Merge branch 'master' into 1492

.appveyor.yml

@@ -59,14 +59,6 @@ test_script:
   - IF "%COMPILER%"=="MINGW" appveyor exit  # skip all further steps
   - cd ../../python-package && python setup.py bdist_wheel --plat-name=win-amd64 --universal
 
-after_test:
-  - cd ../.nuget
-  - python create_nuget.py
-  - nuget.exe pack LightGBM.nuspec
-
-nuget:
-  project_feed: true
-
 artifacts:
 - path: Release/lib_lightgbm.dll
   name: Library
@@ -74,15 +66,13 @@ artifacts:
   name: Exe
 - path: python-package/dist/*
   name: Pip
-- path: .nuget/*.nupkg
-  name: Nuget
 
 deploy:
   release: $(APPVEYOR_REPO_TAG_NAME)
   provider: GitHub
   auth_token:
     secure: KR44XwtxY0cLlVpQwY726BvC6gzT0cYTf0ahJ4cSvvS0UVoSJxkR900ICfVXHRoT
-  artifact: Library,Exe,Pip,Nuget
+  artifact: Library,Exe,Pip
   force_update: true
   draft: true
   on:

.gitignore

@@ -181,6 +181,7 @@ BundleArtifacts/
 ClientBin/
 ~$*
 *~
+.*.swp
 *.dbmdl
 *.dbproj.schemaview
 *.pfx

.nuget/create_nuget.py

 import os
 import sys
-import distutils
-from distutils import file_util
 
-if not os.path.exists("lib"):
-    os.makedirs("lib")
-if not os.path.exists("lib/native"):
-    os.makedirs("lib/native")
-if not os.path.exists("lib/net40"):
-    os.makedirs("lib/net40")
-if not os.path.exists("lib/net45"):
-    os.makedirs("lib/net45")
-distutils.file_util.copy_file("../Release/lightgbm.exe", "./lib/")
-distutils.file_util.copy_file("../Release/lib_lightgbm.dll", "./lib/")
-version = open('../VERSION.txt').read().strip()
-nuget_str = '''<?xml version="1.0"?>
-<package xmlns="http://schemas.microsoft.com/packaging/2013/05/nuspec.xsd">
+from distutils.file_util import copy_file
+
+
+if __name__ == "__main__":
+    source = sys.argv[1]
+    current_dir = os.path.abspath(os.path.dirname(__file__))
+    if not os.path.exists(os.path.join(current_dir, "runtimes/linux-x64/native")):
+        os.makedirs(os.path.join(current_dir, "runtimes/linux-x64/native"))
+    if not os.path.exists(os.path.join(current_dir, "runtimes/osx-x64/native")):
+        os.makedirs(os.path.join(current_dir, "runtimes/osx-x64/native"))
+    if not os.path.exists(os.path.join(current_dir, "runtimes/win-x64/native")):
+        os.makedirs(os.path.join(current_dir, "runtimes/win-x64/native"))
+    copy_file(os.path.join(source, "lib_lightgbm.so"), os.path.join(current_dir, "runtimes/linux-x64/native/lib_lightgbm.so"))
+    copy_file(os.path.join(source, "lib_lightgbm.dylib"), os.path.join(current_dir, "runtimes/osx-x64/native/lib_lightgbm.dylib"))
+    copy_file(os.path.join(source, "lib_lightgbm.dll"), os.path.join(current_dir, "runtimes/win-x64/native/lib_lightgbm.dll"))
+    version = open(os.path.join(current_dir, '../VERSION.txt')).read().strip()
+    nuget_str = '''<?xml version="1.0"?>
+    <package xmlns="http://schemas.microsoft.com/packaging/2013/05/nuspec.xsd">
     <metadata>
         <id>LightGBM</id>
         <version>%s</version>
@@ -25,15 +28,14 @@ nuget_str = '''<?xml version="1.0"?>
         <projectUrl>https://github.com/Microsoft/LightGBM</projectUrl>
         <requireLicenseAcceptance>false</requireLicenseAcceptance>
         <description>A fast, distributed, high performance gradient boosting framework</description>
-    <copyright>Copyright 2017 @ Microsoft</copyright>
+        <copyright>Copyright 2018 @ Microsoft</copyright>
         <tags>machine-learning data-mining distributed native boosting gbdt</tags>
         <dependencies> </dependencies>
     </metadata>
         <files>
-      <file src="lib\**" target="lib"/>
+        <file src="runtimes\**" target="runtimes"/>
         </files>
-</package>
-''' % (version)
-nuget_file = open("LightGBM.nuspec", "w")
-nuget_file.write(nuget_str)
-nuget_file.close()
+    </package>
+    ''' % (version)
+    with open(os.path.join(current_dir, "LightGBM.nuspec"), "w") as nuget_file:
+        nuget_file.write(nuget_str)

.vsts-ci.yml

+phases:
+###########################################
+- phase: Linux
+###########################################
+  variables:
+    PYTHON_VERSION: 3.6
+    CONDA_ENV: test-env
+  queue:
+    name: 'Hosted Linux Preview'
+    parallel: 9
+    matrix:
+      regular:
+        TASK: regular
+      mpi:
+        TASK: mpi
+        PYTHON_VERSION: 2.7
+      pylint: 
+        TASK: pylint
+      inference:
+        TASK: if-else
+      sdist:
+        TASK: sdist
+        PYTHON_VERSION: 3.4
+      bdist:
+        TASK: bdist
+        PYTHON_VERSION: 3.5
+      gpu_1:
+        TASK: gpu 
+        METHOD: source
+      gpu_2:
+        TASK: gpu 
+        METHOD: pip
+  steps:
+  - task: CondaEnvironment@0
+    inputs:
+      environmentName: $(CONDA_ENV)
+      packageSpecs: 'python=$(PYTHON_VERSION)' # Optional
+      createOptions: '-q'
+  - script: | 
+      sudo apt-get update
+      export LGB_VER=$(head -n 1 VERSION.txt)
+      export AMDAPPSDK=$HOME/AMDAPPSDK
+      export LD_LIBRARY_PATH="$AMDAPPSDK/lib/x86_64:$LD_LIBRARY_PATH"
+      export OPENCL_VENDOR_PATH=$AMDAPPSDK/etc/OpenCL/vendors
+      if [[ $TASK == "mpi" ]]; then
+          sudo apt-get install -y libopenmpi-dev openmpi-bin
+      fi
+      if [[ $TASK == "gpu" ]]; then
+          sudo apt-get install --no-install-recommends -y ocl-icd-opencl-dev libboost-dev libboost-system-dev libboost-filesystem-dev
+          wget -q https://github.com/Microsoft/LightGBM/releases/download/v2.0.12/AMD-APP-SDKInstaller-v3.0.130.136-GA-linux64.tar.bz2
+          tar -xjf AMD-APP-SDK*.tar.bz2
+          mkdir -p $OPENCL_VENDOR_PATH
+          sh AMD-APP-SDK*.sh --tar -xf -C $AMDAPPSDK
+          mv $AMDAPPSDK/lib/x86_64/sdk/* $AMDAPPSDK/lib/x86_64/
+          echo libamdocl64.so > $OPENCL_VENDOR_PATH/amdocl64.icd
+      fi
+      bash .vsts-ci/test.sh
+  - task: PublishBuildArtifacts@1
+    inputs:
+      pathtoPublish: '$(Build.ArtifactStagingDirectory)' 
+      artifactName: PackageAssets
+      artifactType: container
+###########################################
+- phase: MacOS
+###########################################
+  variables:
+    PYTHON_VERSION: 3.6
+    CONDA_ENV: test-env
+  queue:
+    name: 'Hosted macOS Preview'
+    parallel: 3
+    matrix:
+      regular:
+        TASK: regular
+        PYTHON_VERSION: 2.7
+      sdist:
+        TASK: sdist
+        PYTHON_VERSION: 3.4
+      bdist:
+        TASK: bdist
+        PYTHON_VERSION: 3.5
+  steps:
+  - script: |
+      test -n $CC  && unset CC
+      test -n $CXX && unset CXX
+      rm '/usr/local/include/c++'
+      brew install gcc@8
+      export CXX=g++-8
+      export CC=gcc-8
+      wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda${PYTHON_VERSION:0:1}-latest-MacOSX-x86_64.sh
+      bash conda.sh -b -p $HOME/miniconda
+      export PATH=$HOME/miniconda/bin:$PATH
+      conda config --set always_yes yes --set changeps1 no
+      conda create -q -n $CONDA_ENV python=$PYTHON_VERSION
+      source activate $CONDA_ENV
+      export LGB_VER=$(head -n 1 VERSION.txt)
+      bash .vsts-ci/test.sh
+  - task: PublishBuildArtifacts@1
+    inputs:
+      pathtoPublish: '$(Build.ArtifactStagingDirectory)' 
+      artifactName: PackageAssets
+      artifactType: container
+###########################################
+- phase: Windows
+###########################################
+  variables:
+    PYTHON_VERSION: 3.6
+    CONDA_ENV: test-env
+  queue:
+    name: 'Hosted VS2017'
+    parallel: 4
+    matrix:
+      regular:
+        TASK: regular
+      sdist:
+        TASK: sdist
+        PYTHON_VERSION: 2.7
+      bdist:
+        TASK: bdist
+        PYTHON_VERSION: 3.5
+      # mingw:
+      #   TASK: mingw
+  steps:
+  - task: CondaEnvironment@0
+    inputs:
+      environmentName: $(CONDA_ENV)
+      packageSpecs: 'python=$(PYTHON_VERSION)' # Optional
+      createOptions: '-q'
+  - powershell: |
+      conda install -q -y -n $env:CONDA_ENV numpy nose scipy scikit-learn pandas matplotlib python-graphviz pytest
+      if ("$env:TASK" -eq "regular") {
+        mkdir build; cd build
+        cmake -DCMAKE_GENERATOR_PLATFORM=x64 .. ; cmake --build . --target ALL_BUILD --config Release
+        cd ../python-package; python setup.py install -p
+        cd ..
+        pytest tests/c_api_test/test_.py
+        cp Release/lib_lightgbm.dll $env:BUILD_ARTIFACTSTAGINGDIRECTORY
+      } 
+      elseif ("$env:TASK" -eq "sdist"){
+        cd python-package; python setup.py sdist --formats gztar
+        cd dist; pip install @(Get-ChildItem *.gz) -v 
+        cd ../..
+      }
+      # elseif ("$env:TASK" -eq "mingw"){
+      #   cd python-package; python setup.py sdist --formats gztar
+      #   cd dist; pip install @(Get-ChildItem *.gz) --install-option=--mingw -v
+      #   cd ../..
+      # }
+      else {
+        cd python-package
+        python setup.py bdist_wheel --plat-name=win-amd64 --universal
+        cd dist; pip install @(Get-ChildItem *.whl)
+        cp @(Get-ChildItem *.whl) $env:BUILD_ARTIFACTSTAGINGDIRECTORY
+        cd ../..
+      }
+      pytest tests/python_package_test
+      cd examples/python-guide
+      @("import matplotlib", "matplotlib.use('Agg')") + (Get-Content "plot_example.py") | Set-Content "plot_example.py"
+      (Get-Content "plot_example.py").replace('graph.render(view=True)', 'graph.render(view=False)') | Set-Content "plot_example.py"
+      foreach ($file in @(Get-ChildItem *.py)) {
+        python $file
+        if ($LastExitCode -ne 0) { $host.SetShouldExit($LastExitCode) }
+      }  # run all examples
+  - task: PublishBuildArtifacts@1
+    inputs:
+      pathtoPublish: '$(Build.ArtifactStagingDirectory)' 
+      artifactName: PackageAssets
+      artifactType: container
 
+################################################################################
+- phase: Package
+################################################################################
+  dependsOn:
+  - Linux
+  - MacOS
+  - Windows
+  queue:
+    name: 'Hosted VS2017'
+  steps:
+  # Download all agent packages from all previous phases
+  - task: DownloadBuildArtifacts@0
+    displayName: Download package assets
+    inputs:
+      artifactName: PackageAssets
+      downloadPath: $(Build.SourcesDirectory)/binaries
+  - powershell: |
+      $client = new-object System.Net.WebClient
+      $client.DownloadFile("https://dist.nuget.org/win-x86-commandline/latest/nuget.exe",".nuget/nuget.exe")
+  - script: |
+      cd .nuget
+      python create_nuget.py %BUILD_SOURCESDIRECTORY%/binaries/PackageAssets
+      nuget.exe pack LightGBM.nuspec
+      xcopy *.nupkg %BUILD_ARTIFACTSTAGINGDIRECTORY%
+  - task: PublishBuildArtifacts@1
+    inputs:
+      pathtoPublish: '$(Build.ArtifactStagingDirectory)' 
+      artifactName: Nuget
+      artifactType: container

.vsts-ci/test.sh

+#!/bin/bash
+
+cd ${BUILD_REPOSITORY_LOCALPATH}
+
+if [[ $TASK == "check-docs" ]]; then
+    if [[ $AGENT_OS == "Linux" ]]; then
+        sudo apt-get install linkchecker -y
+    fi
+    if [[ ${PYTHON_VERSION} == "2.7" ]]; then
+        conda install -y -n $CONDA_ENV mock
+    fi
+    conda install -y -n $CONDA_ENV sphinx "sphinx_rtd_theme>=0.3"  # html5validator
+    pip install rstcheck
+    cd ${BUILD_REPOSITORY_LOCALPATH}/python-package
+    rstcheck --report warning `find . -type f -name "*.rst"` || exit -1
+    cd ${BUILD_REPOSITORY_LOCALPATH}/docs
+    rstcheck --report warning --ignore-directives=autoclass,autofunction `find . -type f -name "*.rst"` || exit -1
+    make html || exit -1
+    find ./_build/html/ -type f -name '*.html' -exec \
+    sed -i -e 's;\(\.\/[^.]*\.\)rst\([^[:space:]]*\);\1html\2;g' {} \;  # emulate js function
+#    html5validator --root ./_build/html/ || exit -1
+    if [[ $AGENT_OS == "Linux" ]]; then
+        linkchecker --config=.linkcheckerrc ./_build/html/*.html || exit -1
+    fi
+    exit 0
+fi
+
+if [[ $TASK == "pylint" ]]; then
+    conda install -y -n $CONDA_ENV pycodestyle
+    pycodestyle --ignore=E501,W503 --exclude=./compute,./docs,./.nuget . || exit -1
+    exit 0
+fi
+
+if [[ $TASK == "if-else" ]]; then
+    conda install -y -n $CONDA_ENV numpy
+    mkdir build && cd build && cmake .. && make lightgbm || exit -1
+    cd ${BUILD_REPOSITORY_LOCALPATH}/tests/cpp_test && ../../lightgbm config=train.conf convert_model_language=cpp convert_model=../../src/boosting/gbdt_prediction.cpp && ../../lightgbm config=predict.conf output_result=origin.pred || exit -1
+    cd ${BUILD_REPOSITORY_LOCALPATH}/build && make lightgbm || exit -1
+    cd ${BUILD_REPOSITORY_LOCALPATH}/tests/cpp_test && ../../lightgbm config=predict.conf output_result=ifelse.pred && python test.py || exit -1
+    exit 0
+fi
+
+conda install -q -y -n $CONDA_ENV numpy nose scipy scikit-learn pandas matplotlib python-graphviz pytest
+
+if [[ $TASK == "sdist" ]]; then
+    cd ${BUILD_REPOSITORY_LOCALPATH}/python-package && python setup.py sdist || exit -1
+    pip install ${BUILD_REPOSITORY_LOCALPATH}/python-package/dist/lightgbm-$LGB_VER.tar.gz -v || exit -1
+    cp ${BUILD_REPOSITORY_LOCALPATH}/python-package/dist/lightgbm-$LGB_VER.tar.gz ${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbm-$LGB_VER.tar.gz
+    pytest ${BUILD_REPOSITORY_LOCALPATH}/tests/python_package_test || exit -1
+    exit 0
+elif [[ $TASK == "bdist" ]]; then
+    if [[ $AGENT_OS == "Darwin" ]]; then
+        cd ${BUILD_REPOSITORY_LOCALPATH}/python-package && python setup.py bdist_wheel --plat-name=macdarwin --universal || exit -1
+        cp dist/lightgbm-$LGB_VER-py2.py3-none-macdarwin.whl ${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbm-$LGB_VER-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.whl
+        mv dist/lightgbm-$LGB_VER-py2.py3-none-macdarwin.whl dist/lightgbm-$LGB_VER-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.whl
+    else
+        cd ${BUILD_REPOSITORY_LOCALPATH}/python-package && python setup.py bdist_wheel --plat-name=manylinux1_x86_64 --universal || exit -1
+        cp dist/lightgbm-$LGB_VER-py2.py3-none-manylinux1_x86_64.whl ${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbm-$LGB_VER-py2.py3-none-manylinux1_x86_64.whl
+    fi
+    pip install ${BUILD_REPOSITORY_LOCALPATH}/python-package/dist/*.whl || exit -1
+    pytest ${BUILD_REPOSITORY_LOCALPATH}/tests/python_package_test || exit -1
+    
+    exit 0
+fi
+
+if [[ $TASK == "gpu" ]]; then
+    sed -i 's/std::string device_type = "cpu";/std::string device_type = "gpu";/' ${BUILD_REPOSITORY_LOCALPATH}/include/LightGBM/config.h
+    grep -q 'std::string device_type = "gpu"' ${BUILD_REPOSITORY_LOCALPATH}/include/LightGBM/config.h || exit -1  # make sure that changes were really done
+    if [[ $METHOD == "pip" ]]; then
+        cd ${BUILD_REPOSITORY_LOCALPATH}/python-package && python setup.py sdist || exit -1
+        pip install ${BUILD_REPOSITORY_LOCALPATH}/python-package/dist/lightgbm-$LGB_VER.tar.gz -v --install-option=--gpu --install-option="--opencl-include-dir=$AMDAPPSDK/include/" || exit -1
+        pytest ${BUILD_REPOSITORY_LOCALPATH}/tests/python_package_test || exit -1
+        exit 0
+    fi
+fi
+
+mkdir build && cd build
+
+if [[ $TASK == "mpi" ]]; then
+    cd ${BUILD_REPOSITORY_LOCALPATH}/python-package && python setup.py sdist || exit -1
+    pip install ${BUILD_REPOSITORY_LOCALPATH}/python-package/dist/lightgbm-$LGB_VER.tar.gz -v --install-option=--mpi || exit -1
+    cd ${BUILD_REPOSITORY_LOCALPATH}/build
+    cmake -DUSE_MPI=ON ..
+elif [[ $TASK == "gpu" ]]; then
+    cmake -DUSE_GPU=ON -DOpenCL_INCLUDE_DIR=$AMDAPPSDK/include/ ..
+else
+    cmake ..
+fi
+
+make _lightgbm || exit -1
+
+cd ${BUILD_REPOSITORY_LOCALPATH}/python-package && python setup.py install --precompile || exit -1
+pytest ${BUILD_REPOSITORY_LOCALPATH} || exit -1
+
+if [[ $TASK == "regular" ]]; then
+    if [[ $AGENT_OS == "Darwin" ]]; then
+        cp ${BUILD_REPOSITORY_LOCALPATH}/lib_lightgbm.so ${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.dylib
+    else
+        cp ${BUILD_REPOSITORY_LOCALPATH}/lib_lightgbm.so ${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.so
+    fi
+    cd ${BUILD_REPOSITORY_LOCALPATH}/examples/python-guide
+    sed -i'.bak' '/import lightgbm as lgb/a\
+import matplotlib\
+matplotlib.use\(\"Agg\"\)\
+' plot_example.py  # prevent interactive window mode
+    sed -i 's/graph.render(view=True)/graph.render(view=False)/' plot_example.py
+    for f in *.py; do python $f || exit -1; done  # run all examples
+fi

CMakeLists.txt

@@ -124,12 +124,12 @@ if(MSVC)
         CMAKE_CXX_FLAGS_RELEASE
         CMAKE_CXX_FLAGS_RELWITHDEBINFO
     )
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /O2 /Ob2 /Oi /Ot /Oy /GL")
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /O2 /Ob2 /Oi /Ot /Oy /GL /MP")
 else()
     SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -funroll-loops")
 endif()
 
-
 SET(LightGBM_HEADER_DIR ${PROJECT_SOURCE_DIR}/include)
 
 SET(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR})

README.md

 LightGBM, Light Gradient Boosting Machine
 =========================================
 
+[![Build Status](https://lightgbm.visualstudio.com/LightGBM-Build/_apis/build/status/LightGBM-Build-CI&branchName=master)]()
 [![Build Status](https://travis-ci.org/Microsoft/LightGBM.svg?branch=master)](https://travis-ci.org/Microsoft/LightGBM)
 [![Windows Build Status](https://ci.appveyor.com/api/projects/status/1ys5ot401m0fep6l/branch/master?svg=true)](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master)
 [![Documentation Status](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/)

docs/Features.rst

@@ -201,9 +201,9 @@ LightGBM supports the following metrics:
 
 -  MAP
 
--  Multi class log loss
+-  Multi-class log loss
 
--  Multi class error rate
+-  Multi-class error rate
 
 -  Fair
 

docs/Quick-Start.rst

@@ -37,8 +37,8 @@ For the setting details, please refer to `Parameters <./Parameters.rst>`__.
 Weight and Query/Group Data
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-LightGBM also supports weighted training, it needs an additional `weight data <./Parameters.rst#io-parameters>`__.
-And it needs an additional `query data <./Parameters.rst#io-parameters>`_ for ranking task.
+LightGBM also supports weighted training, it needs an additional `weight data <./Parameters.rst#weight-data>`__.
+And it needs an additional `query data <./Parameters.rst#query-data>`_ for ranking task.
 
 Also, weight and query data could be specified as columns in training data in the same manner as label.
 

helper/parameter_generator.py

@@ -147,7 +147,7 @@ def GenParameterDescription(sections, descriptions, params_rst):
                 checks_str = ', constraints: ``{0} {1} {2}``'.format(name, sign, number)
             else:
                 checks_str = ''
-            main_desc = '-  ``{0}``, default = ``{1}``, type = {2}{3}{4}{5}'.format(name, default, param_type, options_str, aliases_str, checks_str)
+            main_desc = '-  ``{0}`` :raw-html:`<a id="{0}" title="Permalink to this parameter" href="#{0}">&#x1F517;&#xFE0E;</a>`, default = ``{1}``, type = {2}{3}{4}{5}'.format(name, default, param_type, options_str, aliases_str, checks_str)
             params_to_write.append(main_desc)
             params_to_write.extend([' ' * 3 * int(desc[0][-1]) + '-  ' + desc[1] for desc in param_desc['desc']])
 

python-package/lightgbm/sklearn.py

@@ -134,7 +134,7 @@ class LGBMModel(_LGBMModelBase):
                  min_split_gain=0., min_child_weight=1e-3, min_child_samples=20,
                  subsample=1., subsample_freq=0, colsample_bytree=1.,
                  reg_alpha=0., reg_lambda=0., random_state=None,
-                 n_jobs=-1, silent=True, **kwargs):
+                 n_jobs=-1, silent=True, importance_type='split', **kwargs):
         """Construct a gradient boosting model.
 
         Parameters
@@ -193,6 +193,10 @@ class LGBMModel(_LGBMModelBase):
             Number of parallel threads.
         silent : bool, optional (default=True)
             Whether to print messages while running boosting.
+        importance_type : str, optional (default='split')
+            The type of feature importance to be filled into ``feature_importances_``.
+            If "split", result contains numbers of times the feature is used in a model.
+            If "gain", result contains total gains of splits which use the feature.
         **kwargs : other parameters
             Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.
 
@@ -264,6 +268,7 @@ class LGBMModel(_LGBMModelBase):
         self.random_state = random_state
         self.n_jobs = n_jobs
         self.silent = silent
+        self.importance_type = importance_type
         self._Booster = None
         self._evals_result = None
         self._best_score = None
@@ -399,6 +404,7 @@ class LGBMModel(_LGBMModelBase):
         if 'verbose' not in params and self.silent:
             params['verbose'] = 0
         params.pop('silent', None)
+        params.pop('importance_type', None)
         params.pop('n_estimators', None)
         params.pop('class_weight', None)
         if self._n_classes is not None and self._n_classes > 2:
@@ -606,11 +612,13 @@ class LGBMModel(_LGBMModelBase):
         Note
         ----
         Feature importance in sklearn interface used to normalize to 1,
-        it's deprecated after 2.0.4 and same as Booster.feature_importance() now.
+        it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now.
+        ``importance_type`` attribute is passed to the function
+        to configure the type of importance values to be extracted.
         """
         if self._n_features is None:
             raise LGBMNotFittedError('No feature_importances found. Need to call fit beforehand.')
-        return self.booster_.feature_importance()
+        return self.booster_.feature_importance(importance_type=self.importance_type)
 
 
 class LGBMRegressor(LGBMModel, _LGBMRegressorBase):

src/io/config.cpp

@@ -214,15 +214,13 @@ void Config::CheckParamConflict() {
       Log::Fatal("Number of classes must be 1 for non-multiclass training");
     }
   }
-  if (is_provide_training_metric || !valid.empty()) {
   for (std::string metric_type : metric) {
     bool metric_type_multiclass = (CheckMultiClassObjective(metric_type) 
                                     || metric_type == std::string("multi_logloss")
                                     || metric_type == std::string("multi_error"));
     if ((objective_type_multiclass && !metric_type_multiclass)
       || (!objective_type_multiclass && metric_type_multiclass)) {
-        Log::Fatal("Objective and metrics don't match");
-      }
+      Log::Fatal("Multiclass qbjective and metrics don't match");
     }
   }
 

tests/python_package_test/test_sklearn.py

@@ -166,6 +166,19 @@ class TestSklearn(unittest.TestCase):
         importances = clf.feature_importances_
         self.assertEqual(len(importances), 4)
 
+    def test_feature_importances_type(self):
+        clf = lgb.LGBMClassifier(n_estimators=100)
+        data = load_iris()
+        clf.fit(data.data, data.target)
+        clf.set_params(importance_type='split')
+        importances_split = clf.feature_importances_
+        clf.set_params(importance_type='gain')
+        importances_gain = clf.feature_importances_
+        # Test that the largest element is NOT the same, the smallest can be the same, i.e. zero
+        importance_split_top1 = sorted(importances_split, reverse=True)[0]
+        importance_gain_top1 = sorted(importances_gain, reverse=True)[0]
+        self.assertNotEqual(importance_split_top1, importance_gain_top1)
+
     def test_sklearn_backward_compatibility(self):
         iris = load_iris()
         X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)