Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

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

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

Comments

@jameslamb
Copy link
Collaborator

jameslamb commented Nov 1, 2020

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:

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 <guolin.ke@microsoft.com>’

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 = "jaylamb20@gmail.com"
    , 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
Copy link
Collaborator

@guolinke
Copy link
Collaborator

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
Copy link
Collaborator Author

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
Copy link
Collaborator Author

jameslamb commented Nov 2, 2020

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:

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

@jameslamb
Copy link
Collaborator Author

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.

@StrikerRUS
Copy link
Collaborator

this issue doesn't show up on 32-bit Windows.

Seems that it does.

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
Copy link
Collaborator Author

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

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

3 participants