[R-package] fix unit tests that cause valgrind issues

[jameslamb]

#3443 attempted to fix the issues found by valgrind tests in {lightgbm}'s CRAN submission, documented in #3338 .

valgrind logs: https://www.stats.ox.ac.uk/pub/bdr/memtests/valgrind/lightgbm/tests/testthat.Rout

full logs

==417180== Memcheck, a memory error detector
==417180== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
==417180== Using Valgrind-3.16.1 and LibVEX; rerun with -h for copyright info
==417180== Command: /data/blackswan/ripley/R/R-devel-vg/bin/exec/R -f testthat.R --restore --save --no-readline --vanilla
==417180== 

R Under development (unstable) (2020-09-28 r79268) -- "Unsuffered Consequences"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> 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 col-wise multi-threading, the overhead of testing was 0.592330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[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 0.703014 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 0.176956 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 0.777737 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 col-wise multi-threading, the overhead of testing was 0.832821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[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 0.634000 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 0.569824 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] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.705906 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5211, number of used features: 116
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.912824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5211, number of used features: 116
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.669827 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.814277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.700830 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
[LightGBM] [Info] Start training from score 0.483976
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.480906
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.481574
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.482342
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.481766
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306994+0.00061397"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[4]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[5]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[6]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[7]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[8]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[9]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[10]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306984+0.000613968"
[LightGBM] [Info] Number of positive: 198, number of negative: 202
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.174973 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 row-wise multi-threading, the overhead of testing was 0.058737 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 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 0.109463 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 0.107983 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 0.098009 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 1.020899 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]:  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: -inf
[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: -inf
[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: -inf
[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: -inf
[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: -inf
[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: -inf
[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: -inf
[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: -inf
[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: -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] [Warning] No further splits with positive gain, best gain: -inf
[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: -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] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 35110, number of negative: 34890
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.382028 seconds.
You can set `force_col_wise=true` to remove the overhead.
[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 0.122283 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 0.164994 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 0.177896 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 0.217001 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 0.172755 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 0.355979 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 0.478416 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"
[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: -inf
[1] "[6]:  valid1's auc:0.999667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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: -inf
[1] "[9]:  valid1's auc:0.999997"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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 0.280170 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"
[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: -inf
[1] "[6]:  valid1's binary_error:0.016139"
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040397 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 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 row-wise multi-threading, the overhead of testing was 0.022930 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 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 row-wise multi-threading, the overhead of testing was 0.082773 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 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 0.116001 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 0.094980 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 0.173086 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 0.063966 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 0.174965 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 0.092002 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 0.078933 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 0.079003 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 0.234700 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 0.183467 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 0.086990 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"
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.173983 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 0.189002 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 0.255532 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 0.159646 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 0.135841 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 0.198959 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 row-wise multi-threading, the overhead of testing was 0.046420 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 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 0.120706 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 0.043060 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 0.199547 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 0.121653 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:0.716395+0.018088"
[LightGBM] [Info] Number of positive: 45, number of negative: 35
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.115034 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 0.089988 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 0.102973 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: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Unknown parameter: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.115011 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 35
[LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
[LightGBM] [Warning] Unknown parameter: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.141952 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: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.107004 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: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.062751 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 35
[LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
[LightGBM] [Warning] Unknown parameter: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.185991 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: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Unknown parameter: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.141407 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: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.064852 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 35
[LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
[LightGBM] [Warning] Unknown parameter: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.130405 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 35
[LightGBM] [Info] Number of data points in the train set: 80, number of used features: 1
[LightGBM] [Warning] Unknown parameter: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.254942 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: 0x19897770>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.166584 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 0.349751 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 0.623017 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 0.287775 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 0.632026 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 0.699601 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 0.541556 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
==417180== Invalid write of size 8
==417180==    at 0x1DF0BFD4: LGBM_BoosterGetNumPredict_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:509)
==417180==    by 0x49CDA3: R_doDotCall (svn/R-devel/src/main/dotcode.c:607)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180==    by 0x4EF8C3: Rf_eval (svn/R-devel/src/main/eval.c:850)
==417180==    by 0x4F3B99: do_set (svn/R-devel/src/main/eval.c:2967)
==417180==    by 0x4EFB44: Rf_eval (svn/R-devel/src/main/eval.c:802)
==417180==    by 0x4F27B7: do_begin (svn/R-devel/src/main/eval.c:2515)
==417180==    by 0x4EFB44: Rf_eval (svn/R-devel/src/main/eval.c:802)
==417180==  Address 0x1e06f700 is 4,032 bytes inside a block of size 7,960 alloc'd
==417180==    at 0x483A809: malloc (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:307)
==417180==    by 0x52A8C0: GetNewPage (svn/R-devel/src/main/memory.c:946)
==417180==    by 0x52C4FB: Rf_allocVector3 (svn/R-devel/src/main/memory.c:2784)
==417180==    by 0x587CA2: Rf_allocVector (svn/R-devel/src/include/Rinlinedfuns.h:593)
==417180==    by 0x587CA2: ReadItem (svn/R-devel/src/main/serialize.c:1948)
==417180==    by 0x586F74: ReadItem (svn/R-devel/src/main/serialize.c:1872)
==417180==    by 0x5880A2: ReadItem (svn/R-devel/src/main/serialize.c:2018)
==417180==    by 0x588BC1: ReadBCConsts (svn/R-devel/src/main/serialize.c:2102)
==417180==    by 0x588BC1: ReadBC1 (svn/R-devel/src/main/serialize.c:2119)
==417180==    by 0x588CA5: ReadBCConsts (svn/R-devel/src/main/serialize.c:2088)
==417180==    by 0x588CA5: ReadBC1 (svn/R-devel/src/main/serialize.c:2119)
==417180==    by 0x587F59: ReadBC (svn/R-devel/src/main/serialize.c:2130)
==417180==    by 0x587F59: ReadItem (svn/R-devel/src/main/serialize.c:1967)
==417180==    by 0x586F91: ReadItem (svn/R-devel/src/main/serialize.c:1874)
==417180==    by 0x587702: ReadItem (svn/R-devel/src/main/serialize.c:1962)
==417180==    by 0x587702: ReadItem (svn/R-devel/src/main/serialize.c:1962)
==417180== 
[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 col-wise multi-threading, the overhead of testing was 0.337765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[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 /tmp/Rtmpbi101p/lgb.Dataset_65d9c63e200ad
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.238873 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
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.091547 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 row-wise multi-threading, the overhead of testing was 0.075033 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 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 0.211828 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 0.190981 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@1:0.725+0.0829156  valid's ndcg@2:0.686315+0.0225243  valid's ndcg@3:0.677794+0.0340451"
[1] "[2]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.720986+0.0370912  valid's ndcg@3:0.698464+0.0473417"
[1] "[3]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.730657+0.0661112  valid's ndcg@3:0.711732+0.074403"
[1] "[4]:  valid's ndcg@1:0.775+0.0829156  valid's ndcg@2:0.745986+0.0725754  valid's ndcg@3:0.723464+0.0808668"
[1] "[5]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.730657+0.0661112  valid's ndcg@3:0.711732+0.074403"
[1] "[6]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.720986+0.0506137  valid's ndcg@3:0.710196+0.0719775"
[1] "[7]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.740329+0.0637048  valid's ndcg@3:0.719134+0.0743404"
[1] "[8]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.740329+0.0637048  valid's ndcg@3:0.719134+0.0743404"
[1] "[9]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.740329+0.0637048  valid's ndcg@3:0.725+0.0832215"
[1] "[10]:  valid's ndcg@1:0.75+0.05  valid's ndcg@2:0.730657+0.0661112  valid's ndcg@3:0.711732+0.074403"
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.577516 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: -inf
[1] "[1]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]:  test's l2:1.97215e-31"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]:  test's l2:0"
[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 l2:0"
[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 l2:0"
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.708716 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: -inf
[1] "[1]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]:  test's l2:1.97215e-31"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]:  test's l2:0"
[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 l2:0"
[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 l2:0"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.408796 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 0.576498 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 0.384128 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 0.432696 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 0.397329 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 col-wise multi-threading, the overhead of testing was 0.508779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[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 col-wise multi-threading, the overhead of testing was 0.302556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[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 0.558043 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 0.650130 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 0.379625 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 0.423012 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 0.506047 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 col-wise multi-threading, the overhead of testing was 0.496505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[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 0.082373 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: -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 col-wise multi-threading, the overhead of testing was 0.674454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 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: -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 0.846671 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: -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 0.054956 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: -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] Auto-choosing row-wise multi-threading, the overhead of testing was 0.582258 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: -inf
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.514174 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: -inf
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.580596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
══ testthat results  ═══════════════════════════════════════════════════════════
[ OK: 557 | SKIPPED: 2 | WARNINGS: 0 | FAILED: 0 ]
> 
> proc.time()
    user   system  elapsed 
1379.132  159.815 1449.037 
==417180== 
==417180== HEAP SUMMARY:
==417180==     in use at exit: 236,132,395 bytes in 43,601 blocks
==417180==   total heap usage: 2,361,787 allocs, 2,318,186 frees, 5,577,505,489 bytes allocated
==417180== 
==417180== 16 bytes in 1 blocks are definitely lost in loss record 22 of 2,771
==417180==    at 0x483AE7D: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:342)
==417180==    by 0x1DD8AAFC: allocate (/usr/include/c++/10/ext/new_allocator.h:115)
==417180==    by 0x1DD8AAFC: allocate (/usr/include/c++/10/bits/alloc_traits.h:460)
==417180==    by 0x1DD8AAFC: _M_allocate (/usr/include/c++/10/bits/stl_vector.h:346)
==417180==    by 0x1DD8AAFC: _M_create_storage (/usr/include/c++/10/bits/stl_vector.h:361)
==417180==    by 0x1DD8AAFC: _Vector_base (/usr/include/c++/10/bits/stl_vector.h:305)
==417180==    by 0x1DD8AAFC: vector (/usr/include/c++/10/bits/stl_vector.h:511)
==417180==    by 0x1DD8AAFC: LightGBM::GBDT::SaveModelToString[abi:cxx11](int, int, int) const (packages/tests-vg/lightgbm/src/boosting/gbdt_model_text.cpp:349)
==417180==    by 0x1DEFA77D: SaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:717)
==417180==    by 0x1DEFA77D: LGBM_BoosterSaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:2185)
==417180==    by 0x1DF0C9F7: LGBM_BoosterSaveModelToString_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:657)
==417180==    by 0x49CD21: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180==    by 0x4DEC9D: bcEval (svn/R-devel/src/main/eval.c:7089)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180== 
==417180== 20 bytes in 1 blocks are definitely lost in loss record 26 of 2,771
==417180==    at 0x483AE7D: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:342)
==417180==    by 0x1DF0C379: allocate (/usr/include/c++/10/ext/new_allocator.h:115)
==417180==    by 0x1DF0C379: allocate (/usr/include/c++/10/bits/alloc_traits.h:460)
==417180==    by 0x1DF0C379: _M_allocate (/usr/include/c++/10/bits/stl_vector.h:346)
==417180==    by 0x1DF0C379: _M_create_storage (/usr/include/c++/10/bits/stl_vector.h:361)
==417180==    by 0x1DF0C379: _Vector_base (/usr/include/c++/10/bits/stl_vector.h:305)
==417180==    by 0x1DF0C379: vector (/usr/include/c++/10/bits/stl_vector.h:511)
==417180==    by 0x1DF0C379: LGBM_DatasetSetField_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:220)
==417180==    by 0x49CD87: R_doDotCall (svn/R-devel/src/main/dotcode.c:610)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180==    by 0x4EF8C3: Rf_eval (svn/R-devel/src/main/eval.c:850)
==417180==    by 0x4F27B7: do_begin (svn/R-devel/src/main/eval.c:2515)
==417180==    by 0x4EFB44: Rf_eval (svn/R-devel/src/main/eval.c:802)
==417180==    by 0x4EFB44: Rf_eval (svn/R-devel/src/main/eval.c:802)
==417180== 
==417180== 368 bytes in 1 blocks are possibly lost in loss record 141 of 2,771
==417180==    at 0x483CAE9: calloc (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:760)
==417180==    by 0x401456A: _dl_allocate_tls (in /usr/lib64/ld-2.31.so)
==417180==    by 0x53F912E: pthread_create@@GLIBC_2.2.5 (in /usr/lib64/libpthread-2.31.so)
==417180==    by 0x53C842A: ??? (in /usr/lib64/libgomp.so.1.0.0)
==417180==    by 0x53BFF40: GOMP_parallel (in /usr/lib64/libgomp.so.1.0.0)
==417180==    by 0x1DDF6815: LightGBM::DatasetLoader::ConstructFromSampleData(double**, int**, int, int const*, unsigned long, int) (packages/tests-vg/lightgbm/src/io/dataset_loader.cpp:570)
==417180==    by 0x1DF016C4: LGBM_DatasetCreateFromMats (packages/tests-vg/lightgbm/src/c_api.cpp:1072)
==417180==    by 0x1DF019CD: LGBM_DatasetCreateFromMat (packages/tests-vg/lightgbm/src/c_api.cpp:1006)
==417180==    by 0x1DF0B26F: LGBM_DatasetCreateFromMat_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:110)
==417180==    by 0x49CD21: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180== 
==417180== 1,008 bytes in 1 blocks are definitely lost in loss record 208 of 2,771
==417180==    at 0x483AE7D: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:342)
==417180==    by 0x1DD861DE: allocate (/usr/include/c++/10/ext/new_allocator.h:115)
==417180==    by 0x1DD861DE: allocate (/usr/include/c++/10/bits/alloc_traits.h:460)
==417180==    by 0x1DD861DE: _M_allocate (/usr/include/c++/10/bits/stl_vector.h:346)
==417180==    by 0x1DD861DE: _M_create_storage (/usr/include/c++/10/bits/stl_vector.h:361)
==417180==    by 0x1DD861DE: _Vector_base (/usr/include/c++/10/bits/stl_vector.h:305)
==417180==    by 0x1DD861DE: vector (/usr/include/c++/10/bits/stl_vector.h:524)
==417180==    by 0x1DD861DE: LightGBM::GBDT::FeatureImportance(int, int) const (packages/tests-vg/lightgbm/src/boosting/gbdt_model_text.cpp:595)
==417180==    by 0x1DD8B0A4: LightGBM::GBDT::SaveModelToString[abi:cxx11](int, int, int) const (packages/tests-vg/lightgbm/src/boosting/gbdt_model_text.cpp:368)
==417180==    by 0x1DEFA77D: SaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:717)
==417180==    by 0x1DEFA77D: LGBM_BoosterSaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:2185)
==417180==    by 0x1DF0C9F7: LGBM_BoosterSaveModelToString_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:657)
==417180==    by 0x49CD21: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180==    by 0x4DEC9D: bcEval (svn/R-devel/src/main/eval.c:7089)
==417180== 
==417180== 1,166 (64 direct, 1,102 indirect) bytes in 1 blocks are definitely lost in loss record 221 of 2,771
==417180==    at 0x483AE7D: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:342)
==417180==    by 0x1DD8AA64: allocate (/usr/include/c++/10/ext/new_allocator.h:115)
==417180==    by 0x1DD8AA64: allocate (/usr/include/c++/10/bits/alloc_traits.h:460)
==417180==    by 0x1DD8AA64: _M_allocate (/usr/include/c++/10/bits/stl_vector.h:346)
==417180==    by 0x1DD8AA64: _M_create_storage (/usr/include/c++/10/bits/stl_vector.h:361)
==417180==    by 0x1DD8AA64: _Vector_base (/usr/include/c++/10/bits/stl_vector.h:305)
==417180==    by 0x1DD8AA64: vector (/usr/include/c++/10/bits/stl_vector.h:511)
==417180==    by 0x1DD8AA64: LightGBM::GBDT::SaveModelToString[abi:cxx11](int, int, int) const (packages/tests-vg/lightgbm/src/boosting/gbdt_model_text.cpp:348)
==417180==    by 0x1DEFA77D: SaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:717)
==417180==    by 0x1DEFA77D: LGBM_BoosterSaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:2185)
==417180==    by 0x1DF0C9F7: LGBM_BoosterSaveModelToString_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:657)
==417180==    by 0x49CD21: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180==    by 0x4DEC9D: bcEval (svn/R-devel/src/main/eval.c:7089)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180== 
==417180== 8,193 bytes in 1 blocks are definitely lost in loss record 1,346 of 2,771
==417180==    at 0x483AE7D: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:342)
==417180==    by 0x73852AF: std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >::reserve(unsigned long) (in /usr/lib64/libstdc++.so.6.0.28)
==417180==    by 0x737ADEB: std::__cxx11::basic_stringbuf<char, std::char_traits<char>, std::allocator<char> >::overflow(int) (in /usr/lib64/libstdc++.so.6.0.28)
==417180==    by 0x7383639: std::basic_streambuf<char, std::char_traits<char> >::xsputn(char const*, long) (in /usr/lib64/libstdc++.so.6.0.28)
==417180==    by 0x7375963: std::basic_ostream<char, std::char_traits<char> >& std::__ostream_insert<char, std::char_traits<char> >(std::basic_ostream<char, std::char_traits<char> >&, char const*, long) (in /usr/lib64/libstdc++.so.6.0.28)
==417180==    by 0x1DD8B038: operator<< <char, std::char_traits<char>, std::allocator<char> > (/usr/include/c++/10/bits/basic_string.h:6463)
==417180==    by 0x1DD8B038: LightGBM::GBDT::SaveModelToString[abi:cxx11](int, int, int) const (packages/tests-vg/lightgbm/src/boosting/gbdt_model_text.cpp:363)
==417180==    by 0x1DEFA77D: SaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:717)
==417180==    by 0x1DEFA77D: LGBM_BoosterSaveModelToString (packages/tests-vg/lightgbm/src/c_api.cpp:2185)
==417180==    by 0x1DF0C9F7: LGBM_BoosterSaveModelToString_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:657)
==417180==    by 0x49CD21: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180== 
==417180== 9,192 (624 direct, 8,568 indirect) bytes in 1 blocks are definitely lost in loss record 1,353 of 2,771
==417180==    at 0x483AE7D: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:342)
==417180==    by 0x1DDF6164: LightGBM::DatasetLoader::ConstructFromSampleData(double**, int**, int, int const*, unsigned long, int) (packages/tests-vg/lightgbm/src/io/dataset_loader.cpp:686)
==417180==    by 0x1DF016C4: LGBM_DatasetCreateFromMats (packages/tests-vg/lightgbm/src/c_api.cpp:1072)
==417180==    by 0x1DF019CD: LGBM_DatasetCreateFromMat (packages/tests-vg/lightgbm/src/c_api.cpp:1006)
==417180==    by 0x1DF0B26F: LGBM_DatasetCreateFromMat_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:110)
==417180==    by 0x49CD21: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180==    by 0x4EF8C3: Rf_eval (svn/R-devel/src/main/eval.c:850)
==417180== 
==417180== 1,048,576 bytes in 1 blocks are definitely lost in loss record 2,742 of 2,771
==417180==    at 0x483AE7D: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.16.1/coregrind/m_replacemalloc/vg_replace_malloc.c:342)
==417180==    by 0x1DF0C9BA: allocate (/usr/include/c++/10/ext/new_allocator.h:115)
==417180==    by 0x1DF0C9BA: allocate (/usr/include/c++/10/bits/alloc_traits.h:460)
==417180==    by 0x1DF0C9BA: _M_allocate (/usr/include/c++/10/bits/stl_vector.h:346)
==417180==    by 0x1DF0C9BA: _M_create_storage (/usr/include/c++/10/bits/stl_vector.h:361)
==417180==    by 0x1DF0C9BA: _Vector_base (/usr/include/c++/10/bits/stl_vector.h:305)
==417180==    by 0x1DF0C9BA: vector (/usr/include/c++/10/bits/stl_vector.h:511)
==417180==    by 0x1DF0C9BA: LGBM_BoosterSaveModelToString_R (packages/tests-vg/lightgbm/src/lightgbm_R.cpp:656)
==417180==    by 0x49CD21: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==417180==    by 0x49D2A3: do_dotcall (svn/R-devel/src/main/dotcode.c:1281)
==417180==    by 0x4E14B1: bcEval (svn/R-devel/src/main/eval.c:7078)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180==    by 0x4DEC9D: bcEval (svn/R-devel/src/main/eval.c:7089)
==417180==    by 0x4EF6F7: Rf_eval (svn/R-devel/src/main/eval.c:727)
==417180==    by 0x4F110D: R_execClosure (svn/R-devel/src/main/eval.c:1895)
==417180==    by 0x4F1E03: Rf_applyClosure (svn/R-devel/src/main/eval.c:1821)
==417180== 
==417180== LEAK SUMMARY:
==417180==    definitely lost: 1,058,501 bytes in 7 blocks
==417180==    indirectly lost: 9,670 bytes in 83 blocks
==417180==      possibly lost: 368 bytes in 1 blocks
==417180==    still reachable: 235,062,504 bytes in 43,509 blocks
==417180==         suppressed: 1,352 bytes in 1 blocks
==417180== Reachable blocks (those to which a pointer was found) are not shown.
==417180== To see them, rerun with: --leak-check=full --show-leak-kinds=all
==417180== 
==417180== For lists of detected and suppressed errors, rerun with: -s
==417180== ERROR SUMMARY: 12 errors from 9 contexts (suppressed: 0 from 0)

In #3443 , two R unit tests had to be skipped with testthat::skip() because they caused some minor valgrind issues. See the diff in #3443 for details.

How to close this issue

Remove the calls to testthat::skip() introduced in #3443 . Confirm that your fixes fix the issues by creating a "comment" review with the comment /gha run r-valgrind.

[david-cortes]

These are legit issues caused due to memory leaks. The problem is in the logger which calls Rf_error, triggering a C long jump which bypasses C++ destructors.

[jameslamb]

Thanks for that! I should have come back and updated this issue after seeing your comment in #3016 (comment)

Rf_error used in the logger is a C function which produces an R error. It does not do the same as a C++ exception as it won't trigger stack unwinding (i.e. will lead to memory leaks in some functions). That'd be better replaced by REprintf (prints an error message) and then throwing the R error from the calling function, being careful to destruct any C++ objects if needed.

[github-actions]

This issue has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/microsoft/LightGBM/issues including a reference to this.