[R-package] CMake fixes to support MSVC

As of #2901 , when you build the R package the R core library now needs to be linked to lib_lightgbm. As a result, I think that building the R package is broken for MSVC right now. We have code to create the .lib file for linking but aren't currently linking it.
If you try to build the R package from the version on master with MSVC you will get a linker error.
In this PR, I propose adding that step to CMakeLists.txt. I've also proposed some simplifications to FindLibR.cmake. It seems that the variable LIBR_LIB_DIR isn't actually necessary. It's found using the R code R.home('lib')...there is nothing special about this code. I learned that R.home(some_directory) returns a string with the value of some_directory tacked onto the end of R.home(), regardless of whether or not that exists!

Given that, we might as well just use "${LIBR_HOME}/lib" directly in the search for LIBR_CORE_LIBRARY and not go through that extra step with R.
The code from this PR is working on #2936 , and you can confirm that it works with MSVC in the Azure builds for that PR and with MinGW in the AppVeyor builds for that PR. You can confirm that these changes don't break linux and mac by looking at the Travis builds for that PR.
Splitting this out into its own PR to fix the MSVC problem quicker and to make #2936 easier to review.

I'm OK with these changes. However, I'd suggest to wait resolving of this thread.

thank you! Absolutely agree.

oy, more temporary CTAN failures

close-reopen to trigger CI

I updated this with the changes from #2936 today, then I tested this tonight on my Windows laptop and it is working (details below). @guolinke @StrikerRUS @Laurae2 as a reminder, I think we should merge it without waiting to have CI for Visual Studio. That will be done as part of #2965 .
Right now we have multiple reports that Visual Studio builds of the R package are broken (#2970, #2995), so I think we should merge this ASAP.

how I tested locally + the results

R: 3.6.1
CMake: 3.16
Windows: 10
Visual Studio: 16 2019

I ran the following command to install the R package.
Rscript build_r.R >C:\Users\James\repos\LightGBM\log.txt

A bit of the log went to the console, the rest to the logs.
console logs

log.txt
* checking for file 'lightgbm_r/DESCRIPTION' ... OK
* preparing 'lightgbm':
* checking DESCRIPTION meta-information ... OK
* cleaning src
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
WARNING: directory 'lightgbm/src/compute' is empty
* looking to see if a 'data/datalist' file should be added
* building 'lightgbm_2.3.2.tar.gz'
[1] "Trying to build with: 'Visual Studio 16 2019'"
-- Selecting Windows SDK version 10.0.18362.0 to target Windows 10.0.17763.
-- The C compiler identification is MSVC 19.24.28319.0
-- The CXX compiler identification is MSVC 19.24.28319.0
-- Check for working C compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.24.28314/bin/Hostx64/x64/cl.exe
-- Check for working C compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.24.28314/bin/Hostx64/x64/cl.exe -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.24.28314/bin/Hostx64/x64/cl.exe
-- Check for working CXX compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.24.28314/bin/Hostx64/x64/cl.exe -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found LibR: C:/Program Files/R/R-3.6.1
-- LIBR_EXECUTABLE: C:/Program Files/R/R-3.6.1/bin/x64/R.exe
-- LIBR_INCLUDE_DIRS: C:/Program Files/R/R-3.6.1/include
-- LIBR_CORE_LIBRARY: C:/Program Files/R/R-3.6.1/bin/x64/R.dll
-- Found OpenMP_C: -openmp (found version "2.0")
-- Found OpenMP_CXX: -openmp (found version "2.0")
-- Found OpenMP: TRUE (found version "2.0")
-- Performing Test MM_PREFETCH
-- Performing Test MM_PREFETCH - Failed
-- Performing Test MM_MALLOC
-- Performing Test MM_MALLOC - Failed
-- Configuring done
-- Generating done
-- Build files have been written to: C:/Users/James/AppData/Local/Temp/Rtmpy8QQoo/R.INSTALL20184a692267/lightgbm/src/build
[1] "Building with 'Visual Studio 16 2019' succeeded"
-- Selecting Windows SDK version 10.0.18362.0 to target Windows 10.0.17763.
-- LIBR_EXECUTABLE: C:/Program Files/R/R-3.6.1/bin/x64/R.exe
-- LIBR_INCLUDE_DIRS: C:/Program Files/R/R-3.6.1/include
-- LIBR_CORE_LIBRARY: C:/Program Files/R/R-3.6.1/bin/x64/R.dll
-- Found OpenMP_C: -openmp
-- Found OpenMP_CXX: -openmp
-- Found OpenMP: TRUE
-- Configuring done
-- Generating done
-- Build files have been written to: C:/Users/James/AppData/Local/Temp/Rtmpy8QQoo/R.INSTALL20184a692267/lightgbm/src/build
Microsoft (R) Build Engine version 16.4.0+e901037fe for .NET Framework
Copyright (C) Microsoft Corporation. All rights reserved.

C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\Microsoft.CppBuild.targets(467,5): warning MSB8029: The Intermediate directory or Output directory cannot reside under the Temporary directory as it could lead to issues with incremental build. [C:\Users\James\AppData\Local\Temp\Rtmpy8QQoo\R.INSTALL20184a692267\lightgbm\src\build\ZERO_CHECK.vcxproj]
  Checking Build System
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\Microsoft.CppBuild.targets(467,5): warning MSB8029: The Intermediate directory or Output directory cannot reside under the Temporary directory as it could lead to issues with incremental build. [C:\Users\James\AppData\Local\Temp\Rtmpy8QQoo\R.INSTALL20184a692267\lightgbm\src\build\_lightgbm.vcxproj]
  Building Custom Rule C:/Users/James/AppData/Local/Temp/Rtmpy8QQoo/R.INSTALL20184a692267/lightgbm/src/CMakeLists.txt
  application.cpp
  boosting.cpp
  gbdt.cpp
  gbdt_model_text.cpp
  gbdt_prediction.cpp
  prediction_early_stop.cpp
  bin.cpp
  config.cpp
C:\Users\James\AppData\Local\Temp\Rtmpy8QQoo\R.INSTALL20184a692267\lightgbm\src\src\boosting\prediction_early_stop.cpp(86): warning C4715: 'LightGBM::CreatePredictionEarlyStopInstance': not all control paths return a value [C:\Users\James\AppData\Local\Temp\Rtmpy8QQoo\R.INSTALL20184a692267\lightgbm\src\build\_lightgbm.vcxproj]
  config_auto.cpp
  dataset.cpp
  dataset_loader.cpp
  file_io.cpp
  json11.cpp
  metadata.cpp
  parser.cpp
  tree.cpp
  dcg_calculator.cpp
  metric.cpp
  linker_topo.cpp
  linkers_mpi.cpp
  linkers_socket.cpp
  network.cpp
  objective_function.cpp
  data_parallel_tree_learner.cpp
  feature_parallel_tree_learner.cpp
  gpu_tree_learner.cpp
  serial_tree_learner.cpp
  tree_learner.cpp
  voting_parallel_tree_learner.cpp
  c_api.cpp
  lightgbm_R.cpp
     Creating library C:/Users/James/AppData/Local/Temp/Rtmpy8QQoo/R.INSTALL20184a692267/lightgbm/src/Release/lib_lightgbm.lib and object C:/Users/James/AppData/Local/Temp/Rtmpy8QQoo/R.INSTALL20184a692267/lightgbm/src/Release/lib_lightgbm.exp
  _lightgbm.vcxproj -> C:\Users\James\AppData\Local\Temp\Rtmpy8QQoo\R.INSTALL20184a692267\lightgbm\src\Release\lib_lightgbm.dll
[1] "Found library file: C:\\Users\\James\\AppData\\Local\\Temp\\Rtmpy8QQoo\\R.INSTALL20184a692267/lightgbm/src/Release/lib_lightgbm.dll to move to C:/Users/James/Documents/R/win-library/3.6/00LOCK-lightgbm/00new/lightgbm/libs/x64"
[1] "Removing 'build/' directory"
  converting help for package 'lightgbm'
    agaricus.test                           html
    agaricus.train                          html
    bank                                    html
    dim                                     html
    dimnames.lgb.Dataset                    html
    getinfo                                 html
    lgb.Dataset                             html
    lgb.Dataset.construct                   html
    lgb.Dataset.create.valid                html
    lgb.Dataset.save                        html
    lgb.Dataset.set.categorical             html
    lgb.Dataset.set.reference               html
    lgb.cv                                  html
    lgb.dump                                html
    lgb.get.eval.result                     html
    lgb.importance                          html
    lgb.interprete                          html
    lgb.load                                html
    lgb.model.dt.tree                       html
    lgb.plot.importance                     html
    lgb.plot.interpretation                 html
    lgb.prepare                             html
    lgb.prepare2                            html
    lgb.prepare_rules                       html
    lgb.prepare_rules2                      html
    lgb.save                                html
    lgb.train                               html
    lgb.unloader                            html
    lgb_shared_params                       html
    lightgbm                                html
    predict.lgb.Booster                     html
    readRDS.lgb.Booster                     html
    saveRDS.lgb.Booster                     html
    setinfo                                 html
    slice                                   html

You can see from the logs that this succeeded, and that it used MSVC (didn't fail back to MinGW).
Some issues have reported being able to install the library on Windows and then getting runtime errors, e.g. #2714 and #2970 , so I ran the unit tests to be sure.
cd R-package/tests
Rscript testthat.R  >C:\Users\James\repos\LightGBM\test-log.txt

The tests passed!

[ OK: 706 | SKIPPED: 2 | WARNINGS: 0 | FAILED: 0 ]

[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001037 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]:	train's binary_error:0.0222632
[2]:	train's binary_error:0.0222632
[3]:	train's binary_error:0.0222632
[4]:	train's binary_error:0.0109013
[5]:	train's binary_error:0.0141256
[6]:	train's binary_error:0.0141256
[7]:	train's binary_error:0.0141256
[8]:	train's binary_error:0.0141256
[9]:	train's binary_error:0.00598802
[10]:	train's binary_error:0.00598802
[LightGBM] [Warning] Unknown parameter: min_hess
[LightGBM] [Warning] Unknown parameter: min_hess
[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] [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
[1]:	train's multi_error:0.0466667
[2]:	train's multi_error:0.0466667
[3]:	train's multi_error:0.0466667
[4]:	train's multi_error:0.0333333
[5]:	train's multi_error:0.0466667
[6]:	train's multi_error:0.0333333
[7]:	train's multi_error:0.0333333
[8]:	train's multi_error:0.04
[9]:	train's multi_error:0.0333333
[10]:	train's multi_error:0.04
[11]:	train's multi_error:0.0333333
[12]:	train's multi_error:0.0333333
[13]:	train's multi_error:0.0333333
[14]:	train's multi_error:0.0333333
[15]:	train's multi_error:0.0333333
[16]:	train's multi_error:0.0333333
[17]:	train's multi_error:0.0333333
[18]:	train's multi_error:0.0333333
[19]:	train's multi_error:0.0266667
[20]:	train's multi_error:0.0266667
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001113 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]:	train's binary_error:0.0304007	train's auc:0.972508	train's binary_logloss:0.198597
[2]:	train's binary_error:0.0222632	train's auc:0.995075	train's binary_logloss:0.111535
[3]:	train's binary_error:0.00598802	train's auc:0.997845	train's binary_logloss:0.0480659
[4]:	train's binary_error:0.00122831	train's auc:0.998433	train's binary_logloss:0.0279151
[5]:	train's binary_error:0.00122831	train's auc:0.999354	train's binary_logloss:0.0190479
[6]:	train's binary_error:0.00537387	train's auc:0.98965	train's binary_logloss:0.16706
[7]:	train's binary_error:0	train's auc:1	train's binary_logloss:0.0128449
[8]:	train's binary_error:0	train's auc:1	train's binary_logloss:0.00774702
[9]:	train's binary_error:0	train's auc:1	train's binary_logloss:0.00472108
[10]:	train's binary_error:0	train's auc:1	train's binary_logloss:0.00208929
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001050 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]:	train's binary_error:0.0222632
[2]:	train's binary_error:0.0222632
[3]:	train's binary_error:0.0222632
[4]:	train's binary_error:0.0109013
[5]:	train's binary_error:0.0141256
[6]:	train's binary_error:0.0141256
[7]:	train's binary_error:0.0141256
[8]:	train's binary_error:0.0141256
[9]:	train's binary_error:0.00598802
[10]:	train's binary_error:0.00598802
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001126 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]:	train's l2:0.206337
[2]:	train's l2:0.171229
[3]:	train's l2:0.140871
[4]:	train's l2:0.116282
[5]:	train's l2:0.096364
[6]:	train's l2:0.0802308
[7]:	train's l2:0.0675595
[8]:	train's l2:0.0567154
[9]:	train's l2:0.0482086
[10]:	train's l2:0.0402694
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000983 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] Unknown parameter: labels
[LightGBM] [Warning] Unknown parameter: labels
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580
[LightGBM] [Info] Start training from score -0.071580
[1]:	train's binary_error:0.0222632	valid1's binary_error:0.482573	valid2's binary_error:0.482573
[2]:	train's binary_error:0.0222632	valid1's binary_error:0.482573	valid2's binary_error:0.482573
[3]:	train's binary_error:0.0222632	valid1's binary_error:0.482573	valid2's binary_error:0.482573
[4]:	train's binary_error:0.0109013	valid1's binary_error:0.471211	valid2's binary_error:0.471211
[5]:	train's binary_error:0.0141256	valid1's binary_error:0.467987	valid2's binary_error:0.467987
[6]:	train's binary_error:0.0141256	valid1's binary_error:0.467987	valid2's binary_error:0.467987
[7]:	train's binary_error:0.0141256	valid1's binary_error:0.467987	valid2's binary_error:0.467987
[8]:	train's binary_error:0.0141256	valid1's binary_error:0.467987	valid2's binary_error:0.467987
[9]:	train's binary_error:0.00598802	valid1's binary_error:0.476125	valid2's binary_error:0.476125
[10]:	train's binary_error:0.00598802	valid1's binary_error:0.476125	valid2's binary_error:0.476125
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000868 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 0.000936 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 0.000853 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 0.000882 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 0.000936 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]:	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
[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
[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
[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
[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
[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
[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
[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
[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
[10]:	valid's l2:0.000306984+0.000613968	valid's l1:0.000306984+0.000613968
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 198, number of negative: 202
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000037 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] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 196, number of negative: 204
[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 167
[LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 207, number of negative: 193
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000035 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] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 207, number of negative: 193
[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 167
[LightGBM] [Info] Number of data points in the train set: 400, number of used features: 1
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 192, number of negative: 208
[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 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]:	valid's auc:0.476662+0.0622898	valid's binary_error:0.5+0.0593296
[2]:	valid's auc:0.477476+0.0393392	valid's binary_error:0.554+0.0372022
[3]:	valid's auc:0.456927+0.042898	valid's binary_error:0.526+0.0361109
[4]:	valid's auc:0.419531+0.0344972	valid's binary_error:0.54+0.0289828
[5]:	valid's auc:0.459109+0.0862237	valid's binary_error:0.52+0.0489898
[6]:	valid's auc:0.460522+0.0911246	valid's binary_error:0.528+0.0231517
[7]:	valid's auc:0.456328+0.0540445	valid's binary_error:0.532+0.0386782
[8]:	valid's auc:0.463653+0.0660907	valid's binary_error:0.514+0.0488262
[9]:	valid's auc:0.443017+0.0549965	valid's binary_error:0.55+0.0303315
[10]:	valid's auc:0.477483+0.0763283	valid's binary_error:0.488+0.0549181
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001020 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]:	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
[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
[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
[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
[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
[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
[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
[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
[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
[10]:	train's binary_error:0	train's auc:1	train's binary_logloss:1.539e-05
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[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] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[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] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 35110, number of negative: 34890
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000414 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] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 500, number of negative: 500
[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 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]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[3]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[4]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[5]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[6]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[7]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[8]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[9]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[10]:	valid1's binary_error:0
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 500, number of negative: 500
[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 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]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[3]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[4]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[5]:	valid1's binary_error:0
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[6]:	valid1's binary_error:0
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000052 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]:	valid1's rmse:55
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	valid1's rmse:59.5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[3]:	valid1's rmse:63.55
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[4]:	valid1's rmse:67.195
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[5]:	valid1's rmse:70.4755
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[6]:	valid1's rmse:73.428
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[7]:	valid1's rmse:76.0852
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[8]:	valid1's rmse:78.4766
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[9]:	valid1's rmse:80.629
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[10]:	valid1's rmse:82.5661
[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 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]:	valid1's rmse:55
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	valid1's rmse:59.5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[3]:	valid1's rmse:63.55
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[4]:	valid1's rmse:67.195
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[5]:	valid1's rmse:70.4755
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[6]:	valid1's rmse:73.428
[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 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]:	valid1's rmse:125	valid2's rmse:98.1071
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	valid1's rmse:87.5	valid2's rmse:62.5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[3]:	valid1's rmse:106.25	valid2's rmse:80.0878
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[4]:	valid1's rmse:96.875	valid2's rmse:71.2198
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[5]:	valid1's rmse:101.562	valid2's rmse:75.6386
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[6]:	valid1's rmse:99.2188	valid2's rmse:73.425
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[7]:	valid1's rmse:100.391	valid2's rmse:74.5308
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[8]:	valid1's rmse:99.8047	valid2's rmse:73.9777
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[9]:	valid1's rmse:100.098	valid2's rmse:74.2542
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[10]:	valid1's rmse:99.9512	valid2's rmse:74.1159
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 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]:	train's rmse:25	valid1's rmse:125	valid2's rmse:98.1071
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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
[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
[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
[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
[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
[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
[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
[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
[10]:	train's rmse:0.0488281	valid1's rmse:99.9512	valid2's rmse:74.1159
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000050 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]:	train's rmse:25	valid1's rmse:125	valid2's rmse:98.1071
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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
[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
[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
[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
[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
[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
[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
[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
[10]:	train's rmse:0.0488281	valid1's rmse:99.9512	valid2's rmse:74.1159
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000048 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]:	train's rmse:25	valid1's rmse:125	valid2's rmse:98.1071
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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
[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
[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
[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
[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
[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
[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
[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
[10]:	train's rmse:0.0488281	valid1's rmse:99.9512	valid2's rmse:74.1159
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 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]:	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
[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
[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
[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
[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
[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
[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
[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
[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
[10]:	something-random-we-would-not-hardcode's rmse:0.0488281	valid1's rmse:99.9512	valid2's rmse:74.1159
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 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]:	train's rmse:25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	train's rmse:12.5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[3]:	train's rmse:6.25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[4]:	train's rmse:3.125
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[5]:	train's rmse:1.5625
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[6]:	train's rmse:0.78125
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[7]:	train's rmse:0.390625
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[8]:	train's rmse:0.195312
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[9]:	train's rmse:0.0976562
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[10]:	train's rmse:0.0488281
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 500, number of negative: 500
[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] [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]:	something-random-we-would-not-hardcode's auc:0.58136	valid1's auc:0.429487
[2]:	something-random-we-would-not-hardcode's auc:0.599008	valid1's auc:0.266026
[3]:	something-random-we-would-not-hardcode's auc:0.6328	valid1's auc:0.349359
[4]:	something-random-we-would-not-hardcode's auc:0.655136	valid1's auc:0.394231
[5]:	something-random-we-would-not-hardcode's auc:0.655408	valid1's auc:0.419872
[6]:	something-random-we-would-not-hardcode's auc:0.678784	valid1's auc:0.336538
[7]:	something-random-we-would-not-hardcode's auc:0.682176	valid1's auc:0.416667
[8]:	something-random-we-would-not-hardcode's auc:0.698032	valid1's auc:0.394231
[9]:	something-random-we-would-not-hardcode's auc:0.712672	valid1's auc:0.445513
[10]:	something-random-we-would-not-hardcode's auc:0.723024	valid1's auc:0.471154
[LightGBM] [Warning] Using self-defined objective function
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001120 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]:	train's auc:0.994987	train's error:0.00598802	eval's auc:0.995243	eval's error:0.00558659
[2]:	train's auc:0.99512	train's error:0.00307078	eval's auc:0.995237	eval's error:0.00248293
[3]:	train's auc:0.99009	train's error:0.00598802	eval's auc:0.98843	eval's error:0.00558659
[4]:	train's auc:0.999889	train's error:0.00168893	eval's auc:1	eval's error:0.000620732
[5]:	train's auc:1	train's error:0	eval's auc:1	eval's error:0
[6]:	train's auc:1	train's error:0	eval's auc:1	eval's error:0
[7]:	train's auc:1	train's error:0	eval's auc:1	eval's error:0
[8]:	train's auc:1	train's error:0	eval's auc:1	eval's error:0
[9]:	train's auc:1	train's error:0	eval's auc:1	eval's error:0
[10]:	train's auc:1	train's error:0	eval's auc:1	eval's error:0
[LightGBM] [Info] Saving data to binary file C:\Users\James\AppData\Local\Temp\RtmpwxgWKK\lgb.Dataset_39003f696b0d
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000089 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
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000076 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 40
[LightGBM] [Info] Number of data points in the train set: 4500, number of used features: 20
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 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 0.000070 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 0.000073 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
[1]:	valid's ndcg@1:0.725+0.0829156	valid's ndcg@2:0.686315+0.0225243	valid's ndcg@3:0.677794+0.0340451
[2]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.720986+0.0370912	valid's ndcg@3:0.698464+0.0473417
[3]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.730657+0.0661112	valid's ndcg@3:0.711732+0.074403
[4]:	valid's ndcg@1:0.775+0.0829156	valid's ndcg@2:0.745986+0.0725754	valid's ndcg@3:0.723464+0.0808668
[5]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.730657+0.0661112	valid's ndcg@3:0.711732+0.074403
[6]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.720986+0.0506137	valid's ndcg@3:0.710196+0.0719775
[7]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.740329+0.0637048	valid's ndcg@3:0.719134+0.0743404
[8]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.740329+0.0637048	valid's ndcg@3:0.719134+0.0743404
[9]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.740329+0.0637048	valid's ndcg@3:0.725+0.0832215
[10]:	valid's ndcg@1:0.75+0.05	valid's ndcg@2:0.730657+0.0661112	valid's ndcg@3:0.711732+0.074403
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001096 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]:	test's l2:6.44165e-17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	test's l2:1.97215e-31
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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
[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
[5]:	test's l2:0
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000641 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]:	test's l2:6.44165e-17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[2]:	test's l2:1.97215e-31
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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
[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
[5]:	test's l2:0
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000622 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]:	train's binary_logloss:0.198597
[2]:	train's binary_logloss:0.111535
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000930 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]:	train's binary_logloss:0.198597
[2]:	train's binary_logloss:0.111535
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001026 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]:	train's binary_logloss:0.198597
[2]:	train's binary_logloss:0.111535
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000670 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]:	train's binary_logloss:0.198597
[2]:	train's binary_logloss:0.111535
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001043 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] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001029 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]:	train's binary_logloss:0.198597
[2]:	train's binary_logloss:0.111535
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000351 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] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001058 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]:	train's binary_logloss:0.198597
[2]:	train's binary_logloss:0.111535
[3]:	train's binary_logloss:0.0480659
[4]:	train's binary_logloss:0.0279151
[5]:	train's binary_logloss:0.0190479
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001137 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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 0.000050 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 77
[LightGBM] [Info] Number of data points in the train set: 90, number of used features: 4
[LightGBM] [Info] Start training from score -1.504077
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -0.810930
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001205 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
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
Try to set boost_from_average=false, if your old models produce bad results
[LightGBM] [Info] Number of positive: 3140, number of negative: 3373
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001094 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 232
[LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[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 0.000043 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 77
[LightGBM] [Info] Number of data points in the train set: 90, number of used features: 4
[LightGBM] [Info] Start training from score -1.504077
[LightGBM] [Info] Start training from score -1.098612
[LightGBM] [Info] Start training from score -0.810930
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
== testthat results  ===========================================================
[ OK: 706 | SKIPPED: 2 | WARNINGS: 0 | FAILED: 0 ]

created #3025 to document the two compiler warnings seen in the logs from https://github.com/microsoft/LightGBM/pull/2963#issuecomment-619646274

@jameslamb Seems that this PR broke our builds for R docs.
Can you please take a look?

-- The C compiler identification is GNU 7.3.0
-- The CXX compiler identification is GNU 7.3.0
-- Check for working C compiler: /home/docs/.conda/envs/r_env/bin/x86_64-conda_cos6-linux-gnu-cc
-- Check for working C compiler: /home/docs/.conda/envs/r_env/bin/x86_64-conda_cos6-linux-gnu-cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /home/docs/.conda/envs/r_env/bin/x86_64-conda_cos6-linux-gnu-c++
-- Check for working CXX compiler: /home/docs/.conda/envs/r_env/bin/x86_64-conda_cos6-linux-gnu-c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Configuring incomplete, errors occurred!
See also "/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/lightgbm_r/src/build/CMakeFiles/CMakeOutput.log".

Updating lightgbm documentation
Loading lightgbm
Re-compiling lightgbm
'/home/docs/.conda/envs/r_env/lib/R/bin/R' --no-site-file --no-environ  \
  --no-save --no-restore --quiet CMD INSTALL  \
  '/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/lightgbm_r'  \
  --library='/tmp/RtmpT7BLvD/devtools_install_be97189381e' --no-R --no-data  \
  --no-help --no-demo --no-inst --no-docs --no-exec --no-multiarch  \
  --no-test-load --preclean

* installing *source* package ‘lightgbm’ ...
** libs
installing via 'install.libs.R' to /tmp/RtmpT7BLvD/devtools_install_be97189381e/lightgbm
R version passed into FindLibR.cmake: 3.5.1
CMake Error at /home/docs/.conda/envs/r_env/share/cmake-3.14/Modules/FindPackageHandleStandardArgs.cmake:137 (message):
  Could NOT find LibR (missing: LIBR_CORE_LIBRARY)
Call Stack (most recent call first):
  /home/docs/.conda/envs/r_env/share/cmake-3.14/Modules/FindPackageHandleStandardArgs.cmake:378 (_FPHSA_FAILURE_MESSAGE)
  cmake/modules/FindLibR.cmake:221 (find_package_handle_standard_args)
  CMakeLists.txt:76 (find_package)

make: *** No rule to make target '_lightgbm'.  Stop.
Error in eval(ei, envir) : Cannot find lib_lightgbm.so
* removing ‘/tmp/RtmpT7BLvD/devtools_install_be97189381e/lightgbm’
Error: Command failed (1)
Execution halted

Full logs (clickable):
Read the Docs build information
Build id: 10956193
Project: lightgbm
Version: latest
Commit: 6823af945390ba89b66d7e579501dc87e3cc6162
Date: 2020-05-03T02:56:29.295286Z
State: finished
Success: False

[rtd-command-info] start-time: 2020-05-03T02:56:35.508728Z, end-time: 2020-05-03T02:56:37.804356Z, duration: 2, exit-code: 0
git clone --no-single-branch --depth 50 https://github.com/Microsoft/LightGBM .
Cloning into '.'...

[rtd-command-info] start-time: 2020-05-03T02:56:38.204370Z, end-time: 2020-05-03T02:56:38.583519Z, duration: 0, exit-code: 0
git checkout --force origin/master
Note: checking out 'origin/master'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:

  git checkout -b <new-branch-name>

HEAD is now at 6823af9 [docs] updated bug report template (#3032)

[rtd-command-info] start-time: 2020-05-03T02:56:38.743686Z, end-time: 2020-05-03T02:56:38.995877Z, duration: 0, exit-code: 0
git clean -d -f -f

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python3.7 -mvirtualenv  /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest
Using base prefix '/home/docs/.pyenv/versions/3.7.3'
New python executable in /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/bin/python3.7
Also creating executable in /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/bin/python
Installing setuptools, pip, wheel...
done.

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/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/bin/python -m pip install --upgrade --no-cache-dir pip
Requirement already up-to-date: pip in /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/lib/python3.7/site-packages (20.1)

[rtd-command-info] start-time: 2020-05-03T02:56:48.266392Z, end-time: 2020-05-03T02:57:00.822122Z, duration: 12, exit-code: 0
/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/bin/python -m pip install --upgrade --no-cache-dir Pygments==2.3.1 setuptools==41.0.1 docutils==0.14 mock==1.0.1 pillow==5.4.1 alabaster>=0.7,<0.8,!=0.7.5 commonmark==0.8.1 recommonmark==0.5.0 sphinx<2 sphinx-rtd-theme<0.5 readthedocs-sphinx-ext<1.1
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Building wheels for collected packages: mock, future
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  Created wheel for mock: filename=mock-1.0.1-py3-none-any.whl size=23754 sha256=5601b03737041e5d5a8c8732009ed529e541f800f4cdab9015b1af084706c3cc
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Successfully built mock future
Installing collected packages: Pygments, setuptools, docutils, mock, pillow, alabaster, future, commonmark, snowballstemmer, certifi, chardet, urllib3, idna, requests, imagesize, six, pyparsing, packaging, pytz, babel, sphinxcontrib-websupport, MarkupSafe, Jinja2, sphinx, recommonmark, sphinx-rtd-theme, readthedocs-sphinx-ext
  Attempting uninstall: setuptools
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/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/bin/python -m pip install --exists-action=w --no-cache-dir -r docs/requirements_rtd.txt
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Installing collected packages: sphinxcontrib-applehelp, sphinxcontrib-htmlhelp, sphinxcontrib-serializinghtml, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-devhelp, sphinx, breathe
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[rtd-command-info] start-time: 2020-05-03T02:57:06.498301Z, end-time: 2020-05-03T02:57:06.732298Z, duration: 0, exit-code: 0
cat docs/conf.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# LightGBM documentation build configuration file, created by
# sphinx-quickstart on Thu May  4 14:30:58 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute.
"""Sphinx configuration file."""
import datetime
import os
import sys
import sphinx

from distutils.dir_util import copy_tree
from docutils.parsers.rst import Directive
from sphinx.errors import VersionRequirementError
from subprocess import PIPE, Popen

CURR_PATH = os.path.abspath(os.path.dirname(__file__))
LIB_PATH = os.path.join(CURR_PATH, os.path.pardir, 'python-package')
sys.path.insert(0, LIB_PATH)

# -- mock out modules
try:
    from unittest.mock import Mock  # Python 3.x
except ImportError:
    from mock import Mock  # Python 2.x

MOCK_MODULES = ['numpy', 'scipy', 'scipy.sparse',
                'sklearn', 'matplotlib', 'pandas', 'graphviz']
for mod_name in MOCK_MODULES:
    sys.modules[mod_name] = Mock()

class IgnoredDirective(Directive):
    """Stub for unknown directives."""

    has_content = True

    def run(self):
        """Do nothing."""
        return []

# -- General configuration ------------------------------------------------

os.environ['LIGHTGBM_BUILD_DOC'] = '1'
C_API = os.environ.get('C_API', '').lower().strip() != 'no'
RTD = bool(os.environ.get('READTHEDOCS', ''))

# If your documentation needs a minimal Sphinx version, state it here.
needs_sphinx = '1.3'  # Due to sphinx.ext.napoleon
if needs_sphinx > sphinx.__version__:
    message = 'This project needs at least Sphinx v%s' % needs_sphinx
    raise VersionRequirementError(message)

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
    'sphinx.ext.autodoc',
    'sphinx.ext.autosummary',
    'sphinx.ext.todo',
    'sphinx.ext.viewcode',
    'sphinx.ext.napoleon',
]

autodoc_default_flags = ['members', 'inherited-members', 'show-inheritance']
autodoc_default_options = {
    "members": True,
    "inherited-members": True,
    "show-inheritance": True,
}

# Generate autosummary pages. Output should be set with: `:toctree: pythonapi/`
autosummary_generate = ['Python-API.rst']

# Only the class' docstring is inserted.
autoclass_content = 'class'

# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = 'LightGBM'
copyright = '%s, Microsoft Corporation' % str(datetime.datetime.now().year)
author = 'Microsoft Corporation'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
with open(os.path.join(CURR_PATH, os.path.pardir, 'VERSION.txt'), 'r') as f:
    # The short X.Y version.
    version = f.read().strip()

    # The full version, including alpha/beta/rc tags.
    release = version

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'default'

# -- Configuration for C API docs generation ------------------------------

if C_API:
    extensions.extend([
        'breathe',
    ])
    breathe_projects = {
        "LightGBM": os.path.join(CURR_PATH, 'doxyoutput', 'xml')
    }
    breathe_default_project = "LightGBM"
    breathe_domain_by_extension = {
        "h": "c",
    }
    breathe_show_define_initializer = True
    c_id_attributes = ['LIGHTGBM_C_EXPORT']

# -- Options for HTML output ----------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
html_theme = 'sphinx_rtd_theme'

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
html_theme_options = {
    'includehidden': False,
}

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']

# -- Options for HTMLHelp output ------------------------------------------

# Output file base name for HTML help builder.
htmlhelp_basename = 'LightGBMdoc'

def generate_doxygen_xml(app):
    """Generate XML documentation for C API by Doxygen.

    Parameters
    ----------
    app : object
        The application object representing the Sphinx process.
    """
    doxygen_args = [
        "INPUT={}".format(os.path.join(CURR_PATH, os.path.pardir,
                                       'include', 'LightGBM', 'c_api.h')),
        "OUTPUT_DIRECTORY={}".format(os.path.join(CURR_PATH, 'doxyoutput')),
        "GENERATE_HTML=NO",
        "GENERATE_LATEX=NO",
        "GENERATE_XML=YES",
        "XML_OUTPUT=xml",
        "XML_PROGRAMLISTING=YES",
        r'ALIASES="rst=\verbatim embed:rst:leading-asterisk"',
        r'ALIASES+="endrst=\endverbatim"',
        "ENABLE_PREPROCESSING=YES",
        "MACRO_EXPANSION=YES",
        "EXPAND_ONLY_PREDEF=NO",
        "SKIP_FUNCTION_MACROS=NO",
        "SORT_BRIEF_DOCS=YES",
        "WARN_AS_ERROR=YES",
    ]
    doxygen_input = '\n'.join(doxygen_args)
    is_py3 = sys.version[0] == "3"
    if is_py3:
        doxygen_input = bytes(doxygen_input, "utf-8")
    if not os.path.exists(os.path.join(CURR_PATH, 'doxyoutput')):
        os.makedirs(os.path.join(CURR_PATH, 'doxyoutput'))
    try:
        # Warning! The following code can cause buffer overflows on RTD.
        # Consider suppressing output completely if RTD project silently fails.
        # Refer to https://github.com/svenevs/exhale
        # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111
        process = Popen(["doxygen", "-"],
                        stdin=PIPE, stdout=PIPE, stderr=PIPE)
        stdout, stderr = process.communicate(doxygen_input)
        output = '\n'.join([i.decode('utf-8') if is_py3 else i
                            for i in (stdout, stderr) if i is not None])
        if process.returncode != 0:
            raise RuntimeError(output)
        else:
            print(output)
    except BaseException as e:
        raise Exception("An error has occurred while executing Doxygen\n" + str(e))

def generate_r_docs(app):
    """Generate documentation for R-package.

    Parameters
    ----------
    app : object
        The application object representing the Sphinx process.
    """
    commands = """
    echo 'options(repos = "https://cran.rstudio.com")' > $HOME/.Rprofile
    /home/docs/.conda/bin/conda create -q -y -n r_env \
        r-base=3.5.1=h1e0a451_2 \
        r-devtools=1.13.6=r351h6115d3f_0 \
        r-jsonlite=1.5=r351h96ca727_0 \
        r-matrix=1.2_14=r351h96ca727_0 \
        r-testthat=2.0.0=r351h29659fb_0 \
        cmake=3.14.0=h52cb24c_0 \
        ca-certificates=2019.11.27=0
    /home/docs/.conda/bin/conda install -q -y -n r_env -c conda-forge \
        r-data.table=1.12.8=r35hcdcec82_0 \
        r-pkgdown=1.3.0=r35h6115d3f_1001 \
        r-roxygen2=6.1.1=r35h0357c0b_1001
    source /home/docs/.conda/bin/activate r_env
    export TAR=/bin/tar
    cd {0}
    sed -i'.bak' '/# Build the package (do not touch this line!)/q' build_r.R
    Rscript build_r.R
    Rscript build_r_site.R
    """.format(os.path.join(CURR_PATH, os.path.pardir))
    try:
        # Warning! The following code can cause buffer overflows on RTD.
        # Consider suppressing output completely if RTD project silently fails.
        # Refer to https://github.com/svenevs/exhale
        # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111
        process = Popen(['/bin/bash'],
                        stdin=PIPE, stdout=PIPE, stderr=PIPE,
                        universal_newlines=True)
        stdout, stderr = process.communicate(commands)
        output = '\n'.join([i for i in (stdout, stderr) if i is not None])
        if process.returncode != 0:
            raise RuntimeError(output)
        else:
            print(output)
    except BaseException as e:
        raise Exception("An error has occurred while generating documentation for R-package\n" + str(e))

def setup(app):
    """Add new elements at Sphinx initialization time.

    Parameters
    ----------
    app : object
        The application object representing the Sphinx process.
    """
    first_run = not os.path.exists(os.path.join(CURR_PATH, '_FIRST_RUN.flag'))
    if first_run and RTD:
        open(os.path.join(CURR_PATH, '_FIRST_RUN.flag'), 'w').close()
    if C_API:
        app.connect("builder-inited", generate_doxygen_xml)
    else:
        app.add_directive('doxygenfile', IgnoredDirective)
    if RTD:  # build R docs only on Read the Docs site
        if first_run:
            app.connect("builder-inited", generate_r_docs)
        app.connect("build-finished",
                    lambda app, exception: copy_tree(os.path.join(CURR_PATH, os.path.pardir, "lightgbm_r", "docs"),
                                                     os.path.join(app.outdir, "R"), verbose=0))
    add_js_file = getattr(app, 'add_js_file', False) or app.add_javascript
    add_js_file("js/script.js")

###########################################################################
#          auto-created readthedocs.org specific configuration            #
###########################################################################

#
# The following code was added during an automated build on readthedocs.org
# It is auto created and injected for every build. The result is based on the
# conf.py.tmpl file found in the readthedocs.org codebase:
# https://github.com/rtfd/readthedocs.org/blob/master/readthedocs/doc_builder/templates/doc_builder/conf.py.tmpl
#

import importlib
import sys
import os.path
from six import string_types

from sphinx import version_info

# Get suffix for proper linking to GitHub
# This is deprecated in Sphinx 1.3+,
# as each page can have its own suffix
if globals().get('source_suffix', False):
    if isinstance(source_suffix, string_types):
        SUFFIX = source_suffix
    elif isinstance(source_suffix, (list, tuple)):
        # Sphinx >= 1.3 supports list/tuple to define multiple suffixes
        SUFFIX = source_suffix[0]
    elif isinstance(source_suffix, dict):
        # Sphinx >= 1.8 supports a mapping dictionary for multiple suffixes
        SUFFIX = list(source_suffix.keys())[0]  # make a ``list()`` for py2/py3 compatibility
    else:
        # default to .rst
        SUFFIX = '.rst'
else:
    SUFFIX = '.rst'

# Add RTD Static Path. Add to the end because it overwrites previous files.
if not 'html_static_path' in globals():
    html_static_path = []
if os.path.exists('_static'):
    html_static_path.append('_static')

# Add RTD Theme only if they aren't overriding it already
using_rtd_theme = (
    (
        'html_theme' in globals() and
        html_theme in ['default'] and
        # Allow people to bail with a hack of having an html_style
        'html_style' not in globals()
    ) or 'html_theme' not in globals()
)
if using_rtd_theme:
    theme = importlib.import_module('sphinx_rtd_theme')
    html_theme = 'sphinx_rtd_theme'
    html_style = None
    html_theme_options = {}
    if 'html_theme_path' in globals():
        html_theme_path.append(theme.get_html_theme_path())
    else:
        html_theme_path = [theme.get_html_theme_path()]

if globals().get('websupport2_base_url', False):
    websupport2_base_url = 'https://readthedocs.org/websupport'
    websupport2_static_url = 'https://assets.readthedocs.org/static/'

#Add project information to the template context.
context = {
    'using_theme': using_rtd_theme,
    'html_theme': html_theme,
    'current_version': "latest",
    'version_slug': "latest",
    'MEDIA_URL': "https://media.readthedocs.org/",
    'STATIC_URL': "https://assets.readthedocs.org/static/",
    'PRODUCTION_DOMAIN': "readthedocs.org",
    'versions': [
    ("latest", "/en/latest/"),
    ],
    'downloads': [
    ("pdf", "//lightgbm.readthedocs.io/_/downloads/en/latest/pdf/"),
    ],
    'subprojects': [
    ],
    'slug': 'lightgbm',
    'name': u'lightgbm',
    'rtd_language': u'en',
    'programming_language': u'cpp',
    'canonical_url': 'https://lightgbm.readthedocs.io/en/latest/',
    'analytics_code': 'None',
    'single_version': False,
    'conf_py_path': '/docs/',
    'api_host': 'https://readthedocs.org',
    'github_user': 'Microsoft',
    'github_repo': 'LightGBM',
    'github_version': 'master',
    'display_github': True,
    'bitbucket_user': 'None',
    'bitbucket_repo': 'None',
    'bitbucket_version': 'master',
    'display_bitbucket': False,
    'gitlab_user': 'None',
    'gitlab_repo': 'None',
    'gitlab_version': 'master',
    'display_gitlab': False,
    'READTHEDOCS': True,
    'using_theme': (html_theme == "default"),
    'new_theme': (html_theme == "sphinx_rtd_theme"),
    'source_suffix': SUFFIX,
    'ad_free': False,
    'user_analytics_code': '',
    'global_analytics_code': 'UA-17997319-1',
    'commit': '6823af94',
}

if 'html_context' in globals():

    html_context.update(context)

else:
    html_context = context

# Add custom RTD extension
if 'extensions' in globals():
    # Insert at the beginning because it can interfere
    # with other extensions.
    # See https://github.com/rtfd/readthedocs.org/pull/4054
    extensions.insert(0, "readthedocs_ext.readthedocs")
else:
    extensions = ["readthedocs_ext.readthedocs"]

# Add External version warning banner to the external version documentation
if 'branch' == 'external':
    extensions.insert(1, "readthedocs_ext.external_version_warning")

project_language = 'en'

# User's Sphinx configurations
language_user = globals().get('language', None)
latex_engine_user = globals().get('latex_engine', None)
latex_elements_user = globals().get('latex_elements', None)

# Remove this once xindy gets installed in Docker image and XINDYOPS
# env variable is supported
# https://github.com/rtfd/readthedocs-docker-images/pull/98
latex_use_xindy = False

chinese = any([
    language_user in ('zh_CN', 'zh_TW'),
    project_language in ('zh_CN', 'zh_TW'),
])

japanese = any([
    language_user == 'ja',
    project_language == 'ja',
])

if chinese:
    latex_engine = latex_engine_user or 'xelatex'

    latex_elements_rtd = {
        'preamble': '\\usepackage[UTF8]{ctex}\n',
    }
    latex_elements = latex_elements_user or latex_elements_rtd
elif japanese:
    latex_engine = latex_engine_user or 'platex'

[rtd-command-info] start-time: 2020-05-03T02:57:06.861359Z, end-time: 2020-05-03T03:00:33.319705Z, duration: 206, exit-code: 2
python /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/bin/sphinx-build -T -E -W -b readthedocs -d _build/doctrees-readthedocs -D language=en . _build/html
Running Sphinx v3.0.3
loading translations [en]... done
making output directory... done
[autosummary] generating autosummary for: Python-API.rst
[autosummary] generating autosummary for: /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.Booster.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.Dataset.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.LGBMClassifier.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.LGBMModel.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.LGBMRanker.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.LGBMRegressor.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.create_tree_digraph.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.cv.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.early_stopping.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.plot_importance.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.plot_metric.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.plot_split_value_histogram.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.plot_tree.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.print_evaluation.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.record_evaluation.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.reset_parameter.rst, /home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/pythonapi/lightgbm.train.rst
/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/lib/python3.7/site-packages/sphinx/deprecation.py:52: RemovedInSphinx40Warning: sphinx.builders.html.DirectoryHTMLBuilder is deprecated. Check CHANGES for Sphinx API modifications.
  return getattr(self._module, name)
/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/envs/latest/lib/python3.7/site-packages/readthedocs_ext/readthedocs.py:14: RemovedInSphinx40Warning: sphinx.builders.html.SingleFileHTMLBuilder is deprecated. Check CHANGES for Sphinx API modifications.
  from sphinx.builders.html import (DirectoryHTMLBuilder, SingleFileHTMLBuilder,

Traceback (most recent call last):
  File "/home/docs/checkouts/readthedocs.org/user_builds/lightgbm/checkouts/latest/docs/conf.py", line 258, in generate_r_docs
    raise RuntimeError(output)
RuntimeError: Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /home/docs/.conda/envs/r_env

  added / updated specs:
    - ca-certificates==2019.11.27=0
    - cmake==3.14.0=h52cb24c_0
    - r-base==3.5.1=h1e0a451_2
    - r-devtools==1.13.6=r351h6115d3f_0
    - r-jsonlite==1.5=r351h96ca727_0
    - r-matrix==1.2_14=r351h96ca727_0
    - r-testthat==2.0.0=r351h29659fb_0

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    _r-mutex-1.0.0             |      anacondar_1           3 KB
    binutils_impl_linux-64-2.33.1|       he6710b0_7         3.7 MB
    binutils_linux-64-2.33.1   |      h9595d00_15          26 KB
    bwidget-1.9.11             |                1         116 KB
    bzip2-1.0.8                |       h7b6447c_0          78 KB
    ca-certificates-2019.11.27 |                0         124 KB
    cairo-1.14.12              |       h8948797_3         906 KB
    cmake-3.14.0               |       h52cb24c_0         5.5 MB
    curl-7.67.0                |       hbc83047_0         134 KB
    expat-2.2.6                |       he6710b0_0         146 KB
    fontconfig-2.13.0          |       h9420a91_0         227 KB
    freetype-2.9.1             |       h8a8886c_1         550 KB
    fribidi-1.0.5              |       h7b6447c_0         101 KB
    gcc_impl_linux-64-7.3.0    |       habb00fd_1        41.9 MB
    gcc_linux-64-7.3.0         |      h553295d_15          27 KB
    gfortran_impl_linux-64-7.3.0|       hdf63c60_1         7.8 MB
    gfortran_linux-64-7.3.0    |      h553295d_15          27 KB
    glib-2.63.1                |       h5a9c865_0         2.9 MB
    graphite2-1.3.13           |       h23475e2_0          98 KB
    gsl-2.4                    |       h14c3975_4         1.8 MB
    gxx_impl_linux-64-7.3.0    |       hdf63c60_1        15.0 MB
    gxx_linux-64-7.3.0         |      h553295d_15          26 KB
    harfbuzz-1.8.8             |       hffaf4a1_0         507 KB
    icu-58.2                   |       he6710b0_3        10.5 MB
    jpeg-9b                    |       h024ee3a_2         214 KB
    krb5-1.16.4                |       h173b8e3_0         1.2 MB
    ld_impl_linux-64-2.33.1    |       h53a641e_7         568 KB
    libcurl-7.67.0             |       h20c2e04_0         426 KB
    libgfortran-ng-7.3.0       |       hdf63c60_0        1006 KB
    libpng-1.6.37              |       hbc83047_0         278 KB
    libssh2-1.9.0              |       h1ba5d50_1         269 KB
    libtiff-4.1.0              |       h2733197_0         447 KB
    libuuid-1.0.3              |       h1bed415_2          15 KB
    libxcb-1.13                |       h1bed415_1         421 KB
    libxml2-2.9.9              |       hea5a465_1         1.6 MB
    make-4.2.1                 |       h1bed415_1         415 KB
    ncurses-6.2                |       he6710b0_1         817 KB
    openssl-1.1.1g             |       h7b6447c_0         2.5 MB
    pango-1.42.4               |       h049681c_0         464 KB
    pcre-8.43                  |       he6710b0_0         209 KB
    pixman-0.38.0              |       h7b6447c_0         364 KB
    r-assertthat-0.2.0         |   r351h6115d3f_0          55 KB
    r-base-3.5.1               |       h1e0a451_2        26.5 MB
    r-cli-1.0.0                |   r351h6115d3f_1         310 KB
    r-crayon-1.3.4             |   r351h6115d3f_0         729 KB
    r-curl-3.2                 |   r351hadc6856_1         374 KB
    r-devtools-1.13.6          |   r351h6115d3f_0         665 KB
    r-digest-0.6.15            |   r351h96ca727_0         138 KB
    r-git2r-0.23.0             |   r351h96ca727_1         804 KB
    r-httr-1.3.1               |   r351h6115d3f_1         421 KB
    r-jsonlite-1.5             |   r351h96ca727_0         632 KB
    r-lattice-0.20_35          |   r351h96ca727_0         1.1 MB
    r-magrittr-1.5             |   r351h6115d3f_4         157 KB
    r-matrix-1.2_14            |   r351h96ca727_0         3.3 MB
    r-memoise-1.1.0            |   r351h6115d3f_0          38 KB
    r-mime-0.5                 |   r351h96ca727_0          37 KB
    r-openssl-1.0.2            |   r351h96ca727_1         588 KB
    r-praise-1.0.0             |   r351h6115d3f_4          21 KB
    r-r6-2.2.2                 |   r351h6115d3f_0         245 KB
    r-rlang-0.2.1              |   r351h96ca727_0         744 KB
    r-rstudioapi-0.7           |   r351h6115d3f_0         126 KB
    r-testthat-2.0.0           |   r351h29659fb_0         879 KB
    r-whisker-0.3_2            |   r351hf348343_4          63 KB
    r-withr-2.1.2              |   r351h6115d3f_0         149 KB
    rhash-1.3.8                |       h1ba5d50_0         188 KB
    tktable-2.10               |       h14c3975_0          86 KB
    xz-5.2.5                   |       h7b6447c_0         438 KB
    zstd-1.3.7                 |       h0b5b093_0         401 KB
    ------------------------------------------------------------
                                           Total:       142.0 MB

The following NEW packages will be INSTALLED:

  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main
  _r-mutex           pkgs/r/linux-64::_r-mutex-1.0.0-anacondar_1
  binutils_impl_lin~ pkgs/main/linux-64::binutils_impl_linux-64-2.33.1-he6710b0_7
  binutils_linux-64  pkgs/main/linux-64::binutils_linux-64-2.33.1-h9595d00_15
  bwidget            pkgs/main/linux-64::bwidget-1.9.11-1
  bzip2              pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0
  ca-certificates    pkgs/main/linux-64::ca-certificates-2019.11.27-0
  cairo              pkgs/main/linux-64::cairo-1.14.12-h8948797_3
  cmake              pkgs/main/linux-64::cmake-3.14.0-h52cb24c_0
  curl               pkgs/main/linux-64::curl-7.67.0-hbc83047_0
  expat              pkgs/main/linux-64::expat-2.2.6-he6710b0_0
  fontconfig         pkgs/main/linux-64::fontconfig-2.13.0-h9420a91_0
  freetype           pkgs/main/linux-64::freetype-2.9.1-h8a8886c_1
  fribidi            pkgs/main/linux-64::fribidi-1.0.5-h7b6447c_0
  gcc_impl_linux-64  pkgs/main/linux-64::gcc_impl_linux-64-7.3.0-habb00fd_1
  gcc_linux-64       pkgs/main/linux-64::gcc_linux-64-7.3.0-h553295d_15
  gfortran_impl_lin~ pkgs/main/linux-64::gfortran_impl_linux-64-7.3.0-hdf63c60_1
  gfortran_linux-64  pkgs/main/linux-64::gfortran_linux-64-7.3.0-h553295d_15
  glib               pkgs/main/linux-64::glib-2.63.1-h5a9c865_0
  graphite2          pkgs/main/linux-64::graphite2-1.3.13-h23475e2_0
  gsl                pkgs/main/linux-64::gsl-2.4-h14c3975_4
  gxx_impl_linux-64  pkgs/main/linux-64::gxx_impl_linux-64-7.3.0-hdf63c60_1
  gxx_linux-64       pkgs/main/linux-64::gxx_linux-64-7.3.0-h553295d_15
  harfbuzz           pkgs/main/linux-64::harfbuzz-1.8.8-hffaf4a1_0
  icu                pkgs/main/linux-64::icu-58.2-he6710b0_3
  jpeg               pkgs/main/linux-64::jpeg-9b-h024ee3a_2
  krb5               pkgs/main/linux-64::krb5-1.16.4-h173b8e3_0
  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7
  libcurl            pkgs/main/linux-64::libcurl-7.67.0-h20c2e04_0
  libedit            pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0
  libffi             pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4
  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0
  libgfortran-ng     pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0
  libpng             pkgs/main/linux-64::libpng-1.6.37-hbc83047_0
  libssh2            pkgs/main/linux-64::libssh2-1.9.0-h1ba5d50_1
  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0
  libtiff            pkgs/main/linux-64::libtiff-4.1.0-h2733197_0
  libuuid            pkgs/main/linux-64::libuuid-1.0.3-h1bed415_2
  libxcb             pkgs/main/linux-64::libxcb-1.13-h1bed415_1
  libxml2            pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1
  make               pkgs/main/linux-64::make-4.2.1-h1bed415_1
  ncurses            pkgs/main/linux-64::ncurses-6.2-he6710b0_1
  openssl            pkgs/main/linux-64::openssl-1.1.1g-h7b6447c_0
  pango              pkgs/main/linux-64::pango-1.42.4-h049681c_0
  pcre               pkgs/main/linux-64::pcre-8.43-he6710b0_0
  pixman             pkgs/main/linux-64::pixman-0.38.0-h7b6447c_0
  r-assertthat       pkgs/r/linux-64::r-assertthat-0.2.0-r351h6115d3f_0
  r-base             pkgs/r/linux-64::r-base-3.5.1-h1e0a451_2
  r-cli              pkgs/r/linux-64::r-cli-1.0.0-r351h6115d3f_1
  r-crayon           pkgs/r/linux-64::r-crayon-1.3.4-r351h6115d3f_0
  r-curl             pkgs/r/linux-64::r-curl-3.2-r351hadc6856_1
  r-devtools         pkgs/r/linux-64::r-devtools-1.13.6-r351h6115d3f_0
  r-digest           pkgs/r/linux-64::r-digest-0.6.15-r351h96ca727_0
  r-git2r            pkgs/r/linux-64::r-git2r-0.23.0-r351h96ca727_1
  r-httr             pkgs/r/linux-64::r-httr-1.3.1-r351h6115d3f_1
  r-jsonlite         pkgs/r/linux-64::r-jsonlite-1.5-r351h96ca727_0
  r-lattice          pkgs/r/linux-64::r-lattice-0.20_35-r351h96ca727_0
  r-magrittr         pkgs/r/linux-64::r-magrittr-1.5-r351h6115d3f_4
  r-matrix           pkgs/r/linux-64::r-matrix-1.2_14-r351h96ca727_0
  r-memoise          pkgs/r/linux-64::r-memoise-1.1.0-r351h6115d3f_0
  r-mime             pkgs/r/linux-64::r-mime-0.5-r351h96ca727_0
  r-openssl          pkgs/r/linux-64::r-openssl-1.0.2-r351h96ca727_1
  r-praise           pkgs/r/linux-64::r-praise-1.0.0-r351h6115d3f_4
  r-r6               pkgs/r/linux-64::r-r6-2.2.2-r351h6115d3f_0
  r-rlang            pkgs/r/linux-64::r-rlang-0.2.1-r351h96ca727_0
  r-rstudioapi       pkgs/r/linux-64::r-rstudioapi-0.7-r351h6115d3f_0
  r-testthat         pkgs/r/linux-64::r-testthat-2.0.0-r351h29659fb_0
  r-whisker          pkgs/r/linux-64::r-whisker-0.3_2-r351hf348343_4
  r-withr            pkgs/r/linux-64::r-withr-2.1.2-r351h6115d3f_0
  readline           pkgs/main/linux-64::readline-7.0-h7b6447c_5
  rhash              pkgs/main/linux-64::rhash-1.3.8-h1ba5d50_0
  tk                 pkgs/main/linux-64::tk-8.6.8-hbc83047_0
  tktable            pkgs/main/linux-64::tktable-2.10-h14c3975_0
  xz                 pkgs/main/linux-64::xz-5.2.5-h7b6447c_0
  zlib               pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3
  zstd               pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0

Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /home/docs/.conda/envs/r_env

  added / updated specs:
    - r-data.table==1.12.8=r35hcdcec82_0
    - r-pkgdown==1.3.0=r35h6115d3f_1001
    - r-roxygen2==6.1.1=r35h0357c0b_1001

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    ca-certificates-2<IP_ADDRESS> |       hecc5488_0         146 KB  conda-forge
    openssl-1.1.1g             |       h516909a_0         2.1 MB  conda-forge
    pandoc-<IP_ADDRESS>             |                0        18.7 MB  conda-forge
    r-backports-1.1.6          |    r35hcdcec82_1          76 KB  conda-forge
    r-base64enc-0.1_3          | r35hcdcec82_1003          43 KB  conda-forge
    r-brew-1.0_6               | r35h6115d3f_1002          92 KB  conda-forge
    r-callr-3.4.3              |    r35h6115d3f_0         391 KB  conda-forge
    r-commonmark-1.7           | r35hcdcec82_1001         151 KB  conda-forge
    r-data.table-1.12.8        |    r35hcdcec82_0         1.8 MB  conda-forge
    r-desc-1.2.0               | r35h6115d3f_1002         286 KB  conda-forge
    r-evaluate-0.14            |    r35h6115d3f_1          79 KB  conda-forge
    r-fansi-0.4.1              |    r35hcdcec82_0         195 KB  conda-forge
    r-fs-1.4.1                 |    r35h0357c0b_0         483 KB  conda-forge
    r-glue-1.4.0               |    r35hcdcec82_0         143 KB  conda-forge
    r-highlight-0.5.0          |    r35h0357c0b_0         566 KB  conda-forge
    r-highr-0.8                |    r35h6115d3f_1          47 KB  conda-forge
    r-htmltools-0.4.0          |    r35h0357c0b_0         213 KB  conda-forge
    r-knitr-1.28               |    r35h6115d3f_0         1.3 MB  conda-forge
    r-markdown-1.1             |    r35hcdcec82_0         143 KB  conda-forge
    r-mass-7.3_51.6            |    r35hcdcec82_1         1.1 MB  conda-forge
    r-packrat-0.5.0            | r35h6115d3f_1001         455 KB  conda-forge
    r-pillar-1.3.0             |   r351h6115d3f_0         152 KB
    r-pkgbuild-1.0.7           |    r35h6115d3f_0         151 KB  conda-forge
    r-pkgdown-1.3.0            | r35h6115d3f_1001         505 KB  conda-forge
    r-pkgload-1.0.2            | r35h0357c0b_1001         159 KB  conda-forge
    r-prettyunits-1.1.1        |    r35h6115d3f_0          40 KB  conda-forge
    r-processx-3.4.2           |    r35hcdcec82_0         269 KB  conda-forge
    r-ps-1.3.2                 |    r35hcdcec82_0         224 KB  conda-forge
    r-purrr-0.2.5              |r351h96ca727_1002         283 KB  conda-forge
    r-rcpp-<IP_ADDRESS>             |    r35h0357c0b_0         1.9 MB  conda-forge
    r-rematch2-2.1.2           |    r35h6115d3f_0          52 KB  conda-forge
    r-rmarkdown-2.1            |    r35h6115d3f_0         3.0 MB  conda-forge
    r-roxygen2-6.1.1           | r35h0357c0b_1001         508 KB  conda-forge
    r-rprojroot-1.3_2          | r35h6115d3f_1002          93 KB  conda-forge
    r-rsconnect-0.8.16         |    r35h6115d3f_0         670 KB  conda-forge
    r-stringi-1.4.3            |    r35h0357c0b_2         766 KB  conda-forge
    r-stringr-1.4.0            |    r35h6115d3f_1         207 KB  conda-forge
    r-tibble-1.4.2             |r351h96ca727_1002         229 KB  conda-forge
    r-tinytex-0.22             |    r35h6115d3f_0         110 KB  conda-forge
    r-utf8-1.1.4               | r35hcdcec82_1002         160 KB  conda-forge
    r-xfun-0.13                |    r35h6115d3f_0         194 KB  conda-forge
    r-xml2-1.3.2               |    r35h0357c0b_0         249 KB  conda-forge
    r-yaml-2.2.1               |    r35hcdcec82_0         116 KB  conda-forge
    ------------------------------------------------------------
                                           Total:        38.3 MB

The following NEW packages will be INSTALLED:

  pandoc             conda-forge/linux-64::pandoc-<IP_ADDRESS>-0
  r-backports        conda-forge/linux-64::r-backports-1.1.6-r35hcdcec82_1
  r-base64enc        conda-forge/linux-64::r-base64enc-0.1_3-r35hcdcec82_1003
  r-brew             conda-forge/noarch::r-brew-1.0_6-r35h6115d3f_1002
  r-callr            conda-forge/noarch::r-callr-3.4.3-r35h6115d3f_0
  r-commonmark       conda-forge/linux-64::r-commonmark-1.7-r35hcdcec82_1001
  r-data.table       conda-forge/linux-64::r-data.table-1.12.8-r35hcdcec82_0
  r-desc             conda-forge/noarch::r-desc-1.2.0-r35h6115d3f_1002
  r-evaluate         conda-forge/noarch::r-evaluate-0.14-r35h6115d3f_1
  r-fansi            conda-forge/linux-64::r-fansi-0.4.1-r35hcdcec82_0
  r-fs               conda-forge/linux-64::r-fs-1.4.1-r35h0357c0b_0
  r-glue             conda-forge/linux-64::r-glue-1.4.0-r35hcdcec82_0
  r-highlight        conda-forge/linux-64::r-highlight-0.5.0-r35h0357c0b_0
  r-highr            conda-forge/noarch::r-highr-0.8-r35h6115d3f_1
  r-htmltools        conda-forge/linux-64::r-htmltools-0.4.0-r35h0357c0b_0
  r-knitr            conda-forge/noarch::r-knitr-1.28-r35h6115d3f_0
  r-markdown         conda-forge/linux-64::r-markdown-1.1-r35hcdcec82_0
  r-mass             conda-forge/linux-64::r-mass-7.3_51.6-r35hcdcec82_1
  r-packrat          conda-forge/noarch::r-packrat-0.5.0-r35h6115d3f_1001
  r-pillar           pkgs/r/linux-64::r-pillar-1.3.0-r351h6115d3f_0
  r-pkgbuild         conda-forge/noarch::r-pkgbuild-1.0.7-r35h6115d3f_0
  r-pkgdown          conda-forge/noarch::r-pkgdown-1.3.0-r35h6115d3f_1001
  r-pkgload          conda-forge/linux-64::r-pkgload-1.0.2-r35h0357c0b_1001
  r-prettyunits      conda-forge/noarch::r-prettyunits-1.1.1-r35h6115d3f_0
  r-processx         conda-forge/linux-64::r-processx-3.4.2-r35hcdcec82_0
  r-ps               conda-forge/linux-64::r-ps-1.3.2-r35hcdcec82_0
  r-purrr            conda-forge/linux-64::r-purrr-0.2.5-r351h96ca727_1002
  r-rcpp             conda-forge/linux-64::r-rcpp-<IP_ADDRESS>-r35h0357c0b_0
  r-rematch2         conda-forge/noarch::r-rematch2-2.1.2-r35h6115d3f_0