Running 'testthat.R' [24s]
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
  + )
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003269 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"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003886 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"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 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"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003081 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"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002880 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"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003441 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"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003199 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"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002852 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 0.003738 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 0.003141 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"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003448 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 0.003492 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 0.003612 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 0.003461 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 0.003153 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: 0.000000
  [LightGBM] [Info] Start training from score 0.480906
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Info] Start training from score 0.481574
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Info] Start training from score 0.482342
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Info] Start training from score 0.481766
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [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: 0.000000
  [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: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[2]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306985+0.000613967"
  [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: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306985+0.000613967"
  [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: 0.000000
  [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 l1:0.000306985+0.000613967"
  [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] "[5]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306985+0.000613967"
  [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 l1:0.000306985+0.000613967"
  [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: 0.000000
  [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 l1:0.000306985+0.000613967"
  [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: 0.000000
  [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 l1:0.000306985+0.000613967"
  [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: 0.000000
  [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 l1:0.000306985+0.000613967"
  [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: 0.000000
  [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 l1:0.000306985+0.000613967"
  [LightGBM] [Info] Number of positive: 198, number of negative: 202
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 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 0.001261 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 0.000448 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 0.000469 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 0.000570 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] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002924 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[1]:  train's binary_error:0.00307078  train's auc:0.99996  train's binary_logloss:0.132074"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's binary_error:0.00153539  train's auc:1  train's binary_logloss:0.0444372"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[3]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.0159408"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[4]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00590065"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[5]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00230167"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[6]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00084253"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[7]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.000309409"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[8]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.000113754"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[9]:  train's binary_error:0  train's auc:1  train's binary_logloss:4.1838e-05"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[10]:  train's binary_error:0  train's auc:1  train's binary_logloss:1.539e-05"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [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
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [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
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Info] Number of positive: 35110, number of negative: 34890
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000816 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 12
  [LightGBM] [Info] Number of data points in the train set: 70000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.501571 -> initscore=0.006286
  [LightGBM] [Info] Start training from score 0.006286
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Number of positive: 500, number of negative: 500
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's binary_error:0"
  [LightGBM] [Info] Number of positive: 500, number of negative: 500
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's binary_error:0"
  [LightGBM] [Info] Number of positive: 500, number of negative: 500
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0"
  [LightGBM] [Info] Number of positive: 500, number of negative: 500
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0"
  [LightGBM] [Info] Number of positive: 500, number of negative: 500
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0"
  [LightGBM] [Info] Number of positive: 500, number of negative: 500
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002779 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's auc:0.987036"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's auc:0.987036"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's auc:0.998699"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[4]:  valid1's auc:0.998699"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's auc:0.998699"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[6]:  valid1's auc:0.999667"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[7]:  valid1's auc:0.999806"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's auc:0.999978"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[9]:  valid1's auc:0.999997"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[10]:  valid1's auc:0.999997"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002925 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0.016139"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0.016139"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0.016139"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[4]:  valid1's binary_error:0.016139"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0.016139"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[6]:  valid1's binary_error:0.016139"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's rmse:55"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's rmse:59.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's rmse:63.55"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's rmse:67.195"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's rmse:70.4755"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's rmse:73.428"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's rmse:76.0852"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's rmse:78.4766"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's rmse:80.629"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's rmse:82.5661"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's rmse:55"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's rmse:59.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's rmse:63.55"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's rmse:67.195"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's rmse:70.4755"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's rmse:73.428"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
  [LightGBM] [Info] Start training from score 0.045019
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.1"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.3"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.4"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.6"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.7"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.8"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.9"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's constant_metric:0.2  valid1's increasing_metric:1"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
  [LightGBM] [Info] Start training from score 0.045019
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's increasing_metric:1.1  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's increasing_metric:1.2  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's increasing_metric:1.3  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's increasing_metric:1.4  valid1's constant_metric:0.2"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
  [LightGBM] [Info] Start training from score 0.045019
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's increasing_metric:1.5  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's increasing_metric:1.6  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's increasing_metric:1.7  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's increasing_metric:1.8  valid1's constant_metric:0.2"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
  [LightGBM] [Info] Start training from score 0.045019
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's increasing_metric:1.9  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's increasing_metric:2  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's increasing_metric:2.1  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's increasing_metric:2.2  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's increasing_metric:2.3  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's increasing_metric:2.4  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's increasing_metric:2.5  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's increasing_metric:2.6  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's increasing_metric:2.7  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's increasing_metric:2.8  valid1's constant_metric:0.2"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
  [LightGBM] [Info] Start training from score 0.045019
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's rmse:1.10501  valid1's l2:1.22105  valid1's increasing_metric:2.9  valid1's rmse:1.10501  valid1's l2:1.22105  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's rmse:1.10335  valid1's l2:1.21738  valid1's increasing_metric:3  valid1's rmse:1.10335  valid1's l2:1.21738  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's rmse:1.10199  valid1's l2:1.21438  valid1's increasing_metric:3.1  valid1's rmse:1.10199  valid1's l2:1.21438  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's rmse:1.10198  valid1's l2:1.21436  valid1's increasing_metric:3.2  valid1's rmse:1.10198  valid1's l2:1.21436  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's rmse:1.10128  valid1's l2:1.21282  valid1's increasing_metric:3.3  valid1's rmse:1.10128  valid1's l2:1.21282  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's rmse:1.10101  valid1's l2:1.21222  valid1's increasing_metric:3.4  valid1's rmse:1.10101  valid1's l2:1.21222  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's rmse:1.10065  valid1's l2:1.21143  valid1's increasing_metric:3.5  valid1's rmse:1.10065  valid1's l2:1.21143  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's rmse:1.10011  valid1's l2:1.21025  valid1's increasing_metric:3.6  valid1's rmse:1.10011  valid1's l2:1.21025  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's rmse:1.09999  valid1's l2:1.20997  valid1's increasing_metric:3.7  valid1's rmse:1.09999  valid1's l2:1.20997  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's rmse:1.09954  valid1's l2:1.20898  valid1's increasing_metric:3.8  valid1's rmse:1.09954  valid1's l2:1.20898  valid1's constant_metric:0.2"
  [LightGBM] [Info] Number of positive: 66, number of negative: 54
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 120, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550000 -> initscore=0.200671
  [LightGBM] [Info] Start training from score 0.200671
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.693255"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.691495"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.69009"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.688968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.688534"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689883"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689641"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689532"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.691066"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.690653"
  [LightGBM] [Info] Number of positive: 66, number of negative: 54
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 120, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550000 -> initscore=0.200671
  [LightGBM] [Info] Start training from score 0.200671
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_logloss:0.693255"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_logloss:0.691495"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_logloss:0.69009"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_logloss:0.688968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_logloss:0.688534"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's binary_logloss:0.689883"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's binary_logloss:0.689641"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's binary_logloss:0.689532"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's binary_logloss:0.691066"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's binary_logloss:0.690653"
  [LightGBM] [Info] Number of positive: 66, number of negative: 54
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 120, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550000 -> initscore=0.200671
  [LightGBM] [Info] Start training from score 0.200671
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.693255"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.691495"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.69009"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.688968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.688534"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689883"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689641"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689532"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.691066"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.690653"
  [LightGBM] [Info] Number of positive: 66, number of negative: 54
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 120, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550000 -> initscore=0.200671
  [LightGBM] [Info] Start training from score 0.200671
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_logloss:0.693255"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_logloss:0.691495"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_logloss:0.69009"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_logloss:0.688968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_logloss:0.688534"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's binary_logloss:0.689883"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's binary_logloss:0.689641"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's binary_logloss:0.689532"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's binary_logloss:0.691066"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's binary_logloss:0.690653"
  [LightGBM] [Info] Number of positive: 66, number of negative: 54
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 120, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550000 -> initscore=0.200671
  [LightGBM] [Info] Start training from score 0.200671
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.693255"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.691495"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's binary_error:0.486486  valid1's binary_logloss:0.69009"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.688968"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.688534"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689883"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689641"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.689532"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.691066"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's binary_error:0.432432  valid1's binary_logloss:0.690653"
  [LightGBM] [Info] Number of positive: 66, number of negative: 54
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 120, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550000 -> initscore=0.200671
  [LightGBM] [Info] Start training from score 0.200671
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's constant_metric:0.2"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's mape:1.1  valid1's rmse:55  valid1's l1:55"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's mape:1.19  valid1's rmse:59.5  valid1's l1:59.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's mape:1.271  valid1's rmse:63.55  valid1's l1:63.55"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's mape:1.3439  valid1's rmse:67.195  valid1's l1:67.195"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's mape:1.40951  valid1's rmse:70.4755  valid1's l1:70.4755"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's mape:1.46856  valid1's rmse:73.428  valid1's l1:73.428"
  -- Skip (test_basic.R:1171:3): lgb.train() supports non-ASCII feature names ----
  Reason: UTF-8 feature names are not fully supported in the R package
  
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid1's rmse:125  valid2's rmse:98.1071"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  valid1's rmse:87.5  valid2's rmse:62.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  valid1's rmse:106.25  valid2's rmse:80.0878"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  valid1's rmse:96.875  valid2's rmse:71.2198"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  valid1's rmse:101.562  valid2's rmse:75.6386"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  valid1's rmse:99.2188  valid2's rmse:73.425"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  valid1's rmse:100.391  valid2's rmse:74.5308"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  valid1's rmse:99.8047  valid2's rmse:73.9777"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  valid1's rmse:100.098  valid2's rmse:74.2542"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  valid1's rmse:99.9512  valid2's rmse:74.1159"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000692 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's rmse:25  valid1's rmse:125  valid2's rmse:98.1071"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's rmse:12.5  valid1's rmse:87.5  valid2's rmse:62.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  train's rmse:6.25  valid1's rmse:106.25  valid2's rmse:80.0878"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  train's rmse:3.125  valid1's rmse:96.875  valid2's rmse:71.2198"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  train's rmse:1.5625  valid1's rmse:101.562  valid2's rmse:75.6386"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  train's rmse:0.78125  valid1's rmse:99.2188  valid2's rmse:73.425"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  train's rmse:0.390625  valid1's rmse:100.391  valid2's rmse:74.5308"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  train's rmse:0.195312  valid1's rmse:99.8047  valid2's rmse:73.9777"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  train's rmse:0.0976562  valid1's rmse:100.098  valid2's rmse:74.2542"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  train's rmse:0.0488281  valid1's rmse:99.9512  valid2's rmse:74.1159"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's rmse:25  valid1's rmse:125  valid2's rmse:98.1071"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's rmse:12.5  valid1's rmse:87.5  valid2's rmse:62.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  train's rmse:6.25  valid1's rmse:106.25  valid2's rmse:80.0878"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  train's rmse:3.125  valid1's rmse:96.875  valid2's rmse:71.2198"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  train's rmse:1.5625  valid1's rmse:101.562  valid2's rmse:75.6386"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  train's rmse:0.78125  valid1's rmse:99.2188  valid2's rmse:73.425"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  train's rmse:0.390625  valid1's rmse:100.391  valid2's rmse:74.5308"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  train's rmse:0.195312  valid1's rmse:99.8047  valid2's rmse:73.9777"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  train's rmse:0.0976562  valid1's rmse:100.098  valid2's rmse:74.2542"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  train's rmse:0.0488281  valid1's rmse:99.9512  valid2's rmse:74.1159"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's rmse:25  valid1's rmse:125  valid2's rmse:98.1071"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's rmse:12.5  valid1's rmse:87.5  valid2's rmse:62.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  train's rmse:6.25  valid1's rmse:106.25  valid2's rmse:80.0878"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  train's rmse:3.125  valid1's rmse:96.875  valid2's rmse:71.2198"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  train's rmse:1.5625  valid1's rmse:101.562  valid2's rmse:75.6386"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  train's rmse:0.78125  valid1's rmse:99.2188  valid2's rmse:73.425"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  train's rmse:0.390625  valid1's rmse:100.391  valid2's rmse:74.5308"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  train's rmse:0.195312  valid1's rmse:99.8047  valid2's rmse:73.9777"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  train's rmse:0.0976562  valid1's rmse:100.098  valid2's rmse:74.2542"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  train's rmse:0.0488281  valid1's rmse:99.9512  valid2's rmse:74.1159"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  something-random-we-would-not-hardcode's rmse:25  valid1's rmse:125  valid2's rmse:98.1071"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  something-random-we-would-not-hardcode's rmse:12.5  valid1's rmse:87.5  valid2's rmse:62.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  something-random-we-would-not-hardcode's rmse:6.25  valid1's rmse:106.25  valid2's rmse:80.0878"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  something-random-we-would-not-hardcode's rmse:3.125  valid1's rmse:96.875  valid2's rmse:71.2198"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  something-random-we-would-not-hardcode's rmse:1.5625  valid1's rmse:101.562  valid2's rmse:75.6386"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  something-random-we-would-not-hardcode's rmse:0.78125  valid1's rmse:99.2188  valid2's rmse:73.425"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  something-random-we-would-not-hardcode's rmse:0.390625  valid1's rmse:100.391  valid2's rmse:74.5308"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  something-random-we-would-not-hardcode's rmse:0.195312  valid1's rmse:99.8047  valid2's rmse:73.9777"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  something-random-we-would-not-hardcode's rmse:0.0976562  valid1's rmse:100.098  valid2's rmse:74.2542"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  something-random-we-would-not-hardcode's rmse:0.0488281  valid1's rmse:99.9512  valid2's rmse:74.1159"
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 3
  [LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's rmse:25"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's rmse:12.5"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[3]:  train's rmse:6.25"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[4]:  train's rmse:3.125"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[5]:  train's rmse:1.5625"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[6]:  train's rmse:0.78125"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[7]:  train's rmse:0.390625"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[8]:  train's rmse:0.195312"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[9]:  train's rmse:0.0976562"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[10]:  train's rmse:0.0488281"
  [LightGBM] [Info] Number of positive: 500, number of negative: 500
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 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: 1000, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [1] "[1]:  something-random-we-would-not-hardcode's auc:0.58136  valid1's auc:0.429487"
  [1] "[2]:  something-random-we-would-not-hardcode's auc:0.599008  valid1's auc:0.266026"
  [1] "[3]:  something-random-we-would-not-hardcode's auc:0.6328  valid1's auc:0.349359"
  [1] "[4]:  something-random-we-would-not-hardcode's auc:0.655136  valid1's auc:0.394231"
  [1] "[5]:  something-random-we-would-not-hardcode's auc:0.655408  valid1's auc:0.419872"
  [1] "[6]:  something-random-we-would-not-hardcode's auc:0.678784  valid1's auc:0.336538"
  [1] "[7]:  something-random-we-would-not-hardcode's auc:0.682176  valid1's auc:0.416667"
  [1] "[8]:  something-random-we-would-not-hardcode's auc:0.698032  valid1's auc:0.394231"
  [1] "[9]:  something-random-we-would-not-hardcode's auc:0.712672  valid1's auc:0.445513"
  [1] "[10]:  something-random-we-would-not-hardcode's auc:0.723024  valid1's auc:0.471154"
  [LightGBM] [Info] Number of positive: 50, number of negative: 39
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 89, number of used features: 1
  [LightGBM] [Info] Number of positive: 49, number of negative: 41
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 90, number of used features: 1
  [LightGBM] [Info] Number of positive: 53, number of negative: 38
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 91, number of used features: 1
  [LightGBM] [Info] Number of positive: 46, number of negative: 44
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000570 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 90, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.561798 -> initscore=0.248461
  [LightGBM] [Info] Start training from score 0.248461
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.544444 -> initscore=0.178248
  [LightGBM] [Info] Start training from score 0.178248
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.582418 -> initscore=0.332706
  [LightGBM] [Info] Start training from score 0.332706
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.511111 -> initscore=0.044452
  [LightGBM] [Info] Start training from score 0.044452
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.701123+0.0155541"
  [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 binary_error:0.500565+0.0460701  valid's binary_logloss:0.70447+0.0152787"
  [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 binary_error:0.500565+0.0460701  valid's binary_logloss:0.706572+0.0162531"
  [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] "[4]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.709214+0.0165672"
  [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] "[5]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.710652+0.0172198"
  [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] "[6]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.713091+0.0176604"
  [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] "[7]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.714842+0.0184267"
  [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] "[8]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.714719+0.0178927"
  [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] "[9]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.717162+0.0181993"
  [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] "[10]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.716577+0.0180201"
  [LightGBM] [Info] Number of positive: 45, number of negative: 35
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Info] Number of positive: 40, number of negative: 40
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Info] Number of positive: 47, number of negative: 33
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 42
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.562500 -> initscore=0.251314
  [LightGBM] [Info] Start training from score 0.251314
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.587500 -> initscore=0.353640
  [LightGBM] [Info] Start training from score 0.353640
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid's constant_metric:0.2+0"
  [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 constant_metric:0.2+0"
  [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 constant_metric:0.2+0"
  [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]:  valid's constant_metric:0.2+0"
  [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]:  valid's constant_metric:0.2+0"
  [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]:  valid's constant_metric:0.2+0"
  [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] "[7]:  valid's constant_metric:0.2+0"
  [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]:  valid's constant_metric:0.2+0"
  [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] "[9]:  valid's constant_metric:0.2+0"
  [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]:  valid's constant_metric:0.2+0"
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000581 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000504 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Info] Start training from score 0.024388
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.005573
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.039723
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.029700
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.125712
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid's increasing_metric:4.1+0.141421  valid's constant_metric:0.2+0"
  [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 increasing_metric:4.6+0.141421  valid's constant_metric:0.2+0"
  [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] "[3]:  valid's increasing_metric:5.1+0.141421  valid's constant_metric:0.2+0"
  [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] "[4]:  valid's increasing_metric:5.6+0.141421  valid's constant_metric:0.2+0"
  [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] "[5]:  valid's increasing_metric:6.1+0.141421  valid's constant_metric:0.2+0"
  [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] "[6]:  valid's increasing_metric:6.6+0.141421  valid's constant_metric:0.2+0"
  [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] "[7]:  valid's increasing_metric:7.1+0.141421  valid's constant_metric:0.2+0"
  [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] "[8]:  valid's increasing_metric:7.6+0.141421  valid's constant_metric:0.2+0"
  [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] "[9]:  valid's increasing_metric:8.1+0.141421  valid's constant_metric:0.2+0"
  [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] "[10]:  valid's increasing_metric:8.6+0.141421  valid's constant_metric:0.2+0"
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Warning] Unknown parameter: valids
  [LightGBM] [Warning] Unknown parameter: 0x06b41058>
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 35
  [LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
  [LightGBM] [Info] Start training from score 0.024388
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.005573
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.039723
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.029700
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [LightGBM] [Info] Start training from score 0.125712
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  valid's constant_metric:0.2+0  valid's increasing_metric:9.1+0.141421"
  [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 constant_metric:0.2+0  valid's increasing_metric:9.6+0.141421"
  [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] "[3]:  valid's constant_metric:0.2+0  valid's increasing_metric:10.1+0.141421"
  [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] "[4]:  valid's constant_metric:0.2+0  valid's increasing_metric:10.6+0.141421"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002847 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's l2:0.24804"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's l2:0.246711"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002911 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's l2:0.24804"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's l2:0.246711"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002739 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's l2:0.24804"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's l2:0.246711"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002968 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's l2:0.24804"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's l2:0.246711"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003736 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
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[1]:  train's l2:0.24804"
  [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
  [1] "[2]:  train's l2:0.246711"
  [LightGBM] [Warning] Using self-defined objective function
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002914 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] [Warning] Using self-defined objective function
  [1] "[1]:  train's auc:0.994987  train's error:0.00598802  eval's auc:0.995243  eval's error:0.00558659"
  [1] "[2]:  train's auc:0.99512  train's error:0.00307078  eval's auc:0.995237  eval's error:0.00248293"
  [1] "[3]:  train's auc:0.99009  train's error:0.00598802  eval's auc:0.98843  eval's error:0.00558659"
  [1] "[4]:  train's auc:0.999889  train's error:0.00168893  eval's auc:1  eval's error:0.000620732"
  [1] "[5]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
  [1] "[6]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
  [1] "[7]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
  [1] "[8]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
  [1] "[9]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
  [1] "[10]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
  [LightGBM] [Warning] Using self-defined objective function
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002922 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] [Warning] Using self-defined objective function
  [1] "[1]:  train's error:0.00598802  eval's error:0.00558659"
  [1] "[2]:  train's error:0.00307078  eval's error:0.00248293"
  [1] "[3]:  train's error:0.00598802  eval's error:0.00558659"
  [1] "[4]:  train's error:0.00168893  eval's error:0.000620732"
  [LightGBM] [Info] Saving data to binary file D:\temp\Rtmp4URHZw\lgb.Dataset_2717065b01dbe
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000803 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 32
  [LightGBM] [Info] Number of data points in the train set: 6000, number of used features: 16
  -- FAILURE (test_learning_to_rank.R:52:9): learning-to-rank with lgb.train() wor
  abs(eval_results[[2L]][["value"]] - 0.745986) < TOLERANCE is not TRUE
  
  `actual`:   FALSE
  `expected`: TRUE 
  
  -- FAILURE (test_learning_to_rank.R:53:9): learning-to-rank with lgb.train() wor
  abs(eval_results[[3L]][["value"]] - 0.7351959) < TOLERANCE is not TRUE
  
  `actual`:   FALSE
  `expected`: TRUE 
  
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000687 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 40
  [LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 40
  [LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000708 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 40
  [LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 40
  [LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
  [1] "[1]:  valid's ndcg@1:0.675+0.0829156  valid's ndcg@2:0.655657+0.0625302  valid's ndcg@3:0.648464+0.0613335"
  [1] "[2]:  valid's ndcg@1:0.725+0.108972  valid's ndcg@2:0.666972+0.131409  valid's ndcg@3:0.657124+0.130448"
  [1] "[3]:  valid's ndcg@1:0.65+0.111803  valid's ndcg@2:0.630657+0.125965  valid's ndcg@3:0.646928+0.15518"
  [1] "[4]:  valid's ndcg@1:0.725+0.0829156  valid's ndcg@2:0.647629+0.120353  valid's ndcg@3:0.654052+0.129471"
  [1] "[5]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.662958+0.142544  valid's ndcg@3:0.648186+0.130213"
  [1] "[6]:  valid's ndcg@1:0.725+0.129904  valid's ndcg@2:0.647629+0.108136  valid's ndcg@3:0.648186+0.106655"
  [1] "[7]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.653287+0.14255  valid's ndcg@3:0.64665+0.119557"
  [1] "[8]:  valid's ndcg@1:0.725+0.129904  valid's ndcg@2:0.637958+0.123045  valid's ndcg@3:0.64665+0.119557"
  [1] "[9]:  valid's ndcg@1:0.75+0.15  valid's ndcg@2:0.701643+0.116239  valid's ndcg@3:0.701258+0.102647"
  [1] "[10]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.682301+0.117876  valid's ndcg@3:0.66299+0.121243"
  -- FAILURE (test_learning_to_rank.R:130:5): learning-to-rank with lgb.cv() works
  all(...) is not TRUE
  
  `actual`:   FALSE
  `expected`: TRUE 
  
  -- FAILURE (test_learning_to_rank.R:136:5): learning-to-rank with lgb.cv() works
  all(...) is not TRUE
  
  `actual`:   FALSE
  `expected`: TRUE 
  
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003256 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: 6513, number of used features: 116
  [LightGBM] [Info] Start training from score 0.482113
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[1]:  test's l2:6.44165e-17"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[2]:  test's l2:1.97215e-31"
  [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] "[3]:  test's l2:1.97215e-31"
  [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]:  test's l2:1.97215e-31"
  [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]:  test's l2:1.97215e-31"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003233 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: 6513, number of used features: 116
  [LightGBM] [Info] Start training from score 0.482113
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[1]:  test's l2:6.44165e-17"
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [1] "[2]:  test's l2:1.97215e-31"
  [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] "[3]:  test's l2:1.97215e-31"
  [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]:  test's l2:1.97215e-31"
  [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]:  test's l2:1.97215e-31"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003544 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002841 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002820 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003703 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002994 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] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003146 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002567 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 182
  [LightGBM] [Info] Number of data points in the train set: 1611, number of used features: 91
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003153 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [1] "[3]:  train's binary_logloss:0.0480659"
  [1] "[4]:  train's binary_logloss:0.0279151"
  [1] "[5]:  train's binary_logloss:0.0190479"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003077 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003817 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [1] "[3]:  train's binary_logloss:0.0480659"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004092 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002804 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:0.198597"
  [1] "[2]:  train's binary_logloss:0.111535"
  -- Skip (test_lgb.Booster.R:445:5): Saving a model with unknown importance type 
  Reason: Skipping this test because it causes issues for valgrind
  
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [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
  [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] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003694 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: 6513, number of used features: 116
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000657 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 77
  [LightGBM] [Info] Number of data points in the train set: 90, number of used features: 4
  [LightGBM] [Info] Start training from score -1.504077
  [LightGBM] [Info] Start training from score -1.098612
  [LightGBM] [Info] Start training from score -0.810930
  [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: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003337 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: 6513, number of used features: 116
  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
  [LightGBM] [Info] Start training from score -0.071580
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003931 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: 6513, number of used features: 116
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
  You can set `force_col_wise=true` to remove the overhead.
  [LightGBM] [Info] Total Bins 77
  [LightGBM] [Info] Number of data points in the train set: 90, number of used features: 4
  [LightGBM] [Info] Start training from score -1.504077
  [LightGBM] [Info] Start training from score -1.098612
  [LightGBM] [Info] Start training from score -0.810930
  [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: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003012 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: 6513, number of used features: 116
  [LightGBM] [Info] Start training from score 0.482113
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003656 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: 6513, number of used features: 116
  [LightGBM] [Info] Start training from score 0.482113
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003235 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: 6513, number of used features: 116
  [LightGBM] [Info] Start training from score 0.482113
  [LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
  -- Skip (test_utils.R:70:5): lgb.last_error() correctly returns errors from the 
  Reason: Skipping this test because it causes valgrind to think there is a memory leak, and needs to be rethought
  
  -- Skipped tests  --------------------------------------------------------------
  * Skipping this test because it causes issues for valgrind (1)
  * Skipping this test because it causes valgrind to think there is a memory leak, and needs to be rethought (1)
  * UTF-8 feature names are not fully supported in the R package (1)
  
  == testthat results  ===========================================================
  FAILURE (test_learning_to_rank.R:52:9): learning-to-rank with lgb.train() works as expected
  FAILURE (test_learning_to_rank.R:53:9): learning-to-rank with lgb.train() works as expected
  FAILURE (test_learning_to_rank.R:130:5): learning-to-rank with lgb.cv() works as expected
  FAILURE (test_learning_to_rank.R:136:5): learning-to-rank with lgb.cv() works as expected
  
  [ FAIL 4 | WARN 0 | SKIP 3 | PASS 597 ]
  Error: Test failures
  Execution halted