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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 = 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 0.185100
#> 2   1  0.60 0.168600
#> 3   2  1.56 0.153600
#> 4   2  2.39 0.140000
#> 5   2  3.11 0.127600
#> 6   3  6.02 0.116200
#> 7   3  8.30 0.105900
#> 8   3 10.02 0.096490
#> 9   3 11.37 0.087920
#> 10  3 12.46 0.080110
#> 11  3 13.34 0.072990
#> 12  5 14.11 0.066510
#> 13  5 14.77 0.060600
#> 14  5 15.32 0.055220
#> 15  5 15.78 0.050310
#> 16  5 16.17 0.045840
#> 17  5 16.50 0.041770
#> 18  6 16.78 0.038060
#> 19  6 17.04 0.034680
#> 20  6 17.26 0.031600
#> 21  6 17.45 0.028790
#> 22  6 17.61 0.026230
#> 23  6 17.74 0.023900
#> 24  6 17.85 0.021780
#> 25  6 17.95 0.019840
#> 26  6 18.03 0.018080
#> 27  6 18.09 0.016470
#> 28  6 18.15 0.015010
#> 29  6 18.20 0.013680
#> 30  6 18.24 0.012460
#> 31  6 18.27 0.011360
#> 32  6 18.30 0.010350
#> 33  6 18.32 0.009427
#> 34  6 18.34 0.008590
#> 35  6 18.36 0.007827
#> 36  6 18.37 0.007131
#> 37  6 18.38 0.006498
#> 38  6 18.39 0.005921
#> 39  6 18.40 0.005395
#> 40  6 18.41 0.004915
#> 41  6 18.41 0.004479
#> 42  6 18.42 0.004081
#> 43  6 18.42 0.003718
#> 44  6 18.43 0.003388
#> 
#> $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      1          1        1   112
#>   [8,]  37   9      0        180        1   151
#>   [9,]  38  13      1          0        1   161
#>  [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,]  38  16      1         10        0   160
#>  [15,]  38  12      1         11        1    92
#>  [16,]  42   3      1          1        1   130
#>  [17,]  37   1      1          0        1   146
#>  [18,]  42   2      0        180        1   100
#>  [19,]  40   6      0        180        1   138
#>  [20,]  42   2      0        180        0   100
#>  [21,]  43   3      1          0        1   100
#>  [22,]  40   1      1          0        1   145
#>  [23,]  43   4      1          0        1   130
#>  [24,]  42   4      0        180        0   162
#>  [25,]  43   2      1          1        1   116
#>  [26,]  42   2      0        180        1   124
#>  [27,]  41  10      1          8        0   150
#>  [28,]  44   2      1          1        1   150
#>  [29,]  43   2      0        180        1   140
#>  [30,]  45   2      0        180        1   140
#>  [31,]  45   3      0        150        0   130
#>  [32,]  44   3      1          0        1   180
#>  [33,]  47   6      1          0        1   116
#>  [34,]  46  13      1         10        0   100
#>  [35,]  44   0      1          0        1    96
#>  [36,]  47   4      1          3        1   160
#>  [37,]  43   3      1          0        1   124
#>  [38,]  49   5      0         73        1   136
#>  [39,]  45   5      0          5        0   141
#>  [40,]  46   6      1          0        1   100
#>  [41,]  44   4      1          0        1   114
#>  [42,]  47   2      0        180        0   108
#>  [43,]  46   5      1          3        0   130
#>  [44,]  46   4      0        180        1   121
#>  [45,]  44   2      0        180        0   142
#>  [46,]  45   9      1          0        1   145
#>  [47,]  47   3      1          1        1   120
#>  [48,]  48   3      0        180        0   154
#>  [49,]  47   9      1          6        0   170
#>  [50,]  49   4      0        180        0   117
#>  [51,]  47  10      0         10        1   140
#>  [52,]  50   1      1          0        1   129
#>  [53,]  48   2      1          0        0   184
#>  [54,]  47   7      0        180        0   145
#>  [55,]  50   4      1          1        0   125
#>  [56,]  50   6      1          2        1   140
#>  [57,]  49   7      1          7        1   110
#>  [58,]  46   3      1          1        1   140
#>  [59,]  49   2      0          2        0   105
#>  [60,]  51   1      0          1        1   145
#>  [61,]  49  15      1         11        1   160
#>  [62,]  49  23      0        179        1   112
#>  [63,]  52   2      0        180        1   170
#>  [64,]  50   9      0        180        0   130
#>  [65,]  49   7      1          4        1    90
#>  [66,]  47   8      0        180        0   160
#>  [67,]  47   6      0        180        1   162
#>  [68,]  51   8      0        180        1   140
#>  [69,]  52   2      0        180        0   155
#>  [70,]  46   3      0        180        1   120
#>  [71,]  46   1      1          1        0   145
#>  [72,]  50   4      1          1        0   150
#>  [73,]  48   7      1          0        1   110
#>  [74,]  48  17      1         10        0   111
#>  [75,]  47   2      1          1        0   110
#>  [76,]  52   4      1          4        0   152
#>  [77,]  49   9      1          3        0   102
#>  [78,]  49  15      0        180        1   160
#>  [79,]  53   5      0        180        1   140
#>  [80,]  53   7      1          0        0   199
#>  [81,]  51   3      1          1        0   140
#>  [82,]  50   2      0          5        1   106
#>  [83,]  50  10      1          6        0   122
#>  [84,]  53   8      1          7        0   160
#>  [85,]  51  25      1          1        0   202
#>  [86,]  49   5      1          2        1   150
#>  [87,]  53   4      0          4        0   140
#>  [88,]  48  11      1         10        0   120
#>  [89,]  53   4      1          0        1   156
#>  [90,]  51  13      0         99        1   160
#>  [91,]  54   9      1          0        1   138
#>  [92,]  49  16      0         16        0   125
#>  [93,]  55   3      1          1        0   150
#>  [94,]  54   9      1          1        0   130
#>  [95,]  55   4      1          2        0   150
#>  [96,]  51  13      1         11        0   145
#>  [97,]  54   4      1          0        1   121
#>  [98,]  52   4      0        180        0   183
#>  [99,]  50   3      0        174        1   153
#> [100,]  49   6      1          0        1   130
#> [101,]  49   1      0          1        1   110
#> [102,]  50   7      1          1        0   156
#> [103,]  53   9      0          9        1    95
#> [104,]  53   8      1          0        1   130
#> [105,]  50   7      1          0        1   127
#> [106,]  56   4      1          1        1   130
#> [107,]  55   1      0        180        0   127
#> [108,]  55   2      0          2        0   145
#> [109,]  54   1      0        180        0   162
#> [110,]  54   7      1          0        1   100
#> [111,]  56   3      0        180        1   193
#> [112,]  52   8      0        180        0   119
#> [113,]  55   6      0        180        0   170
#> [114,]  52  16      0         16        0   152
#> [115,]  52  16      1         14        0   170
#> [116,]  55   6      0        180        1   100
#> [117,]  55   6      1          5        1   138
#> [118,]  54  12      1          0        1   190
#> [119,]  54   7      1          2        0   129
#> [120,]  54   2      1          1        0   135
#> [121,]  54   2      1          1        1   176
#> [122,]  57   5      1          3        1   138
#> [123,]  57   1      0        180        1   156
#> [124,]  57   1      0          1        1   100
#> [125,]  52   2      0        180        0   140
#> [126,]  52  15      1         14        0   130
#> [127,]  56  14      1         11        0   130
#> [128,]  53   3      1          0        1   200
#> [129,]  58   8      0          8        1   130
#> [130,]  54   5      0        180        1   108
#> [131,]  55   3      1          1        1   156
#> [132,]  53  21      1         13        1   130
#> [133,]  59   3      1          1        0   172
#> [134,]  57   4      0        180        1   119
#> [135,]  58   6      1          0        1    90
#> [136,]  53  15      1         10        1   130
#> [137,]  54  17      1          8        1   227
#> [138,]  55  13      0        166        1   140
#> [139,]  56   5      0          5        1   150
#> [140,]  54  23      1          8        0   120
#> [141,]  53   4      0        147        1   145
#> [142,]  53   7      1          0        1   120
#> [143,]  57  11      1         10        1   129
#> [144,]  55   3      1          2        0   140
#> [145,]  55   5      0          5        1   131
#> [146,]  54   7      1          0        1   141
#> [147,]  56   4      0          4        0   164
#> [148,]  59  15      1         10        0   140
#> [149,]  58   9      1          0        1   180
#> [150,]  58   1      1          1        1   200
#> [151,]  55   5      1          0        0   140
#> [152,]  56   7      1          5        1   120
#> [153,]  55   2      0          2        0   106
#> [154,]  59   9      1          1        1   125
#> [155,]  57   1      0        180        0   148
#> [156,]  60  11      1          9        0   106
#> [157,]  58   4      1          0        1   160
#> [158,]  57   2      0          2        1   120
#> [159,]  57   5      0        180        1   130
#> [160,]  58  11      1          9        1   124
#> [161,]  55   5      1          0        1   160
#> [162,]  57  10      1          9        0   103
#> [163,]  59   4      1          0        1   152
#> [164,]  58   4      1          3        0   120
#> [165,]  59   2      1          1        0   140
#> [166,]  58  14      1          6        0   190
#> [167,]  61   4      1          3        0   151
#> [168,]  61   9      1          8        0   150
#> [169,]  58   1      0          1        1   100
#> [170,]  57   2      1          0        1   116
#> [171,]  57  11      0        180        1   150
#> [172,]  61   3      0         17        0   143
#> [173,]  56  14      0         45        0   130
#> [174,]  57   3      1          2        0   120
#> [175,]  58  19      1         13        1   140
#> [176,]  59   9      1          0        1    80
#> [177,]  58  11      0        172        1   135
#> [178,]  60  12      1          0        1   114
#> [179,]  61   4      1          0        1   115
#> [180,]  56   8      1          8        0   120
#> [181,]  61  13      1         12        1   130
#> [182,]  59  11      1          8        1   190
#> [183,]  57   1      0          1        0   126
#> [184,]  57  15      1         13        1   110
#> [185,]  59   5      1          2        0   182
#> [186,]  58   5      1          1        1   135
#> [187,]  59  10      0        180        0   160
#> [188,]  61   8      0         77        0   120
#> [189,]  58   8      1          5        0   152
#> [190,]  57   3      1          0        0   100
#> [191,]  61  18      0        170        0   140
#> [192,]  61  28      1          7        0   133
#> [193,]  58   8      1          3        1   150
#> [194,]  61   7      0          7        1   150
#> [195,]  62   4      1          0        0   160
#> [196,]  58   3      1          0        1   146
#> [197,]  58   2      0         30        0   202
#> [198,]  59   1      0        180        0   155
#> [199,]  63   6      0         28        1   120
#> [200,]  61  13      0         13        0   120
#> [201,]  57  18      1          9        1    93
#> [202,]  57   2      1          1        0   159
#> [203,]  62  17      1         10        1   180
#> [204,]  58   7      0        180        1   150
#> [205,]  61   7      0        180        0   135
#> [206,]  63   4      1          3        0   222
#> [207,]  62   3      0        180        1   105
#> [208,]  63   4      0        180        1   190
#> [209,]  63  15      1         10        1   126
#> [210,]  64   4      0        180        0   130
#> [211,]  60  18      1         13        0   132
#> [212,]  58   9      1          9        0   110
#> [213,]  62   7      0          7        0   150
#> [214,]  59   1      0         22        1   162
#> [215,]  60   7      1          5        1   141
#> [216,]  60   7      0          7        0   140
#> [217,]  59   5      1          1        0   148
#> [218,]  65  13      0        180        1   100
#> [219,]  63   1      0          1        0   162
#> [220,]  62   6      0        180        0   170
#> [221,]  64  10      1          9        0   160
#> [222,]  62   6      0          6        0   120
#> [223,]  63  12      1         10        0   200
#> [224,]  59  10      0        180        1   130
#> [225,]  61   6      1          1        1   117
#> [226,]  64  12      1         11        0   160
#> [227,]  64   6      1          0        1   140
#> [228,]  63  14      1          9        0   123
#> [229,]  63   4      1          3        0   162
#> [230,]  66   3      1          1        0   127
#> [231,]  61  10      1          2        1   194
#> [232,]  63  12      1          9        0   114
#> [233,]  63   7      0        180        0   120
#> [234,]  66   5      1          0        1   110
#> [235,]  65  10      1          8        1   120
#> [236,]  64   0      0          0        1    90
#> [237,]  60   6      0        180        0   130
#> [238,]  64  21      1         10        0   190
#> [239,]  64   9      0        180        0   150
#> [240,]  65   3      0        180        1   190
#> [241,]  63  16      1          7        1   110
#> [242,]  64   7      0        180        1   120
#> [243,]  66   6      1          1        1   130
#> [244,]  62   3      1          1        1   199
#> [245,]  65   6      0          9        0   112
#> [246,]  65   3      1          0        1    80
#> [247,]  63   5      1          4        0   170
#> [248,]  63   2      1          1        0   180
#> [249,]  62  13      1         11        0   180
#> [250,]  66  18      1          5        0   142
#> [251,]  66  16      1         11        1   169
#> [252,]  62   9      0        180        0   145
#> [253,]  61  14      1          5        0   140
#> [254,]  61  15      1         10        0   130
#> [255,]  63   9      1          8        1   160
#> [256,]  63   2      1          0        0   140
#> [257,]  64  19      1          8        1   160
#> [258,]  65   8      1          0        1   140
#> [259,]  67   6      0        180        1   170
#> [260,]  68   5      1          4        1   150
#> [261,]  64  13      1         12        1   150
#> [262,]  64   6      1          0        1   125
#> [263,]  66  13      1          0        0   118
#> [264,]  64  14      1         13        1   150
#> [265,]  65   3      0          3        0   105
#> [266,]  64   0      0          0        1   148
#> [267,]  67   4      1          3        0   130
#> [268,]  66   3      1          0        1   135
#> [269,]  66   6      1          0        1   140
#> [270,]  65   2      1          1        1   170
#> [271,]  68   1      0        180        1   166
#> [272,]  64  10      1          9        1   110
#> [273,]  63   7      1          0        0   162
#> [274,]  67   8      1          1        1   130
#> [275,]  63  10      0         16        1   160
#> [276,]  66  14      0        180        0   130
#> [277,]  68  18      0        180        1   260
#> [278,]  65  17      1         14        1   100
#> [279,]  63   1      1          0        1   155
#> [280,]  63  10      0         18        1   130
#> [281,]  67  11      0         11        0   150
#> [282,]  68  11      0        180        0   160
#> [283,]  66  12      1         10        1   150
#> [284,]  65  15      1         12        1   150
#> [285,]  66  11      1          0        0   100
#> [286,]  65   4      1          2        1   145
#> [287,]  63   2      0        180        0   150
#> [288,]  65  11      1          6        0   130
#> [289,]  69  21      1         10        0   180
#> [290,]  66   9      1          8        0   130
#> [291,]  68  14      1         13        1   140
#> [292,]  69   1      1          0        0   170
#> [293,]  67   1      0        180        1   160
#> [294,]  68  10      1         10        1   150
#> [295,]  67   7      1          4        1   130
#> [296,]  63   2      1          0        0    99
#> [297,]  67   2      0        180        0   184
#> [298,]  66  19      1         12        1   150
#> [299,]  69   6      0         99        1   140
#> [300,]  65   4      1          1        0   130
#> [301,]  64   4      0        179        0   160
#> [302,]  66   4      0        180        1   130
#> [303,]  64   0      1          0        1   118
#> [304,]  67   2      0         18        0   131
#> [305,]  66   4      0        180        0   177
#> [306,]  65  13      1         12        1   130
#> [307,]  69   8      0         93        0   140
#> [308,]  68  18      1          0        1   160
#> [309,]  65   6      0        101        1   115
#> [310,]  68   4      0          4        1   190
#> [311,]  71   3      0          5        0   112
#> [312,]  68   7      0        150        0   210
#> [313,]  71  20      1          0        1   160
#> [314,]  67   2      0        180        0   128
#> [315,]  66   9      1          3        1   151
#> [316,]  70  14      0        171        0   166
#> [317,]  66   4      0        180        0   130
#> [318,]  67  10      1          9        0   200
#> [319,]  67   6      1          4        0   130
#> [320,]  68  18      1         14        1   170
#> [321,]  69   0      0          0        1   148
#> [322,]  65   2      0        180        0   130
#> [323,]  68   7      1          0        1   150
#> [324,]  69   3      1          2        0   151
#> [325,]  67  14      1         13        0   130
#> [326,]  65  14      1         13        1   150
#> [327,]  69   8      0        180        1   180
#> [328,]  66   2      0          2        1   228
#> [329,]  71   6      0         45        1   158
#> [330,]  69   5      0          5        1   142
#> [331,]  71   3      0        103        0   133
#> [332,]  69   3      0          3        1   130
#> [333,]  70  22      1         13        0   103
#> [334,]  67   5      0          5        0   130
#> [335,]  68   6      0        180        0   145
#> [336,]  69   6      1          4        1   174
#> [337,]  68  18      0         18        1   100
#> [338,]  69  11      1          0        1   120
#> [339,]  66   2      0        180        0   130
#> [340,]  69   4      1          3        0   132
#> [341,]  68   2      0          7        1   130
#> [342,]  67  13      1          9        0   130
#> [343,]  70   9      0        180        1   142
#> [344,]  67  22      1          1        1   140
#> [345,]  67  12      1          8        0   120
#> [346,]  69   1      0          1        1   110
#> [347,]  67   4      0         60        1   136
#> [348,]  69   5      0         76        0   120
#> [349,]  67   8      1          0        1   130
#> [350,]  72  13      1         11        1   195
#> [351,]  72  30      1          0        1   145
#> [352,]  68   7      1          2        0   135
#> [353,]  69  10      1          6        1   120
#> [354,]  70  11      0        180        1   210
#> [355,]  72  19      1          8        0   120
#> [356,]  72  12      1         10        0   170
#> [357,]  67   8      0        180        1   170
#> [358,]  67   9      0        180        0   158
#> [359,]  70   5      0        180        0   150
#> [360,]  72   2      0          2        1   100
#> [361,]  67   4      1          1        0   134
#> [362,]  71   1      0        173        1   188
#> [363,]  68  23      0        180        1   220
#> [364,]  70   3      0        180        0   121
#> [365,]  69   3      0        180        0   220
#> [366,]  68   4      1          3        0   210
#> [367,]  71   5      0        180        0   191
#> [368,]  72  16      1          1        1   130
#> [369,]  70   4      0        180        0   180
#> [370,]  69   1      1          0        0   155
#> [371,]  73   6      1          0        1   270
#> [372,]  72   8      1          1        1   150
#> [373,]  71   2      1          0        1   180
#> [374,]  73   7      0          7        1   140
#> [375,]  70   3      0          3        1   159
#> [376,]  70  13      1          9        0   100
#> [377,]  73   0      0        180        1   161
#> [378,]  74   8      1          0        1    85
#> [379,]  73   4      0        180        1   154
#> [380,]  71  15      1         11        0   165
#> [381,]  68   9      0        180        1   120
#> [382,]  74   0      1          0        1    90
#> [383,]  73   3      1          0        1   136
#> [384,]  71  17      1         11        0   160
#> [385,]  71   8      1          7        0   149
#> [386,]  73  10      1          8        0   106
#> [387,]  69  12      1          1        1   149
#> [388,]  70  26      1         11        1   120
#> [389,]  74   4      0          4        0   120
#> [390,]  73   4      0         58        1   160
#> [391,]  70   3      0        180        1   154
#> [392,]  73   6      0        180        0   110
#> [393,]  72   8      1          0        1   140
#> [394,]  74   3      0          3        1   150
#> [395,]  73  17      1         11        0   140
#> [396,]  71  13      1          8        0   121
#> [397,]  69   2      1          1        1    80
#> [398,]  70   4      1          0        1   140
#> [399,]  74   7      1          0        1   117
#> [400,]  69   7      0        180        1   144
#> [401,]  72  15      1         13        0   156
#> [402,]  70   8      0          8        0   120
#> [403,]  75   1      0          1        0   133
#> [404,]  75   2      1          1        0   145
#> [405,]  72  10      1          9        1   160
#> [406,]  73  10      1         10        1   120
#> [407,]  74  15      1          9        1   179
#> [408,]  71   2      0         10        1   112
#> [409,]  73   1      0          1        1    80
#> [410,]  75   9      1          7        0   140
#> [411,]  75  13      1          1        1   130
#> [412,]  71  11      1          8        0   110
#> [413,]  71   4      0          4        0   134
#> [414,]  72  15      1         12        1   120
#> [415,]  73  10      1          8        0   120
#> [416,]  70   7      1          4        0   184
#> [417,]  72   7      0         57        1   145
#> [418,]  73  10      0        180        0   162
#> [419,]  72  11      0         11        1   140
#> [420,]  70   3      0          3        0   150
#> [421,]  73   5      1          3        1   112
#> [422,]  73  12      1         12        1   140
#> [423,]  75   1      0        180        1   140
#> [424,]  72   4      1          0        1   197
#> [425,]  71   3      1          0        0   144
#> [426,]  73   5      0        180        0   126
#> [427,]  73   4      0        180        0   124
#> [428,]  76   3      1          0        1   120
#> [429,]  77   4      0          4        0   185
#> [430,]  72   7      1          2        0   142
#> [431,]  73  15      0         15        1   160
#> [432,]  71  16      0        180        0   140
#> [433,]  74   7      0        180        1   150
#> [434,]  74   3      0          3        1   128
#> [435,]  76   1      0        180        0   114
#> [436,]  74   2      1          1        0   140
#> [437,]  75  23      1         14        1   110
#> [438,]  72   4      0         85        1   120
#> [439,]  72   4      1          3        0   160
#> [440,]  76  17      1          0        1   200
#> [441,]  73   4      1          3        1   125
#> [442,]  76  13      1         10        0   110
#> [443,]  75   0      0          0        1   130
#> [444,]  73  13      1         11        0   195
#> [445,]  75  12      0         12        1   160
#> [446,]  74   8      1          0        1   105
#> [447,]  76  13      1          8        1   148
#> [448,]  74   6      0        180        0   160
#> [449,]  76   4      0          4        1   155
#> [450,]  74   2      0        180        0   111
#> [451,]  73   1      0         52        1   105
#> [452,]  73   0      0        180        0   156
#> [453,]  72   5      0        180        0   120
#> [454,]  76   5      0        180        0   185
#> [455,]  74  10      1          0        1   135
#> [456,]  74   8      1          8        1   170
#> [457,]  75   9      0        180        1   140
#> [458,]  73  33      1         12        1   175
#> [459,]  77  12      1          9        1   100
#> [460,]  73  10      1          9        0   146
#> [461,]  77  12      0        180        0   130
#> [462,]  77   1      1          0        1    90
#> [463,]  76  12      1         11        1   120
#> [464,]  78   5      1          0        1   170
#> [465,]  73   7      1          0        0   174
#> [466,]  74   6      0         79        1   140
#> [467,]  75   6      0        180        0   150
#> [468,]  79  10      1          8        0   190
#> [469,]  78  18      0         18        1   144
#> [470,]  77   3      0        180        0   110
#> [471,]  76  29      0         47        0    90
#> [472,]  73  11      1          2        1   110
#> [473,]  74   2      0        180        0   100
#> [474,]  74  15      0        180        1   172
#> [475,]  78   8      1          6        1   110
#> [476,]  76  13      1          1        1   170
#> [477,]  80  10      1          6        1   147
#> [478,]  78   0      0        180        1   212
#> [479,]  78  13      1          5        0   130
#> [480,]  75  12      1          1        1   120
#> [481,]  78  15      0        180        1   270
#> [482,]  80   8      0          8        1   120
#> [483,]  75  13      1          6        0   150
#> [484,]  74  10      1          8        0   135
#> [485,]  76   1      0          1        1    83
#> [486,]  79   4      0         80        0   145
#> [487,]  78  12      1          9        0   150
#> [488,]  75   4      1          0        0   212
#> [489,]  78  10      0        180        1   130
#> [490,]  75  11      1          4        0   162
#> [491,]  75   3      0          3        0     0
#> [492,]  76   7      0         29        1   150
#> [493,]  77  24      0         24        1   160
#> [494,]  79   8      0         32        1   120
#> [495,]  78   6      1          0        1   240
#> [496,]  76   3      1          0        1   140
#> [497,]  78  11      1          1        1   140
#> [498,]  79  11      0        180        0   160
#> [499,]  79   2      1          0        1   121
#> [500,]  78  14      1          0        1   140
#> [501,]  81   1      0          1        0   130
#> [502,]  78  11      1          8        1   118
#> [503,]  76   4      0          4        1   160
#> [504,]  79   4      0          4        1   125
#> [505,]  76  12      1         10        1   127
#> [506,]  77   6      0          6        1   107
#> [507,]  80   3      1          0        1   120
#> [508,]  75   2      1          1        1   204
#> [509,]  76   1      0          1        1   140
#> [510,]  77  31      1          3        1   161
#> [511,]  76   1      0          1        1    90
#> [512,]  78   7      1          0        1   110
#> [513,]  79   3      0          3        0   120
#> [514,]  77   7      0        180        1   170
#> [515,]  77   6      0          6        1   144
#> [516,]  79   4      1          0        1   120
#> [517,]  81   1      0        180        0   120
#> [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,]  80  15      1          0        1    90
#> [524,]  81   4      1          2        1   126
#> [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   2      0        175        0   172
#> [530,]  78   7      0          7        1   147
#> [531,]  77  13      1          0        1   190
#> [532,]  78   4      0        180        0   175
#> [533,]  79   3      0          3        1   101
#> [534,]  81   4      1          1        1   104
#> [535,]  78  20      1          0        1   109
#> [536,]  80   1      0          1        0   100
#> [537,]  78   3      1          1        1   152
#> [538,]  77  10      1          8        1   130
#> [539,]  77   5      0         85        0   188
#> [540,]  80   2      1          1        0   168
#> [541,]  79   6      0          6        0   152
#> [542,]  80   6      1          0        1   119
#> [543,]  78   2      0        180        0   148
#> [544,]  82   1      1          0        1    82
#> [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,]  84  22      1         10        0   180
#> [551,]  83   9      1          5        1   170
#> [552,]  82   5      0        180        0   110
#> [553,]  83   5      0        180        0   148
#> [554,]  79   7      1          6        0   130
#> [555,]  81  11      1          8        0   160
#> [556,]  80  11      1          8        0   170
#> [557,]  79   4      0          4        1   183
#> [558,]  79   1      0        180        1   170
#> [559,]  84   5      1          1        1    85
#> [560,]  81  20      1          9        0   170
#> [561,]  81  16      0         16        1   110
#> [562,]  80   6      1          0        1   150
#> [563,]  80  11      1          8        0   110
#> [564,]  81   2      1          1        0   198
#> [565,]  84   4      0        167        0   198
#> [566,]  80   3      1          1        1   230
#> [567,]  84   5      0        180        1   203
#> [568,]  84   4      0          4        1    85
#> [569,]  81   1      0          1        1   150
#> [570,]  81   4      0         90        1   138
#> [571,]  79   9      1          8        0   150
#> [572,]  85   3      1          2        1   160
#> [573,]  79   4      0          4        1    60
#> [574,]  80   6      0         71        1   189
#> [575,]  83   1      0          1        1   100
#> [576,]  82  19      0         19        0   120
#> [577,]  81  14      1         12        1   128
#> [578,]  83   2      0        154        0   130
#> [579,]  82   0      0          2        1   100
#> [580,]  85   9      1          6        1   160
#> [581,]  83   1      0        180        0   160
#> [582,]  81   4      0          4        0   175
#> [583,]  81   1      0          1        1   145
#> [584,]  82  16      1          8        0   103
#> [585,]  82   5      1          0        1   146
#> [586,]  83  12      1          2        1   170
#> [587,]  81  19      1         14        0   120
#> [588,]  82   3      1          2        0   130
#> [589,]  82  15      1          0        0   183
#> [590,]  80   2      0         88        0   135
#> [591,]  83   7      0        126        0   135
#> [592,]  86   8      0          8        1   132
#> [593,]  81  16      1          9        0   180
#> [594,]  86   3      0          3        1   140
#> [595,]  82   9      0        180        1   134
#> [596,]  81  13      0        180        0   152
#> [597,]  85   3      0          3        1   118
#> [598,]  83   9      0        180        1   149
#> [599,]  82   1      0        180        1   193
#> [600,]  83   4      0          4        0   130
#> [601,]  86  12      1          0        1   132
#> [602,]  84  16      0         70        1   150
#> [603,]  83  19      0         43        0   150
#> [604,]  84   3      1          2        0   125
#> [605,]  88  14      1          3        1   130
#> [606,]  83  13      1         12        0   170
#> [607,]  84   7      1          2        0   148
#> [608,]  87   2      0        180        0   113
#> [609,]  84   3      0        180        1   170
#> [610,]  86   4      0         38        1   122
#> [611,]  85   3      0          3        1   113
#> [612,]  86   6      0          6        1   117
#> [613,]  84  13      0         62        1   100
#> [614,]  86   6      1          1        0   112
#> [615,]  88   4      0          4        0   100
#> [616,]  83  20      1          3        1   150
#> [617,]  85  22      0         22        1   184
#> [618,]  86   9      1          7        1   142
#> [619,]  87   2      0        180        1   130
#> [620,]  86   6      0         46        0   173
#> [621,]  88   3      0        115        0   110
#> [622,]  83   3      0          3        1   130
#> [623,]  87   8      0          8        1   157
#> [624,]  86  15      1          8        1   109
#> [625,]  88   4      0          4        0    86
#> [626,]  89   5      0        119        1   140
#> [627,]  87   1      0          1        0   170
#> [628,]  84   2      0        110        1   174
#> [629,]  87  29      0         29        1    97
#> [630,]  84   0      0        180        1   136
#> [631,]  89  10      0         46        1   170
#> [632,]  90  14      0         14        1   100
#> [633,]  86   4      0        180        1   145
#> [634,]  91   8      0          8        0   100
#> [635,]  87   2      0        180        0   160
#> [636,]  86   3      1          0        1    80
#> [637,]  88   7      0         24        0   119
#> [638,]  88   8      0         50        1   154
#> [639,]  90  11      1         10        1   186
#> [640,]  87   6      0        126        1   168
#> [641,]  86  10      0        180        1   137
#> [642,]  86   9      1          7        0   130
#> [643,]  90   4      1          0        0   121
#> [644,]  91   1      0          1        1    74
#> [645,]  87  43      0        178        1   130
#> [646,]  90   5      1          0        1   125
#> [647,]  88   3      1          2        0   159
#> [648,]  92   1      0          1        1   167
#> [649,]  91   3      0         33        1   137
#> [650,]  87   7      0         74        1   105
#> [651,]  89   2      0        168        0   118
#> [652,]  91   5      0        169        1   176
#> [653,]  89  52      0         52        1   130
#> [654,]  90  18      0        180        0   188
#> [655,]  91   4      1          0        1   120
#> [656,]  90  19      1         11        1   129
#> [657,]  94   6      0         50        0    78
#> [658,]  90   1      0          1        1   118
#> [659,]  93   8      0        179        1   110
#> [660,]  94   8      0          8        1   142
#> [661,]  92   4      0         76        1   149
#> [662,]  91   1      0        180        0   158
#> [663,]  96   3      0         12        1    97
#> [664,]  95   8      1          5        1   150
#> [665,]  91   7      0          7        0   135
#> [666,]  92   5      0         69        0   139
#> [667,]  92   2      0          2        0   112
#> [668,]  93   4      0        180        1   135
#> [669,]  96   3      1          0        1   104
#> [670,]  96  15      1          0        1   140
#> 
#> $y
#>   [1] 180.0+   5.0+   2.0+   5.0+ 180.0+   5.0+   2.0+ 180.0+ 180.0+ 180.0+
#>  [11] 180.0+  12.0    5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [21]   3.0  180.0+ 180.0+ 180.0+   2.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [31] 150.0  180.0+   6.0+ 180.0+ 180.0+ 180.0+ 180.0+  73.0    5.0+ 180.0+
#>  [41] 180.0+ 180.0+   5.0+ 180.0+ 180.0+ 177.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [51]  10.0+ 172.0+ 180.0+ 180.0+ 180.0+ 180.0+   7.0  180.0+   2.0    1.0 
#>  [61] 179.0+ 179.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [71] 180.0+ 180.0+   7.0   88.0+ 180.0+   4.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [81] 180.0+   5.0  180.0+ 180.0+ 180.0+ 180.0+   4.0+ 180.0+ 166.0+  99.0 
#>  [91] 180.0+  16.0+ 180.0+ 180.0+ 180.0+  13.0+ 180.0+ 180.0+ 174.0+   6.0+
#> [101]   1.0  180.0+   9.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0  180.0+   7.0+
#> [111] 180.0+ 180.0+ 180.0+  16.0+  16.0  180.0+ 180.0+  12.0+ 180.0+ 180.0+
#> [121] 180.0+ 140.0  180.0+   1.0  180.0+ 180.0+ 180.0+ 180.0+   8.0+ 180.0+
#> [131] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 171.0+ 166.0+   5.0+ 180.0+
#> [141] 147.0+ 180.0+ 180.0+ 180.0+   5.0+ 180.0+   4.0+ 180.0+   9.0+   1.0 
#> [151] 180.0+ 180.0+   2.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0  180.0+ 180.0+
#> [161] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 171.0+ 180.0+ 180.0+   1.0  180.0+
#> [171] 180.0+  17.0   45.0    3.0+  19.0    9.0+ 172.0+ 172.0+ 180.0+   8.0 
#> [181] 180.0+ 180.0+   1.0+  15.0  180.0+ 180.0+ 180.0+  77.0    8.0+ 180.0+
#> [191] 170.0   94.0  180.0+   7.0  180.0+   3.0+  30.0  180.0+  28.0   13.0+
#> [201]  18.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [211] 180.0+   9.0    7.0+  22.0   84.0    7.0+ 180.0+ 180.0+   1.0  180.0+
#> [221] 167.0    6.0+ 180.0+ 180.0+ 180.0+  12.0  180.0+  14.0+ 180.0+   3.0+
#> [231]  88.0   12.0  180.0+ 180.0+ 180.0+   0.5  180.0+ 180.0+ 180.0+ 180.0+
#> [241] 180.0+ 180.0+ 180.0+ 180.0+   9.0    3.0  180.0+ 180.0+ 180.0+  18.0+
#> [251] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0+ 103.0   15.0  180.0+   5.0+
#> [261]  13.0  180.0+ 166.0+  14.0+   3.0    0.5+ 180.0+   3.0+ 180.0+ 175.0+
#> [271] 180.0+ 180.0+   7.0+   8.0   16.0  180.0+ 180.0+ 180.0+   1.0+  18.0 
#> [281]  11.0+ 180.0+  80.0   15.0+ 180.0+   4.0+ 180.0+ 180.0+ 174.0+ 180.0+
#> [291] 180.0+ 175.0  180.0+  10.0  180.0+ 180.0+ 180.0+  19.0+  99.0  180.0+
#> [301] 179.0+ 180.0+   0.5   18.0  180.0+ 180.0+  93.0   18.0+ 101.0    4.0 
#> [311]   5.0  150.0  180.0+ 180.0+ 180.0+ 171.0  180.0+ 174.0+   6.0  180.0+
#> [321]   0.5  180.0+ 180.0+ 180.0+ 180.0+  14.0+ 180.0+   2.0   45.0    5.0+
#> [331] 103.0    3.0+ 180.0+   5.0+ 180.0+  97.0   18.0  180.0+ 180.0+ 180.0+
#> [341]   7.0   13.0+ 180.0+  51.0  180.0+   1.0   60.0   76.0  180.0+ 132.0 
#> [351] 162.0    7.0+ 180.0+ 180.0+ 180.0+  12.0  180.0+ 180.0+ 180.0+   2.0 
#> [361]  76.0  173.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+  16.0+ 180.0+ 180.0+
#> [371]   6.0  180.0+ 180.0+   7.0+   3.0+  13.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [381] 180.0+   0.5  180.0+ 180.0+   8.0   87.0   12.0  180.0+   4.0+  58.0 
#> [391] 180.0+ 180.0+ 180.0+   3.0  180.0+ 175.0    2.0  180.0+ 180.0+ 180.0+
#> [401] 180.0+   8.0+   1.0  180.0+ 159.0   15.0  180.0+  10.0    1.0  180.0+
#> [411]  13.0  180.0+   4.0+ 180.0+  10.0  104.0+  57.0  180.0+  11.0    3.0+
#> [421]   5.0   12.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   4.0+   7.0 
#> [431]  15.0+ 180.0+ 180.0+   3.0  180.0+ 180.0+ 180.0+  85.0  180.0+  17.0+
#> [441] 180.0+ 174.0+   0.5  180.0+  12.0  180.0+ 180.0+ 180.0+   4.0  180.0+
#> [451]  52.0  180.0+ 180.0+ 180.0+ 180.0+   8.0  180.0+  33.0  180.0+ 180.0+
#> [461] 180.0+   1.0   12.0  180.0+   7.0+  79.0  180.0+ 180.0+  18.0  180.0+
#> [471]  47.0   11.0  180.0+ 180.0+   8.0+ 180.0+  10.0  180.0+ 172.0   12.0 
#> [481] 180.0+   8.0  180.0+ 180.0+   1.0   80.0  180.0+   4.0+ 180.0+ 152.0+
#> [491]   3.0   29.0   24.0   32.0  180.0+   3.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [501]   1.0   11.0    4.0    4.0  180.0+   6.0    3.0+   2.0+   1.0  171.0 
#> [511]   1.0   43.0    3.0  180.0+   6.0  138.0  180.0+   8.0   40.0   59.0 
#> [521]  17.0  161.0  180.0+  93.0  164.0  118.0  173.0  180.0+ 175.0+   7.0+
#> [531]  22.0  180.0+   3.0   71.0   20.0+   1.0    3.0+  10.0   85.0   10.0 
#> [541]   6.0+   6.0  180.0+   1.0  180.0+ 108.0  180.0+ 125.0  180.0+ 180.0+
#> [551]   9.0+ 180.0+ 180.0+ 180.0+ 180.0+ 169.0    4.0  180.0+ 180.0+  20.0 
#> [561]  16.0  180.0+ 180.0+ 180.0+ 167.0    3.0+ 180.0+   4.0    1.0   90.0 
#> [571] 180.0+ 180.0+   4.0   71.0    1.0   19.0  180.0+ 154.0    2.0  180.0+
#> [581] 180.0+   4.0+   1.0   16.0+   5.0+  77.0  180.0+   3.0   83.0   88.0 
#> [591] 126.0    8.0  180.0+   3.0  180.0+ 180.0+   3.0+ 180.0+ 180.0+   4.0+
#> [601] 180.0+  70.0   43.0  180.0+  14.0   13.0  180.0+ 180.0+ 180.0+  38.0 
#> [611]   3.0+   6.0+  62.0    6.0+   4.0+  20.0   22.0   11.0  180.0+  46.0 
#> [621] 115.0    3.0+   8.0+ 180.0+   4.0  119.0    1.0+ 110.0   29.0  180.0+
#> [631]  46.0   14.0  180.0+   8.0  180.0+   3.0   24.0   50.0   11.0  126.0 
#> [641] 180.0+ 180.0+   4.0    1.0  178.0+  89.0   75.0    1.0   33.0   74.0 
#> [651] 168.0  169.0   52.0  180.0+   4.0  180.0+  50.0    1.0+ 179.0+   8.0+
#> [661]  76.0  180.0+  12.0    8.0    7.0+  69.0    2.0  180.0+   3.0   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 
#>   0.8371673