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[R-package] learning-to-rank tests are broken on Solaris 10 and 32-bit Windows #3513

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jameslamb opened this issue Nov 1, 2020 · 7 comments
Open

[R-package] learning-to-rank tests are broken on Solaris 10 and 32-bit Windows #3513

jameslamb opened this issue Nov 1, 2020 · 7 comments
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@jameslamb
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jameslamb commented Nov 1, 2020 •
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I ran the R tests on Solaris using R Hub tonight, and found that they're broken in one of the two Solaris environments that platform supports.

✅ Oracle Solaris 10, x86, 32 bit, R-release, Oracle Developer Studio 12.6
❌ Oracle Solaris 10, x86, 32 bit, R-release

I don't THINK this will block our next attempt at CRAN in #3484 . It looks like CRAN's Solaris environment is the "Oracle Developer Studio" one, based on https://cran.r-project.org/web/checks/check_flavors.html#r-patched-solaris-x86.

Screen Shot 2020-10-31 at 10 18 19 PM

The tests that are failing are both learning-to-rank tests checking the values of the NDCG at different positions...so I'm guessing the failures are related to the changes in #3425 .

logs from the failing tests

[LightGBM] [Info] Total Bins 40
[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
[1] "[1]:  valid's ndcg@1:0.675+0.0829156  valid's ndcg@2:0.655657+0.0625302  valid's ndcg@3:0.648464+0.0613335"
[1] "[2]:  valid's ndcg@1:0.725+0.108972  valid's ndcg@2:0.666972+0.131409  valid's ndcg@3:0.657124+0.130448"
[1] "[3]:  valid's ndcg@1:0.65+0.111803  valid's ndcg@2:0.630657+0.125965  valid's ndcg@3:0.646928+0.15518"
[1] "[4]:  valid's ndcg@1:0.725+0.0829156  valid's ndcg@2:0.647629+0.120353  valid's ndcg@3:0.654052+0.129471"
[1] "[5]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.662958+0.142544  valid's ndcg@3:0.648186+0.130213"
[1] "[6]:  valid's ndcg@1:0.725+0.129904  valid's ndcg@2:0.647629+0.108136  valid's ndcg@3:0.648186+0.106655"
[1] "[7]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.653287+0.14255  valid's ndcg@3:0.64665+0.119557"
[1] "[8]:  valid's ndcg@1:0.725+0.129904  valid's ndcg@2:0.637958+0.123045  valid's ndcg@3:0.64665+0.119557"
[1] "[9]:  valid's ndcg@1:0.75+0.15  valid's ndcg@2:0.711315+0.101634  valid's ndcg@3:0.702794+0.100252"
[1] "[10]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.682301+0.117876  valid's ndcg@3:0.66299+0.121243"
── FAILURE (test_learning_to_rank.R:125:5): learning-to-rank with lgb.cv() works
all(...) is not TRUE

`actual`:   FALSE
`expected`: TRUE 

── FAILURE (test_learning_to_rank.R:131:5): learning-to-rank with lgb.cv() works
all(...) is not TRUE

`actual`:   FALSE
`expected`: TRUE

The test this comes from:

LightGBM/R-package/tests/testthat/test_learning_to_rank.R

Line 131 in 13194d2

expect_true(all(abs(unlist(eval_results[["ndcg@3"]][["eval"]]) - ndcg3_values) < TOLERANCE))

full test results 

R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: i386-pc-solaris2.10 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(lightgbm)
Loading required package: R6
> 
> test_check(
+     package = "lightgbm"
+     , stop_on_failure = TRUE
+     , stop_on_warning = FALSE
+ )
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001250 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]:  train's binary_logloss:0.314167  test's binary_logloss:0.317777"
[1] "[2]:  train's binary_logloss:0.187654  test's binary_logloss:0.187981"
[1] "[3]:  train's binary_logloss:0.109209  test's binary_logloss:0.109949"
[1] "[4]:  train's binary_logloss:0.0755423  test's binary_logloss:0.0772008"
[1] "[5]:  train's binary_logloss:0.0528045  test's binary_logloss:0.0533291"
[1] "[6]:  train's binary_logloss:0.0395797  test's binary_logloss:0.0380824"
[1] "[7]:  train's binary_logloss:0.0287269  test's binary_logloss:0.0255364"
[1] "[8]:  train's binary_logloss:0.0224443  test's binary_logloss:0.0195616"
[1] "[9]:  train's binary_logloss:0.016621  test's binary_logloss:0.017834"
[1] "[10]:  train's binary_logloss:0.0112055  test's binary_logloss:0.0125538"
[1] "[11]:  train's binary_logloss:0.00759638  test's binary_logloss:0.00842372"
[1] "[12]:  train's binary_logloss:0.0054887  test's binary_logloss:0.00631812"
[1] "[13]:  train's binary_logloss:0.00399548  test's binary_logloss:0.00454944"
[1] "[14]:  train's binary_logloss:0.00283135  test's binary_logloss:0.00323724"
[1] "[15]:  train's binary_logloss:0.00215378  test's binary_logloss:0.00256697"
[1] "[16]:  train's binary_logloss:0.00156723  test's binary_logloss:0.00181753"
[1] "[17]:  train's binary_logloss:0.00120077  test's binary_logloss:0.00144437"
[1] "[18]:  train's binary_logloss:0.000934889  test's binary_logloss:0.00111807"
[1] "[19]:  train's binary_logloss:0.000719878  test's binary_logloss:0.000878304"
[1] "[20]:  train's binary_logloss:0.000558692  test's binary_logloss:0.000712272"
[1] "[21]:  train's binary_logloss:0.000400916  test's binary_logloss:0.000492223"
[1] "[22]:  train's binary_logloss:0.000315938  test's binary_logloss:0.000402804"
[1] "[23]:  train's binary_logloss:0.000238113  test's binary_logloss:0.000288682"
[1] "[24]:  train's binary_logloss:0.000190248  test's binary_logloss:0.000237835"
[1] "[25]:  train's binary_logloss:0.000148322  test's binary_logloss:0.000174674"
[1] "[26]:  train's binary_logloss:0.000120581  test's binary_logloss:0.000139513"
[1] "[27]:  train's binary_logloss:0.000102756  test's binary_logloss:0.000118804"
[1] "[28]:  train's binary_logloss:7.83011e-05  test's binary_logloss:8.40978e-05"
[1] "[29]:  train's binary_logloss:6.29191e-05  test's binary_logloss:6.8803e-05"
[1] "[30]:  train's binary_logloss:5.28039e-05  test's binary_logloss:5.89864e-05"
[1] "[31]:  train's binary_logloss:4.51561e-05  test's binary_logloss:4.91874e-05"
[1] "[32]:  train's binary_logloss:3.89402e-05  test's binary_logloss:4.13015e-05"
[1] "[33]:  train's binary_logloss:3.24434e-05  test's binary_logloss:3.52605e-05"
[1] "[34]:  train's binary_logloss:2.65255e-05  test's binary_logloss:2.86338e-05"
[1] "[35]:  train's binary_logloss:2.19277e-05  test's binary_logloss:2.3937e-05"
[1] "[36]:  train's binary_logloss:1.86469e-05  test's binary_logloss:2.05375e-05"
[1] "[37]:  train's binary_logloss:1.49881e-05  test's binary_logloss:1.53852e-05"
[1] "[38]:  train's binary_logloss:1.2103e-05  test's binary_logloss:1.20722e-05"
[1] "[39]:  train's binary_logloss:1.02027e-05  test's binary_logloss:1.0578e-05"
[1] "[40]:  train's binary_logloss:8.91561e-06  test's binary_logloss:8.8323e-06"
[1] "[41]:  train's binary_logloss:7.4855e-06  test's binary_logloss:7.58441e-06"
[1] "[42]:  train's binary_logloss:6.21179e-06  test's binary_logloss:6.14299e-06"
[1] "[43]:  train's binary_logloss:5.06413e-06  test's binary_logloss:5.13576e-06"
[1] "[44]:  train's binary_logloss:4.2029e-06  test's binary_logloss:4.53605e-06"
[1] "[45]:  train's binary_logloss:3.47042e-06  test's binary_logloss:3.73234e-06"
[1] "[46]:  train's binary_logloss:2.78181e-06  test's binary_logloss:3.02556e-06"
[1] "[47]:  train's binary_logloss:2.19819e-06  test's binary_logloss:2.3666e-06"
[1] "[48]:  train's binary_logloss:1.80519e-06  test's binary_logloss:1.92932e-06"
[1] "[49]:  train's binary_logloss:1.50192e-06  test's binary_logloss:1.64658e-06"
[1] "[50]:  train's binary_logloss:1.20212e-06  test's binary_logloss:1.33316e-06"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001232 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 0.000023 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
[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 0.001289 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:0.167059"
[1] "[7]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.0128449"
[1] "[8]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00774702"
[1] "[9]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00472108"
[1] "[10]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00208929"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001259 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]:  train's binary_error:0.0222632"
[1] "[2]:  train's binary_error:0.0222632"
[1] "[3]:  train's binary_error:0.0222632"
[1] "[4]:  train's binary_error:0.0109013"
[1] "[5]:  train's binary_error:0.0141256"
[1] "[6]:  train's binary_error:0.0141256"
[1] "[7]:  train's binary_error:0.0141256"
[1] "[8]:  train's binary_error:0.0141256"
[1] "[9]:  train's binary_error:0.00598802"
[1] "[10]:  train's binary_error:0.00598802"
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001568 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 0.001231 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]:  train's binary_error:0.0222632  train's auc:0.981784  valid1's binary_error:0.0222632  valid1's auc:0.981784  valid2's binary_error:0.0222632  valid2's auc:0.981784"
[1] "[2]:  train's binary_error:0.0222632  train's auc:0.981784  valid1's binary_error:0.0222632  valid1's auc:0.981784  valid2's binary_error:0.0222632  valid2's auc:0.981784"
[1] "[3]:  train's binary_error:0.0222632  train's auc:0.992951  valid1's binary_error:0.0222632  valid1's auc:0.992951  valid2's binary_error:0.0222632  valid2's auc:0.992951"
[1] "[4]:  train's binary_error:0.0109013  train's auc:0.992951  valid1's binary_error:0.0109013  valid1's auc:0.992951  valid2's binary_error:0.0109013  valid2's auc:0.992951"
[1] "[5]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
[1] "[6]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
[1] "[7]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
[1] "[8]:  train's binary_error:0.0141256  train's auc:0.994714  valid1's binary_error:0.0141256  valid1's auc:0.994714  valid2's binary_error:0.0141256  valid2's auc:0.994714"
[1] "[9]:  train's binary_error:0.00598802  train's auc:0.993175  valid1's binary_error:0.00598802  valid1's auc:0.993175  valid2's binary_error:0.00598802  valid2's auc:0.993175"
[1] "[10]:  train's binary_error:0.00598802  train's auc:0.998242  valid1's binary_error:0.00598802  valid1's auc:0.998242  valid2's binary_error:0.00598802  valid2's auc:0.998242"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001213 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]:  train's binary_logloss:0.179606"
[1] "[2]:  train's binary_logloss:0.0975448"
[1] "[3]:  train's binary_logloss:0.0384292"
[1] "[4]:  train's binary_logloss:0.0582241"
[1] "[5]:  train's binary_logloss:0.0595215"
[1] "[6]:  train's binary_logloss:0.0609174"
[1] "[7]:  train's binary_logloss:0.317567"
[1] "[8]:  train's binary_logloss:0.0104223"
[1] "[9]:  train's binary_logloss:0.00497498"
[1] "[10]:  train's binary_logloss:0.00283557"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001231 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]:  train's binary_logloss:0.179606"
[1] "[2]:  train's binary_logloss:0.0975448"
[1] "[3]:  train's binary_logloss:0.0384292"
[1] "[4]:  train's binary_logloss:0.0582241"
[1] "[5]:  train's binary_logloss:0.0595215"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001244 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[1] "[6]:  train's binary_logloss:0.0609174"
[1] "[7]:  train's binary_logloss:0.317567"
[1] "[8]:  train's binary_logloss:0.0104223"
[1] "[9]:  train's binary_logloss:0.00497498"
[1] "[10]:  train's binary_logloss:0.00283557"
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001075 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5211, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001065 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5211, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001059 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001067 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001069 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
[LightGBM] [Info] Start training from score 0.483976
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Start training from score 0.480906
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Start training from score 0.481574
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Start training from score 0.482342
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Start training from score 0.481766
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[1]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306994+0.00061397"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[2]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[3]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[1] "[4]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[1] "[5]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[1] "[6]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[1] "[7]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[1] "[8]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[1] "[9]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] 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] Stopped training because there are no more leaves that meet the split requirements
[1] "[10]:  valid's l2:0.000306984+0.000613968  valid's l1:0.000306986+0.000613967"
[LightGBM] [Info] Number of positive: 198, number of negative: 202
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 167
[LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
[LightGBM] [Info] Number of positive: 196, number of negative: 204
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000015 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 0.000015 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 0.000015 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 0.000015 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 167
[LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.495000 -> initscore=-0.020001
[LightGBM] [Info] Start training from score -0.020001
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490000 -> initscore=-0.040005
[LightGBM] [Info] Start training from score -0.040005
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.517500 -> initscore=0.070029
[LightGBM] [Info] Start training from score 0.070029
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.517500 -> initscore=0.070029
[LightGBM] [Info] Start training from score 0.070029
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.480000 -> initscore=-0.080043
[LightGBM] [Info] Start training from score -0.080043
[1] "[1]:  valid's auc:0.476662+0.0622898  valid's binary_error:0.5+0.0593296"
[1] "[2]:  valid's auc:0.477476+0.0393392  valid's binary_error:0.554+0.0372022"
[1] "[3]:  valid's auc:0.456927+0.042898  valid's binary_error:0.526+0.0361109"
[1] "[4]:  valid's auc:0.419531+0.0344972  valid's binary_error:0.54+0.0289828"
[1] "[5]:  valid's auc:0.459109+0.0862237  valid's binary_error:0.52+0.0489898"
[1] "[6]:  valid's auc:0.460522+0.0911246  valid's binary_error:0.528+0.0231517"
[1] "[7]:  valid's auc:0.456328+0.0540445  valid's binary_error:0.532+0.0386782"
[1] "[8]:  valid's auc:0.463653+0.0660907  valid's binary_error:0.514+0.0488262"
[1] "[9]:  valid's auc:0.443017+0.0549965  valid's binary_error:0.55+0.0303315"
[1] "[10]:  valid's auc:0.477483+0.0763283  valid's binary_error:0.488+0.0549181"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001221 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[1]:  train's binary_error:0.00307078  train's auc:0.99996  train's binary_logloss:0.132074"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[2]:  train's binary_error:0.00153539  train's auc:1  train's binary_logloss:0.0444372"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[3]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.0159408"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[4]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00590065"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[5]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00230167"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[6]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.00084253"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[7]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.000309409"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[8]:  train's binary_error:0  train's auc:1  train's binary_logloss:0.000113754"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[9]:  train's binary_error:0  train's auc:1  train's binary_logloss:4.1838e-05"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[10]:  train's binary_error:0  train's auc:1  train's binary_logloss:1.539e-05"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Number of positive: 35110, number of negative: 34890
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12
[LightGBM] [Info] Number of data points in the train set: 70000, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.501571 -> initscore=0.006286
[LightGBM] [Info] Start training from score 0.006286
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Info] Number of positive: 500, number of negative: 500
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000029 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 0.000031 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 0.000028 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 0.000031 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 0.000031 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 0.000030 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 0.001219 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  valid1's auc:0.987036"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  valid1's auc:0.987036"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  valid1's auc:0.998699"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[4]:  valid1's auc:0.998699"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  valid1's auc:0.998699"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[6]:  valid1's auc:0.999667"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[7]:  valid1's auc:0.999806"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[8]:  valid1's auc:0.999978"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[9]:  valid1's auc:0.999997"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[10]:  valid1's auc:0.999997"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001229 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[4]:  valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[6]:  valid1's binary_error:0.016139"
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000031 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  valid1's rmse:55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  valid1's rmse:59.5"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  valid1's rmse:63.55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[4]:  valid1's rmse:67.195"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  valid1's rmse:70.4755"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[6]:  valid1's rmse:73.428"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[7]:  valid1's rmse:76.0852"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[8]:  valid1's rmse:78.4766"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[9]:  valid1's rmse:80.629"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[10]:  valid1's rmse:82.5661"
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000031 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  valid1's rmse:55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  valid1's rmse:59.5"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  valid1's rmse:63.55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[4]:  valid1's rmse:67.195"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  valid1's rmse:70.4755"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[6]:  valid1's rmse:73.428"
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 35
[LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
[LightGBM] [Info] Start training from score 0.045019
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.1"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.2"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.3"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[4]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.4"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.5"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[6]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.6"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[7]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.7"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[8]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.8"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[9]:  valid1's constant_metric:0.2  valid1's increasing_metric:0.9"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[10]:  valid1's constant_metric:0.2  valid1's increasing_metric:1"
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000015 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 0.000014 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 0.000018 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 0.000014 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 0.000012 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 0.000014 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 0.000014 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 0.000013 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 0.000015 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 0.000014 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 0.000029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  valid1's mape:1.1  valid1's rmse:55  valid1's l1:55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  valid1's mape:1.19  valid1's rmse:59.5  valid1's l1:59.5"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  valid1's mape:1.271  valid1's rmse:63.55  valid1's l1:63.55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[4]:  valid1's mape:1.3439  valid1's rmse:67.195  valid1's l1:67.195"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  valid1's mape:1.40951  valid1's rmse:70.4755  valid1's l1:70.4755"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[6]:  valid1's mape:1.46856  valid1's rmse:73.428  valid1's l1:73.428"
── Skip (test_basic.R:1171:3): lgb.train() supports non-ASCII feature names ────
Reason: UTF-8 feature names are not fully supported in the R package

[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000029 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 0.000030 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 0.000032 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 0.000032 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 0.000032 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 0.000031 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  train's rmse:25"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  train's rmse:12.5"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  train's rmse:6.25"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[4]:  train's rmse:3.125"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  train's rmse:1.5625"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[6]:  train's rmse:0.78125"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[7]:  train's rmse:0.390625"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[8]:  train's rmse:0.195312"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[9]:  train's rmse:0.0976562"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[10]:  train's rmse:0.0488281"
[LightGBM] [Info] Number of positive: 500, number of negative: 500
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000029 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[1] "[1]:  something-random-we-would-not-hardcode's auc:0.58136  valid1's auc:0.429487"
[1] "[2]:  something-random-we-would-not-hardcode's auc:0.599008  valid1's auc:0.266026"
[1] "[3]:  something-random-we-would-not-hardcode's auc:0.6328  valid1's auc:0.349359"
[1] "[4]:  something-random-we-would-not-hardcode's auc:0.655136  valid1's auc:0.394231"
[1] "[5]:  something-random-we-would-not-hardcode's auc:0.655408  valid1's auc:0.419872"
[1] "[6]:  something-random-we-would-not-hardcode's auc:0.678784  valid1's auc:0.336538"
[1] "[7]:  something-random-we-would-not-hardcode's auc:0.682176  valid1's auc:0.416667"
[1] "[8]:  something-random-we-would-not-hardcode's auc:0.698032  valid1's auc:0.394231"
[1] "[9]:  something-random-we-would-not-hardcode's auc:0.712672  valid1's auc:0.445513"
[1] "[10]:  something-random-we-would-not-hardcode's auc:0.723024  valid1's auc:0.471154"
[LightGBM] [Info] Number of positive: 50, number of negative: 39
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000011 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 0.000008 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 0.000008 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 0.000008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 42
[LightGBM] [Info] Number of data points in the train set: 90, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.561798 -> initscore=0.248461
[LightGBM] [Info] Start training from score 0.248461
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.544444 -> initscore=0.178248
[LightGBM] [Info] Start training from score 0.178248
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.582418 -> initscore=0.332706
[LightGBM] [Info] Start training from score 0.332706
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.511111 -> initscore=0.044452
[LightGBM] [Info] Start training from score 0.044452
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[1]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.701123+0.0155541"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[2]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.70447+0.0152787"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[3]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.706572+0.0162531"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[4]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.709214+0.0165672"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[5]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.710652+0.0172198"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[6]:  valid's binary_error:0.500565+0.0460701  valid's binary_logloss:0.713091+0.0176604"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[7]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.714842+0.0184267"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[8]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.714719+0.0178927"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[9]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.717162+0.0181993"
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[1] "[10]:  valid's binary_error:0.508899+0.0347887  valid's binary_logloss:0.716577+0.0180201"
[LightGBM] [Info] Number of positive: 45, number of negative: 35
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000011 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 0.000008 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 0.000008 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Unknown parameter: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000013 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000012 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000012 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000010 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000010 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Unknown parameter: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000013 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000009 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000008 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000008 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: c4154e8>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000009 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 0.001272 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 0.001295 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 0.001334 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 0.001281 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 0.001278 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 0.001209 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Warning] Using self-defined objective function
[1] "[1]:  train's auc:0.994987  train's error:0.00598802  eval's auc:0.995243  eval's error:0.00558659"
[1] "[2]:  train's auc:0.99512  train's error:0.00307078  eval's auc:0.995237  eval's error:0.00248293"
[1] "[3]:  train's auc:0.99009  train's error:0.00598802  eval's auc:0.98843  eval's error:0.00558659"
[1] "[4]:  train's auc:0.999889  train's error:0.00168893  eval's auc:1  eval's error:0.000620732"
[1] "[5]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
[1] "[6]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
[1] "[7]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
[1] "[8]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
[1] "[9]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
[1] "[10]:  train's auc:1  train's error:0  eval's auc:1  eval's error:0"
[LightGBM] [Warning] Using self-defined objective function
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001273 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Warning] Using self-defined objective function
[1] "[1]:  train's error:0.00598802  eval's error:0.00558659"
[1] "[2]:  train's error:0.00307078  eval's error:0.00248293"
[1] "[3]:  train's error:0.00598802  eval's error:0.00558659"
[1] "[4]:  train's error:0.00168893  eval's error:0.000620732"
[LightGBM] [Info] Saving data to binary file /export/home/X7hzECR/Rtemp/Rtmpd5KsmG/working_dir/Rtmpi3f75n/lgb.Dataset_7186408c4917
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32
[LightGBM] [Info] Number of data points in the train set: 6000, number of used features: 16
── FAILURE (test_learning_to_rank.R:49:5): learning-to-rank with lgb.train() wor
abs(eval_results[[2L]][["value"]] - 0.745986) < TOLERANCE is not TRUE

`actual`:   FALSE
`expected`: TRUE 

── FAILURE (test_learning_to_rank.R:50:5): learning-to-rank with lgb.train() wor
abs(eval_results[[3L]][["value"]] - 0.7351959) < TOLERANCE is not TRUE

`actual`:   FALSE
`expected`: TRUE 

[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 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 0.000217 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 0.000214 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 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 40
[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
[1] "[1]:  valid's ndcg@1:0.675+0.0829156  valid's ndcg@2:0.655657+0.0625302  valid's ndcg@3:0.648464+0.0613335"
[1] "[2]:  valid's ndcg@1:0.725+0.108972  valid's ndcg@2:0.666972+0.131409  valid's ndcg@3:0.657124+0.130448"
[1] "[3]:  valid's ndcg@1:0.65+0.111803  valid's ndcg@2:0.630657+0.125965  valid's ndcg@3:0.646928+0.15518"
[1] "[4]:  valid's ndcg@1:0.725+0.0829156  valid's ndcg@2:0.647629+0.120353  valid's ndcg@3:0.654052+0.129471"
[1] "[5]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.662958+0.142544  valid's ndcg@3:0.648186+0.130213"
[1] "[6]:  valid's ndcg@1:0.725+0.129904  valid's ndcg@2:0.647629+0.108136  valid's ndcg@3:0.648186+0.106655"
[1] "[7]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.653287+0.14255  valid's ndcg@3:0.64665+0.119557"
[1] "[8]:  valid's ndcg@1:0.725+0.129904  valid's ndcg@2:0.637958+0.123045  valid's ndcg@3:0.64665+0.119557"
[1] "[9]:  valid's ndcg@1:0.75+0.15  valid's ndcg@2:0.711315+0.101634  valid's ndcg@3:0.702794+0.100252"
[1] "[10]:  valid's ndcg@1:0.75+0.165831  valid's ndcg@2:0.682301+0.117876  valid's ndcg@3:0.66299+0.121243"
── FAILURE (test_learning_to_rank.R:125:5): learning-to-rank with lgb.cv() works
all(...) is not TRUE

`actual`:   FALSE
`expected`: TRUE 

── FAILURE (test_learning_to_rank.R:131:5): learning-to-rank with lgb.cv() works
all(...) is not TRUE

`actual`:   FALSE
`expected`: TRUE 

[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001347 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[1]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[2]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[3]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[4]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[5]:  test's l2:6.44165e-17"
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001320 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[1] "[1]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[2]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[3]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[4]:  test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[5]:  test's l2:6.44165e-17"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001212 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 0.001201 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 0.001202 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 0.001215 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 0.001208 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001249 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]:  train's binary_logloss:0.198597"
[1] "[2]:  train's binary_logloss:0.111535"
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 182
[LightGBM] [Info] Number of data points in the train set: 1611, number of used features: 91
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001219 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 0.001217 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 0.001214 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 0.001214 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 0.001221 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]:  train's binary_logloss:0.198597"
[1] "[2]:  train's binary_logloss:0.111535"
── Skip (test_lgb.Booster.R:445:5): Saving a model with unknown importance type 
Reason: Skipping this test because it causes issues for valgrind

[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000019 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000019 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000014 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000014 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001242 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000021 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: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001242 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001249 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000020 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: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: -Inf
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001313 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001317 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001333 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: 0.000000
── Skip (test_utils.R:70:5): lgb.last_error() correctly returns errors from the 
Reason: Skipping this test because it causes valgrind to think there is a memory leak, and needs to be rethought

── Skipped tests  ──────────────────────────────────────────────────────────────
â—� Skipping this test because it causes issues for valgrind (1)
â—� Skipping this test because it causes valgrind to think there is a memory leak, and needs to be rethought (1)
â—� UTF-8 feature names are not fully supported in the R package (1)

�� testthat results  �����������������������������������������������������������
FAILURE (test_learning_to_rank.R:49:5): learning-to-rank with lgb.train() works as expected
FAILURE (test_learning_to_rank.R:50:5): learning-to-rank with lgb.train() works as expected
FAILURE (test_learning_to_rank.R:125:5): learning-to-rank with lgb.cv() works as expected
FAILURE (test_learning_to_rank.R:131:5): learning-to-rank with lgb.cv() works as expected

[ FAIL 4 | WARN 0 | SKIP 3 | PASS 597 ]
Error: Test failures
Execution halted
R CMD CHECK results 
* using log directory ‘/export/home/X7hzECR/lightgbm.Rcheck’
* using R version 4.0.3 (2020-10-10)
* using platform: i386-pc-solaris2.10 (32-bit)
* using session charset: UTF-8
* using option ‘--as-cran’
* checking for file ‘lightgbm/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘lightgbm’ version ‘3.0.0.99’
* package encoding: UTF-8
* checking CRAN incoming feasibility ... NOTE
Maintainer: ‘Guolin Ke <[email protected]>’

New submission

Package was archived on CRAN

Possibly mis-spelled words in DESCRIPTION:
  Guolin (26:52)
  Ke (26:48)
  al (26:62)
  et (26:59)

CRAN repository db overrides:
  X-CRAN-Comment: Archived on 2020-10-02 for corrupting R's memory.

  See the valgrind report of out-of-bounds write.
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘lightgbm’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking for future file timestamps ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... WARNING
  Output from running autoreconf:
  /opt/csw/share/aclocal/gtk.m4:7: warning: underquoted definition of AM_PATH_GTK
  /opt/csw/share/aclocal/gtk.m4:7:   run info Automake 'Extending aclocal'
  /opt/csw/share/aclocal/gtk.m4:7:   or see https://www.gnu.org/software/automake/manual/automake.html#Extending-aclocal
A complete check needs the 'checkbashisms' script.
See section ‘Configure and cleanup’ in the ‘Writing R Extensions’
manual.
Files ‘README.md’ or ‘NEWS.md’ cannot be checked without ‘pandoc’ being installed.
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in shell scripts ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compilation flags used ... NOTE
Compilation used the following non-portable flag(s):
  ‘-march=pentiumpro’
* checking compiled code ... OK
* checking examples ... OK
* checking examples with --run-donttest ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ... ERROR
  Running ‘testthat.R’ [11s/12s]
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
  
  ── Skipped tests  ──────────────────────────────────────────────────────────────
  â—� Skipping this test because it causes issues for valgrind (1)
  â—� Skipping this test because it causes valgrind to think there is a memory leak, and needs to be rethought (1)
  â—� UTF-8 feature names are not fully supported in the R package (1)
  
  �� testthat results  �����������������������������������������������������������
  FAILURE (test_learning_to_rank.R:49:5): learning-to-rank with lgb.train() works as expected
  FAILURE (test_learning_to_rank.R:50:5): learning-to-rank with lgb.train() works as expected
  FAILURE (test_learning_to_rank.R:125:5): learning-to-rank with lgb.cv() works as expected
  FAILURE (test_learning_to_rank.R:131:5): learning-to-rank with lgb.cv() works as expected
  
  [ FAIL 4 | WARN 0 | SKIP 3 | PASS 597 ]
  Error: Test failures
  Execution halted
* checking PDF version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE
Status: 1 ERROR, 1 WARNING, 2 NOTEs

How to test this

in a shell

sh build-cran-package.sh

in R

result <- rhub::check(
    path = "lightgbm_3.0.0.99.tar.gz"
    , email = "[email protected]"
    , check_args = c(
        "--as-cran"
    )
    , platform = c(
        "solaris-x86-patched"
        , "solaris-x86-patched-ods"
    )
    , env_vars = c(
        "R_COMPILE_AND_INSTALL_PACKAGES" = "always"
        , "_R_CHECK_FORCE_SUGGESTS_" = "true"
        , "_R_CHECK_CRAN_INCOMING_USE_ASPELL_" = "true"
    )
)
@StrikerRUS
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As a quick workaround with the aim to try upload 3.1.0 to CRAN we can just skip problematic files/tests/asserts.
https://github.com/dmlc/xgboost/blob/cf4f019ed63b94a8ba15d854a44feeaeeec385b0/R-package/tests/testthat/test_basic.R#L14
https://github.com/dmlc/xgboost/blob/cf4f019ed63b94a8ba15d854a44feeaeeec385b0/R-package/tests/testthat/test_basic.R#L239-L240

@guolinke
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guolinke commented Nov 2, 2020

@jameslamb , does the solaris generated the different NDCG scores rather than other platforms ?
it may be caused by the floating-point sum error accumulation, which may be different in different platforms.
Can we show its values, and try smaller thresholds?

@jameslamb
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As a quick workaround with the aim to try upload 3.1.0 to CRAN we can just skip problematic files/tests/asserts.

Yes if we can't fix it quickly enough, that's probably fine.

Can we show its values, and try smaller thresholds?

Yeah I was thinking the same thing. I can change the tests so that the error messages show exact values.

jameslamb added a commit to jameslamb/LightGBM that referenced this issue Nov 2, 2020
@jameslamb
[R-package] fix learning-to-rank tests on Solaris (fixes microsoft#3513)
2882143
@jameslamb
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jameslamb commented Nov 2, 2020 •
edited
Loading

Ok I was able to get better errors.

── FAILURE (test_learning_to_rank.R:144:5): learning-to-rank with lgb.cv() works
eval_results[["ndcg@2"]][["eval"]][[7L]] not equal to ndcg2_values[[7L]].
1/1 mismatches
[1] 0.653 - 0.663 == -0.00967

── FAILURE (test_learning_to_rank.R:160:5): learning-to-rank with lgb.cv() works
eval_results[["ndcg@3"]][["eval"]][[7L]] not equal to ndcg3_values[[7L]].
1/1 mismatches
[1] 0.647 - 0.648 == -0.00154

This is from the following test code:

LightGBM/R-package/tests/testthat/test_learning_to_rank.R

Line 53 in cf69591

test_that("learning-to-rank with lgb.cv() works as expected", {

@jameslamb
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I checked Windows builds...this issue doesn't show up on 32-bit Windows. So it's not a problem like "we have some lost precision on 32-bit systems". I think the issue really might be specific to Solaris.

This was referenced Nov 7, 2020
Merged
Merged
@StrikerRUS
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this issue doesn't show up on 32-bit Windows.

Seems that it does.

LightGBM/R-package/tests/testthat/test_learning_to_rank.R

Lines 58 to 62 in c5d9d24

test_that("learning-to-rank with lgb.cv() works as expected", {
testthat::skip_if(
ON_SOLARIS || ON_32_BIT_WINDOWS
, message = "Skipping on Solaris and 32-bit Windows"
)

@StrikerRUS StrikerRUS changed the title [R-package] learning-to-rank tests are broken on Solaris 10 [R-package] learning-to-rank tests are broken on Solaris 10 and 32-bit Windows Jan 27, 2021
@jameslamb
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Thanks for updating. I think that comment was written before we discovered that the 32-bit Windows jobs were silently not running in CI: #3588

@StrikerRUS StrikerRUS mentioned this issue Jan 28, 2021
Merged
@jameslamb jameslamb mentioned this issue May 21, 2022
Merged
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@jameslamb @guolinke @StrikerRUS