Running ‘testthat.R’ [1241s/238s]
Running R code in ‘testthat.R’ had CPU time 5.2 times elapsed time
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(lightgbm)
  Loading required package: R6
  > 
  > test_check(
  +     package = "lightgbm"
  +     , stop_on_failure = TRUE
  +     , stop_on_warning = FALSE
  +     , reporter = testthat::SummaryReporter$new()
  + )
  Predictor: 
  Predictor: W....W.W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.395880 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [1] "[1]:  train's binary_logloss:0.314167  test's binary_logloss:0.317777"
  [1] "[2]:  train's binary_logloss:0.187654  test's binary_logloss:0.187981"
  [1] "[3]:  train's binary_logloss:0.109209  test's binary_logloss:0.109949"
  [1] "[4]:  train's binary_logloss:0.0755423  test's binary_logloss:0.0772008"
  [1] "[5]:  train's binary_logloss:0.0528045  test's binary_logloss:0.0533291"
  [1] "[6]:  train's binary_logloss:0.0395797  test's binary_logloss:0.0380824"
  [1] "[7]:  train's binary_logloss:0.0287269  test's binary_logloss:0.0255364"
  [1] "[8]:  train's binary_logloss:0.0224443  test's binary_logloss:0.0195616"
  [1] "[9]:  train's binary_logloss:0.016621  test's binary_logloss:0.017834"
  [1] "[10]:  train's binary_logloss:0.0112055  test's binary_logloss:0.0125538"
  [1] "[11]:  train's binary_logloss:0.00759638  test's binary_logloss:0.00842372"
  [1] "[12]:  train's binary_logloss:0.0054887  test's binary_logloss:0.00631812"
  [1] "[13]:  train's binary_logloss:0.00399548  test's binary_logloss:0.00454944"
  [1] "[14]:  train's binary_logloss:0.00283135  test's binary_logloss:0.00323724"
  [1] "[15]:  train's binary_logloss:0.00215378  test's binary_logloss:0.00256697"
  [1] "[16]:  train's binary_logloss:0.00156723  test's binary_logloss:0.00181753"
  [1] "[17]:  train's binary_logloss:0.00120077  test's binary_logloss:0.00144437"
  [1] "[18]:  train's binary_logloss:0.000934889  test's binary_logloss:0.00111807"
  [1] "[19]:  train's binary_logloss:0.000719878  test's binary_logloss:0.000878304"
  [1] "[20]:  train's binary_logloss:0.000558692  test's binary_logloss:0.000712272"
  [1] "[21]:  train's binary_logloss:0.000400916  test's binary_logloss:0.000492223"
  [1] "[22]:  train's binary_logloss:0.000315938  test's binary_logloss:0.000402804"
  [1] "[23]:  train's binary_logloss:0.000238113  test's binary_logloss:0.000288682"
  [1] "[24]:  train's binary_logloss:0.000190248  test's binary_logloss:0.000237835"
  [1] "[25]:  train's binary_logloss:0.000148322  test's binary_logloss:0.000174674"
  [1] "[26]:  train's binary_logloss:0.000120581  test's binary_logloss:0.000139513"
  [1] "[27]:  train's binary_logloss:0.000102756  test's binary_logloss:0.000118804"
  [1] "[28]:  train's binary_logloss:7.83011e-05  test's binary_logloss:8.40978e-05"
  [1] "[29]:  train's binary_logloss:6.29191e-05  test's binary_logloss:6.8803e-05"
  [1] "[30]:  train's binary_logloss:5.28039e-05  test's binary_logloss:5.89864e-05"
  [1] "[31]:  train's binary_logloss:4.51561e-05  test's binary_logloss:4.91874e-05"
  [1] "[32]:  train's binary_logloss:3.89402e-05  test's binary_logloss:4.13015e-05"
  [1] "[33]:  train's binary_logloss:3.24434e-05  test's binary_logloss:3.52605e-05"
  [1] "[34]:  train's binary_logloss:2.65255e-05  test's binary_logloss:2.86338e-05"
  [1] "[35]:  train's binary_logloss:2.19277e-05  test's binary_logloss:2.3937e-05"
  [1] "[36]:  train's binary_logloss:1.86469e-05  test's binary_logloss:2.05375e-05"
  [1] "[37]:  train's binary_logloss:1.49881e-05  test's binary_logloss:1.53852e-05"
  [1] "[38]:  train's binary_logloss:1.2103e-05  test's binary_logloss:1.20722e-05"
  [1] "[39]:  train's binary_logloss:1.02027e-05  test's binary_logloss:1.0578e-05"
  [1] "[40]:  train's binary_logloss:8.91561e-06  test's binary_logloss:8.8323e-06"
  [1] "[41]:  train's binary_logloss:7.4855e-06  test's binary_logloss:7.58441e-06"
  [1] "[42]:  train's binary_logloss:6.21179e-06  test's binary_logloss:6.14299e-06"
  [1] "[43]:  train's binary_logloss:5.06413e-06  test's binary_logloss:5.13576e-06"
  [1] "[44]:  train's binary_logloss:4.2029e-06  test's binary_logloss:4.53605e-06"
  [1] "[45]:  train's binary_logloss:3.47042e-06  test's binary_logloss:3.73234e-06"
  [1] "[46]:  train's binary_logloss:2.78181e-06  test's binary_logloss:3.02556e-06"
  [1] "[47]:  train's binary_logloss:2.19819e-06  test's binary_logloss:2.3666e-06"
  [1] "[48]:  train's binary_logloss:1.80519e-06  test's binary_logloss:1.92932e-06"
  [1] "[49]:  train's binary_logloss:1.50192e-06  test's binary_logloss:1.64658e-06"
  [1] "[50]:  train's binary_logloss:1.20212e-06  test's binary_logloss:1.33316e-06"
  ....
  basic: 
  lightgbm(): W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.441277 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [1] "[1]:  train's binary_error:0.0222632"
  [1] "[2]:  train's binary_error:0.0222632"
  [1] "[3]:  train's binary_error:0.0222632"
  [1] "[4]:  train's binary_error:0.0109013"
  [1] "[5]:  train's binary_error:0.0141256"
  [1] "[6]:  train's binary_error:0.0141256"
  [1] "[7]:  train's binary_error:0.0141256"
  [1] "[8]:  train's binary_error:0.0141256"
  [1] "[9]:  train's binary_error:0.00598802"
  [1] "[10]:  train's binary_error:0.00598802"
  .....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115782 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 98
  [LightGBM] [Info] Number of data points in the train set: 150, number of used features: 4
  [LightGBM] [Info] Start training from score -1.098612
  [LightGBM] [Info] Start training from score -1.098612
  [LightGBM] [Info] Start training from score -1.098612
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  train's multi_error:0.0466667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[11]:  train's multi_error:0.0333333"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[12]:  train's multi_error:0.0266667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[13]:  train's multi_error:0.0266667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[14]:  train's multi_error:0.0266667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[15]:  train's multi_error:0.0266667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[16]:  train's multi_error:0.0333333"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[17]:  train's multi_error:0.0266667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[18]:  train's multi_error:0.0333333"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[19]:  train's multi_error:0.0333333"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[20]:  train's multi_error:0.0333333"
  ...W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.521893 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [1] "[1]:  train's binary_error:0.0304007  train's auc:0.972508  train's binary_logloss:0.198597"
  [1] "[2]:  train's binary_error:0.0222632  train's auc:0.995075  train's binary_logloss:0.111535"
  [1] "[3]:  train's binary_error:0.00598802  train's auc:0.997845  train's binary_logloss:0.0480659"
  [1] "[4]:  train's binary_error:0.00122831  train's auc:0.998433  train's binary_logloss:0.0279151"
  [1] "[5]:  train's binary_error:0.00122831  train's auc:0.999354  train's binary_logloss:0.0190479"
  [1] "[6]:  train's binary_error:0.00537387  train's auc:0.98965  train's binary_logloss:0.16706"
  [1] "[7]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.0128449"
  [1] "[8]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00774702"
  [1] "[9]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00472108"
  [1] "[10]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00208929"
  ..W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.503233 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [1] "[1]:  train's binary_error:0.0222632"
  [1] "[2]:  train's binary_error:0.0222632"
  [1] "[3]:  train's binary_error:0.0222632"
  [1] "[4]:  train's binary_error:0.0109013"
  [1] "[5]:  train's binary_error:0.0141256"
  [1] "[6]:  train's binary_error:0.0141256"
  [1] "[7]:  train's binary_error:0.0141256"
  [1] "[8]:  train's binary_error:0.0141256"
  [1] "[9]:  train's binary_error:0.00598802"
  [1] "[10]:  train's binary_error:0.00598802"
  ..W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.553030 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] Start training from score 0.482113
  [1] "[1]:  train's l2:0.206337"
  [1] "[2]:  train's l2:0.171229"
  [1] "[3]:  train's l2:0.140871"
  [1] "[4]:  train's l2:0.116282"
  [1] "[5]:  train's l2:0.096364"
  [1] "[6]:  train's l2:0.0802308"
  [1] "[7]:  train's l2:0.0675595"
  [1] "[8]:  train's l2:0.0567154"
  [1] "[9]:  train's l2:0.0482086"
  [1] "[10]:  train's l2:0.0402694"
  ....W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.680688 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [1] "[1]:  train's binary_error:0.0222632  train's auc:0.981784  valid1's binary_error:0.0222632  valid1's auc:0.981784  valid2's binary_error:0.0222632  valid2's auc:0.981784"
  [1] "[2]:  train's binary_error:0.0222632  train's auc:0.981784  valid1's binary_error:0.0222632  valid1's auc:0.981784  valid2's binary_error:0.0222632  valid2's auc:0.981784"
  [1] "[3]:  train's binary_error:0.0222632  train's auc:0.992951  valid1's binary_error:0.0222632  valid1's auc:0.992951  valid2's binary_error:0.0222632  valid2's auc:0.992951"
  [1] "[4]:  train's binary_error:0.0109013  train's auc:0.992951  valid1's binary_error:0.0109013  valid1's auc:0.992951  valid2's binary_error:0.0109013  valid2's auc:0.992951"
  [1] "[5]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
  [1] "[6]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
  [1] "[7]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
  [1] "[8]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
  [1] "[9]:  train's binary_error:0.00598802  train's auc:0.993175  valid1's binary_error:0.00598802  valid1's auc:0.993175  valid2's binary_error:0.00598802  valid2's auc:0.993175"
  [1] "[10]:  train's binary_error:0.00598802  train's auc:0.998242  valid1's binary_error:0.00598802  valid1's auc:0.998242  valid2's binary_error:0.00598802  valid2's auc:0.998242"
  .......
  training continuation: [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.442017 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [1] "[1]:  train's binary_logloss:0.179606"
  [1] "[2]:  train's binary_logloss:0.0975448"
  [1] "[3]:  train's binary_logloss:0.0384292"
  [1] "[4]:  train's binary_logloss:0.0582241"
  [1] "[5]:  train's binary_logloss:0.0595215"
  [1] "[6]:  train's binary_logloss:0.0609174"
  [1] "[7]:  train's binary_logloss:0.317567"
  [1] "[8]:  train's binary_logloss:0.0104223"
  [1] "[9]:  train's binary_logloss:0.00497498"
  [1] "[10]:  train's binary_logloss:0.00283557"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.560606 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [1] "[1]:  train's binary_logloss:0.179606"
  [1] "[2]:  train's binary_logloss:0.0975448"
  [1] "[3]:  train's binary_logloss:0.0384292"
  [1] "[4]:  train's binary_logloss:0.0582241"
  [1] "[5]:  train's binary_logloss:0.0595215"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.585014 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 214
  [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
  [1] "[6]:  train's binary_logloss:0.0609174"
  [1] "[7]:  train's binary_logloss:0.317567"
  [1] "[8]:  train's binary_logloss:0.0104223"
  [1] "[9]:  train's binary_logloss:0.00497498"
  [1] "[10]:  train's binary_logloss:0.00283557"
  .
  lgb.cv(): W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.512095 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 232
  [LightGBM] [Info] Number of data points in the train set: 5211, number of used features: 116
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.412332 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 232
  [LightGBM] [Info] Number of data points in the train set: 5211, number of used features: 116
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.441114 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 232
  [LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.560270 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 232
  [LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.490349 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 232
  [LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
  [LightGBM] [Info] Start training from score 0.483976
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.480906
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.481574
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.482342
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.481766
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306994+0.00061397"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [1] "[4]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [1] "[5]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [1] "[6]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [1] "[7]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [1] "[8]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [1] "[9]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
  [1] "[10]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
  .........W[LightGBM] [Info] Number of positive: 198, number of negative: 202
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.122766 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 167
  [LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
  [LightGBM] [Info] Number of positive: 196, number of negative: 204
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.140335 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 167
  [LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
  [LightGBM] [Info] Number of positive: 207, number of negative: 193
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.139744 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 167
  [LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
  [LightGBM] [Info] Number of positive: 207, number of negative: 193
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.095421 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 167
  [LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
  [LightGBM] [Info] Number of positive: 192, number of negative: 208
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.138382 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 167
  [LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.495000 -> initscore=-0.020001
  [LightGBM] [Info] Start training from score -0.020001
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490000 -> initscore=-0.040005
  [LightGBM] [Info] Start training from score -0.040005
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.517500 -> initscore=0.070029
  [LightGBM] [Info] Start training from score 0.070029
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.517500 -> initscore=0.070029
  [LightGBM] [Info] Start training from score 0.070029
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.480000 -> initscore=-0.080043
  [LightGBM] [Info] Start training from score -0.080043
  [1] "[1]:  valid's auc:0.476662+0.0622898  valid's binary_error:0.5+0.0593296"
  [1] "[2]:  valid's auc:0.477476+0.0393392  valid's binary_error:0.554+0.0372022"
  [1] "[3]:  valid's auc:0.456927+0.042898  valid's binary_error:0.526+0.0361109"
  [1] "[4]:  valid's auc:0.419531+0.0344972  valid's binary_error:0.54+0.0289828"
  [1] "[5]:  valid's auc:0.459109+0.0862237  valid's binary_error:0.52+0.0489898"
  [1] "[6]:  valid's auc:0.460522+0.0911246  valid's binary_error:0.528+0.0231517"
  [1] "[7]:  valid's auc:0.456328+0.0540445  valid's binary_error:0.532+0.0386782"
  [1] "[8]:  valid's auc:0.463653+0.0660907  valid's binary_error:0.514+0.0488262"
  [1] "[9]:  valid's auc:0.443017+0.0549965  valid's binary_error:0.55+0.0303315"
  [1] "[10]:  valid's auc:0.477483+0.0763283  valid's binary_error:0.488+0.0549181"
  .....[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.128767 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.120977 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.128940 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.163560 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.116476 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Info] Start training from score 0.052402
  [LightGBM] [Info] Start training from score 0.017850
  [LightGBM] [Info] Start training from score -0.018739
  [LightGBM] [Info] Start training from score -0.029298
  [LightGBM] [Info] Start training from score 0.027539
  [1] "[1]:  valid's l2:3.40838+0.231034"
  [1] "[2]:  valid's l2:3.02136+0.216706"
  [1] "[3]:  valid's l2:2.68787+0.209507"
  [1] "[4]:  valid's l2:2.40599+0.192969"
  [1] "[5]:  valid's l2:2.15761+0.187944"
  [1] "[6]:  valid's l2:1.94667+0.172578"
  [1] "[7]:  valid's l2:1.75663+0.167009"
  [1] "[8]:  valid's l2:1.59496+0.150831"
  [1] "[9]:  valid's l2:1.44947+0.144699"
  [1] "[10]:  valid's l2:1.31792+0.139097"
  .[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.166469 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.133430 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.145875 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.139385 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.180481 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 255
  [LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
  [LightGBM] [Info] Start training from score 0.115703
  [LightGBM] [Info] Start training from score 0.123541
  [LightGBM] [Info] Start training from score 0.076232
  [LightGBM] [Info] Start training from score 0.094848
  [LightGBM] [Info] Start training from score 0.150201
  [1] "[1]:  valid's l2:3.58556+0.201544"
  [1] "[2]:  valid's l2:2.9047+0.163529"
  [1] "[3]:  valid's l2:2.35329+0.13272"
  [1] "[4]:  valid's l2:1.90673+0.107749"
  [1] "[5]:  valid's l2:1.54508+0.0875109"
  [1] "[6]:  valid's l2:1.25218+0.0711496"
  [1] "[7]:  valid's l2:1.01499+0.057881"
  [1] "[8]:  valid's l2:0.82292+0.0471195"
  [1] "[9]:  valid's l2:0.667388+0.0383999"
  [1] "[10]:  valid's l2:0.541453+0.0313252"
  ..W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.345393 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 232
  [LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.469210 seconds.
  You can set `force_row_wise=true` to remove the overhead.
  And if memory is not enough, you can set `force_col_wise=true`.
  [LightGBM] [Info] Total Bins 232
  [LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
  Killed