haghish / mlim Goto Github PK

> dane_elnet <- mlim(as.data.frame(x_miss), m=1, seed = 2022, tuning_time = 900, algos = c("ELNET"), report = "imputation_ELNET.md")

Random Forest preimputation in progress...


data 1, iteration 1 (RAM = 138.897 GiB):

  |                                                                                                                      |   0%
21:40:03.915: GLM_1_AutoML_1_20220908_214002 [GLM def_1] failed: java.lang.ArrayIndexOutOfBoundsException: Index 57 out of bounds for length 57
21:40:03.935: Empty leaderboard.
AutoML was not able to build any model within a max runtime constraint of 900 seconds, you may want to increase this value before retrying.
21:50:18.391: New models will be added to existing leaderboard mlim@@TNM (leaderboard frame=null) with already 0 models.
21:50:18.769: GLM_2_AutoML_2_20220908_215018 [GLM def_1] failed: java.lang.ArrayIndexOutOfBoundsException: Index 57 out of bounds for length 57
21:50:18.785: Empty leaderboard.
AutoML was not able to build any model within a max runtime constraint of 900 seconds, you may want to increase this value before retrying.
21:20:07.760: New models will be added to existing leaderboard mlim@@TNM (leaderboard frame=null) with already 0 models.
21:20:07.990: GLM_3_AutoML_3_20220909_212007 [GLM def_1] failed: java.lang.ArrayIndexOutOfBoundsException
21:20:08.1: Empty leaderboard.
AutoML was not able to build any model within a max runtime constraint of 900 seconds, you may want to increase this value before retrying.connection to JAVA server failed...

Error in value[[3L]](cond) : Java server crashed. perhaps a RAM problem?
In addition: Warning message:
In .automl.fetch_state(project_name) :
  The leaderboard contains zero models: try running AutoML for longer (the default is 1 hour).

> str(x_miss)
'data.frame':	1641 obs. of  42 variables:
 $ Wiek               : num  69.6 73.1 76.5 65.1 63.3 48.4 68.8 69.5 78.2 71.1 ...
 $ EBRT_BT            : Factor w/ 2 levels "BT BOOST","EBRT": 2 2 2 2 2 2 2 2 2 2 ...
 $ GGG                : num  2 5 1 4 1 3 2 1 5 1 ...
 $ cores              : num  6 6 10 6 6 NA NA 6 12 6 ...
 $ cores_positive     : num  6 1 6 5 2 NA NA 2 12 1 ...
 $ cores_positive_proc: num  1 0.167 0.6 0.833 0.333 ...
 $ max_prccancer      : num  NA 50 100 100 NA NA 100 50 NA 20 ...
 $ TURP               : Factor w/ 2 levels "0_No","1_Yes": 1 1 1 1 1 1 1 1 1 1 ...
 $ V_prostata         : num  31.9 35.7 67.2 38.5 26.5 35.9 NA 60 NA 156 ...
 $ MR_pre_EPE         : Factor w/ 2 levels "0_No","1_Yes": 2 NA NA NA NA NA 1 NA NA NA ...
 $ MR_pre_SVI         : Factor w/ 2 levels "0_No","1_Yes": 2 NA NA NA NA NA 1 NA NA NA ...
 $ PSA_density        : num  0.72 0.59 1.29 1.09 1.53 4.74 NA 0.25 NA 0.07 ...
 $ TNM                : Factor w/ 7 levels "T1c","T2a","T2b",..: 7 3 3 6 1 6 NA 3 5 2 ...
 $ ZUBROD             : num  0 1 1 0 0 0 0 0 0 0 ...
 $ PSAmax             : num  23 21 86.4 41.8 40.5 ...
 $ Risk_Group         : Factor w/ 4 levels "1_low_IR","2_high_IR",..: 4 4 3 4 3 4 3 2 4 2 ...
 $ ADT_pre_RT         : Factor w/ 2 levels "0_No","1_Yes": 2 2 2 2 2 2 2 2 2 1 ...
 $ ADT_intractu_RT    : Factor w/ 2 levels "0_No","1_Yes": 2 2 2 2 2 2 2 2 2 1 ...
 $ ADT_ADJ            : Factor w/ 2 levels "0_No","1_Yes": 1 2 2 2 2 2 2 2 2 1 ...
 $ ADT_typ            : Factor w/ 5 levels "0_brak","analog",..: 3 3 3 3 3 3 3 3 3 1 ...
 $ ADT_czas_pre_RT    : num  100 110 104 92 74 76 101 90 113 1 ...
 $ ADT_ADJ_CZAS       : num  0 2.66 51.12 74.8 9.63 ...
 $ ADT_czas_suma      : num  5.81 11.4 59.1 80.35 13.7 ...
 $ czas_PSA_pre       : num  NA NA 0.76 8.74 1.38 NA NA 6.44 NA 0.66 ...
 $ PSA_pre_RT         : num  0.08 0.04 0 41.8 23.28 ...
 $ czas_RT            : num  38 40 62 50 61 39 50 50 58 46 ...
 $ DCp                : num  38 42 44 54 58 60 60 62 72 68 ...
 $ N_RT               : num  1 1 1 1 1 1 0 1 1 0 ...
 $ DCn                : num  38 42 44 50 44 50 0 50 43.2 0 ...
 $ DCbt               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BTfx               : num  0 0 0 0 0 0 0 0 0 0 ...
 $ BED3               : num  63.3 70 73.3 90 96.7 ...
 $ BED1_5             : num  88.7 98 102.7 126 135.3 ...
 $ FU                 : num  0.5 4 53.2 102.2 11.6 ...
 $ Zgon               : num  1 1 1 1 1 1 1 1 1 1 ...
 $ BC                 : num  0 0 0 0 0 1 0 0 1 1 ...
 $ MFS_24             : num  1 0 1 1 0 1 1 1 1 1 ...
 $ FFM                : num  0 0 0 0 0 1 0 0 0 1 ...
 $ BC_czas            : num  0.53 3.98 53.15 102.23 11.63 ...
 $ FFM_czas           : num  0.53 3.98 53.15 102.23 11.63 ...
 $ MFS_czas_24        : num  23.72 3.98 53.15 102.76 11.63 ...
 $ OS                 : num  23.7 112.5 53.2 102.8 84.3 ...

> caret::nearZeroVar(x_miss)
integer(0)

naniar::vis_miss(x_miss, sort_miss = T)