Last updated on 2022-09-11 12:56:21 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 3.3.2 | 389.32 | 178.05 | 567.37 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 3.3.2 | 317.94 | 336.03 | 653.97 | OK | |
| r-devel-linux-x86_64-fedora-clang | 3.3.2 | 1028.61 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 3.3.2 | 725.34 | OK | |||
| r-devel-windows-x86_64 | 3.3.2 | 350.00 | 190.00 | 540.00 | OK | |
| r-patched-linux-x86_64 | 3.3.2 | 332.04 | 194.95 | 526.99 | OK | |
| r-release-linux-x86_64 | 3.3.2 | 331.31 | 187.99 | 519.30 | OK | |
| r-release-macos-arm64 | 3.3.2 | 215.00 | NOTE | |||
| r-release-macos-x86_64 | 3.3.2 | 260.00 | NOTE | |||
| r-release-windows-x86_64 | 3.3.2 | 401.00 | 220.00 | 621.00 | OK | |
| r-oldrel-macos-arm64 | 3.3.2 | 234.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 3.3.2 | 277.00 | NOTE | |||
| r-oldrel-windows-ix86+x86_64 | 3.3.2 | 787.00 | 248.00 | 1035.00 | NOTE |
Version: 3.3.2
Check: tests
Result: ERROR
Running 'testthat.R' [84s/44s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(lightgbm)
Loading required package: R6
>
> test_check(
+ package = "lightgbm"
+ , stop_on_failure = TRUE
+ , stop_on_warning = FALSE
+ , reporter = testthat::SummaryReporter$new()
+ )
Predictor:
Predictor: W....W.W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.044127 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]: train's binary_logloss:0.314167 test's binary_logloss:0.317777"
[1] "[2]: train's binary_logloss:0.187654 test's binary_logloss:0.187981"
[1] "[3]: train's binary_logloss:0.109209 test's binary_logloss:0.109949"
[1] "[4]: train's binary_logloss:0.0755423 test's binary_logloss:0.0772008"
[1] "[5]: train's binary_logloss:0.0528045 test's binary_logloss:0.0533291"
[1] "[6]: train's binary_logloss:0.0395797 test's binary_logloss:0.0380824"
[1] "[7]: train's binary_logloss:0.0287269 test's binary_logloss:0.0255364"
[1] "[8]: train's binary_logloss:0.0224443 test's binary_logloss:0.0195616"
[1] "[9]: train's binary_logloss:0.016621 test's binary_logloss:0.017834"
[1] "[10]: train's binary_logloss:0.0112055 test's binary_logloss:0.0125538"
[1] "[11]: train's binary_logloss:0.00759638 test's binary_logloss:0.00842372"
[1] "[12]: train's binary_logloss:0.0054887 test's binary_logloss:0.00631812"
[1] "[13]: train's binary_logloss:0.00399548 test's binary_logloss:0.00454944"
[1] "[14]: train's binary_logloss:0.00283135 test's binary_logloss:0.00323724"
[1] "[15]: train's binary_logloss:0.00215378 test's binary_logloss:0.00256697"
[1] "[16]: train's binary_logloss:0.00156723 test's binary_logloss:0.00181753"
[1] "[17]: train's binary_logloss:0.00120077 test's binary_logloss:0.00144437"
[1] "[18]: train's binary_logloss:0.000934889 test's binary_logloss:0.00111807"
[1] "[19]: train's binary_logloss:0.000719878 test's binary_logloss:0.000878304"
[1] "[20]: train's binary_logloss:0.000558692 test's binary_logloss:0.000712272"
[1] "[21]: train's binary_logloss:0.000400916 test's binary_logloss:0.000492223"
[1] "[22]: train's binary_logloss:0.000315938 test's binary_logloss:0.000402804"
[1] "[23]: train's binary_logloss:0.000238113 test's binary_logloss:0.000288682"
[1] "[24]: train's binary_logloss:0.000190248 test's binary_logloss:0.000237835"
[1] "[25]: train's binary_logloss:0.000148322 test's binary_logloss:0.000174674"
[1] "[26]: train's binary_logloss:0.000120581 test's binary_logloss:0.000139513"
[1] "[27]: train's binary_logloss:0.000102756 test's binary_logloss:0.000118804"
[1] "[28]: train's binary_logloss:7.83011e-05 test's binary_logloss:8.40978e-05"
[1] "[29]: train's binary_logloss:6.29191e-05 test's binary_logloss:6.8803e-05"
[1] "[30]: train's binary_logloss:5.28039e-05 test's binary_logloss:5.89864e-05"
[1] "[31]: train's binary_logloss:4.51561e-05 test's binary_logloss:4.91874e-05"
[1] "[32]: train's binary_logloss:3.89402e-05 test's binary_logloss:4.13015e-05"
[1] "[33]: train's binary_logloss:3.24434e-05 test's binary_logloss:3.52605e-05"
[1] "[34]: train's binary_logloss:2.65255e-05 test's binary_logloss:2.86338e-05"
[1] "[35]: train's binary_logloss:2.19277e-05 test's binary_logloss:2.3937e-05"
[1] "[36]: train's binary_logloss:1.86469e-05 test's binary_logloss:2.05375e-05"
[1] "[37]: train's binary_logloss:1.49881e-05 test's binary_logloss:1.53852e-05"
[1] "[38]: train's binary_logloss:1.2103e-05 test's binary_logloss:1.20722e-05"
[1] "[39]: train's binary_logloss:1.02027e-05 test's binary_logloss:1.0578e-05"
[1] "[40]: train's binary_logloss:8.91561e-06 test's binary_logloss:8.8323e-06"
[1] "[41]: train's binary_logloss:7.4855e-06 test's binary_logloss:7.58441e-06"
[1] "[42]: train's binary_logloss:6.21179e-06 test's binary_logloss:6.14299e-06"
[1] "[43]: train's binary_logloss:5.06413e-06 test's binary_logloss:5.13576e-06"
[1] "[44]: train's binary_logloss:4.2029e-06 test's binary_logloss:4.53605e-06"
[1] "[45]: train's binary_logloss:3.47042e-06 test's binary_logloss:3.73234e-06"
[1] "[46]: train's binary_logloss:2.78181e-06 test's binary_logloss:3.02556e-06"
[1] "[47]: train's binary_logloss:2.19819e-06 test's binary_logloss:2.3666e-06"
[1] "[48]: train's binary_logloss:1.80519e-06 test's binary_logloss:1.92932e-06"
[1] "[49]: train's binary_logloss:1.50192e-06 test's binary_logloss:1.64658e-06"
[1] "[50]: train's binary_logloss:1.20212e-06 test's binary_logloss:1.33316e-06"
....
basic:
lightgbm(): W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000900 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]: train's binary_error:0.0222632"
[1] "[2]: train's binary_error:0.0222632"
[1] "[3]: train's binary_error:0.0222632"
[1] "[4]: train's binary_error:0.0109013"
[1] "[5]: train's binary_error:0.0141256"
[1] "[6]: train's binary_error:0.0141256"
[1] "[7]: train's binary_error:0.0141256"
[1] "[8]: train's binary_error:0.0141256"
[1] "[9]: train's binary_error:0.00598802"
[1] "[10]: train's binary_error:0.00598802"
.....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 98
[LightGBM] [Info] Number of data points in the train set: 150, number of used features: 4
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[4]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[7]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[8]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[9]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[10]: train's multi_error:0.0466667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[11]: train's multi_error:0.0333333"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[12]: train's multi_error:0.0266667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[13]: train's multi_error:0.0266667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[14]: train's multi_error:0.0266667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[15]: train's multi_error:0.0266667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[16]: train's multi_error:0.0333333"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[17]: train's multi_error:0.0266667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[18]: train's multi_error:0.0333333"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[19]: train's multi_error:0.0333333"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[20]: train's multi_error:0.0333333"
...W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002121 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]: train's binary_error:0.0304007 train's auc:0.972508 train's binary_logloss:0.198597"
[1] "[2]: train's binary_error:0.0222632 train's auc:0.995075 train's binary_logloss:0.111535"
[1] "[3]: train's binary_error:0.00598802 train's auc:0.997845 train's binary_logloss:0.0480659"
[1] "[4]: train's binary_error:0.00122831 train's auc:0.998433 train's binary_logloss:0.0279151"
[1] "[5]: train's binary_error:0.00122831 train's auc:0.999354 train's binary_logloss:0.0190479"
[1] "[6]: train's binary_error:0.00537387 train's auc:0.98965 train's binary_logloss:0.16706"
[1] "[7]: train's binary_error:0 train's auc:1 train's binary_logloss:0.0128449"
[1] "[8]: train's binary_error:0 train's auc:1 train's binary_logloss:0.00774702"
[1] "[9]: train's binary_error:0 train's auc:1 train's binary_logloss:0.00472108"
[1] "[10]: train's binary_error:0 train's auc:1 train's binary_logloss:0.00208929"
..W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000939 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]: train's binary_error:0.0222632"
[1] "[2]: train's binary_error:0.0222632"
[1] "[3]: train's binary_error:0.0222632"
[1] "[4]: train's binary_error:0.0109013"
[1] "[5]: train's binary_error:0.0141256"
[1] "[6]: train's binary_error:0.0141256"
[1] "[7]: train's binary_error:0.0141256"
[1] "[8]: train's binary_error:0.0141256"
[1] "[9]: train's binary_error:0.00598802"
[1] "[10]: train's binary_error:0.00598802"
..W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000939 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] Start training from score 0.482113
[1] "[1]: train's l2:0.206337"
[1] "[2]: train's l2:0.171229"
[1] "[3]: train's l2:0.140871"
[1] "[4]: train's l2:0.116282"
[1] "[5]: train's l2:0.096364"
[1] "[6]: train's l2:0.0802308"
[1] "[7]: train's l2:0.0675595"
[1] "[8]: train's l2:0.0567154"
[1] "[9]: train's l2:0.0482086"
[1] "[10]: train's l2:0.0402694"
....W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000897 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1] "[1]: train's binary_error:0.0222632 train's auc:0.981784 valid1's binary_error:0.0222632 valid1's auc:0.981784 valid2's binary_error:0.0222632 valid2's auc:0.981784"
[1] "[2]: train's binary_error:0.0222632 train's auc:0.981784 valid1's binary_error:0.0222632 valid1's auc:0.981784 valid2's binary_error:0.0222632 valid2's auc:0.981784"
[1] "[3]: train's binary_error:0.0222632 train's auc:0.992951 valid1's binary_error:0.0222632 valid1's auc:0.992951 valid2's binary_error:0.0222632 valid2's auc:0.992951"
[1] "[4]: train's binary_error:0.0109013 train's auc:0.992951 valid1's binary_error:0.0109013 valid1's auc:0.992951 valid2's binary_error:0.0109013 valid2's auc:0.992951"
[1] "[5]: train's binary_error:0.0141256 train's auc:0.994714 valid1's binary_error:0.0141256 valid1's auc:0.994714 valid2's binary_error:0.0141256 valid2's auc:0.994714"
[1] "[6]: train's binary_error:0.0141256 train's auc:0.994714 valid1's binary_error:0.0141256 valid1's auc:0.994714 valid2's binary_error:0.0141256 valid2's auc:0.994714"
[1] "[7]: train's binary_error:0.0141256 train's auc:0.994714 valid1's binary_error:0.0141256 valid1's auc:0.994714 valid2's binary_error:0.0141256 valid2's auc:0.994714"
[1] "[8]: train's binary_error:0.0141256 train's auc:0.994714 valid1's binary_error:0.0141256 valid1's auc:0.994714 valid2's binary_error:0.0141256 valid2's auc:0.994714"
[1] "[9]: train's binary_error:0.00598802 train's auc:0.993175 valid1's binary_error:0.00598802 valid1's auc:0.993175 valid2's binary_error:0.00598802 valid2's auc:0.993175"
[1] "[10]: train's binary_error:0.00598802 train's auc:0.998242 valid1's binary_error:0.00598802 valid1's auc:0.998242 valid2's binary_error:0.00598802 valid2's auc:0.998242"
.......
training continuation: [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000893 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 0.001973 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 0.000912 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[1] "[6]: train's binary_logloss:0.0609174"
[1] "[7]: train's binary_logloss:0.317567"
[1] "[8]: train's binary_logloss:0.0104223"
[1] "[9]: train's binary_logloss:0.00497498"
[1] "[10]: train's binary_logloss:0.00283557"
.
lgb.cv(): W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000810 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 0.001081 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 0.000791 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 0.000804 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 0.000802 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 5210, number of used features: 116
[LightGBM] [Info] Start training from score 0.483976
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.480906
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.481574
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.482342
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.481766
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306994+0.00061397"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[4]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[5]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[6]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[7]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[8]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[9]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[10]: valid's l2:0.000306984+0.000613968 valid's l1:0.000306984+0.000613968"
.........W[LightGBM] [Info] Number of positive: 198, number of negative: 202
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 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 0.000206 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 0.000203 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 0.000200 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 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 167
[LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.495000 -> initscore=-0.020001
[LightGBM] [Info] Start training from score -0.020001
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490000 -> initscore=-0.040005
[LightGBM] [Info] Start training from score -0.040005
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.517500 -> initscore=0.070029
[LightGBM] [Info] Start training from score 0.070029
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.517500 -> initscore=0.070029
[LightGBM] [Info] Start training from score 0.070029
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.480000 -> initscore=-0.080043
[LightGBM] [Info] Start training from score -0.080043
[1] "[1]: valid's auc:0.476662+0.0622898 valid's binary_error:0.5+0.0593296"
[1] "[2]: valid's auc:0.477476+0.0393392 valid's binary_error:0.554+0.0372022"
[1] "[3]: valid's auc:0.456927+0.042898 valid's binary_error:0.526+0.0361109"
[1] "[4]: valid's auc:0.419531+0.0344972 valid's binary_error:0.54+0.0289828"
[1] "[5]: valid's auc:0.459109+0.0862237 valid's binary_error:0.52+0.0489898"
[1] "[6]: valid's auc:0.460522+0.0911246 valid's binary_error:0.528+0.0231517"
[1] "[7]: valid's auc:0.456328+0.0540445 valid's binary_error:0.532+0.0386782"
[1] "[8]: valid's auc:0.463653+0.0660907 valid's binary_error:0.514+0.0488262"
[1] "[9]: valid's auc:0.443017+0.0549965 valid's binary_error:0.55+0.0303315"
[1] "[10]: valid's auc:0.477483+0.0763283 valid's binary_error:0.488+0.0549181"
.....[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] Start training from score 0.052402
[LightGBM] [Info] Start training from score 0.017850
[LightGBM] [Info] Start training from score -0.018739
[LightGBM] [Info] Start training from score -0.029298
[LightGBM] [Info] Start training from score 0.027539
[1] "[1]: valid's l2:3.40838+0.231034"
[1] "[2]: valid's l2:3.02136+0.216706"
[1] "[3]: valid's l2:2.68787+0.209507"
[1] "[4]: valid's l2:2.40599+0.192969"
[1] "[5]: valid's l2:2.15761+0.187944"
[1] "[6]: valid's l2:1.94667+0.172578"
[1] "[7]: valid's l2:1.75663+0.167009"
[1] "[8]: valid's l2:1.59496+0.150831"
[1] "[9]: valid's l2:1.44947+0.144699"
[1] "[10]: valid's l2:1.31792+0.139097"
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] Start training from score 0.115703
[LightGBM] [Info] Start training from score 0.123541
[LightGBM] [Info] Start training from score 0.076232
[LightGBM] [Info] Start training from score 0.094848
[LightGBM] [Info] Start training from score 0.150201
[1] "[1]: valid's l2:3.58556+0.201544"
[1] "[2]: valid's l2:2.9047+0.163529"
[1] "[3]: valid's l2:2.35329+0.13272"
[1] "[4]: valid's l2:1.90673+0.107749"
[1] "[5]: valid's l2:1.54508+0.0875109"
[1] "[6]: valid's l2:1.25218+0.0711496"
[1] "[7]: valid's l2:1.01499+0.057881"
[1] "[8]: valid's l2:0.82292+0.0471195"
[1] "[9]: valid's l2:0.667388+0.0383999"
[1] "[10]: valid's l2:0.541453+0.0313252"
..W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001596 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001587 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001606 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
[LightGBM] [Info] Start training from score 0.485260
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.478812
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.482266
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid's l2:0.202301+0.000155342"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: valid's l2:0.163898+0.000140233"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: valid's l2:0.132794+0.000144812"
[LightGBM] [Warning] No further splits with positive gain, best 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 l2:0.107602+0.000161317"
[LightGBM] [Warning] No further splits with positive gain, best 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 l2:0.0871985+0.000182805"
W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001606 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001586 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001587 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
[LightGBM] [Info] Start training from score 0.485260
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.478812
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.482266
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid's l2:0.202301"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: valid's l2:0.163898"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: valid's l2:0.132794"
[LightGBM] [Warning] No further splits with positive gain, best 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 l2:0.107602"
[LightGBM] [Warning] No further splits with positive gain, best 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 l2:0.0871985"
....
lgb.train(): W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000895 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: train's binary_error:0.00307078 train's auc:0.99996 train's binary_logloss:0.132074"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: train's binary_error:0.00153539 train's auc:1 train's binary_logloss:0.0444372"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: train's binary_error:0 train's auc:1 train's binary_logloss:0.0159408"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[4]: train's binary_error:0 train's auc:1 train's binary_logloss:0.00590065"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: train's binary_error:0 train's auc:1 train's binary_logloss:0.00230167"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: train's binary_error:0 train's auc:1 train's binary_logloss:0.00084253"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[7]: train's binary_error:0 train's auc:1 train's binary_logloss:0.000309409"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[8]: train's binary_error:0 train's auc:1 train's binary_logloss:0.000113754"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[9]: train's binary_error:0 train's auc:1 train's binary_logloss:4.1838e-05"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[10]: train's binary_error:0 train's auc:1 train's binary_logloss:1.539e-05"
.............[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
........[LightGBM] [Info] Number of positive: 35110, number of negative: 34890
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000544 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 0.000247 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 row-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [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 0.000221 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 0.000203 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 0.000213 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 0.000216 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 0.001989 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid1's auc:0.987036"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: valid1's auc:0.987036"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: valid1's auc:0.998699"
[1] "[4]: valid1's auc:0.998699"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: valid1's auc:0.998699"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: valid1's auc:0.999667"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[7]: valid1's auc:0.999806"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[8]: valid1's auc:0.999978"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[9]: valid1's auc:0.999997"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[10]: valid1's auc:0.999997"
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002302 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 214
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 107
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: valid1's binary_error:0.016139"
[1] "[4]: valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: valid1's binary_error:0.016139"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: valid1's binary_error:0.016139"
..........[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 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 0.000203 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 0.000195 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 0.000215 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 0.000212 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 0.000189 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 0.000193 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 0.000197 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 0.000190 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 0.000203 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 0.000203 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 0.000180 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 0.000213 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 0.000072 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid1's mape:1.1 valid1's rmse:55 valid1's l1:55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: valid1's mape:1.19 valid1's rmse:59.5 valid1's l1:59.5"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: valid1's mape:1.271 valid1's rmse:63.55 valid1's l1:63.55"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[4]: valid1's mape:1.3439 valid1's rmse:67.195 valid1's l1:67.195"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: valid1's mape:1.40951 valid1's rmse:70.4755 valid1's l1:70.4755"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: valid1's mape:1.46856 valid1's rmse:73.428 valid1's l1:73.428"
.....[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000058 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 140
[LightGBM] [Info] Number of data points in the train set: 100, number of used features: 4
[LightGBM] [Info] Start training from score 0.045019
[LightGBM] [Warning] No further splits with positive gain, best 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[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 74
[LightGBM] [Info] Number of data points in the train set: 32, number of used features: 10
[LightGBM] [Info] Start training from score 20.090625
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: train's l2:34.4887"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: train's l2:33.8024"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: train's l2:33.1297"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[4]: train's l2:32.4704"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: train's l2:31.8243"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: train's l2:31.191"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[7]: train's l2:30.5703"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[8]: train's l2:29.9619"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[9]: train's l2:29.3657"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[10]: train's l2:28.7813"
...[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 74
[LightGBM] [Info] Number of data points in the train set: 32, number of used features: 10
[LightGBM] [Info] Start training from score 20.090625
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: train's l2:34.4887"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: train's l2:33.8024"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: train's l2:33.1297"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[4]: train's l2:32.4704"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: train's l2:31.8243"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: train's l2:31.191"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[7]: train's l2:30.5703"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[8]: train's l2:29.9619"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[9]: train's l2:29.3657"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[10]: train's l2:28.7813"
...[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 57
[LightGBM] [Info] Number of data points in the train set: 32, number of used features: 10
[LightGBM] [Info] Start training from score 20.090625
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: train's l2:34.4954"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: train's l2:33.8156"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: train's l2:33.1493"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[4]: train's l2:32.4963"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: train's l2:31.8563"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: train's l2:31.2291"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[7]: train's l2:30.6143"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[8]: train's l2:30.0117"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[9]: train's l2:29.4211"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[10]: train's l2:28.8423"
...W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000075 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"
....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000077 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"
....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000075 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"
....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000075 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"
....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000073 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"
....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000080 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"
..W[LightGBM] [Info] Number of positive: 500, number of negative: 500
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 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"
....W....[LightGBM] [Info] Number of positive: 50, number of negative: 39
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 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 0.000047 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 0.000045 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 0.000044 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 42
[LightGBM] [Info] Number of data points in the train set: 90, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.561798 -> initscore=0.248461
[LightGBM] [Info] Start training from score 0.248461
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.544444 -> initscore=0.178248
[LightGBM] [Info] Start training from score 0.178248
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.582418 -> initscore=0.332706
[LightGBM] [Info] Start training from score 0.332706
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.511111 -> initscore=0.044452
[LightGBM] [Info] Start training from score 0.044452
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid's binary_error:0.500565+0.0460701 valid's binary_logloss:0.701123+0.0155541"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: valid's binary_error:0.500565+0.0460701 valid's binary_logloss:0.70447+0.0152787"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: valid's binary_error:0.500565+0.0460701 valid's binary_logloss:0.706572+0.0162531"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[4]: valid's binary_error:0.500565+0.0460701 valid's binary_logloss:0.709214+0.0165672"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[5]: valid's binary_error:0.500565+0.0460701 valid's binary_logloss:0.710652+0.0172198"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[6]: valid's binary_error:0.500565+0.0460701 valid's binary_logloss:0.713091+0.0176604"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[7]: valid's binary_error:0.508899+0.0347887 valid's binary_logloss:0.714842+0.0184267"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[8]: valid's binary_error:0.508899+0.0347887 valid's binary_logloss:0.714719+0.0178927"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[9]: valid's binary_error:0.508899+0.0347887 valid's binary_logloss:0.717162+0.0181993"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[10]: valid's binary_error:0.508899+0.0347887 valid's binary_logloss:0.716395+0.018088"
....[LightGBM] [Info] Number of positive: 45, number of negative: 35
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000050 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 0.000042 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 0.000041 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000048 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000046 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000048 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000073 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000048 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 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"
.....
linear learner: [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 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 0.137767
[1] "[1]: train's l2:2.84778"
[1] "[2]: train's l2:2.5242"
[1] "[3]: train's l2:2.24798"
[1] "[4]: train's l2:2.00427"
[1] "[5]: train's l2:1.79784"
[1] "[6]: train's l2:1.61418"
[1] "[7]: train's l2:1.45586"
[1] "[8]: train's l2:1.32013"
[1] "[9]: train's l2:1.19755"
[1] "[10]: train's l2:1.09283"
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000057 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 0.338905
[1] "[1]: train's l2:3.40843"
[1] "[2]: train's l2:2.76158"
[1] "[3]: train's l2:2.23763"
[1] "[4]: train's l2:1.81323"
[1] "[5]: train's l2:1.46947"
[1] "[6]: train's l2:1.19102"
[1] "[7]: train's l2:0.96548"
[1] "[8]: train's l2:0.78279"
[1] "[9]: train's l2:0.634812"
[1] "[10]: train's l2:0.514949"
..[LightGBM] [Fatal] Cannot change linear_tree after constructed Dataset handle.
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 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: 100, number of used features: 1
[LightGBM] [Info] Start training from score 0.172413
[1] "[1]: train's l2:2.40089"
[1] "[2]: train's l2:2.14212"
[1] "[3]: train's l2:1.92264"
[1] "[4]: train's l2:1.72221"
[1] "[5]: train's l2:1.55745"
[1] "[6]: train's l2:1.4075"
[1] "[7]: train's l2:1.27998"
[1] "[8]: train's l2:1.16551"
[1] "[9]: train's l2:1.06822"
[1] "[10]: train's l2:0.97992"
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33
[LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
[LightGBM] [Info] Start training from score -0.048498
[1] "[1]: train's l2:3.56595"
[1] "[2]: train's l2:2.88907"
[1] "[3]: train's l2:2.34276"
[1] "[4]: train's l2:1.89763"
[1] "[5]: train's l2:1.53941"
[1] "[6]: train's l2:1.24722"
[1] "[7]: train's l2:1.0124"
[1] "[8]: train's l2:0.820467"
[1] "[9]: train's l2:0.66657"
[1] "[10]: train's l2:0.54045"
..[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 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: 100, number of used features: 1
[LightGBM] [Info] Start training from score 0.172413
[1] "[1]: train's l2:2.41018"
[1] "[2]: train's l2:2.15266"
[1] "[3]: train's l2:1.92549"
[1] "[4]: train's l2:1.71392"
[1] "[5]: train's l2:1.54274"
[1] "[6]: train's l2:1.39293"
[1] "[7]: train's l2:1.271"
[1] "[8]: train's l2:1.16194"
[1] "[9]: train's l2:1.06917"
[1] "[10]: train's l2:0.992576"
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33
[LightGBM] [Info] Number of data points in the train set: 100, number of used features: 1
[LightGBM] [Info] Start training from score -0.048498
[1] "[1]: train's l2:3.57471"
[1] "[2]: train's l2:2.8932"
[1] "[3]: train's l2:2.34421"
[1] "[4]: train's l2:1.90154"
[1] "[5]: train's l2:1.54348"
[1] "[6]: train's l2:1.24892"
[1] "[7]: train's l2:1.01391"
[1] "[8]: train's l2:0.821055"
[1] "[9]: train's l2:0.66692"
[1] "[10]: train's l2:0.540703"
..W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 38
[LightGBM] [Info] Number of data points in the train set: 100, number of used features: 2
[LightGBM] [Info] Start training from score 0.137507
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 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: 100, number of used features: 2
[LightGBM] [Info] Start training from score 0.140709
[1] "[1]: train's l2:2.81786"
[1] "[2]: train's l2:2.49518"
[1] "[3]: train's l2:2.22344"
[1] "[4]: train's l2:1.98477"
[1] "[5]: train's l2:1.77604"
[1] "[6]: train's l2:1.59305"
[1] "[7]: train's l2:1.43703"
[1] "[8]: train's l2:1.30009"
[1] "[9]: train's l2:1.18123"
[1] "[10]: train's l2:1.07721"
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 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: 100, number of used features: 2
[LightGBM] [Info] Start training from score 0.274022
[1] "[1]: train's l2:3.37302"
[1] "[2]: train's l2:2.7329"
[1] "[3]: train's l2:2.21441"
[1] "[4]: train's l2:1.79444"
[1] "[5]: train's l2:1.45425"
[1] "[6]: train's l2:1.17871"
[1] "[7]: train's l2:0.955515"
[1] "[8]: train's l2:0.774729"
[1] "[9]: train's l2:0.628292"
[1] "[10]: train's l2:0.509678"
..
interaction constraints: ...[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000928 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 0.000923 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 0.000961 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 0.049639 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 0.000937 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"
.
monotone constraints: [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 610
[LightGBM] [Info] Number of data points in the train set: 3000, number of used features: 3
[LightGBM] [Info] Start training from score -358.923775
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000132 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 610
[LightGBM] [Info] Number of data points in the train set: 3000, number of used features: 3
[LightGBM] [Info] Start training from score -358.923775
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 610
[LightGBM] [Info] Number of data points in the train set: 3000, number of used features: 3
[LightGBM] [Info] Start training from score -358.923775
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 3000, number of used features: 3
[LightGBM] [Info] Start training from score -5.149260
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000155 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 3000, number of used features: 3
[LightGBM] [Info] Start training from score -5.149260
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 3000, number of used features: 3
[LightGBM] [Info] Start training from score -5.149260
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
.
custom_objective:
Test models with custom objective: [LightGBM] [Warning] Using self-defined objective function
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000861 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 0.000905 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"
.......
dataset:
testing lgb.Dataset functionality: WW..[LightGBM] [Info] Saving data to binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda776ae826c4
[LightGBM] [Info] Load from binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda776ae826c4
WW..WWW.W.W.W........W..WW.W........................[LightGBM] [Fatal] Initial score size doesn't match data size
.W.WW......................[LightGBM] [Info] Saving data to binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda7751cddaa0
[LightGBM] [Info] Load from binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda7751cddaa0
[LightGBM] [Info] Number of positive: 13, number of negative: 87
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 100, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.130000 -> initscore=-1.900959
[LightGBM] [Info] Start training from score -1.900959
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best 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] Saving data to binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda776fe4a425
[LightGBM] [Info] Load from binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda776fe4a425
[LightGBM] [Info] Number of positive: 9, number of negative: 58
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 67, number of used features: 23
[LightGBM] [Info] Number of positive: 8, number of negative: 59
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 67, number of used features: 23
[LightGBM] [Info] Number of positive: 9, number of negative: 57
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 46
[LightGBM] [Info] Number of data points in the train set: 66, number of used features: 23
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.134328 -> initscore=-1.863218
[LightGBM] [Info] Start training from score -1.863218
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.119403 -> initscore=-1.998096
[LightGBM] [Info] Start training from score -1.998096
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136364 -> initscore=-1.845827
[LightGBM] [Info] Start training from score -1.845827
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: valid's binary_logloss:0.279414+0.0167009"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.145125+0.0306369"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.113776+0.0170876"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.111976+0.0374821"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.0966582+0.0285143"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.0865012+0.0219174"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.103477+0.0535833"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.10095+0.0584932"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.108361+0.0735154"
[LightGBM] [Warning] No further splits with positive gain, best 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_logloss:0.102928+0.0643592"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.0999163+0.0796126"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.0952751+0.0882803"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.103852+0.0954681"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.101612+0.107453"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.104854+0.107106"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.104574+0.116598"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.0956509+0.101251"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.0996179+0.114974"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.0913103+0.105155"
[LightGBM] [Warning] No further splits with positive gain, best 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]: valid's binary_logloss:0.0946521+0.11431"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[21]: valid's binary_logloss:0.0979668+0.124126"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[22]: valid's binary_logloss:0.0969992+0.115547"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[23]: valid's binary_logloss:0.104527+0.123895"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[24]: valid's binary_logloss:0.107342+0.131438"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[25]: valid's binary_logloss:0.106014+0.125752"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[26]: valid's binary_logloss:0.116506+0.133072"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[27]: valid's binary_logloss:0.119467+0.138957"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[28]: valid's binary_logloss:0.106518+0.128288"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[29]: valid's binary_logloss:0.118423+0.141227"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[30]: valid's binary_logloss:0.12338+0.147676"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[31]: valid's binary_logloss:0.123616+0.138676"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[32]: valid's binary_logloss:0.126272+0.150046"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[33]: valid's binary_logloss:0.138234+0.15078"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[34]: valid's binary_logloss:0.132632+0.13679"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[35]: valid's binary_logloss:0.14058+0.151539"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[36]: valid's binary_logloss:0.150265+0.156074"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[37]: valid's binary_logloss:0.140275+0.153453"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[38]: valid's binary_logloss:0.15395+0.157308"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[39]: valid's binary_logloss:0.152835+0.167185"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[40]: valid's binary_logloss:0.147393+0.141337"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[41]: valid's binary_logloss:0.1445+0.12219"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[42]: valid's binary_logloss:0.151162+0.131083"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[43]: valid's binary_logloss:0.148884+0.131826"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[44]: valid's binary_logloss:0.154864+0.137458"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[45]: valid's binary_logloss:0.133984+0.111943"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[46]: valid's binary_logloss:0.123453+0.101176"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[47]: valid's binary_logloss:0.133002+0.111579"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[48]: valid's binary_logloss:0.13919+0.118066"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[49]: valid's binary_logloss:0.135524+0.114905"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[50]: valid's binary_logloss:0.143256+0.124112"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[51]: valid's binary_logloss:0.14774+0.129636"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[52]: valid's binary_logloss:0.14825+0.130326"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[53]: valid's binary_logloss:0.136665+0.114743"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[54]: valid's binary_logloss:0.137499+0.115857"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[55]: valid's binary_logloss:0.144269+0.124948"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[56]: valid's binary_logloss:0.149565+0.132107"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[57]: valid's binary_logloss:0.142714+0.122854"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[58]: valid's binary_logloss:0.146239+0.127606"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[59]: valid's binary_logloss:0.137767+0.116215"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[60]: valid's binary_logloss:0.147556+0.129386"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[61]: valid's binary_logloss:0.152326+0.135853"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[62]: valid's binary_logloss:0.147743+0.12964"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[63]: valid's binary_logloss:0.153347+0.13724"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[64]: valid's binary_logloss:0.15782+0.143333"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[65]: valid's binary_logloss:0.157954+0.143515"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[66]: valid's binary_logloss:0.154795+0.13921"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[67]: valid's binary_logloss:0.155441+0.140089"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[68]: valid's binary_logloss:0.162146+0.149243"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[69]: valid's binary_logloss:0.162745+0.150063"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[70]: valid's binary_logloss:0.155413+0.140052"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[71]: valid's binary_logloss:0.156071+0.140948"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[72]: valid's binary_logloss:0.160202+0.146585"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[73]: valid's binary_logloss:0.163868+0.151601"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[74]: valid's binary_logloss:0.162803+0.150142"
[LightGBM] [Warning] No further splits with positive gain, best 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] "[75]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[76]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[77]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[78]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[79]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[80]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[81]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[82]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[83]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[84]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[85]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[86]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[87]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[88]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[89]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[90]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[91]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[92]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[93]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[94]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[95]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[96]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[97]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[98]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[99]: valid's binary_logloss:0.153939+0.138046"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[100]: valid's binary_logloss:0.153939+0.138046"
...[LightGBM] [Info] Construct bin mappers from text data time 0.01 seconds
...[LightGBM] [Info] Construct bin mappers from text data time 0.00 seconds
..........
learning_to_rank:
Learning to rank: [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000092 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 32
[LightGBM] [Info] Number of data points in the train set: 6000, number of used features: 16
.................W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 40
[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000073 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 40
[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000120 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 40
[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000072 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 40
[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
[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@2:0.662958+0.128753 valid's ndcg@3:0.648186+0.11714"
[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"
..............................
lgb.Booster:
Booster: W....
lgb.get.eval.result: ......W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002761 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: test's l2:1.97215e-31"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: test's l2:0"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[4]: test's l2:0"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[5]: test's l2:0"
.W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000946 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[1]: test's l2:6.44165e-17"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[2]: test's l2:1.97215e-31"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[1] "[3]: test's l2:0"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[4]: test's l2:0"
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
[1] "[5]: test's l2:0"
.
lgb.load(): W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000904 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"
.....W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000923 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 col-wise multi-threading, the overhead of testing was 0.000055 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 0.137767
...W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003379 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"
...W.....W..W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000877 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"
...
Booster: [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000870 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
....W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000895 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 0.000407 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] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000934 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] [Info] Saving data to binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda774130d534
[LightGBM] [Info] Load from binary file /tmp/RtmpWvzeOY/lgb.Dataset_eda774130d534
..W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000880 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"
......W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000890 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
..W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000999 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"
....W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001273 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] [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 0.000929 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 0.000917 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
..
save_model: W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002787 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"
...W[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002786 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] [Fatal] Unknown importance type: only support split=0 and gain=1
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 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 0.000066 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
......................Error in self$construct() :
Attempting to create a Dataset without any raw data. This can happen if you have called Dataset$finalize() or if this Dataset was saved with saveRDS(). To avoid this error in the future, use lgb.Dataset.save() or Dataset$save_binary() to save lightgbm Datasets.
.[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000073 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 0.000065 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 0.000071 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Start training from score 0.016891
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score 0.014176
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Start training from score -0.114604
[LightGBM] [Warning] No further splits with positive gain, best 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
.....................
saveRDS.lgb.Booster() and readRDS.lgb.Booster(): [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000890 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] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 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 0.167151
...
lgb.convert_with_rules:
lgb.convert_with_rules(): .........................................................................................................................................
lgb.importance:
lgb.importance: ...........
lgb.interprete:
lgb.interpete: [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001187 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
....W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000080 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 77
[LightGBM] [Info] Number of data points in the train set: 90, number of used features: 4
[LightGBM] [Info] Start training from score -1.504077
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -0.810930
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
....
lgb.plot.importance:
lgb.plot.importance(): [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000955 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: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
.........
lgb.plot.interpretation:
lgb.plot.interpretation: [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000972 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
..W[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 77
[LightGBM] [Info] Number of data points in the train set: 90, number of used features: 4
[LightGBM] [Info] Start training from score -1.504077
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -0.810930
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
.
lgb.unloader:
lgb.unloader: W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000964 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
...W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000961 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
W[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.013452 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Info] Start training from score 0.482113
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
....
metrics:
.METRICS_HIGHER_BETTER(): ...
parameters:
feature penalties: WWWWWWWWWWW.......
parameter aliases: ............
utils:
lgb.params2str: ....
lgb.check.eval: ..........
lgb.check.wrapper_param: .......
weighted_loss:
Case weights are respected: [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000059 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 0.000062 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 0.000056 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 0.000063 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
...
== Warnings ====================================================================
1. Predictor$finalize() should not fail (test_Predictor.R:7:5) - lgb.train: Found the following passed through '...': objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
2. predictions do not fail for integer input (test_Predictor.R:33:5) - lgb.train: Found the following passed through '...': objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
3. start_iteration works correctly (test_Predictor.R:62:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
4. train and predict binary classification (test_basic.R:72:3) - lgb.train: Found the following passed through '...': num_leaves, objective, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
5. train and predict softmax (test_basic.R:100:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, min_data, min_hessian, objective, metric, num_class. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
6. use of multiple eval metrics works (test_basic.R:125:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
7. lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for binary classification (test_basic.R:147:3) - lgb.train: Found the following passed through '...': num_leaves, objective, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
8. lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for regression (test_basic.R:163:3) - lgb.train: Found the following passed through '...': num_leaves, objective, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
9. lightgbm() performs evaluation on validation sets if they are provided (test_basic.R:202:3) - lgb.train: Found the following passed through '...': num_leaves, objective, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
10. cv works (test_basic.R:272:3) - lgb.cv: Found the following passed through '...': min_data, learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.cv for documentation on how to call this function.
11. lightgbm.cv() gives the correct best_score and best_iter for a metric where higher values are better (test_basic.R:329:3) - lgb.cv: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.cv for documentation on how to call this function.
12. lgb.cv() respects showsd argument (test_basic.R:397:3) - lgb.cv: Found the following passed through '...': min_data. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.cv for documentation on how to call this function.
13. lgb.cv() respects showsd argument (test_basic.R:407:3) - lgb.cv: Found the following passed through '...': min_data. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.cv for documentation on how to call this function.
14. lgb.train() works as expected with multiple eval metrics (test_basic.R:429:3) - lgb.train: Found the following passed through '...': learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
15. when early stopping is not activated, best_iter and best_score come from valids and not training data (test_basic.R:1345:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
16. when early stopping is not activated, best_iter and best_score come from valids and not training data (test_basic.R:1367:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
17. when early stopping is not activated, best_iter and best_score come from valids and not training data (test_basic.R:1390:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
18. when early stopping is not activated, best_iter and best_score come from valids and not training data (test_basic.R:1414:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
19. when early stopping is not activated, best_iter and best_score come from valids and not training data (test_basic.R:1438:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
20. when early stopping is not activated, best_iter and best_score come from valids and not training data (test_basic.R:1463:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
21. lightgbm.train() gives the correct best_score and best_iter for a metric where higher values are better (test_basic.R:1495:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
22. using lightgbm() without early stopping, best_iter and best_score come from valids and not training data (test_basic.R:1548:3) - lgb.train: Found the following passed through '...': num_leaves. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
23. lgb.train() works with linear learners and data where a feature has only 1 non-NA value (test_basic.R:1923:3) - lgb.Dataset: Found the following passed through '...': feature_pre_filter. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.Dataset for documentation on how to call this function.
24. lgb.Dataset: basic construction, saving, loading (test_dataset.R:16:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
25. lgb.Dataset: basic construction, saving, loading (test_dataset.R:16:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
26. lgb.Dataset: basic construction, saving, loading (test_dataset.R:26:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
27. lgb.Dataset: basic construction, saving, loading (test_dataset.R:26:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
28. lgb.Dataset: getinfo & setinfo (test_dataset.R:34:3) - Calling setinfo() on a lgb.Dataset is deprecated. Use set_field() instead.
29. lgb.Dataset: getinfo & setinfo (test_dataset.R:35:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
30. lgb.Dataset: getinfo & setinfo (test_dataset.R:36:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
31. lgb.Dataset: getinfo & setinfo (test_dataset.R:38:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
32. lgb.Dataset: getinfo & setinfo (test_dataset.R:39:3) - Calling getinfo() on a lgb.Dataset is deprecated. Use get_field() instead.
33. lgb.Dataset: getinfo & setinfo (test_dataset.R:42:3) - Calling setinfo() on a lgb.Dataset is deprecated. Use set_field() instead.
34. Dataset$slice() supports passing additional parameters through '...' (test_dataset.R:73:3) - Dataset$slice(): Found the following passed through '...': feature_pre_filter. These are ignored and should be removed. To change the parameters of a Dataset produced by Dataset$slice(), use Dataset$set_params(). To modify attributes like 'init_score', use Dataset$set_field(). In future releases of lightgbm, this warning will become an error.
35. Dataset$slice() supports passing Dataset attributes through '...' (test_dataset.R:88:3) - Dataset$slice(): Found the following passed through '...': init_score. These are ignored and should be removed. To change the parameters of a Dataset produced by Dataset$slice(), use Dataset$set_params(). To modify attributes like 'init_score', use Dataset$set_field(). In future releases of lightgbm, this warning will become an error.
36. Dataset$slice() supports passing Dataset attributes through '...' (test_dataset.R:94:3) - Dataset$getinfo() is deprecated and will be removed in a future release. Use Dataset$get_field() instead.
37. Dataset$slice() supports passing Dataset attributes through '...' (test_dataset.R:95:3) - Dataset$getinfo() is deprecated and will be removed in a future release. Use Dataset$get_field() instead.
38. lgb.Dataset$setinfo() should convert 'group' to integer (test_dataset.R:268:3) - Dataset$getinfo() is deprecated and will be removed in a future release. Use Dataset$get_field() instead.
39. lgb.Dataset$setinfo() should convert 'group' to integer (test_dataset.R:271:3) - Dataset$setinfo() is deprecated and will be removed in a future release. Use Dataset$set_field() instead.
40. lgb.Dataset$setinfo() should convert 'group' to integer (test_dataset.R:272:3) - Dataset$getinfo() is deprecated and will be removed in a future release. Use Dataset$get_field() instead.
41. learning-to-rank with lgb.cv() works as expected (test_learning_to_rank.R:84:5) - lgb.cv: Found the following passed through '...': min_data, learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.cv for documentation on how to call this function.
42. Booster$finalize() should not fail (test_lgb.Booster.R:10:5) - lgb.train: Found the following passed through '...': objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
43. lgb.get.eval.result() should throw an informative error for incorrect data_name (test_lgb.Booster.R:60:5) - lgb.train: Found the following passed through '...': min_data, learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
44. lgb.get.eval.result() should throw an informative error for incorrect eval_name (test_lgb.Booster.R:93:5) - lgb.train: Found the following passed through '...': min_data, learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
45. lgb.load() gives the expected error messages given different incorrect inputs (test_lgb.Booster.R:127:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
46. Loading a Booster from a text file works (test_lgb.Booster.R:171:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
47. Loading a Booster from a string works (test_lgb.Booster.R:244:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
48. Saving a large model to string should work (test_lgb.Booster.R:274:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
49. Saving a large model to JSON should work (test_lgb.Booster.R:316:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
50. If a string and a file are both passed to lgb.load() the file is used model_str is totally ignored (test_lgb.Booster.R:344:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
51. Creating a Booster from a Dataset with an existing predictor should work (test_lgb.Booster.R:398:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
52. Booster$rollback_one_iter() should work as expected (test_lgb.Booster.R:485:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
53. Booster$update() passing a train_set works as expected (test_lgb.Booster.R:517:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
54. Booster$update() passing a train_set works as expected (test_lgb.Booster.R:538:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
55. Booster$update() throws an informative error if you provide a non-Dataset to update() (test_lgb.Booster.R:561:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
56. Saving a model with different feature importance types works (test_lgb.Booster.R:652:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
57. Saving a model with unknown importance type fails (test_lgb.Booster.R:705:5) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
58. lgb.intereprete works as expected for multiclass classification (test_lgb.interprete.R:82:5) - lgb.train: Found the following passed through '...': min_data. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
59. lgb.plot.interepretation works as expected for multiclass classification (test_lgb.plot.interpretation.R:80:5) - lgb.train: Found the following passed through '...': min_data. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
60. lgb.unloader works as expected (test_lgb.unloader.R:7:5) - lgb.train: Found the following passed through '...': min_data, learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
61. lgb.unloader finds all boosters and removes them (test_lgb.unloader.R:27:5) - lgb.train: Found the following passed through '...': min_data, learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
62. lgb.unloader finds all boosters and removes them (test_lgb.unloader.R:37:5) - lgb.train: Found the following passed through '...': min_data, learning_rate. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
63. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
64. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
65. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
66. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
67. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
68. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
69. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
70. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
71. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
72. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
73. Feature penalties work properly (test_parameters.R:14:3) - lgb.train: Found the following passed through '...': num_leaves, learning_rate, objective, feature_penalty, metric. These will be used, but in future releases of lightgbm, this warning will become an error. Add these to 'params' instead. See ?lgb.train for documentation on how to call this function.
== Failed ======================================================================
-- 1. Failure (test_basic.R:1276:5): lgb.train() supports non-ASCII feature name
dumped_model[["feature_names"]] not identical to iconv(feature_names, to = "UTF-8").
4/4 mismatches
x[1]: "F_é\u009b¶"
y[1]: "F_<U+96F6>"
x[2]: "F_äž\u0080"
y[2]: "F_<U+4E00>"
x[3]: "F_äº\u008c"
y[3]: "F_<U+4E8C>"
x[4]: "F_äž\u0089"
y[4]: "F_<U+4E09>"
== DONE ========================================================================
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 3.3.2
Check: installed package size
Result: NOTE
installed size is 5.8Mb
sub-directories of 1Mb or more:
libs 5.2Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64