140
#> [156,]  58   1      1          1        1   200
#> [157,]  55   5      1          0        0   140
#> [158,]  55   2      0          2        0   106
#> [159,]  59   9      1          1        1   125
#> [160,]  57   1      0        180        0   148
#> [161,]  60  11      1          9        0   106
#> [162,]  59   3      0        180        0   120
#> [163,]  58   4      1          0        1   160
#> [164,]  57   2      0          2        1   120
#> [165,]  60   5      1          1        0   138
#> [166,]  57   5      0        180        1   130
#> [167,]  58  11      1          9        1   124
#> [168,]  55   5      1          0        1   160
#> [169,]  57  10      1          9        0   103
#> [170,]  59   6      1          0        1   140
#> [171,]  59   5      0        180        1   155
#> [172,]  59   4      1          0        1   152
#> [173,]  58  26      1          0        1   189
#> [174,]  61   9      0          9        1   160
#> [175,]  58   4      1          3        0   120
#> [176,]  59   2      1          1        0   140
#> [177,]  58   8      0        161        1   140
#> [178,]  61   9      1          8        0   150
#> [179,]  61   3      1          2        1   102
#> [180,]  58   1      0          1        1   100
#> [181,]  61  20      1         13        0   130
#> [182,]  57  13      1         10        0   110
#> [183,]  57   4      1          3        0   138
#> [184,]  61   3      0         17        0   143
#> [185,]  56  14      0         45        0   130
#> [186,]  57   3      1          2        0   120
#> [187,]  59   9      1          0        1    80
#> [188,]  58  11      0        172        1   135
#> [189,]  55   9      1          7        1   135
#> [190,]  61  13      1         12        1   130
#> [191,]  59  11      1          8        1   190
#> [192,]  57  15      1         13        1   110
#> [193,]  59   5      1          2        0   182
#> [194,]  58   5      1          1        1   135
#> [195,]  59  10      0        180        0   160
#> [196,]  61   8      0         77        0   120
#> [197,]  61  13      0         13        0   210
#> [198,]  62   7      1          2        1   180
#> [199,]  57   3      1          0        0   100
#> [200,]  61  18      0        170        0   140
#> [201,]  61  28      1          7        0   133
#> [202,]  58   8      1          3        1   150
#> [203,]  57   7      0        169        0   180
#> [204,]  61   7      0          7        1   150
#> [205,]  59  13      1          2        0   198
#> [206,]  57  12      1          9        1   120
#> [207,]  60  17      1          8        1   140
#> [208,]  62   4      1          3        0   173
#> [209,]  58   2      0         30        0   202
#> [210,]  59   1      0        180        0   155
#> [211,]  59  16      1          9        1   133
#> [212,]  63   6      0         28        1   120
#> [213,]  61  13      0         13        0   120
#> [214,]  57  18      1          9        1    93
#> [215,]  61   5      0          5        1   160
#> [216,]  58  11      1          9        0   179
#> [217,]  57   2      1          1        0   159
#> [218,]  62   1      1          0        1   172
#> [219,]  58   7      0        180        1   150
#> [220,]  63   3      1          1        0   180
#> [221,]  63   1      0        180        1   130
#> [222,]  61   7      0        180        0   135
#> [223,]  62   3      0        180        1   105
#> [224,]  63   4      0        180        1   190
#> [225,]  64   4      0        180        0   130
#> [226,]  63   4      1          1        0   155
#> [227,]  59   8      0        180        1   140
#> [228,]  61   9      1          9        1   150
#> [229,]  58   9      1          9        0   110
#> [230,]  62   7      0          7        0   150
#> [231,]  59   1      0         22        1   162
#> [232,]  59   4      0        180        0   196
#> [233,]  59   5      1          1        0   148
#> [234,]  63   1      0          1        0   130
#> [235,]  62   6      0        180        0   170
#> [236,]  61  15      1         13        0   170
#> [237,]  60   3      0          3        0   168
#> [238,]  64  10      1          9        0   160
#> [239,]  63  12      1         10        0   200
#> [240,]  59  10      0        180        1   130
#> [241,]  60   8      0         17        1   130
#> [242,]  64  12      1         11        0   160
#> [243,]  64   6      1          0        1   140
#> [244,]  63  10      1          0        1   148
#> [245,]  63  14      1          9        0   123
#> [246,]  65  36      1         11        0   140
#> [247,]  66   3      1          1        0   127
#> [248,]  64  32      1          9        1   160
#> [249,]  63  12      1          9        0   114
#> [250,]  63   7      0        180        0   120
#> [251,]  65  10      1          8        1   120
#> [252,]  64   0      0          0        1    90
#> [253,]  64  21      1         10        0   190
#> [254,]  65   3      0        180        1   190
#> [255,]  63  16      1          7        1   110
#> [256,]  64   7      0        180        1   120
#> [257,]  63  12      0         12        1   150
#> [258,]  62   3      1          1        1   199
#> [259,]  65   6      0          9        0   112
#> [260,]  63   2      1          1        0   180
#> [261,]  67  11      0         11        1   100
#> [262,]  66  18      1          5        0   142
#> [263,]  66  16      1         11        1   169
#> [264,]  61  14      1          5        0   140
#> [265,]  61  15      1         10        0   130
#> [266,]  63   3      1          2        0   120
#> [267,]  65   8      1          0        1   140
#> [268,]  67   6      0        180        1   170
#> [269,]  68   5      1          4        1   150
#> [270,]  66   7      1          0        1   115
#> [271,]  65   3      0          3        0   105
#> [272,]  66   3      1          0        1   135
#> [273,]  66   6      1          0        1   140
#> [274,]  68   1      0        180        1   166
#> [275,]  64  10      1          9        1   110
#> [276,]  63   7      1          0        0   162
#> [277,]  67   8      1          1        1   130
#> [278,]  63  10      0         16        1   160
#> [279,]  66  14      0        180        0   130
#> [280,]  68  18      0        180        1   260
#> [281,]  63   8      1          1        1   162
#> [282,]  65  18      1          3        0   120
#> [283,]  63   1      1          0        1   155
#> [284,]  63  10      0         18        1   130
#> [285,]  67  11      0         11        0   150
#> [286,]  68  14      0         79        0   172
#> [287,]  66  12      1         10        1   150
#> [288,]  65  15      1         12        1   150
#> [289,]  69  12      0         15        1   140
#> [290,]  66  15      1         13        1   160
#> [291,]  69  21      1         10        0   180
#> [292,]  66   9      1          8        0   130
#> [293,]  63   8      0        180        1   120
#> [294,]  68  14      1         13        1   140
#> [295,]  65   8      1          0        1    90
#> [296,]  66   3      0          3        1   138
#> [297,]  69   1      1          0        0   170
#> [298,]  68  10      1         10        1   150
#> [299,]  65   1      1          0        0   133
#> [300,]  67   2      0        180        0   184
#> [301,]  65   6      0          6        0    80
#> [302,]  66  19      1         12        1   150
#> [303,]  67  12      1         12        0   160
#> [304,]  69   6      0         99        1   140
#> [305,]  64   4      0        179        0   160
#> [306,]  66   4      0        180        1   130
#> [307,]  70  15      1         12        1   132
#> [308,]  64   4      0        180        1   140
#> [309,]  66   7      1          5        1   131
#> [310,]  66   4      0        180        0   177
#> [311,]  69   4      1          3        1   150
#> [312,]  69  17      1         10        0   140
#> [313,]  69   8      0         93        0   140
#> [314,]  64  21      0         21        1   155
#> [315,]  66   6      0        180        0   140
#> [316,]  65   1      0          1        1   120
#> [317,]  68  18      1          0        1   160
#> [318,]  68   4      0          4        1   190
#> [319,]  71   3      0          5        0   112
#> [320,]  68   7      0        150        0   210
#> [321,]  67   2      0        180        0   128
#> [322,]  66   1      1          1        1   165
#> [323,]  70   4      1          0        1   180
#> [324,]  70  14      0        171        0   166
#> [325,]  66   4      0        180        0   130
#> [326,]  67  10      1          9        0   200
#> [327,]  68  18      1         14        1   170
#> [328,]  65   2      0        180        0   130
#> [329,]  68   7      1          0        1   150
#> [330,]  67  14      1         13        0   130
#> [331,]  69   8      0        180        1   180
#> [332,]  66   2      0          2        1   228
#> [333,]  71   6      0         45        1   158
#> [334,]  69   5      0          5        1   142
#> [335,]  69   3      0          3        1   130
#> [336,]  70  22      1         13        0   103
#> [337,]  69   8      1          5        1   195
#> [338,]  72   3      1          0        1   132
#> [339,]  69   8      1          7        1   108
#> [340,]  67   3      0        180        0   110
#> [341,]  66   2      1          1        0   123
#> [342,]  69  19      0        180        0   130
#> [343,]  68  18      0         18        1   100
#> [344,]  67  14      0        172        1   140
#> [345,]  69  11      1          0        1   120
#> [346,]  66   2      0        180        0   130
#> [347,]  67   7      1          4        0   122
#> [348,]  68   2      0          7        1   130
#> [349,]  67  13      1          9        0   130
#> [350,]  70   9      0        180        1   142
#> [351,]  67  22      1          1        1   140
#> [352,]  68   3      0         19        0   135
#> [353,]  67  12      1          8        0   120
#> [354,]  69   1      0          1        1   110
#> [355,]  67   1      0          1        1    60
#> [356,]  67   4      0         60        1   136
#> [357,]  69   5      0         76        0   120
#> [358,]  67   8      1          0        1   130
#> [359,]  72  13      1         11        1   195
#> [360,]  70  35      1          0        1   105
#> [361,]  68   7      1          2        0   135
#> [362,]  73  20      1          0        1   170
#> [363,]  69  10      1          6        1   120
#> [364,]  70  11      0        180        1   210
#> [365,]  67   5      1          0        1   147
#> [366,]  67   9      0        180        0   158
#> [367,]  73  13      0        152        1   130
#> [368,]  70   5      0        180        0   150
#> [369,]  72   2      0          2        1   100
#> [370,]  67   4      1          1        0   134
#> [371,]  72   6      1          5        0   115
#> [372,]  71   1      0        173        1   188
#> [373,]  68  23      0        180        1   220
#> [374,]  69   3      0        180        0   220
#> [375,]  71   3      1          2        0   150
#> [376,]  68   4      1          3        0   210
#> [377,]  71   5      0        180        0   191
#> [378,]  73   6      0        180        1   117
#> [379,]  69   8      1          1        0   164
#> [380,]  68   7      0        180        1   130
#> [381,]  72  16      1          1        1   130
#> [382,]  70   4      0        180        0   180
#> [383,]  69   1      1          0        0   155
#> [384,]  73   6      1          0        1   270
#> [385,]  72   8      1          1        1   150
#> [386,]  71   2      1          0        1   180
#> [387,]  73   7      0          7        1   140
#> [388,]  68  15      1         13        1   130
#> [389,]  70   3      0          3        1   159
#> [390,]  70  13      1          9        0   100
#> [391,]  72   6      0        180        1   130
#> [392,]  73   0      0        180        1   161
#> [393,]  74   8      1          0        1    85
#> [394,]  73   4      0        180        1   154
#> [395,]  69   2      1          0        1   110
#> [396,]  71   3      1          1        0   150
#> [397,]  71  15      1         11        0   165
#> [398,]  71  20      1         10        0   140
#> [399,]  73   3      1          0        1   136
#> [400,]  70   5      1          0        1   190
#> [401,]  71  17      1         11        0   160
#> [402,]  73  10      1          8        0   106
#> [403,]  69  12      1          1        1   149
#> [404,]  74   4      0          4        0   120
#> [405,]  72   5      1          3        1   160
#> [406,]  73   6      0        180        0   110
#> [407,]  71   7      1          2        0   143
#> [408,]  72   8      1          0        1   140
#> [409,]  74   3      0          3        1   150
#> [410,]  73  17      1         11        0   140
#> [411,]  71  13      1          8        0   121
#> [412,]  71  14      1         13        1   170
#> [413,]  74   7      1          0        1   117
#> [414,]  69   7      0        180        1   144
#> [415,]  70   8      0          8        0   120
#> [416,]  71  10      1          9        1   120
#> [417,]  75   1      0          1        0   133
#> [418,]  75   2      1          1        0   145
#> [419,]  73  10      1          9        1   146
#> [420,]  72  10      1          9        1   160
#> [421,]  71   2      0         10        1   112
#> [422,]  73   1      0          1        1    80
#> [423,]  75   9      1          7        0   140
#> [424,]  75  13      1          1        1   130
#> [425,]  71  11      1          8        0   110
#> [426,]  72  15      1         12        1   120
#> [427,]  72   1      1          1        0   168
#> [428,]  73  10      0        180        0   162
#> [429,]  76  25      1         12        1   170
#> [430,]  73  12      1         12        1   140
#> [431,]  72   2      0        180        0   120
#> [432,]  72   4      1          0        1   197
#> [433,]  73   5      0        180        0   126
#> [434,]  73   4      0        180        0   124
#> [435,]  76   3      1          0        1   120
#> [436,]  72   5      0        180        0   154
#> [437,]  72   3      0        180        0   160
#> [438,]  76   5      0          5        1   130
#> [439,]  75   3      1          1        0   180
#> [440,]  73  15      0         15        1   160
#> [441,]  71  16      0        180        0   140
#> [442,]  73  10      1         10        0   124
#> [443,]  74   7      0        180        1   150
#> [444,]  74   3      0          3        1   128
#> [445,]  76   1      0        180        0   114
#> [446,]  74   2      1          1        0   140
#> [447,]  76   8      1          0        1   141
#> [448,]  74  19      1          4        1   200
#> [449,]  73   6      0          6        1   114
#> [450,]  76  17      1          0        1   200
#> [451,]  73   4      1          3        1   125
#> [452,]  75   7      0          7        0   190
#> [453,]  75   0      0          0        1   130
#> [454,]  75  12      0         12        1   160
#> [455,]  74   8      1          0        1   105
#> [456,]  75   4      1          2        1   188
#> [457,]  75   1      0          1        1   125
#> [458,]  73   1      0         52        1   105
#> [459,]  72   5      0        180        0   120
#> [460,]  76  44      1         10        0   105
#> [461,]  74  10      1          0        1   135
#> [462,]  74   8      1          8        1   170
#> [463,]  75   9      0        180        1   140
#> [464,]  77   5      1          0        0   123
#> [465,]  77  12      1          9        1   100
#> [466,]  73  10      1          9        0   146
#> [467,]  77  12      0        180        0   130
#> [468,]  76  12      1         11        1   120
#> [469,]  78   5      1          0        1   170
#> [470,]  74   6      0         79        1   140
#> [471,]  75   3      1          1        1   171
#> [472,]  78  18      0         18        1   144
#> [473,]  77   3      0        180        0   110
#> [474,]  76  29      0         47        0    90
#> [475,]  73   8      1          1        1   162
#> [476,]  74   2      0        180        0   100
#> [477,]  78   7      0          7        1   133
#> [478,]  78   8      1          6        1   110
#> [479,]  74   7      0          7        0   161
#> [480,]  76  13      1          1        1   170
#> [481,]  79   6      0        180        0   170
#> [482,]  80  10      1          6        1   147
#> [483,]  78  13      1          5        0   130
#> [484,]  75   5      0        119        1   150
#> [485,]  75  12      1          1        1   120
#> [486,]  80   8      0          8        1   120
#> [487,]  75  13      1          6        0   150
#> [488,]  76   1      0          1        1    83
#> [489,]  79   4      0         80        0   145
#> [490,]  78   2      1          1        0   130
#> [491,]  75   4      1          0        0   212
#> [492,]  77   2      1          0        1   143
#> [493,]  78  10      0        180        1   130
#> [494,]  76  11      1          0        0   120
#> [495,]  75   3      0          3        0     0
#> [496,]  77  24      0         24        1   160
#> [497,]  80   6      0          6        1   150
#> [498,]  78   6      1          0        1   240
#> [499,]  78  11      1          1        1   140
#> [500,]  79  11      0        180        0   160
#> [501,]  78  14      1          0        1   140
#> [502,]  81   1      0          1        0   130
#> [503,]  78  11      1          8        1   118
#> [504,]  79   4      0          4        1   125
#> [505,]  76  10      1          8        0   180
#> [506,]  76  12      1         10        1   127
#> [507,]  77   6      0          6        1   107
#> [508,]  80   3      1          0        1   120
#> [509,]  78  11      0        180        1   135
#> [510,]  76   1      0          1        1    90
#> [511,]  79   3      0          3        0   120
#> [512,]  77   6      0          6        1   144
#> [513,]  79   4      1          0        1   120
#> [514,]  81   1      0        180        0   120
#> [515,]  82   5      0          8        1   120
#> [516,]  80  17      1         12        0   100
#> [517,]  76   7      0        161        0   151
#> [518,]  79  10      0         10        1   120
#> [519,]  80  15      1          0        1    90
#> [520,]  80   9      0        118        1   186
#> [521,]  78  32      0        180        1   130
#> [522,]  79   1      0         37        1   140
#> [523,]  81   3      0        180        0   184
#> [524,]  81   2      0        175        0   172
#> [525,]  78   7      0          7        1   147
#> [526,]  77  13      1          0        1   190
#> [527,]  78  15      0         15        0   165
#> [528,]  80   5      1          1        1   108
#> [529,]  78   4      0        180        0   175
#> [530,]  78  26      1          5        0   194
#> [531,]  76   1      0        166        0   131
#> [532,]  81   4      1          1        1   104
#> [533,]  78  20      1          0        1   109
#> [534,]  78   3      1          1        1   152
#> [535,]  77  10      1          8        1   130
#> [536,]  77   5      0         85        0   188
#> [537,]  79   6      0          6        0   152
#> [538,]  79  10      0        180        1   150
#> [539,]  78  12      0        180        0   134
#> [540,]  79   1      0        125        0   193
#> [541,]  82  21      1          2        0   155
#> [542,]  84  22      1         10        0   180
#> [543,]  80   6      0          6        1   110
#> [544,]  83   9      1          5        1   170
#> [545,]  82   5      0        180        0   110
#> [546,]  81  11      1          8        0   160
#> [547,]  81   5      0        177        0    41
#> [548,]  80  11      1          8        0   170
#> [549,]  78  23      1         10        1   145
#> [550,]  82   8      1          1        0   128
#> [551,]  81  15      0        180        1   140
#> [552,]  80   7      1          0        1   146
#> [553,]  84   5      1          1        1    85
#> [554,]  81  16      0         16        1   110
#> [555,]  80   6      1          0        1   150
#> [556,]  80  11      1          8        0   110
#> [557,]  81   8      0        180        0   146
#> [558,]  80   8      1          7        0   160
#> [559,]  79   0      1          0        1    96
#> [560,]  85   4      0        180        0    90
#> [561,]  81   2      1          1        0   198
#> [562,]  83   2      0          2        1   155
#> [563,]  82   6      0        128        1   100
#> [564,]  84   4      0        167        0   198
#> [565,]  82  23      1          0        0   110
#> [566,]  84   5      0        180        1   203
#> [567,]  81   1      0          1        1   150
#> [568,]  84   1      0         38        1   205
#> [569,]  81   4      0         90        1   138
#> [570,]  80  13      1          8        1   140
#> [571,]  84   4      0         89        1   129
#> [572,]  79   4      0          4        1    60
#> [573,]  80   6      0         71        1   189
#> [574,]  83   1      0          1        1   100
#> [575,]  82  19      0         19        0   120
#> [576,]  83   9      0        180        0   198
#> [577,]  79  14      1          0        0   110
#> [578,]  83   3      0        114        0    98
#> [579,]  83   2      0        154        0   130
#> [580,]  85   9      1          6        1   160
#> [581,]  83   1      0        180        0   160
#> [582,]  84  15      1         13        1   110
#> [583,]  81   1      0          1        1   145
#> [584,]  81  12      0         12        1   163
#> [585,]  82  16      1          8        0   103
#> [586,]  82   5      1          0        1   146
#> [587,]  81   4      0          4        0   160
#> [588,]  86  12      0        180        1   120
#> [589,]  81  19      1         14        0   120
#> [590,]  80   2      0         88        0   135
#> [591,]  86   8      0          8        1   132
#> [592,]  81  16      1          9        0   180
#> [593,]  84   6      0        165        0   145
#> [594,]  86   3      0          3        1   140
#> [595,]  82   9      0        180        1   134
#> [596,]  84   3      0        180        1   120
#> [597,]  81   2      1          0        1   118
#> [598,]  81   4      0        180        0   160
#> [599,]  83   9      0        180        1   149
#> [600,]  82   1      0        180        1   193
#> [601,]  83   4      0          4        0   130
#> [602,]  82  14      1         11        1   103
#> [603,]  86   6      1          0        1   140
#> [604,]  84  16      0         70        1   150
#> [605,]  83  19      0         43        0   150
#> [606,]  84   3      1          2        0   125
#> [607,]  83  10      1          0        1   190
#> [608,]  86   2      0        180        1   169
#> [609,]  88  14      1          3        1   130
#> [610,]  84   3      0          3        1   121
#> [611,]  83  13      1         12        0   170
#> [612,]  84   9      0         92        1   110
#> [613,]  84   3      0        180        1   170
#> [614,]  86   4      0         38        1   122
#> [615,]  86  13      0        177        0   163
#> [616,]  85   3      0          3        1   113
#> [617,]  86   6      0          6        1   117
#> [618,]  84  13      0         62        1   100
#> [619,]  86   6      1          1        0   112
#> [620,]  88   4      0          4        0   100
#> [621,]  83   9      0         65        1   150
#> [622,]  86   9      1          7        1   142
#> [623,]  88   3      0        115        0   110
#> [624,]  88   2      0        180        1    68
#> [625,]  83   3      0          3        1   130
#> [626,]  87   8      0          8        1   157
#> [627,]  87   1      0          1        0   170
#> [628,]  84   2      0        110        1   174
#> [629,]  87  29      0         29        1    97
#> [630,]  87  15      1          9        1   138
#> [631,]  84   0      0        180        1   136
#> [632,]  89  10      0         46        1   170
#> [633,]  86   4      0        180        1   145
#> [634,]  87   2      0        180        0   160
#> [635,]  91  10      0        145        0   135
#> [636,]  86   3      1          0        1    80
#> [637,]  88   7      0         24        0   119
#> [638,]  90  11      1         10        1   186
#> [639,]  87   6      0        126        1   168
#> [640,]  86   9      1          7        0   130
#> [641,]  91   1      0          1        1    74
#> [642,]  87   5      0         36        1   150
#> [643,]  88   3      1          2        0   159
#> [644,]  92   1      0          1        1   167
#> [645,]  91   3      0         33        1   137
#> [646,]  88   5      0        158        0   100
#> [647,]  87   7      0         74        1   105
#> [648,]  89   2      0        168        0   118
#> [649,]  91   5      0        169        1   176
#> [650,]  92   7      0          7        1   110
#> [651,]  89   4      0          4        1   159
#> [652,]  91   0      0          0        0     0
#> [653,]  89  14      0        180        1    84
#> [654,]  90  18      0        180        0   188
#> [655,]  91   4      1          0        1   120
#> [656,]  94   6      0         50        0    78
#> [657,]  90   1      0          1        1   118
#> [658,]  91   2      0          2        1   116
#> [659,]  93   8      0        179        1   110
#> [660,]  94   8      0          8        1   142
#> [661,]  92   4      0         76        1   149
#> [662,]  90   3      0         67        0   162
#> [663,]  96   3      0         12        1    97
#> [664,]  95   8      1          5        1   150
#> [665,]  94   3      0         26        1   144
#> [666,]  91  12      0         53        1   212
#> [667,]  91   7      0          7        0   135
#> [668,]  92   5      0         69        0   139
#> [669,]  93   4      0        180        1   135
#> [670,]  96  15      1          0        1   140
#> 
#> $y
#>   [1] 180.0+   5.0+   2.0+ 180.0+ 180.0+ 180.0+ 115.0  180.0+  12.0    5.0+
#>  [11] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0+ 180.0+   5.0+   2.0+ 180.0+
#>  [21] 180.0+ 180.0+   3.0  180.0+ 180.0+ 180.0+   2.0+ 180.0+ 155.0+ 180.0+
#>  [31] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   5.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [41] 180.0+ 180.0+ 150.0  180.0+ 180.0+ 180.0+ 180.0+   5.0+ 161.0+ 180.0+
#>  [51] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+  10.0+ 180.0+
#>  [61] 180.0+ 180.0+   7.0  180.0+ 180.0+   2.0    1.0  179.0+ 180.0+ 180.0+
#>  [71] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [81]  88.0+ 180.0+   4.0+ 180.0+  77.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [91] 180.0+ 180.0+ 180.0+   4.0+ 180.0+  99.0  180.0+  16.0+ 180.0+ 152.0+
#> [101]   7.0+   6.0+ 180.0+ 180.0+ 180.0+  13.0+ 171.0+ 180.0+   6.0+   1.0 
#> [111] 180.0+   9.0+   2.0  180.0+   7.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [121] 180.0+ 180.0+  16.0+ 180.0+  16.0   15.0+ 180.0+ 180.0+  12.0+   2.0 
#> [131]   3.0+ 180.0+ 140.0  180.0+   1.0  165.0  180.0+ 180.0+ 180.0+ 180.0+
#> [141]   8.0+ 180.0+ 180.0+ 180.0+ 171.0+  15.0  166.0+ 180.0+   4.0+ 147.0+
#> [151] 180.0+ 180.0+ 180.0+   4.0+ 180.0+   1.0  180.0+   2.0+ 180.0+ 180.0+
#> [161] 180.0+ 180.0+ 180.0+   2.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+  64.0 
#> [171] 180.0+ 180.0+ 180.0+   9.0+ 180.0+ 180.0+ 161.0+ 180.0+   3.0    1.0 
#> [181] 180.0+ 180.0+ 180.0+  17.0   45.0    3.0+   9.0+ 172.0+  24.0  180.0+
#> [191] 180.0+  15.0  180.0+ 180.0+ 180.0+  77.0   13.0+ 180.0+ 180.0+ 170.0 
#> [201]  94.0  180.0+ 169.0    7.0  180.0+ 180.0+ 180.0+ 180.0+  30.0  180.0+
#> [211] 180.0+  28.0   13.0+  18.0    5.0+ 180.0+ 180.0+   1.0  180.0+ 180.0+
#> [221] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   4.0+ 180.0+ 180.0+   9.0    7.0+
#> [231]  22.0  180.0+ 180.0+   1.0  180.0+ 180.0+   3.0+ 167.0  180.0+ 180.0+
#> [241]  17.0   12.0  180.0+ 180.0+  14.0+  36.0    3.0+ 180.0+  12.0  180.0+
#> [251] 180.0+   0.5  180.0+ 180.0+ 180.0+ 180.0+  12.0  180.0+   9.0  180.0+
#> [261]  11.0+  18.0+ 180.0+ 180.0+ 180.0+   3.0+  15.0  180.0+   5.0+ 179.0+
#> [271]   3.0    3.0+ 180.0+ 180.0+ 180.0+   7.0+   8.0   16.0  180.0+ 180.0+
#> [281] 180.0+ 123.0+   1.0+  18.0   11.0+  79.0   80.0   15.0+  15.0  180.0+
#> [291] 174.0+ 180.0+ 180.0+ 180.0+   8.0+   3.0  175.0   10.0  180.0+ 180.0+
#> [301]   6.0   19.0+  12.0   99.0  179.0+ 180.0+ 180.0+ 180.0+   7.0+ 180.0+
#> [311] 152.0+ 180.0+  93.0   21.0+ 180.0+   1.0   18.0+   4.0    5.0  150.0 
#> [321] 180.0+   1.0  180.0+ 171.0  180.0+ 174.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [331] 180.0+   2.0   45.0    5.0+   3.0+ 180.0+ 180.0+ 180.0+   8.0+ 180.0+
#> [341]   2.0+ 180.0+  18.0  172.0+ 180.0+ 180.0+   7.0    7.0   13.0+ 180.0+
#> [351]  51.0   19.0  180.0+   1.0    1.0   60.0   76.0  180.0+ 132.0  180.0+
#> [361]   7.0+ 124.0  180.0+ 180.0+ 180.0+ 180.0+ 152.0  180.0+   2.0   76.0 
#> [371] 180.0+ 173.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [381]  16.0+ 180.0+ 180.0+   6.0  180.0+ 180.0+   7.0+  15.0    3.0+  13.0+
#> [391] 180.0+ 180.0+ 180.0+ 180.0+   2.0    3.0+ 180.0+  20.0  180.0+ 180.0+
#> [401] 180.0+  87.0   12.0    4.0+ 180.0+ 180.0+ 180.0+ 180.0+   3.0  180.0+
#> [411] 175.0   14.0+ 180.0+ 180.0+   8.0+ 179.0+   1.0  180.0+ 180.0+ 159.0 
#> [421]  10.0    1.0  180.0+  13.0  180.0+ 180.0+   1.0  180.0+ 180.0+  12.0 
#> [431] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   5.0  180.0+  15.0+
#> [441] 180.0+  10.0  180.0+   3.0  180.0+ 180.0+ 180.0+ 180.0+   6.0   17.0+
#> [451] 180.0+   7.0    0.5   12.0  180.0+  46.0    1.0   52.0  180.0+ 180.0+
#> [461] 180.0+   8.0  180.0+   5.0  180.0+ 180.0+ 180.0+  12.0  180.0+  79.0 
#> [471]   3.0   18.0  180.0+  47.0  180.0+ 180.0+   7.0    8.0+   7.0  180.0+
#> [481] 180.0+  10.0  172.0  119.0   12.0    8.0  180.0+   1.0   80.0  180.0+
#> [491]   4.0+   2.0  180.0+  11.0    3.0   24.0    6.0  180.0+ 180.0+ 180.0+
#> [501] 180.0+   1.0   11.0    4.0   10.0+ 180.0+   6.0    3.0+ 180.0+   1.0 
#> [511]   3.0    6.0  138.0  180.0+   8.0   17.0  161.0   10.0+ 180.0+ 118.0 
#> [521] 180.0+  37.0  180.0+ 175.0+   7.0+  22.0   15.0+   5.0+ 180.0+ 171.0+
#> [531] 166.0+  71.0   20.0+   3.0+  10.0   85.0    6.0+ 180.0+ 180.0+ 125.0 
#> [541] 180.0+ 180.0+   6.0    9.0+ 180.0+ 180.0+ 177.0+ 169.0   70.0  180.0+
#> [551] 180.0+   7.0+ 180.0+  16.0  180.0+ 180.0+ 180.0+ 180.0+   0.5  180.0+
#> [561] 180.0+   2.0  128.0  167.0   62.0  180.0+   1.0   38.0   90.0  180.0+
#> [571]  89.0    4.0   71.0    1.0   19.0  180.0+ 180.0+ 114.0  154.0  180.0+
#> [581] 180.0+ 180.0+   1.0   12.0   16.0+   5.0+   4.0+ 180.0+ 180.0+  88.0 
#> [591]   8.0  180.0+ 165.0    3.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [601]   4.0+ 174.0    6.0   70.0   43.0  180.0+ 180.0+ 180.0+  14.0    3.0 
#> [611]  13.0   92.0  180.0+  38.0  177.0    3.0+   6.0+  62.0    6.0+   4.0+
#> [621]  65.0   11.0  115.0  180.0+   3.0+   8.0+   1.0+ 110.0   29.0  180.0+
#> [631] 180.0+  46.0  180.0+ 180.0+ 145.0    3.0   24.0   11.0  126.0  180.0+
#> [641]   1.0   36.0   75.0    1.0   33.0  158.0   74.0  168.0  169.0    7.0 
#> [651]   4.0    0.5  180.0+ 180.0+   4.0   50.0    1.0+   2.0  179.0+   8.0+
#> [661]  76.0   67.0   12.0    8.0   26.0   53.0    7.0+  69.0  180.0+  15.0+
#> 
#> $weights
#> NULL
#> 
#> $offset
#> NULL
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
#> Warning: Multiple lambdas have been fit. Lambda will be set to 0.01 (see parameter 's').

# Score the predictions
predictions$score()
#> surv.cindex 
#>   0.8203326