Skip to contents

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 = lrn("surv.glmnet")
print(learner)
#> 
#> ── <LearnerSurvGlmnet> (surv.glmnet): Regularized Generalized Linear Model ─────
#> • Model: -
#> • Parameters: use_pred_offset=TRUE
#> • Packages: mlr3, mlr3proba, mlr3extralearners, and glmnet
#> • Predict Types: [crank], distr, and lp
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: offset, selected_features, and weights
#> • Other settings: use_weights = 'use'

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

# Create train and test set
ids = 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 0.182400
#> 2   1  0.60 0.166200
#> 3   1  1.11 0.151400
#> 4   2  1.97 0.137900
#> 5   3  3.93 0.125700
#> 6   3  6.74 0.114500
#> 7   3  8.79 0.104400
#> 8   3 10.37 0.095080
#> 9   3 11.63 0.086640
#> 10  3 12.64 0.078940
#> 11  4 13.48 0.071930
#> 12  4 14.21 0.065540
#> 13  4 14.82 0.059720
#> 14  4 15.33 0.054410
#> 15  4 15.76 0.049580
#> 16  4 16.12 0.045170
#> 17  5 16.43 0.041160
#> 18  5 16.70 0.037500
#> 19  5 16.93 0.034170
#> 20  5 17.12 0.031140
#> 21  5 17.29 0.028370
#> 22  6 17.43 0.025850
#> 23  6 17.56 0.023550
#> 24  6 17.67 0.021460
#> 25  6 17.77 0.019550
#> 26  6 17.84 0.017820
#> 27  6 17.91 0.016230
#> 28  6 17.96 0.014790
#> 29  6 18.01 0.013480
#> 30  6 18.05 0.012280
#> 31  6 18.08 0.011190
#> 32  6 18.11 0.010200
#> 33  6 18.13 0.009290
#> 34  6 18.15 0.008464
#> 35  6 18.17 0.007713
#> 36  6 18.18 0.007027
#> 37  6 18.19 0.006403
#> 38  6 18.20 0.005834
#> 39  6 18.21 0.005316
#> 40  6 18.22 0.004844
#> 41  6 18.22 0.004413
#> 42  6 18.23 0.004021
#> 43  6 18.23 0.003664
#> 44  6 18.23 0.003339
#> 
#> $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   5      1          2        0   172
#>   [5,]  35  10      1          9        0   106
#>   [6,]  34   5      0          5        0   120
#>   [7,]  35   2      0        180        0   121
#>   [8,]  35   2      1          1        1   112
#>   [9,]  38   2      0        115        0   150
#>  [10,]  36   1      0        180        1   155
#>  [11,]  35   0      0        180        1   119
#>  [12,]  38  12      1          8        1   120
#>  [13,]  36   5      1          0        1   115
#>  [14,]  33   6      1          1        1   115
#>  [15,]  38  16      1         10        0   160
#>  [16,]  38  12      1         11        1    92
#>  [17,]  40  12      1          9        0   153
#>  [18,]  37   1      1          0        1   146
#>  [19,]  42   2      0        180        1   100
#>  [20,]  38   5      1          3        0   125
#>  [21,]  42   2      0          2        0   140
#>  [22,]  40   6      0        180        1   138
#>  [23,]  40  11      1         10        1   120
#>  [24,]  42   2      0        180        0   100
#>  [25,]  43   3      1          0        1   100
#>  [26,]  41   2      1          1        0   166
#>  [27,]  40   1      1          0        1   145
#>  [28,]  42   4      0        180        0   162
#>  [29,]  40   3      1          1        0   170
#>  [30,]  42  12      1         10        1   170
#>  [31,]  43   2      1          1        1   116
#>  [32,]  41  10      1          8        0   150
#>  [33,]  44   3      0        180        0   141
#>  [34,]  41  13      1          1        0   140
#>  [35,]  45   9      1          7        0   110
#>  [36,]  41   5      1          4        1   141
#>  [37,]  45   2      0        180        1   140
#>  [38,]  46   2      1          1        0   126
#>  [39,]  47   4      1          3        0   118
#>  [40,]  48  15      0        180        1   160
#>  [41,]  46   7      1          2        0   166
#>  [42,]  43  29      0        180        1   180
#>  [43,]  45   4      1          0        0   124
#>  [44,]  46  13      1         10        0   100
#>  [45,]  44   0      1          0        1    96
#>  [46,]  43   3      1          0        1   124
#>  [47,]  45   8      1          0        1   117
#>  [48,]  45   5      0          5        0   141
#>  [49,]  46   6      1          0        1   100
#>  [50,]  44   4      1          0        1   114
#>  [51,]  44   9      1          8        1   135
#>  [52,]  45   5      0        180        1   190
#>  [53,]  46   5      1          3        0   130
#>  [54,]  44   2      0        180        0   142
#>  [55,]  46  15      0        180        1   120
#>  [56,]  45   9      1          0        1   145
#>  [57,]  47   3      1          1        1   120
#>  [58,]  48  12      1         11        0   200
#>  [59,]  47   5      1          3        1   130
#>  [60,]  47   9      1          6        0   170
#>  [61,]  46   3      1          0        1   119
#>  [62,]  49   4      0        180        0   117
#>  [63,]  47  10      0         10        1   140
#>  [64,]  50   1      1          0        1   129
#>  [65,]  48   2      1          0        0   184
#>  [66,]  47   7      0        180        0   145
#>  [67,]  50   4      1          1        0   125
#>  [68,]  49   7      1          7        1   110
#>  [69,]  46   3      1          1        1   140
#>  [70,]  50   7      0        180        1   110
#>  [71,]  49   2      0          2        0   105
#>  [72,]  51   1      0          1        1   145
#>  [73,]  47   2      0        180        0   150
#>  [74,]  49  23      0        179        1   112
#>  [75,]  46   6      1          0        1   156
#>  [76,]  52   2      0        180        1   170
#>  [77,]  50   7      1          0        1    92
#>  [78,]  49   7      1          4        1    90
#>  [79,]  47   6      0        180        1   162
#>  [80,]  51   8      0        180        1   140
#>  [81,]  52   2      0        180        0   155
#>  [82,]  46   1      1          1        0   145
#>  [83,]  50   4      1          1        0   150
#>  [84,]  53   8      0         36        1   160
#>  [85,]  47   2      1          1        0   110
#>  [86,]  52   4      1          4        0   152
#>  [87,]  49   9      1          3        0   102
#>  [88,]  49  15      0        180        1   160
#>  [89,]  53   5      0        180        1   140
#>  [90,]  53   5      0         77        0   159
#>  [91,]  53   7      1          0        0   199
#>  [92,]  54   6      1          3        0   129
#>  [93,]  50   2      0          5        1   106
#>  [94,]  53   8      1          7        0   160
#>  [95,]  48   3      1          2        0   150
#>  [96,]  51  25      1          1        0   202
#>  [97,]  53   4      0          4        0   140
#>  [98,]  52  14      1          7        1   200
#>  [99,]  53   4      1          0        1   156
#> [100,]  49  16      0         16        0   125
#> [101,]  52   7      1          2        0   154
#> [102,]  55   6      1          2        1   114
#> [103,]  54   9      1          1        0   130
#> [104,]  52   4      0        180        1   180
#> [105,]  51  13      1         11        0   145
#> [106,]  50   5      1          4        1   150
#> [107,]  54   4      1          0        1   121
#> [108,]  50   3      0        174        1   153
#> [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,]  53   8      1          0        1   130
#> [114,]  52   5      0        175        1   117
#> [115,]  55   2      0          2        0   145
#> [116,]  54   7      1          0        1   100
#> [117,]  56   3      0        180        1   193
#> [118,]  55   5      1          4        1   120
#> [119,]  53  18      1          9        1   150
#> [120,]  54   3      0        180        1   180
#> [121,]  55   6      0        180        0   170
#> [122,]  52  16      0         16        0   152
#> [123,]  53  10      1          9        0   172
#> [124,]  52  16      1         14        0   170
#> [125,]  53  15      0         15        1    90
#> [126,]  53   4      0        180        1   150
#> [127,]  55   6      0        180        1   100
#> [128,]  55   6      1          5        1   138
#> [129,]  54  12      1          0        1   190
#> [130,]  55   2      0        134        1   140
#> [131,]  54   3      0        180        0   128
#> [132,]  55   1      0          2        0   130
#> [133,]  57   3      0          3        0   120
#> [134,]  54   7      1          2        0   129
#> [135,]  54   2      1          1        0   135
#> [136,]  57   5      1          3        1   138
#> [137,]  57   1      0        180        1   156
#> [138,]  57   1      0          1        1   100
#> [139,]  56   4      1          0        1   140
#> [140,]  52  15      1         14        0   130
#> [141,]  56  14      1         11        0   130
#> [142,]  53   3      1          0        1   200
#> [143,]  55   3      1          1        1   156
#> [144,]  53  21      1         13        1   130
#> [145,]  59   3      1          1        0   172
#> [146,]  57   4      0        180        1   119
#> [147,]  53  15      1         10        1   130
#> [148,]  54  17      1          8        1   227
#> [149,]  56   5      0          5        1   150
#> [150,]  53   7      1          0        1   120
#> [151,]  55   3      1          2        0   140
#> [152,]  55   5      0          5        1   131
#> [153,]  59  15      1         10        0   140
#> [154,]  58   9      1          0        1   180
#> [155,]  55   5      1          0        0   140
#> [156,]  55   2      0          2        0   106
#> [157,]  60  11      1          9        0   106
#> [158,]  58   4      1          0        1   160
#> [159,]  57   2      0          2        1   120
#> [160,]  60   5      1          1        0   138
#> [161,]  57   5      0        180        1   130
#> [162,]  58  11      1          9        1   124
#> [163,]  57  10      1          9        0   103
#> [164,]  59   6      1          0        1   140
#> [165,]  59   4      1          0        1   152
#> [166,]  58   4      1          3        0   120
#> [167,]  60   0      1          0        1    80
#> [168,]  59   2      1          1        0   140
#> [169,]  58  14      1          6        0   190
#> [170,]  61   4      1          3        0   151
#> [171,]  61   9      1          8        0   150
#> [172,]  61   3      1          2        1   102
#> [173,]  58   1      0          1        1   100
#> [174,]  61  20      1         13        0   130
#> [175,]  57  13      1         10        0   110
#> [176,]  57   2      1          0        1   116
#> [177,]  58  10      0         10        1   150
#> [178,]  57   4      1          3        0   138
#> [179,]  57  11      0        180        1   150
#> [180,]  56  14      0         45        0   130
#> [181,]  57   3      1          2        0   120
#> [182,]  58  19      1         13        1   140
#> [183,]  56  13      1          6        1   158
#> [184,]  59   9      1          0        1    80
#> [185,]  55   4      1          3        1   160
#> [186,]  58  11      0        172        1   135
#> [187,]  60  12      1          0        1   114
#> [188,]  55   9      1          7        1   135
#> [189,]  61   4      1          0        1   115
#> [190,]  56   8      1          8        0   120
#> [191,]  61  13      1         12        1   130
#> [192,]  59  11      1          8        1   190
#> [193,]  57   1      0          1        0   126
#> [194,]  57  15      1         13        1   110
#> [195,]  59   5      1          2        0   182
#> [196,]  59  10      0        180        0   160
#> [197,]  61  13      0         13        0   210
#> [198,]  58   8      1          5        0   152
#> [199,]  62  10      1          0        1   153
#> [200,]  62   7      1          2        1   180
#> [201,]  57   3      1          0        0   100
#> [202,]  61  18      0        170        0   140
#> [203,]  58   8      1          3        1   150
#> [204,]  57   7      0        169        0   180
#> [205,]  61   6      0          6        0   134
#> [206,]  59  13      1          2        0   198
#> [207,]  57  12      1          9        1   120
#> [208,]  62   4      1          0        0   160
#> [209,]  60  17      1          8        1   140
#> [210,]  58   3      1          0        1   146
#> [211,]  58   2      0         30        0   202
#> [212,]  59   1      0        180        0   155
#> [213,]  61  13      0         13        0   120
#> [214,]  61   5      0          5        1   110
#> [215,]  57  18      1          9        1    93
#> [216,]  58  11      1          9        0   179
#> [217,]  57   2      1          1        0   159
#> [218,]  62  17      1         10        1   180
#> [219,]  62   1      1          0        1   172
#> [220,]  63   3      1          1        0   180
#> [221,]  63   1      0        180        1   130
#> [222,]  63   4      0        180        1   190
#> [223,]  63  15      1         10        1   126
#> [224,]  64   4      0        180        0   130
#> [225,]  63   4      1          1        0   155
#> [226,]  60  18      1         13        0   132
#> [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   5      1          1        0   148
#> [233,]  63   1      0          1        0   162
#> [234,]  63   1      0          1        0   130
#> [235,]  60   3      0          3        0   168
#> [236,]  63  12      1         10        0   200
#> [237,]  59  10      0        180        1   130
#> [238,]  61   6      1          1        1   117
#> [239,]  64  12      1         11        0   160
#> [240,]  66   1      1          0        1   120
#> [241,]  64   6      1          0        1   140
#> [242,]  63  10      1          0        1   148
#> [243,]  65  36      1         11        0   140
#> [244,]  61  10      1          2        1   194
#> [245,]  64  32      1          9        1   160
#> [246,]  66   5      1          0        1   110
#> [247,]  65   8      1          0        0   168
#> [248,]  65  10      1          8        1   120
#> [249,]  64   0      0          0        1    90
#> [250,]  64  21      1         10        0   190
#> [251,]  61  12      1         11        0   154
#> [252,]  65   3      0        180        1   190
#> [253,]  64   7      0        180        1   120
#> [254,]  66   6      1          1        1   130
#> [255,]  62   3      1          1        1   199
#> [256,]  65   3      1          0        1    80
#> [257,]  63   5      1          4        0   170
#> [258,]  63   2      1          1        0   180
#> [259,]  67  11      0         11        1   100
#> [260,]  62   9      0        180        0   145
#> [261,]  61  14      1          5        0   140
#> [262,]  63   9      1          8        1   160
#> [263,]  63   3      1          2        0   120
#> [264,]  63   2      1          0        0   140
#> [265,]  64  19      1          8        1   160
#> [266,]  65   8      1          0        1   140
#> [267,]  65  15      1         11        1   160
#> [268,]  68   5      1          4        1   150
#> [269,]  64   6      1          0        1   125
#> [270,]  66   7      1          0        1   115
#> [271,]  64  14      1         13        1   150
#> [272,]  65   3      0          3        0   105
#> [273,]  64   0      0          0        1   148
#> [274,]  67   4      1          3        0   130
#> [275,]  66   3      1          0        1   135
#> [276,]  66   6      1          0        1   140
#> [277,]  63   7      1          0        0   162
#> [278,]  67   8      1          1        1   130
#> [279,]  68   5      0          5        1    90
#> [280,]  63  10      0         16        1   160
#> [281,]  64   1      0          1        1   120
#> [282,]  68  18      0        180        1   260
#> [283,]  65  17      1         14        1   100
#> [284,]  67  11      0         11        0   150
#> [285,]  68  11      0        180        0   160
#> [286,]  68  14      0         79        0   172
#> [287,]  66  12      1         10        1   150
#> [288,]  65  15      1         12        1   150
#> [289,]  65   4      1          2        1   145
#> [290,]  66  15      1         13        1   160
#> [291,]  63   2      0        180        0   150
#> [292,]  65  11      1          6        0   130
#> [293,]  69  21      1         10        0   180
#> [294,]  69   6      0        180        1   100
#> [295,]  66   9      1          8        0   130
#> [296,]  63   8      0        180        1   120
#> [297,]  68  14      1         13        1   140
#> [298,]  65   8      1          0        1    90
#> [299,]  66   3      0          3        1   138
#> [300,]  69   1      1          0        0   170
#> [301,]  67   1      0        180        1   160
#> [302,]  68  10      1         10        1   150
#> [303,]  67   7      1          4        1   130
#> [304,]  63   2      1          0        0    99
#> [305,]  67   2      0        180        0   184
#> [306,]  65   6      0          6        0    80
#> [307,]  65  10      1          1        1   148
#> [308,]  66  19      1         12        1   150
#> [309,]  67  12      1         12        0   160
#> [310,]  69   6      0         99        1   140
#> [311,]  65   4      1          1        0   130
#> [312,]  66   4      0        180        1   130
#> [313,]  70  15      1         12        1   132
#> [314,]  64   4      0        180        1   140
#> [315,]  64   0      1          0        1   118
#> [316,]  68   4      1          0        1   160
#> [317,]  65  13      1         12        1   130
#> [318,]  69  17      1         10        0   140
#> [319,]  64  21      0         21        1   155
#> [320,]  66   6      0        180        0   140
#> [321,]  68  18      1          0        1   160
#> [322,]  71   3      0          5        0   112
#> [323,]  70   7      1          0        1   190
#> [324,]  68   7      0        150        0   210
#> [325,]  71  20      1          0        1   160
#> [326,]  67   2      0        180        0   128
#> [327,]  66   9      1          3        1   151
#> [328,]  70   4      1          0        1   180
#> [329,]  69   8      0        180        1   153
#> [330,]  66   4      0        180        0   130
#> [331,]  67  10      1          9        0   200
#> [332,]  67   6      1          4        0   130
#> [333,]  68  18      1         14        1   170
#> [334,]  65   2      0        180        0   130
#> [335,]  68   7      1          0        1   150
#> [336,]  69   3      1          2        0   151
#> [337,]  65  14      1         13        1   150
#> [338,]  69   8      0        180        1   180
#> [339,]  71   7      0          7        0   230
#> [340,]  66   2      0          2        1   228
#> [341,]  69   5      0          5        1   142
#> [342,]  71   3      0        103        0   133
#> [343,]  69   3      0          3        1   130
#> [344,]  70  22      1         13        0   103
#> [345,]  67   5      0          5        0   130
#> [346,]  72   3      1          0        1   132
#> [347,]  67   3      0        180        0   110
#> [348,]  66   2      1          1        0   123
#> [349,]  69  19      0        180        0   130
#> [350,]  67  14      0        172        1   140
#> [351,]  69  11      1          0        1   120
#> [352,]  69   4      1          3        0   132
#> [353,]  68   2      0          7        1   130
#> [354,]  69   8      1          2        0   121
#> [355,]  67  13      1          9        0   130
#> [356,]  70   9      0        180        1   142
#> [357,]  72   5      1          4        0   170
#> [358,]  67  22      1          1        1   140
#> [359,]  68   3      0         19        0   135
#> [360,]  67   1      0          1        1    60
#> [361,]  67   4      0         60        1   136
#> [362,]  69   5      0         76        0   120
#> [363,]  67   8      1          0        1   130
#> [364,]  68  10      1          8        1   160
#> [365,]  70  35      1          0        1   105
#> [366,]  72  30      1          0        1   145
#> [367,]  68   7      1          2        0   135
#> [368,]  73  20      1          0        1   170
#> [369,]  71   6      0          9        0   120
#> [370,]  69  10      1          6        1   120
#> [371,]  70  11      0        180        1   210
#> [372,]  72  19      1          8        0   120
#> [373,]  72  12      1         10        0   170
#> [374,]  67   8      0        180        1   170
#> [375,]  67   9      0        180        0   158
#> [376,]  72   2      0          2        1   100
#> [377,]  72   6      1          5        0   115
#> [378,]  71   1      0        173        1   188
#> [379,]  70   3      0        180        0   121
#> [380,]  69   3      0        180        0   220
#> [381,]  71   3      1          2        0   150
#> [382,]  68   4      1          3        0   210
#> [383,]  71   5      0        180        0   191
#> [384,]  73   6      0        180        1   117
#> [385,]  69   8      1          1        0   164
#> [386,]  68   7      0        180        1   130
#> [387,]  72  16      1          1        1   130
#> [388,]  70   4      0        180        0   180
#> [389,]  69   1      1          0        0   155
#> [390,]  73   6      1          0        1   270
#> [391,]  72   8      1          1        1   150
#> [392,]  73   7      0          7        1   140
#> [393,]  68  15      1         13        1   130
#> [394,]  70   3      0          3        1   159
#> [395,]  70  13      1          9        0   100
#> [396,]  73   0      0        180        1   161
#> [397,]  74   8      1          0        1    85
#> [398,]  69   2      1          0        1   110
#> [399,]  71   3      1          1        0   150
#> [400,]  74  20      0         20        1   180
#> [401,]  68   9      0        180        1   120
#> [402,]  71  20      1         10        0   140
#> [403,]  73   3      1          0        1   136
#> [404,]  71   8      1          7        0   149
#> [405,]  73  10      1          8        0   106
#> [406,]  69  12      1          1        1   149
#> [407,]  70  26      1         11        1   120
#> [408,]  70   3      0        180        1   154
#> [409,]  71   7      1          2        0   143
#> [410,]  72   8      1          0        1   140
#> [411,]  71  13      1          8        0   121
#> [412,]  70   4      1          0        1   140
#> [413,]  71  14      1         13        1   170
#> [414,]  72  10      1          8        1   153
#> [415,]  72  15      1         13        0   156
#> [416,]  70   8      0          8        0   120
#> [417,]  75   2      1          1        0   145
#> [418,]  72  10      1          9        1   160
#> [419,]  74  15      1          9        1   179
#> [420,]  71   2      0         10        1   112
#> [421,]  73   1      0          1        1    80
#> [422,]  75   9      1          7        0   140
#> [423,]  75  13      1          1        1   130
#> [424,]  72  15      1         12        1   120
#> [425,]  70   7      1          4        0   184
#> [426,]  73  10      0        180        0   162
#> [427,]  72  11      0         11        1   140
#> [428,]  73   5      1          3        1   112
#> [429,]  76  25      1         12        1   170
#> [430,]  73  12      1         12        1   140
#> [431,]  72   2      0        180        0   120
#> [432,]  71   3      1          0        0   144
#> [433,]  74  34      1          8        1   233
#> [434,]  76   3      1          0        1   120
#> [435,]  71  32      1         12        1   107
#> [436,]  72   5      0        180        0   154
#> [437,]  76   5      0          5        1   130
#> [438,]  77  11      0         11        1   150
#> [439,]  75   3      1          1        0   180
#> [440,]  71  16      0        180        0   140
#> [441,]  73  10      1         10        0   124
#> [442,]  74   7      0        180        1   150
#> [443,]  74   3      0          3        1   128
#> [444,]  76   1      0        180        0   114
#> [445,]  74   2      1          1        0   140
#> [446,]  76   8      1          0        1   141
#> [447,]  74  19      1          4        1   200
#> [448,]  73   6      0          6        1   114
#> [449,]  75  23      1         14        1   110
#> [450,]  74   2      0        180        0   190
#> [451,]  72   4      0         85        1   120
#> [452,]  72   4      1          3        0   160
#> [453,]  76  17      1          0        1   200
#> [454,]  73   4      1          3        1   125
#> [455,]  75   0      0          0        1   130
#> [456,]  73  13      1         11        0   195
#> [457,]  74   8      1          0        1   105
#> [458,]  76  13      1          8        1   148
#> [459,]  74   6      0        180        0   160
#> [460,]  76   4      0          4        1   155
#> [461,]  73   1      0         52        1   105
#> [462,]  73   0      0        180        0   156
#> [463,]  74  10      1          0        1   135
#> [464,]  76   5      1          0        1   167
#> [465,]  74   8      1          8        1   170
#> [466,]  75   9      0        180        1   140
#> [467,]  73  33      1         12        1   175
#> [468,]  77   5      1          0        0   123
#> [469,]  73  10      1          9        0   146
#> [470,]  76  12      1         11        1   120
#> [471,]  78   5      1          0        1   170
#> [472,]  73   7      1          0        0   174
#> [473,]  75   3      1          1        1   171
#> [474,]  74   9      1          8        0   200
#> [475,]  75   6      0        180        0   150
#> [476,]  78  18      0         18        1   144
#> [477,]  76  29      0         47        0    90
#> [478,]  73  11      1          2        1   110
#> [479,]  74   2      0        180        0   100
#> [480,]  78   7      0          7        1   133
#> [481,]  78   8      1          6        1   110
#> [482,]  76  13      1          1        1   170
#> [483,]  79   6      0        180        0   170
#> [484,]  75   5      0        119        1   150
#> [485,]  75  12      1          1        1   120
#> [486,]  78  15      0        180        1   270
#> [487,]  80   8      0          8        1   120
#> [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,]  78  10      0        180        1   130
#> [493,]  76  11      1          0        0   120
#> [494,]  75   3      0          3        0     0
#> [495,]  76   7      0         29        1   150
#> [496,]  77  24      0         24        1   160
#> [497,]  80   9      0         23        1   128
#> [498,]  78   6      1          0        1   240
#> [499,]  76   3      1          0        1   140
#> [500,]  78  11      1          1        1   140
#> [501,]  79  11      0        180        0   160
#> [502,]  79   2      1          0        1   121
#> [503,]  81   1      0          1        0   130
#> [504,]  78  11      1          8        1   118
#> [505,]  76   4      0          4        1   160
#> [506,]  79   4      0          4        1   125
#> [507,]  76  10      1          8        0   180
#> [508,]  76  12      1         10        1   127
#> [509,]  77   6      0          6        1   107
#> [510,]  80   3      1          0        1   120
#> [511,]  75   2      1          1        1   204
#> [512,]  77  31      1          3        1   161
#> [513,]  78   7      1          0        1   110
#> [514,]  79   3      0          3        0   120
#> [515,]  77   6      0          6        1   144
#> [516,]  81   1      0        180        0   120
#> [517,]  80  15      1         12        1   150
#> [518,]  82   5      0          8        1   120
#> [519,]  80  40      1          0        1   138
#> [520,]  78   4      0         59        1   112
#> [521,]  80  17      1         12        0   100
#> [522,]  76   7      0        161        0   151
#> [523,]  79  10      0         10        1   120
#> [524,]  80  15      1          0        1    90
#> [525,]  79  28      0        164        0   100
#> [526,]  80   9      0        118        1   186
#> [527,]  80   6      0        173        1   160
#> [528,]  78  32      0        180        1   130
#> [529,]  81   3      0        180        0   184
#> [530,]  78   7      0          7        1   147
#> [531,]  77  13      1          0        1   190
#> [532,]  78  15      0         15        0   165
#> [533,]  80   5      1          1        1   108
#> [534,]  78   4      0        180        0   175
#> [535,]  79   3      0          3        1   101
#> [536,]  81   4      1          1        1   104
#> [537,]  78  20      1          0        1   109
#> [538,]  78   3      1          1        1   152
#> [539,]  77  10      1          8        1   130
#> [540,]  82   3      1          1        1   144
#> [541,]  80   2      1          1        0   168
#> [542,]  80   6      1          0        1   119
#> [543,]  78   2      0        180        0   148
#> [544,]  80   5      0          5        1   130
#> [545,]  77   4      0        180        1    98
#> [546,]  81   1      0        108        0   129
#> [547,]  78  12      0        180        0   134
#> [548,]  79   1      0        125        0   193
#> [549,]  82  21      1          2        0   155
#> [550,]  80   6      0          6        1   110
#> [551,]  82   5      0        180        0   110
#> [552,]  83   5      0        180        0   148
#> [553,]  79   7      1          6        0   130
#> [554,]  81  11      1          8        0   160
#> [555,]  80  11      1          8        0   170
#> [556,]  78  23      1         10        1   145
#> [557,]  78   9      1          4        1   120
#> [558,]  82   8      1          1        0   128
#> [559,]  79   1      0        180        1   170
#> [560,]  81  20      1          9        0   170
#> [561,]  83   8      0          8        0   115
#> [562,]  81  16      0         16        1   110
#> [563,]  80   6      1          0        1   150
#> [564,]  80  11      1          8        0   110
#> [565,]  81   8      0        180        0   146
#> [566,]  80   8      1          7        0   160
#> [567,]  79   7      0        177        0   197
#> [568,]  79   0      1          0        1    96
#> [569,]  85   4      0        180        0    90
#> [570,]  81   2      1          1        0   198
#> [571,]  83   2      0          2        1   155
#> [572,]  82   6      0        128        1   100
#> [573,]  80   3      1          1        1   230
#> [574,]  82  23      1          0        0   110
#> [575,]  84   4      0          4        1    85
#> [576,]  81   1      0          1        1   150
#> [577,]  84   1      0         38        1   205
#> [578,]  83   3      0        180        0   174
#> [579,]  85   3      1          2        1   160
#> [580,]  80  13      1          8        1   140
#> [581,]  80   6      0         71        1   189
#> [582,]  82  19      0         19        0   120
#> [583,]  80  30      1         13        0   220
#> [584,]  83   9      0        180        0   198
#> [585,]  79  14      1          0        0   110
#> [586,]  83   3      0        114        0    98
#> [587,]  81  14      1         12        1   128
#> [588,]  82   0      0          2        1   100
#> [589,]  84  15      1         13        1   110
#> [590,]  81   1      0          1        1   145
#> [591,]  81  12      0         12        1   163
#> [592,]  82  16      1          8        0   103
#> [593,]  82   5      1          0        1   146
#> [594,]  81   4      0          4        0   160
#> [595,]  86  12      0        180        1   120
#> [596,]  81  19      1         14        0   120
#> [597,]  80   2      0         88        0   135
#> [598,]  83   7      0        126        0   135
#> [599,]  86   8      0          8        1   132
#> [600,]  81  16      1          9        0   180
#> [601,]  84   6      0        165        0   145
#> [602,]  84   3      0        180        1   120
#> [603,]  81   4      0        180        0   160
#> [604,]  82   1      0        180        1   193
#> [605,]  83   4      0          4        0   130
#> [606,]  87   2      0          5        1   137
#> [607,]  86   6      1          0        1   140
#> [608,]  84  16      0         70        1   150
#> [609,]  83  19      0         43        0   150
#> [610,]  84   3      1          2        0   125
#> [611,]  86   2      0        180        1   169
#> [612,]  88  14      1          3        1   130
#> [613,]  84   3      0          3        1   121
#> [614,]  84   7      1          2        0   148
#> [615,]  84   9      0         92        1   110
#> [616,]  84   3      0        180        1   170
#> [617,]  86   4      0         38        1   122
#> [618,]  82   4      0          4        0   130
#> [619,]  85   3      0          3        1   113
#> [620,]  86   6      0          6        1   117
#> [621,]  83  20      1          3        1   150
#> [622,]  88   4      0          4        1   115
#> [623,]  85  22      0         22        1   184
#> [624,]  83   9      0         65        1   150
#> [625,]  86   9      1          7        1   142
#> [626,]  86   6      0         46        0   173
#> [627,]  88   3      0        115        0   110
#> [628,]  86  15      1          8        1   109
#> [629,]  88   4      0          4        0    86
#> [630,]  89   4      0          4        1   153
#> [631,]  89   5      0        119        1   140
#> [632,]  84   2      0        110        1   174
#> [633,]  87  15      1          9        1   138
#> [634,]  84   0      0        180        1   136
#> [635,]  89  10      0         46        1   170
#> [636,]  90  14      0         14        1   100
#> [637,]  88   1      0          1        0   135
#> [638,]  86   4      0        180        1   145
#> [639,]  91   8      0          8        0   100
#> [640,]  87   2      0        180        0   160
#> [641,]  87   6      1          0        0   125
#> [642,]  91  10      0        145        0   135
#> [643,]  88   7      0         24        0   119
#> [644,]  90  11      1         10        1   186
#> [645,]  86  10      0        180        1   137
#> [646,]  90   4      1          0        0   121
#> [647,]  87  43      0        178        1   130
#> [648,]  87   5      0         36        1   150
#> [649,]  90   5      1          0        1   125
#> [650,]  88   3      1          2        0   159
#> [651,]  92   1      0          1        1   167
#> [652,]  89  12      1          0        1   130
#> [653,]  89   2      0        168        0   118
#> [654,]  89  52      0         52        1   130
#> [655,]  92   7      0          7        1   110
#> [656,]  91   0      0          0        0     0
#> [657,]  89  14      0        180        1    84
#> [658,]  94   6      0         50        0    78
#> [659,]  90   1      0          1        1   118
#> [660,]  93   8      0        179        1   110
#> [661,]  94   8      0          8        1   142
#> [662,]  92   4      0         76        1   149
#> [663,]  91   1      0        180        0   158
#> [664,]  96   3      0         12        1    97
#> [665,]  95   8      1          5        1   150
#> [666,]  91  12      0         53        1   212
#> [667,]  91   7      0          7        0   135
#> [668,]  93   0      1          0        1   122
#> [669,]  92   5      0         69        0   139
#> [670,]  93   4      0        180        1   135
#> 
#> $y
#>   [1] 180.0+   5.0+   2.0+   5.0+ 180.0+   5.0+ 180.0+   2.0+ 115.0  180.0+
#>  [11] 180.0+  12.0    5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   5.0+
#>  [21]   2.0+ 180.0+ 180.0+ 180.0+   3.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [31]   2.0+ 180.0+ 180.0+ 180.0+ 180.0+   5.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [41] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   5.0+ 180.0+ 180.0+
#>  [51] 180.0+ 180.0+   5.0+ 180.0+ 180.0+ 177.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [61] 180.0+ 180.0+  10.0+ 172.0+ 180.0+ 180.0+ 180.0+   7.0  180.0+ 180.0+
#>  [71]   2.0    1.0  180.0+ 179.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [81] 180.0+ 180.0+ 180.0+  36.0  180.0+   4.0+ 180.0+ 180.0+ 180.0+  77.0 
#>  [91] 180.0+ 180.0+   5.0  180.0+ 180.0+ 180.0+   4.0+  85.0  166.0+  16.0+
#> [101]   7.0+   6.0+ 180.0+ 180.0+  13.0+ 171.0+ 180.0+ 174.0+   6.0+   1.0 
#> [111] 180.0+   9.0+ 180.0+ 175.0+   2.0    7.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [121] 180.0+  16.0+ 180.0+  16.0   15.0+ 180.0+ 180.0+ 180.0+  12.0+ 134.0+
#> [131] 180.0+   2.0    3.0+ 180.0+ 180.0+ 140.0  180.0+   1.0  165.0  180.0+
#> [141] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 171.0+   5.0+ 180.0+
#> [151] 180.0+   5.0+ 180.0+   9.0+ 180.0+   2.0+ 180.0+ 180.0+   2.0  180.0+
#> [161] 180.0+ 180.0+ 180.0+  64.0  180.0+ 180.0+   0.5  180.0+ 171.0+ 180.0+
#> [171] 180.0+   3.0    1.0  180.0+ 180.0+ 180.0+  10.0+ 180.0+ 180.0+  45.0 
#> [181]   3.0+  19.0  180.0+   9.0+ 180.0+ 172.0+ 172.0+  24.0  180.0+   8.0 
#> [191] 180.0+ 180.0+   1.0+  15.0  180.0+ 180.0+  13.0+   8.0+ 180.0+ 180.0+
#> [201] 180.0+ 170.0  180.0+ 169.0    6.0  180.0+ 180.0+ 180.0+ 180.0+   3.0+
#> [211]  30.0  180.0+  13.0+   5.0   18.0  180.0+ 180.0+ 180.0+   1.0  180.0+
#> [221] 180.0+ 180.0+ 180.0+ 180.0+   4.0+ 180.0+ 180.0+ 180.0+   9.0    7.0+
#> [231]  22.0  180.0+   1.0    1.0    3.0+ 180.0+ 180.0+ 180.0+  12.0  180.0+
#> [241] 180.0+ 180.0+  36.0   88.0  180.0+ 180.0+ 180.0+ 180.0+   0.5  180.0+
#> [251]  12.0+ 180.0+ 180.0+ 180.0+ 180.0+   3.0  180.0+ 180.0+  11.0+ 180.0+
#> [261] 180.0+ 180.0+   3.0+   2.0+ 103.0   15.0  180.0+   5.0+ 180.0+ 179.0+
#> [271]  14.0+   3.0    0.5+ 180.0+   3.0+ 180.0+   7.0+   8.0    5.0   16.0 
#> [281]   1.0  180.0+ 180.0+  11.0+ 180.0+  79.0   80.0   15.0+   4.0+ 180.0+
#> [291] 180.0+ 180.0+ 174.0+ 180.0+ 180.0+ 180.0+ 180.0+   8.0+   3.0  175.0 
#> [301] 180.0+  10.0  180.0+ 180.0+ 180.0+   6.0  180.0+  19.0+  12.0   99.0 
#> [311] 180.0+ 180.0+ 180.0+ 180.0+   0.5  180.0+ 180.0+ 180.0+  21.0+ 180.0+
#> [321]  18.0+   5.0    7.0+ 150.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [331] 174.0+   6.0  180.0+ 180.0+ 180.0+ 180.0+  14.0+ 180.0+   7.0+   2.0 
#> [341]   5.0+ 103.0    3.0+ 180.0+   5.0+ 180.0+ 180.0+   2.0+ 180.0+ 172.0+
#> [351] 180.0+ 180.0+   7.0    8.0+  13.0+ 180.0+ 180.0+  51.0   19.0    1.0 
#> [361]  60.0   76.0  180.0+  10.0+ 180.0+ 162.0    7.0+ 124.0    9.0  180.0+
#> [371] 180.0+ 180.0+  12.0  180.0+ 180.0+   2.0  180.0+ 173.0+ 180.0+ 180.0+
#> [381] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+  16.0+ 180.0+ 180.0+   6.0 
#> [391] 180.0+   7.0+  15.0    3.0+  13.0+ 180.0+ 180.0+   2.0    3.0+  20.0 
#> [401] 180.0+  20.0  180.0+   8.0   87.0   12.0  180.0+ 180.0+ 180.0+ 180.0+
#> [411] 175.0  180.0+  14.0+  10.0+ 180.0+   8.0+ 180.0+ 159.0  180.0+  10.0 
#> [421]   1.0  180.0+  13.0  180.0+ 104.0+ 180.0+  11.0    5.0  180.0+  12.0 
#> [431] 180.0+ 180.0+  34.0  180.0+ 177.0+ 180.0+   5.0   11.0+ 180.0+ 180.0+
#> [441]  10.0  180.0+   3.0  180.0+ 180.0+ 180.0+ 180.0+   6.0  180.0+ 180.0+
#> [451]  85.0  180.0+  17.0+ 180.0+   0.5  180.0+ 180.0+ 180.0+ 180.0+   4.0 
#> [461]  52.0  180.0+ 180.0+ 180.0+   8.0  180.0+  33.0    5.0  180.0+  12.0 
#> [471] 180.0+   7.0+   3.0  168.0+ 180.0+  18.0   47.0   11.0  180.0+   7.0 
#> [481]   8.0+ 180.0+ 180.0+ 119.0   12.0  180.0+   8.0    1.0   80.0  180.0+
#> [491]   4.0+ 180.0+  11.0    3.0   29.0   24.0   23.0  180.0+   3.0+ 180.0+
#> [501] 180.0+ 180.0+   1.0   11.0    4.0    4.0   10.0+ 180.0+   6.0    3.0+
#> [511]   2.0+ 171.0   43.0    3.0    6.0  180.0+ 180.0+   8.0   40.0   59.0 
#> [521]  17.0  161.0   10.0+ 180.0+ 164.0  118.0  173.0  180.0+ 180.0+   7.0+
#> [531]  22.0   15.0+   5.0+ 180.0+   3.0   71.0   20.0+   3.0+  10.0  180.0+
#> [541]  10.0    6.0  180.0+   5.0  180.0+ 108.0  180.0+ 125.0  180.0+   6.0 
#> [551] 180.0+ 180.0+ 180.0+ 180.0+ 169.0   70.0  180.0+ 180.0+ 180.0+  20.0 
#> [561]   8.0+  16.0  180.0+ 180.0+ 180.0+ 180.0+ 177.0+   0.5  180.0+ 180.0+
#> [571]   2.0  128.0    3.0+  62.0    4.0    1.0   38.0  180.0+ 180.0+ 180.0+
#> [581]  71.0   19.0   30.0  180.0+ 180.0+ 114.0  180.0+   2.0  180.0+   1.0 
#> [591]  12.0   16.0+   5.0+   4.0+ 180.0+ 180.0+  88.0  126.0    8.0  180.0+
#> [601] 165.0  180.0+ 180.0+ 180.0+   4.0+   5.0    6.0   70.0   43.0  180.0+
#> [611] 180.0+  14.0    3.0  180.0+  92.0  180.0+  38.0    4.0    3.0+   6.0+
#> [621]  20.0    4.0   22.0   65.0   11.0   46.0  115.0  180.0+   4.0    4.0 
#> [631] 119.0  110.0  180.0+ 180.0+  46.0   14.0    1.0+ 180.0+   8.0  180.0+
#> [641]  25.0  145.0   24.0   11.0  180.0+   4.0  178.0+  36.0   89.0   75.0 
#> [651]   1.0  180.0+ 168.0   52.0    7.0    0.5  180.0+  50.0    1.0+ 179.0+
#> [661]   8.0+  76.0  180.0+  12.0    8.0   53.0    7.0+   0.5   69.0  180.0+
#> 
#> $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 
#>   0.8517356