[CI] Upload Doxygen to correct destination

The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.

.clang-tidy

@@ -1,21 +1,21 @@
-Checks: 'modernize-*,-modernize-make-*,-modernize-raw-string-literal,google-*,-google-default-arguments,-clang-diagnostic-#pragma-messages,readability-identifier-naming'
+Checks: 'modernize-*,-modernize-make-*,-modernize-use-auto,-modernize-raw-string-literal,-modernize-avoid-c-arrays,-modernize-use-trailing-return-type,google-*,-google-default-arguments,-clang-diagnostic-#pragma-messages,readability-identifier-naming'
 CheckOptions:
-  - { key: readability-identifier-naming.ClassCase,           value: CamelCase  }
-  - { key: readability-identifier-naming.StructCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.TypeAliasCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.TypedefCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.TypeTemplateParameterCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.MemberCase,          value: lower_case  }
-  - { key: readability-identifier-naming.PrivateMemberSuffix,           value: '_'  }
-  - { key: readability-identifier-naming.ProtectedMemberSuffix,           value: '_'  }
-  - { key: readability-identifier-naming.EnumCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.EnumConstant,          value: CamelCase  }
-  - { key: readability-identifier-naming.EnumConstantPrefix,          value: k  }
-  - { key: readability-identifier-naming.GlobalConstantCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.GlobalConstantPrefix,          value: k  }
-  - { key: readability-identifier-naming.StaticConstantCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.StaticConstantPrefix,          value: k  }
-  - { key: readability-identifier-naming.ConstexprVariableCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.ConstexprVariablePrefix,          value: k  }
-  - { key: readability-identifier-naming.FunctionCase,          value: CamelCase  }
-  - { key: readability-identifier-naming.NamespaceCase,       value: lower_case }
+  - { key: readability-identifier-naming.ClassCase,                 value: CamelCase  }
+  - { key: readability-identifier-naming.StructCase,                value: CamelCase  }
+  - { key: readability-identifier-naming.TypeAliasCase,             value: CamelCase  }
+  - { key: readability-identifier-naming.TypedefCase,               value: CamelCase  }
+  - { key: readability-identifier-naming.TypeTemplateParameterCase, value: CamelCase  }
+  - { key: readability-identifier-naming.MemberCase,                value: lower_case }
+  - { key: readability-identifier-naming.PrivateMemberSuffix,       value: '_'        }
+  - { key: readability-identifier-naming.ProtectedMemberSuffix,     value: '_'        }
+  - { key: readability-identifier-naming.EnumCase,                  value: CamelCase  }
+  - { key: readability-identifier-naming.EnumConstant,              value: CamelCase  }
+  - { key: readability-identifier-naming.EnumConstantPrefix,        value: k          }
+  - { key: readability-identifier-naming.GlobalConstantCase,        value: CamelCase  }
+  - { key: readability-identifier-naming.GlobalConstantPrefix,      value: k          }
+  - { key: readability-identifier-naming.StaticConstantCase,        value: CamelCase  }
+  - { key: readability-identifier-naming.StaticConstantPrefix,      value: k          }
+  - { key: readability-identifier-naming.ConstexprVariableCase,     value: CamelCase  }
+  - { key: readability-identifier-naming.ConstexprVariablePrefix,   value: k          }
+  - { key: readability-identifier-naming.FunctionCase,              value: CamelCase  }
+  - { key: readability-identifier-naming.NamespaceCase,             value: lower_case }

.github/FUNDING.yml

@@ -0,0 +1,2 @@
+open_collective: xgboost
+custom: https://xgboost.ai/sponsors

.github/workflows/main.yml

@@ -0,0 +1,356 @@
+# This is a basic workflow to help you get started with Actions
+
+name: XGBoost-CI
+
+# Controls when the action will run. Triggers the workflow on push or pull request
+# events but only for the master branch
+on: [push, pull_request]
+
+env:
+  R_PACKAGES: c('XML', 'igraph', 'data.table', 'magrittr', 'ggplot2', 'DiagrammeR', 'Ckmeans.1d.dp', 'vcd', 'testthat', 'lintr', 'knitr', 'rmarkdown', 'e1071', 'cplm', 'devtools', 'float', 'titanic')
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  gtest-cpu:
+    name: Test Google C++ test (CPU)
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos-10.15]
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+    - name: Install system packages
+      run: |
+        brew install lz4 ninja libomp
+    - name: Build gtest binary
+      run: |
+        mkdir build
+        cd build
+        cmake .. -DGOOGLE_TEST=ON -DUSE_OPENMP=ON -DUSE_DMLC_GTEST=ON -DPLUGIN_LZ4=ON -DPLUGIN_DENSE_PARSER=ON -GNinja
+        ninja -v
+    - name: Run gtest binary
+      run: |
+        cd build
+        ctest --extra-verbose
+
+  gtest-cpu-nonomp:
+    name: Test Google C++ unittest (CPU Non-OMP)
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+    - name: Install system packages
+      run: |
+        sudo apt-get install -y --no-install-recommends ninja-build
+    - name: Build and install XGBoost
+      shell: bash -l {0}
+      run: |
+        mkdir build
+        cd build
+        cmake .. -GNinja -DGOOGLE_TEST=ON  -DUSE_DMLC_GTEST=ON -DUSE_OPENMP=OFF
+        ninja -v
+    - name: Run gtest binary
+      run: |
+        cd build
+        ctest --extra-verbose
+
+  c-api-demo:
+    name: Test installing XGBoost lib + building the C API demo
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: ["ubuntu-latest"]
+        python-version: ["3.8"]
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+    - name: Install system packages
+      run: |
+        sudo apt-get install -y --no-install-recommends ninja-build
+    - uses: conda-incubator/setup-miniconda@v2
+      with:
+        auto-update-conda: true
+        python-version: ${{ matrix.python-version }}
+    - name: Display Conda env
+      shell: bash -l {0}
+      run: |
+        conda info
+        conda list
+    - name: Build and install XGBoost
+      shell: bash -l {0}
+      run: |
+        mkdir build
+        cd build
+        cmake .. -DBUILD_STATIC_LIB=ON -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -GNinja
+        ninja -v install
+    - name: Build and run C API demo
+      shell: bash -l {0}
+      run: |
+        cd demo/c-api/
+        mkdir build
+        cd build
+        cmake .. -GNinja -DCMAKE_PREFIX_PATH=$CONDA_PREFIX
+        ninja -v
+        cd ..
+        ./build/api-demo
+
+  test-with-jvm:
+    name: Test JVM on OS ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [windows-latest, ubuntu-latest]
+
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+
+    - uses: actions/setup-java@v1
+      with:
+        java-version: 1.8
+
+    - name: Cache Maven packages
+      uses: actions/cache@v2
+      with:
+        path: ~/.m2
+        key: ${{ runner.os }}-m2-${{ hashFiles('./jvm-packages/pom.xml') }}
+        restore-keys: ${{ runner.os }}-m2
+
+    - name: Test XGBoost4J
+      run: |
+        cd jvm-packages
+        mvn test -B -pl :xgboost4j_2.12
+
+    - name: Test XGBoost4J-Spark
+      run: |
+        rm -rfv build/
+        cd jvm-packages
+        mvn -B test
+      if: matrix.os == 'ubuntu-latest'  # Distributed training doesn't work on Windows
+      env:
+        RABIT_MOCK: ON
+
+  lint:
+    runs-on: ubuntu-latest
+    name: Code linting for Python and C++
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+    - uses: actions/setup-python@v2
+      with:
+        python-version: '3.7'
+        architecture: 'x64'
+    - name: Install Python packages
+      run: |
+        python -m pip install wheel setuptools
+        python -m pip install pylint cpplint numpy scipy scikit-learn
+    - name: Run lint
+      run: |
+        make lint
+
+  doxygen:
+    runs-on: ubuntu-latest
+    name: Generate C/C++ API doc using Doxygen
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+    - uses: actions/setup-python@v2
+      with:
+        python-version: '3.7'
+        architecture: 'x64'
+    - name: Install system packages
+      run: |
+        sudo apt-get install -y --no-install-recommends doxygen graphviz ninja-build
+        python -m pip install wheel setuptools
+        python -m pip install awscli
+    - name: Run Doxygen
+      run: |
+        mkdir build
+        cd build
+        cmake .. -DBUILD_C_DOC=ON -GNinja
+        ninja -v doc_doxygen
+    - name: Extract branch name
+      shell: bash
+      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
+      id: extract_branch
+      if: github.ref == 'refs/heads/master' || contains(github.ref, 'refs/heads/release_')
+    - name: Publish
+      run: |
+        cd build/
+        tar cvjf ${{ steps.extract_branch.outputs.branch }}.tar.bz2 doc_doxygen/
+        python -m awscli s3 cp ./${{ steps.extract_branch.outputs.branch }}.tar.bz2 s3://xgboost-docs/doxygen/ --acl public-read
+      if: github.ref == 'refs/heads/master' || contains(github.ref, 'refs/heads/release_')
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID_IAM_S3_UPLOADER }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY_IAM_S3_UPLOADER }}
+
+  sphinx:
+    runs-on: ubuntu-latest
+    name: Build docs using Sphinx
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+    - uses: actions/setup-python@v2
+      with:
+        python-version: '3.7'
+        architecture: 'x64'
+    - name: Install system packages
+      run: |
+        sudo apt-get install -y --no-install-recommends graphviz
+        python -m pip install wheel setuptools
+        python -m pip install -r doc/requirements.txt
+    - name: Extract branch name
+      shell: bash
+      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
+      id: extract_branch
+      if: github.ref == 'refs/heads/master' || contains(github.ref, 'refs/heads/release_')
+    - name: Run Sphinx
+      run: |
+        make -C doc html
+      env:
+        SPHINX_GIT_BRANCH: ${{ steps.extract_branch.outputs.branch }}
+
+  lintr:
+    runs-on: ${{ matrix.config.os }}
+    name: Run R linters on OS ${{ matrix.config.os }}, R ${{ matrix.config.r }}, Compiler ${{ matrix.config.compiler }}, Build ${{ matrix.config.build }}
+    strategy:
+      matrix:
+        config:
+          - {os: windows-latest, r: 'release', compiler: 'mingw', build: 'autotools'}
+    env:
+      R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
+      RSPM: ${{ matrix.config.rspm }}
+
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+
+    - uses: r-lib/actions/setup-r@master
+      with:
+        r-version: ${{ matrix.config.r }}
+
+    - name: Cache R packages
+      uses: actions/cache@v2
+      with:
+        path: ${{ env.R_LIBS_USER }}
+        key: ${{ runner.os }}-r-${{ matrix.config.r }}-1-${{ hashFiles('R-package/DESCRIPTION') }}
+        restore-keys: ${{ runner.os }}-r-${{ matrix.config.r }}-2-
+
+    - name: Install dependencies
+      shell: Rscript {0}
+      run: |
+        install.packages(${{ env.R_PACKAGES }},
+                         repos = 'http://cloud.r-project.org',
+                         dependencies = c('Depends', 'Imports', 'LinkingTo'))
+
+    - name: Run lintr
+      run: |
+        cd R-package
+        R.exe CMD INSTALL .
+        Rscript.exe tests/helper_scripts/run_lint.R
+
+  test-with-R:
+    runs-on: ${{ matrix.config.os }}
+    name: Test R on OS ${{ matrix.config.os }}, R ${{ matrix.config.r }}, Compiler ${{ matrix.config.compiler }}, Build ${{ matrix.config.build }}
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: windows-2016, r: 'release', compiler: 'mingw', build: 'autotools'}
+          - {os: windows-2016, r: 'release', compiler: 'msvc', build: 'cmake'}
+          - {os: windows-2016, r: 'release', compiler: 'mingw', build: 'cmake'}
+    env:
+      R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
+      RSPM: ${{ matrix.config.rspm }}
+
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+
+    - uses: r-lib/actions/setup-r@master
+      with:
+        r-version: ${{ matrix.config.r }}
+
+    - name: Cache R packages
+      uses: actions/cache@v2
+      with:
+        path: ${{ env.R_LIBS_USER }}
+        key: ${{ runner.os }}-r-${{ matrix.config.r }}-1-${{ hashFiles('R-package/DESCRIPTION') }}
+        restore-keys: ${{ runner.os }}-r-${{ matrix.config.r }}-2-
+
+    - name: Install dependencies
+      shell: Rscript {0}
+      run: |
+        install.packages(${{ env.R_PACKAGES }},
+                         repos = 'http://cloud.r-project.org',
+                         dependencies = c('Depends', 'Imports', 'LinkingTo'))
+
+    - uses: actions/setup-python@v2
+      with:
+        python-version: '3.7'
+        architecture: 'x64'
+
+    - name: Test R
+      run: |
+        python tests/ci_build/test_r_package.py --compiler="${{ matrix.config.compiler }}" --build-tool="${{ matrix.config.build }}"
+
+  test-R-CRAN:
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {r: 'release'}
+
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+
+    - uses: r-lib/actions/setup-r@master
+      with:
+        r-version: ${{ matrix.config.r }}
+
+    - uses: r-lib/actions/setup-tinytex@master
+
+    - name: Cache R packages
+      uses: actions/cache@v2
+      with:
+        path: ${{ env.R_LIBS_USER }}
+        key: ${{ runner.os }}-r-${{ matrix.config.r }}-1-${{ hashFiles('R-package/DESCRIPTION') }}
+        restore-keys: ${{ runner.os }}-r-${{ matrix.config.r }}-2-
+
+    - name: Install system packages
+      run: |
+        sudo apt-get update && sudo apt-get install libcurl4-openssl-dev libssl-dev libssh2-1-dev libgit2-dev
+
+    - name: Install dependencies
+      shell: Rscript {0}
+      run: |
+        install.packages(${{ env.R_PACKAGES }},
+                         repos = 'http://cloud.r-project.org',
+                         dependencies = c('Depends', 'Imports', 'LinkingTo'))
+
+    - name: Check R Package
+      run: |
+        # Print stacktrace upon success of failure
+        make Rcheck || tests/ci_build/print_r_stacktrace.sh fail
+        tests/ci_build/print_r_stacktrace.sh success

.github/workflows/r_nold.yml

@@ -0,0 +1,44 @@
+# Run R tests with noLD R. Only triggered by a pull request review
+# See discussion at https://github.com/dmlc/xgboost/pull/6378
+
+name: XGBoost-R-noLD
+
+on:
+  pull_request_review_comment:
+    types: [created]
+
+env:
+  R_PACKAGES: c('XML', 'igraph', 'data.table', 'magrittr', 'ggplot2', 'DiagrammeR', 'Ckmeans.1d.dp', 'vcd', 'testthat', 'lintr', 'knitr', 'rmarkdown', 'e1071', 'cplm', 'devtools', 'float', 'titanic')
+
+jobs:
+  test-R-noLD:
+    if: github.event.comment.body == '/gha run r-nold-test' && contains('OWNER,MEMBER,COLLABORATOR', github.event.comment.author_association)
+    timeout-minutes: 120
+    runs-on: ubuntu-latest
+    container: rhub/debian-gcc-devel-nold
+    steps:
+    - name: Install git and system packages
+      shell: bash
+      run: |
+        apt-get update && apt-get install -y git libcurl4-openssl-dev libssl-dev libssh2-1-dev libgit2-dev libxml2-dev
+
+    - uses: actions/checkout@v2
+      with:
+        submodules: 'true'
+
+    - name: Install dependencies
+      shell: bash
+      run: |
+        cat > install_libs.R <<EOT
+          install.packages(${{ env.R_PACKAGES }},
+                           repos = 'http://cloud.r-project.org',
+                           dependencies = c('Depends', 'Imports', 'LinkingTo'))
+        EOT
+        /tmp/R-devel/bin/Rscript install_libs.R
+
+    - name: Run R tests
+      shell: bash
+      run: |
+        cd R-package && \
+        /tmp/R-devel/bin/R CMD INSTALL . && \
+        /tmp/R-devel/bin/R -q -e "library(testthat); setwd('tests'); source('testthat.R')"

.gitignore

@@ -17,7 +17,7 @@
 *.tar.gz
 *conf
 *buffer
-*model
+*.model
 *pyc
 *.train
 *.test
@@ -51,6 +51,7 @@ Debug
 #.Rbuildignore
 R-package.Rproj
 *.cache*
+.mypy_cache/
 # java
 java/xgboost4j/target
 java/xgboost4j/tmp
@@ -65,14 +66,12 @@ nb-configuration*
 .pydevproject
 .settings/
 build
-config.mk
 /xgboost
 *.data
 build_plugin
-.idea
 recommonmark/
 tags
-*.iml
+TAGS
 *.class
 target
 *.swp
@@ -90,4 +89,29 @@ lib/
 # spark
 metastore_db
 
-plugin/updater_gpu/test/cpp/data
+/include/xgboost/build_config.h
+
+# files from R-package source install
+**/config.status
+R-package/src/Makevars
+*.lib
+
+# Visual Studio Code
+/.vscode/
+
+# IntelliJ/CLion
+.idea
+*.iml
+/cmake-build-debug/
+
+# GDB
+.gdb_history
+
+# Python joblib.Memory used in pytest.
+cachedir/
+
+# Files from local Dask work
+dask-worker-space/
+
+# Jupyter notebook checkpoints
+.ipynb_checkpoints/

.gitmodules

@@ -1,9 +1,9 @@
 [submodule "dmlc-core"]
 	path = dmlc-core
 	url = https://github.com/dmlc/dmlc-core
-[submodule "rabit"]
-	path = rabit
-	url = https://github.com/dmlc/rabit
 [submodule "cub"]
 	path = cub
 	url = https://github.com/NVlabs/cub
+[submodule "gputreeshap"]
+	path = gputreeshap
+	url = https://github.com/rapidsai/gputreeshap.git

.travis.yml

@@ -1,77 +1,62 @@
-# disable sudo for container build.
 sudo: required
 
-# Enabling test on Linux and OS X
-os:
-  - linux
-  - osx
+dist: bionic
 
-osx_image: xcode8
-
-group: deprecated-2017Q4
-
-# Use Build Matrix to do lint and build seperately
 env:
-  matrix:
-    # code lint
-    - TASK=lint
-    # r package test
-    - TASK=r_test
-    # python package test
-    - TASK=python_test
-    - TASK=python_lightweight_test
-    # java package test
-    - TASK=java_test
-    # cmake test
-    - TASK=cmake_test
-    # c++ test
-    - TASK=cpp_test
-    # distributed test
-    - TASK=distributed_test
-    # address sanitizer test
-    - TASK=sanitizer_test
+  global:
+    - secure: "PR16i9F8QtNwn99C5NDp8nptAS+97xwDtXEJJfEiEVhxPaaRkOp0MPWhogCaK0Eclxk1TqkgWbdXFknwGycX620AzZWa/A1K3gAs+GrpzqhnPMuoBJ0Z9qxXTbSJvCyvMbYwVrjaxc/zWqdMU8waWz8A7iqKGKs/SqbQ3rO6v7c="
+    - secure: "dAGAjBokqm/0nVoLMofQni/fWIBcYSmdq4XvCBX1ZAMDsWnuOfz/4XCY6h2lEI1rVHZQ+UdZkc9PioOHGPZh5BnvE49/xVVWr9c4/61lrDOlkD01ZjSAeoV0fAZq+93V/wPl4QV+MM+Sem9hNNzFSbN5VsQLAiWCSapWsLdKzqA="
 
-matrix:
-  exclude:
-    - os: osx
-      env: TASK=lint
-    - os: osx
-      env: TASK=cmake_test
+jobs:
+  include:
     - os: linux
-      env: TASK=r_test
+      arch: amd64
+      env: TASK=python_sdist_test
+    - os: linux
+      arch: arm64
+      env: TASK=python_sdist_test
+    - os: linux
+      arch: arm64
+      env: TASK=python_test
+      services:
+        - docker
     - os: osx
-      env: TASK=python_lightweight_test
+      arch: amd64
+      osx_image: xcode10.2
+      env: TASK=python_test
     - os: osx
-      env: TASK=cpp_test
+      arch: amd64
+      osx_image: xcode10.2
+      env: TASK=python_sdist_test
     - os: osx
-      env: TASK=distributed_test
-    - os: osx
-      env: TASK=sanitizer_test
+      arch: amd64
+      osx_image: xcode10.2
+      env: TASK=java_test
+    - os: linux
+      arch: s390x
+      env: TASK=s390x_test
 
-# dependent apt packages
+# dependent brew packages
 addons:
-  apt:
-    sources:
-      - llvm-toolchain-trusty-5.0
-      - ubuntu-toolchain-r-test
-      - george-edison55-precise-backports
+  homebrew:
     packages:
-      - clang
-      - clang-tidy-5.0
-      - cmake-data
-      - doxygen
-      - wget
-      - libcurl4-openssl-dev
-      - unzip
+      - cmake
+      - libomp
       - graphviz
-      - gcc-4.8
-      - g++-4.8
-      - gcc-7
-      - g++-7
+      - openssl
+      - libgit2
+      - lz4
+      - wget
+      - r
+    update: true
+  apt:
+    packages:
+      - snapd
+      - unzip
 
 before_install:
-  - source dmlc-core/scripts/travis/travis_setup_env.sh
-  - export PYTHONPATH=${PYTHONPATH}:${PWD}/python-package
+  - source tests/travis/travis_setup_env.sh
+  - if [ "${TASK}" != "python_sdist_test" ]; then export PYTHONPATH=${PYTHONPATH}:${PWD}/python-package; fi
   - echo "MAVEN_OPTS='-Xmx2g -XX:MaxPermSize=1024m -XX:ReservedCodeCacheSize=512m -Dorg.slf4j.simpleLogger.defaultLogLevel=error'" > ~/.mavenrc
 
 install:
@@ -86,7 +71,7 @@ cache:
     - ${HOME}/.cache/pip
 
 before_cache:
-  - dmlc-core/scripts/travis/travis_before_cache.sh
+  - tests/travis/travis_before_cache.sh
 
 after_failure:
   - tests/travis/travis_after_failure.sh

CMakeLists.txt

@@ -1,264 +1,338 @@
-cmake_minimum_required (VERSION 3.2)
-project(xgboost)
+cmake_minimum_required(VERSION 3.13)
+project(xgboost LANGUAGES CXX C VERSION 1.3.3)
 include(cmake/Utils.cmake)
-list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake/modules")
-find_package(OpenMP)
+list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
+cmake_policy(SET CMP0022 NEW)
+cmake_policy(SET CMP0079 NEW)
+set(CMAKE_POLICY_DEFAULT_CMP0063 NEW)
+cmake_policy(SET CMP0063 NEW)
 
+if ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
+  cmake_policy(SET CMP0077 NEW)
+endif ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
+
+message(STATUS "CMake version ${CMAKE_VERSION}")
+
+if (CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.0)
+  message(FATAL_ERROR "GCC version must be at least 5.0!")
+endif()
+
+include(${xgboost_SOURCE_DIR}/cmake/FindPrefetchIntrinsics.cmake)
+find_prefetch_intrinsics()
+include(${xgboost_SOURCE_DIR}/cmake/Version.cmake)
+write_version()
 set_default_configuration_release()
-msvc_use_static_runtime()
 
-# Options
-option(USE_CUDA  "Build with GPU acceleration")
+#-- Options
+## User options
+option(BUILD_C_DOC "Build documentation for C APIs using Doxygen." OFF)
+option(USE_OPENMP "Build with OpenMP support." ON)
+option(BUILD_STATIC_LIB "Build static library" OFF)
+option(RABIT_BUILD_MPI "Build MPI" OFF)
+## Bindings
 option(JVM_BINDINGS "Build JVM bindings" OFF)
-option(GOOGLE_TEST "Build google tests" OFF)
 option(R_LIB "Build shared library for R package" OFF)
+## Dev
+option(USE_DEBUG_OUTPUT "Dump internal training results like gradients and predictions to stdout.
+Should only be used for debugging." OFF)
+option(FORCE_COLORED_OUTPUT "Force colored output from compilers, useful when ninja is used instead of make." OFF)
+option(ENABLE_ALL_WARNINGS "Enable all compiler warnings. Only effective for GCC/Clang" OFF)
+option(LOG_CAPI_INVOCATION "Log all C API invocations for debugging" OFF)
+option(GOOGLE_TEST "Build google tests" OFF)
+option(USE_DMLC_GTEST "Use google tests bundled with dmlc-core submodule" OFF)
+option(USE_DEVICE_DEBUG "Generate CUDA device debug info." OFF)
+option(USE_NVTX "Build with cuda profiling annotations. Developers only." OFF)
+set(NVTX_HEADER_DIR "" CACHE PATH "Path to the stand-alone nvtx header")
+option(RABIT_MOCK "Build rabit with mock" OFF)
+option(HIDE_CXX_SYMBOLS "Build shared library and hide all C++ symbols" OFF)
+## CUDA
+option(USE_CUDA  "Build with GPU acceleration" OFF)
+option(USE_NCCL  "Build with NCCL to enable distributed GPU support." OFF)
+option(BUILD_WITH_SHARED_NCCL "Build with shared NCCL library." OFF)
 set(GPU_COMPUTE_VER "" CACHE STRING
-  "Space separated list of compute versions to be built against, e.g. '35 61'")
+  "Semicolon separated list of compute versions to be built against, e.g. '35;61'")
+## Copied From dmlc
+option(USE_HDFS "Build with HDFS support" OFF)
+option(USE_AZURE "Build with AZURE support" OFF)
+option(USE_S3 "Build with S3 support" OFF)
+## Sanitizers
 option(USE_SANITIZER "Use santizer flags" OFF)
 option(SANITIZER_PATH "Path to sanitizes.")
 set(ENABLED_SANITIZERS "address" "leak" CACHE STRING
   "Semicolon separated list of sanitizer names. E.g 'address;leak'. Supported sanitizers are
-address, leak and thread.")
-
-# Plugins
+address, leak, undefined and thread.")
+## Plugins
 option(PLUGIN_LZ4 "Build lz4 plugin" OFF)
 option(PLUGIN_DENSE_PARSER "Build dense parser plugin" OFF)
+option(PLUGIN_RMM "Build with RAPIDS Memory Manager (RMM)" OFF)
+## TODO: 1. Add check if DPC++ compiler is used for building
+option(PLUGIN_UPDATER_ONEAPI "DPC++ updater" OFF)
+option(ADD_PKGCONFIG "Add xgboost.pc into system." ON)
 
-# Deprecation warning
-if(USE_AVX)
-  message(WARNING "The option 'USE_AVX' is deprecated as experimental AVX features have been removed from xgboost.")
-endif()
+#-- Checks for building XGBoost
+if (USE_DEBUG_OUTPUT AND (NOT (CMAKE_BUILD_TYPE MATCHES Debug)))
+  message(SEND_ERROR "Do not enable `USE_DEBUG_OUTPUT' with release build.")
+endif (USE_DEBUG_OUTPUT AND (NOT (CMAKE_BUILD_TYPE MATCHES Debug)))
+if (USE_NCCL AND NOT (USE_CUDA))
+  message(SEND_ERROR "`USE_NCCL` must be enabled with `USE_CUDA` flag.")
+endif (USE_NCCL AND NOT (USE_CUDA))
+if (USE_DEVICE_DEBUG AND NOT (USE_CUDA))
+  message(SEND_ERROR "`USE_DEVICE_DEBUG` must be enabled with `USE_CUDA` flag.")
+endif (USE_DEVICE_DEBUG AND NOT (USE_CUDA))
+if (BUILD_WITH_SHARED_NCCL AND (NOT USE_NCCL))
+  message(SEND_ERROR "Build XGBoost with -DUSE_NCCL=ON to enable BUILD_WITH_SHARED_NCCL.")
+endif (BUILD_WITH_SHARED_NCCL AND (NOT USE_NCCL))
+if (JVM_BINDINGS AND R_LIB)
+  message(SEND_ERROR "`R_LIB' is not compatible with `JVM_BINDINGS' as they both have customized configurations.")
+endif (JVM_BINDINGS AND R_LIB)
+if (R_LIB AND GOOGLE_TEST)
+  message(WARNING "Some C++ unittests will fail with `R_LIB` enabled,
+ as R package redirects some functions to R runtime implementation.")
+endif (R_LIB AND GOOGLE_TEST)
+if (USE_AVX)
+  message(SEND_ERROR  "The option 'USE_AVX' is deprecated as experimental AVX features have been removed from XGBoost.")
+endif (USE_AVX)
+if (PLUGIN_RMM AND NOT (USE_CUDA))
+  message(SEND_ERROR "`PLUGIN_RMM` must be enabled with `USE_CUDA` flag.")
+endif (PLUGIN_RMM AND NOT (USE_CUDA))
+if (PLUGIN_RMM AND NOT ((CMAKE_CXX_COMPILER_ID STREQUAL "Clang") OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")))
+  message(SEND_ERROR "`PLUGIN_RMM` must be used with GCC or Clang compiler.")
+endif (PLUGIN_RMM AND NOT ((CMAKE_CXX_COMPILER_ID STREQUAL "Clang") OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")))
+if (PLUGIN_RMM AND NOT (CMAKE_SYSTEM_NAME STREQUAL "Linux"))
+  message(SEND_ERROR "`PLUGIN_RMM` must be used with Linux.")
+endif (PLUGIN_RMM AND NOT (CMAKE_SYSTEM_NAME STREQUAL "Linux"))
+if (ENABLE_ALL_WARNINGS)
+  if ((NOT CMAKE_CXX_COMPILER_ID MATCHES "Clang") AND (NOT CMAKE_CXX_COMPILER_ID STREQUAL "GNU"))
+    message(SEND_ERROR "ENABLE_ALL_WARNINGS is only available for Clang and GCC.")
+  endif ((NOT CMAKE_CXX_COMPILER_ID MATCHES "Clang") AND (NOT CMAKE_CXX_COMPILER_ID STREQUAL "GNU"))
+endif (ENABLE_ALL_WARNINGS)
+if (BUILD_STATIC_LIB AND (R_LIB OR JVM_BINDINGS))
+  message(SEND_ERROR "Cannot build a static library libxgboost.a when R or JVM packages are enabled.")
+endif (BUILD_STATIC_LIB AND (R_LIB OR JVM_BINDINGS))
 
-# Compiler flags
-set(CMAKE_CXX_STANDARD 11)
-set(CMAKE_CXX_STANDARD_REQUIRED ON)
-if(OpenMP_CXX_FOUND OR OPENMP_FOUND)
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
-endif()
-set(CMAKE_POSITION_INDEPENDENT_CODE ON)
-if(MSVC)
-  # Multithreaded compilation
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP")
-else()
-  # Correct error for GCC 5 and cuda
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_MWAITXINTRIN_H_INCLUDED -D_FORCE_INLINES")
-  # Performance
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -funroll-loops")
-endif()
-if(WIN32 AND MINGW)
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -static-libstdc++")
-endif()
-
-# Sanitizer
-if(USE_SANITIZER)
+#-- Sanitizer
+if (USE_SANITIZER)
   include(cmake/Sanitizer.cmake)
   enable_sanitizers("${ENABLED_SANITIZERS}")
-endif(USE_SANITIZER)
+endif (USE_SANITIZER)
+
+if (USE_CUDA)
+  SET(USE_OPENMP ON CACHE BOOL "CUDA requires OpenMP" FORCE)
+  # `export CXX=' is ignored by CMake CUDA.
+  set(CMAKE_CUDA_HOST_COMPILER ${CMAKE_CXX_COMPILER})
+  message(STATUS "Configured CUDA host compiler: ${CMAKE_CUDA_HOST_COMPILER}")
+
+  enable_language(CUDA)
+  if (${CMAKE_CUDA_COMPILER_VERSION} VERSION_LESS 10.0)
+    message(FATAL_ERROR "CUDA version must be at least 10.0!")
+  endif()
+  set(GEN_CODE "")
+  format_gencode_flags("${GPU_COMPUTE_VER}" GEN_CODE)
+  add_subdirectory(${PROJECT_SOURCE_DIR}/gputreeshap)
+endif (USE_CUDA)
+
+if (FORCE_COLORED_OUTPUT AND (CMAKE_GENERATOR STREQUAL "Ninja") AND
+    ((CMAKE_CXX_COMPILER_ID STREQUAL "GNU") OR
+      (CMAKE_CXX_COMPILER_ID STREQUAL "Clang")))
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fdiagnostics-color=always")
+endif()
+
+find_package(Threads REQUIRED)
+
+if (USE_OPENMP)
+  if (APPLE)
+    # Require CMake 3.16+ on Mac OSX, as previous versions of CMake had trouble locating
+    # OpenMP on Mac. See https://github.com/dmlc/xgboost/pull/5146#issuecomment-568312706
+    cmake_minimum_required(VERSION 3.16)
+  endif (APPLE)
+  find_package(OpenMP REQUIRED)
+endif (USE_OPENMP)
 
 # dmlc-core
-add_subdirectory(dmlc-core)
-set(LINK_LIBRARIES dmlc rabit)
-
-# enable custom logging
-add_definitions(-DDMLC_LOG_CUSTOMIZE=1)
-
-# compiled code customizations for R package
-if(R_LIB)
-  add_definitions(
-    -DXGBOOST_STRICT_R_MODE=1
-    -DXGBOOST_CUSTOMIZE_GLOBAL_PRNG=1
-    -DDMLC_LOG_BEFORE_THROW=0
-    -DDMLC_DISABLE_STDIN=1
-    -DDMLC_LOG_CUSTOMIZE=1
-    -DRABIT_CUSTOMIZE_MSG_
-    -DRABIT_STRICT_CXX98_
-  )
-endif()
-
-# Gather source files
-include_directories (
-    ${PROJECT_SOURCE_DIR}/include
-    ${PROJECT_SOURCE_DIR}/dmlc-core/include
-    ${PROJECT_SOURCE_DIR}/rabit/include
-)
-
-file(GLOB_RECURSE SOURCES
-    src/*.cc
-    src/*.h
-    include/*.h
-)
-
-# Only add main function for executable target
-list(REMOVE_ITEM SOURCES ${PROJECT_SOURCE_DIR}/src/cli_main.cc)
-
-file(GLOB_RECURSE TEST_SOURCES "tests/cpp/*.cc")
-
-file(GLOB_RECURSE CUDA_SOURCES
-    src/*.cu
-    src/*.cuh
-)
-
-# Add plugins to source files
-if(PLUGIN_LZ4)
-  list(APPEND SOURCES plugin/lz4/sparse_page_lz4_format.cc)
-  link_libraries(lz4)
-endif()
-if(PLUGIN_DENSE_PARSER)
-  list(APPEND SOURCES plugin/dense_parser/dense_libsvm.cc)
-endif()
+msvc_use_static_runtime()
+add_subdirectory(${xgboost_SOURCE_DIR}/dmlc-core)
+set_target_properties(dmlc PROPERTIES
+  CXX_STANDARD 14
+  CXX_STANDARD_REQUIRED ON
+  POSITION_INDEPENDENT_CODE ON)
+if (MSVC)
+  target_compile_options(dmlc PRIVATE
+                         -D_CRT_SECURE_NO_WARNINGS -D_CRT_SECURE_NO_DEPRECATE)
+  if (TARGET dmlc_unit_tests)
+    target_compile_options(dmlc_unit_tests PRIVATE
+                           -D_CRT_SECURE_NO_WARNINGS -D_CRT_SECURE_NO_DEPRECATE)
+  endif (TARGET dmlc_unit_tests)
+endif (MSVC)
+if (ENABLE_ALL_WARNINGS)
+  target_compile_options(dmlc PRIVATE -Wall -Wextra)
+endif (ENABLE_ALL_WARNINGS)
 
 # rabit
-# TODO: Create rabit cmakelists.txt
-set(RABIT_SOURCES
-    rabit/src/allreduce_base.cc
-    rabit/src/allreduce_robust.cc
-    rabit/src/engine.cc
-    rabit/src/c_api.cc
-)
-set(RABIT_EMPTY_SOURCES
-    rabit/src/engine_empty.cc
-    rabit/src/c_api.cc
-)
-if(MINGW OR R_LIB)
-  # build a dummy rabit library
-  add_library(rabit STATIC ${RABIT_EMPTY_SOURCES})
-else()
-  add_library(rabit STATIC ${RABIT_SOURCES})
-endif()
+add_subdirectory(rabit)
 
-if(USE_CUDA)
-  find_package(CUDA 8.0 REQUIRED)
-  cmake_minimum_required(VERSION 3.5)
+# core xgboost
+add_subdirectory(${xgboost_SOURCE_DIR}/src)
+target_link_libraries(objxgboost PUBLIC dmlc)
 
-  add_definitions(-DXGBOOST_USE_CUDA)
+# Exports some R specific definitions and objects
+if (R_LIB)
+  add_subdirectory(${xgboost_SOURCE_DIR}/R-package)
+endif (R_LIB)
 
-  include_directories(cub)
+# Plugin
+add_subdirectory(${xgboost_SOURCE_DIR}/plugin)
 
-  if(USE_NCCL)
-    find_package(Nccl REQUIRED)
-    include_directories(${NCCL_INCLUDE_DIR})
-    add_definitions(-DXGBOOST_USE_NCCL)
-  endif()
+#-- library
+if (BUILD_STATIC_LIB)
+  add_library(xgboost STATIC)
+else (BUILD_STATIC_LIB)
+  add_library(xgboost SHARED)
+endif (BUILD_STATIC_LIB)
+target_link_libraries(xgboost PRIVATE objxgboost)
 
-  set(GENCODE_FLAGS "")
-  format_gencode_flags("${GPU_COMPUTE_VER}" GENCODE_FLAGS)
-  message("cuda architecture flags: ${GENCODE_FLAGS}")
+if (USE_CUDA)
+  xgboost_set_cuda_flags(xgboost)
+endif (USE_CUDA)
 
-  set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS};--expt-extended-lambda;--expt-relaxed-constexpr;${GENCODE_FLAGS};-lineinfo;")
-  if(NOT MSVC)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS};-Xcompiler -fPIC; -Xcompiler -Werror; -std=c++11")
-  endif()
+#-- Hide all C++ symbols
+if (HIDE_CXX_SYMBOLS)
+  foreach(target objxgboost xgboost dmlc)
+    set_target_properties(${target} PROPERTIES CXX_VISIBILITY_PRESET hidden)
+  endforeach()
+endif (HIDE_CXX_SYMBOLS)
 
-  cuda_add_library(gpuxgboost ${CUDA_SOURCES} STATIC)
+target_include_directories(xgboost
+  INTERFACE
+  $<INSTALL_INTERFACE:${CMAKE_INSTALL_PREFIX}/include>
+  $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/include>)
 
-  if(USE_NCCL)
-    link_directories(${NCCL_LIBRARY})
-    target_link_libraries(gpuxgboost ${NCCL_LIB_NAME})
-  endif()
-  list(APPEND LINK_LIBRARIES gpuxgboost)
-endif()
+# This creates its own shared library `xgboost4j'.
+if (JVM_BINDINGS)
+  add_subdirectory(${xgboost_SOURCE_DIR}/jvm-packages)
+endif (JVM_BINDINGS)
+#-- End shared library
 
+#-- CLI for xgboost
+add_executable(runxgboost ${xgboost_SOURCE_DIR}/src/cli_main.cc)
+target_link_libraries(runxgboost PRIVATE objxgboost)
+if (USE_NVTX)
+  enable_nvtx(runxgboost)
+endif (USE_NVTX)
 
-# flags and sources for R-package
-if(R_LIB)
-  file(GLOB_RECURSE R_SOURCES
-    R-package/src/*.h
-    R-package/src/*.c
-    R-package/src/*.cc
-  )
-  list(APPEND SOURCES ${R_SOURCES})
-endif()
+target_include_directories(runxgboost
+  PRIVATE
+  ${xgboost_SOURCE_DIR}/include
+  ${xgboost_SOURCE_DIR}/dmlc-core/include
+  ${xgboost_SOURCE_DIR}/rabit/include)
+set_target_properties(
+  runxgboost PROPERTIES
+  OUTPUT_NAME xgboost
+  CXX_STANDARD 14
+  CXX_STANDARD_REQUIRED ON)
+#-- End CLI for xgboost
 
-add_library(objxgboost OBJECT ${SOURCES})
+set_output_directory(runxgboost ${xgboost_SOURCE_DIR})
+set_output_directory(xgboost ${xgboost_SOURCE_DIR}/lib)
+# Ensure these two targets do not build simultaneously, as they produce outputs with conflicting names
+add_dependencies(xgboost runxgboost)
 
-
-# building shared library for R package
-if(R_LIB)
-  find_package(LibR REQUIRED)
-
-  list(APPEND LINK_LIBRARIES "${LIBR_CORE_LIBRARY}")
-  MESSAGE(STATUS "LIBR_CORE_LIBRARY " ${LIBR_CORE_LIBRARY})
-
-  include_directories(
-    "${LIBR_INCLUDE_DIRS}"
-    "${PROJECT_SOURCE_DIR}"
-  )
-
-  # Shared library target for the R package
-  add_library(xgboost SHARED $<TARGET_OBJECTS:objxgboost>)
-  target_link_libraries(xgboost ${LINK_LIBRARIES})
-  # R uses no lib prefix in shared library names of its packages
+#-- Installing XGBoost
+if (R_LIB)
+  include(cmake/RPackageInstallTargetSetup.cmake)
   set_target_properties(xgboost PROPERTIES PREFIX "")
-  if(APPLE)
+  if (APPLE)
     set_target_properties(xgboost PROPERTIES SUFFIX ".so")
-  endif()
-
-  setup_rpackage_install_target(xgboost ${CMAKE_CURRENT_BINARY_DIR})
-  # use a dummy location for any other remaining installs
+  endif (APPLE)
+  setup_rpackage_install_target(xgboost "${CMAKE_CURRENT_BINARY_DIR}/R-package-install")
   set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_BINARY_DIR}/dummy_inst")
+endif (R_LIB)
+if (MINGW)
+  set_target_properties(xgboost PROPERTIES PREFIX "")
+endif (MINGW)
 
-# main targets: shared library & exe
-else()
-  # Executable
-  add_executable(runxgboost $<TARGET_OBJECTS:objxgboost> src/cli_main.cc)
-  set_target_properties(runxgboost PROPERTIES
-    OUTPUT_NAME xgboost
-  )
-  set_output_directory(runxgboost ${PROJECT_SOURCE_DIR})
-  target_link_libraries(runxgboost ${LINK_LIBRARIES})
+if (BUILD_C_DOC)
+  include(cmake/Doc.cmake)
+  run_doxygen()
+endif (BUILD_C_DOC)
 
-  # Shared library
-  add_library(xgboost SHARED $<TARGET_OBJECTS:objxgboost>)
-  target_link_libraries(xgboost ${LINK_LIBRARIES})
-  set_output_directory(xgboost ${PROJECT_SOURCE_DIR}/lib)
-  if(MINGW)
-    # remove the 'lib' prefix to conform to windows convention for shared library names
-    set_target_properties(xgboost PROPERTIES PREFIX "")
-  endif()
+include(GNUInstallDirs)
+# Install all headers.  Please note that currently the C++ headers does not form an "API".
+install(DIRECTORY ${xgboost_SOURCE_DIR}/include/xgboost
+  DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
 
-  #Ensure these two targets do not build simultaneously, as they produce outputs with conflicting names
-  add_dependencies(xgboost runxgboost)
-endif()
+# Install libraries. If `xgboost` is a static lib, specify `objxgboost` also, to avoid the
+# following error:
+#
+#  > install(EXPORT ...) includes target "xgboost" which requires target "objxgboost" that is not
+#  > in any export set.
+#
+# https://github.com/dmlc/xgboost/issues/6085
+if (BUILD_STATIC_LIB)
+  set(INSTALL_TARGETS xgboost runxgboost objxgboost dmlc)
+else (BUILD_STATIC_LIB)
+  set(INSTALL_TARGETS xgboost runxgboost)
+endif (BUILD_STATIC_LIB)
 
+install(TARGETS ${INSTALL_TARGETS}
+  EXPORT XGBoostTargets
+  ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
+  LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
+  RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
+  INCLUDES DESTINATION ${LIBLEGACY_INCLUDE_DIRS})
+install(EXPORT XGBoostTargets
+  FILE XGBoostTargets.cmake
+  NAMESPACE xgboost::
+  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/xgboost)
 
-# JVM
-if(JVM_BINDINGS)
-    find_package(JNI QUIET REQUIRED)
+include(CMakePackageConfigHelpers)
+configure_package_config_file(
+  ${CMAKE_CURRENT_LIST_DIR}/cmake/xgboost-config.cmake.in
+  ${CMAKE_CURRENT_BINARY_DIR}/cmake/xgboost-config.cmake
+  INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/xgboost)
+write_basic_package_version_file(
+  ${CMAKE_BINARY_DIR}/cmake/xgboost-config-version.cmake
+  VERSION ${XGBOOST_VERSION}
+  COMPATIBILITY AnyNewerVersion)
+install(
+  FILES
+  ${CMAKE_BINARY_DIR}/cmake/xgboost-config.cmake
+  ${CMAKE_BINARY_DIR}/cmake/xgboost-config-version.cmake
+  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/xgboost)
 
-    include_directories(${JNI_INCLUDE_DIRS} jvm-packages/xgboost4j/src/native)
-
-    add_library(xgboost4j SHARED
-        $<TARGET_OBJECTS:objxgboost>
-        jvm-packages/xgboost4j/src/native/xgboost4j.cpp)
-    set_output_directory(xgboost4j ${PROJECT_SOURCE_DIR}/lib)
-    target_link_libraries(xgboost4j
-        ${LINK_LIBRARIES}
-        ${JAVA_JVM_LIBRARY})
-endif()
-
-
-# Test
-if(GOOGLE_TEST)
+#-- Test
+if (GOOGLE_TEST)
   enable_testing()
-  find_package(GTest REQUIRED)
+  # Unittests.
+  add_subdirectory(${xgboost_SOURCE_DIR}/tests/cpp)
+  add_test(
+    NAME TestXGBoostLib
+    COMMAND testxgboost
+    WORKING_DIRECTORY ${xgboost_BINARY_DIR})
 
-  auto_source_group("${TEST_SOURCES}")
-  include_directories(${GTEST_INCLUDE_DIRS})
+  # CLI tests
+  configure_file(
+    ${xgboost_SOURCE_DIR}/tests/cli/machine.conf.in
+    ${xgboost_BINARY_DIR}/tests/cli/machine.conf
+    @ONLY)
+  add_test(
+    NAME TestXGBoostCLI
+    COMMAND runxgboost ${xgboost_BINARY_DIR}/tests/cli/machine.conf
+    WORKING_DIRECTORY ${xgboost_BINARY_DIR})
+  set_tests_properties(TestXGBoostCLI
+    PROPERTIES
+    PASS_REGULAR_EXPRESSION ".*test-rmse:0.087.*")
+endif (GOOGLE_TEST)
 
-  if(USE_CUDA)
-    file(GLOB_RECURSE CUDA_TEST_SOURCES "tests/cpp/*.cu")
-    cuda_compile(CUDA_TEST_OBJS ${CUDA_TEST_SOURCES})
-  else()
-    set(CUDA_TEST_OBJS "")
-  endif()
+# For MSVC: Call msvc_use_static_runtime() once again to completely
+# replace /MD with /MT. See https://github.com/dmlc/xgboost/issues/4462
+# for issues caused by mixing of /MD and /MT flags
+msvc_use_static_runtime()
 
-  add_executable(testxgboost ${TEST_SOURCES} ${CUDA_TEST_OBJS} $<TARGET_OBJECTS:objxgboost>)
-  set_output_directory(testxgboost ${PROJECT_SOURCE_DIR})
-  target_link_libraries(testxgboost ${GTEST_LIBRARIES} ${LINK_LIBRARIES})
+# Add xgboost.pc
+if (ADD_PKGCONFIG)
+  configure_file(${xgboost_SOURCE_DIR}/cmake/xgboost.pc.in ${xgboost_BINARY_DIR}/xgboost.pc @ONLY)
 
-  add_test(TestXGBoost testxgboost)
-endif()
-
-
-# Group sources
-auto_source_group("${SOURCES}")
+  install(
+    FILES ${xgboost_BINARY_DIR}/xgboost.pc
+    DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
+endif (ADD_PKGCONFIG)

CONTRIBUTORS.md

@@ -2,34 +2,44 @@ Contributors of DMLC/XGBoost
 ============================
 XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users.
 
+Project Management Committee(PMC) 
+----------
+The Project Management Committee(PMC) consists group of active committers that moderate the discussion, manage the project release, and proposes new committer/PMC members. 
+
+* [Tianqi Chen](https://github.com/tqchen), University of Washington
+  - Tianqi is a Ph.D. student working on large-scale machine learning. He is the creator of the project.
+* [Michael Benesty](https://github.com/pommedeterresautee)
+  - Michael is a lawyer and data scientist in France. He is the creator of XGBoost interactive analysis module in R.
+* [Yuan Tang](https://github.com/terrytangyuan), Ant Group
+  - Yuan is a software engineer in Ant Group. He contributed mostly in R and Python packages.
+* [Nan Zhu](https://github.com/CodingCat), Uber
+  - Nan is a software engineer in Uber. He contributed mostly in JVM packages.
+* [Jiaming Yuan](https://github.com/trivialfis)
+  - Jiaming contributed to the GPU algorithms. He has also introduced new abstractions to improve the quality of the C++ codebase.
+* [Hyunsu Cho](http://hyunsu-cho.io/), NVIDIA
+  - Hyunsu is the maintainer of the XGBoost Python package. He also manages the Jenkins continuous integration system (https://xgboost-ci.net/). He is the initial author of the CPU 'hist' updater.
+* [Rory Mitchell](https://github.com/RAMitchell), University of Waikato
+  - Rory is a Ph.D. student at University of Waikato. He is the original creator of the GPU training algorithms. He improved the CMake build system and continuous integration. 
+* [Hongliang Liu](https://github.com/phunterlau)
+
+
 Committers
 ----------
 Committers are people who have made substantial contribution to the project and granted write access to the project.
-* [Tianqi Chen](https://github.com/tqchen), University of Washington
-  - Tianqi is a Ph.D. student working on large-scale machine learning. He is the creator of the project.
+
 * [Tong He](https://github.com/hetong007), Amazon AI
   - Tong is an applied scientist in Amazon AI. He is the maintainer of XGBoost R package.
 * [Vadim Khotilovich](https://github.com/khotilov)
   - Vadim contributes many improvements in R and core packages.
 * [Bing Xu](https://github.com/antinucleon)
   - Bing is the original creator of XGBoost Python package and currently the maintainer of [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl).
-* [Michael Benesty](https://github.com/pommedeterresautee)
-  - Michael is a lawyer and data scientist in France. He is the creator of XGBoost interactive analysis module in R.
-* [Yuan Tang](https://github.com/terrytangyuan), Ant Financial
-  - Yuan is a software engineer in Ant Financial. He contributed mostly in R and Python packages.
-* [Nan Zhu](https://github.com/CodingCat), Uber
-  - Nan is a software engineer in Uber. He contributed mostly in JVM packages.
 * [Sergei Lebedev](https://github.com/superbobry), Criteo
   - Sergei is a software engineer in Criteo. He contributed mostly in JVM packages.
-* [Hongliang Liu](https://github.com/phunterlau)
 * [Scott Lundberg](http://scottlundberg.com/), University of Washington
   - Scott is a Ph.D. student at University of Washington. He is the creator of SHAP, a unified approach to explain the output of machine learning models such as decision tree ensembles. He also helps maintain the XGBoost Julia package.
-* [Rory Mitchell](https://github.com/RAMitchell), University of Waikato
-  - Rory is a Ph.D. student at University of Waikato. He is the original creator of the GPU training algorithms. He improved the CMake build system and continuous integration. 
-* [Hyunsu Cho](http://hyunsu-cho.io/), Amazon AI
-  - Hyunsu is an applied scientist in Amazon AI. He is the maintainer of the XGBoost Python package. He also manages the Jenkins continuous integration system (https://xgboost-ci.net/). He is the initial author of the CPU 'hist' updater. 
-* [Jiaming](https://github.com/trivialfis)
-  - Jiaming contributed to the GPU algorithms. He has also introduced new abstractions to improve the quality of the C++ codebase.
+* [Egor Smirnov](https://github.com/SmirnovEgorRu), Intel
+  - Egor has led a major effort to improve the performance of XGBoost on multi-core CPUs.
+
 
 Become a Committer
 ------------------
@@ -85,4 +95,12 @@ List of Contributors
 * [Andrew Thia](https://github.com/BlueTea88)
   - Andrew Thia implemented feature interaction constraints
 * [Wei Tian](https://github.com/weitian)
-* [Chen Qin] (https://github.com/chenqin)
+* [Chen Qin](https://github.com/chenqin)
+* [Sam Wilkinson](https://samwilkinson.io)
+* [Matthew Jones](https://github.com/mt-jones)
+* [Jiaxiang Li](https://github.com/JiaxiangBU)
+* [Bryan Woods](https://github.com/bryan-woods)
+  - Bryan added support for cross-validation for the ranking objective
+* [Haoda Fu](https://github.com/fuhaoda)
+* [Evan Kepner](https://github.com/EvanKepner)
+  - Evan Kepner added support for os.PathLike file paths in Python

Jenkinsfile

@@ -3,106 +3,389 @@
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
+// Command to run command inside a docker container
+dockerRun = 'tests/ci_build/ci_build.sh'
+
+// Which CUDA version to use when building reference distribution wheel
+ref_cuda_ver = '10.0'
+
 import groovy.transform.Field
 
-/* Unrestricted tasks: tasks that do NOT generate artifacts */
-
-// Command to run command inside a docker container
-def dockerRun = 'tests/ci_build/ci_build.sh'
-// Utility functions
 @Field
-def utils
-
-def buildMatrix = [
-    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "9.2", "multiGpu": true],
-    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "9.2" ],
-    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
-    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": false, "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
-]
+def commit_id   // necessary to pass a variable from one stage to another
 
 pipeline {
-    // Each stage specify its own agent
-    agent none
+  // Each stage specify its own agent
+  agent none
 
-    // Setup common job properties
-    options {
-        ansiColor('xterm')
-        timestamps()
-        timeout(time: 120, unit: 'MINUTES')
-        buildDiscarder(logRotator(numToKeepStr: '10'))
-    }
+  environment {
+    DOCKER_CACHE_ECR_ID = '492475357299'
+    DOCKER_CACHE_ECR_REGION = 'us-west-2'
+  }
 
-    // Build stages
-    stages {
-        stage('Jenkins: Get sources') {
-            agent {
-                label 'unrestricted'
-            }
-            steps {
-                script {
-                    utils = load('tests/ci_build/jenkins_tools.Groovy')
-                    utils.checkoutSrcs()
-                }
-                stash name: 'srcs', excludes: '.git/'
-                milestone label: 'Sources ready', ordinal: 1
-            }
-        }
-        stage('Jenkins: Build & Test') {
-            steps {
-                script {
-                    parallel (buildMatrix.findAll{it['enabled']}.collectEntries{ c ->
-                        def buildName = utils.getBuildName(c)
-                        utils.buildFactory(buildName, c, false, this.&buildPlatformCmake)
-                    })
-                }
-            }
+  // Setup common job properties
+  options {
+    ansiColor('xterm')
+    timestamps()
+    timeout(time: 240, unit: 'MINUTES')
+    buildDiscarder(logRotator(numToKeepStr: '10'))
+    preserveStashes()
+  }
+
+  // Build stages
+  stages {
+    stage('Jenkins Linux: Initialize') {
+      agent { label 'job_initializer' }
+      steps {
+        script {
+          def buildNumber = env.BUILD_NUMBER as int
+          if (buildNumber > 1) milestone(buildNumber - 1)
+          milestone(buildNumber)
+
+          checkoutSrcs()
+          commit_id = "${GIT_COMMIT}"
         }
+        sh 'python3 tests/jenkins_get_approval.py'
+        stash name: 'srcs'
+      }
     }
+    stage('Jenkins Linux: Build') {
+      agent none
+      steps {
+        script {
+          parallel ([
+            'clang-tidy': { ClangTidy() },
+            'build-cpu': { BuildCPU() },
+            'build-cpu-rabit-mock': { BuildCPUMock() },
+            // Build reference, distribution-ready Python wheel with CUDA 10.0
+            // using CentOS 6 image
+            'build-gpu-cuda10.0': { BuildCUDA(cuda_version: '10.0') },
+            // The build-gpu-* builds below use Ubuntu image
+            'build-gpu-cuda10.1': { BuildCUDA(cuda_version: '10.1') },
+            'build-gpu-cuda10.2': { BuildCUDA(cuda_version: '10.2', build_rmm: true) },
+            'build-gpu-cuda11.0': { BuildCUDA(cuda_version: '11.0') },
+            'build-jvm-packages-gpu-cuda10.0': { BuildJVMPackagesWithCUDA(spark_version: '3.0.0', cuda_version: '10.0') },
+            'build-jvm-packages': { BuildJVMPackages(spark_version: '3.0.0') },
+            'build-jvm-doc': { BuildJVMDoc() }
+          ])
+        }
+      }
+    }
+    stage('Jenkins Linux: Test') {
+      agent none
+      steps {
+        script {
+          parallel ([
+            'test-python-cpu': { TestPythonCPU() },
+            // artifact_cuda_version doesn't apply to RMM tests; RMM tests will always match CUDA version between artifact and host env
+            'test-python-gpu-cuda10.2': { TestPythonGPU(artifact_cuda_version: '10.0', host_cuda_version: '10.2', test_rmm: true) },
+            'test-python-gpu-cuda11.0-cross': { TestPythonGPU(artifact_cuda_version: '10.0', host_cuda_version: '11.0') },
+            'test-python-gpu-cuda11.0': { TestPythonGPU(artifact_cuda_version: '11.0', host_cuda_version: '11.0') },
+            'test-python-mgpu-cuda10.2': { TestPythonGPU(artifact_cuda_version: '10.0', host_cuda_version: '10.2', multi_gpu: true, test_rmm: true) },
+            'test-cpp-gpu-cuda10.2': { TestCppGPU(artifact_cuda_version: '10.2', host_cuda_version: '10.2', test_rmm: true) },
+            'test-cpp-gpu-cuda11.0': { TestCppGPU(artifact_cuda_version: '11.0', host_cuda_version: '11.0') },
+            'test-jvm-jdk8': { CrossTestJVMwithJDK(jdk_version: '8', spark_version: '3.0.0') },
+            'test-jvm-jdk11': { CrossTestJVMwithJDK(jdk_version: '11') },
+            'test-jvm-jdk12': { CrossTestJVMwithJDK(jdk_version: '12') }
+          ])
+        }
+      }
+    }
+    stage('Jenkins Linux: Deploy') {
+      agent none
+      steps {
+        script {
+          parallel ([
+            'deploy-jvm-packages': { DeployJVMPackages(spark_version: '3.0.0') }
+          ])
+        }
+      }
+    }
+  }
 }
 
-/**
- * Build platform and test it via cmake.
- */
-def buildPlatformCmake(buildName, conf, nodeReq, dockerTarget) {
-    def opts = utils.cmakeOptions(conf)
-    // Destination dir for artifacts
-    def distDir = "dist/${buildName}"
-    def dockerArgs = ""
-    if (conf["withGpu"]) {
-        dockerArgs = "--build-arg CUDA_VERSION=" + conf["cudaVersion"]
-    }
-    def test_suite = conf["withGpu"] ? (conf["multiGpu"] ? "mgpu" : "gpu") : "cpu"
-    // Build node - this is returned result
-    retry(3) {
-        node(nodeReq) {
-            unstash name: 'srcs'
-            echo """
-            |===== XGBoost CMake build =====
-            |  dockerTarget: ${dockerTarget}
-            |  cmakeOpts   : ${opts}
-            |=========================
-            """.stripMargin('|')
-            // Invoke command inside docker
-            sh """
-            ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/build_via_cmake.sh ${opts}
-            ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/test_${test_suite}.sh
-            """
-            if (!conf["multiGpu"]) {
-                sh """
-                ${dockerRun} ${dockerTarget} ${dockerArgs} bash -c "cd python-package; rm -f dist/*; python setup.py bdist_wheel --universal"
-                rm -rf "${distDir}"; mkdir -p "${distDir}/py"
-                cp xgboost "${distDir}"
-                cp -r python-package/dist "${distDir}/py"
-                # Test the wheel for compatibility on a barebones CPU container
-                ${dockerRun} release ${dockerArgs} bash -c " \
-                    pip install --user python-package/dist/xgboost-*-none-any.whl && \
-                    python -m nose -v tests/python"
-                # Test the wheel for compatibility on CUDA 10.0 container
-                ${dockerRun} gpu --build-arg CUDA_VERSION=10.0 bash -c " \
-                    pip install --user python-package/dist/xgboost-*-none-any.whl && \
-                    python -m nose -v --eval-attr='(not slow) and (not mgpu)' tests/python-gpu"
-                """
-            }
-        }
+// check out source code from git
+def checkoutSrcs() {
+  retry(5) {
+    try {
+      timeout(time: 2, unit: 'MINUTES') {
+        checkout scm
+        sh 'git submodule update --init'
+      }
+    } catch (exc) {
+      deleteDir()
+      error "Failed to fetch source codes"
     }
+  }
+}
+
+def GetCUDABuildContainerType(cuda_version) {
+  return (cuda_version == ref_cuda_ver) ? 'gpu_build_centos6' : 'gpu_build'
+}
+
+def ClangTidy() {
+  node('linux && cpu_build') {
+    unstash name: 'srcs'
+    echo "Running clang-tidy job..."
+    def container_type = "clang_tidy"
+    def docker_binary = "docker"
+    def dockerArgs = "--build-arg CUDA_VERSION_ARG=10.1"
+    sh """
+    ${dockerRun} ${container_type} ${docker_binary} ${dockerArgs} python3 tests/ci_build/tidy.py
+    """
+    deleteDir()
+  }
+}
+
+def BuildCPU() {
+  node('linux && cpu') {
+    unstash name: 'srcs'
+    echo "Build CPU"
+    def container_type = "cpu"
+    def docker_binary = "docker"
+    sh """
+    ${dockerRun} ${container_type} ${docker_binary} rm -fv dmlc-core/include/dmlc/build_config_default.h
+      # This step is not necessary, but here we include it, to ensure that DMLC_CORE_USE_CMAKE flag is correctly propagated
+      # We want to make sure that we use the configured header build/dmlc/build_config.h instead of include/dmlc/build_config_default.h.
+      # See discussion at https://github.com/dmlc/xgboost/issues/5510
+    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_via_cmake.sh -DPLUGIN_LZ4=ON -DPLUGIN_DENSE_PARSER=ON
+    ${dockerRun} ${container_type} ${docker_binary} bash -c "cd build && ctest --extra-verbose"
+    """
+    // Sanitizer test
+    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='-e ASAN_SYMBOLIZER_PATH=/usr/bin/llvm-symbolizer -e ASAN_OPTIONS=symbolize=1 -e UBSAN_OPTIONS=print_stacktrace=1:log_path=ubsan_error.log --cap-add SYS_PTRACE'"
+    sh """
+    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_via_cmake.sh -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak;undefined" \
+      -DCMAKE_BUILD_TYPE=Debug -DSANITIZER_PATH=/usr/lib/x86_64-linux-gnu/
+    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} bash -c "cd build && ctest --exclude-regex AllTestsInDMLCUnitTests --extra-verbose"
+    """
+
+    stash name: 'xgboost_cli', includes: 'xgboost'
+    deleteDir()
+  }
+}
+
+def BuildCPUMock() {
+  node('linux && cpu') {
+    unstash name: 'srcs'
+    echo "Build CPU with rabit mock"
+    def container_type = "cpu"
+    def docker_binary = "docker"
+    sh """
+    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_mock_cmake.sh
+    """
+    echo 'Stashing rabit C++ test executable (xgboost)...'
+    stash name: 'xgboost_rabit_tests', includes: 'xgboost'
+    deleteDir()
+  }
+}
+
+def BuildCUDA(args) {
+  node('linux && cpu_build') {
+    unstash name: 'srcs'
+    echo "Build with CUDA ${args.cuda_version}"
+    def container_type = GetCUDABuildContainerType(args.cuda_version)
+    def docker_binary = "docker"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.cuda_version}"
+    def arch_flag = ""
+    if (env.BRANCH_NAME != 'master' && !(env.BRANCH_NAME.startsWith('release'))) {
+      arch_flag = "-DGPU_COMPUTE_VER=75"
+    }
+    def wheel_tag = "manylinux2010_x86_64"
+    sh """
+    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_via_cmake.sh -DUSE_CUDA=ON -DUSE_NCCL=ON -DOPEN_MP:BOOL=ON -DHIDE_CXX_SYMBOLS=ON ${arch_flag}
+    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} bash -c "cd python-package && rm -rf dist/* && python setup.py bdist_wheel --universal"
+    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} python tests/ci_build/rename_whl.py python-package/dist/*.whl ${commit_id} ${wheel_tag}
+    """
+    if (args.cuda_version == ref_cuda_ver) {
+      sh """
+      ${dockerRun} auditwheel_x86_64 ${docker_binary} auditwheel repair --plat ${wheel_tag} python-package/dist/*.whl
+      mv -v wheelhouse/*.whl python-package/dist/
+      # Make sure that libgomp.so is vendored in the wheel
+      ${dockerRun} auditwheel_x86_64 ${docker_binary} bash -c "unzip -l python-package/dist/*.whl | grep libgomp  || exit -1"
+      """
+    }
+    echo 'Stashing Python wheel...'
+    stash name: "xgboost_whl_cuda${args.cuda_version}", includes: 'python-package/dist/*.whl'
+    if (args.cuda_version == ref_cuda_ver && (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release'))) {
+      echo 'Uploading Python wheel...'
+      path = ("${BRANCH_NAME}" == 'master') ? '' : "${BRANCH_NAME}/"
+      s3Upload bucket: 'xgboost-nightly-builds', path: path, acl: 'PublicRead', workingDir: 'python-package/dist', includePathPattern:'**/*.whl'
+    }
+    echo 'Stashing C++ test executable (testxgboost)...'
+    stash name: "xgboost_cpp_tests_cuda${args.cuda_version}", includes: 'build/testxgboost'
+    if (args.build_rmm) {
+      echo "Build with CUDA ${args.cuda_version} and RMM"
+      container_type = "rmm"
+      docker_binary = "docker"
+      docker_args = "--build-arg CUDA_VERSION_ARG=${args.cuda_version}"
+      sh """
+      rm -rf build/
+      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_via_cmake.sh --conda-env=gpu_test -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON ${arch_flag}
+      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} bash -c "cd python-package && rm -rf dist/* && python setup.py bdist_wheel --universal"
+      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} python tests/ci_build/rename_whl.py python-package/dist/*.whl ${commit_id} manylinux2010_x86_64
+      """
+      echo 'Stashing Python wheel...'
+      stash name: "xgboost_whl_rmm_cuda${args.cuda_version}", includes: 'python-package/dist/*.whl'
+      echo 'Stashing C++ test executable (testxgboost)...'
+      stash name: "xgboost_cpp_tests_rmm_cuda${args.cuda_version}", includes: 'build/testxgboost'
+    }
+    deleteDir()
+  }
+}
+
+def BuildJVMPackagesWithCUDA(args) {
+  node('linux && mgpu') {
+    unstash name: 'srcs'
+    echo "Build XGBoost4J-Spark with Spark ${args.spark_version}, CUDA ${args.cuda_version}"
+    def container_type = "jvm_gpu_build"
+    def docker_binary = "nvidia-docker"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.cuda_version}"
+    def arch_flag = ""
+    if (env.BRANCH_NAME != 'master' && !(env.BRANCH_NAME.startsWith('release'))) {
+      arch_flag = "-DGPU_COMPUTE_VER=75"
+    }
+    // Use only 4 CPU cores
+    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='--cpuset-cpus 0-3'"
+    sh """
+    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_jvm_packages.sh ${args.spark_version} -Duse.cuda=ON $arch_flag
+    """
+    echo "Stashing XGBoost4J JAR with CUDA ${args.cuda_version} ..."
+    stash name: 'xgboost4j_jar_gpu', includes: "jvm-packages/xgboost4j-gpu/target/*.jar,jvm-packages/xgboost4j-spark-gpu/target/*.jar"
+    deleteDir()
+  }
+}
+
+def BuildJVMPackages(args) {
+  node('linux && cpu') {
+    unstash name: 'srcs'
+    echo "Build XGBoost4J-Spark with Spark ${args.spark_version}"
+    def container_type = "jvm"
+    def docker_binary = "docker"
+    // Use only 4 CPU cores
+    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='--cpuset-cpus 0-3'"
+    sh """
+    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_jvm_packages.sh ${args.spark_version}
+    """
+    echo 'Stashing XGBoost4J JAR...'
+    stash name: 'xgboost4j_jar', includes: "jvm-packages/xgboost4j/target/*.jar,jvm-packages/xgboost4j-spark/target/*.jar,jvm-packages/xgboost4j-example/target/*.jar"
+    deleteDir()
+  }
+}
+
+def BuildJVMDoc() {
+  node('linux && cpu') {
+    unstash name: 'srcs'
+    echo "Building JVM doc..."
+    def container_type = "jvm"
+    def docker_binary = "docker"
+    sh """
+    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/build_jvm_doc.sh ${BRANCH_NAME}
+    """
+    if (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release')) {
+      echo 'Uploading doc...'
+      s3Upload file: "jvm-packages/${BRANCH_NAME}.tar.bz2", bucket: 'xgboost-docs', acl: 'PublicRead', path: "${BRANCH_NAME}.tar.bz2"
+    }
+    deleteDir()
+  }
+}
+
+def TestPythonCPU() {
+  node('linux && cpu') {
+    unstash name: "xgboost_whl_cuda${ref_cuda_ver}"
+    unstash name: 'srcs'
+    unstash name: 'xgboost_cli'
+    echo "Test Python CPU"
+    def container_type = "cpu"
+    def docker_binary = "docker"
+    sh """
+    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/test_python.sh cpu
+    """
+    deleteDir()
+  }
+}
+
+def TestPythonGPU(args) {
+  def nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
+  def artifact_cuda_version = (args.artifact_cuda_version) ?: ref_cuda_ver
+  node(nodeReq) {
+    unstash name: "xgboost_whl_cuda${artifact_cuda_version}"
+    unstash name: "xgboost_cpp_tests_cuda${artifact_cuda_version}"
+    unstash name: 'srcs'
+    echo "Test Python GPU: CUDA ${args.host_cuda_version}"
+    def container_type = "gpu"
+    def docker_binary = "nvidia-docker"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
+    def mgpu_indicator = (args.multi_gpu) ? 'mgpu' : 'gpu'
+    // Allocate extra space in /dev/shm to enable NCCL
+    def docker_extra_params = (args.multi_gpu) ? "CI_DOCKER_EXTRA_PARAMS_INIT='--shm-size=4g'" : ''
+    sh "${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_python.sh ${mgpu_indicator}"
+    if (args.test_rmm) {
+      sh "rm -rfv build/ python-package/dist/"
+      unstash name: "xgboost_whl_rmm_cuda${args.host_cuda_version}"
+      unstash name: "xgboost_cpp_tests_rmm_cuda${args.host_cuda_version}"
+      sh "${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_python.sh ${mgpu_indicator} --use-rmm-pool"
+    }
+    deleteDir()
+  }
+}
+
+def TestCppGPU(args) {
+  def nodeReq = 'linux && mgpu'
+  def artifact_cuda_version = (args.artifact_cuda_version) ?: ref_cuda_ver
+  node(nodeReq) {
+    unstash name: "xgboost_cpp_tests_cuda${artifact_cuda_version}"
+    unstash name: 'srcs'
+    echo "Test C++, CUDA ${args.host_cuda_version}"
+    def container_type = "gpu"
+    def docker_binary = "nvidia-docker"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
+    sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost"
+    if (args.test_rmm) {
+      sh "rm -rfv build/"
+      unstash name: "xgboost_cpp_tests_rmm_cuda${args.host_cuda_version}"
+      echo "Test C++, CUDA ${args.host_cuda_version} with RMM"
+      container_type = "rmm"
+      docker_binary = "nvidia-docker"
+      docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
+      sh """
+      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} bash -c "source activate gpu_test && build/testxgboost --use-rmm-pool --gtest_filter=-*DeathTest.*"
+      """
+    }
+    deleteDir()
+  }
+}
+
+def CrossTestJVMwithJDK(args) {
+  node('linux && cpu') {
+    unstash name: 'xgboost4j_jar'
+    unstash name: 'srcs'
+    if (args.spark_version != null) {
+      echo "Test XGBoost4J on a machine with JDK ${args.jdk_version}, Spark ${args.spark_version}"
+    } else {
+      echo "Test XGBoost4J on a machine with JDK ${args.jdk_version}"
+    }
+    def container_type = "jvm_cross"
+    def docker_binary = "docker"
+    def spark_arg = (args.spark_version != null) ? "--build-arg SPARK_VERSION=${args.spark_version}" : ""
+    def docker_args = "--build-arg JDK_VERSION=${args.jdk_version} ${spark_arg}"
+    // Run integration tests only when spark_version is given
+    def docker_extra_params = (args.spark_version != null) ? "CI_DOCKER_EXTRA_PARAMS_INIT='-e RUN_INTEGRATION_TEST=1'" : ""
+    sh """
+    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_jvm_cross.sh
+    """
+    deleteDir()
+  }
+}
+
+def DeployJVMPackages(args) {
+  node('linux && cpu') {
+    unstash name: 'srcs'
+    if (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release')) {
+      echo 'Deploying to xgboost-maven-repo S3 repo...'
+      sh """
+      ${dockerRun} jvm_gpu_build docker --build-arg CUDA_VERSION_ARG=10.0 tests/ci_build/deploy_jvm_packages.sh ${args.spark_version}
+      """
+    }
+    deleteDir()
+  }
 }

Jenkinsfile-restricted

@@ -1,123 +0,0 @@
-#!/usr/bin/groovy
-// -*- mode: groovy -*-
-// Jenkins pipeline
-// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
-
-import groovy.transform.Field
-
-/* Restricted tasks: tasks generating artifacts, such as binary wheels and
-                     documentation */
-
-// Command to run command inside a docker container
-def dockerRun = 'tests/ci_build/ci_build.sh'
-// Utility functions
-@Field
-def utils
-@Field
-def commit_id
-@Field
-def branch_name
-
-def buildMatrix = [
-    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "9.2" ],
-    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
-    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": false, "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
-]
-
-pipeline {
-    // Each stage specify its own agent
-    agent none
-
-    // Setup common job properties
-    options {
-        ansiColor('xterm')
-        timestamps()
-        timeout(time: 120, unit: 'MINUTES')
-        buildDiscarder(logRotator(numToKeepStr: '10'))
-    }
-
-    // Build stages
-    stages {
-        stage('Jenkins: Get sources') {
-            agent {
-                label 'restricted'
-            }
-            steps {
-                script {
-                    utils = load('tests/ci_build/jenkins_tools.Groovy')
-                    utils.checkoutSrcs()
-                    commit_id = "${GIT_COMMIT}"
-                    branch_name = "${GIT_LOCAL_BRANCH}"
-                }
-                stash name: 'srcs', excludes: '.git/'
-                milestone label: 'Sources ready', ordinal: 1
-            }
-        }
-        stage('Jenkins: Build doc') {
-            steps {
-                script {
-                    retry(3) {
-                        node('linux && cpu && restricted') {
-                            unstash name: 'srcs'
-                            echo 'Building doc...'
-                            dir ('jvm-packages') {
-                                sh "bash ./build_doc.sh ${commit_id}"
-                                archiveArtifacts artifacts: "${commit_id}.tar.bz2", allowEmptyArchive: true
-                                echo 'Deploying doc...'
-                                withAWS(credentials:'xgboost-doc-bucket') {
-                                    s3Upload file: "${commit_id}.tar.bz2", bucket: 'xgboost-docs', acl: 'PublicRead', path: "${branch_name}.tar.bz2"
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-        }
-
-        stage('Jenkins: Build artifacts') {
-            steps {
-                script {
-                    parallel (buildMatrix.findAll{it['enabled']}.collectEntries{ c ->
-                        def buildName = utils.getBuildName(c)
-                        utils.buildFactory(buildName, c, true, this.&buildPlatformCmake)
-                    })
-                }
-            }
-        }
-    }
-}
-
-/**
- * Build platform and test it via cmake.
- */
-def buildPlatformCmake(buildName, conf, nodeReq, dockerTarget) {
-    def opts = utils.cmakeOptions(conf)
-    // Destination dir for artifacts
-    def distDir = "dist/${buildName}"
-    def dockerArgs = ""
-    if(conf["withGpu"]){
-        dockerArgs = "--build-arg CUDA_VERSION=" + conf["cudaVersion"]
-    }
-    // Build node - this is returned result
-    retry(3) {
-        node(nodeReq) {
-            unstash name: 'srcs'
-            echo """
-            |===== XGBoost CMake build =====
-            |  dockerTarget: ${dockerTarget}
-            |  cmakeOpts   : ${opts}
-            |=========================
-            """.stripMargin('|')
-            // Invoke command inside docker
-            sh """
-            ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/build_via_cmake.sh ${opts}
-            ${dockerRun} ${dockerTarget} ${dockerArgs} bash -c "cd python-package; rm -f dist/*; python setup.py bdist_wheel --universal"
-            rm -rf "${distDir}"; mkdir -p "${distDir}/py"
-            cp xgboost "${distDir}"
-            cp -r lib "${distDir}"
-            cp -r python-package/dist "${distDir}/py"
-            """
-            archiveArtifacts artifacts: "${distDir}/**/*.*", allowEmptyArchive: true
-        }
-    }
-}

Jenkinsfile-win64

@@ -0,0 +1,143 @@
+#!/usr/bin/groovy
+// -*- mode: groovy -*-
+
+/* Jenkins pipeline for Windows AMD64 target */
+
+import groovy.transform.Field
+
+@Field
+def commit_id   // necessary to pass a variable from one stage to another
+
+pipeline {
+  agent none
+
+  // Setup common job properties
+  options {
+    timestamps()
+    timeout(time: 240, unit: 'MINUTES')
+    buildDiscarder(logRotator(numToKeepStr: '10'))
+    preserveStashes()
+  }
+
+  // Build stages
+  stages {
+    stage('Jenkins Win64: Initialize') {
+      agent { label 'job_initializer' }
+      steps {
+        script {
+          def buildNumber = env.BUILD_NUMBER as int
+          if (buildNumber > 1) milestone(buildNumber - 1)
+          milestone(buildNumber)
+          checkoutSrcs()
+          commit_id = "${GIT_COMMIT}"
+        }
+        sh 'python3 tests/jenkins_get_approval.py'
+        stash name: 'srcs'
+      }
+    }
+    stage('Jenkins Win64: Build') {
+      agent none
+      steps {
+        script {
+          parallel ([
+            'build-win64-cuda10.1': { BuildWin64() }
+          ])
+        }
+      }
+    }
+    stage('Jenkins Win64: Test') {
+      agent none
+      steps {
+        script {
+          parallel ([
+            'test-win64-cuda10.1': { TestWin64() },
+          ])
+        }
+      }
+    }
+  }
+}
+
+// check out source code from git
+def checkoutSrcs() {
+  retry(5) {
+    try {
+      timeout(time: 2, unit: 'MINUTES') {
+        checkout scm
+        sh 'git submodule update --init'
+      }
+    } catch (exc) {
+      deleteDir()
+      error "Failed to fetch source codes"
+    }
+  }
+}
+
+def BuildWin64() {
+  node('win64 && cuda10_unified') {
+    unstash name: 'srcs'
+    echo "Building XGBoost for Windows AMD64 target..."
+    bat "nvcc --version"
+    def arch_flag = ""
+    if (env.BRANCH_NAME != 'master' && !(env.BRANCH_NAME.startsWith('release'))) {
+      arch_flag = "-DGPU_COMPUTE_VER=75"
+    }
+    bat """
+    mkdir build
+    cd build
+    cmake .. -G"Visual Studio 15 2017 Win64" -DUSE_CUDA=ON -DCMAKE_VERBOSE_MAKEFILE=ON -DGOOGLE_TEST=ON -DUSE_DMLC_GTEST=ON ${arch_flag} -DCMAKE_UNITY_BUILD=ON
+    """
+    bat """
+    cd build
+    "C:\\Program Files (x86)\\Microsoft Visual Studio\\2017\\Community\\MSBuild\\15.0\\Bin\\MSBuild.exe" xgboost.sln /m /p:Configuration=Release /nodeReuse:false
+    """
+    bat """
+    cd python-package
+    conda activate && python setup.py bdist_wheel --universal && for /R %%i in (dist\\*.whl) DO python ../tests/ci_build/rename_whl.py "%%i" ${commit_id} win_amd64
+    """
+    echo "Insert vcomp140.dll (OpenMP runtime) into the wheel..."
+    bat """
+    cd python-package\\dist
+    COPY /B ..\\..\\tests\\ci_build\\insert_vcomp140.py
+    conda activate && python insert_vcomp140.py *.whl
+    """
+    echo 'Stashing Python wheel...'
+    stash name: 'xgboost_whl', includes: 'python-package/dist/*.whl'
+    if (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release')) {
+      echo 'Uploading Python wheel...'
+      path = ("${BRANCH_NAME}" == 'master') ? '' : "${BRANCH_NAME}/"
+      s3Upload bucket: 'xgboost-nightly-builds', path: path, acl: 'PublicRead', workingDir: 'python-package/dist', includePathPattern:'**/*.whl'
+    }
+    echo 'Stashing C++ test executable (testxgboost)...'
+    stash name: 'xgboost_cpp_tests', includes: 'build/testxgboost.exe'
+    stash name: 'xgboost_cli', includes: 'xgboost.exe'
+    deleteDir()
+  }
+}
+
+def TestWin64() {
+  node('win64 && cuda10_unified') {
+    unstash name: 'srcs'
+    unstash name: 'xgboost_whl'
+    unstash name: 'xgboost_cli'
+    unstash name: 'xgboost_cpp_tests'
+    echo "Test Win64"
+    bat "nvcc --version"
+    echo "Running C++ tests..."
+    bat "build\\testxgboost.exe"
+    echo "Installing Python dependencies..."
+    def env_name = 'win64_' + UUID.randomUUID().toString().replaceAll('-', '')
+    bat "conda env create -n ${env_name} --file=tests/ci_build/conda_env/win64_test.yml"
+    echo "Installing Python wheel..."
+    bat """
+    conda activate ${env_name} && for /R %%i in (python-package\\dist\\*.whl) DO python -m pip install "%%i"
+    """
+    echo "Running Python tests..."
+    bat "conda activate ${env_name} && python -m pytest -v -s -rxXs --fulltrace tests\\python"
+    bat """
+    conda activate ${env_name} && python -m pytest -v -s -rxXs --fulltrace -m "(not slow) and (not mgpu)" tests\\python-gpu
+    """
+    bat "conda env remove --name ${env_name}"
+    deleteDir()
+  }
+}

LICENSE

@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright (c) 2018 by Contributors
+   Copyright (c) 2019 by Contributors
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

Makefile

@@ -1,11 +1,3 @@
-ifndef config
-ifneq ("$(wildcard ./config.mk)","")
-	config = config.mk
-else
-	config = make/config.mk
-endif
-endif
-
 ifndef DMLC_CORE
 	DMLC_CORE = dmlc-core
 endif
@@ -30,23 +22,8 @@ ifndef MAKE_OK
 endif
 $(warning MAKE [$(MAKE)] - $(if $(MAKE_OK),checked OK,PROBLEM))
 
-ifeq ($(OS), Windows_NT)
-	UNAME="Windows"
-else
-	UNAME=$(shell uname)
-endif
-
-include $(config)
-ifeq ($(USE_OPENMP), 0)
-	export NO_OPENMP = 1
-endif
 include $(DMLC_CORE)/make/dmlc.mk
 
-# include the plugins
-ifdef XGB_PLUGINS
-include $(XGB_PLUGINS)
-endif
-
 # set compiler defaults for OSX versus *nix
 # let people override either
 OS := $(shell uname)
@@ -67,126 +44,40 @@ export CXX = g++
 endif
 endif
 
-export LDFLAGS= -pthread -lm $(ADD_LDFLAGS) $(DMLC_LDFLAGS) $(PLUGIN_LDFLAGS)
-export CFLAGS= -DDMLC_LOG_CUSTOMIZE=1 -std=c++11 -Wall -Wno-unknown-pragmas -Iinclude $(ADD_CFLAGS) $(PLUGIN_CFLAGS)
+export CFLAGS= -DDMLC_LOG_CUSTOMIZE=1 -std=c++14 -Wall -Wno-unknown-pragmas -Iinclude $(ADD_CFLAGS)
 CFLAGS += -I$(DMLC_CORE)/include -I$(RABIT)/include -I$(GTEST_PATH)/include
-#java include path
-export JAVAINCFLAGS = -I${JAVA_HOME}/include -I./java
 
 ifeq ($(TEST_COVER), 1)
 	CFLAGS += -g -O0 -fprofile-arcs -ftest-coverage
 else
 	CFLAGS += -O3 -funroll-loops
-ifeq ($(USE_SSE), 1)
-	CFLAGS += -msse2
-endif
 endif
 
 ifndef LINT_LANG
 	LINT_LANG= "all"
 endif
 
-ifeq ($(UNAME), Windows)
-	XGBOOST_DYLIB = lib/xgboost.dll
-	JAVAINCFLAGS += -I${JAVA_HOME}/include/win32
-else
-ifeq ($(UNAME), Darwin)
-	XGBOOST_DYLIB = lib/libxgboost.dylib
-	CFLAGS += -fPIC
-else
-	XGBOOST_DYLIB = lib/libxgboost.so
-	CFLAGS += -fPIC
-endif
-endif
-
-ifeq ($(UNAME), Linux)
-	LDFLAGS += -lrt
-	JAVAINCFLAGS += -I${JAVA_HOME}/include/linux
-endif
-
-ifeq ($(UNAME), Darwin)
-	JAVAINCFLAGS += -I${JAVA_HOME}/include/darwin
-endif
-
-OPENMP_FLAGS =
-ifeq ($(USE_OPENMP), 1)
-	OPENMP_FLAGS = -fopenmp
-else
-	OPENMP_FLAGS = -DDISABLE_OPENMP
-endif
-CFLAGS += $(OPENMP_FLAGS)
-
 # specify tensor path
-.PHONY: clean all lint clean_all doxygen rcpplint pypack Rpack Rbuild Rcheck java pylint
-
-all: lib/libxgboost.a $(XGBOOST_DYLIB) xgboost
-
-$(DMLC_CORE)/libdmlc.a: $(wildcard $(DMLC_CORE)/src/*.cc $(DMLC_CORE)/src/*/*.cc)
-	+ cd $(DMLC_CORE); "$(MAKE)" libdmlc.a config=$(ROOTDIR)/$(config); cd $(ROOTDIR)
-
-$(RABIT)/lib/$(LIB_RABIT): $(wildcard $(RABIT)/src/*.cc)
-	+ cd $(RABIT); "$(MAKE)" lib/$(LIB_RABIT) USE_SSE=$(USE_SSE); cd $(ROOTDIR)
-
-jvm: jvm-packages/lib/libxgboost4j.so
-
-SRC = $(wildcard src/*.cc src/*/*.cc)
-ALL_OBJ = $(patsubst src/%.cc, build/%.o, $(SRC)) $(PLUGIN_OBJS)
-AMALGA_OBJ = amalgamation/xgboost-all0.o
-LIB_DEP = $(DMLC_CORE)/libdmlc.a $(RABIT)/lib/$(LIB_RABIT)
-ALL_DEP = $(filter-out build/cli_main.o, $(ALL_OBJ)) $(LIB_DEP)
-CLI_OBJ = build/cli_main.o
-include tests/cpp/xgboost_test.mk
+.PHONY: clean all lint clean_all doxygen rcpplint pypack Rpack Rbuild Rcheck
 
 build/%.o: src/%.cc
 	@mkdir -p $(@D)
 	$(CXX) $(CFLAGS) -MM -MT build/$*.o $< >build/$*.d
 	$(CXX) -c $(CFLAGS) $< -o $@
 
-build_plugin/%.o: plugin/%.cc
-	@mkdir -p $(@D)
-	$(CXX) $(CFLAGS) -MM -MT build_plugin/$*.o $< >build_plugin/$*.d
-	$(CXX) -c $(CFLAGS) $< -o $@
-
 # The should be equivalent to $(ALL_OBJ)  except for build/cli_main.o
 amalgamation/xgboost-all0.o: amalgamation/xgboost-all0.cc
 	$(CXX) -c $(CFLAGS) $< -o $@
 
-# Equivalent to lib/libxgboost_all.so
-lib/libxgboost_all.so: $(AMALGA_OBJ) $(LIB_DEP)
-	@mkdir -p $(@D)
-	$(CXX) $(CFLAGS) -shared -o $@ $(filter %.o %.a, $^) $(LDFLAGS)
-
-lib/libxgboost.a: $(ALL_DEP)
-	@mkdir -p $(@D)
-	ar crv $@ $(filter %.o, $?)
-
-lib/xgboost.dll lib/libxgboost.so lib/libxgboost.dylib: $(ALL_DEP)
-	@mkdir -p $(@D)
-	$(CXX) $(CFLAGS) -shared -o $@ $(filter %.o %a,  $^) $(LDFLAGS)
-
-jvm-packages/lib/libxgboost4j.so: jvm-packages/xgboost4j/src/native/xgboost4j.cpp $(ALL_DEP)
-	@mkdir -p $(@D)
-	$(CXX) $(CFLAGS) $(JAVAINCFLAGS) -shared -o $@ $(filter %.cpp %.o %.a, $^) $(LDFLAGS)
-
-
-xgboost: $(CLI_OBJ) $(ALL_DEP)
-	$(CXX) $(CFLAGS) -o $@  $(filter %.o %.a, $^)  $(LDFLAGS)
-
 rcpplint:
-	python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
+	python3 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
 
 lint: rcpplint
-	python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} include src plugin python-package
-
-pylint:
-	flake8 --ignore E501 python-package
-	flake8 --ignore E501 tests/python
-
-test: $(ALL_TEST)
-	$(ALL_TEST)
-
-check: test
-	./tests/cpp/xgboost_test
+	python3 dmlc-core/scripts/lint.py --exclude_path python-package/xgboost/dmlc-core \
+	  python-package/xgboost/include python-package/xgboost/lib \
+	  python-package/xgboost/make python-package/xgboost/rabit \
+	  python-package/xgboost/src --pylint-rc ${PWD}/python-package/.pylintrc xgboost \
+	  ${LINT_LANG} include src python-package
 
 ifeq ($(TEST_COVER), 1)
 cover: check
@@ -196,7 +87,7 @@ cover: check
 endif
 
 clean:
-	$(RM) -rf build build_plugin lib bin *~ */*~ */*/*~ */*/*/*~ */*.o */*/*.o */*/*/*.o #xgboost
+	$(RM) -rf build lib bin *~ */*~ */*/*~ */*/*/*~ */*.o */*/*.o */*/*/*.o #xgboost
 	$(RM) -rf build_tests *.gcov tests/cpp/xgboost_test
 	if [ -d "R-package/src" ]; then \
 		cd R-package/src; \
@@ -208,36 +99,9 @@ clean_all: clean
 	cd $(DMLC_CORE); "$(MAKE)" clean; cd $(ROOTDIR)
 	cd $(RABIT); "$(MAKE)" clean; cd $(ROOTDIR)
 
-doxygen:
-	doxygen doc/Doxyfile
-
-# create standalone python tar file.
-pypack: ${XGBOOST_DYLIB}
-	cp ${XGBOOST_DYLIB} python-package/xgboost
-	cd python-package; tar cf xgboost.tar xgboost; cd ..
-
 # create pip source dist (sdist) pack for PyPI
 pippack: clean_all
-	rm -rf xgboost-python
-# remove symlinked directories in python-package/xgboost
-	rm -rf python-package/xgboost/lib
-	rm -rf python-package/xgboost/dmlc-core
-	rm -rf python-package/xgboost/include
-	rm -rf python-package/xgboost/make
-	rm -rf python-package/xgboost/rabit
-	rm -rf python-package/xgboost/src
-	cp -r python-package xgboost-python
-	cp -r Makefile xgboost-python/xgboost/
-	cp -r make xgboost-python/xgboost/
-	cp -r src xgboost-python/xgboost/
-	cp -r tests xgboost-python/xgboost/
-	cp -r include xgboost-python/xgboost/
-	cp -r dmlc-core xgboost-python/xgboost/
-	cp -r rabit xgboost-python/xgboost/
-# Use setup_pip.py instead of setup.py
-	mv xgboost-python/setup_pip.py xgboost-python/setup.py
-# Build sdist tarball
-	cd xgboost-python; python setup.py sdist; mv dist/*.tar.gz ..; cd ..
+	cd python-package; python setup.py sdist; mv dist/*.tar.gz ..; cd ..
 
 # Script to make a clean installable R package.
 Rpack: clean_all
@@ -258,19 +122,30 @@ Rpack: clean_all
 	cp -r dmlc-core/include xgboost/src/dmlc-core/include
 	cp -r dmlc-core/src xgboost/src/dmlc-core/src
 	cp ./LICENSE xgboost
-	cat R-package/src/Makevars.in|sed '2s/.*/PKGROOT=./' | sed '3s/.*/ENABLE_STD_THREAD=0/' > xgboost/src/Makevars.in
+# Modify PKGROOT in Makevars.in
+	cat R-package/src/Makevars.in|sed '2s/.*/PKGROOT=./' > xgboost/src/Makevars.in
+# Configure Makevars.win (Windows-specific Makevars, likely using MinGW)
 	cp xgboost/src/Makevars.in xgboost/src/Makevars.win
-	sed -i -e 's/@OPENMP_CXXFLAGS@/$$\(SHLIB_OPENMP_CFLAGS\)/g' xgboost/src/Makevars.win
+	cat xgboost/src/Makevars.in| sed '3s/.*/ENABLE_STD_THREAD=0/' > xgboost/src/Makevars.win
+	sed -i -e 's/@OPENMP_CXXFLAGS@/$$\(SHLIB_OPENMP_CXXFLAGS\)/g' xgboost/src/Makevars.win
+	sed -i -e 's/-pthread/$$\(SHLIB_PTHREAD_FLAGS\)/g' xgboost/src/Makevars.win
+	sed -i -e 's/@ENDIAN_FLAG@/-DDMLC_CMAKE_LITTLE_ENDIAN=1/g' xgboost/src/Makevars.win
+	sed -i -e 's/@BACKTRACE_LIB@//g' xgboost/src/Makevars.win
+	sed -i -e 's/@OPENMP_LIB@//g' xgboost/src/Makevars.win
+	rm -f xgboost/src/Makevars.win-e   # OSX sed create this extra file; remove it
 	bash R-package/remove_warning_suppression_pragma.sh
+	bash xgboost/remove_warning_suppression_pragma.sh
 	rm xgboost/remove_warning_suppression_pragma.sh
+	rm -rfv xgboost/tests/helper_scripts/
+
+R ?= R
 
 Rbuild: Rpack
-	R CMD build --no-build-vignettes xgboost
+	$(R) CMD build xgboost
 	rm -rf xgboost
 
 Rcheck: Rbuild
-	R CMD check xgboost*.tar.gz
+	$(R) CMD check --as-cran xgboost*.tar.gz
 
 -include build/*.d
 -include build/*/*.d
--include build_plugin/*/*.d

NEWS.md

@@ -3,6 +3,990 @@ XGBoost Change Log
 
 This file records the changes in xgboost library in reverse chronological order.
 
+## v1.2.0 (2020.08.22)
+
+### XGBoost4J-Spark now supports the GPU algorithm (#5171)
+* Now XGBoost4J-Spark is able to leverage NVIDIA GPU hardware to speed up training.
+* There is on-going work for accelerating the rest of the data pipeline with NVIDIA GPUs (#5950, #5972).
+
+### XGBoost now supports CUDA 11 (#5808)
+* It is now possible to build XGBoost with CUDA 11. Note that we do not yet distribute pre-built binaries built with CUDA 11; all current distributions use CUDA 10.0.
+
+### Better guidance for persisting XGBoost models in an R environment (#5940, #5964)
+* Users are strongly encouraged to use `xgb.save()` and `xgb.save.raw()` instead of `saveRDS()`. This is so that the persisted models can be accessed with future releases of XGBoost.
+* The previous release (1.1.0) had problems loading models that were saved with `saveRDS()`. This release adds a compatibility layer to restore access to the old RDS files. Note that this is meant to be a temporary measure; users are advised to stop using `saveRDS()` and migrate to `xgb.save()` and `xgb.save.raw()`.
+
+### New objectives and metrics
+* The pseudo-Huber loss `reg:pseudohubererror` is added (#5647). The corresponding metric is `mphe`. Right now, the slope is hard-coded to 1.
+* The Accelerated Failure Time objective for survival analysis (`survival:aft`) is now accelerated on GPUs (#5714, #5716). The survival metrics `aft-nloglik` and `interval-regression-accuracy` are also accelerated on GPUs.
+
+### Improved integration with scikit-learn
+* Added `n_features_in_` attribute to the scikit-learn interface to store the number of features used (#5780). This is useful for integrating with some scikit-learn features such as `StackingClassifier`.  See [this link](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep010/proposal.html) for more details.
+* `XGBoostError` now inherits `ValueError`, which conforms scikit-learn's exception requirement (#5696).
+
+### Improved integration with Dask
+* The XGBoost Dask API now exposes an asynchronous interface (#5862). See [the document](https://xgboost.readthedocs.io/en/latest/tutorials/dask.html#working-with-asyncio) for details.
+* Zero-copy ingestion of GPU arrays via `DaskDeviceQuantileDMatrix` (#5623, #5799, #5800, #5803, #5837, #5874, #5901): Previously, the Dask interface had to make 2 data copies: one for concatenating the Dask partition/block into a single block and another for internal representation. To save memory, we introduce `DaskDeviceQuantileDMatrix`. As long as Dask partitions are resident in the GPU memory, `DaskDeviceQuantileDMatrix` is able to ingest them directly without making copies. This matrix type wraps `DeviceQuantileDMatrix`.
+* The prediction function now returns GPU Series type if the input is from Dask-cuDF (#5710). This is to preserve the input data type.
+
+### Robust handling of external data types (#5689, #5893)
+- As we support more and more external data types, the handling logic has proliferated all over the code base and became hard to keep track. It also became unclear how missing values and threads are handled. We refactored the Python package code to collect all data handling logic to a central location, and now we have an explicit list of of all supported data types.
+
+### Improvements in GPU-side data matrix (`DeviceQuantileDMatrix`)
+* The GPU-side data matrix now implements its own quantile sketching logic, so that data don't have to be transported back to the main memory (#5700, #5747, #5760, #5846, #5870, #5898). The GK sketching algorithm is also now better documented.
+  - Now we can load extremely sparse dataset like URL, although performance is still sub-optimal.
+* The GPU-side data matrix now exposes an iterative interface (#5783), so that users are able to construct a matrix from a data iterator. See the [Python demo](https://github.com/dmlc/xgboost/blob/release_1.2.0/demo/guide-python/data_iterator.py).
+
+### New language binding: Swift (#5728)
+* Visit https://github.com/kongzii/SwiftXGBoost for more details.
+
+### Robust model serialization with JSON (#5772, #5804, #5831, #5857, #5934)
+* We continue efforts from the 1.0.0 release to adopt JSON as the format to save and load models robustly.
+* JSON model IO is significantly faster and produces smaller model files.
+* Round-trip reproducibility is guaranteed, via the introduction of an efficient float-to-string conversion algorithm known as [the Ryū algorithm](https://dl.acm.org/doi/10.1145/3192366.3192369). The conversion is locale-independent, producing consistent numeric representation regardless of the locale setting of the user's machine.
+* We fixed an issue in loading large JSON files to memory.
+* It is now possible to load a JSON file from a remote source such as S3.
+
+### Performance improvements
+* CPU hist tree method optimization
+  - Skip missing lookup in hist row partitioning if data is dense. (#5644)
+  - Specialize training procedures for CPU hist tree method on distributed environment. (#5557)
+  - Add single point histogram for CPU hist.  Previously gradient histogram for CPU hist is hard coded to be 64 bit, now users can specify the parameter `single_precision_histogram` to use 32 bit histogram instead for faster training performance. (#5624, #5811)
+* GPU hist tree method optimization
+  - Removed some unnecessary synchronizations and better memory allocation pattern. (#5707)
+  - Optimize GPU Hist for wide dataset.  Previously for wide dataset the atomic operation is performed on global memory, now it can run on shared memory for faster histogram building. But there's a known small regression on GeForce cards with dense data. (#5795, #5926, #5948, #5631)
+
+### API additions
+* Support passing fmap to importance plot (#5719). Now importance plot can show actual names of features instead of default ones.
+* Support 64bit seed. (#5643)
+* A new C API `XGBoosterGetNumFeature` is added for getting number of features in booster (#5856).
+* Feature names and feature types are now stored in C++ core and saved in binary DMatrix (#5858).
+
+### Breaking: The `predict()` method of `DaskXGBClassifier` now produces class predictions (#5986). Use `predict_proba()` to obtain probability predictions.
+* Previously, `DaskXGBClassifier.predict()` produced probability predictions. This is inconsistent with the behavior of other scikit-learn classifiers, where `predict()` returns class predictions. We make a breaking change in 1.2.0 release so that `DaskXGBClassifier.predict()` now correctly produces class predictions and thus behave like other scikit-learn classifiers. Furthermore, we introduce the `predict_proba()` method for obtaining probability predictions, again to be in line with other scikit-learn classifiers.
+
+### Breaking: Custom evaluation metric now receives raw prediction (#5954)
+* Previously, the custom evaluation metric received a transformed prediction result when used with a classifier. Now the custom metric will receive a raw (untransformed) prediction and will need to transform the prediction itself.  See [demo/guide-python/custom\_softmax.py](https://github.com/dmlc/xgboost/blob/release_1.2.0/demo/guide-python/custom_softmax.py) for an example.
+* This change is to make the custom metric behave consistently with the custom objective, which already receives raw prediction (#5564).
+
+### Breaking: XGBoost4J-Spark now requires Spark 3.0 and Scala 2.12 (#5836, #5890)
+* Starting with version 3.0, Spark can manage GPU resources and allocate them among executors.
+* Spark 3.0 dropped support for Scala 2.11 and now only supports Scala 2.12. Thus, XGBoost4J-Spark also only supports Scala 2.12.
+
+### Breaking: XGBoost Python package now requires Python 3.6 and later (#5715)
+* Python 3.6 has many useful features such as f-strings.
+
+### Breaking: XGBoost now adopts the C++14 standard (#5664)
+* Make sure to use a sufficiently modern C++ compiler that supports C++14, such as Visual Studio 2017, GCC 5.0+, and Clang 3.4+.
+
+### Bug-fixes
+* Fix a data race in the prediction function (#5853). As a byproduct, the prediction function now uses a thread-local data store and became thread-safe.
+* Restore capability to run prediction when the test input has fewer features than the training data (#5955). This capability is necessary to support predicting with LIBSVM inputs. The previous release (1.1) had broken this capability, so we restore it in this version with better tests.
+* Fix OpenMP build with CMake for R package, to support CMake 3.13 (#5895).
+* Fix Windows 2016 build (#5902, #5918).
+* Fix edge cases in scikit-learn interface with Pandas input by disabling feature validation. (#5953)
+* [R] Enable weighted learning to rank (#5945)
+* [R] Fix early stopping with custom objective (#5923)
+* Fix NDK Build (#5886)
+* Add missing explicit template specializations for greater portability (#5921)
+* Handle empty rows in data iterators correctly (#5929). This bug affects file loader and JVM data frames.
+* Fix `IsDense` (#5702)
+* [jvm-packages] Fix wrong method name `setAllowZeroForMissingValue` (#5740)
+* Fix shape inference for Dask predict (#5989)
+
+### Usability Improvements, Documentation
+* [Doc] Document that CUDA 10.0 is required (#5872)
+* Refactored command line interface (CLI). Now CLI is able to handle user errors and output basic document. (#5574)
+* Better error handling in Python: use `raise from` syntax to preserve full stacktrace (#5787).
+* The JSON model dump now has a formal schema (#5660, #5818). The benefit is to prevent `dump_model()` function from breaking. See [this document](https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html#difference-between-saving-model-and-dumping-model) to understand the difference between saving and dumping models.
+* Add a reference to the GPU external memory paper (#5684)
+* Document more objective parameters in the R package (#5682)
+* Document the existence of pre-built binary wheels for MacOS (#5711)
+* Remove `max.depth` in the R gblinear example. (#5753)
+* Added conda environment file for building docs (#5773)
+* Mention dask blog post in the doc, which introduces using Dask with GPU and some internal workings. (#5789)
+* Fix rendering of Markdown docs (#5821)
+* Document new objectives and metrics available on GPUs (#5909)
+* Better message when no GPU is found. (#5594)
+* Remove the use of `silent` parameter from R demos. (#5675)
+* Don't use masked array in array interface. (#5730)
+* Update affiliation of @terrytangyuan: Ant Financial -> Ant Group (#5827)
+* Move dask tutorial closer other distributed tutorials (#5613)
+* Update XGBoost + Dask overview documentation (#5961)
+* Show `n_estimators` in the docstring of the scikit-learn interface (#6041)
+* Fix a type in a doctring of the scikit-learn interface (#5980)
+
+### Maintenance: testing, continuous integration, build system
+* [CI] Remove CUDA 9.0 from CI (#5674, #5745)
+* Require CUDA 10.0+ in CMake build (#5718)
+* [R] Remove dependency on gendef for Visual Studio builds (fixes #5608) (#5764). This enables building XGBoost with GPU support with R 4.x.
+* [R-package] Reduce duplication in configure.ac (#5693)
+* Bump com.esotericsoftware to 4.0.2 (#5690)
+* Migrate some tests from AppVeyor to GitHub Actions to speed up the tests. (#5911, #5917, #5919, #5922, #5928)
+* Reduce cost of the Jenkins CI server (#5884, #5904, #5892). We now enforce a daily budget via an automated monitor. We also dramatically reduced the workload for the Windows platform, since the cloud VM cost is vastly greater for Windows.
+* [R] Set up automated R linter (#5944)
+* [R] replace uses of T and F with TRUE and FALSE (#5778)
+* Update Docker container 'CPU' (#5956)
+* Simplify CMake build with modern CMake techniques (#5871)
+* Use `hypothesis` package for testing (#5759, #5835, #5849).
+* Define `_CRT_SECURE_NO_WARNINGS` to remove unneeded warnings in MSVC (#5434)
+* Run all Python demos in CI, to ensure that they don't break (#5651)
+* Enhance nvtx support (#5636). Now we can use unified timer between CPU and GPU. Also CMake is able to find nvtx automatically.
+* Speed up python test. (#5752)
+* Add helper for generating batches of data. (#5756)
+* Add c-api-demo to .gitignore (#5855)
+* Add option to enable all compiler warnings in GCC/Clang (#5897)
+* Make Python model compatibility test runnable locally (#5941)
+* Add cupy to Windows CI (#5797)
+* [CI] Fix cuDF install; merge 'gpu' and 'cudf' test suite (#5814)
+* Update rabit submodule (#5680, #5876)
+* Force colored output for Ninja build. (#5959)
+* [CI] Assign larger /dev/shm to NCCL (#5966)
+* Add missing Pytest marks to AsyncIO unit test (#5968)
+* [CI] Use latest cuDF and dask-cudf (#6048)
+* Add CMake flag to log C API invocations, to aid debugging (#5925)
+* Fix a unit test on CLI, to handle RC versions (#6050)
+* [CI] Use mgpu machine to run gpu hist unit tests (#6050)
+* [CI] Build GPU-enabled JAR artifact and deploy to xgboost-maven-repo (#6050)
+
+### Maintenance: Refactor code for legibility and maintainability
+* Remove dead code in DMatrix initialization. (#5635)
+* Catch dmlc error by ref. (#5678)
+* Refactor the `gpu_hist` split evaluation in preparation for batched nodes enumeration. (#5610)
+* Remove column major specialization. (#5755)
+* Remove unused imports in Python (#5776)
+* Avoid including `c_api.h` in header files. (#5782)
+* Remove unweighted GK quantile, which is unused. (#5816)
+* Add Python binding for rabit ops. (#5743)
+* Implement `Empty` method for host device vector. (#5781)
+* Remove print (#5867)
+* Enforce tree order in JSON (#5974)
+
+### Acknowledgement
+**Contributors**: Nan Zhu (@CodingCat), @LionOrCatThatIsTheQuestion, Dmitry Mottl (@Mottl), Rory Mitchell (@RAMitchell), @ShvetsKS, Alex Wozniakowski (@a-wozniakowski), Alexander Gugel (@alexanderGugel), @anttisaukko, @boxdot, Andy Adinets (@canonizer), Ram Rachum (@cool-RR), Elliot Hershberg (@elliothershberg), Jason E. Aten, Ph.D. (@glycerine), Philip Hyunsu Cho (@hcho3), @jameskrach, James Lamb (@jameslamb), James Bourbeau (@jrbourbeau), Peter Jung (@kongzii), Lorenz Walthert (@lorenzwalthert), Oleksandr Kuvshynov (@okuvshynov), Rong Ou (@rongou), Shaochen Shi (@shishaochen), Yuan Tang (@terrytangyuan), Jiaming Yuan (@trivialfis), Bobby Wang (@wbo4958), Zhang Zhang (@zhangzhang10)
+
+**Reviewers**: Nan Zhu (@CodingCat), @LionOrCatThatIsTheQuestion, Hao Yang (@QuantHao), Rory Mitchell (@RAMitchell), @ShvetsKS, Egor Smirnov (@SmirnovEgorRu), Alex Wozniakowski (@a-wozniakowski), Amit Kumar (@aktech), Avinash Barnwal (@avinashbarnwal), @boxdot, Andy Adinets (@canonizer), Chandra Shekhar Reddy (@chandrureddy), Ram Rachum (@cool-RR), Cristiano Goncalves (@cristianogoncalves), Elliot Hershberg (@elliothershberg), Jason E. Aten, Ph.D. (@glycerine), Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), James Lamb (@jameslamb), James Bourbeau (@jrbourbeau), Lee Drake (@leedrake5), DougM (@mengdong), Oleksandr Kuvshynov (@okuvshynov), RongOu (@rongou), Shaochen Shi (@shishaochen), Xu Xiao (@sperlingxx), Yuan Tang (@terrytangyuan), Theodore Vasiloudis (@thvasilo), Jiaming Yuan (@trivialfis), Bobby Wang (@wbo4958), Zhang Zhang (@zhangzhang10)
+
+## v1.1.1 (2020.06.06)
+This patch release applies the following patches to 1.1.0 release:
+
+* CPU performance improvement in the PyPI wheels (#5720)
+* Fix loading old model (#5724)
+* Install pkg-config file (#5744)
+
+## v1.1.0 (2020.05.17)
+
+### Better performance on multi-core CPUs (#5244, #5334, #5522)
+* Poor performance scaling of the `hist` algorithm for multi-core CPUs has been under investigation (#3810). #5244 concludes the ongoing effort to improve performance scaling on multi-CPUs, in particular Intel CPUs. Roadmap: #5104
+* #5334 makes steps toward reducing memory consumption for the `hist` tree method on CPU.
+* #5522 optimizes random number generation for data sampling.
+
+### Deterministic GPU algorithm for regression and classification (#5361)
+* GPU algorithm for regression and classification tasks is now deterministic.
+* Roadmap: #5023. Currently only single-GPU training is deterministic. Distributed training with multiple GPUs is not yet deterministic.
+
+### Improve external memory support on GPUs (#5093, #5365)
+* Starting from 1.0.0 release, we added support for external memory on GPUs to enable training with larger datasets. Gradient-based sampling (#5093) speeds up the external memory algorithm by intelligently sampling a subset of the training data to copy into the GPU memory. [Learn more about out-of-core GPU gradient boosting.](https://arxiv.org/abs/2005.09148)
+* GPU-side data sketching now works with data from external memory (#5365).
+
+### Parameter validation: detection of unused or incorrect parameters (#5477, #5569, #5508)
+* Mis-spelled training parameter is a common user mistake. In previous versions of XGBoost, mis-spelled parameters were silently ignored. Starting with 1.0.0 release, XGBoost will produce a warning message if there is any unused training parameters. The 1.1.0 release makes parameter validation available to the scikit-learn interface (#5477) and the R binding (#5569).
+
+### Thread-safe, in-place prediction method (#5389, #5512)
+* Previously, the prediction method was not thread-safe (#5339). This release adds a new API function `inplace_predict()` that is thread-safe. It is now possible to serve concurrent requests for prediction using a shared model object.
+* It is now possible to compute prediction in-place for selected data formats (`numpy.ndarray` / `scipy.sparse.csr_matrix` / `cupy.ndarray` / `cudf.DataFrame` / `pd.DataFrame`) without creating a `DMatrix` object.
+
+### Addition of Accelerated Failure Time objective for survival analysis (#4763, #5473, #5486, #5552, #5553)
+* Survival analysis (regression) models the time it takes for an event of interest to occur. The target label is potentially censored, i.e. the label is a range rather than a single number. We added a new objective `survival:aft` to support survival analysis. Also added is the new API to specify the ranged labels. Check out [the tutorial](https://xgboost.readthedocs.io/en/release_1.1.0/tutorials/aft_survival_analysis.html) and the [demos](https://github.com/dmlc/xgboost/tree/release_1.1.0/demo/aft_survival).
+* GPU support is work in progress (#5714).
+
+### Improved installation experience on Mac OSX (#5597, #5602, #5606, #5701)
+* It only takes two commands to install the XGBoost Python package: `brew install libomp` followed by `pip install xgboost`. The installed XGBoost will use all CPU cores. Even better, starting with this release, we distribute pre-compiled binary wheels targeting Mac OSX. Now the install command `pip install xgboost` finishes instantly, as it no longer compiles the C++ source of XGBoost. The last three Mac versions (High Sierra, Mojave, Catalina) are supported.
+* R package: the 1.1.0 release fixes the error `Initializing libomp.dylib, but found libomp.dylib already initialized` (#5701)
+
+### Ranking metrics are now accelerated on GPUs (#5380, #5387, #5398)
+
+### GPU-side data matrix to ingest data directly from other GPU libraries (#5420, #5465)
+* Previously, data on GPU memory had to be copied back to the main memory before it could be used by XGBoost. Starting with 1.1.0 release, XGBoost provides a dedicated interface (`DeviceQuantileDMatrix`) so that it can ingest data from GPU memory directly. The result is that XGBoost interoperates better with GPU-accelerated data science libraries, such as cuDF, cuPy, and PyTorch.
+* Set device in device dmatrix. (#5596)
+
+### Robust model serialization with JSON (#5123, #5217)
+* We continue efforts from the 1.0.0 release to adopt JSON as the format to save and load models robustly. Refer to the release note for 1.0.0 to learn more.
+* It is now possible to store internal configuration of the trained model (`Booster`) object in R as a JSON string (#5123, #5217).
+
+### Improved integration with Dask
+* Pass through `verbose` parameter for dask fit (#5413)
+* Use `DMLC_TASK_ID`. (#5415)
+* Order the prediction result. (#5416)
+* Honor `nthreads` from dask worker. (#5414)
+* Enable grid searching with scikit-learn. (#5417)
+* Check non-equal when setting threads. (#5421)
+* Accept other inputs for prediction. (#5428)
+* Fix missing value for scikit-learn interface. (#5435)
+
+### XGBoost4J-Spark: Check number of columns in the data iterator (#5202, #5303)
+* Before, the native layer in XGBoost did not know the number of columns (features) ahead of time and had to guess the number of columns by counting the feature index when ingesting data. This method has a failure more in distributed setting: if the training data is highly sparse, some features may be completely missing in one or more worker partitions. Thus, one or more workers may deduce an incorrect data shape, leading to crashes or silently wrong models.
+* Enforce correct data shape by passing the number of columns explicitly from the JVM layer into the native layer.
+
+### Major refactoring of the `DMatrix` class
+* Continued from 1.0.0 release.
+* Remove update prediction cache from predictors. (#5312)
+* Predict on Ellpack. (#5327)
+* Partial rewrite EllpackPage (#5352)
+* Use ellpack for prediction only when sparsepage doesn't exist. (#5504)
+* RFC: #4354, Roadmap: #5143
+
+### Breaking: XGBoost Python package now requires Pip 19.0 and higher (#5589)
+* Your Linux machine may have an old version of Pip and may attempt to install a source package, leading to long installation time. This is because we are now using `manylinux2010` tag in the binary wheel release. Ensure you have Pip 19.0 or newer by running `python3 -m pip -V` to check the version. Upgrade Pip with command
+```
+python3 -m pip install --upgrade pip
+```
+Upgrading to latest pip allows us to depend on newer versions of system libraries. [TensorFlow](https://www.tensorflow.org/install/pip) also requires Pip 19.0+.
+
+### Breaking: GPU algorithm now requires CUDA 10.0 and higher (#5649)
+* CUDA 10.0 is necessary to make the GPU algorithm deterministic (#5361).
+
+### Breaking: `silent` parameter is now removed (#5476)
+* Please use `verbosity` instead.
+
+### Breaking: Set `output_margin` to True for custom objectives (#5564)
+* Now both R and Python interface custom objectives get un-transformed (raw) prediction outputs.
+
+### Breaking: `Makefile` is now removed. We use CMake exclusively to build XGBoost (#5513)
+* Exception: the R package uses Autotools, as the CRAN ecosystem did not yet adopt CMake widely.
+
+### Breaking: `distcol` updater is now removed (#5507)
+* The `distcol` updater has been long broken, and currently we lack resources to implement a working implementation from scratch.
+
+### Deprecation notices
+* **Python 3.5**. This release is the last release to support Python 3.5. The following release (1.2.0) will require Python 3.6.
+* **Scala 2.11**. Currently XGBoost4J supports Scala 2.11. However, if a future release of XGBoost adopts Spark 3, it will not support Scala 2.11, as Spark 3 requires Scala 2.12+. We do not yet know which XGBoost release will adopt Spark 3.
+
+### Known limitations
+* (Python package) When early stopping is activated with `early_stopping_rounds` at training time, the prediction method (`xgb.predict()`) behaves in a surprising way. If XGBoost runs for M rounds and chooses iteration N (N < M) as the best iteration, then the prediction method will use M trees by default. To use the best iteration (N trees), users will need to manually take the best iteration field `bst.best_iteration` and pass it as the `ntree_limit` argument to `xgb.predict()`. See #5209 and #4052 for additional context.
+* GPU ranking objective is currently not deterministic (#5561).
+* When training parameter `reg_lambda` is set to zero, some leaf nodes may be assigned a NaN value. (See [discussion](https://discuss.xgboost.ai/t/still-getting-unexplained-nans-new-replication-code/1383/9).) For now, please set `reg_lambda` to a nonzero value.
+
+### Community and Governance
+* The XGBoost Project Management Committee (PMC) is pleased to announce a new committer: Egor Smirnov (@SmirnovEgorRu). He has led a major initiative to improve the performance of XGBoost on multi-core CPUs.
+
+### Bug-fixes
+* Improved compatibility with scikit-learn (#5255, #5505, #5538)
+* Remove f-string, since it's not supported by Python 3.5 (#5330). Note that Python 3.5 support is deprecated and schedule to be dropped in the upcoming release (1.2.0).
+* Fix the pruner so that it doesn't prune the same branch twice (#5335)
+* Enforce only major version in JSON model schema (#5336). Any major revision of the model schema would bump up the major version.
+* Fix a small typo in sklearn.py that broke multiple eval metrics (#5341)
+* Restore loading model from a memory buffer (#5360)
+* Define lazy isinstance for Python compat (#5364)
+* [R] fixed uses of `class()` (#5426)
+* Force compressed buffer to be 4 bytes aligned, to keep cuda-memcheck happy (#5441)
+* Remove warning for calling host function (`std::max`) on a GPU device (#5453)
+* Fix uninitialized value bug in xgboost callback (#5463)
+* Fix model dump in CLI (#5485)
+* Fix out-of-bound array access in `WQSummary::SetPrune()` (#5493)
+* Ensure that configured `dmlc/build_config.h` is picked up by Rabit and XGBoost, to fix build on Alpine (#5514)
+* Fix a misspelled method, made in a git merge (#5509)
+* Fix a bug in binary model serialization (#5532)
+* Fix CLI model IO (#5535)
+* Don't use `uint` for threads (#5542)
+* Fix R interaction constraints to handle more than 100000 features (#5543)
+* [jvm-packages] XGBoost Spark should deal with NaN when parsing evaluation output (#5546)
+* GPU-side data sketching is now aware of query groups in learning-to-rank data (#5551)
+* Fix DMatrix slicing for newly added fields (#5552)
+* Fix configuration status with loading binary model (#5562)
+* Fix build when OpenMP is disabled (#5566)
+* R compatibility patches (#5577, #5600)
+* gpu\_hist performance fixes (#5558)
+* Don't set seed on CLI interface (#5563)
+* [R] When serializing model, preserve model attributes related to early stopping (#5573)
+* Avoid rabit calls in learner configuration (#5581)
+* Hide C++ symbols in libxgboost.so when building Python wheel (#5590). This fixes apache/incubator-tvm#4953.
+* Fix compilation on Mac OSX High Sierra (10.13) (#5597)
+* Fix build on big endian CPUs (#5617)
+* Resolve crash due to use of `vector<bool>::iterator` (#5642)
+* Validation JSON model dump using JSON schema (#5660)
+
+### Performance improvements
+* Wide dataset quantile performance improvement (#5306)
+* Reduce memory usage of GPU-side data sketching (#5407)
+* Reduce span check overhead (#5464)
+* Serialise booster after training to free up GPU memory (#5484)
+* Use the maximum amount of GPU shared memory available to speed up the histogram kernel (#5491)
+* Use non-synchronising scan in Thrust (#5560)
+* Use `cudaDeviceGetAttribute()` instead of `cudaGetDeviceProperties()` for speed (#5570)
+
+### API changes
+* Support importing data from a Pandas SparseArray (#5431)
+* `HostDeviceVector` (vector shared between CPU and GPU memory) now exposes `HostSpan` interface, to enable access on the CPU side with bound check (#5459)
+* Accept other gradient types for `SplitEntry` (#5467)
+
+### Usability Improvements, Documentation
+* Add `JVM_CHECK_CALL` to prevent C++ exceptions from leaking into the JVM layer (#5199)
+* Updated Windows build docs (#5283)
+* Update affiliation of @hcho3 (#5292)
+* Display Sponsor button, link to OpenCollective (#5325)
+* Update docs for GPU external memory (#5332)
+* Add link to GPU documentation (#5437)
+* Small updates to GPU documentation (#5483)
+* Edits on tutorial for XGBoost job on Kubernetes (#5487)
+* Add reference to GPU external memory (#5490)
+* Fix typos (#5346, #5371, #5384, #5399, #5482, #5515)
+* Update Python doc (#5517)
+* Add Neptune and Optuna to list of examples (#5528)
+* Raise error if the number of data weights doesn't match the number of data sets (#5540)
+* Add a note about GPU ranking (#5572)
+* Clarify meaning of `training` parameter in the C API function `XGBoosterPredict()` (#5604)
+* Better error handling for situations where existing trees cannot be modified (#5406, #5418). This feature is enabled when `process_type` is set to `update`.
+
+### Maintenance: testing, continuous integration, build system
+* Add C++ test coverage for data sketching (#5251)
+* Ignore gdb\_history (#5257)
+* Rewrite setup.py. (#5271, #5280)
+* Use `scikit-learn` in extra dependencies (#5310)
+* Add CMake option to build static library (#5397)
+* [R] changed FindLibR to take advantage of CMake cache (#5427)
+* [R] fixed inconsistency in R -e calls in FindLibR.cmake (#5438)
+* Refactor tests with data generator (#5439)
+* Resolve failing Travis CI (#5445)
+* Update dmlc-core. (#5466)
+* [CI] Use clang-tidy 10 (#5469)
+* De-duplicate code for checking maximum number of nodes (#5497)
+* [CI] Use Ubuntu 18.04 LTS in JVM CI, because 19.04 is EOL (#5537)
+* [jvm-packages] [CI] Create a Maven repository to host SNAPSHOT JARs (#5533)
+* [jvm-packages] [CI] Publish XGBoost4J JARs with Scala 2.11 and 2.12 (#5539)
+* [CI] Use Vault repository to re-gain access to devtoolset-4 (#5589)
+
+### Maintenance: Refactor code for legibility and maintainability
+* Move prediction cache to Learner (#5220, #5302)
+* Remove SimpleCSRSource (#5315)
+* Refactor SparsePageSource, delete cache files after use (#5321)
+* Remove unnecessary DMatrix methods (#5324)
+* Split up `LearnerImpl` (#5350)
+* Move segment sorter to common (#5378)
+* Move thread local entry into Learner (#5396)
+* Split up test helpers header (#5455)
+* Requires setting leaf stat when expanding tree (#5501)
+* Purge device\_helpers.cuh (#5534)
+* Use thrust functions instead of custom functions (#5544)
+
+### Acknowledgement
+**Contributors**: Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), @ShvetsKS, Egor Smirnov (@SmirnovEgorRu), Andrew Kane (@ankane), Avinash Barnwal (@avinashbarnwal), Bart Broere (@bartbroere), Andy Adinets (@canonizer), Chen Qin (@chenqin), Daiki Katsuragawa (@daikikatsuragawa), David Díaz Vico (@daviddiazvico), Darius Kharazi (@dkharazi), Darby Payne (@dpayne), Jason E. Aten, Ph.D. (@glycerine), Philip Hyunsu Cho (@hcho3), James Lamb (@jameslamb), Jan Borchmann (@jborchma), Kamil A. Kaczmarek (@kamil-kaczmarek), Melissa Kohl (@mjkohl32), Nicolas Scozzaro (@nscozzaro), Paul Kaefer (@paulkaefer), Rong Ou (@rongou), Samrat Pandiri (@samratp), Sriram Chandramouli (@sriramch), Yuan Tang (@terrytangyuan), Jiaming Yuan (@trivialfis), Liang-Chi Hsieh (@viirya), Bobby Wang (@wbo4958), Zhang Zhang (@zhangzhang10),
+
+**Reviewers**: Nan Zhu (@CodingCat), @LeZhengThu, Rory Mitchell (@RAMitchell), @ShvetsKS, Egor Smirnov (@SmirnovEgorRu), Steve Bronder (@SteveBronder), Nikita Titov (@StrikerRUS), Andrew Kane (@ankane), Avinash Barnwal (@avinashbarnwal), @brydag, Andy Adinets (@canonizer), Chandra Shekhar Reddy (@chandrureddy), Chen Qin (@chenqin), Codecov (@codecov-io), David Díaz Vico (@daviddiazvico), Darby Payne (@dpayne), Jason E. Aten, Ph.D. (@glycerine), Philip Hyunsu Cho (@hcho3), James Lamb (@jameslamb), @johnny-cat, Mu Li (@mli), Mate Soos (@msoos), @rnyak, Rong Ou (@rongou), Sriram Chandramouli (@sriramch), Toby Dylan Hocking (@tdhock), Yuan Tang (@terrytangyuan), Oleksandr Pryimak (@trams), Jiaming Yuan (@trivialfis), Liang-Chi Hsieh (@viirya), Bobby Wang (@wbo4958),
+
+## v1.0.2 (2020.03.03)
+This patch release applies the following patches to 1.0.0 release:
+
+* Fix a small typo in sklearn.py that broke multiple eval metrics (#5341)
+* Restore loading model from buffer (#5360)
+* Use type name for data type check (#5364)
+
+## v1.0.1 (2020.02.21)
+This release is identical to the 1.0.0 release, except that it fixes a small bug that rendered 1.0.0 incompatible with Python 3.5. See #5328.
+
+## v1.0.0 (2020.02.19)
+This release marks a major milestone for the XGBoost project.
+
+### Apache-style governance, contribution policy, and semantic versioning (#4646, #4659)
+* Starting with 1.0.0 release, the XGBoost Project is adopting Apache-style governance. The full community guideline is [available in the doc website](https://xgboost.readthedocs.io/en/release_1.0.0/contrib/community.html). Note that we now have Project Management Committee (PMC) who would steward the project on the long-term basis. The PMC is also entrusted to run and fund the project's continuous integration (CI) infrastructure (https://xgboost-ci.net).
+* We also adopt the [semantic versioning](https://semver.org/). See [our release versioning policy](https://xgboost.readthedocs.io/en/release_1.0.0/contrib/release.html).
+
+### Better performance scaling for multi-core CPUs (#4502, #4529, #4716, #4851, #5008, #5107, #5138, #5156)
+* Poor performance scaling of the `hist` algorithm for multi-core CPUs has been under investigation (#3810). Previous effort #4529 was replaced with a series of pull requests (#5107, #5138, #5156) aimed at achieving the same performance benefits while keeping the C++ codebase legible. The latest performance benchmark results show [up to 5x speedup on Intel CPUs with many cores](https://github.com/dmlc/xgboost/pull/5156#issuecomment-580024413). Note: #5244, which concludes the effort, will become part of the upcoming release 1.1.0.
+
+### Improved installation experience on Mac OSX (#4672, #5074, #5080, #5146, #5240)
+* It used to be quite complicated to install XGBoost on Mac OSX. XGBoost uses OpenMP to distribute work among multiple CPU cores, and Mac's default C++ compiler (Apple Clang) does not come with OpenMP. Existing work-around (using another C++ compiler) was complex and prone to fail with cryptic diagnosis (#4933, #4949, #4969).
+* Now it only takes two commands to install XGBoost: `brew install libomp` followed by `pip install xgboost`. The installed XGBoost will use all CPU cores.
+* Even better, XGBoost is now available from Homebrew: `brew install xgboost`. See Homebrew/homebrew-core#50467.
+* Previously, if you installed the XGBoost R package using the command `install.packages('xgboost')`, it could only use a single CPU core and you would experience slow training performance. With 1.0.0 release, the R package will use all CPU cores out of box.
+
+### Distributed XGBoost now available on Kubernetes (#4621, #4939)
+* Check out the [tutorial for setting up distributed XGBoost on a Kubernetes cluster](https://xgboost.readthedocs.io/en/release_1.0.0/tutorials/kubernetes.html).
+
+### Ruby binding for XGBoost (#4856)
+
+### New Native Dask interface for multi-GPU and multi-node scaling (#4473, #4507, #4617, #4819, #4907, #4914, #4941, #4942, #4951, #4973, #5048, #5077, #5144, #5270)
+* XGBoost now integrates seamlessly with [Dask](https://dask.org/), a lightweight distributed framework for data processing. Together with the first-class support for cuDF data frames (see below), it is now easier than ever to create end-to-end data pipeline running on one or more NVIDIA GPUs.
+* Multi-GPU training with Dask is now up to 20% faster than the previous release (#4914, #4951).
+
+### First-class support for cuDF data frames and cuPy arrays (#4737, #4745, #4794, #4850, #4891, #4902, #4918, #4927, #4928, #5053, #5189, #5194, #5206, #5219, #5225)
+* [cuDF](https://github.com/rapidsai/cudf) is a data frame library for loading and processing tabular data on NVIDIA GPUs. It provides a Pandas-like API.
+* [cuPy](https://github.com/cupy/cupy) implements a NumPy-compatible multi-dimensional array on NVIDIA GPUs.
+* Now users can keep the data on the GPU memory throughout the end-to-end data pipeline, obviating the need for copying data between the main memory and GPU memory.
+* XGBoost can accept any data structure that exposes `__array_interface__` signature, opening way to support other columar formats that are compatible with Apache Arrow.
+
+### [Feature interaction constraint](https://xgboost.readthedocs.io/en/release_1.0.0/tutorials/feature_interaction_constraint.html) is now available with `approx` and `gpu_hist` algorithms (#4534, #4587, #4596, #5034).
+
+### Learning to rank is now GPU accelerated (#4873, #5004, #5129)
+* Supported ranking objectives: NDGC, Map, Pairwise.
+* [Up to 2x improved training performance on GPUs](https://devblogs.nvidia.com/learning-to-rank-with-xgboost-and-gpu/).
+
+### Enable `gamma` parameter for GPU training (#4874, #4953)
+* The `gamma` parameter specifies the minimum loss reduction required to add a new split in a tree. A larger value for `gamma` has the effect of pre-pruning the tree, by making harder to add splits.
+
+### External memory for GPU training (#4486, #4526, #4747, #4833, #4879, #5014)
+* It is now possible to use NVIDIA GPUs even when the size of training data exceeds the available GPU memory. Note that the external memory support for GPU is still experimental. #5093 will further improve performance and will become part of the upcoming release 1.1.0.
+* RFC for enabling external memory with GPU algorithms: #4357
+
+### Improve Scikit-Learn interface (#4558, #4842, #4929, #5049, #5151, #5130, #5227)
+* Many users of XGBoost enjoy the convenience and breadth of Scikit-Learn ecosystem. In this release, we revise the Scikit-Learn API of XGBoost (`XGBRegressor`, `XGBClassifier`, and `XGBRanker`) to achieve feature parity with the traditional XGBoost interface (`xgboost.train()`).
+* Insert check to validate data shapes.
+* Produce an error message if `eval_set` is not a tuple. An error message is better than silently crashing.
+* Allow using `numpy.RandomState` object.
+* Add `n_jobs` as an alias of `nthread`.
+* Roadmap: #5152
+
+### XGBoost4J-Spark: Redesigning checkpointing mechanism
+* RFC is available at #4786
+* Clean up checkpoint file after a successful training job (#4754): The current implementation in XGBoost4J-Spark does not clean up the checkpoint file after a successful training job. If the user runs another job with the same checkpointing directory, she will get a wrong model because the second job will re-use the checkpoint file left over from the first job. To prevent this scenario, we propose to always clean up the checkpoint file after every successful training job.
+* Avoid Multiple Jobs for Checkpointing (#5082): The current method for checkpoint is to collect the booster produced at the last iteration of each checkpoint internal to Driver and persist it in HDFS. The major issue with this approach is that it needs to re-perform the data preparation for training if the user did not choose to cache the training dataset. To avoid re-performing data prep, we build external-memory checkpointing in the XGBoost4J layer as well.
+* Enable deterministic repartitioning when checkpoint is enabled (#4807): Distributed algorithm for gradient boosting assumes a fixed partition of the training data between multiple iterations. In previous versions, there was no guarantee that data partition would stay the same, especially when a worker goes down and some data had to recovered from previous checkpoint. In this release, we make data partition deterministic by using the data hash value of each data row in computing the partition.
+
+### XGBoost4J-Spark: handle errors thrown by the native code (#4560)
+* All core logic of XGBoost is written in C++, so XGBoost4J-Spark internally uses the C++ code via Java Native Interface (JNI). #4560 adds a proper error handling for any errors or exceptions arising from the C++ code, so that the XGBoost Spark application can be torn down in an orderly fashion.
+
+### XGBoost4J-Spark: Refine method to count the number of alive cores  (#4858)
+* The `SparkParallelismTracker` class ensures that sufficient number of executor cores are alive. To that end, it is important to query the number of alive cores reliably.
+
+### XGBoost4J: Add `BigDenseMatrix` to store more than `Integer.MAX_VALUE` elements (#4383)
+
+### Robust model serialization with JSON (#4632, #4708, #4739, #4868, #4936, #4945, #4974, #5086, #5087, #5089, #5091, #5094, #5110, #5111, #5112, #5120, #5137, #5218, #5222, #5236, #5245, #5248, #5281)
+* In this release, we introduce an experimental support of using [JSON](https://www.json.org/json-en.html) for serializing (saving/loading) XGBoost models and related hyperparameters for training. We would like to eventually replace the old binary format with JSON, since it is an open format and parsers are available in many programming languages and platforms. See [the documentation for model I/O using JSON](https://xgboost.readthedocs.io/en/release_1.0.0/tutorials/saving_model.html). #3980 explains why JSON was chosen over other alternatives.
+* To maximize interoperability and compatibility of the serialized models, we now split serialization into two parts (#4855):
+  1. Model, e.g. decision trees and strictly related metadata like `num_features`.
+  2. Internal configuration, consisting of training parameters and other configurable parameters. For example, `max_delta_step`, `tree_method`, `objective`, `predictor`, `gpu_id`.
+
+  Previously, users often ran into issues where the model file produced by one machine could not load or run on another machine. For example, models trained using a machine with an NVIDIA GPU could not run on another machine without a GPU (#5291, #5234). The reason is that the old binary format saved some internal configuration that were not universally applicable to all machines, e.g. `predictor='gpu_predictor'`.
+
+  Now, model saving function (`Booster.save_model()` in Python) will save only the model, without internal configuration. This will guarantee that your model file would be used anywhere. Internal configuration will be serialized in limited circumstances such as:
+  * Multiple nodes in a distributed system exchange model details over the network.
+  * Model checkpointing, to recover from possible crashes.
+
+  This work proved to be useful for parameter validation as well (see below).
+* Starting with 1.0.0 release, we will use semantic versioning to indicate whether the model produced by one version of XGBoost would be compatible with another version of XGBoost. Any change in the major version indicates a breaking change in the serialization format.
+* We now provide a robust method to save and load scikit-learn related attributes (#5245). Previously, we used Python pickle to save Python attributes related to `XGBClassifier`, `XGBRegressor`, and `XGBRanker` objects. The attributes are necessary to properly interact with scikit-learn. See #4639 for more details. The use of pickling hampered interoperability, as a pickle from one machine may not necessarily work on another machine. Starting with this release, we use an alternative method to serialize the scikit-learn related attributes. The use of Python pickle is now discouraged (#5236, #5281).
+
+### Parameter validation: detection of unused or incorrect parameters (#4553, #4577, #4738, #4801, #4961, #5101, #5157, #5167, #5256)
+* Mis-spelled training parameter is a common user mistake. In previous versions of XGBoost, mis-spelled parameters were silently ignored. Starting with 1.0.0 release, XGBoost will produce a warning message if there is any unused training parameters. Currently, parameter validation is available to R users and Python XGBoost API users. We are working to extend its support to scikit-learn users.
+* Configuration steps now have well-defined semantics (#4542, #4738), so we know exactly where and how the internal configurable parameters are changed.
+* The user can now use `save_config()` function to inspect all (used) training parameters. This is helpful for debugging model performance.
+
+### Allow individual workers to recover from faults (#4808, #4966)
+* Status quo: if a worker fails, all workers are shut down and restarted, and learning resumes from the last checkpoint. This involves requesting resources from the scheduler (e.g. Spark) and shuffling all the data again from scratch. Both of these operations can be quite costly and block training for extended periods of time, especially if the training data is big and the number of worker nodes is in the hundreds.
+* The proposed solution is to recover the single node that failed, instead of shutting down all workers. The rest of the clusters wait until the single failed worker is bootstrapped and catches up with the rest.
+* See roadmap at #4753. Note that this is work in progress. In particular, the feature is not yet available from XGBoost4J-Spark.
+
+### Accurate prediction for DART models
+* Use DART tree weights when computing SHAPs (#5050)
+* Don't drop trees during DART prediction by default (#5115)
+* Fix DART prediction in R (#5204)
+
+### Make external memory more robust
+* Fix issues with training with external memory on cpu (#4487)
+* Fix crash with approx tree method on cpu (#4510)
+* Fix external memory race in `exact` (#4980). Note: `dmlc::ThreadedIter` is not actually thread-safe. We would like to re-design it in the long term.
+
+### Major refactoring of the `DMatrix` class (#4686, #4744, #4748, #5044, #5092, #5108, #5188, #5198)
+* Goal 1: improve performance and reduce memory consumption. Right now, if the user trains a model with a NumPy array as training data, the array gets copies 2-3 times before training begins. We'd like to reduce duplication of the data matrix.
+* Goal 2: Expose a common interface to external data, unify the way DMatrix objects are constructed and simplify the process of adding new external data sources. This work is essential for ingesting cuPy arrays.
+* Goal 3: Handle missing values consistently.
+* RFC: #4354, Roadmap: #5143
+* This work is also relevant to external memory support on GPUs.
+
+### Breaking: XGBoost Python package now requires Python 3.5 or newer (#5021, #5274)
+* Python 3.4 has reached its end-of-life on March 16, 2019, so we now require Python 3.5 or newer.
+
+### Breaking: GPU algorithm now requires CUDA 9.0 and higher (#4527, #4580)
+
+### Breaking: `n_gpus` parameter removed; multi-GPU training now requires a distributed framework (#4579, #4749, #4773, #4810, #4867, #4908)
+* #4531 proposed removing support for single-process multi-GPU training. Contributors would focus on multi-GPU support through distributed frameworks such as Dask and Spark, where the framework would be expected to assign a worker process for each GPU independently. By delegating GPU management and data movement to the distributed framework, we can greatly simplify the core XGBoost codebase, make multi-GPU training more robust, and reduce burden for future development.
+
+### Breaking: Some deprecated features have been removed
+* ``gpu_exact`` training method (#4527, #4742, #4777). Use ``gpu_hist`` instead.
+* ``learning_rates`` parameter in Python (#5155). Use the callback API instead.
+* ``num_roots`` (#5059, #5165), since the current training code always uses a single root node.
+* GPU-specific objectives (#4690), such as `gpu:reg:linear`. Use objectives without `gpu:` prefix; GPU will be used automatically if your machine has one.
+
+### Breaking: the C API function `XGBoosterPredict()` now asks for an extra parameter `training`.
+
+### Breaking: We now use CMake exclusively to build XGBoost. `Makefile` is being sunset.
+* Exception: the R package uses Autotools, as the CRAN ecosystem did not yet adopt CMake widely.
+
+### Performance improvements
+* Smarter choice of histogram construction for distributed `gpu_hist` (#4519)
+* Optimizations for quantization on device (#4572)
+* Introduce caching memory allocator to avoid latency associated with GPU memory allocation (#4554, #4615)
+* Optimize the initialization stage of the CPU `hist` algorithm for sparse datasets (#4625)
+* Prevent unnecessary data copies from GPU memory to the host (#4795)
+* Improve operation efficiency for single prediction (#5016)
+* Group builder modified for incremental building, to speed up building large `DMatrix` (#5098)
+
+### Bug-fixes
+* Eliminate `FutureWarning: Series.base is deprecated` (#4337)
+* Ensure pandas DataFrame column names are treated as strings in type error message (#4481)
+* [jvm-packages] Add back `reg:linear` for scala, as it is only deprecated and not meant to be removed yet (#4490)
+* Fix library loading for Cygwin users (#4499)
+* Fix prediction from loaded pickle (#4516)
+* Enforce exclusion between `pred_interactions=True` and `pred_interactions=True` (#4522)
+* Do not return dangling reference to local `std::string` (#4543)
+* Set the appropriate device before freeing device memory (#4566)
+* Mark `SparsePageDmatrix` destructor default. (#4568)
+* Choose the appropriate tree method only when the tree method is 'auto' (#4571)
+* Fix `benchmark_tree.py` (#4593)
+* [jvm-packages] Fix silly bug in feature scoring (#4604)
+* Fix GPU predictor when the test data matrix has different number of features than the training data matrix used to train the model (#4613)
+* Fix external memory for get column batches. (#4622)
+* [R] Use built-in label when xgb.DMatrix is given to xgb.cv() (#4631)
+* Fix early stopping in the Python package (#4638)
+* Fix AUC error in distributed mode caused by imbalanced dataset (#4645, #4798)
+* [jvm-packages] Expose `setMissing` method in `XGBoostClassificationModel` / `XGBoostRegressionModel` (#4643)
+* Remove initializing stringstream reference. (#4788)
+* [R] `xgb.get.handle` now checks all class listed of `object` (#4800)
+* Do not use `gpu_predictor` unless data comes from GPU (#4836)
+* Fix data loading (#4862)
+* Workaround `isnan` across different environments. (#4883)
+* [jvm-packages] Handle Long-type parameter (#4885)
+* Don't `set_params` at the end of `set_state` (#4947). Ensure that the model does not change after pickling and unpickling multiple times.
+* C++ exceptions should not crash OpenMP loops (#4960)
+* Fix `usegpu` flag in DART. (#4984)
+* Run training with empty `DMatrix` (#4990, #5159)
+* Ensure that no two processes can use the same GPU (#4990)
+* Fix repeated split and 0 cover nodes (#5010)
+* Reset histogram hit counter between multiple data batches (#5035)
+* Fix `feature_name` crated from int64index dataframe. (#5081)
+* Don't use 0 for "fresh leaf" (#5084)
+* Throw error when user attempts to use multi-GPU training and XGBoost has not been compiled with NCCL (#5170)
+* Fix metric name loading (#5122)
+* Quick fix for memory leak in CPU `hist` algorithm (#5153)
+* Fix wrapping GPU ID and prevent data copying (#5160)
+* Fix signature of Span constructor (#5166)
+* Lazy initialization of device vector, so that XGBoost compiled with CUDA can run on a machine without any GPU (#5173)
+* Model loading should not change system locale (#5314)
+* Distributed training jobs would sometimes hang; revert Rabit to fix this regression (dmlc/rabit#132, #5237)
+
+### API changes
+* Add support for cross-validation using query ID (#4474)
+* Enable feature importance property for DART model (#4525)
+* Add `rmsle` metric and `reg:squaredlogerror` objective (#4541)
+* All objective and evaluation metrics are now exposed to JVM packages (#4560)
+* `dump_model()` and `get_dump()` now support exporting in GraphViz language (#4602)
+* Support metrics `ndcg-` and `map-` (#4635)
+* [jvm-packages] Allow chaining prediction (transform) in XGBoost4J-Spark (#4667)
+* [jvm-packages] Add option to bypass missing value check in the Spark layer (#4805). Only use this option if you know what you are doing.
+* [jvm-packages] Add public group getter (#4838)
+* `XGDMatrixSetGroup` C API is now deprecated (#4864). Use `XGDMatrixSetUIntInfo` instead.
+* [R] Added new `train_folds` parameter to `xgb.cv()` (#5114)
+* Ingest meta information from Pandas DataFrame, such as data weights (#5216)
+
+### Maintenance: Refactor code for legibility and maintainability
+* De-duplicate GPU parameters (#4454)
+* Simplify INI-style config reader using C++11 STL (#4478, #4521)
+* Refactor histogram building code for `gpu_hist` (#4528)
+* Overload device memory allocator, to enable instrumentation for compiling memory usage statistics (#4532)
+* Refactor out row partitioning logic from `gpu_hist` (#4554)
+* Remove an unused variable (#4588)
+* Implement tree model dump with code generator, to de-duplicate code for generating dumps in 3 different formats (#4602)
+* Remove `RowSet` class which is no longer being used (#4697)
+* Remove some unused functions as reported by cppcheck (#4743)
+* Mimic CUDA assert output in Span check (#4762)
+* [jvm-packages] Refactor `XGBoost.scala` to put all params processing in one place (#4815)
+* Add some comments for GPU row partitioner (#4832)
+* Span: use `size_t' for index_type,  add `front' and `back'. (#4935)
+* Remove dead code in `exact` algorithm (#5034, #5105)
+* Unify integer types used for row and column indices (#5034)
+* Extract feature interaction constraint from `SplitEvaluator` class. (#5034)
+* [Breaking] De-duplicate paramters and docstrings in the constructors of Scikit-Learn models (#5130)
+* Remove benchmark code from GPU tests (#5141)
+* Clean up Python 2 compatibility code. (#5161)
+* Extensible binary serialization format for `DMatrix::MetaInfo` (#5187). This will be useful for implementing censored labels for survival analysis applications.
+* Cleanup clang-tidy warnings. (#5247)
+
+### Maintenance: testing, continuous integration, build system
+* Use `yaml.safe_load` instead of `yaml.load`. (#4537)
+* Ensure GCC is at least 5.x (#4538)
+* Remove all mention of `reg:linear` from tests (#4544)
+* [jvm-packages] Upgrade to Scala 2.12 (#4574)
+* [jvm-packages] Update kryo dependency to 2.22 (#4575)
+* [CI] Specify account ID when logging into ECR Docker registry (#4584)
+* Use Sphinx 2.1+ to compile documentation (#4609)
+* Make Pandas optional for running Python unit tests (#4620)
+* Fix spark tests on machines with many cores (#4634)
+* [jvm-packages] Update local dev build process (#4640)
+* Add optional dependencies to setup.py (#4655)
+* [jvm-packages] Fix maven warnings (#4664)
+* Remove extraneous files from the R package, to comply with CRAN policy (#4699)
+* Remove VC-2013 support, since it is not C++11 compliant (#4701)
+* [CI] Fix broken installation of Pandas (#4704, #4722)
+* [jvm-packages] Clean up temporary files afer running tests (#4706)
+* Specify version macro in CMake. (#4730)
+* Include dmlc-tracker into XGBoost Python package (#4731)
+* [CI] Use long key ID for Ubuntu repository fingerprints. (#4783)
+* Remove plugin, cuda related code in automake & autoconf files (#4789)
+* Skip related tests when scikit-learn is not installed. (#4791)
+* Ignore vscode and clion files (#4866)
+* Use bundled Google Test by default (#4900)
+* [CI] Raise timeout threshold in Jenkins (#4938)
+* Copy CMake parameter from dmlc-core. (#4948)
+* Set correct file permission. (#4964)
+* [CI] Update lint configuration to support latest pylint convention (#4971)
+* [CI] Upload nightly builds to S3 (#4976, #4979)
+* Add asan.so.5 to cmake script. (#4999)
+* [CI] Fix Travis tests. (#5062)
+* [CI] Locate vcomp140.dll from System32 directory (#5078)
+* Implement training observer to dump internal states of objects (#5088). This will be useful for debugging.
+* Fix visual studio output library directories (#5119)
+* [jvm-packages] Comply with scala style convention + fix broken unit test (#5134)
+* [CI] Repair download URL for Maven 3.6.1 (#5139)
+* Don't use modernize-use-trailing-return-type in clang-tidy. (#5169)
+* Explicitly use UTF-8 codepage when using MSVC (#5197)
+* Add CMake option to run Undefined Behavior Sanitizer (UBSan) (#5211)
+* Make some GPU tests deterministic (#5229)
+* [R] Robust endian detection in CRAN xgboost build (#5232)
+* Support FreeBSD (#5233)
+* Make `pip install xgboost*.tar.gz` work by fixing build-python.sh (#5241)
+* Fix compilation error due to 64-bit integer narrowing to `size_t` (#5250)
+* Remove use of `std::cout` from R package, to comply with CRAN policy (#5261)
+* Update DMLC-Core submodule (#4674, #4688, #4726, #4924)
+* Update Rabit submodule (#4560, #4667, #4718, #4808, #4966, #5237)
+
+### Usability Improvements, Documentation
+* Add Random Forest API to Python API doc (#4500)
+* Fix Python demo and doc. (#4545)
+* Remove doc about not supporting cuda 10.1 (#4578)
+* Address some sphinx warnings and errors, add doc for building doc. (#4589)
+* Add instruction to run formatting checks locally (#4591)
+* Fix docstring for `XGBModel.predict()` (#4592)
+* Doc and demo for customized metric and objective (#4598, #4608)
+* Add to documentation how to run tests locally (#4610)
+* Empty evaluation list in early stopping should produce meaningful error message (#4633)
+* Fixed year to 2019 in conf.py, helpers.h and LICENSE (#4661)
+* Minor updates to links and grammar (#4673)
+* Remove `silent` in doc (#4689)
+* Remove old Python trouble shooting doc (#4729)
+* Add `os.PathLike` support for file paths to DMatrix and Booster Python classes (#4757)
+* Update XGBoost4J-Spark doc (#4804)
+* Regular formatting for evaluation metrics (#4803)
+* [jvm-packages] Refine documentation for handling missing values in XGBoost4J-Spark (#4805)
+* Monitor for distributed envorinment (#4829). This is useful for identifying performance bottleneck.
+* Add check for length of weights and produce a good error message (#4872)
+* Fix DMatrix doc (#4884)
+* Export C++ headers in CMake installation (#4897)
+* Update license year in README.md to 2019 (#4940)
+* Fix incorrectly displayed Note in the doc (#4943)
+* Follow PEP 257 Docstring Conventions (#4959)
+* Document minimum version required for Google Test (#5001)
+* Add better error message for invalid feature names (#5024)
+* Some guidelines on device memory usage (#5038)
+* [doc] Some notes for external memory. (#5065)
+* Update document for `tree_method` (#5106)
+* Update demo for ranking. (#5154)
+* Add new lines for Spark XGBoost missing values section (#5180)
+* Fix simple typo: utilty -> utility (#5182)
+* Update R doc by roxygen2 (#5201)
+* [R] Direct user to use `set.seed()` instead of setting `seed` parameter (#5125)
+* Add Optuna badge to `README.md` (#5208)
+* Fix compilation error in `c-api-demo.c` (#5215)
+
+### Acknowledgement
+**Contributors**: Nan Zhu (@CodingCat), Crissman Loomis (@Crissman), Cyprien Ricque (@Cyprien-Ricque), Evan Kepner (@EvanKepner), K.O. (@Hi-king), KaiJin Ji (@KerryJi), Peter Badida (@KeyWeeUsr), Kodi Arfer (@Kodiologist), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), Jacob Kim (@TheJacobKim), Vibhu Jawa (@VibhuJawa), Marcos (@astrowonk), Andy Adinets (@canonizer), Chen Qin (@chenqin), Christopher Cowden (@cowden), @cpfarrell, @david-cortes, Liangcai Li (@firestarman), @fuhaoda, Philip Hyunsu Cho (@hcho3), @here-nagini, Tong He (@hetong007), Michal Kurka (@michalkurka), Honza Sterba (@honzasterba), @iblumin, @koertkuipers, mattn (@mattn), Mingjie Tang (@merlintang), OrdoAbChao (@mglowacki100), Matthew Jones (@mt-jones), mitama (@nigimitama), Nathan Moore (@nmoorenz), Daniel Stahl (@phillyfan1138), Michaël Benesty (@pommedeterresautee), Rong Ou (@rongou), Sebastian (@sfahnens), Xu Xiao (@sperlingxx), @sriramch, Sean Owen (@srowen), Stephanie Yang (@stpyang), Yuan Tang (@terrytangyuan), Mathew Wicks (@thesuperzapper), Tim Gates (@timgates42), TinkleG (@tinkle1129), Oleksandr Pryimak (@trams), Jiaming Yuan (@trivialfis), Matvey Turkov (@turk0v), Bobby Wang (@wbo4958), yage (@yage99), @yellowdolphin
+
+**Reviewers**: Nan Zhu (@CodingCat), Crissman Loomis (@Crissman), Cyprien Ricque (@Cyprien-Ricque), Evan Kepner (@EvanKepner), John Zedlewski (@JohnZed), KOLANICH (@KOLANICH), KaiJin Ji (@KerryJi), Kodi Arfer (@Kodiologist), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), Nikita Titov (@StrikerRUS), Jacob Kim (@TheJacobKim), Vibhu Jawa (@VibhuJawa), Andrew Kane (@ankane), Arno Candel (@arnocandel), Marcos (@astrowonk), Bryan Woods (@bryan-woods), Andy Adinets (@canonizer), Chen Qin (@chenqin), Thomas Franke (@coding-komek), Peter  (@codingforfun), @cpfarrell, Joshua Patterson (@datametrician), @fuhaoda, Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), Honza Sterba (@honzasterba), @iblumin, @jakirkham, Vadim Khotilovich (@khotilov), Keith Kraus (@kkraus14), @koertkuipers, @melonki, Mingjie Tang (@merlintang), OrdoAbChao (@mglowacki100), Daniel Mahler (@mhlr), Matthew Rocklin (@mrocklin), Matthew Jones (@mt-jones), Michaël Benesty (@pommedeterresautee), PSEUDOTENSOR / Jonathan McKinney (@pseudotensor), Rong Ou (@rongou), Vladimir (@sh1ng), Scott Lundberg (@slundberg), Xu Xiao (@sperlingxx), @sriramch, Pasha Stetsenko (@st-pasha), Stephanie Yang (@stpyang), Yuan Tang (@terrytangyuan), Mathew Wicks (@thesuperzapper), Theodore Vasiloudis (@thvasilo), TinkleG (@tinkle1129), Oleksandr Pryimak (@trams), Jiaming Yuan (@trivialfis), Bobby Wang (@wbo4958), yage (@yage99), @yellowdolphin, Yin Lou (@yinlou)
+
+## v0.90 (2019.05.18)
+
+### XGBoost Python package drops Python 2.x (#4379, #4381)
+Python 2.x is reaching its end-of-life at the end of this year. [Many scientific Python packages are now moving to drop Python 2.x](https://python3statement.org/).
+
+### XGBoost4J-Spark now requires Spark 2.4.x (#4377)
+* Spark 2.3 is reaching its end-of-life soon. See discussion at #4389.
+* **Consistent handling of missing values** (#4309, #4349, #4411): Many users had reported issue with inconsistent predictions between XGBoost4J-Spark and the Python XGBoost package. The issue was caused by Spark mis-handling non-zero missing values (NaN, -1, 999 etc). We now alert the user whenever Spark doesn't handle missing values correctly (#4309, #4349). See [the tutorial for dealing with missing values in XGBoost4J-Spark](https://xgboost.readthedocs.io/en/release_0.90/jvm/xgboost4j_spark_tutorial.html#dealing-with-missing-values). This fix also depends on the availability of Spark 2.4.x.
+
+### Roadmap: better performance scaling for multi-core CPUs (#4310)
+* Poor performance scaling of the `hist` algorithm for multi-core CPUs has been under investigation (#3810). #4310 optimizes quantile sketches and other pre-processing tasks. Special thanks to @SmirnovEgorRu.
+
+### Roadmap: Harden distributed training (#4250)
+* Make distributed training in XGBoost more robust by hardening [Rabit](https://github.com/dmlc/rabit), which implements [the AllReduce primitive](https://en.wikipedia.org/wiki/Reduce_%28parallel_pattern%29). In particular, improve test coverage on mechanisms for fault tolerance and recovery. Special thanks to @chenqin.
+
+### New feature: Multi-class metric functions for GPUs (#4368)
+* Metrics for multi-class classification have been ported to GPU: `merror`, `mlogloss`. Special thanks to @trivialfis.
+* With supported metrics, XGBoost will select the correct devices based on your system and `n_gpus` parameter.
+
+### New feature: Scikit-learn-like random forest API (#4148, #4255, #4258)
+* XGBoost Python package now offers `XGBRFClassifier` and `XGBRFRegressor` API to train random forests. See [the tutorial](https://xgboost.readthedocs.io/en/release_0.90/tutorials/rf.html). Special thanks to @canonizer
+
+### New feature: use external memory in GPU predictor (#4284, #4396, #4438, #4457)
+* It is now possible to make predictions on GPU when the input is read from external memory. This is useful when you want to make predictions with big dataset that does not fit into the GPU memory. Special thanks to @rongou, @canonizer, @sriramch.
+
+  ```python
+  dtest = xgboost.DMatrix('test_data.libsvm#dtest.cache')
+  bst.set_param('predictor', 'gpu_predictor')
+  bst.predict(dtest)
+  ```
+
+* Coming soon: GPU training (`gpu_hist`) with external memory
+
+### New feature: XGBoost can now handle comments in LIBSVM files (#4430)
+* Special thanks to @trivialfis and @hcho3
+
+### New feature: Embed XGBoost in your C/C++ applications using CMake (#4323, #4333, #4453)
+* It is now easier than ever to embed XGBoost in your C/C++ applications. In your CMakeLists.txt, add `xgboost::xgboost` as a linked library:
+
+  ```cmake
+  find_package(xgboost REQUIRED)
+  add_executable(api-demo c-api-demo.c)
+  target_link_libraries(api-demo xgboost::xgboost)
+  ```
+
+  [XGBoost C API documentation is available.](https://xgboost.readthedocs.io/en/release_0.90/dev) Special thanks to @trivialfis
+
+### Performance improvements
+* Use feature interaction constraints to narrow split search space (#4341, #4428)
+* Additional optimizations for `gpu_hist` (#4248, #4283)
+* Reduce OpenMP thread launches in `gpu_hist` (#4343)
+* Additional optimizations for multi-node multi-GPU random forests. (#4238)
+* Allocate unique prediction buffer for each input matrix, to avoid re-sizing GPU array (#4275)
+* Remove various synchronisations from CUDA API calls (#4205)
+* XGBoost4J-Spark
+  - Allow the user to control whether to cache partitioned training data, to potentially reduce execution time (#4268)
+
+### Bug-fixes
+* Fix node reuse in `hist` (#4404)
+* Fix GPU histogram allocation (#4347)
+* Fix matrix attributes not sliced (#4311)
+* Revise AUC and AUCPR metrics now work with weighted ranking task (#4216, #4436)
+* Fix timer invocation for InitDataOnce() in `gpu_hist` (#4206)
+* Fix R-devel errors (#4251)
+* Make gradient update in GPU linear updater thread-safe (#4259)
+* Prevent out-of-range access in column matrix (#4231)
+* Don't store DMatrix handle in Python object until it's initialized, to improve exception safety (#4317)
+* XGBoost4J-Spark
+  - Fix non-deterministic order within a zipped partition on prediction (#4388)
+  - Remove race condition on tracker shutdown (#4224)
+  - Allow set the parameter `maxLeaves`. (#4226)
+  - Allow partial evaluation of dataframe before prediction (#4407)
+  - Automatically set `maximize_evaluation_metrics` if not explicitly given (#4446)
+
+### API changes
+* Deprecate `reg:linear` in favor of `reg:squarederror`. (#4267, #4427)
+* Add attribute getter and setter to the Booster object in XGBoost4J (#4336)
+
+### Maintenance: Refactor C++ code for legibility and maintainability
+* Fix clang-tidy warnings. (#4149)
+* Remove deprecated C APIs. (#4266)
+* Use Monitor class to time functions in `hist`. (#4273)
+* Retire DVec class in favour of c++20 style span for device memory. (#4293)
+* Improve HostDeviceVector exception safety (#4301)
+
+### Maintenance: testing, continuous integration, build system
+* **Major refactor of CMakeLists.txt** (#4323, #4333, #4453): adopt modern CMake and export XGBoost as a target
+* **Major improvement in Jenkins CI pipeline** (#4234)
+  - Migrate all Linux tests to Jenkins (#4401)
+  - Builds and tests are now de-coupled, to test an artifact against multiple versions of CUDA, JDK, and other dependencies (#4401)
+  - Add Windows GPU to Jenkins CI pipeline (#4463, #4469)
+* Support CUDA 10.1 (#4223, #4232, #4265, #4468)
+* Python wheels are now built with CUDA 9.0, so that JIT is not required on Volta architecture (#4459)
+* Integrate with NVTX CUDA profiler (#4205)
+* Add a test for cpu predictor using external memory (#4308)
+* Refactor tests to get rid of duplication (#4358)
+* Remove test dependency on `craigcitro/r-travis`, since it's deprecated (#4353)
+* Add files from local R build to `.gitignore` (#4346)
+* Make XGBoost4J compatible with Java 9+ by revising NativeLibLoader (#4351)
+* Jenkins build for CUDA 10.0 (#4281)
+* Remove remaining `silent` and `debug_verbose` in Python tests (#4299)
+* Use all cores to build XGBoost4J lib on linux (#4304)
+* Upgrade Jenkins Linux build environment to GCC 5.3.1, CMake 3.6.0 (#4306)
+* Make CMakeLists.txt compatible with CMake 3.3 (#4420)
+* Add OpenMP option in CMakeLists.txt (#4339)
+* Get rid of a few trivial compiler warnings (#4312)
+* Add external Docker build cache, to speed up builds on Jenkins CI (#4331, #4334, #4458)
+* Fix Windows tests (#4403)
+* Fix a broken python test (#4395)
+* Use a fixed seed to split data in XGBoost4J-Spark tests, for reproducibility (#4417)
+* Add additional Python tests to test training under constraints (#4426)
+* Enable building with shared NCCL. (#4447)
+
+### Usability Improvements, Documentation
+* Document limitation of one-split-at-a-time Greedy tree learning heuristic (#4233)
+* Update build doc: PyPI wheel now support multi-GPU (#4219)
+* Fix docs for `num_parallel_tree` (#4221)
+* Fix document about `colsample_by*` parameter (#4340)
+* Make the train and test input with same colnames. (#4329)
+* Update R contribute link. (#4236)
+* Fix travis R tests (#4277)
+* Log version number in crash log in XGBoost4J-Spark (#4271, #4303)
+* Allow supression of Rabit output in Booster::train in XGBoost4J (#4262)
+* Add tutorial on handling missing values in XGBoost4J-Spark (#4425)
+* Fix typos (#4345, #4393, #4432, #4435)
+* Added language classifier in setup.py (#4327)
+* Added Travis CI badge (#4344)
+* Add BentoML to use case section (#4400)
+* Remove subtly sexist remark (#4418)
+* Add R vignette about parsing JSON dumps (#4439)
+
+### Acknowledgement
+**Contributors**: Nan Zhu (@CodingCat), Adam Pocock (@Craigacp), Daniel Hen (@Daniel8hen), Jiaxiang Li (@JiaxiangBU), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), Andy Adinets (@canonizer), Jonas (@elcombato), Harry Braviner (@harrybraviner), Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), James Lamb (@jameslamb), Jean-Francois Zinque (@jeffzi), Yang Yang (@jokerkeny), Mayank Suman (@mayanksuman), jess (@monkeywithacupcake), Hajime Morrita (@omo), Ravi Kalia (@project-delphi), @ras44, Rong Ou (@rongou), Shaochen Shi (@shishaochen), Xu Xiao (@sperlingxx), @sriramch, Jiaming Yuan (@trivialfis), Christopher Suchanek (@wsuchy), Bozhao (@yubozhao)
+
+**Reviewers**: Nan Zhu (@CodingCat), Adam Pocock (@Craigacp), Daniel Hen (@Daniel8hen), Jiaxiang Li (@JiaxiangBU), Laurae (@Laurae2), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), @alois-bissuel, Andy Adinets (@canonizer), Chen Qin (@chenqin), Harry Braviner (@harrybraviner), Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), @jakirkham, James Lamb (@jameslamb), Julien Schueller (@jschueller), Mayank Suman (@mayanksuman), Hajime Morrita (@omo), Rong Ou (@rongou), Sara Robinson (@sararob), Shaochen Shi (@shishaochen), Xu Xiao (@sperlingxx), @sriramch, Sean Owen (@srowen), Sergei Lebedev (@superbobry), Yuan (Terry) Tang (@terrytangyuan), Theodore Vasiloudis (@thvasilo), Matthew Tovbin (@tovbinm), Jiaming Yuan (@trivialfis), Xin Yin (@xydrolase)
+
+## v0.82 (2019.03.03)
+This release is packed with many new features and bug fixes.
+
+### Roadmap: better performance scaling for multi-core CPUs (#3957)
+* Poor performance scaling of the `hist` algorithm for multi-core CPUs has been under investigation (#3810). #3957 marks an important step toward better performance scaling, by using software pre-fetching and replacing STL vectors with C-style arrays. Special thanks to @Laurae2 and @SmirnovEgorRu.
+* See #3810 for latest progress on this roadmap.
+
+### New feature: Distributed Fast Histogram Algorithm (`hist`) (#4011, #4102, #4140, #4128)
+* It is now possible to run the `hist` algorithm in distributed setting. Special thanks to @CodingCat. The benefits include:
+  1. Faster local computation via feature binning
+  2. Support for monotonic constraints and feature interaction constraints
+  3. Simpler codebase than `approx`, allowing for future improvement
+* Depth-wise tree growing is now performed in a separate code path, so that cross-node syncronization is performed only once per level.
+
+### New feature: Multi-Node, Multi-GPU training (#4095)
+* Distributed training is now able to utilize clusters equipped with NVIDIA GPUs. In particular, the rabit AllReduce layer will communicate GPU device information. Special thanks to @mt-jones, @RAMitchell, @rongou, @trivialfis, @canonizer, and @jeffdk.
+* Resource management systems will be able to assign a rank for each GPU in the cluster.
+* In Dask, users will be able to construct a collection of XGBoost processes over an inhomogeneous device cluster (i.e. workers with different number and/or kinds of GPUs).
+
+### New feature: Multiple validation datasets in XGBoost4J-Spark (#3904, #3910)
+* You can now track the performance of the model during training with multiple evaluation datasets. By specifying `eval_sets` or call `setEvalSets` over a `XGBoostClassifier` or `XGBoostRegressor`, you can pass in multiple evaluation datasets typed as a `Map` from `String` to `DataFrame`. Special thanks to @CodingCat.
+* See the usage of multiple validation datasets [here](https://github.com/dmlc/xgboost/blob/0c1d5f1120c0a159f2567b267f0ec4ffadee00d0/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala#L66-L78)
+
+### New feature: Additional metric functions for GPUs (#3952)
+* Element-wise metrics have been ported to GPU: `rmse`, `mae`, `logloss`, `poisson-nloglik`, `gamma-deviance`, `gamma-nloglik`, `error`, `tweedie-nloglik`. Special thanks to @trivialfis and @RAMitchell.
+* With supported metrics, XGBoost will select the correct devices based on your system and `n_gpus` parameter.
+
+### New feature: Column sampling at individual nodes (splits) (#3971)
+* Columns (features) can now be sampled at individual tree nodes, in addition to per-tree and per-level sampling. To enable per-node sampling, set `colsample_bynode` parameter, which represents the fraction of columns sampled at each node. This parameter is set to 1.0 by default (i.e. no sampling per node). Special thanks to @canonizer.
+* The `colsample_bynode` parameter works cumulatively with other `colsample_by*` parameters: for example, `{'colsample_bynode':0.5, 'colsample_bytree':0.5}` with 100 columns will give 25 features to choose from at each split.
+
+### Major API change: consistent logging level via `verbosity` (#3982, #4002, #4138)
+* XGBoost now allows fine-grained control over logging. You can set `verbosity` to 0 (silent), 1 (warning), 2 (info), and 3 (debug). This is useful for controlling the amount of logging outputs. Special thanks to @trivialfis.
+* Parameters `silent` and `debug_verbose` are now deprecated.
+* Note: Sometimes XGBoost tries to change configurations based on heuristics, which is displayed as warning message.  If there's unexpected behaviour, please try to increase value of verbosity.
+
+### Major bug fix: external memory (#4040, #4193)
+* Clarify object ownership in multi-threaded prefetcher, to avoid memory error.
+* Correctly merge two column batches (which uses [CSC layout](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS))).
+* Add unit tests for external memory.
+* Special thanks to @trivialfis and @hcho3.
+
+### Major bug fix: early stopping fixed in XGBoost4J and XGBoost4J-Spark (#3928, #4176)
+* Early stopping in XGBoost4J and XGBoost4J-Spark is now consistent with its counterpart in the Python package. Training stops if the current iteration is `earlyStoppingSteps` away from the best iteration. If there are multiple evaluation sets, only the last one is used to determinate early stop.
+* See the updated documentation [here](https://xgboost.readthedocs.io/en/release_0.82/jvm/xgboost4j_spark_tutorial.html#early-stopping)
+* Special thanks to @CodingCat, @yanboliang, and @mingyang.
+
+### Major bug fix: infrequent features should not crash distributed training (#4045)
+* For infrequently occuring features, some partitions may not get any instance. This scenario used to crash distributed training due to mal-formed ranges. The problem has now been fixed.
+* In practice, one-hot-encoded categorical variables tend to produce rare features, particularly when the cardinality is high.
+* Special thanks to @CodingCat.
+
+### Performance improvements
+* Faster, more space-efficient radix sorting in `gpu_hist` (#3895)
+* Subtraction trick in histogram calculation in `gpu_hist` (#3945)
+* More performant re-partition in XGBoost4J-Spark (#4049)
+
+### Bug-fixes
+* Fix semantics of `gpu_id` when running multiple XGBoost processes on a multi-GPU machine (#3851)
+* Fix page storage path for external memory on Windows (#3869)
+* Fix configuration setup so that DART utilizes GPU (#4024)
+* Eliminate NAN values from SHAP prediction (#3943)
+* Prevent empty quantile sketches in `hist` (#4155)
+* Enable running objectives with 0 GPU (#3878)
+* Parameters are no longer dependent on system locale (#3891, #3907)
+* Use consistent data type in the GPU coordinate descent code (#3917)
+* Remove undefined behavior in the CLI config parser on the ARM platform (#3976)
+* Initialize counters in GPU AllReduce (#3987)
+* Prevent deadlocks in GPU AllReduce (#4113)
+* Load correct values from sliced NumPy arrays (#4147, #4165)
+* Fix incorrect GPU device selection (#4161)
+* Make feature binning logic in `hist` aware of query groups when running a ranking task (#4115). For ranking task, query groups are weighted, not individual instances.
+* Generate correct C++ exception type for `LOG(FATAL)` macro (#4159)
+* Python package
+  - Python package should run on system without `PATH` environment variable (#3845)
+  - Fix `coef_` and `intercept_` signature to be compatible with `sklearn.RFECV` (#3873)
+  - Use UTF-8 encoding in Python package README, to support non-English locale (#3867)
+  - Add AUC-PR to list of metrics to maximize for early stopping (#3936)
+  - Allow loading pickles without `self.booster` attribute, for backward compatibility (#3938, #3944)
+  - White-list DART for feature importances (#4073)
+  - Update usage of [h2oai/datatable](https://github.com/h2oai/datatable) (#4123)
+* XGBoost4J-Spark
+  - Address scalability issue in prediction (#4033)
+  - Enforce the use of per-group weights for ranking task (#4118)
+  - Fix vector size of `rawPredictionCol` in `XGBoostClassificationModel` (#3932)
+  - More robust error handling in Spark tracker (#4046, #4108)
+  - Fix return type of `setEvalSets` (#4105)
+  - Return correct value of `getMaxLeaves` (#4114)
+
+### API changes
+* Add experimental parameter `single_precision_histogram` to use single-precision histograms for the `gpu_hist` algorithm (#3965)
+* Python package
+  - Add option to select type of feature importances in the scikit-learn inferface (#3876)
+  - Add `trees_to_df()` method to dump decision trees as Pandas data frame (#4153)
+  - Add options to control node shapes in the GraphViz plotting function (#3859)
+  - Add `xgb_model` option to `XGBClassifier`, to load previously saved model (#4092)
+  - Passing lists into `DMatrix` is now deprecated (#3970)
+* XGBoost4J
+  - Support multiple feature importance features (#3801)
+
+### Maintenance: Refactor C++ code for legibility and maintainability
+* Refactor `hist` algorithm code and add unit tests (#3836)
+* Minor refactoring of split evaluator in `gpu_hist` (#3889)
+* Removed unused leaf vector field in the tree model (#3989)
+* Simplify the tree representation by combining `TreeModel` and `RegTree` classes (#3995)
+* Simplify and harden tree expansion code (#4008, #4015)
+* De-duplicate parameter classes in the linear model algorithms (#4013)
+* Robust handling of ranges with C++20 span in `gpu_exact` and `gpu_coord_descent` (#4020, #4029)
+* Simplify tree training code (#3825). Also use Span class for robust handling of ranges.
+
+### Maintenance: testing, continuous integration, build system
+* Disallow `std::regex` since it's not supported by GCC 4.8.x (#3870)
+* Add multi-GPU tests for coordinate descent algorithm for linear models (#3893, #3974)
+* Enforce naming style in Python lint (#3896)
+* Refactor Python tests (#3897, #3901): Use pytest exclusively, display full trace upon failure
+* Address `DeprecationWarning` when using Python collections (#3909)
+* Use correct group for maven site plugin (#3937)
+* Jenkins CI is now using on-demand EC2 instances exclusively, due to unreliability of Spot instances (#3948)
+* Better GPU performance logging (#3945)
+* Fix GPU tests on machines with only 1 GPU (#4053)
+* Eliminate CRAN check warnings and notes (#3988)
+* Add unit tests for tree serialization (#3989)
+* Add unit tests for tree fitting functions in `hist` (#4155)
+* Add a unit test for `gpu_exact` algorithm (#4020)
+* Correct JVM CMake GPU flag (#4071)
+* Fix failing Travis CI on Mac (#4086)
+* Speed up Jenkins by not compiling CMake (#4099)
+* Analyze C++ and CUDA code using clang-tidy, as part of Jenkins CI pipeline (#4034)
+* Fix broken R test: Install Homebrew GCC (#4142)
+* Check for empty datasets in GPU unit tests (#4151)
+* Fix Windows compilation (#4139)
+* Comply with latest convention of cpplint (#4157)
+* Fix a unit test in `gpu_hist` (#4158)
+* Speed up data generation in Python tests (#4164)
+
+### Usability Improvements
+* Add link to [InfoWorld 2019 Technology of the Year Award](https://www.infoworld.com/article/3336072/application-development/infoworlds-2019-technology-of-the-year-award-winners.html) (#4116)
+* Remove outdated AWS YARN tutorial (#3885)
+* Document current limitation in number of features (#3886)
+* Remove unnecessary warning when `gblinear` is selected (#3888)
+* Document limitation of CSV parser: header not supported (#3934)
+* Log training parameters in XGBoost4J-Spark (#4091)
+* Clarify early stopping behavior in the scikit-learn interface (#3967)
+* Clarify behavior of `max_depth` parameter (#4078)
+* Revise Python docstrings for ranking task (#4121). In particular, weights must be per-group in learning-to-rank setting.
+* Document parameter `num_parallel_tree` (#4022)
+* Add Jenkins status badge (#4090)
+* Warn users against using internal functions of `Booster` object (#4066)
+* Reformat `benchmark_tree.py` to comply with Python style convention (#4126)
+* Clarify a comment in `objectiveTrait` (#4174)
+* Fix typos and broken links in documentation (#3890, #3872, #3902, #3919, #3975, #4027, #4156, #4167)
+
+### Acknowledgement
+**Contributors** (in no particular order): Jiaming Yuan (@trivialfis), Hyunsu Cho (@hcho3), Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), Yanbo Liang (@yanboliang), Andy Adinets (@canonizer), Tong He (@hetong007), Yuan Tang (@terrytangyuan)
+
+**First-time Contributors** (in no particular order): Jelle Zijlstra (@JelleZijlstra), Jiacheng Xu (@jiachengxu), @ajing, Kashif Rasul (@kashif), @theycallhimavi, Joey Gao (@pjgao), Prabakaran Kumaresshan (@nixphix), Huafeng Wang (@huafengw), @lyxthe, Sam Wilkinson (@scwilkinson), Tatsuhito Kato (@stabacov), Shayak Banerjee (@shayakbanerjee), Kodi Arfer (@Kodiologist), @KyleLi1985, Egor Smirnov (@SmirnovEgorRu), @tmitanitky, Pasha Stetsenko (@st-pasha), Kenichi Nagahara (@keni-chi), Abhai Kollara Dilip (@abhaikollara), Patrick Ford (@pford221), @hshujuan, Matthew Jones (@mt-jones), Thejaswi Rao (@teju85), Adam November (@anovember)
+
+**First-time Reviewers** (in no particular order): Mingyang Hu (@mingyang), Theodore Vasiloudis (@thvasilo), Jakub Troszok (@troszok), Rong Ou (@rongou), @Denisevi4, Matthew Jones (@mt-jones), Jeff Kaplan (@jeffdk)
+
 ## v0.81 (2018.11.04)
 ### New feature: feature interaction constraints
 * Users are now able to control which features (independent variables) are allowed to interact by specifying feature interaction constraints (#3466).
@@ -179,7 +1163,7 @@ This file records the changes in xgboost library in reverse chronological order.
   - Latest master: https://xgboost.readthedocs.io/en/latest
   - 0.80 stable: https://xgboost.readthedocs.io/en/release_0.80
   - 0.72 stable: https://xgboost.readthedocs.io/en/release_0.72
-* Ranking task now uses instance weights (#3379)
+* Support for per-group weights in ranking objective (#3379)
 * Fix inaccurate decimal parsing (#3546)
 * New functionality
   - Query ID column support in LIBSVM data files (#2749). This is convenient for performing ranking task in distributed setting.

R-package/CMakeLists.txt

@@ -0,0 +1,44 @@
+find_package(LibR REQUIRED)
+message(STATUS "LIBR_CORE_LIBRARY " ${LIBR_CORE_LIBRARY})
+
+file(GLOB_RECURSE R_SOURCES
+  ${CMAKE_CURRENT_LIST_DIR}/src/*.cc
+  ${CMAKE_CURRENT_LIST_DIR}/src/*.c)
+# Use object library to expose symbols
+add_library(xgboost-r OBJECT ${R_SOURCES})
+if (ENABLE_ALL_WARNINGS)
+  target_compile_options(xgboost-r PRIVATE -Wall -Wextra)
+endif (ENABLE_ALL_WARNINGS)
+target_compile_definitions(xgboost-r
+  PUBLIC
+  -DXGBOOST_STRICT_R_MODE=1
+  -DXGBOOST_CUSTOMIZE_GLOBAL_PRNG=1
+  -DDMLC_LOG_BEFORE_THROW=0
+  -DDMLC_DISABLE_STDIN=1
+  -DDMLC_LOG_CUSTOMIZE=1
+  -DRABIT_CUSTOMIZE_MSG_
+  -DRABIT_STRICT_CXX98_)
+target_include_directories(xgboost-r
+  PRIVATE
+  ${LIBR_INCLUDE_DIRS}
+  ${PROJECT_SOURCE_DIR}/include
+  ${PROJECT_SOURCE_DIR}/dmlc-core/include
+  ${PROJECT_SOURCE_DIR}/rabit/include)
+target_link_libraries(xgboost-r PUBLIC ${LIBR_CORE_LIBRARY})
+if (USE_OPENMP)
+  find_package(OpenMP REQUIRED)
+  target_link_libraries(xgboost-r PUBLIC OpenMP::OpenMP_CXX OpenMP::OpenMP_C)
+endif (USE_OPENMP)
+set_target_properties(
+  xgboost-r PROPERTIES
+  CXX_STANDARD 14
+  CXX_STANDARD_REQUIRED ON
+  POSITION_INDEPENDENT_CODE ON)
+
+# Get compilation and link flags of xgboost-r and propagate to objxgboost
+target_link_libraries(objxgboost PUBLIC xgboost-r)
+# Add all objects of xgboost-r to objxgboost
+target_sources(objxgboost INTERFACE $<TARGET_OBJECTS:xgboost-r>)
+
+set(LIBR_HOME "${LIBR_HOME}" PARENT_SCOPE)
+set(LIBR_EXECUTABLE "${LIBR_EXECUTABLE}" PARENT_SCOPE)

R-package/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
-Version: 0.81.0.1
-Date: 2018-08-13
+Version: 1.3.3.1
+Date: 2020-08-28
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),
@@ -31,9 +31,9 @@ Authors@R: c(
   )
 Description: Extreme Gradient Boosting, which is an efficient implementation
     of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
-    This package is its R interface. The package includes efficient linear 
-    model solver and tree learning algorithms. The package can automatically 
-    do parallel computation on a single machine which could be more than 10 
+    This package is its R interface. The package includes efficient linear
+    model solver and tree learning algorithms. The package can automatically
+    do parallel computation on a single machine which could be more than 10
     times faster than existing gradient boosting packages. It supports
     various objective functions, including regression, classification and ranking.
     The package is made to be extensible, so that users are also allowed to define
@@ -52,7 +52,11 @@ Suggests:
     vcd (>= 1.3),
     testthat,
     lintr,
-    igraph (>= 1.0.1)
+    igraph (>= 1.0.1),
+    jsonlite,
+    float,
+    crayon,
+    titanic
 Depends:
     R (>= 3.3.0)
 Imports:
@@ -60,6 +64,5 @@ Imports:
     methods,
     data.table (>= 1.9.6),
     magrittr (>= 1.5),
-    stringi (>= 0.5.2)
-RoxygenNote: 6.1.0
-SystemRequirements: GNU make, C++11
+RoxygenNote: 7.1.1
+SystemRequirements: GNU make, C++14

R-package/NAMESPACE

@@ -14,6 +14,7 @@ S3method(setinfo,xgb.DMatrix)
 S3method(slice,xgb.DMatrix)
 export("xgb.attr<-")
 export("xgb.attributes<-")
+export("xgb.config<-")
 export("xgb.parameters<-")
 export(cb.cv.predict)
 export(cb.early.stop)
@@ -30,23 +31,29 @@ export(xgb.DMatrix)
 export(xgb.DMatrix.save)
 export(xgb.attr)
 export(xgb.attributes)
+export(xgb.config)
 export(xgb.create.features)
 export(xgb.cv)
 export(xgb.dump)
 export(xgb.gblinear.history)
 export(xgb.ggplot.deepness)
 export(xgb.ggplot.importance)
+export(xgb.ggplot.shap.summary)
 export(xgb.importance)
 export(xgb.load)
+export(xgb.load.raw)
 export(xgb.model.dt.tree)
 export(xgb.plot.deepness)
 export(xgb.plot.importance)
 export(xgb.plot.multi.trees)
 export(xgb.plot.shap)
+export(xgb.plot.shap.summary)
 export(xgb.plot.tree)
 export(xgb.save)
 export(xgb.save.raw)
+export(xgb.serialize)
 export(xgb.train)
+export(xgb.unserialize)
 export(xgboost)
 import(methods)
 importClassesFrom(Matrix,dgCMatrix)
@@ -74,11 +81,6 @@ importFrom(graphics,title)
 importFrom(magrittr,"%>%")
 importFrom(stats,median)
 importFrom(stats,predict)
-importFrom(stringi,stri_detect_regex)
-importFrom(stringi,stri_match_first_regex)
-importFrom(stringi,stri_replace_all_regex)
-importFrom(stringi,stri_replace_first_regex)
-importFrom(stringi,stri_split_regex)
 importFrom(utils,head)
 importFrom(utils,object.size)
 importFrom(utils,str)

R-package/R/callbacks.R

@@ -1,26 +1,26 @@
 #' Callback closures for booster training.
 #'
 #' These are used to perform various service tasks either during boosting iterations or at the end.
-#' This approach helps to modularize many of such tasks without bloating the main training methods, 
+#' This approach helps to modularize many of such tasks without bloating the main training methods,
 #' and it offers .
-#' 
+#'
 #' @details
 #' By default, a callback function is run after each boosting iteration.
 #' An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
-#' 
-#' When a callback function has \code{finalize} parameter, its finalizer part will also be run after 
+#'
+#' When a callback function has \code{finalize} parameter, its finalizer part will also be run after
 #' the boosting is completed.
-#' 
-#' WARNING: side-effects!!! Be aware that these callback functions access and modify things in 
+#'
+#' WARNING: side-effects!!! Be aware that these callback functions access and modify things in
 #' the environment from which they are called from, which is a fairly uncommon thing to do in R.
-#' 
-#' To write a custom callback closure, make sure you first understand the main concepts about R envoronments.
-#' Check either R documentation on \code{\link[base]{environment}} or the 
-#' \href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R" 
+#'
+#' To write a custom callback closure, make sure you first understand the main concepts about R environments.
+#' Check either R documentation on \code{\link[base]{environment}} or the
+#' \href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
 #' book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
-#' choose ones that do something similar to what you want to achieve. Also, you would need to get familiar 
+#' choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
 #' with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
-#' 
+#'
 #' @seealso
 #' \code{\link{cb.print.evaluation}},
 #' \code{\link{cb.evaluation.log}},
@@ -30,43 +30,43 @@
 #' \code{\link{cb.cv.predict}},
 #' \code{\link{xgb.train}},
 #' \code{\link{xgb.cv}}
-#' 
+#'
 #' @name callbacks
 NULL
 
 #
 # Callbacks -------------------------------------------------------------------
-# 
+#
 
 #' Callback closure for printing the result of evaluation
-#' 
+#'
 #' @param period  results would be printed every number of periods
 #' @param showsd  whether standard deviations should be printed (when available)
-#' 
+#'
 #' @details
 #' The callback function prints the result of evaluation at every \code{period} iterations.
 #' The initial and the last iteration's evaluations are always printed.
-#' 
+#'
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{bst_evaluation} (also \code{bst_evaluation_err} when available),
 #' \code{iteration},
 #' \code{begin_iteration},
 #' \code{end_iteration}.
-#' 
+#'
 #' @seealso
 #' \code{\link{callbacks}}
-#' 
+#'
 #' @export
 cb.print.evaluation <- function(period = 1, showsd = TRUE) {
-  
+
   callback <- function(env = parent.frame()) {
     if (length(env$bst_evaluation) == 0 ||
         period == 0 ||
-        NVL(env$rank, 0) != 0 )
+        NVL(env$rank, 0) != 0)
       return()
-    
-    i <- env$iteration 
-    if ((i-1) %% period == 0 ||
+
+    i <- env$iteration
+    if ((i - 1) %% period == 0 ||
         i == env$begin_iteration ||
         i == env$end_iteration) {
       stdev <- if (showsd) env$bst_evaluation_err else NULL
@@ -81,72 +81,72 @@ cb.print.evaluation <- function(period = 1, showsd = TRUE) {
 
 
 #' Callback closure for logging the evaluation history
-#' 
+#'
 #' @details
 #' This callback function appends the current iteration evaluation results \code{bst_evaluation}
 #' available in the calling parent frame to the \code{evaluation_log} list in a calling frame.
-#' 
-#' The finalizer callback (called with \code{finalize = TURE} in the end) converts 
+#'
+#' The finalizer callback (called with \code{finalize = TURE} in the end) converts
 #' the \code{evaluation_log} list into a final data.table.
-#' 
-#' The iteration evaluation result \code{bst_evaluation} must be a named numeric vector. 
-#' 
-#' Note: in the column names of the final data.table, the dash '-' character is replaced with 
+#'
+#' The iteration evaluation result \code{bst_evaluation} must be a named numeric vector.
+#'
+#' Note: in the column names of the final data.table, the dash '-' character is replaced with
 #' the underscore '_' in order to make the column names more like regular R identifiers.
-#' 
+#'
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{evaluation_log},
 #' \code{bst_evaluation},
 #' \code{iteration}.
-#' 
+#'
 #' @seealso
 #' \code{\link{callbacks}}
-#' 
+#'
 #' @export
 cb.evaluation.log <- function() {
 
   mnames <- NULL
-  
+
   init <- function(env) {
     if (!is.list(env$evaluation_log))
       stop("'evaluation_log' has to be a list")
     mnames <<- names(env$bst_evaluation)
     if (is.null(mnames) || any(mnames == ""))
       stop("bst_evaluation must have non-empty names")
-    
+
     mnames <<- gsub('-', '_', names(env$bst_evaluation))
-    if(!is.null(env$bst_evaluation_err))
+    if (!is.null(env$bst_evaluation_err))
       mnames <<- c(paste0(mnames, '_mean'), paste0(mnames, '_std'))
   }
-  
+
   finalizer <- function(env) {
     env$evaluation_log <- as.data.table(t(simplify2array(env$evaluation_log)))
     setnames(env$evaluation_log, c('iter', mnames))
-    
-    if(!is.null(env$bst_evaluation_err)) {
+
+    if (!is.null(env$bst_evaluation_err)) {
       # rearrange col order from _mean,_mean,...,_std,_std,...
       # to be _mean,_std,_mean,_std,...
       len <- length(mnames)
-      means <- mnames[seq_len(len/2)]
-      stds <- mnames[(len/2 + 1):len]
+      means <- mnames[seq_len(len / 2)]
+      stds <- mnames[(len / 2 + 1):len]
       cnames <- numeric(len)
       cnames[c(TRUE, FALSE)] <- means
       cnames[c(FALSE, TRUE)] <- stds
       env$evaluation_log <- env$evaluation_log[, c('iter', cnames), with = FALSE]
     }
   }
-  
+
   callback <- function(env = parent.frame(), finalize = FALSE) {
     if (is.null(mnames))
       init(env)
 
     if (finalize)
       return(finalizer(env))
-    
+
     ev <- env$bst_evaluation
-    if(!is.null(env$bst_evaluation_err))
+    if (!is.null(env$bst_evaluation_err))
       ev <- c(ev, env$bst_evaluation_err)
-    env$evaluation_log <- c(env$evaluation_log, 
+    env$evaluation_log <- c(env$evaluation_log,
                             list(c(iter = env$iteration, ev)))
   }
   attr(callback, 'call') <- match.call()
@@ -154,21 +154,21 @@ cb.evaluation.log <- function() {
   callback
 }
 
-#' Callback closure for restetting the booster's parameters at each iteration.
-#' 
+#' Callback closure for resetting the booster's parameters at each iteration.
+#'
 #' @param new_params a list where each element corresponds to a parameter that needs to be reset.
-#'        Each element's value must be either a vector of values of length \code{nrounds} 
-#'        to be set at each iteration, 
-#'        or a function of two parameters \code{learning_rates(iteration, nrounds)} 
-#'        which returns a new parameter value by using the current iteration number 
+#'        Each element's value must be either a vector of values of length \code{nrounds}
+#'        to be set at each iteration,
+#'        or a function of two parameters \code{learning_rates(iteration, nrounds)}
+#'        which returns a new parameter value by using the current iteration number
 #'        and the total number of boosting rounds.
-#' 
-#' @details 
+#'
+#' @details
 #' This is a "pre-iteration" callback function used to reset booster's parameters
 #' at the beginning of each iteration.
-#' 
-#' Note that when training is resumed from some previous model, and a function is used to 
-#' reset a parameter value, the \code{nrounds} argument in this function would be the 
+#'
+#' Note that when training is resumed from some previous model, and a function is used to
+#' reset a parameter value, the \code{nrounds} argument in this function would be the
 #' the number of boosting rounds in the current training.
 #'
 #' Callback function expects the following values to be set in its calling frame:
@@ -176,32 +176,32 @@ cb.evaluation.log <- function() {
 #' \code{iteration},
 #' \code{begin_iteration},
 #' \code{end_iteration}.
-#' 
+#'
 #' @seealso
 #' \code{\link{callbacks}}
-#' 
+#'
 #' @export
 cb.reset.parameters <- function(new_params) {
 
-  if (typeof(new_params) != "list") 
+  if (typeof(new_params) != "list")
     stop("'new_params' must be a list")
   pnames <- gsub("\\.", "_", names(new_params))
   nrounds <- NULL
-  
+
   # run some checks in the begining
   init <- function(env) {
     nrounds <<- env$end_iteration - env$begin_iteration + 1
-    
+
     if (is.null(env$bst) && is.null(env$bst_folds))
       stop("Parent frame has neither 'bst' nor 'bst_folds'")
-    
+
     # Some parameters are not allowed to be changed,
     # since changing them would simply wreck some chaos
-    not_allowed <- pnames %in% 
+    not_allowed <- pnames %in%
       c('num_class', 'num_output_group', 'size_leaf_vector', 'updater_seq')
     if (any(not_allowed))
       stop('Parameters ', paste(pnames[not_allowed]), " cannot be changed during boosting.")
-    
+
     for (n in pnames) {
       p <- new_params[[n]]
       if (is.function(p)) {
@@ -215,18 +215,18 @@ cb.reset.parameters <- function(new_params) {
       }
     }
   }
-  
+
   callback <- function(env = parent.frame()) {
     if (is.null(nrounds))
       init(env)
-    
+
     i <- env$iteration
     pars <- lapply(new_params, function(p) {
       if (is.function(p))
         return(p(i, nrounds))
       p[i]
     })
-    
+
     if (!is.null(env$bst)) {
       xgb.parameters(env$bst$handle) <- pars
     } else {
@@ -242,23 +242,23 @@ cb.reset.parameters <- function(new_params) {
 
 
 #' Callback closure to activate the early stopping.
-#' 
-#' @param stopping_rounds The number of rounds with no improvement in 
+#'
+#' @param stopping_rounds The number of rounds with no improvement in
 #'        the evaluation metric in order to stop the training.
 #' @param maximize whether to maximize the evaluation metric
 #' @param metric_name the name of an evaluation column to use as a criteria for early
 #'        stopping. If not set, the last column would be used.
-#'        Let's say the test data in \code{watchlist} was labelled as \code{dtest}, 
-#'        and one wants to use the AUC in test data for early stopping regardless of where 
+#'        Let's say the test data in \code{watchlist} was labelled as \code{dtest},
+#'        and one wants to use the AUC in test data for early stopping regardless of where
 #'        it is in the \code{watchlist}, then one of the following would need to be set:
 #'        \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
 #'        All dash '-' characters in metric names are considered equivalent to '_'.
 #' @param verbose whether to print the early stopping information.
-#' 
+#'
 #' @details
-#' This callback function determines the condition for early stopping 
+#' This callback function determines the condition for early stopping
 #' by setting the \code{stop_condition = TRUE} flag in its calling frame.
-#' 
+#'
 #' The following additional fields are assigned to the model's R object:
 #' \itemize{
 #' \item \code{best_score} the evaluation score at the best iteration
@@ -266,13 +266,13 @@ cb.reset.parameters <- function(new_params) {
 #' \item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
 #'      It differs from \code{best_iteration} in multiclass or random forest settings.
 #' }
-#' 
+#'
 #' The Same values are also stored as xgb-attributes:
 #' \itemize{
 #' \item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
 #' \item \code{best_msg} message string is also stored.
 #' }
-#' 
+#'
 #' At least one data element is required in the evaluation watchlist for early stopping to work.
 #'
 #' Callback function expects the following values to be set in its calling frame:
@@ -284,13 +284,13 @@ cb.reset.parameters <- function(new_params) {
 #' \code{begin_iteration},
 #' \code{end_iteration},
 #' \code{num_parallel_tree}.
-#' 
+#'
 #' @seealso
 #' \code{\link{callbacks}},
 #' \code{\link{xgb.attr}}
-#' 
+#'
 #' @export
-cb.early.stop <- function(stopping_rounds, maximize = FALSE, 
+cb.early.stop <- function(stopping_rounds, maximize = FALSE,
                           metric_name = NULL, verbose = TRUE) {
   # state variables
   best_iteration <- -1
@@ -298,11 +298,11 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
   best_score <- Inf
   best_msg <- NULL
   metric_idx <- 1
-  
+
   init <- function(env) {
     if (length(env$bst_evaluation) == 0)
       stop("For early stopping, watchlist must have at least one element")
-    
+
     eval_names <- gsub('-', '_', names(env$bst_evaluation))
     if (!is.null(metric_name)) {
       metric_idx <<- which(gsub('-', '_', metric_name) == eval_names)
@@ -314,25 +314,25 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
         length(env$bst_evaluation) > 1) {
       metric_idx <<- length(eval_names)
       if (verbose)
-        cat('Multiple eval metrics are present. Will use ', 
+        cat('Multiple eval metrics are present. Will use ',
             eval_names[metric_idx], ' for early stopping.\n', sep = '')
     }
-    
+
     metric_name <<- eval_names[metric_idx]
-    
+
     # maximize is usually NULL when not set in xgb.train and built-in metrics
     if (is.null(maximize))
       maximize <<- grepl('(_auc|_map|_ndcg)', metric_name)
 
     if (verbose && NVL(env$rank, 0) == 0)
-      cat("Will train until ", metric_name, " hasn't improved in ", 
+      cat("Will train until ", metric_name, " hasn't improved in ",
           stopping_rounds, " rounds.\n\n", sep = '')
 
     best_iteration <<- 1
     if (maximize) best_score <<- -Inf
-    
+
     env$stop_condition <- FALSE
-    
+
     if (!is.null(env$bst)) {
       if (!inherits(env$bst, 'xgb.Booster'))
         stop("'bst' in the parent frame must be an 'xgb.Booster'")
@@ -348,16 +348,22 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
       stop("Parent frame has neither 'bst' nor ('bst_folds' and 'basket')")
     }
   }
-  
+
   finalizer <- function(env) {
     if (!is.null(env$bst)) {
-      attr_best_score = as.numeric(xgb.attr(env$bst$handle, 'best_score'))
-      if (best_score != attr_best_score)
-        stop("Inconsistent 'best_score' values between the closure state: ", best_score,
-             " and the xgb.attr: ", attr_best_score)
-      env$bst$best_iteration = best_iteration
-      env$bst$best_ntreelimit = best_ntreelimit
-      env$bst$best_score = best_score
+      attr_best_score <- as.numeric(xgb.attr(env$bst$handle, 'best_score'))
+      if (best_score != attr_best_score) {
+        # If the difference is too big, throw an error
+        if (abs(best_score - attr_best_score) >= 1e-14) {
+          stop("Inconsistent 'best_score' values between the closure state: ", best_score,
+               " and the xgb.attr: ", attr_best_score)
+        }
+        # If the difference is due to floating-point truncation, update best_score
+        best_score <- attr_best_score
+      }
+      env$bst$best_iteration <- best_iteration
+      env$bst$best_ntreelimit <- best_ntreelimit
+      env$bst$best_score <- best_score
     } else {
       env$basket$best_iteration <- best_iteration
       env$basket$best_ntreelimit <- best_ntreelimit
@@ -367,16 +373,16 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
   callback <- function(env = parent.frame(), finalize = FALSE) {
     if (best_iteration < 0)
       init(env)
-    
+
     if (finalize)
       return(finalizer(env))
-    
+
     i <- env$iteration
-    score = env$bst_evaluation[metric_idx]
-    
-    if (( maximize && score > best_score) ||
+    score <- env$bst_evaluation[metric_idx]
+
+    if ((maximize && score > best_score) ||
         (!maximize && score < best_score)) {
-      
+
       best_msg <<- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
       best_score <<- score
       best_iteration <<- i
@@ -403,37 +409,37 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
 
 
 #' Callback closure for saving a model file.
-#' 
-#' @param save_period save the model to disk after every 
+#'
+#' @param save_period save the model to disk after every
 #'        \code{save_period} iterations; 0 means save the model at the end.
 #' @param save_name the name or path for the saved model file.
-#'        It can contain a \code{\link[base]{sprintf}} formatting specifier 
+#'        It can contain a \code{\link[base]{sprintf}} formatting specifier
 #'        to include the integer iteration number in the file name.
-#'        E.g., with \code{save_name} = 'xgboost_%04d.model', 
+#'        E.g., with \code{save_name} = 'xgboost_%04d.model',
 #'        the file saved at iteration 50 would be named "xgboost_0050.model".
-#' 
-#' @details 
+#'
+#' @details
 #' This callback function allows to save an xgb-model file, either periodically after each \code{save_period}'s or at the end.
-#' 
+#'
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{bst},
 #' \code{iteration},
 #' \code{begin_iteration},
 #' \code{end_iteration}.
-#' 
+#'
 #' @seealso
 #' \code{\link{callbacks}}
-#' 
+#'
 #' @export
 cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
-  
+
   if (save_period < 0)
     stop("'save_period' cannot be negative")
 
   callback <- function(env = parent.frame()) {
     if (is.null(env$bst))
       stop("'save_model' callback requires the 'bst' booster object in its calling frame")
-    
+
     if ((save_period > 0 && (env$iteration - env$begin_iteration) %% save_period == 0) ||
         (save_period == 0 && env$iteration == env$end_iteration))
       xgb.save(env$bst, sprintf(save_name, env$iteration))
@@ -445,16 +451,16 @@ cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
 
 
 #' Callback closure for returning cross-validation based predictions.
-#' 
+#'
 #' @param save_models a flag for whether to save the folds' models.
-#' 
-#' @details 
+#'
+#' @details
 #' This callback function saves predictions for all of the test folds,
 #' and also allows to save the folds' models.
-#' 
+#'
 #' It is a "finalizer" callback and it uses early stopping information whenever it is available,
 #' thus it must be run after the early stopping callback if the early stopping is used.
-#' 
+#'
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{bst_folds},
 #' \code{basket},
@@ -463,36 +469,36 @@ cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
 #' \code{params},
 #' \code{num_parallel_tree},
 #' \code{num_class}.
-#' 
-#' @return 
+#'
+#' @return
 #' Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
-#' depending on the number of prediction outputs per data row. The order of predictions corresponds 
-#' to the order of rows in the original dataset. Note that when a custom \code{folds} list is 
-#' provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a 
-#' non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be 
-#' meaningful when user-profided folds have overlapping indices as in, e.g., random sampling splits.
+#' depending on the number of prediction outputs per data row. The order of predictions corresponds
+#' to the order of rows in the original dataset. Note that when a custom \code{folds} list is
+#' provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a
+#' non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
+#' meaningful when user-provided folds have overlapping indices as in, e.g., random sampling splits.
 #' When some of the indices in the training dataset are not included into user-provided \code{folds},
 #' their prediction value would be \code{NA}.
-#' 
+#'
 #' @seealso
 #' \code{\link{callbacks}}
-#' 
+#'
 #' @export
 cb.cv.predict <- function(save_models = FALSE) {
-  
+
   finalizer <- function(env) {
     if (is.null(env$basket) || is.null(env$bst_folds))
       stop("'cb.cv.predict' callback requires 'basket' and 'bst_folds' lists in its calling frame")
-    
+
     N <- nrow(env$data)
-    pred <- 
+    pred <-
       if (env$num_class > 1) {
         matrix(NA_real_, N, env$num_class)
       } else {
         rep(NA_real_, N)
       }
 
-    ntreelimit <- NVL(env$basket$best_ntreelimit, 
+    ntreelimit <- NVL(env$basket$best_ntreelimit,
                       env$end_iteration * env$num_parallel_tree)
     if (NVL(env$params[['booster']], '') == 'gblinear') {
       ntreelimit <- 0 # must be 0 for gblinear
@@ -500,7 +506,7 @@ cb.cv.predict <- function(save_models = FALSE) {
     for (fd in env$bst_folds) {
       pr <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit, reshape = TRUE)
       if (is.matrix(pred)) {
-        pred[fd$index,] <- pr
+        pred[fd$index, ] <- pr
       } else {
         pred[fd$index] <- pr
       }
@@ -569,7 +575,7 @@ cb.cv.predict <- function(save_models = FALSE) {
 #' # Extract the coefficients' path and plot them vs boosting iteration number:
 #' coef_path <- xgb.gblinear.history(bst)
 #' matplot(coef_path, type = 'l')
-#' 
+#'
 #' # With the deterministic coordinate descent updater, it is safer to use higher learning rates.
 #' # Will try the classical componentwise boosting which selects a single best feature per round:
 #' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8,
@@ -586,7 +592,7 @@ cb.cv.predict <- function(save_models = FALSE) {
 #' # coefficients in the CV fold #3
 #' xgb.gblinear.history(bst)[[3]] %>% matplot(type = 'l')
 #'
-#' 
+#'
 #' #### Multiclass classification:
 #' #
 #' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
@@ -613,9 +619,7 @@ cb.gblinear.history <- function(sparse=FALSE) {
 
   init <- function(env) {
     if (!is.null(env$bst)) { # xgb.train:
-      coef_path <- list()
     } else if (!is.null(env$bst_folds)) { # xgb.cv:
-      coef_path <- rep(list(), length(env$bst_folds))
     } else stop("Parent frame has neither 'bst' nor 'bst_folds'")
   }
 
@@ -681,9 +685,9 @@ cb.gblinear.history <- function(sparse=FALSE) {
 #'        using the \code{cb.gblinear.history()} callback.
 #' @param class_index zero-based class index to extract the coefficients for only that
 #'        specific class in a multinomial multiclass model. When it is NULL, all the
-#'        coeffients are returned. Has no effect in non-multiclass models.
+#'        coefficients are returned. Has no effect in non-multiclass models.
 #'
-#' @return 
+#' @return
 #' For an \code{xgb.train} result, a matrix (either dense or sparse) with the columns
 #' corresponding to iteration's coefficients (in the order as \code{xgb.dump()} would
 #' return) and the rows corresponding to boosting iterations.
@@ -705,11 +709,11 @@ xgb.gblinear.history <- function(model, class_index = NULL) {
   if (!is_cv) {
     # extract num_class & num_feat from the internal model
     dmp <- xgb.dump(model)
-    if(length(dmp) < 2 || dmp[2] != "bias:")
+    if (length(dmp) < 2 || dmp[2] != "bias:")
       stop("It does not appear to be a gblinear model")
-    dmp <- dmp[-c(1,2)]
+    dmp <- dmp[-c(1, 2)]
     n <- which(dmp == 'weight:')
-    if(length(n) != 1)
+    if (length(n) != 1)
       stop("It does not appear to be a gblinear model")
     num_class <- n - 1
     num_feat <- (length(dmp) - 4) / num_class
@@ -731,10 +735,10 @@ xgb.gblinear.history <- function(model, class_index = NULL) {
   coef_path <- environment(model$callbacks$cb.gblinear.history)[["coefs"]]
   if (!is.null(class_index) && num_class > 1) {
     coef_path <- if (is.list(coef_path)) {
-      lapply(coef_path, 
-             function(x) x[, seq(1 + class_index, by=num_class, length.out=num_feat)])
+      lapply(coef_path,
+             function(x) x[, seq(1 + class_index, by = num_class, length.out = num_feat)])
     } else {
-      coef_path <- coef_path[, seq(1 + class_index, by=num_class, length.out=num_feat)]
+      coef_path <- coef_path[, seq(1 + class_index, by = num_class, length.out = num_feat)]
     }
   }
   coef_path
@@ -743,7 +747,7 @@ xgb.gblinear.history <- function(model, class_index = NULL) {
 
 #
 # Internal utility functions for callbacks ------------------------------------
-# 
+#
 
 # Format the evaluation metric string
 format.eval.string <- function(iter, eval_res, eval_err = NULL) {
@@ -773,7 +777,7 @@ callback.calls <- function(cb_list) {
   unlist(lapply(cb_list, function(x) attr(x, 'call')))
 }
 
-# Add a callback cb to the list and make sure that 
+# Add a callback cb to the list and make sure that
 # cb.early.stop and cb.cv.predict are at the end of the list
 # with cb.cv.predict being the last (when present)
 add.cb <- function(cb_list, cb) {
@@ -782,11 +786,11 @@ add.cb <- function(cb_list, cb) {
   if ('cb.early.stop' %in% names(cb_list)) {
     cb_list <- c(cb_list, cb_list['cb.early.stop'])
     # this removes only the first one
-    cb_list['cb.early.stop'] <- NULL 
+    cb_list['cb.early.stop'] <- NULL
   }
   if ('cb.cv.predict' %in% names(cb_list)) {
     cb_list <- c(cb_list, cb_list['cb.cv.predict'])
-    cb_list['cb.cv.predict'] <- NULL 
+    cb_list['cb.cv.predict'] <- NULL
   }
   cb_list
 }
@@ -796,7 +800,7 @@ categorize.callbacks <- function(cb_list) {
   list(
     pre_iter = Filter(function(x) {
         pre <- attr(x, 'is_pre_iteration')
-        !is.null(pre) && pre 
+        !is.null(pre) && pre
       }, cb_list),
     post_iter = Filter(function(x) {
         pre <- attr(x, 'is_pre_iteration')

R-package/R/utils.R

@@ -20,6 +20,12 @@ NVL <- function(x, val) {
   stop("typeof(x) == ", typeof(x), " is not supported by NVL")
 }
 
+# List of classification and ranking objectives
+.CLASSIFICATION_OBJECTIVES <- function() {
+  return(c('binary:logistic', 'binary:logitraw', 'binary:hinge', 'multi:softmax',
+           'multi:softprob', 'rank:pairwise', 'rank:ndcg', 'rank:map'))
+}
+
 
 #
 # Low-level functions for boosting --------------------------------------------
@@ -28,12 +34,12 @@ NVL <- function(x, val) {
 # Merges booster params with whatever is provided in ...
 # plus runs some checks
 check.booster.params <- function(params, ...) {
-  if (typeof(params) != "list") 
+  if (!identical(class(params), "list"))
     stop("params must be a list")
-  
+
   # in R interface, allow for '.' instead of '_' in parameter names
   names(params) <- gsub("\\.", "_", names(params))
-  
+
   # merge parameters from the params and the dots-expansion
   dot_params <- list(...)
   names(dot_params) <- gsub("\\.", "_", names(dot_params))
@@ -41,15 +47,15 @@ check.booster.params <- function(params, ...) {
                        names(dot_params))) > 0)
     stop("Same parameters in 'params' and in the call are not allowed. Please check your 'params' list.")
   params <- c(params, dot_params)
-  
+
   # providing a parameter multiple times makes sense only for 'eval_metric'
   name_freqs <- table(names(params))
   multi_names <- setdiff(names(name_freqs[name_freqs > 1]), 'eval_metric')
   if (length(multi_names) > 0) {
     warning("The following parameters were provided multiple times:\n\t",
             paste(multi_names, collapse = ', '), "\n  Only the last value for each of them will be used.\n")
-    # While xgboost internals would choose the last value for a multiple-times parameter, 
-    # enforce it here in R as well (b/c multi-parameters might be used further in R code, 
+    # While xgboost internals would choose the last value for a multiple-times parameter,
+    # enforce it here in R as well (b/c multi-parameters might be used further in R code,
     # and R takes the 1st value when multiple elements with the same name are present in a list).
     for (n in multi_names) {
       del_idx <- which(n == names(params))
@@ -57,35 +63,35 @@ check.booster.params <- function(params, ...) {
       params[[del_idx]] <- NULL
     }
   }
-  
+
   # for multiclass, expect num_class to be set
   if (typeof(params[['objective']]) == "character" &&
       substr(NVL(params[['objective']], 'x'), 1, 6) == 'multi:' &&
       as.numeric(NVL(params[['num_class']], 0)) < 2) {
         stop("'num_class' > 1 parameter must be set for multiclass classification")
   }
-  
+
   # monotone_constraints parser
-  
+
   if (!is.null(params[['monotone_constraints']]) &&
       typeof(params[['monotone_constraints']]) != "character") {
-        vec2str = paste(params[['monotone_constraints']], collapse = ',')
-        vec2str = paste0('(', vec2str, ')')
-        params[['monotone_constraints']] = vec2str
+        vec2str <- paste(params[['monotone_constraints']], collapse = ',')
+        vec2str <- paste0('(', vec2str, ')')
+        params[['monotone_constraints']] <- vec2str
   }
-  
+
   # interaction constraints parser (convert from list of column indices to string)
-  if (!is.null(params[['interaction_constraints']]) && 
+  if (!is.null(params[['interaction_constraints']]) &&
       typeof(params[['interaction_constraints']]) != "character"){
     # check input class
-    if (class(params[['interaction_constraints']]) != 'list') stop('interaction_constraints should be class list')
-    if (!all(unique(sapply(params[['interaction_constraints']], class)) %in% c('numeric','integer'))) {
+    if (!identical(class(params[['interaction_constraints']]), 'list')) stop('interaction_constraints should be class list')
+    if (!all(unique(sapply(params[['interaction_constraints']], class)) %in% c('numeric', 'integer'))) {
       stop('interaction_constraints should be a list of numeric/integer vectors')
     }
 
     # recast parameter as string
-    interaction_constraints <- sapply(params[['interaction_constraints']], function(x) paste0('[', paste(x, collapse=','), ']'))
-    params[['interaction_constraints']] <- paste0('[', paste(interaction_constraints, collapse=','), ']')
+    interaction_constraints <- sapply(params[['interaction_constraints']], function(x) paste0('[', paste(x, collapse = ','), ']'))
+    params[['interaction_constraints']] <- paste0('[', paste(interaction_constraints, collapse = ','), ']')
   }
   return(params)
 }
@@ -96,10 +102,10 @@ check.booster.params <- function(params, ...) {
 check.custom.obj <- function(env = parent.frame()) {
   if (!is.null(env$params[['objective']]) && !is.null(env$obj))
     stop("Setting objectives in 'params' and 'obj' at the same time is not allowed")
-  
+
   if (!is.null(env$obj) && typeof(env$obj) != 'closure')
     stop("'obj' must be a function")
-  
+
   # handle the case when custom objective function was provided through params
   if (!is.null(env$params[['objective']]) &&
       typeof(env$params$objective) == 'closure') {
@@ -113,21 +119,21 @@ check.custom.obj <- function(env = parent.frame()) {
 check.custom.eval <- function(env = parent.frame()) {
   if (!is.null(env$params[['eval_metric']]) && !is.null(env$feval))
     stop("Setting evaluation metrics in 'params' and 'feval' at the same time is not allowed")
-  
+
   if (!is.null(env$feval) && typeof(env$feval) != 'closure')
     stop("'feval' must be a function")
-  
+
   # handle a situation when custom eval function was provided through params
   if (!is.null(env$params[['eval_metric']]) &&
       typeof(env$params$eval_metric) == 'closure') {
     env$feval <- env$params$eval_metric
     env$params$eval_metric <- NULL
   }
-  
+
   # require maximize to be set when custom feval and early stopping are used together
   if (!is.null(env$feval) &&
       is.null(env$maximize) && (
-        !is.null(env$early_stopping_rounds) || 
+        !is.null(env$early_stopping_rounds) ||
         has.callbacks(env$callbacks, 'cb.early.stop')))
     stop("Please set 'maximize' to indicate whether the evaluation metric needs to be maximized or not")
 }
@@ -145,7 +151,8 @@ xgb.iter.update <- function(booster_handle, dtrain, iter, obj = NULL) {
   if (is.null(obj)) {
     .Call(XGBoosterUpdateOneIter_R, booster_handle, as.integer(iter), dtrain)
   } else {
-    pred <- predict(booster_handle, dtrain)
+    pred <- predict(booster_handle, dtrain, outputmargin = TRUE, training = TRUE,
+                    ntreelimit = 0)
     gpair <- obj(pred, dtrain)
     .Call(XGBoosterBoostOneIter_R, booster_handle, dtrain, gpair$grad, gpair$hess)
   }
@@ -154,25 +161,24 @@ xgb.iter.update <- function(booster_handle, dtrain, iter, obj = NULL) {
 
 
 # Evaluate one iteration.
-# Returns a named vector of evaluation metrics 
+# Returns a named vector of evaluation metrics
 # with the names in a 'datasetname-metricname' format.
 xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
   if (!identical(class(booster_handle), "xgb.Booster.handle"))
     stop("class of booster_handle must be xgb.Booster.handle")
 
-  if (length(watchlist) == 0) 
+  if (length(watchlist) == 0)
     return(NULL)
-  
+
   evnames <- names(watchlist)
   if (is.null(feval)) {
     msg <- .Call(XGBoosterEvalOneIter_R, booster_handle, as.integer(iter), watchlist, as.list(evnames))
-    msg <- stri_split_regex(msg, '(\\s+|:|\\s+)')[[1]][-1]
-    res <- as.numeric(msg[c(FALSE,TRUE)]) # even indices are the values
-    names(res) <- msg[c(TRUE,FALSE)]      # odds are the names
+    mat <- matrix(strsplit(msg, '\\s+|:')[[1]][-1], nrow = 2)
+    res <- structure(as.numeric(mat[2, ]), names = mat[1, ])
   } else {
     res <- sapply(seq_along(watchlist), function(j) {
       w <- watchlist[[j]]
-      preds <- predict(booster_handle, w) # predict using all trees
+      preds <- predict(booster_handle, w, outputmargin = TRUE, ntreelimit = 0) # predict using all trees
       eval_res <- feval(preds, w)
       out <- eval_res$value
       names(out) <- paste0(evnames[j], "-", eval_res$metric)
@@ -187,13 +193,23 @@ xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
 # Helper functions for cross validation ---------------------------------------
 #
 
+# Possibly convert the labels into factors, depending on the objective.
+# The labels are converted into factors only when the given objective refers to the classification
+# or ranking tasks.
+convert.labels <- function(labels, objective_name) {
+  if (objective_name %in% .CLASSIFICATION_OBJECTIVES()) {
+    return(as.factor(labels))
+  } else {
+    return(labels)
+  }
+}
+
 # Generates random (stratified if needed) CV folds
 generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
-  
+
   # cannot do it for rank
-  if (exists('objective', where = params) &&
-      is.character(params$objective) &&
-      strtrim(params$objective, 5) == 'rank:') {
+  objective <- params$objective
+  if (is.character(objective) && strtrim(objective, 5) == 'rank:') {
     stop("\n\tAutomatic generation of CV-folds is not implemented for ranking!\n",
          "\tConsider providing pre-computed CV-folds through the 'folds=' parameter.\n")
   }
@@ -206,18 +222,16 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
     #  - For classification, need to convert y labels to factor before making the folds,
     #    and then do stratification by factor levels.
     #  - For regression, leave y numeric and do stratification by quantiles.
-    if (exists('objective', where = params) &&
-        is.character(params$objective)) {
-      # If 'objective' provided in params, assume that y is a classification label
-      # unless objective is reg:linear
-      if (params$objective != 'reg:linear')
-        y <- factor(y)
+    if (is.character(objective)) {
+      y <- convert.labels(y, params$objective)
     } else {
-      # If no 'objective' given in params, it means that user either wants to use
-      # the default 'reg:linear' objective or has provided a custom obj function.
-      # Here, assume classification setting when y has 5 or less unique values:
-      if (length(unique(y)) <= 5)
+      # If no 'objective' given in params, it means that user either wants to
+      # use the default 'reg:squarederror' objective or has provided a custom
+      # obj function.  Here, assume classification setting when y has 5 or less
+      # unique values:
+      if (length(unique(y)) <= 5) {
         y <- factor(y)
+      }
     }
     folds <- xgb.createFolds(y, nfold)
   } else {
@@ -293,29 +307,91 @@ xgb.createFolds <- function(y, k = 10)
 #
 
 #' Deprecation notices.
-#' 
+#'
 #' At this time, some of the parameter names were changed in order to make the code style more uniform.
 #' The deprecated parameters would be removed in the next release.
-#' 
+#'
 #' To see all the current deprecated and new parameters, check the \code{xgboost:::depr_par_lut} table.
-#' 
-#' A deprecation warning is shown when any of the deprecated parameters is used in a call. 
-#' An additional warning is shown when there was a partial match to a deprecated parameter 
+#'
+#' A deprecation warning is shown when any of the deprecated parameters is used in a call.
+#' An additional warning is shown when there was a partial match to a deprecated parameter
 #' (as R is able to partially match parameter names).
-#' 
+#'
 #' @name xgboost-deprecated
 NULL
 
+#' Do not use \code{\link[base]{saveRDS}} or \code{\link[base]{save}} for long-term archival of
+#' models. Instead, use \code{\link{xgb.save}} or \code{\link{xgb.save.raw}}.
+#'
+#' It is a common practice to use the built-in \code{\link[base]{saveRDS}} function (or
+#' \code{\link[base]{save}}) to persist R objects to the disk. While it is possible to persist
+#' \code{xgb.Booster} objects using \code{\link[base]{saveRDS}}, it is not advisable to do so if
+#' the model is to be accessed in the future. If you train a model with the current version of
+#' XGBoost and persist it with \code{\link[base]{saveRDS}}, the model is not guaranteed to be
+#' accessible in later releases of XGBoost. To ensure that your model can be accessed in future
+#' releases of XGBoost, use \code{\link{xgb.save}} or \code{\link{xgb.save.raw}} instead.
+#'
+#' @details
+#' Use \code{\link{xgb.save}} to save the XGBoost model as a stand-alone file. You may opt into
+#' the JSON format by specifying the JSON extension. To read the model back, use
+#' \code{\link{xgb.load}}.
+#'
+#' Use \code{\link{xgb.save.raw}} to save the XGBoost model as a sequence (vector) of raw bytes
+#' in a future-proof manner. Future releases of XGBoost will be able to read the raw bytes and
+#' re-construct the corresponding model. To read the model back, use \code{\link{xgb.load.raw}}.
+#' The \code{\link{xgb.save.raw}} function is useful if you'd like to persist the XGBoost model
+#' as part of another R object.
+#'
+#' Note: Do not use \code{\link{xgb.serialize}} to store models long-term. It persists not only the
+#' model but also internal configurations and parameters, and its format is not stable across
+#' multiple XGBoost versions. Use \code{\link{xgb.serialize}} only for checkpointing.
+#'
+#' For more details and explanation about model persistence and archival, consult the page
+#' \url{https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html}.
+#'
+#' @examples
+#' data(agaricus.train, package='xgboost')
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
+#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+#'
+#' # Save as a stand-alone file; load it with xgb.load()
+#' xgb.save(bst, 'xgb.model')
+#' bst2 <- xgb.load('xgb.model')
+#'
+#' # Save as a stand-alone file (JSON); load it with xgb.load()
+#' xgb.save(bst, 'xgb.model.json')
+#' bst2 <- xgb.load('xgb.model.json')
+#' if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
+#'
+#' # Save as a raw byte vector; load it with xgb.load.raw()
+#' xgb_bytes <- xgb.save.raw(bst)
+#' bst2 <- xgb.load.raw(xgb_bytes)
+#'
+#' # Persist XGBoost model as part of another R object
+#' obj <- list(xgb_model_bytes = xgb.save.raw(bst), description = "My first XGBoost model")
+#' # Persist the R object. Here, saveRDS() is okay, since it doesn't persist
+#' # xgb.Booster directly. What's being persisted is the future-proof byte representation
+#' # as given by xgb.save.raw().
+#' saveRDS(obj, 'my_object.rds')
+#' # Read back the R object
+#' obj2 <- readRDS('my_object.rds')
+#' # Re-construct xgb.Booster object from the bytes
+#' bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
+#' if (file.exists('my_object.rds')) file.remove('my_object.rds')
+#'
+#' @name a-compatibility-note-for-saveRDS-save
+NULL
+
 # Lookup table for the deprecated parameters bookkeeping
 depr_par_lut <- matrix(c(
-  'print.every.n', 'print_every_n', 
+  'print.every.n', 'print_every_n',
   'early.stop.round', 'early_stopping_rounds',
   'training.data', 'data',
   'with.stats', 'with_stats',
   'numberOfClusters', 'n_clusters',
   'features.keep', 'features_keep',
-  'plot.height','plot_height',
-  'plot.width','plot_width',
+  'plot.height', 'plot_height',
+  'plot.width', 'plot_width',
   'n_first_tree', 'trees',
   'dummy', 'DUMMY'
 ), ncol = 2, byrow = TRUE)
@@ -328,20 +404,20 @@ colnames(depr_par_lut) <- c('old', 'new')
 check.deprecation <- function(..., env = parent.frame()) {
   pars <- list(...)
   # exact and partial matches
-  all_match <- pmatch(names(pars), depr_par_lut[,1])
+  all_match <- pmatch(names(pars), depr_par_lut[, 1])
   # indices of matched pars' names
   idx_pars <- which(!is.na(all_match))
   if (length(idx_pars) == 0) return()
   # indices of matched LUT rows
   idx_lut <- all_match[idx_pars]
   # which of idx_lut were the exact matches?
-  ex_match <- depr_par_lut[idx_lut,1] %in% names(pars)
+  ex_match <- depr_par_lut[idx_lut, 1] %in% names(pars)
   for (i in seq_along(idx_pars)) {
     pars_par <- names(pars)[idx_pars[i]]
     old_par <- depr_par_lut[idx_lut[i], 1]
     new_par <- depr_par_lut[idx_lut[i], 2]
     if (!ex_match[i]) {
-      warning("'", pars_par, "' was partially matched to '", old_par,"'")
+      warning("'", pars_par, "' was partially matched to '", old_par, "'")
     }
     .Deprecated(new_par, old = old_par, package = 'xgboost')
     if (new_par != 'NULL') {

R-package/R/xgb.Booster.R

@@ -1,24 +1,39 @@
 # Construct an internal xgboost Booster and return a handle to it.
 # internal utility function
-xgb.Booster.handle <- function(params = list(), cachelist = list(), modelfile = NULL) {
+xgb.Booster.handle <- function(params = list(), cachelist = list(),
+                               modelfile = NULL) {
   if (typeof(cachelist) != "list" ||
       !all(vapply(cachelist, inherits, logical(1), what = 'xgb.DMatrix'))) {
     stop("cachelist must be a list of xgb.DMatrix objects")
   }
-
-  handle <- .Call(XGBoosterCreate_R, cachelist)
+  ## Load existing model, dispatch for on disk model file and in memory buffer
   if (!is.null(modelfile)) {
     if (typeof(modelfile) == "character") {
+      ## A filename
+      handle <- .Call(XGBoosterCreate_R, cachelist)
       .Call(XGBoosterLoadModel_R, handle, modelfile[1])
+      class(handle) <- "xgb.Booster.handle"
+      if (length(params) > 0) {
+        xgb.parameters(handle) <- params
+      }
+      return(handle)
     } else if (typeof(modelfile) == "raw") {
-      .Call(XGBoosterLoadModelFromRaw_R, handle, modelfile)
+      ## A memory buffer
+      bst <- xgb.unserialize(modelfile)
+      xgb.parameters(bst) <- params
+      return (bst)
     } else if (inherits(modelfile, "xgb.Booster")) {
+      ## A booster object
       bst <- xgb.Booster.complete(modelfile, saveraw = TRUE)
-      .Call(XGBoosterLoadModelFromRaw_R, handle, bst$raw)
+      bst <- xgb.unserialize(bst$raw)
+      xgb.parameters(bst) <- params
+      return (bst)
     } else {
       stop("modelfile must be either character filename, or raw booster dump, or xgb.Booster object")
     }
   }
+  ## Create new model
+  handle <- .Call(XGBoosterCreate_R, cachelist)
   class(handle) <- "xgb.Booster.handle"
   if (length(params) > 0) {
     xgb.parameters(handle) <- params
@@ -48,14 +63,16 @@ is.null.handle <- function(handle) {
   return(FALSE)
 }
 
-# Return a verified to be valid handle out of either xgb.Booster.handle or xgb.Booster
-# internal utility function
+# Return a verified to be valid handle out of either xgb.Booster.handle or
+# xgb.Booster internal utility function
 xgb.get.handle <- function(object) {
-  handle <- switch(class(object)[1],
-    xgb.Booster = object$handle,
-    xgb.Booster.handle = object,
+  if (inherits(object, "xgb.Booster")) {
+    handle <- object$handle
+  } else if (inherits(object, "xgb.Booster.handle")) {
+    handle <- object
+  } else {
     stop("argument must be of either xgb.Booster or xgb.Booster.handle class")
-  )
+  }
   if (is.null.handle(handle)) {
     stop("invalid xgb.Booster.handle")
   }
@@ -81,7 +98,7 @@ xgb.get.handle <- function(object) {
 #' its handle (pointer) to an internal xgboost model would be invalid. The majority of xgboost methods
 #' should still work for such a model object since those methods would be using
 #' \code{xgb.Booster.complete} internally. However, one might find it to be more efficient to call the
-#' \code{xgb.Booster.complete} function explicitely once after loading a model as an R-object.
+#' \code{xgb.Booster.complete} function explicitly once after loading a model as an R-object.
 #' That would prevent further repeated implicit reconstruction of an internal booster model.
 #'
 #' @return
@@ -94,7 +111,10 @@ xgb.get.handle <- function(object) {
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #' saveRDS(bst, "xgb.model.rds")
 #'
+#' # Warning: The resulting RDS file is only compatible with the current XGBoost version.
+#' # Refer to the section titled "a-compatibility-note-for-saveRDS-save".
 #' bst1 <- readRDS("xgb.model.rds")
+#' if (file.exists("xgb.model.rds")) file.remove("xgb.model.rds")
 #' # the handle is invalid:
 #' print(bst1$handle)
 #'
@@ -110,9 +130,29 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
   if (is.null.handle(object$handle)) {
     object$handle <- xgb.Booster.handle(modelfile = object$raw)
   } else {
-    if (is.null(object$raw) && saveraw)
-      object$raw <- xgb.save.raw(object$handle)
+    if (is.null(object$raw) && saveraw) {
+      object$raw <- xgb.serialize(object$handle)
+    }
   }
+
+  attrs <- xgb.attributes(object)
+  if (!is.null(attrs$best_ntreelimit)) {
+    object$best_ntreelimit <- as.integer(attrs$best_ntreelimit)
+  }
+  if (!is.null(attrs$best_iteration)) {
+    ## Convert from 0 based back to 1 based.
+    object$best_iteration <- as.integer(attrs$best_iteration) + 1
+  }
+  if (!is.null(attrs$best_score)) {
+    object$best_score <- as.numeric(attrs$best_score)
+  }
+  if (!is.null(attrs$best_msg)) {
+    object$best_msg <- attrs$best_msg
+  }
+  if (!is.null(attrs$niter)) {
+    object$niter <- as.integer(attrs$niter)
+  }
+
   return(object)
 }
 
@@ -136,6 +176,8 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' @param reshape whether to reshape the vector of predictions to a matrix form when there are several
 #'        prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
 #'        or predinteraction flags is TRUE.
+#' @param training whether is the prediction result used for training.  For dart booster,
+#'        training predicting will perform dropout.
 #' @param ... Parameters passed to \code{predict.xgb.Booster}
 #'
 #' @details
@@ -162,7 +204,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'
 #' With \code{predinteraction = TRUE}, SHAP values of contributions of interaction of each pair of features
 #' are computed. Note that this operation might be rather expensive in terms of compute and memory.
-#' Since it quadratically depends on the number of features, it is recommended to perfom selection
+#' Since it quadratically depends on the number of features, it is recommended to perform selection
 #' of the most important features first. See below about the format of the returned results.
 #'
 #' @return
@@ -190,7 +232,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'
 #' @seealso
 #' \code{\link{xgb.train}}.
-#' 
+#'
 #' @references
 #'
 #' Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
@@ -285,7 +327,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' @export
 predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
                                 predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
-                                reshape = FALSE, ...) {
+                                reshape = FALSE, training = FALSE, ...) {
 
   object <- xgb.Booster.complete(object, saveraw = FALSE)
   if (!inherits(newdata, "xgb.DMatrix"))
@@ -304,7 +346,8 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) +
     8L * as.logical(approxcontrib) + 16L * as.logical(predinteraction)
 
-  ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1], as.integer(ntreelimit))
+  ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1],
+               as.integer(ntreelimit), as.integer(training))
 
   n_ret <- length(ret)
   n_row <- nrow(newdata)
@@ -329,8 +372,8 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
       matrix(ret, nrow = n_row, byrow = TRUE, dimnames = list(NULL, cnames))
     } else {
       arr <- array(ret, c(n_col1, n_group, n_row),
-                   dimnames = list(cnames, NULL, NULL)) %>% aperm(c(2,3,1)) # [group, row, col]
-      lapply(seq_len(n_group), function(g) arr[g,,])
+                   dimnames = list(cnames, NULL, NULL)) %>% aperm(c(2, 3, 1)) # [group, row, col]
+      lapply(seq_len(n_group), function(g) arr[g, , ])
     }
   } else if (predinteraction) {
     n_col1 <- ncol(newdata) + 1
@@ -339,11 +382,11 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
     ret <- if (n_ret == n_row) {
       matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
     } else if (n_group == 1) {
-      array(ret, c(n_col1, n_col1, n_row), dimnames = list(cnames, cnames, NULL)) %>% aperm(c(3,1,2))
+      array(ret, c(n_col1, n_col1, n_row), dimnames = list(cnames, cnames, NULL)) %>% aperm(c(3, 1, 2))
     } else {
       arr <- array(ret, c(n_col1, n_col1, n_group, n_row),
-                   dimnames = list(cnames, cnames, NULL, NULL)) %>% aperm(c(3,4,1,2)) # [group, row, col1, col2]
-      lapply(seq_len(n_group), function(g) arr[g,,,])
+                   dimnames = list(cnames, cnames, NULL, NULL)) %>% aperm(c(3, 4, 1, 2)) # [group, row, col1, col2]
+      lapply(seq_len(n_group), function(g) arr[g, , , ])
     }
   } else if (reshape && npred_per_case > 1) {
     ret <- matrix(ret, nrow = n_row, byrow = TRUE)
@@ -393,7 +436,7 @@ predict.xgb.Booster.handle <- function(object, ...) {
 #' That would only matter if attributes need to be set many times.
 #' Note, however, that when feeding a handle of an \code{xgb.Booster} object to the attribute setters,
 #' the raw model cache of an \code{xgb.Booster} object would not be automatically updated,
-#' and it would be user's responsibility to call \code{xgb.save.raw} to update it.
+#' and it would be user's responsibility to call \code{xgb.serialize} to update it.
 #'
 #' The \code{xgb.attributes<-} setter either updates the existing or adds one or several attributes,
 #' but it doesn't delete the other existing attributes.
@@ -418,6 +461,7 @@ predict.xgb.Booster.handle <- function(object, ...) {
 #'
 #' xgb.save(bst, 'xgb.model')
 #' bst1 <- xgb.load('xgb.model')
+#' if (file.exists('xgb.model')) file.remove('xgb.model')
 #' print(xgb.attr(bst1, "my_attribute"))
 #' print(xgb.attributes(bst1))
 #'
@@ -451,7 +495,7 @@ xgb.attr <- function(object, name) {
   }
   .Call(XGBoosterSetAttr_R, handle, as.character(name[1]), value)
   if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.save.raw(object$handle)
+    object$raw <- xgb.serialize(object$handle)
   }
   object
 }
@@ -491,11 +535,41 @@ xgb.attributes <- function(object) {
     .Call(XGBoosterSetAttr_R, handle, names(a[i]), a[[i]])
   }
   if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.save.raw(object$handle)
+    object$raw <- xgb.serialize(object$handle)
   }
   object
 }
 
+#' Accessors for model parameters as JSON string.
+#'
+#' @param object Object of class \code{xgb.Booster}
+#' @param value A JSON string.
+#'
+#' @examples
+#' data(agaricus.train, package='xgboost')
+#' train <- agaricus.train
+#'
+#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
+#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+#' config <- xgb.config(bst)
+#'
+#' @rdname xgb.config
+#' @export
+xgb.config <- function(object) {
+  handle <- xgb.get.handle(object)
+  .Call(XGBoosterSaveJsonConfig_R, handle);
+}
+
+#' @rdname xgb.config
+#' @export
+`xgb.config<-` <- function(object, value) {
+  handle <- xgb.get.handle(object)
+  .Call(XGBoosterLoadJsonConfig_R, handle, value)
+  object$raw <- NULL  # force renew the raw buffer
+  object <- xgb.Booster.complete(object)
+  object
+}
+
 #' Accessors for model parameters.
 #'
 #' Only the setter for xgboost parameters is currently implemented.
@@ -532,7 +606,7 @@ xgb.attributes <- function(object) {
     .Call(XGBoosterSetParam_R, handle, names(p[i]), p[[i]])
   }
   if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.save.raw(object$handle)
+    object$raw <- xgb.serialize(object$handle)
   }
   object
 }
@@ -585,7 +659,7 @@ print.xgb.Booster <- function(x, verbose = FALSE, ...) {
 
   if (!is.null(x$params)) {
     cat('params (as set within xgb.train):\n')
-    cat( '  ',
+    cat('  ',
          paste(names(x$params),
                paste0('"', unlist(x$params), '"'),
                sep = ' = ', collapse = ', '), '\n', sep = '')
@@ -598,9 +672,9 @@ print.xgb.Booster <- function(x, verbose = FALSE, ...) {
   if (length(attrs) > 0) {
     cat('xgb.attributes:\n')
     if (verbose) {
-      cat( paste(paste0('  ',names(attrs)),
-                 paste0('"', unlist(attrs), '"'),
-                 sep = ' = ', collapse = '\n'), '\n', sep = '')
+        cat(paste(paste0('  ', names(attrs)),
+                  paste0('"', unlist(attrs), '"'),
+                  sep = ' = ', collapse = '\n'), '\n', sep = '')
     } else {
       cat('  ', paste(names(attrs), collapse = ', '), '\n', sep = '')
     }
@@ -622,7 +696,7 @@ print.xgb.Booster <- function(x, verbose = FALSE, ...) {
   #cat('ntree: ', xgb.ntree(x), '\n', sep='')
 
   for (n in setdiff(names(x), c('handle', 'raw', 'call', 'params', 'callbacks',
-                                'evaluation_log','niter','feature_names'))) {
+                                'evaluation_log', 'niter', 'feature_names'))) {
     if (is.atomic(x[[n]])) {
       cat(n, ':', x[[n]], '\n', sep = ' ')
     } else {

R-package/R/xgb.DMatrix.R

@@ -1,24 +1,25 @@
 #' Construct xgb.DMatrix object
-#' 
+#'
 #' Construct xgb.DMatrix object from either a dense matrix, a sparse matrix, or a local file.
 #' Supported input file formats are either a libsvm text file or a binary file that was created previously by
 #' \code{\link{xgb.DMatrix.save}}).
-#' 
-#' @param data a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character 
+#'
+#' @param data a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character
 #'        string representing a filename.
 #' @param info a named list of additional information to store in the \code{xgb.DMatrix} object.
-#'        See \code{\link{setinfo}} for the specific allowed kinds of 
+#'        See \code{\link{setinfo}} for the specific allowed kinds of
 #' @param missing a float value to represents missing values in data (used only when input is a dense matrix).
 #'        It is useful when a 0 or some other extreme value represents missing values in data.
 #' @param silent whether to suppress printing an informational message after loading from a file.
 #' @param ... the \code{info} data could be passed directly as parameters, without creating an \code{info} list.
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
 #' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
 #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
+#' if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
 #' @export
 xgb.DMatrix <- function(data, info = list(), missing = NA, silent = FALSE, ...) {
   cnames <- NULL
@@ -78,23 +79,23 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
 
 
 #' Dimensions of xgb.DMatrix
-#' 
+#'
 #' Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
 #' @param x Object of class \code{xgb.DMatrix}
-#' 
+#'
 #' @details
-#' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also 
+#' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
 #' be directly used with an \code{xgb.DMatrix} object.
 #'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' 
+#'
 #' stopifnot(nrow(dtrain) == nrow(train$data))
 #' stopifnot(ncol(dtrain) == ncol(train$data))
 #' stopifnot(all(dim(dtrain) == dim(train$data)))
-#' 
+#'
 #' @export
 dim.xgb.DMatrix <- function(x) {
   c(.Call(XGDMatrixNumRow_R, x), .Call(XGDMatrixNumCol_R, x))
@@ -102,14 +103,14 @@ dim.xgb.DMatrix <- function(x) {
 
 
 #' Handling of column names of \code{xgb.DMatrix}
-#' 
-#' Only column names are supported for \code{xgb.DMatrix}, thus setting of 
-#' row names would have no effect and returnten row names would be NULL.
-#' 
+#'
+#' Only column names are supported for \code{xgb.DMatrix}, thus setting of
+#' row names would have no effect and returned row names would be NULL.
+#'
 #' @param x object of class \code{xgb.DMatrix}
 #' @param value a list of two elements: the first one is ignored
-#'        and the second one is column names 
-#' 
+#'        and the second one is column names
+#'
 #' @details
 #' Generic \code{dimnames} methods are used by \code{colnames}.
 #' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
@@ -122,7 +123,7 @@ dim.xgb.DMatrix <- function(x) {
 #' colnames(dtrain)
 #' colnames(dtrain) <- make.names(1:ncol(train$data))
 #' print(dtrain, verbose=TRUE)
-#' 
+#'
 #' @rdname dimnames.xgb.DMatrix
 #' @export
 dimnames.xgb.DMatrix <- function(x) {
@@ -140,8 +141,8 @@ dimnames.xgb.DMatrix <- function(x) {
     attr(x, '.Dimnames') <- NULL
     return(x)
   }
-  if (ncol(x) != length(value[[2]])) 
-    stop("can't assign ", length(value[[2]]), " colnames to a ", 
+  if (ncol(x) != length(value[[2]]))
+    stop("can't assign ", length(value[[2]]), " colnames to a ",
          ncol(x), " column xgb.DMatrix")
   attr(x, '.Dimnames') <- value
   x
@@ -149,33 +150,33 @@ dimnames.xgb.DMatrix <- function(x) {
 
 
 #' Get information of an xgb.DMatrix object
-#' 
+#'
 #' Get information of an xgb.DMatrix object
 #' @param object Object of class \code{xgb.DMatrix}
 #' @param name the name of the information field to get (see details)
 #' @param ... other parameters
-#' 
+#'
 #' @details
 #' The \code{name} field can be one of the following:
-#' 
+#'
 #' \itemize{
 #'     \item \code{label}: label Xgboost learn from ;
 #'     \item \code{weight}: to do a weight rescale ;
 #'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
 #'     \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
-#'     
+#'
 #' }
-#' 
+#'
 #' \code{group} can be setup by \code{setinfo} but can't be retrieved by \code{getinfo}.
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' 
+#'
 #' labels <- getinfo(dtrain, 'label')
 #' setinfo(dtrain, 'label', 1-labels)
-#' 
+#'
 #' labels2 <- getinfo(dtrain, 'label')
 #' stopifnot(all(labels2 == 1-labels))
 #' @rdname getinfo
@@ -187,9 +188,10 @@ getinfo <- function(object, ...) UseMethod("getinfo")
 getinfo.xgb.DMatrix <- function(object, name, ...) {
   if (typeof(name) != "character" ||
       length(name) != 1 ||
-      !name %in% c('label', 'weight', 'base_margin', 'nrow')) {
+      !name %in% c('label', 'weight', 'base_margin', 'nrow',
+                   'label_lower_bound', 'label_upper_bound')) {
     stop("getinfo: name must be one of the following\n",
-         "    'label', 'weight', 'base_margin', 'nrow'")
+         "    'label', 'weight', 'base_margin', 'nrow', 'label_lower_bound', 'label_upper_bound'")
   }
   if (name != "nrow"){
     ret <- .Call(XGDMatrixGetInfo_R, object, name)
@@ -202,9 +204,9 @@ getinfo.xgb.DMatrix <- function(object, name, ...) {
 
 
 #' Set information of an xgb.DMatrix object
-#' 
+#'
 #' Set information of an xgb.DMatrix object
-#' 
+#'
 #' @param object Object of class "xgb.DMatrix"
 #' @param name the name of the field to get
 #' @param info the specific field of information to set
@@ -212,19 +214,19 @@ getinfo.xgb.DMatrix <- function(object, name, ...) {
 #'
 #' @details
 #' The \code{name} field can be one of the following:
-#' 
+#'
 #' \itemize{
 #'     \item \code{label}: label Xgboost learn from ;
 #'     \item \code{weight}: to do a weight rescale ;
 #'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
 #'     \item \code{group}: number of rows in each group (to use with \code{rank:pairwise} objective).
 #' }
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' 
+#'
 #' labels <- getinfo(dtrain, 'label')
 #' setinfo(dtrain, 'label', 1-labels)
 #' labels2 <- getinfo(dtrain, 'label')
@@ -242,9 +244,19 @@ setinfo.xgb.DMatrix <- function(object, name, info, ...) {
     .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
     return(TRUE)
   }
-  if (name == "weight") {
+  if (name == "label_lower_bound") {
     if (length(info) != nrow(object))
-      stop("The length of weights must equal to the number of rows in the input data")
+      stop("The length of lower-bound labels must equal to the number of rows in the input data")
+    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
+    return(TRUE)
+  }
+  if (name == "label_upper_bound") {
+    if (length(info) != nrow(object))
+      stop("The length of upper-bound labels must equal to the number of rows in the input data")
+    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
+    return(TRUE)
+  }
+  if (name == "weight") {
     .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
     return(TRUE)
   }
@@ -266,27 +278,27 @@ setinfo.xgb.DMatrix <- function(object, name, info, ...) {
 
 
 #' Get a new DMatrix containing the specified rows of
-#' orginal xgb.DMatrix object
+#' original xgb.DMatrix object
 #'
 #' Get a new DMatrix containing the specified rows of
-#' orginal xgb.DMatrix object
-#' 
+#' original xgb.DMatrix object
+#'
 #' @param object Object of class "xgb.DMatrix"
 #' @param idxset a integer vector of indices of rows needed
 #' @param colset currently not used (columns subsetting is not available)
 #' @param ... other parameters (currently not used)
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' 
+#'
 #' dsub <- slice(dtrain, 1:42)
 #' labels1 <- getinfo(dsub, 'label')
 #' dsub <- dtrain[1:42, ]
 #' labels2 <- getinfo(dsub, 'label')
 #' all.equal(labels1, labels2)
-#' 
+#'
 #' @rdname slice.xgb.DMatrix
 #' @export
 slice <- function(object, ...) UseMethod("slice")
@@ -301,12 +313,17 @@ slice.xgb.DMatrix <- function(object, idxset, ...) {
 
   attr_list <- attributes(object)
   nr <- nrow(object)
-  len <- sapply(attr_list, length)
+  len <- sapply(attr_list, NROW)
   ind <- which(len == nr)
   if (length(ind) > 0) {
     nms <- names(attr_list)[ind]
     for (i in seq_along(ind)) {
-      attr(ret, nms[i]) <- attr(object, nms[i])[idxset]
+      obj_attr <- attr(object, nms[i])
+      if (NCOL(obj_attr) > 1) {
+        attr(ret, nms[i]) <- obj_attr[idxset, ]
+      } else {
+        attr(ret, nms[i]) <- obj_attr[idxset]
+      }
     }
   }
   return(structure(ret, class = "xgb.DMatrix"))
@@ -320,30 +337,30 @@ slice.xgb.DMatrix <- function(object, idxset, ...) {
 
 
 #' Print xgb.DMatrix
-#' 
-#' Print information about xgb.DMatrix. 
+#'
+#' Print information about xgb.DMatrix.
 #' Currently it displays dimensions and presence of info-fields and colnames.
-#' 
+#'
 #' @param x an xgb.DMatrix object
 #' @param verbose whether to print colnames (when present)
 #' @param ... not currently used
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' 
+#'
 #' dtrain
 #' print(dtrain, verbose=TRUE)
-#' 
+#'
 #' @method print xgb.DMatrix
 #' @export
 print.xgb.DMatrix <- function(x, verbose = FALSE, ...) {
   cat('xgb.DMatrix  dim:', nrow(x), 'x', ncol(x), ' info: ')
-  infos <- c()
-  if(length(getinfo(x, 'label')) > 0) infos <- 'label'
-  if(length(getinfo(x, 'weight')) > 0) infos <- c(infos, 'weight')
-  if(length(getinfo(x, 'base_margin')) > 0) infos <- c(infos, 'base_margin')
+  infos <- character(0)
+  if (length(getinfo(x, 'label')) > 0) infos <- 'label'
+  if (length(getinfo(x, 'weight')) > 0) infos <- c(infos, 'weight')
+  if (length(getinfo(x, 'base_margin')) > 0) infos <- c(infos, 'base_margin')
   if (length(infos) == 0) infos <- 'NA'
   cat(infos)
   cnames <- colnames(x)

R-package/R/xgb.DMatrix.save.R

@@ -1,23 +1,24 @@
 #' Save xgb.DMatrix object to binary file
-#' 
+#'
 #' Save xgb.DMatrix object to binary file
-#' 
+#'
 #' @param dmatrix the \code{xgb.DMatrix} object
 #' @param fname the name of the file to write.
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
 #' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
 #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
+#' if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
 #' @export
 xgb.DMatrix.save <- function(dmatrix, fname) {
   if (typeof(fname) != "character")
     stop("fname must be character")
   if (!inherits(dmatrix, "xgb.DMatrix"))
     stop("dmatrix must be xgb.DMatrix")
-  
+
   .Call(XGDMatrixSaveBinary_R, dmatrix, fname[1], 0L)
   return(TRUE)
 }

R-package/R/xgb.create.features.R

@@ -1,50 +1,50 @@
 #' Create new features from a previously learned model
-#' 
+#'
 #' May improve the learning by adding new features to the training data based on the decision trees from a previously learned model.
-#' 
+#'
 #' @param model decision tree boosting model learned on the original data
 #' @param data original data (usually provided as a \code{dgCMatrix} matrix)
 #' @param ... currently not used
-#' 
+#'
 #' @return \code{dgCMatrix} matrix including both the original data and the new features.
 #'
-#' @details 
+#' @details
 #' This is the function inspired from the paragraph 3.1 of the paper:
-#' 
+#'
 #' \strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
-#' 
-#' \emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, 
+#'
+#' \emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers,
 #' Joaquin Quinonero Candela)}
-#'  
+#'
 #' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
-#' 
+#'
 #' \url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
-#' 
+#'
 #' Extract explaining the method:
-#' 
+#'
 #' "We found that boosted decision trees are a powerful and very
 #' convenient way to implement non-linear and tuple transformations
 #' of the kind we just described. We treat each individual
 #' tree as a categorical feature that takes as value the
-#' index of the leaf an instance ends up falling in. We use 
-#' 1-of-K coding of this type of features. 
-#' 
-#' For example, consider the boosted tree model in Figure 1 with 2 subtrees, 
+#' index of the leaf an instance ends up falling in. We use
+#' 1-of-K coding of this type of features.
+#'
+#' For example, consider the boosted tree model in Figure 1 with 2 subtrees,
 #' where the first subtree has 3 leafs and the second 2 leafs. If an
 #' instance ends up in leaf 2 in the first subtree and leaf 1 in
 #' second subtree, the overall input to the linear classifier will
 #' be the binary vector \code{[0, 1, 0, 1, 0]}, where the first 3 entries
 #' correspond to the leaves of the first subtree and last 2 to
 #' those of the second subtree.
-#' 
+#'
 #' [...]
-#' 
+#'
 #' We can understand boosted decision tree
 #' based transformation as a supervised feature encoding that
 #' converts a real-valued vector into a compact binary-valued
 #' vector. A traversal from root node to a leaf node represents
 #' a rule on certain features."
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
@@ -55,33 +55,33 @@
 #' nrounds = 4
 #'
 #' bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
-#' 
+#'
 #' # Model accuracy without new features
 #' accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
 #'                    length(agaricus.test$label)
-#' 
+#'
 #' # Convert previous features to one hot encoding
 #' new.features.train <- xgb.create.features(model = bst, agaricus.train$data)
 #' new.features.test <- xgb.create.features(model = bst, agaricus.test$data)
-#' 
+#'
 #' # learning with new features
 #' new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
 #' new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
 #' watchlist <- list(train = new.dtrain)
 #' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
-#' 
+#'
 #' # Model accuracy with new features
 #' accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /
 #'                   length(agaricus.test$label)
-#' 
+#'
 #' # Here the accuracy was already good and is now perfect.
 #' cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
 #'           accuracy.after, "!\n"))
-#' 
+#'
 #' @export
 xgb.create.features <- function(model, data, ...){
   check.deprecation(...)
   pred_with_leaf <- predict(model, data, predleaf = TRUE)
   cols <- lapply(as.data.frame(pred_with_leaf), factor)
-  cbind(data, sparse.model.matrix( ~ . -1, cols))
+  cbind(data, sparse.model.matrix(~ . -1, cols)) # nolint
 }

R-package/R/xgb.cv.R

@@ -1,13 +1,16 @@
 #' Cross Validation
-#' 
+#'
 #' The cross validation function of xgboost
-#' 
-#' @param params the list of parameters. Commonly used ones are:
+#'
+#' @param params the list of parameters. The complete list of parameters is
+#'   available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
+#'   is a shorter summary:
 #' \itemize{
 #'   \item \code{objective} objective function, common ones are
 #'   \itemize{
-#'     \item \code{reg:linear} linear regression
-#'     \item \code{binary:logistic} logistic regression for classification
+#'     \item \code{reg:squarederror} Regression with squared loss.
+#'     \item \code{binary:logistic} logistic regression for classification.
+#'     \item See \code{\link[=xgb.train]{xgb.train}()} for complete list of objectives.
 #'   }
 #'   \item \code{eta} step size of each boosting step
 #'   \item \code{max_depth} maximum depth of the tree
@@ -18,12 +21,12 @@
 #'   See also demo/ for walkthrough example in R.
 #' @param data takes an \code{xgb.DMatrix}, \code{matrix}, or \code{dgCMatrix} as the input.
 #' @param nrounds the max number of iterations
-#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples. 
+#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
 #' @param label vector of response values. Should be provided only when data is an R-matrix.
-#' @param missing is only used when input is a dense matrix. By default is set to NA, which means 
-#'        that NA values should be considered as 'missing' by the algorithm. 
+#' @param missing is only used when input is a dense matrix. By default is set to NA, which means
+#'        that NA values should be considered as 'missing' by the algorithm.
 #'        Sometimes, 0 or other extreme value might be used to represent missing values.
-#' @param prediction A logical value indicating whether to return the test fold predictions 
+#' @param prediction A logical value indicating whether to return the test fold predictions
 #'        from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.
 #' @param showsd \code{boolean}, whether to show standard deviation of cross validation
 #' @param metrics, list of evaluation metrics to be used in cross validation,
@@ -33,26 +36,30 @@
 #'   \item \code{error} binary classification error rate
 #'   \item \code{rmse} Rooted mean square error
 #'   \item \code{logloss} negative log-likelihood function
+#'   \item \code{mae} Mean absolute error
+#'   \item \code{mape} Mean absolute percentage error
 #'   \item \code{auc} Area under curve
 #'   \item \code{aucpr} Area under PR curve
 #'   \item \code{merror} Exact matching error, used to evaluate multi-class classification
 #' }
-#' @param obj customized objective function. Returns gradient and second order 
+#' @param obj customized objective function. Returns gradient and second order
 #'        gradient with given prediction and dtrain.
-#' @param feval custimized evaluation function. Returns 
-#'        \code{list(metric='metric-name', value='metric-value')} with given 
+#' @param feval customized evaluation function. Returns
+#'        \code{list(metric='metric-name', value='metric-value')} with given
 #'        prediction and dtrain.
-#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified 
+#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
 #'        by the values of outcome labels.
 #' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
-#'        (each element must be a vector of test fold's indices). When folds are supplied, 
+#'        (each element must be a vector of test fold's indices). When folds are supplied,
 #'        the \code{nfold} and \code{stratified} parameters are ignored.
+#' @param train_folds \code{list} list specifying which indicies to use for training. If \code{NULL}
+#'        (the default) all indices not specified in \code{folds} will be used for training.
 #' @param verbose \code{boolean}, print the statistics during the process
 #' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
-#'        Default is 1 which means all messages are printed. This parameter is passed to the 
+#'        Default is 1 which means all messages are printed. This parameter is passed to the
 #'        \code{\link{cb.print.evaluation}} callback.
-#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered. 
-#'        If set to an integer \code{k}, training with a validation set will stop if the performance 
+#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
+#'        If set to an integer \code{k}, training with a validation set will stop if the performance
 #'        doesn't improve for \code{k} rounds.
 #'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
 #' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
@@ -60,46 +67,46 @@
 #'        When it is \code{TRUE}, it means the larger the evaluation score the better.
 #'        This parameter is passed to the \code{\link{cb.early.stop}} callback.
 #' @param callbacks a list of callback functions to perform various task during boosting.
-#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
-#'        parameters' values. User can provide either existing or their own callback methods in order 
+#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
+#'        parameters' values. User can provide either existing or their own callback methods in order
 #'        to customize the training process.
 #' @param ... other parameters to pass to \code{params}.
-#' 
-#' @details 
-#' The original sample is randomly partitioned into \code{nfold} equal size subsamples. 
-#' 
-#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data. 
-#' 
-#' The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
-#' 
-#' All observations are used for both training and validation.
-#' 
-#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
 #'
-#' @return 
+#' @details
+#' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
+#'
+#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
+#'
+#' The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
+#'
+#' All observations are used for both training and validation.
+#'
+#' Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
+#'
+#' @return
 #' An object of class \code{xgb.cv.synchronous} with the following elements:
 #' \itemize{
 #'   \item \code{call} a function call.
-#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not 
+#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
 #'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-#'   \item \code{callbacks} callback functions that were either automatically assigned or 
+#'   \item \code{callbacks} callback functions that were either automatically assigned or
 #'         explicitly passed.
-#'   \item \code{evaluation_log} evaluation history storead as a \code{data.table} with the
-#'         first column corresponding to iteration number and the rest corresponding to the 
+#'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
+#'         first column corresponding to iteration number and the rest corresponding to the
 #'         CV-based evaluation means and standard deviations for the training and test CV-sets.
 #'         It is created by the \code{\link{cb.evaluation.log}} callback.
 #'   \item \code{niter} number of boosting iterations.
 #'   \item \code{nfeatures} number of features in training data.
-#'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds} 
+#'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
 #'         parameter or randomly generated.
 #'   \item \code{best_iteration} iteration number with the best evaluation metric value
 #'         (only available with early stopping).
-#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
+#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
 #'         which could further be used in \code{predict} method
 #'         (only available with early stopping).
-#'   \item \code{pred} CV prediction values available when \code{prediction} is set. 
+#'   \item \code{pred} CV prediction values available when \code{prediction} is set.
 #'         It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-#'   \item \code{models} a liost of the CV folds' models. It is only available with the explicit 
+#'   \item \code{models} a list of the CV folds' models. It is only available with the explicit
 #'         setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
 #' }
 #'
@@ -110,50 +117,57 @@
 #'                   max_depth = 3, eta = 1, objective = "binary:logistic")
 #' print(cv)
 #' print(cv, verbose=TRUE)
-#' 
+#'
 #' @export
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
                    prediction = FALSE, showsd = TRUE, metrics=list(),
-                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, 
+                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, train_folds = NULL,
                    verbose = TRUE, print_every_n=1L,
                    early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
 
   check.deprecation(...)
-  
+
   params <- check.booster.params(params, ...)
   # TODO: should we deprecate the redundant 'metrics' parameter?
   for (m in metrics)
     params <- c(params, list("eval_metric" = m))
-  
+
   check.custom.obj()
   check.custom.eval()
 
   #if (is.null(params[['eval_metric']]) && is.null(feval))
   #  stop("Either 'eval_metric' or 'feval' must be provided for CV")
-  
+
   # Check the labels
-  if ( (inherits(data, 'xgb.DMatrix') && is.null(getinfo(data, 'label'))) ||
-       (!inherits(data, 'xgb.DMatrix') && is.null(label)))
+  if ((inherits(data, 'xgb.DMatrix') && is.null(getinfo(data, 'label'))) ||
+      (!inherits(data, 'xgb.DMatrix') && is.null(label))) {
     stop("Labels must be provided for CV either through xgb.DMatrix, or through 'label=' when 'data' is matrix")
-  
+  } else if (inherits(data, 'xgb.DMatrix')) {
+    if (!is.null(label))
+      warning("xgb.cv: label will be ignored, since data is of type xgb.DMatrix")
+    cv_label <- getinfo(data, 'label')
+  } else {
+    cv_label <- label
+  }
+
   # CV folds
-  if(!is.null(folds)) {
-    if(!is.list(folds) || length(folds) < 2)
+  if (!is.null(folds)) {
+    if (!is.list(folds) || length(folds) < 2)
       stop("'folds' must be a list with 2 or more elements that are vectors of indices for each CV-fold")
     nfold <- length(folds)
   } else {
     if (nfold <= 1)
       stop("'nfold' must be > 1")
-    folds <- generate.cv.folds(nfold, nrow(data), stratified, label, params)
+    folds <- generate.cv.folds(nfold, nrow(data), stratified, cv_label, params)
   }
-  
+
   # Potential TODO: sequential CV
   #if (strategy == 'sequential')
   #  stop('Sequential CV strategy is not yet implemented')
 
   # verbosity & evaluation printing callback:
   params <- c(params, list(silent = 1))
-  print_every_n <- max( as.integer(print_every_n), 1L)
+  print_every_n <- max(as.integer(print_every_n), 1L)
   if (!has.callbacks(callbacks, 'cb.print.evaluation') && verbose) {
     callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n, showsd = showsd))
   }
@@ -166,7 +180,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   stop_condition <- FALSE
   if (!is.null(early_stopping_rounds) &&
       !has.callbacks(callbacks, 'cb.early.stop')) {
-    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds, 
+    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
                                                  maximize = maximize, verbose = verbose))
   }
   # CV-predictions callback
@@ -177,42 +191,47 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   # Sort the callbacks into categories
   cb <- categorize.callbacks(callbacks)
 
-  
+
   # create the booster-folds
+  # train_folds
   dall <- xgb.get.DMatrix(data, label, missing)
   bst_folds <- lapply(seq_along(folds), function(k) {
     dtest  <- slice(dall, folds[[k]])
-    dtrain <- slice(dall, unlist(folds[-k]))
+    # code originally contributed by @RolandASc on stackoverflow
+    if (is.null(train_folds))
+       dtrain <- slice(dall, unlist(folds[-k]))
+    else
+       dtrain <- slice(dall, train_folds[[k]])
     handle <- xgb.Booster.handle(params, list(dtrain, dtest))
-    list(dtrain = dtrain, bst = handle, watchlist = list(train = dtrain, test=dtest), index = folds[[k]])
+    list(dtrain = dtrain, bst = handle, watchlist = list(train = dtrain, test = dtest), index = folds[[k]])
   })
   rm(dall)
   # a "basket" to collect some results from callbacks
   basket <- list()
 
   # extract parameters that can affect the relationship b/w #trees and #iterations
-  num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1)
-  num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)
+  num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1) # nolint
+  num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1) # nolint
 
   # those are fixed for CV (no training continuation)
   begin_iteration <- 1
   end_iteration <- nrounds
-  
+
   # synchronous CV boosting: run CV folds' models within each iteration
   for (iteration in begin_iteration:end_iteration) {
-    
+
     for (f in cb$pre_iter) f()
-    
+
     msg <- lapply(bst_folds, function(fd) {
       xgb.iter.update(fd$bst, fd$dtrain, iteration - 1, obj)
       xgb.iter.eval(fd$bst, fd$watchlist, iteration - 1, feval)
     })
     msg <- simplify2array(msg)
     bst_evaluation <- rowMeans(msg)
-    bst_evaluation_err <- sqrt(rowMeans(msg^2) - bst_evaluation^2)
-    
+    bst_evaluation_err <- sqrt(rowMeans(msg^2) - bst_evaluation^2) # nolint
+
     for (f in cb$post_iter) f()
-    
+
     if (stop_condition) break
   }
   for (f in cb$finalize) f(finalize = TRUE)
@@ -236,17 +255,17 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 
 
 #' Print xgb.cv result
-#' 
+#'
 #' Prints formatted results of \code{xgb.cv}.
-#' 
+#'
 #' @param x an \code{xgb.cv.synchronous} object
 #' @param verbose whether to print detailed data
 #' @param ... passed to \code{data.table.print}
-#' 
+#'
 #' @details
-#' When not verbose, it would only print the evaluation results, 
+#' When not verbose, it would only print the evaluation results,
 #' including the best iteration (when available).
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
@@ -254,13 +273,13 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #' print(cv)
 #' print(cv, verbose=TRUE)
-#' 
+#'
 #' @rdname print.xgb.cv
 #' @method print xgb.cv.synchronous
 #' @export
 print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
   cat('##### xgb.cv ', length(x$folds), '-folds\n', sep = '')
-  
+
   if (verbose) {
     if (!is.null(x$call)) {
       cat('call:\n  ')
@@ -268,10 +287,10 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
     }
     if (!is.null(x$params)) {
       cat('params (as set within xgb.cv):\n')
-      cat( '  ', 
-           paste(names(x$params), 
-                 paste0('"', unlist(x$params), '"'),
-                 sep = ' = ', collapse = ', '), '\n', sep = '')
+      cat('  ',
+          paste(names(x$params),
+                paste0('"', unlist(x$params), '"'),
+                sep = ' = ', collapse = ', '), '\n', sep = '')
     }
     if (!is.null(x$callbacks) && length(x$callbacks) > 0) {
       cat('callbacks:\n')
@@ -280,9 +299,9 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
         print(x)
       })
     }
-    
+
     for (n in c('niter', 'best_iteration', 'best_ntreelimit')) {
-      if (is.null(x[[n]])) 
+      if (is.null(x[[n]]))
         next
       cat(n, ': ', x[[n]], '\n', sep = '')
     }
@@ -293,10 +312,10 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
     }
   }
 
-  if (verbose) 
+  if (verbose)
     cat('evaluation_log:\n')
   print(x$evaluation_log, row.names = FALSE, ...)
-  
+
   if (!is.null(x$best_iteration)) {
     cat('Best iteration:\n')
     print(x$evaluation_log[x$best_iteration], row.names = FALSE, ...)

R-package/R/xgb.dump.R

@@ -1,15 +1,15 @@
 #' Dump an xgboost model in text format.
-#' 
+#'
 #' Dump an xgboost model in text format.
-#' 
+#'
 #' @param model the model object.
-#' @param fname the name of the text file where to save the model text dump. 
+#' @param fname the name of the text file where to save the model text dump.
 #'        If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.
 #' @param fmap feature map file representing feature types.
-#'        Detailed description could be found at 
+#'        Detailed description could be found at
 #'        \url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
 #'        See demo/ for walkthrough example in R, and
-#'        \url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt} 
+#'        \url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt}
 #'        for example Format.
 #' @param with_stats whether to dump some additional statistics about the splits.
 #'        When this option is on, the model dump contains two additional values:
@@ -27,18 +27,18 @@
 #' data(agaricus.test, package='xgboost')
 #' train <- agaricus.train
 #' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
+#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #' # save the model in file 'xgb.model.dump'
 #' dump_path = file.path(tempdir(), 'model.dump')
 #' xgb.dump(bst, dump_path, with_stats = TRUE)
-#' 
+#'
 #' # print the model without saving it to a file
 #' print(xgb.dump(bst, with_stats = TRUE))
-#' 
+#'
 #' # print in JSON format:
 #' cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))
-#' 
+#'
 #' @export
 xgb.dump <- function(model, fname = NULL, fmap = "", with_stats=FALSE,
                      dump_format = c("text", "json"), ...) {
@@ -50,19 +50,19 @@ xgb.dump <- function(model, fname = NULL, fmap = "", with_stats=FALSE,
     stop("fname: argument must be a character string (when provided)")
   if (!(is.null(fmap) || is.character(fmap)))
     stop("fmap: argument must be a character string (when provided)")
-  
+
   model <- xgb.Booster.complete(model)
   model_dump <- .Call(XGBoosterDumpModel_R, model$handle, NVL(fmap, "")[1], as.integer(with_stats),
                       as.character(dump_format))
 
-  if (is.null(fname)) 
-    model_dump <- stri_replace_all_regex(model_dump, '\t', '')
-  
+  if (is.null(fname))
+    model_dump <- gsub('\t', '', model_dump, fixed = TRUE)
+
   if (dump_format == "text")
-    model_dump <- unlist(stri_split_regex(model_dump, '\n'))
-  
+    model_dump <- unlist(strsplit(model_dump, '\n', fixed = TRUE))
+
   model_dump <- grep('^\\s*$', model_dump, invert = TRUE, value = TRUE)
-  
+
   if (is.null(fname)) {
     return(model_dump)
   } else {

R-package/R/xgb.ggplot.R

@@ -3,9 +3,9 @@
 
 #' @rdname xgb.plot.importance
 #' @export
-xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, 
+xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL,
                                   rel_to_first = FALSE, n_clusters = c(1:10), ...) {
-  
+
   importance_matrix <- xgb.plot.importance(importance_matrix, top_n = top_n, measure = measure,
                                            rel_to_first = rel_to_first, plot = FALSE, ...)
   if (!requireNamespace("ggplot2", quietly = TRUE)) {
@@ -14,21 +14,21 @@ xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measur
   if (!requireNamespace("Ckmeans.1d.dp", quietly = TRUE)) {
     stop("Ckmeans.1d.dp package is required", call. = FALSE)
   }
-  
+
   clusters <- suppressWarnings(
     Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix$Importance, n_clusters)
   )
   importance_matrix[, Cluster := as.character(clusters$cluster)]
 
   plot <-
-    ggplot2::ggplot(importance_matrix, 
+    ggplot2::ggplot(importance_matrix,
                     ggplot2::aes(x = factor(Feature, levels = rev(Feature)), y = Importance, width = 0.5),
-                    environment = environment()) + 
-    ggplot2::geom_bar(ggplot2::aes(fill = Cluster), stat = "identity", position = "identity") + 
-    ggplot2::coord_flip() + 
-    ggplot2::xlab("Features") + 
-    ggplot2::ggtitle("Feature importance") + 
-    ggplot2::theme(plot.title = ggplot2::element_text(lineheight = .9, face = "bold"), 
+                    environment = environment()) +
+    ggplot2::geom_bar(ggplot2::aes(fill = Cluster), stat = "identity", position = "identity") +
+    ggplot2::coord_flip() +
+    ggplot2::xlab("Features") +
+    ggplot2::ggtitle("Feature importance") +
+    ggplot2::theme(plot.title = ggplot2::element_text(lineheight = .9, face = "bold"),
                    panel.grid.major.y = ggplot2::element_blank())
   return(plot)
 }
@@ -42,7 +42,7 @@ xgb.ggplot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med
     stop("ggplot2 package is required for plotting the graph deepness.", call. = FALSE)
 
   which <- match.arg(which)
-  
+
   dt_depths <- xgb.plot.deepness(model = model, plot = FALSE)
   dt_summaries <- dt_depths[, .(.N, Cover = mean(Cover)), Depth]
   setkey(dt_summaries, 'Depth')
@@ -60,30 +60,30 @@ xgb.ggplot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med
         axis.ticks = ggplot2::element_blank(),
         axis.text.x = ggplot2::element_blank()
       )
-  
-    p2 <- 
+
+    p2 <-
       ggplot2::ggplot(dt_summaries) +
-      ggplot2::geom_bar(ggplot2::aes(x = Depth, y = Cover), stat = "Identity") + 
+      ggplot2::geom_bar(ggplot2::aes(x = Depth, y = Cover), stat = "Identity") +
       ggplot2::xlab("Leaf depth") +
       ggplot2::ylab("Weighted cover")
-  
+
     multiplot(p1, p2, cols = 1)
     return(invisible(list(p1, p2)))
-    
+
   } else if (which == "max.depth") {
     p <-
       ggplot2::ggplot(dt_depths[, max(Depth), Tree]) +
       ggplot2::geom_jitter(ggplot2::aes(x = Tree, y = V1),
-                           height = 0.15, alpha=0.4, size=3, stroke=0) + 
+                           height = 0.15, alpha = 0.4, size = 3, stroke = 0) +
       ggplot2::xlab("tree #") +
       ggplot2::ylab("Max tree leaf depth")
     return(p)
-    
+
   } else if (which == "med.depth") {
     p <-
       ggplot2::ggplot(dt_depths[, median(as.numeric(Depth)), Tree]) +
       ggplot2::geom_jitter(ggplot2::aes(x = Tree, y = V1),
-                           height = 0.15, alpha=0.4, size=3, stroke=0) + 
+                           height = 0.15, alpha = 0.4, size = 3, stroke = 0) +
       ggplot2::xlab("tree #") +
       ggplot2::ylab("Median tree leaf depth")
     return(p)
@@ -92,24 +92,102 @@ xgb.ggplot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med
     p <-
       ggplot2::ggplot(dt_depths[, median(abs(Weight)), Tree]) +
       ggplot2::geom_point(ggplot2::aes(x = Tree, y = V1),
-                          alpha=0.4, size=3, stroke=0) + 
+                          alpha = 0.4, size = 3, stroke = 0) +
       ggplot2::xlab("tree #") +
       ggplot2::ylab("Median absolute leaf weight")
     return(p)
   }
 }
 
+#' @rdname xgb.plot.shap.summary
+#' @export
+xgb.ggplot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = NULL,
+                                    trees = NULL, target_class = NULL, approxcontrib = FALSE, subsample = NULL) {
+  data_list <- xgb.shap.data(
+    data = data,
+    shap_contrib = shap_contrib,
+    features = features,
+    top_n = top_n,
+    model = model,
+    trees = trees,
+    target_class = target_class,
+    approxcontrib = approxcontrib,
+    subsample = subsample,
+    max_observations = 10000  # 10,000 samples per feature.
+  )
+  p_data <- prepare.ggplot.shap.data(data_list, normalize = TRUE)
+  # Reverse factor levels so that the first level is at the top of the plot
+  p_data[, "feature" := factor(feature, rev(levels(feature)))]
+  p <- ggplot2::ggplot(p_data, ggplot2::aes(x = feature, y = p_data$shap_value, colour = p_data$feature_value)) +
+    ggplot2::geom_jitter(alpha = 0.5, width = 0.1) +
+    ggplot2::scale_colour_viridis_c(limits = c(-3, 3), option = "plasma", direction = -1) +
+    ggplot2::geom_abline(slope = 0, intercept = 0, colour = "darkgrey") +
+    ggplot2::coord_flip()
+
+  p
+}
+
+#' Combine and melt feature values and SHAP contributions for sample
+#' observations.
+#'
+#' Conforms to data format required for ggplot functions.
+#'
+#' Internal utility function.
+#'
+#' @param data_list List containing 'data' and 'shap_contrib' returned by
+#'   \code{xgb.shap.data()}.
+#' @param normalize Whether to standardize feature values to have mean 0 and
+#'   standard deviation 1 (useful for comparing multiple features on the same
+#'   plot). Default \code{FALSE}.
+#'
+#' @return A data.table containing the observation ID, the feature name, the
+#'   feature value (normalized if specified), and the SHAP contribution value.
+prepare.ggplot.shap.data <- function(data_list, normalize = FALSE) {
+  data <- data_list[["data"]]
+  shap_contrib <- data_list[["shap_contrib"]]
+
+  data <- data.table::as.data.table(as.matrix(data))
+  if (normalize) {
+    data[, (names(data)) := lapply(.SD, normalize)]
+  }
+  data[, "id" := seq_len(nrow(data))]
+  data_m <- data.table::melt.data.table(data, id.vars = "id", variable.name = "feature", value.name = "feature_value")
+
+  shap_contrib <- data.table::as.data.table(as.matrix(shap_contrib))
+  shap_contrib[, "id" := seq_len(nrow(shap_contrib))]
+  shap_contrib_m <- data.table::melt.data.table(shap_contrib, id.vars = "id", variable.name = "feature", value.name = "shap_value")
+
+  p_data <- data.table::merge.data.table(data_m, shap_contrib_m, by = c("id", "feature"))
+
+  p_data
+}
+
+#' Scale feature value to have mean 0, standard deviation 1
+#'
+#' This is used to compare multiple features on the same plot.
+#' Internal utility function
+#'
+#' @param x Numeric vector
+#'
+#' @return Numeric vector with mean 0 and sd 1.
+normalize <- function(x) {
+  loc <- mean(x, na.rm = TRUE)
+  scale <- stats::sd(x, na.rm = TRUE)
+
+  (x - loc) / scale
+}
+
 # Plot multiple ggplot graph aligned by rows and columns.
 # ... the plots
 # cols number of columns
 # internal utility function
 multiplot <- function(..., cols = 1) {
   plots <- list(...)
-  num_plots = length(plots)
-  
+  num_plots <- length(plots)
+
   layout <- matrix(seq(1, cols * ceiling(num_plots / cols)),
                    ncol = cols, nrow = ceiling(num_plots / cols))
-  
+
   if (num_plots == 1) {
     print(plots[[1]])
   } else {
@@ -118,7 +196,7 @@ multiplot <- function(..., cols = 1) {
     for (i in 1:num_plots) {
       # Get the i,j matrix positions of the regions that contain this subplot
       matchidx <- as.data.table(which(layout == i, arr.ind = TRUE))
-      
+
       print(
         plots[[i]], vp = grid::viewport(
           layout.pos.row = matchidx$row,
@@ -131,5 +209,5 @@ multiplot <- function(..., cols = 1) {
 
 globalVariables(c(
   "Cluster", "ggplot", "aes", "geom_bar", "coord_flip", "xlab", "ylab", "ggtitle", "theme",
-  "element_blank", "element_text", "V1", "Weight"
+  "element_blank", "element_text", "V1", "Weight", "feature"
 ))

R-package/R/xgb.importance.R

@@ -1,66 +1,66 @@
 #' Importance of features in a model.
-#' 
+#'
 #' Creates a \code{data.table} of feature importances in a model.
-#' 
+#'
 #' @param feature_names character vector of feature names. If the model already
 #'       contains feature names, those would be used when \code{feature_names=NULL} (default value).
 #'       Non-null \code{feature_names} could be provided to override those in the model.
 #' @param model object of class \code{xgb.Booster}.
 #' @param trees (only for the gbtree booster) an integer vector of tree indices that should be included
 #'          into the importance calculation. If set to \code{NULL}, all trees of the model are parsed.
-#'          It could be useful, e.g., in multiclass classification to get feature importances 
+#'          It could be useful, e.g., in multiclass classification to get feature importances
 #'          for each class separately. IMPORTANT: the tree index in xgboost models
 #'          is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).
 #' @param data deprecated.
 #' @param label deprecated.
 #' @param target deprecated.
 #'
-#' @details 
-#' 
+#' @details
+#'
 #' This function works for both linear and tree models.
-#' 
-#' For linear models, the importance is the absolute magnitude of linear coefficients. 
-#' For that reason, in order to obtain a meaningful ranking by importance for a linear model, 
-#' the features need to be on the same scale (which you also would want to do when using either 
+#'
+#' For linear models, the importance is the absolute magnitude of linear coefficients.
+#' For that reason, in order to obtain a meaningful ranking by importance for a linear model,
+#' the features need to be on the same scale (which you also would want to do when using either
 #' L1 or L2 regularization).
-#' 
+#'
 #' @return
-#' 
+#'
 #' For a tree model, a \code{data.table} with the following columns:
 #' \itemize{
 #'   \item \code{Features} names of the features used in the model;
 #'   \item \code{Gain} represents fractional contribution of each feature to the model based on
-#'        the total gain of this feature's splits. Higher percentage means a more important 
+#'        the total gain of this feature's splits. Higher percentage means a more important
 #'        predictive feature.
 #'   \item \code{Cover} metric of the number of observation related to this feature;
 #'   \item \code{Frequency} percentage representing the relative number of times
 #'        a feature have been used in trees.
 #' }
-#' 
+#'
 #' A linear model's importance \code{data.table} has the following columns:
 #' \itemize{
 #'   \item \code{Features} names of the features used in the model;
 #'   \item \code{Weight} the linear coefficient of this feature;
 #'   \item \code{Class} (only for multiclass models) class label.
 #' }
-#' 
-#' If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names}, 
+#'
+#' If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names},
 #' index of the features will be used instead. Because the index is extracted from the model dump
 #' (based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
-#' 
+#'
 #' @examples
-#' 
+#'
 #' # binomial classification using gbtree:
 #' data(agaricus.train, package='xgboost')
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #' xgb.importance(model = bst)
-#' 
+#'
 #' # binomial classification using gblinear:
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear", 
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear",
 #'                eta = 0.3, nthread = 1, nrounds = 20, objective = "binary:logistic")
 #' xgb.importance(model = bst)
-#' 
+#'
 #' # multiclass classification using gbtree:
 #' nclass <- 3
 #' nrounds <- 10
@@ -73,7 +73,7 @@
 #' xgb.importance(model = mbst, trees = seq(from=0, by=nclass, length.out=nrounds))
 #' xgb.importance(model = mbst, trees = seq(from=1, by=nclass, length.out=nrounds))
 #' xgb.importance(model = mbst, trees = seq(from=2, by=nclass, length.out=nrounds))
-#' 
+#'
 #' # multiclass classification using gblinear:
 #' mbst <- xgboost(data = scale(as.matrix(iris[, -5])), label = as.numeric(iris$Species) - 1,
 #'                booster = "gblinear", eta = 0.2, nthread = 1, nrounds = 15,
@@ -83,33 +83,33 @@
 #' @export
 xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
                            data = NULL, label = NULL, target = NULL){
-  
+
   if (!(is.null(data) && is.null(label) && is.null(target)))
     warning("xgb.importance: parameters 'data', 'label' and 'target' are deprecated")
-  
+
   if (!inherits(model, "xgb.Booster"))
     stop("model: must be an object of class xgb.Booster")
-  
+
   if (is.null(feature_names) && !is.null(model$feature_names))
     feature_names <- model$feature_names
-  
+
   if (!(is.null(feature_names) || is.character(feature_names)))
     stop("feature_names: Has to be a character vector")
 
   model_text_dump <- xgb.dump(model = model, with_stats = TRUE)
-  
+
   # linear model
-  if(model_text_dump[2] == "bias:"){
+  if (model_text_dump[2] == "bias:"){
     weights <- which(model_text_dump == "weight:") %>%
                {model_text_dump[(. + 1):length(model_text_dump)]} %>%
                as.numeric
-    
+
     num_class <- NVL(model$params$num_class, 1)
-    if(is.null(feature_names)) 
+    if (is.null(feature_names))
       feature_names <- seq(to = length(weights) / num_class) - 1
     if (length(feature_names) * num_class != length(weights))
       stop("feature_names length does not match the number of features used in the model")
-    
+
     result <- if (num_class == 1) {
       data.table(Feature = feature_names, Weight = weights)[order(-abs(Weight))]
     } else {
@@ -117,18 +117,17 @@ xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
                  Weight = weights,
                  Class = seq_len(num_class) - 1)[order(Class, -abs(Weight))]
     }
-  } else { 
-  # tree model
-    result <- xgb.model.dt.tree(feature_names = feature_names,
-                                text = model_text_dump,
-                                trees = trees)[
-      Feature != "Leaf", .(Gain = sum(Quality), 
-                           Cover = sum(Cover), 
-                           Frequency = .N), by = Feature][
-      ,`:=`(Gain = Gain / sum(Gain), 
-            Cover = Cover / sum(Cover),
-            Frequency = Frequency / sum(Frequency))][
-      order(Gain, decreasing = TRUE)]
+  } else { # tree model
+      result <- xgb.model.dt.tree(feature_names = feature_names,
+                                  text = model_text_dump,
+                                  trees = trees)[
+          Feature != "Leaf", .(Gain = sum(Quality),
+                               Cover = sum(Cover),
+                               Frequency = .N), by = Feature][
+        , `:=`(Gain = Gain / sum(Gain),
+               Cover = Cover / sum(Cover),
+               Frequency = Frequency / sum(Frequency))][
+          order(Gain, decreasing = TRUE)]
   }
   result
 }

R-package/R/xgb.load.R

@@ -1,33 +1,34 @@
 #' Load xgboost model from binary file
-#' 
-#' Load xgboost model from the binary model file. 
-#' 
+#'
+#' Load xgboost model from the binary model file.
+#'
 #' @param modelfile the name of the binary input file.
-#' 
-#' @details 
+#'
+#' @details
 #' The input file is expected to contain a model saved in an xgboost-internal binary format
-#' using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some 
-#' appropriate methods from other xgboost interfaces. E.g., a model trained in Python and 
+#' using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some
+#' appropriate methods from other xgboost interfaces. E.g., a model trained in Python and
 #' saved from there in xgboost format, could be loaded from R.
-#' 
+#'
 #' Note: a model saved as an R-object, has to be loaded using corresponding R-methods,
 #' not \code{xgb.load}.
-#' 
-#' @return 
+#'
+#' @return
 #' An object of \code{xgb.Booster} class.
-#' 
-#' @seealso 
-#' \code{\link{xgb.save}}, \code{\link{xgb.Booster.complete}}. 
-#' 
+#'
+#' @seealso
+#' \code{\link{xgb.save}}, \code{\link{xgb.Booster.complete}}.
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #' train <- agaricus.train
 #' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
+#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
 #'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
+#' if (file.exists('xgb.model')) file.remove('xgb.model')
 #' pred <- predict(bst, test$data)
 #' @export
 xgb.load <- function(modelfile) {

R-package/R/xgb.load.raw.R

@@ -0,0 +1,14 @@
+#' Load serialised xgboost model from R's raw vector
+#'
+#' User can generate raw memory buffer by calling xgb.save.raw
+#'
+#' @param buffer the buffer returned by xgb.save.raw
+#'
+#' @export
+xgb.load.raw <- function(buffer) {
+  cachelist <- list()
+  handle <- .Call(XGBoosterCreate_R, cachelist)
+  .Call(XGBoosterLoadModelFromRaw_R, handle, buffer)
+  class(handle) <- "xgb.Booster.handle"
+  return (handle)
+}

R-package/R/xgb.model.dt.tree.R

@@ -1,12 +1,12 @@
 #' Parse a boosted tree model text dump
-#' 
+#'
 #' Parse a boosted tree model text dump into a \code{data.table} structure.
-#' 
+#'
 #' @param feature_names character vector of feature names. If the model already
 #'          contains feature names, those would be used when \code{feature_names=NULL} (default value).
 #'          Non-null \code{feature_names} could be provided to override those in the model.
 #' @param model object of class \code{xgb.Booster}
-#' @param text \code{character} vector previously generated by the \code{xgb.dump} 
+#' @param text \code{character} vector previously generated by the \code{xgb.dump}
 #'          function  (where parameter \code{with_stats = TRUE} should have been set).
 #'          \code{text} takes precedence over \code{model}.
 #' @param trees an integer vector of tree indices that should be parsed.
@@ -18,11 +18,11 @@
 #'          represented as integers (when FALSE) or as "Tree-Node" character strings (when FALSE).
 #' @param ... currently not used.
 #'
-#' @return 
+#' @return
 #' A \code{data.table} with detailed information about model trees' nodes.
 #'
 #' The columns of the \code{data.table} are:
-#' 
+#'
 #' \itemize{
 #'  \item \code{Tree}: integer ID of a tree in a model (zero-based index)
 #'  \item \code{Node}: integer ID of a node in a tree (zero-based index)
@@ -36,109 +36,111 @@
 #'  \item \code{Quality}: either the split gain (change in loss) or the leaf value
 #'  \item \code{Cover}: metric related to the number of observation either seen by a split
 #'                      or collected by a leaf during training.
-#' } 
-#' 
+#' }
+#'
 #' When \code{use_int_id=FALSE}, columns "Yes", "No", and "Missing" point to model-wide node identifiers
-#' in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from 
+#' in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from
 #' the corresponding trees in the "Node" column.
-#' 
+#'
 #' @examples
 #' # Basic use:
-#' 
+#'
 #' data(agaricus.train, package='xgboost')
-#' 
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
+#'
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
 #'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
-#' 
+#'
 #' (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
-#' 
-#' # This bst model already has feature_names stored with it, so those would be used when 
+#'
+#' # This bst model already has feature_names stored with it, so those would be used when
 #' # feature_names is not set:
 #' (dt <- xgb.model.dt.tree(model = bst))
-#' 
+#'
 #' # How to match feature names of splits that are following a current 'Yes' branch:
-#' 
+#'
 #' merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
-#'  
+#'
 #' @export
 xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
                               trees = NULL, use_int_id = FALSE, ...){
   check.deprecation(...)
-  
+
   if (!inherits(model, "xgb.Booster") && !is.character(text)) {
     stop("Either 'model' must be an object of class xgb.Booster\n",
          "  or 'text' must be a character vector with the result of xgb.dump\n",
          "  (or NULL if 'model' was provided).")
   }
-  
+
   if (is.null(feature_names) && !is.null(model) && !is.null(model$feature_names))
     feature_names <- model$feature_names
-  
+
   if (!(is.null(feature_names) || is.character(feature_names))) {
     stop("feature_names: must be a character vector")
   }
-  
+
   if (!(is.null(trees) || is.numeric(trees))) {
     stop("trees: must be a vector of integers.")
   }
-  
+
   if (is.null(text)){
     text <- xgb.dump(model = model, with_stats = TRUE)
   }
-  
+
   if (length(text) < 2 ||
-      sum(stri_detect_regex(text, 'yes=(\\d+),no=(\\d+)')) < 1) {
+      sum(grepl('yes=(\\d+),no=(\\d+)', text)) < 1) {
     stop("Non-tree model detected! This function can only be used with tree models.")
   }
-  
-  position <- which(!is.na(stri_match_first_regex(text, "booster")))
-  
+
+  position <- which(grepl("booster", text, fixed = TRUE))
+
   add.tree.id <- function(node, tree) if (use_int_id) node else paste(tree, node, sep = "-")
-  
+
   anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
-  
+
   td <- data.table(t = text)
   td[position, Tree := 1L]
   td[, Tree := cumsum(ifelse(is.na(Tree), 0L, Tree)) - 1L]
-  
+
   if (is.null(trees)) {
     trees <- 0:max(td$Tree)
   } else {
     trees <- trees[trees >= 0 & trees <= max(td$Tree)]
   }
   td <- td[Tree %in% trees & !grepl('^booster', t)]
-  
-  td[, Node := stri_match_first_regex(t, "(\\d+):")[,2] %>% as.integer ]
+
+  td[, Node := as.integer(sub("^([0-9]+):.*", "\\1", t))]
   if (!use_int_id) td[, ID := add.tree.id(Node, Tree)]
-  td[, isLeaf := !is.na(stri_match_first_regex(t, "leaf"))]
+  td[, isLeaf := grepl("leaf", t, fixed = TRUE)]
 
   # parse branch lines
   branch_rx <- paste0("f(\\d+)<(", anynumber_regex, ")\\] yes=(\\d+),no=(\\d+),missing=(\\d+),",
                       "gain=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
   branch_cols <- c("Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
-  td[isLeaf == FALSE, 
+  td[isLeaf == FALSE,
      (branch_cols) := {
-      # skip some indices with spurious capture groups from anynumber_regex
-      xtr <- stri_match_first_regex(t, branch_rx)[, c(2,3,5,6,7,8,10), drop = FALSE]
-      xtr[, 3:5] <- add.tree.id(xtr[, 3:5], Tree)
-      lapply(seq_len(ncol(xtr)), function(i) xtr[,i])
+       matches <- regmatches(t, regexec(branch_rx, t))
+       # skip some indices with spurious capture groups from anynumber_regex
+       xtr <- do.call(rbind, matches)[, c(2, 3, 5, 6, 7, 8, 10), drop = FALSE]
+       xtr[, 3:5] <- add.tree.id(xtr[, 3:5], Tree)
+       as.data.table(xtr)
     }]
   # assign feature_names when available
   if (!is.null(feature_names)) {
     if (length(feature_names) <= max(as.numeric(td$Feature), na.rm = TRUE))
       stop("feature_names has less elements than there are features used in the model")
-    td[isLeaf == FALSE, Feature := feature_names[as.numeric(Feature) + 1] ]
+    td[isLeaf == FALSE, Feature := feature_names[as.numeric(Feature) + 1]]
   }
-  
+
   # parse leaf lines
   leaf_rx <- paste0("leaf=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
   leaf_cols <- c("Feature", "Quality", "Cover")
   td[isLeaf == TRUE,
      (leaf_cols) := {
-      xtr <- stri_match_first_regex(t, leaf_rx)[, c(2,4)]
-      c("Leaf", lapply(seq_len(ncol(xtr)), function(i) xtr[,i]))
+       matches <- regmatches(t, regexec(leaf_rx, t))
+       xtr <- do.call(rbind, matches)[, c(2, 4)]
+       c("Leaf", as.data.table(xtr))
     }]
-  
+
   # convert some columns to numeric
   numeric_cols <- c("Split", "Quality", "Cover")
   td[, (numeric_cols) := lapply(.SD, as.numeric), .SDcols = numeric_cols]
@@ -146,14 +148,14 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
     int_cols <- c("Yes", "No", "Missing")
     td[, (int_cols) := lapply(.SD, as.integer), .SDcols = int_cols]
   }
-  
+
   td[, t := NULL]
   td[, isLeaf := NULL]
-  
+
   td[order(Tree, Node)]
 }
 
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c("Tree", "Node", "ID", "Feature", "t", "isLeaf",".SD", ".SDcols"))
+globalVariables(c("Tree", "Node", "ID", "Feature", "t", "isLeaf", ".SD", ".SDcols"))

R-package/R/xgb.plot.deepness.R

@@ -2,48 +2,48 @@
 #'
 #' Visualizes distributions related to depth of tree leafs.
 #' \code{xgb.plot.deepness} uses base R graphics, while \code{xgb.ggplot.deepness} uses the ggplot backend.
-#' 
+#'
 #' @param model either an \code{xgb.Booster} model generated by the \code{xgb.train} function
 #'        or a data.table result of the \code{xgb.model.dt.tree} function.
-#' @param plot (base R barplot) whether a barplot should be produced. 
+#' @param plot (base R barplot) whether a barplot should be produced.
 #'        If FALSE, only a data.table is returned.
 #' @param which which distribution to plot (see details).
 #' @param ... other parameters passed to \code{barplot} or \code{plot}.
-#' 
+#'
 #' @details
-#' 
+#'
 #' When \code{which="2x1"}, two distributions with respect to the leaf depth
 #' are plotted on top of each other:
 #' \itemize{
 #'  \item the distribution of the number of leafs in a tree model at a certain depth;
-#'  \item the distribution of average weighted number of observations ("cover") 
+#'  \item the distribution of average weighted number of observations ("cover")
 #'        ending up in leafs at certain depth.
 #' }
-#' Those could be helpful in determining sensible ranges of the \code{max_depth} 
+#' Those could be helpful in determining sensible ranges of the \code{max_depth}
 #' and \code{min_child_weight} parameters.
-#' 
+#'
 #' When \code{which="max.depth"} or \code{which="med.depth"}, plots of either maximum or median depth
 #' per tree with respect to tree number are created. And \code{which="med.weight"} allows to see how
 #' a tree's median absolute leaf weight changes through the iterations.
 #'
 #' This function was inspired by the blog post
-#' \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}.
-#' 
+#' \url{https://github.com/aysent/random-forest-leaf-visualization}.
+#'
 #' @return
-#' 
+#'
 #' Other than producing plots (when \code{plot=TRUE}), the \code{xgb.plot.deepness} function
 #' silently returns a processed data.table where each row corresponds to a terminal leaf in a tree model,
 #' and contains information about leaf's depth, cover, and weight (which is used in calculating predictions).
-#' 
+#'
 #' The \code{xgb.ggplot.deepness} silently returns either a list of two ggplot graphs when \code{which="2x1"}
 #' or a single ggplot graph for the other \code{which} options.
 #'
-#' @seealso 
-#' 
+#' @seealso
+#'
 #' \code{\link{xgb.train}}, \code{\link{xgb.model.dt.tree}}.
-#' 
+#'
 #' @examples
-#' 
+#'
 #' data(agaricus.train, package='xgboost')
 #'
 #' # Change max_depth to a higher number to get a more significant result
@@ -53,16 +53,16 @@
 #'
 #' xgb.plot.deepness(bst)
 #' xgb.ggplot.deepness(bst)
-#' 
+#'
 #' xgb.plot.deepness(bst, which='max.depth', pch=16, col=rgb(0,0,1,0.3), cex=2)
-#' 
+#'
 #' xgb.plot.deepness(bst, which='med.weight', pch=16, col=rgb(0,0,1,0.3), cex=2)
 #'
 #' @rdname xgb.plot.deepness
 #' @export
 xgb.plot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med.depth", "med.weight"),
                               plot = TRUE, ...) {
-  
+
   if (!(inherits(model, "xgb.Booster") || is.data.table(model)))
     stop("model: Has to be either an xgb.Booster model generaged by the xgb.train function\n",
          "or a data.table result of the xgb.importance function")
@@ -71,32 +71,32 @@ xgb.plot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med.d
     stop("igraph package is required for plotting the graph deepness.", call. = FALSE)
 
   which <- match.arg(which)
-  
+
   dt_tree <- model
   if (inherits(model, "xgb.Booster"))
     dt_tree <- xgb.model.dt.tree(model = model)
-  
+
   if (!all(c("Feature", "Tree", "ID", "Yes", "No", "Cover") %in% colnames(dt_tree)))
     stop("Model tree columns are not as expected!\n",
          "  Note that this function works only for tree models.")
-  
+
   dt_depths <- merge(get.leaf.depth(dt_tree), dt_tree[, .(ID, Cover, Weight = Quality)], by = "ID")
   setkeyv(dt_depths, c("Tree", "ID"))
   # count by depth levels, and also calculate average cover at a depth
   dt_summaries <- dt_depths[, .(.N, Cover = mean(Cover)), Depth]
   setkey(dt_summaries, "Depth")
-  
+
   if (plot) {
     if (which == "2x1") {
       op <- par(no.readonly = TRUE)
-      par(mfrow = c(2,1),
-          oma = c(3,1,3,1) + 0.1,
-          mar = c(1,4,1,0) + 0.1)
+      par(mfrow = c(2, 1),
+          oma = c(3, 1, 3, 1) + 0.1,
+          mar = c(1, 4, 1, 0) + 0.1)
 
       dt_summaries[, barplot(N, border = NA, ylab = 'Number of leafs', ...)]
 
       dt_summaries[, barplot(Cover, border = NA, ylab = "Weighted cover", names.arg = Depth, ...)]
-    
+
       title("Model complexity", xlab = "Leaf depth", outer = TRUE, line = 1)
       par(op)
     } else if (which == "max.depth") {
@@ -123,14 +123,14 @@ get.leaf.depth <- function(dt_tree) {
       dt_tree[Feature != "Leaf", .(ID, To = No, Tree)]
     ))
   # whether "To" is a leaf:
-  dt_edges <- 
+  dt_edges <-
     merge(dt_edges,
           dt_tree[Feature == "Leaf", .(ID, Leaf = TRUE)],
           all.x = TRUE, by.x = "To", by.y = "ID")
   dt_edges[is.na(Leaf), Leaf := FALSE]
 
   dt_edges[, {
-    graph <- igraph::graph_from_data_frame(.SD[,.(ID, To)])
+    graph <- igraph::graph_from_data_frame(.SD[, .(ID, To)])
     # min(ID) in a tree is a root node
     paths_tmp <- igraph::shortest_paths(graph, from = min(ID), to = To[Leaf == TRUE])
     # list of paths to each leaf in a tree

R-package/R/xgb.plot.importance.R

@@ -5,16 +5,16 @@
 #'
 #' @param importance_matrix a \code{data.table} returned by \code{\link{xgb.importance}}.
 #' @param top_n maximal number of top features to include into the plot.
-#' @param measure the name of importance measure to plot. 
+#' @param measure the name of importance measure to plot.
 #'        When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.
 #' @param rel_to_first whether importance values should be represented as relative to the highest ranked feature.
 #'        See Details.
 #' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.
 #'        When it is NULL, the existing \code{par('mar')} is used.
 #' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}.
-#' @param plot (base R barplot) whether a barplot should be produced. 
+#' @param plot (base R barplot) whether a barplot should be produced.
 #'        If FALSE, only a data.table is returned.
-#' @param n_clusters (ggplot only) a \code{numeric} vector containing the min and the max range 
+#' @param n_clusters (ggplot only) a \code{numeric} vector containing the min and the max range
 #'        of the possible number of clusters of bars.
 #' @param ... other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).
 #'
@@ -22,27 +22,27 @@
 #' The graph represents each feature as a horizontal bar of length proportional to the importance of a feature.
 #' Features are shown ranked in a decreasing importance order.
 #' It works for importances from both \code{gblinear} and \code{gbtree} models.
-#' 
+#'
 #' When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
-#' For gbtree model, that would mean being normalized to the total of 1 
+#' For gbtree model, that would mean being normalized to the total of 1
 #' ("what is feature's importance contribution relative to the whole model?").
 #' For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
-#' Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of 
+#' Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
 #' "what is feature's importance contribution relative to the most important feature?"
-#' 
-#' The ggplot-backend method also performs 1-D custering of the importance values, 
-#' with bar colors coresponding to different clusters that have somewhat similar importance values.
-#' 
+#'
+#' The ggplot-backend method also performs 1-D clustering of the importance values,
+#' with bar colors corresponding to different clusters that have somewhat similar importance values.
+#'
 #' @return
 #' The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})
 #' and silently returns a processed data.table with \code{n_top} features sorted by importance.
-#' 
+#'
 #' The \code{xgb.ggplot.importance} function returns a ggplot graph which could be customized afterwards.
 #' E.g., to change the title of the graph, add \code{+ ggtitle("A GRAPH NAME")} to the result.
 #'
 #' @seealso
 #' \code{\link[graphics]{barplot}}.
-#' 
+#'
 #' @examples
 #' data(agaricus.train)
 #'
@@ -50,15 +50,15 @@
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #'
 #' importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
-#' 
+#'
 #' xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")
-#' 
+#'
 #' (gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
 #' gg + ggplot2::ylab("Frequency")
 #'
 #' @rdname xgb.plot.importance
 #' @export
-xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, 
+xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL,
                                 rel_to_first = FALSE, left_margin = 10, cex = NULL, plot = TRUE, ...) {
   check.deprecation(...)
   if (!is.data.table(importance_matrix))  {
@@ -80,42 +80,41 @@ xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure
     if (!"Feature" %in% imp_names)
       stop("Importance matrix column names are not as expected!")
   }
-  
+
   # also aggregate, just in case when the values were not yet summed up by feature
   importance_matrix <- importance_matrix[, Importance := sum(get(measure)), by = Feature]
-  
+
   # make sure it's ordered
   importance_matrix <- importance_matrix[order(-abs(Importance))]
-  
+
   if (!is.null(top_n)) {
     top_n <- min(top_n, nrow(importance_matrix))
     importance_matrix <- head(importance_matrix, top_n)
   }
   if (rel_to_first) {
-    importance_matrix[, Importance := Importance/max(abs(Importance))]
+    importance_matrix[, Importance := Importance / max(abs(Importance))]
   }
   if (is.null(cex)) {
-    cex <- 2.5/log2(1 + nrow(importance_matrix))
+    cex <- 2.5 / log2(1 + nrow(importance_matrix))
   }
-  
+
   if (plot) {
-    op <- par(no.readonly = TRUE)
-    mar <- op$mar
+    original_mar <- par()$mar
+
+    # reset margins so this function doesn't have side effects
+    on.exit({par(mar = original_mar)})
+
+    mar <- original_mar
     if (!is.null(left_margin))
       mar[2] <- left_margin
     par(mar = mar)
-    
+
     # reverse the order of rows to have the highest ranked at the top
-    importance_matrix[nrow(importance_matrix):1,
+    importance_matrix[rev(seq_len(nrow(importance_matrix))),
                       barplot(Importance, horiz = TRUE, border = NA, cex.names = cex,
                               names.arg = Feature, las = 1, ...)]
-    grid(NULL, NA)
-    # redraw over the grid
-    importance_matrix[nrow(importance_matrix):1,
-                      barplot(Importance, horiz = TRUE, border = NA, add = TRUE)]
-    par(op)
   }
-  
+
   invisible(importance_matrix)
 }
 

R-package/R/xgb.plot.multi.trees.R

@@ -9,7 +9,7 @@
 #' @param plot_height height in pixels of the graph to produce
 #' @param render a logical flag for whether the graph should be rendered (see Value).
 #' @param ... currently not used
-#' 
+#'
 #' @details
 #'
 #' This function tries to capture the complexity of a gradient boosted tree model
@@ -67,58 +67,55 @@ xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5,
 
   # first number of the path represents the tree, then the following numbers are related to the path to follow
   # root init
-  root.nodes <- tree.matrix[stri_detect_regex(ID, "\\d+-0"), ID]
+  root.nodes <- tree.matrix[Node == 0, ID]
   tree.matrix[ID %in% root.nodes, abs.node.position := root.nodes]
 
   precedent.nodes <- root.nodes
 
-  while(tree.matrix[,sum(is.na(abs.node.position))] > 0) {
+  while (tree.matrix[, sum(is.na(abs.node.position))] > 0) {
     yes.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(Yes)]
     no.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(No)]
     yes.nodes.abs.pos <- yes.row.nodes[, abs.node.position] %>% paste0("_0")
     no.nodes.abs.pos <- no.row.nodes[, abs.node.position] %>% paste0("_1")
-    
+
     tree.matrix[ID %in% yes.row.nodes[, Yes], abs.node.position := yes.nodes.abs.pos]
     tree.matrix[ID %in% no.row.nodes[, No], abs.node.position := no.nodes.abs.pos]
     precedent.nodes <- c(yes.nodes.abs.pos, no.nodes.abs.pos)
   }
-  
+
   tree.matrix[!is.na(Yes), Yes := paste0(abs.node.position, "_0")]
   tree.matrix[!is.na(No), No := paste0(abs.node.position, "_1")]
-  
-  remove.tree <- . %>% stri_replace_first_regex(pattern = "^\\d+-", replacement = "")
-  
-  tree.matrix[,`:=`(abs.node.position = remove.tree(abs.node.position),
-                    Yes = remove.tree(Yes),
-                    No = remove.tree(No))]
-  
+
+  for (nm in c("abs.node.position", "Yes", "No"))
+    data.table::set(tree.matrix, j = nm, value = sub("^\\d+-", "", tree.matrix[[nm]]))
+
   nodes.dt <- tree.matrix[
         , .(Quality = sum(Quality))
         , by = .(abs.node.position, Feature)
       ][, .(Text = paste0(Feature[1:min(length(Feature), features_keep)],
                           " (",
-                          format(Quality[1:min(length(Quality), features_keep)], digits=5),
+                          format(Quality[1:min(length(Quality), features_keep)], digits = 5),
                           ")") %>%
                    paste0(collapse = "\n"))
         , by = abs.node.position]
-  
+
   edges.dt <- tree.matrix[Feature != "Leaf", .(abs.node.position, Yes)] %>%
-    list(tree.matrix[Feature != "Leaf",.(abs.node.position, No)]) %>%
+    list(tree.matrix[Feature != "Leaf", .(abs.node.position, No)]) %>%
     rbindlist() %>%
     setnames(c("From", "To")) %>%
     .[, .N, .(From, To)] %>%
-    .[, N:=NULL]
-  
+    .[, N := NULL]
+
   nodes <- DiagrammeR::create_node_df(
     n = nrow(nodes.dt),
-    label = nodes.dt[,Text]
+    label = nodes.dt[, Text]
   )
-  
+
   edges <- DiagrammeR::create_edge_df(
-    from = match(edges.dt[,From], nodes.dt[,abs.node.position]),
-    to = match(edges.dt[,To], nodes.dt[,abs.node.position]),
+    from = match(edges.dt[, From], nodes.dt[, abs.node.position]),
+    to = match(edges.dt[, To], nodes.dt[, abs.node.position]),
     rel = "leading_to")
-  
+
   graph <- DiagrammeR::create_graph(
       nodes_df = nodes,
       edges_df = edges,

R-package/R/xgb.plot.shap.R

@@ -1,9 +1,9 @@
 #' SHAP contribution dependency plots
 #'
 #' Visualizing the SHAP feature contribution to prediction dependencies on feature value.
-#' 
+#'
 #' @param data data as a \code{matrix} or \code{dgCMatrix}.
-#' @param shap_contrib a matrix of SHAP contributions that was computed earlier for the above 
+#' @param shap_contrib a matrix of SHAP contributions that was computed earlier for the above
 #'          \code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.
 #' @param features a vector of either column indices or of feature names to plot. When it is NULL,
 #'          feature importance is calculated, and \code{top_n} high ranked features are taken.
@@ -31,32 +31,32 @@
 #' @param plot_loess whether to plot loess-smoothed curves. The smoothing is only done for features with
 #'          more than 5 distinct values.
 #' @param col_loess a color to use for the loess curves.
-#' @param span_loess the \code{span} paramerer in \code{\link[stats]{loess}}'s call.
+#' @param span_loess the \code{span} parameter in \code{\link[stats]{loess}}'s call.
 #' @param which whether to do univariate or bivariate plotting. NOTE: only 1D is implemented so far.
 #' @param plot whether a plot should be drawn. If FALSE, only a lits of matrices is returned.
 #' @param ... other parameters passed to \code{plot}.
-#' 
+#'
 #' @details
-#' 
+#'
 #' These scatterplots represent how SHAP feature contributions depend of feature values.
 #' The similarity to partial dependency plots is that they also give an idea for how feature values
 #' affect predictions. However, in partial dependency plots, we usually see marginal dependencies
 #' of model prediction on feature value, while SHAP contribution dependency plots display the estimated
 #' contributions of a feature to model prediction for each individual case.
-#' 
+#'
 #' When \code{plot_loess = TRUE} is set, feature values are rounded to 3 significant digits and
 #' weighted LOESS is computed and plotted, where weights are the numbers of data points
 #' at each rounded value.
-#' 
+#'
 #' Note: SHAP contributions are shown on the scale of model margin. E.g., for a logistic binomial objective,
 #' the margin is prediction before a sigmoidal transform into probability-like values.
 #' Also, since SHAP stands for "SHapley Additive exPlanation" (model prediction = sum of SHAP
 #' contributions for all features + bias), depending on the objective used, transforming SHAP
 #' contributions for a feature from the marginal to the prediction space is not necessarily
 #' a meaningful thing to do.
-#' 
+#'
 #' @return
-#' 
+#'
 #' In addition to producing plots (when \code{plot=TRUE}), it silently returns a list of two matrices:
 #' \itemize{
 #'  \item \code{data} the values of selected features;
@@ -70,17 +70,18 @@
 #' Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", \url{https://arxiv.org/abs/1706.06060}
 #'
 #' @examples
-#' 
+#'
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #'
-#' bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50, 
+#' bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
 #'                eta = 0.1, max_depth = 3, subsample = .5,
 #'                method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
 #'
 #' xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
 #' contr <- predict(bst, agaricus.test$data, predcontrib = TRUE)
 #' xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
+#' xgb.ggplot.shap.summary(agaricus.test$data, contr, model = bst, top_n = 12)  # Summary plot
 #'
 #' # multiclass example - plots for each class separately:
 #' nclass <- 3
@@ -99,7 +100,8 @@
 #'               n_col = 2, col = col, pch = 16, pch_NA = 17)
 #' xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4,
 #'               n_col = 2, col = col, pch = 16, pch_NA = 17)
-#' 
+#' xgb.ggplot.shap.summary(x, model = mbst, target_class = 0, top_n = 4)  # Summary plot
+#'
 #' @rdname xgb.plot.shap
 #' @export
 xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL,
@@ -109,69 +111,33 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
                           plot_NA = TRUE, col_NA = rgb(0.7, 0, 1, 0.6), pch_NA = '.', pos_NA = 1.07,
                           plot_loess = TRUE, col_loess = 2, span_loess = 0.5,
                           which = c("1d", "2d"), plot = TRUE, ...) {
-  
-  if (!is.matrix(data) && !inherits(data, "dgCMatrix"))
-    stop("data: must be either matrix or dgCMatrix")
-
-  if (is.null(shap_contrib) && (is.null(model) || !inherits(model, "xgb.Booster")))
-    stop("when shap_contrib is not provided, one must provide an xgb.Booster model")
-
-  if (is.null(features) && (is.null(model) || !inherits(model, "xgb.Booster")))
-    stop("when features are not provided, one must provide an xgb.Booster model to rank the features")
-
-  if (!is.null(shap_contrib) &&
-      (!is.matrix(shap_contrib) || nrow(shap_contrib) != nrow(data) || ncol(shap_contrib) != ncol(data) + 1))
-    stop("shap_contrib is not compatible with the provided data")
-  
-  nsample <- if (is.null(subsample)) min(100000, nrow(data)) else as.integer(subsample * nrow(data))
-  idx <- sample(1:nrow(data), nsample)
-  data <- data[idx,]
-
-  if (is.null(shap_contrib)) {
-    shap_contrib <- predict(model, data, predcontrib = TRUE, approxcontrib = approxcontrib)
-  } else {
-    shap_contrib <- shap_contrib[idx,]
-  }
+  data_list <- xgb.shap.data(
+    data = data,
+    shap_contrib = shap_contrib,
+    features = features,
+    top_n = top_n,
+    model = model,
+    trees = trees,
+    target_class = target_class,
+    approxcontrib = approxcontrib,
+    subsample = subsample,
+    max_observations = 100000
+  )
+  data <- data_list[["data"]]
+  shap_contrib <- data_list[["shap_contrib"]]
+  features <- colnames(data)
 
   which <- match.arg(which)
   if (which == "2d")
     stop("2D plots are not implemented yet")
 
-  if (is.null(features)) {
-    imp <- xgb.importance(model = model, trees = trees)
-    top_n <- as.integer(top_n[1])
-    if (top_n < 1 && top_n > 100)
-      stop("top_n: must be an integer within [1, 100]")
-    features <- imp$Feature[1:min(top_n, NROW(imp))]
-  }
-  
-  if (is.character(features)) {
-    if (is.null(colnames(data)))
-      stop("Either provide `data` with column names or provide `features` as column indices")
-    features <- match(features, colnames(data))
-  }
-  
   if (n_col > length(features)) n_col <- length(features)
-
-  if (is.list(shap_contrib)) { # multiclass: either choose a class or merge
-    shap_contrib <- if (!is.null(target_class)) shap_contrib[[target_class + 1]]
-                    else Reduce("+", lapply(shap_contrib, abs))
-  }
-
-  shap_contrib <- shap_contrib[, features, drop = FALSE]
-  data <- data[, features, drop = FALSE]
-  cols <- colnames(data)
-  if (is.null(cols)) cols <- colnames(shap_contrib)
-  if (is.null(cols)) cols <- paste0('X', 1:ncol(data))
-  colnames(data) <- cols
-  colnames(shap_contrib) <- cols
-  
   if (plot && which == "1d") {
     op <- par(mfrow = c(ceiling(length(features) / n_col), n_col),
-              oma = c(0,0,0,0) + 0.2,
-              mar = c(3.5,3.5,0,0) + 0.1,
+              oma = c(0, 0, 0, 0) + 0.2,
+              mar = c(3.5, 3.5, 0, 0) + 0.1,
               mgp = c(1.7, 0.6, 0))
-    for (f in cols) {
+    for (f in features) {
       ord <- order(data[, f])
       x <- data[, f][ord]
       y <- shap_contrib[, f][ord]
@@ -192,7 +158,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
       grid()
       if (plot_loess) {
         # compress x to 3 digits, and mean-aggredate y
-        zz <- data.table(x = signif(x, 3), y)[, .(.N, y=mean(y)), x]
+        zz <- data.table(x = signif(x, 3), y)[, .(.N, y = mean(y)), x]
         if (nrow(zz) <= 5) {
           lines(zz$x, zz$y, col = col_loess)
         } else {
@@ -216,3 +182,108 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
   }
   invisible(list(data = data, shap_contrib = shap_contrib))
 }
+
+#' SHAP contribution dependency summary plot
+#'
+#' Compare SHAP contributions of different features.
+#'
+#' A point plot (each point representing one sample from \code{data}) is
+#' produced for each feature, with the points plotted on the SHAP value axis.
+#' Each point (observation) is coloured based on its feature value. The plot
+#' hence allows us to see which features have a negative / positive contribution
+#' on the model prediction, and whether the contribution is different for larger
+#' or smaller values of the feature. We effectively try to replicate the
+#' \code{summary_plot} function from https://github.com/slundberg/shap.
+#'
+#' @inheritParams xgb.plot.shap
+#'
+#' @return A \code{ggplot2} object.
+#' @export
+#'
+#' @examples # See \code{\link{xgb.plot.shap}}.
+#' @seealso \code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
+#'   \url{https://github.com/slundberg/shap}
+xgb.plot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = NULL,
+                                  trees = NULL, target_class = NULL, approxcontrib = FALSE, subsample = NULL) {
+  # Only ggplot implementation is available.
+  xgb.ggplot.shap.summary(data, shap_contrib, features, top_n, model, trees, target_class, approxcontrib, subsample)
+}
+
+#' Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc.
+#' Internal utility function.
+#'
+#' @inheritParams xgb.plot.shap
+#' @keywords internal
+#'
+#' @return A list containing: 'data', a matrix containing sample observations
+#'   and their feature values; 'shap_contrib', a matrix containing the SHAP contribution
+#'   values for these observations.
+xgb.shap.data <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL,
+                          trees = NULL, target_class = NULL, approxcontrib = FALSE,
+                          subsample = NULL, max_observations = 100000) {
+  if (!is.matrix(data) && !inherits(data, "dgCMatrix"))
+    stop("data: must be either matrix or dgCMatrix")
+
+  if (is.null(shap_contrib) && (is.null(model) || !inherits(model, "xgb.Booster")))
+    stop("when shap_contrib is not provided, one must provide an xgb.Booster model")
+
+  if (is.null(features) && (is.null(model) || !inherits(model, "xgb.Booster")))
+    stop("when features are not provided, one must provide an xgb.Booster model to rank the features")
+
+  if (!is.null(shap_contrib) &&
+      (!is.matrix(shap_contrib) || nrow(shap_contrib) != nrow(data) || ncol(shap_contrib) != ncol(data) + 1))
+    stop("shap_contrib is not compatible with the provided data")
+
+  if (is.character(features) && is.null(colnames(data)))
+    stop("either provide `data` with column names or provide `features` as column indices")
+
+  if (is.null(model$feature_names) && model$nfeatures != ncol(data))
+    stop("if model has no feature_names, columns in `data` must match features in model")
+
+  if (!is.null(subsample)) {
+    idx <- sample(x = seq_len(nrow(data)), size = as.integer(subsample * nrow(data)), replace = FALSE)
+  } else {
+    idx <- seq_len(min(nrow(data), max_observations))
+  }
+  data <- data[idx, ]
+  if (is.null(colnames(data))) {
+    colnames(data) <- paste0("X", seq_len(ncol(data)))
+  }
+
+  if (!is.null(shap_contrib)) {
+    if (is.list(shap_contrib)) { # multiclass: either choose a class or merge
+      shap_contrib <- if (!is.null(target_class)) shap_contrib[[target_class + 1]] else Reduce("+", lapply(shap_contrib, abs))
+    }
+    shap_contrib <- shap_contrib[idx, ]
+    if (is.null(colnames(shap_contrib))) {
+      colnames(shap_contrib) <- paste0("X", seq_len(ncol(data)))
+    }
+  } else {
+    shap_contrib <- predict(model, newdata = data, predcontrib = TRUE, approxcontrib = approxcontrib)
+    if (is.list(shap_contrib)) { # multiclass: either choose a class or merge
+      shap_contrib <- if (!is.null(target_class)) shap_contrib[[target_class + 1]] else Reduce("+", lapply(shap_contrib, abs))
+    }
+  }
+
+  if (is.null(features)) {
+    if (!is.null(model$feature_names)) {
+      imp <- xgb.importance(model = model, trees = trees)
+    } else {
+      imp <- xgb.importance(model = model, trees = trees, feature_names = colnames(data))
+    }
+    top_n <- top_n[1]
+    if (top_n < 1 | top_n > 100) stop("top_n: must be an integer within [1, 100]")
+    features <- imp$Feature[1:min(top_n, NROW(imp))]
+  }
+  if (is.character(features)) {
+    features <- match(features, colnames(data))
+  }
+
+  shap_contrib <- shap_contrib[, features, drop = FALSE]
+  data <- data[, features, drop = FALSE]
+
+  list(
+    data = data,
+    shap_contrib = shap_contrib
+  )
+}

R-package/R/xgb.plot.tree.R

@@ -1,7 +1,7 @@
 #' Plot a boosted tree model
-#' 
-#' Read a tree model text dump and plot the model. 
-#' 
+#'
+#' Read a tree model text dump and plot the model.
+#'
 #' @param feature_names names of each feature as a \code{character} vector.
 #' @param model produced by the \code{xgb.train} function.
 #' @param trees an integer vector of tree indices that should be visualized.
@@ -14,10 +14,10 @@
 #' @param show_node_id a logical flag for whether to show node id's in the graph.
 #' @param ... currently not used.
 #'
-#' @details 
-#' 
+#' @details
+#'
 #' The content of each node is organised that way:
-#' 
+#'
 #' \itemize{
 #'  \item Feature name.
 #'  \item \code{Cover}: The sum of second order gradient of training data classified to the leaf.
@@ -27,21 +27,21 @@
 #'  \item \code{Gain} (for split nodes): the information gain metric of a split
 #'        (corresponds to the importance of the node in the model).
 #'  \item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
-#' } 
+#' }
 #' The tree root nodes also indicate the Tree index (0-based).
-#' 
+#'
 #' The "Yes" branches are marked by the "< split_value" label.
 #' The branches that also used for missing values are marked as bold
 #' (as in "carrying extra capacity").
-#' 
+#'
 #' This function uses \href{http://www.graphviz.org/}{GraphViz} as a backend of DiagrammeR.
-#'  
+#'
 #' @return
-#' 
+#'
 #' When \code{render = TRUE}:
 #' returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
 #' Similar to ggplot objects, it needs to be printed to see it when not running from command line.
-#' 
+#'
 #' When \code{render = FALSE}:
 #' silently returns a graph object which is of DiagrammeR's class \code{dgr_graph}.
 #' This could be useful if one wants to modify some of the graph attributes
@@ -49,23 +49,23 @@
 #'
 #' @examples
 #' data(agaricus.train, package='xgboost')
-#' 
+#'
 #' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
 #'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 #' # plot all the trees
 #' xgb.plot.tree(model = bst)
 #' # plot only the first tree and display the node ID:
 #' xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
-#' 
+#'
 #' \dontrun{
-#' # Below is an example of how to save this plot to a file. 
+#' # Below is an example of how to save this plot to a file.
 #' # Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
 #' library(DiagrammeR)
 #' gr <- xgb.plot.tree(model=bst, trees=0:1, render=FALSE)
 #' export_graph(gr, 'tree.pdf', width=1500, height=1900)
 #' export_graph(gr, 'tree.png', width=1500, height=1900)
 #' }
-#' 
+#'
 #' @export
 xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot_width = NULL, plot_height = NULL,
                           render = TRUE, show_node_id = FALSE, ...){
@@ -77,18 +77,18 @@ xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot
   if (!requireNamespace("DiagrammeR", quietly = TRUE)) {
     stop("DiagrammeR package is required for xgb.plot.tree", call. = FALSE)
   }
-  
+
   dt <- xgb.model.dt.tree(feature_names = feature_names, model = model, trees = trees)
 
-  dt[, label:= paste0(Feature, "\nCover: ", Cover, ifelse(Feature == "Leaf", "\nValue: ", "\nGain: "), Quality)]
+  dt[, label := paste0(Feature, "\nCover: ", Cover, ifelse(Feature == "Leaf", "\nValue: ", "\nGain: "), Quality)]
   if (show_node_id)
     dt[, label := paste0(ID, ": ", label)]
   dt[Node == 0, label := paste0("Tree ", Tree, "\n", label)]
-  dt[, shape:= "rectangle"][Feature == "Leaf", shape:= "oval"]
-  dt[, filledcolor:= "Beige"][Feature == "Leaf", filledcolor:= "Khaki"]
+  dt[, shape := "rectangle"][Feature == "Leaf", shape := "oval"]
+  dt[, filledcolor := "Beige"][Feature == "Leaf", filledcolor := "Khaki"]
   # in order to draw the first tree on top:
   dt <- dt[order(-Tree)]
-  
+
   nodes <- DiagrammeR::create_node_df(
     n         = nrow(dt),
     ID        = dt$ID,
@@ -97,7 +97,7 @@ xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot
     shape     = dt$shape,
     data      = dt$Feature,
     fontcolor = "black")
-  
+
   edges <- DiagrammeR::create_edge_df(
     from  = match(dt[Feature != "Leaf", c(ID)] %>% rep(2), dt$ID),
     to    = match(dt[Feature != "Leaf", c(Yes, No)], dt$ID),
@@ -126,9 +126,9 @@ xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot
       attr_type = "edge",
       attr  = c("color", "arrowsize", "arrowhead", "fontname"),
       value = c("DimGray", "1.5", "vee", "Helvetica"))
-  
+
   if (!render) return(invisible(graph))
-  
+
   DiagrammeR::render_graph(graph, width = plot_width, height = plot_height)
 }
 

R-package/R/xgb.save.R

@@ -1,32 +1,37 @@
 #' Save xgboost model to binary file
-#' 
+#'
 #' Save xgboost model to a file in binary format.
-#' 
+#'
 #' @param model model object of \code{xgb.Booster} class.
 #' @param fname name of the file to write.
-#' 
-#' @details 
-#' This methods allows to save a model in an xgboost-internal binary format which is universal 
+#'
+#' @details
+#' This methods allows to save a model in an xgboost-internal binary format which is universal
 #' among the various xgboost interfaces. In R, the saved model file could be read-in later
-#' using either the \code{\link{xgb.load}} function or the \code{xgb_model} parameter 
+#' using either the \code{\link{xgb.load}} function or the \code{xgb_model} parameter
 #' of \code{\link{xgb.train}}.
-#' 
-#' Note: a model can also be saved as an R-object (e.g., by using \code{\link[base]{readRDS}} 
-#' or \code{\link[base]{save}}). However, it would then only be compatible with R, and 
-#' corresponding R-methods would need to be used to load it.
-#' 
-#' @seealso 
-#' \code{\link{xgb.load}}, \code{\link{xgb.Booster.complete}}. 
-#' 
+#'
+#' Note: a model can also be saved as an R-object (e.g., by using \code{\link[base]{readRDS}}
+#' or \code{\link[base]{save}}). However, it would then only be compatible with R, and
+#' corresponding R-methods would need to be used to load it. Moreover, persisting the model with
+#' \code{\link[base]{readRDS}} or \code{\link[base]{save}}) will cause compatibility problems in
+#' future versions of XGBoost. Consult \code{\link{a-compatibility-note-for-saveRDS-save}} to learn
+#' how to persist models in a future-proof way, i.e. to make the model accessible in future
+#' releases of XGBoost.
+#'
+#' @seealso
+#' \code{\link{xgb.load}}, \code{\link{xgb.Booster.complete}}.
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #' train <- agaricus.train
 #' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
+#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
 #'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
+#' if (file.exists('xgb.model')) file.remove('xgb.model')
 #' pred <- predict(bst, test$data)
 #' @export
 xgb.save <- function(model, fname) {

R-package/R/xgb.save.raw.R

@@ -1,23 +1,23 @@
 #' Save xgboost model to R's raw vector,
-#' user can call xgb.load to load the model back from raw vector
-#' 
+#' user can call xgb.load.raw to load the model back from raw vector
+#'
 #' Save xgboost model from xgboost or xgb.train
-#' 
+#'
 #' @param model the model object.
-#' 
+#'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #' train <- agaricus.train
 #' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
+#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
 #'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 #' raw <- xgb.save.raw(bst)
-#' bst <- xgb.load(raw)
+#' bst <- xgb.load.raw(raw)
 #' pred <- predict(bst, test$data)
 #'
 #' @export
 xgb.save.raw <- function(model) {
-  model <- xgb.get.handle(model)
-  .Call(XGBoosterModelToRaw_R, model)
+  handle <- xgb.get.handle(model)
+  .Call(XGBoosterModelToRaw_R, handle)
 }

R-package/R/xgb.serialize.R

@@ -0,0 +1,21 @@
+#' Serialize the booster instance into R's raw vector.  The serialization method differs
+#' from \code{\link{xgb.save.raw}} as the latter one saves only the model but not
+#' parameters.  This serialization format is not stable across different xgboost versions.
+#'
+#' @param booster the booster instance
+#'
+#' @examples
+#' data(agaricus.train, package='xgboost')
+#' data(agaricus.test, package='xgboost')
+#' train <- agaricus.train
+#' test <- agaricus.test
+#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
+#'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
+#' raw <- xgb.serialize(bst)
+#' bst <- xgb.unserialize(raw)
+#'
+#' @export
+xgb.serialize <- function(booster) {
+  handle <- xgb.get.handle(booster)
+  .Call(XGBoosterSerializeToBuffer_R, handle)
+}

R-package/R/xgb.train.R

@@ -1,85 +1,95 @@
 #' eXtreme Gradient Boosting Training
-#' 
+#'
 #' \code{xgb.train} is an advanced interface for training an xgboost model.
 #' The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
 #'
-#' @param params the list of parameters. 
-#'        The complete list of parameters is available at \url{http://xgboost.readthedocs.io/en/latest/parameter.html}.
-#'        Below is a shorter summary:
-#' 
+#' @param params the list of parameters. The complete list of parameters is
+#'   available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
+#'   is a shorter summary:
+#'
 #' 1. General Parameters
-#' 
+#'
 #' \itemize{
 #'   \item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
 #' }
-#'  
+#'
 #' 2. Booster Parameters
-#' 
+#'
 #' 2.1. Parameter for Tree Booster
-#' 
+#'
 #' \itemize{
 #'   \item \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1} when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model more robust to overfitting but slower to compute. Default: 0.3
-#'   \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. 
+#'   \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.
 #'   \item \code{max_depth} maximum depth of a tree. Default: 6
 #'   \item \code{min_child_weight} minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1
-#'   \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1 
+#'   \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1
 #'   \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
 #'   \item \code{num_parallel_tree} Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample  < 1}  and \code{round = 1}) accordingly. Default: 1
 #'   \item \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length equals to the number of features in the training data. \code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.
 #'   \item \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions. Each item of the list represents one permitted interaction where specified features are allowed to interact with each other. Feature index values should start from \code{0} (\code{0} references the first column).  Leave argument unspecified for no interaction constraints.
 #' }
-#' 
+#'
 #' 2.2. Parameter for Linear Booster
-#'  
+#'
 #' \itemize{
 #'   \item \code{lambda} L2 regularization term on weights. Default: 0
 #'   \item \code{lambda_bias} L2 regularization term on bias. Default: 0
 #'   \item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
 #' }
-#' 
-#' 3. Task Parameters 
-#' 
+#'
+#' 3. Task Parameters
+#'
 #' \itemize{
 #' \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
 #'   \itemize{
-#'     \item \code{reg:linear} linear regression (Default).
+#'     \item \code{reg:squarederror} Regression with squared loss (Default).
+#'     \item \code{reg:squaredlogerror}: regression with squared log loss \eqn{1/2 * (log(pred + 1) - log(label + 1))^2}. All inputs are required to be greater than -1. Also, see metric rmsle for possible issue with this objective.
 #'     \item \code{reg:logistic} logistic regression.
+#'     \item \code{reg:pseudohubererror}: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
 #'     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
 #'     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
-#'     \item \code{num_class} set the number of classes. To use only with multiclass objectives.
+#'     \item \code{binary:hinge}: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
+#'     \item \code{count:poisson}: poisson regression for count data, output mean of poisson distribution. \code{max_delta_step} is set to 0.7 by default in poisson regression (used to safeguard optimization).
+#'     \item \code{survival:cox}: Cox regression for right censored survival time data (negative values are considered right censored). Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function \code{h(t) = h0(t) * HR)}.
+#'     \item \code{survival:aft}: Accelerated failure time model for censored survival time data. See \href{https://xgboost.readthedocs.io/en/latest/tutorials/aft_survival_analysis.html}{Survival Analysis with Accelerated Failure Time} for details.
+#'     \item \code{aft_loss_distribution}: Probabilty Density Function used by \code{survival:aft} and \code{aft-nloglik} metric.
 #'     \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{num_class - 1}.
 #'     \item \code{multi:softprob} same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
 #'     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
+#'     \item \code{rank:ndcg}: Use LambdaMART to perform list-wise ranking where \href{https://en.wikipedia.org/wiki/Discounted_cumulative_gain}{Normalized Discounted Cumulative Gain (NDCG)} is maximized.
+#'     \item \code{rank:map}: Use LambdaMART to perform list-wise ranking where \href{https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision}{Mean Average Precision (MAP)} is maximized.
+#'     \item \code{reg:gamma}: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be \href{https://en.wikipedia.org/wiki/Gamma_distribution#Applications}{gamma-distributed}.
+#'     \item \code{reg:tweedie}: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be \href{https://en.wikipedia.org/wiki/Tweedie_distribution#Applications}{Tweedie-distributed}.
 #'   }
 #'   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
 #'   \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
 #' }
-#' 
+#'
 #' @param data training dataset. \code{xgb.train} accepts only an \code{xgb.DMatrix} as the input.
 #'        \code{xgboost}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.
 #' @param nrounds max number of boosting iterations.
 #' @param watchlist named list of xgb.DMatrix datasets to use for evaluating model performance.
 #'        Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
-#'        of these datasets during each boosting iteration, and stored in the end as a field named 
-#'        \code{evaluation_log} in the resulting object. When either \code{verbose>=1} or 
+#'        of these datasets during each boosting iteration, and stored in the end as a field named
+#'        \code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
 #'        \code{\link{cb.print.evaluation}} callback is engaged, the performance results are continuously
-#'        printed out during the training. 
+#'        printed out during the training.
 #'        E.g., specifying \code{watchlist=list(validation1=mat1, validation2=mat2)} allows to track
 #'        the performance of each round's model on mat1 and mat2.
-#' @param obj customized objective function. Returns gradient and second order 
+#' @param obj customized objective function. Returns gradient and second order
 #'        gradient with given prediction and dtrain.
-#' @param feval custimized evaluation function. Returns 
-#'        \code{list(metric='metric-name', value='metric-value')} with given 
+#' @param feval customized evaluation function. Returns
+#'        \code{list(metric='metric-name', value='metric-value')} with given
 #'        prediction and dtrain.
 #' @param verbose If 0, xgboost will stay silent. If 1, it will print information about performance.
 #'        If 2, some additional information will be printed out.
-#'        Note that setting \code{verbose > 0} automatically engages the 
+#'        Note that setting \code{verbose > 0} automatically engages the
 #'        \code{cb.print.evaluation(period=1)} callback function.
 #' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
-#'        Default is 1 which means all messages are printed. This parameter is passed to the 
+#'        Default is 1 which means all messages are printed. This parameter is passed to the
 #'        \code{\link{cb.print.evaluation}} callback.
-#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered. 
-#'        If set to an integer \code{k}, training with a validation set will stop if the performance 
+#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
+#'        If set to an integer \code{k}, training with a validation set will stop if the performance
 #'        doesn't improve for \code{k} rounds.
 #'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
 #' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
@@ -90,48 +100,50 @@
 #'        0 means save at the end. The saving is handled by the \code{\link{cb.save.model}} callback.
 #' @param save_name the name or path for periodically saved model file.
 #' @param xgb_model a previously built model to continue the training from.
-#'        Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a 
+#'        Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
 #'        file with a previously saved model.
 #' @param callbacks a list of callback functions to perform various task during boosting.
-#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
-#'        parameters' values. User can provide either existing or their own callback methods in order 
+#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
+#'        parameters' values. User can provide either existing or their own callback methods in order
 #'        to customize the training process.
 #' @param ... other parameters to pass to \code{params}.
-#' @param label vector of response values. Should not be provided when data is 
+#' @param label vector of response values. Should not be provided when data is
 #'        a local data file name or an \code{xgb.DMatrix}.
 #' @param missing by default is set to NA, which means that NA values should be considered as 'missing'
 #'        by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values.
 #'        This parameter is only used when input is a dense matrix.
 #' @param weight a vector indicating the weight for each row of the input.
-#' 
-#' @details 
-#' These are the training functions for \code{xgboost}. 
-#' 
-#' The \code{xgb.train} interface supports advanced features such as \code{watchlist}, 
-#' customized objective and evaluation metric functions, therefore it is more flexible 
+#'
+#' @details
+#' These are the training functions for \code{xgboost}.
+#'
+#' The \code{xgb.train} interface supports advanced features such as \code{watchlist},
+#' customized objective and evaluation metric functions, therefore it is more flexible
 #' than the \code{xgboost} interface.
 #'
-#' Parallelization is automatically enabled if \code{OpenMP} is present. 
+#' Parallelization is automatically enabled if \code{OpenMP} is present.
 #' Number of threads can also be manually specified via \code{nthread} parameter.
-#' 
+#'
 #' The evaluation metric is chosen automatically by Xgboost (according to the objective)
 #' when the \code{eval_metric} parameter is not provided.
-#' User may set one or several \code{eval_metric} parameters. 
+#' User may set one or several \code{eval_metric} parameters.
 #' Note that when using a customized metric, only this single metric can be used.
-#' The folloiwing is the list of built-in metrics for which Xgboost provides optimized implementation:
+#' The following is the list of built-in metrics for which Xgboost provides optimized implementation:
 #'   \itemize{
-#'      \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
-#'      \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
-#'      \item \code{mlogloss} multiclass logloss. \url{http://wiki.fast.ai/index.php/Log_Loss}
+#'      \item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
+#'      \item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
+#'      \item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
 #'      \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
 #'            By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
 #'            Different threshold (e.g., 0.) could be specified as "error@0."
 #'      \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
-#'      \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
+#'      \item \code{mae} Mean absolute error
+#'      \item \code{mape} Mean absolute percentage error
+#'      \item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
 #'      \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
-#'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
+#'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
 #'   }
-#' 
+#'
 #' The following callbacks are automatically created when certain parameters are set:
 #' \itemize{
 #'   \item \code{cb.print.evaluation} is turned on when \code{verbose > 0};
@@ -140,38 +152,38 @@
 #'   \item \code{cb.early.stop}: when \code{early_stopping_rounds} is set.
 #'   \item \code{cb.save.model}: when \code{save_period > 0} is set.
 #' }
-#' 
-#' @return 
+#'
+#' @return
 #' An object of class \code{xgb.Booster} with the following elements:
 #' \itemize{
 #'   \item \code{handle} a handle (pointer) to the xgboost model in memory.
 #'   \item \code{raw} a cached memory dump of the xgboost model saved as R's \code{raw} type.
 #'   \item \code{niter} number of boosting iterations.
-#'   \item \code{evaluation_log} evaluation history storead as a \code{data.table} with the
+#'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
 #'         first column corresponding to iteration number and the rest corresponding to evaluation
 #'         metrics' values. It is created by the \code{\link{cb.evaluation.log}} callback.
 #'   \item \code{call} a function call.
-#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not 
+#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
 #'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-#'   \item \code{callbacks} callback functions that were either automatically assigned or 
-#'         explicitely passed.
+#'   \item \code{callbacks} callback functions that were either automatically assigned or
+#'         explicitly passed.
 #'   \item \code{best_iteration} iteration number with the best evaluation metric value
 #'         (only available with early stopping).
-#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
+#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
 #'         which could further be used in \code{predict} method
 #'         (only available with early stopping).
 #'   \item \code{best_score} the best evaluation metric value during early stopping.
 #'         (only available with early stopping).
 #'   \item \code{feature_names} names of the training dataset features
-#'         (only when comun names were defined in training data).
+#'         (only when column names were defined in training data).
 #'   \item \code{nfeatures} number of features in training data.
 #' }
-#' 
+#'
 #' @seealso
 #' \code{\link{callbacks}},
 #' \code{\link{predict.xgb.Booster}},
 #' \code{\link{xgb.cv}}
-#' 
+#'
 #' @references
 #'
 #' Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System",
@@ -180,17 +192,17 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
-#' 
+#'
 #' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 #' dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 #' watchlist <- list(train = dtrain, eval = dtest)
-#' 
+#'
 #' ## A simple xgb.train example:
-#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2, 
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
 #'               objective = "binary:logistic", eval_metric = "auc")
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
-#' 
-#' 
+#'
+#'
 #' ## An xgb.train example where custom objective and evaluation metric are used:
 #' logregobj <- function(preds, dtrain) {
 #'    labels <- getinfo(dtrain, "label")
@@ -204,58 +216,58 @@
 #'   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
 #'   return(list(metric = "error", value = err))
 #' }
-#' 
+#'
 #' # These functions could be used by passing them either:
 #' #  as 'objective' and 'eval_metric' parameters in the params list:
-#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2, 
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
 #'               objective = logregobj, eval_metric = evalerror)
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
-#' 
+#'
 #' #  or through the ... arguments:
-#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2)
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 #'                  objective = logregobj, eval_metric = evalerror)
-#' 
+#'
 #' #  or as dedicated 'obj' and 'feval' parameters of xgb.train:
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 #'                  obj = logregobj, feval = evalerror)
-#' 
-#' 
+#'
+#'
 #' ## An xgb.train example of using variable learning rates at each iteration:
-#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
+#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
 #'               objective = "binary:logistic", eval_metric = "auc")
 #' my_etas <- list(eta = c(0.5, 0.1))
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 #'                  callbacks = list(cb.reset.parameters(my_etas)))
-#' 
+#'
 #' ## Early stopping:
 #' bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
 #'                  early_stopping_rounds = 3)
-#' 
+#'
 #' ## An 'xgboost' interface example:
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, 
-#'                max_depth = 2, eta = 1, nthread = 2, nrounds = 2, 
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
+#'                max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
 #'                objective = "binary:logistic")
 #' pred <- predict(bst, agaricus.test$data)
-#' 
+#'
 #' @rdname xgb.train
 #' @export
 xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
                       obj = NULL, feval = NULL, verbose = 1, print_every_n = 1L,
                       early_stopping_rounds = NULL, maximize = NULL,
-                      save_period = NULL, save_name = "xgboost.model", 
+                      save_period = NULL, save_name = "xgboost.model",
                       xgb_model = NULL, callbacks = list(), ...) {
-  
+
   check.deprecation(...)
-  
+
   params <- check.booster.params(params, ...)
 
   check.custom.obj()
   check.custom.eval()
-  
+
   # data & watchlist checks
   dtrain <- data
-  if (!inherits(dtrain, "xgb.DMatrix")) 
+  if (!inherits(dtrain, "xgb.DMatrix"))
     stop("second argument dtrain must be xgb.DMatrix")
   if (length(watchlist) > 0) {
     if (typeof(watchlist) != "list" ||
@@ -267,8 +279,8 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
   }
 
   # evaluation printing callback
-  params <- c(params, list(silent = ifelse(verbose > 1, 0, 1)))
-  print_every_n <- max( as.integer(print_every_n), 1L)
+  params <- c(params)
+  print_every_n <- max(as.integer(print_every_n), 1L)
   if (!has.callbacks(callbacks, 'cb.print.evaluation') &&
       verbose) {
     callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n))
@@ -288,11 +300,16 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
   stop_condition <- FALSE
   if (!is.null(early_stopping_rounds) &&
       !has.callbacks(callbacks, 'cb.early.stop')) {
-    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds, 
+    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
                                                  maximize = maximize, verbose = verbose))
   }
+
   # Sort the callbacks into categories
   cb <- categorize.callbacks(callbacks)
+  params['validate_parameters'] <- TRUE
+  if (!is.null(params[['seed']])) {
+    warning("xgb.train: `seed` is ignored in R package.  Use `set.seed()` instead.")
+  }
 
   # The tree updating process would need slightly different handling
   is_update <- NVL(params[['process_type']], '.') == 'update'
@@ -313,27 +330,23 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
       niter_init <- xgb.ntree(bst) %/% (num_parallel_tree * num_class)
     }
   }
-  if(is_update && nrounds > niter_init)
+  if (is_update && nrounds > niter_init)
     stop("nrounds cannot be larger than ", niter_init, " (nrounds of xgb_model)")
 
-  # TODO: distributed code
-  rank <- 0
-  
   niter_skip <- ifelse(is_update, 0, niter_init)
   begin_iteration <- niter_skip + 1
   end_iteration <- niter_skip + nrounds
-  
+
   # the main loop for boosting iterations
   for (iteration in begin_iteration:end_iteration) {
-    
+
     for (f in cb$pre_iter) f()
-    
+
     xgb.iter.update(bst$handle, dtrain, iteration - 1, obj)
-    
-    bst_evaluation <- numeric(0)
+
     if (length(watchlist) > 0)
       bst_evaluation <- xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval)
-    
+
     xgb.attr(bst$handle, 'niter') <- iteration - 1
 
     for (f in cb$post_iter) f()
@@ -341,11 +354,11 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
     if (stop_condition) break
   }
   for (f in cb$finalize) f(finalize = TRUE)
-  
+
   bst <- xgb.Booster.complete(bst, saveraw = TRUE)
-  
+
   # store the total number of boosting iterations
-  bst$niter = end_iteration
+  bst$niter <- end_iteration
 
   # store the evaluation results
   if (length(evaluation_log) > 0 &&

R-package/R/xgb.unserialize.R

@@ -0,0 +1,31 @@
+#' Load the instance back from \code{\link{xgb.serialize}}
+#'
+#' @param buffer the buffer containing booster instance saved by \code{\link{xgb.serialize}}
+#'
+#' @export
+xgb.unserialize <- function(buffer) {
+  cachelist <- list()
+  handle <- .Call(XGBoosterCreate_R, cachelist)
+  tryCatch(
+    .Call(XGBoosterUnserializeFromBuffer_R, handle, buffer),
+    error = function(e) {
+      error_msg <- conditionMessage(e)
+      m <- regexec("(src[\\\\/]learner.cc:[0-9]+): Check failed: (header == serialisation_header_)",
+                   error_msg, perl = TRUE)
+      groups <- regmatches(error_msg, m)[[1]]
+      if (length(groups) == 3) {
+        warning(paste("The model had been generated by XGBoost version 1.0.0 or earlier and was ",
+                      "loaded from a RDS file. We strongly ADVISE AGAINST using saveRDS() ",
+                      "function, to ensure that your model can be read in current and upcoming ",
+                      "XGBoost releases. Please use xgb.save() instead to preserve models for the ",
+                      "long term. For more details and explanation, see ",
+                      "https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html",
+                      sep = ""))
+        .Call(XGBoosterLoadModelFromRaw_R, handle, buffer)
+      } else {
+        stop(e)
+      }
+    })
+  class(handle) <- "xgb.Booster.handle"
+  return (handle)
+}

R-package/R/xgboost.R

@@ -5,8 +5,8 @@
 #' @export
 xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
                     params = list(), nrounds,
-                    verbose = 1, print_every_n = 1L, 
-                    early_stopping_rounds = NULL, maximize = NULL, 
+                    verbose = 1, print_every_n = 1L,
+                    early_stopping_rounds = NULL, maximize = NULL,
                     save_period = NULL, save_name = "xgboost.model",
                     xgb_model = NULL, callbacks = list(), ...) {
 
@@ -18,16 +18,16 @@ xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
                    early_stopping_rounds = early_stopping_rounds, maximize = maximize,
                    save_period = save_period, save_name = save_name,
                    xgb_model = xgb_model, callbacks = callbacks, ...)
-  return(bst)
+  return (bst)
 }
 
 #' Training part from Mushroom Data Set
-#' 
+#'
 #' This data set is originally from the Mushroom data set,
 #' UCI Machine Learning Repository.
-#' 
+#'
 #' This data set includes the following fields:
-#' 
+#'
 #' \itemize{
 #'  \item \code{label} the label for each record
 #'  \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
@@ -35,16 +35,16 @@ xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
 #'
 #' @references
 #' https://archive.ics.uci.edu/ml/datasets/Mushroom
-#' 
-#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository 
-#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, 
+#'
+#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
+#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
 #' School of Information and Computer Science.
-#' 
+#'
 #' @docType data
 #' @keywords datasets
 #' @name agaricus.train
 #' @usage data(agaricus.train)
-#' @format A list containing a label vector, and a dgCMatrix object with 6513 
+#' @format A list containing a label vector, and a dgCMatrix object with 6513
 #' rows and 127 variables
 NULL
 
@@ -52,9 +52,9 @@ NULL
 #'
 #' This data set is originally from the Mushroom data set,
 #' UCI Machine Learning Repository.
-#' 
+#'
 #' This data set includes the following fields:
-#' 
+#'
 #' \itemize{
 #'  \item \code{label} the label for each record
 #'  \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
@@ -62,16 +62,16 @@ NULL
 #'
 #' @references
 #' https://archive.ics.uci.edu/ml/datasets/Mushroom
-#' 
-#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository 
-#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, 
+#'
+#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
+#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
 #' School of Information and Computer Science.
-#' 
+#'
 #' @docType data
 #' @keywords datasets
 #' @name agaricus.test
 #' @usage data(agaricus.test)
-#' @format A list containing a label vector, and a dgCMatrix object with 1611 
+#' @format A list containing a label vector, and a dgCMatrix object with 1611
 #' rows and 126 variables
 NULL
 
@@ -91,11 +91,6 @@ NULL
 #' @importFrom data.table setkeyv
 #' @importFrom data.table setnames
 #' @importFrom magrittr %>%
-#' @importFrom stringi stri_detect_regex
-#' @importFrom stringi stri_match_first_regex
-#' @importFrom stringi stri_replace_first_regex
-#' @importFrom stringi stri_replace_all_regex
-#' @importFrom stringi stri_split_regex
 #' @importFrom utils object.size str tail
 #' @importFrom stats predict
 #' @importFrom stats median
@@ -107,7 +102,7 @@ NULL
 #' @importFrom graphics par
 #' @importFrom graphics title
 #' @importFrom grDevices rgb
-#' 
+#'
 #' @import methods
 #' @useDynLib xgboost, .registration = TRUE
 NULL

R-package/README.md

@@ -30,4 +30,4 @@ Examples
 Development
 -----------
 
-* See the [R Package section](https://xgboost.readthedocs.io/en/latest/how_to/contribute.html#r-package) of the contributors guide.
+* See the [R Package section](https://xgboost.readthedocs.io/en/latest/contribute.html#r-package) of the contributors guide.

R-package/cleanup

@@ -1,3 +1,4 @@
 #!/bin/sh
 
 rm -f src/Makevars
+rm -f CMakeLists.txt

R-package/configure.ac

@@ -1,31 +1,54 @@
 ### configure.ac					-*- Autoconf -*-
 
-AC_PREREQ(2.62)
+AC_PREREQ(2.69)
 
 AC_INIT([xgboost],[0.6-3],[],[xgboost],[])
 
+# Use this line to set CC variable to a C compiler
+AC_PROG_CC
+
+### Check whether backtrace() is part of libc or the external lib libexecinfo
+AC_MSG_CHECKING([Backtrace lib])
+AC_MSG_RESULT([])
+AC_CHECK_LIB([execinfo], [backtrace], [BACKTRACE_LIB=-lexecinfo], [BACKTRACE_LIB=''])
+
+### Endian detection
+AC_MSG_CHECKING([endian])
+AC_MSG_RESULT([])
+AC_RUN_IFELSE([AC_LANG_PROGRAM([[#include <stdint.h>]], [[const uint16_t endianness = 256; return !!(*(const uint8_t *)&endianness);]])],
+  [ENDIAN_FLAG="-DDMLC_CMAKE_LITTLE_ENDIAN=1"],
+  [ENDIAN_FLAG="-DDMLC_CMAKE_LITTLE_ENDIAN=0"])
+
 OPENMP_CXXFLAGS=""
 
 if test `uname -s` = "Linux"
 then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CFLAGS)"
+  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)"
 fi
 
 if test `uname -s` = "Darwin"
 then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CFLAGS)"
+  OPENMP_CXXFLAGS='-Xclang -fopenmp'
+  OPENMP_LIB='-lomp'
   ac_pkg_openmp=no
   AC_MSG_CHECKING([whether OpenMP will work in a package])
-  AC_LANG_CONFTEST(
-  [AC_LANG_PROGRAM([[#include <omp.h>]], [[ return omp_get_num_threads (); ]])])
-  PKG_CFLAGS="${OPENMP_CFLAGS}" PKG_LIBS="${OPENMP_CFLAGS}" "$RBIN" CMD SHLIB conftest.c 1>&AS_MESSAGE_LOG_FD 2>&AS_MESSAGE_LOG_FD && "$RBIN" --vanilla -q -e "dyn.load(paste('conftest',.Platform\$dynlib.ext,sep=''))" 1>&AS_MESSAGE_LOG_FD 2>&AS_MESSAGE_LOG_FD && ac_pkg_openmp=yes
+  AC_LANG_CONFTEST([AC_LANG_PROGRAM([[#include <omp.h>]], [[ return (omp_get_max_threads() <= 1); ]])])
+  ${CC} -o conftest conftest.c ${OPENMP_LIB} ${OPENMP_CXXFLAGS} 2>/dev/null && ./conftest && ac_pkg_openmp=yes
   AC_MSG_RESULT([${ac_pkg_openmp}])
   if test "${ac_pkg_openmp}" = no; then
     OPENMP_CXXFLAGS=''
+    OPENMP_LIB=''
+    echo '*****************************************************************************************'
+    echo '         OpenMP is unavailable on this Mac OSX system. Training speed may be suboptimal.'
+    echo '         To use all CPU cores for training jobs, you should install OpenMP by running\n'
+    echo '             brew install libomp'
+    echo '*****************************************************************************************'
   fi
 fi
 
 AC_SUBST(OPENMP_CXXFLAGS)
+AC_SUBST(OPENMP_LIB)
+AC_SUBST(ENDIAN_FLAG)
+AC_SUBST(BACKTRACE_LIB)
 AC_CONFIG_FILES([src/Makevars])
 AC_OUTPUT
-

R-package/demo/README.md

@@ -17,4 +17,4 @@ Benchmarks
 Notes
 ====
 * Contribution of examples, benchmarks is more than welcomed!
-* If you like to share how you use xgboost to solve your problem, send a pull request:)
+* If you like to share how you use xgboost to solve your problem, send a pull request :)

R-package/demo/basic_walkthrough.R

@@ -3,8 +3,8 @@ require(methods)
 
 # we load in the agaricus dataset
 # In this example, we are aiming to predict whether a mushroom is edible
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 train <- agaricus.train
 test <- agaricus.test
 # the loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1}
@@ -26,7 +26,7 @@ bst <- xgboost(data = as.matrix(train$data), label = train$label, max_depth = 2,
 # you can also put in xgb.DMatrix object, which stores label, data and other meta datas needed for advanced features
 print("Training xgboost with xgb.DMatrix")
 dtrain <- xgb.DMatrix(data = train$data, label = train$label)
-bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, nthread = 2, 
+bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, nthread = 2,
                objective = "binary:logistic")
 
 # Verbose = 0,1,2
@@ -46,7 +46,7 @@ bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2,
 
 #--------------------basic prediction using xgboost--------------
 # you can do prediction using the following line
-# you can put in Matrix, sparseMatrix, or xgb.DMatrix 
+# you can put in Matrix, sparseMatrix, or xgb.DMatrix
 pred <- predict(bst, test$data)
 err <- mean(as.numeric(pred > 0.5) != test$label)
 print(paste("test-error=", err))
@@ -58,31 +58,31 @@ xgb.save(bst, "xgboost.model")
 bst2 <- xgb.load("xgboost.model")
 pred2 <- predict(bst2, test$data)
 # pred2 should be identical to pred
-print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
+print(paste("sum(abs(pred2-pred))=", sum(abs(pred2 - pred))))
 
 # save model to R's raw vector
-raw = xgb.save.raw(bst)
+raw <- xgb.save.raw(bst)
 # load binary model to R
 bst3 <- xgb.load(raw)
 pred3 <- predict(bst3, test$data)
 # pred3 should be identical to pred
-print(paste("sum(abs(pred3-pred))=", sum(abs(pred3-pred))))
+print(paste("sum(abs(pred3-pred))=", sum(abs(pred3 - pred))))
 
 #----------------Advanced features --------------
 # to use advanced features, we need to put data in xgb.DMatrix
-dtrain <- xgb.DMatrix(data = train$data, label=train$label)
-dtest <- xgb.DMatrix(data = test$data, label=test$label)
+dtrain <- xgb.DMatrix(data = train$data, label = train$label)
+dtest <- xgb.DMatrix(data = test$data, label = test$label)
 #---------------Using watchlist----------------
 # watchlist is a list of xgb.DMatrix, each of them is tagged with name
-watchlist <- list(train=dtrain, test=dtest)
+watchlist <- list(train = dtrain, test = dtest)
 # to train with watchlist, use xgb.train, which contains more advanced features
-# watchlist allows us to monitor the evaluation result on all data in the list 
+# watchlist allows us to monitor the evaluation result on all data in the list
 print("Train xgboost using xgb.train with watchlist")
-bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nrounds=2, watchlist=watchlist,
+bst <- xgb.train(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, watchlist = watchlist,
                  nthread = 2, objective = "binary:logistic")
 # we can change evaluation metrics, or use multiple evaluation metrics
 print("train xgboost using xgb.train with watchlist, watch logloss and error")
-bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nrounds=2, watchlist=watchlist,
+bst <- xgb.train(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, watchlist = watchlist,
                  eval_metric = "error", eval_metric = "logloss",
                  nthread = 2, objective = "binary:logistic")
 
@@ -90,17 +90,17 @@ bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nrounds=2, watchlist=watchlist
 xgb.DMatrix.save(dtrain, "dtrain.buffer")
 # to load it in, simply call xgb.DMatrix
 dtrain2 <- xgb.DMatrix("dtrain.buffer")
-bst <- xgb.train(data=dtrain2, max_depth=2, eta=1, nrounds=2, watchlist=watchlist,
+bst <- xgb.train(data = dtrain2, max_depth = 2, eta = 1, nrounds = 2, watchlist = watchlist,
                  nthread = 2, objective = "binary:logistic")
 # information can be extracted from xgb.DMatrix using getinfo
-label = getinfo(dtest, "label")
+label <- getinfo(dtest, "label")
 pred <- predict(bst, dtest)
-err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
+err <- as.numeric(sum(as.integer(pred > 0.5) != label)) / length(label)
 print(paste("test-error=", err))
 
 # You can dump the tree you learned using xgb.dump into a text file
-dump_path = file.path(tempdir(), 'dump.raw.txt')
-xgb.dump(bst, dump_path, with_stats = T)
+dump_path <- file.path(tempdir(), 'dump.raw.txt')
+xgb.dump(bst, dump_path, with_stats = TRUE)
 
 # Finally, you can check which features are the most important.
 print("Most important features (look at column Gain):")

R-package/demo/boost_from_prediction.R

@@ -1,7 +1,7 @@
 require(xgboost)
 # load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
@@ -11,12 +11,12 @@ watchlist <- list(eval = dtest, train = dtrain)
 #
 print('start running example to start from a initial prediction')
 # train xgboost for 1 round
-param <- list(max_depth=2, eta=1, nthread = 2, silent=1, objective='binary:logistic')
+param <- list(max_depth = 2, eta = 1, nthread = 2, objective = 'binary:logistic')
 bst <- xgb.train(param, dtrain, 1, watchlist)
 # Note: we need the margin value instead of transformed prediction in set_base_margin
 # do predict with output_margin=TRUE, will always give you margin values before logistic transformation
-ptrain <- predict(bst, dtrain, outputmargin=TRUE)
-ptest  <- predict(bst, dtest, outputmargin=TRUE)
+ptrain <- predict(bst, dtrain, outputmargin = TRUE)
+ptest  <- predict(bst, dtest, outputmargin = TRUE)
 # set the base_margin property of dtrain and dtest
 # base margin is the base prediction we will boost from
 setinfo(dtrain, "base_margin", ptrain)

R-package/demo/caret_wrapper.R

@@ -1,5 +1,5 @@
 # install development version of caret library that contains xgboost models
-devtools::install_github("topepo/caret/pkg/caret") 
+devtools::install_github("topepo/caret/pkg/caret")
 require(caret)
 require(xgboost)
 require(data.table)
@@ -9,17 +9,17 @@ require(e1071)
 # Load Arthritis dataset in memory.
 data(Arthritis)
 # Create a copy of the dataset with data.table package (data.table is 100% compliant with R dataframe but its syntax is a lot more consistent and its performance are really good).
-df <- data.table(Arthritis, keep.rownames = F)
+df <- data.table(Arthritis, keep.rownames = FALSE)
 
 # Let's add some new categorical features to see if it helps. Of course these feature are highly correlated to the Age feature. Usually it's not a good thing in ML, but Tree algorithms (including boosted trees) are able to select the best features, even in case of highly correlated features.
 # For the first feature we create groups of age by rounding the real age. Note that we transform it to factor (categorical data) so the algorithm treat them as independant values.
-df[,AgeDiscret:= as.factor(round(Age/10,0))]
+df[, AgeDiscret := as.factor(round(Age / 10, 0))]
 
 # Here is an even stronger simplification of the real age with an arbitrary split at 30 years old. I choose this value based on nothing. We will see later if simplifying the information based on arbitrary values is a good strategy (I am sure you already have an idea of how well it will work!).
-df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
+df[, AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
 
 # We remove ID as there is nothing to learn from this feature (it will just add some noise as the dataset is small).
-df[,ID:=NULL]
+df[, ID := NULL]
 
 #-------------Basic Training using XGBoost in caret Library-----------------
 # Set up control parameters for caret::train

R-package/demo/create_sparse_matrix.R

@@ -6,10 +6,10 @@ if (!require(vcd)) {
   require(vcd)
 }
 # According to its documentation, Xgboost works only on numbers.
-# Sometimes the dataset we have to work on have categorical data. 
+# Sometimes the dataset we have to work on have categorical data.
 # A categorical variable is one which have a fixed number of values. By example, if for each observation a variable called "Colour" can have only "red", "blue" or "green" as value, it is a categorical variable.
 #
-# In R, categorical variable is called Factor. 
+# In R, categorical variable is called Factor.
 # Type ?factor in console for more information.
 #
 # In this demo we will see how to transform a dense dataframe with categorical variables to a sparse matrix before analyzing it in Xgboost.
@@ -19,7 +19,7 @@ if (!require(vcd)) {
 data(Arthritis)
 
 # create a copy of the dataset with data.table package (data.table is 100% compliant with R dataframe but its syntax is a lot more consistent and its performance are really good).
-df <- data.table(Arthritis, keep.rownames = F)
+df <- data.table(Arthritis, keep.rownames = FALSE)
 
 # Let's have a look to the data.table
 cat("Print the dataset\n")
@@ -32,17 +32,17 @@ str(df)
 # Let's add some new categorical features to see if it helps. Of course these feature are highly correlated to the Age feature. Usually it's not a good thing in ML, but Tree algorithms (including boosted trees) are able to select the best features, even in case of highly correlated features.
 
 # For the first feature we create groups of age by rounding the real age. Note that we transform it to factor (categorical data) so the algorithm treat them as independant values.
-df[,AgeDiscret:= as.factor(round(Age/10,0))]
+df[, AgeDiscret := as.factor(round(Age / 10, 0))]
 
 # Here is an even stronger simplification of the real age with an arbitrary split at 30 years old. I choose this value based on nothing. We will see later if simplifying the information based on arbitrary values is a good strategy (I am sure you already have an idea of how well it will work!).
-df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
+df[, AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
 
 # We remove ID as there is nothing to learn from this feature (it will just add some noise as the dataset is small).
-df[,ID:=NULL]
+df[, ID := NULL]
 
 # List the different values for the column Treatment: Placebo, Treated.
 cat("Values of the categorical feature Treatment\n")
-print(levels(df[,Treatment]))
+print(levels(df[, Treatment]))
 
 # Next step, we will transform the categorical data to dummy variables.
 # This method is also called one hot encoding.
@@ -52,16 +52,16 @@ print(levels(df[,Treatment]))
 #
 # Formulae Improved~.-1 used below means transform all categorical features but column Improved to binary values.
 # Column Improved is excluded because it will be our output column, the one we want to predict.
-sparse_matrix = sparse.model.matrix(Improved~.-1, data = df)
+sparse_matrix <- sparse.model.matrix(Improved ~ . - 1, data = df)
 
 cat("Encoding of the sparse Matrix\n")
 print(sparse_matrix)
 
 # Create the output vector (not sparse)
-# 1. Set, for all rows, field in Y column to 0; 
-# 2. set Y to 1 when Improved == Marked; 
+# 1. Set, for all rows, field in Y column to 0;
+# 2. set Y to 1 when Improved == Marked;
 # 3. Return Y column
-output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
+output_vector <- df[, Y := 0][Improved == "Marked", Y := 1][, Y]
 
 # Following is the same process as other demo
 cat("Learning...\n")

R-package/demo/cross_validation.R

@@ -1,25 +1,25 @@
 require(xgboost)
 # load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
 nrounds <- 2
-param <- list(max_depth=2, eta=1, silent=1, nthread=2, objective='binary:logistic')
+param <- list(max_depth = 2, eta = 1, nthread = 2, objective = 'binary:logistic')
 
 cat('running cross validation\n')
 # do cross validation, this will print result out as
 # [iteration]  metric_name:mean_value+std_value
 # std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold=5, metrics={'error'})
+xgb.cv(param, dtrain, nrounds, nfold = 5, metrics = {'error'})
 
 cat('running cross validation, disable standard deviation display\n')
 # do cross validation, this will print result out as
 # [iteration]  metric_name:mean_value+std_value
 # std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold=5,
-       metrics='error', showsd = FALSE)
+xgb.cv(param, dtrain, nrounds, nfold = 5,
+       metrics = 'error', showsd = FALSE)
 
 ###
 # you can also do cross validation with cutomized loss function
@@ -29,18 +29,18 @@ print ('running cross validation, with cutomsized loss function')
 
 logregobj <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
-  preds <- 1/(1 + exp(-preds))
+  preds <- 1 / (1 + exp(-preds))
   grad <- preds - labels
   hess <- preds * (1 - preds)
   return(list(grad = grad, hess = hess))
 }
 evalerror <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
   return(list(metric = "error", value = err))
 }
 
-param <- list(max_depth=2, eta=1, silent=1,
+param <- list(max_depth = 2, eta = 1,
               objective = logregobj, eval_metric = evalerror)
 # train with customized objective
 xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5)

R-package/demo/custom_objective.R

@@ -1,7 +1,7 @@
 require(xgboost)
 # load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
@@ -15,7 +15,7 @@ num_round <- 2
 # this is loglikelihood loss
 logregobj <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
-  preds <- 1/(1 + exp(-preds))
+  preds <- 1 / (1 + exp(-preds))
   grad <- preds - labels
   hess <- preds * (1 - preds)
   return(list(grad = grad, hess = hess))
@@ -29,36 +29,36 @@ logregobj <- function(preds, dtrain) {
 # Take this in mind when you use the customization, and maybe you need write customized evaluation function
 evalerror <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
   return(list(metric = "error", value = err))
 }
 
-param <- list(max_depth=2, eta=1, nthread = 2, silent=1, 
-              objective=logregobj, eval_metric=evalerror)
+param <- list(max_depth = 2, eta = 1, nthread  =  2, verbosity = 0,
+              objective = logregobj, eval_metric = evalerror)
 print ('start training with user customized objective')
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train
 bst <- xgb.train(param, dtrain, num_round, watchlist)
 
 #
-# there can be cases where you want additional information 
+# there can be cases where you want additional information
 # being considered besides the property of DMatrix you can get by getinfo
 # you can set additional information as attributes if DMatrix
 
-# set label attribute of dtrain to be label, we use label as an example, it can be anything 
+# set label attribute of dtrain to be label, we use label as an example, it can be anything
 attr(dtrain, 'label') <- getinfo(dtrain, 'label')
 # this is new customized objective, where you can access things you set
 # same thing applies to customized evaluation function
 logregobjattr <- function(preds, dtrain) {
   # now you can access the attribute in customized function
   labels <- attr(dtrain, 'label')
-  preds <- 1/(1 + exp(-preds))
+  preds <- 1 / (1 + exp(-preds))
   grad <- preds - labels
   hess <- preds * (1 - preds)
   return(list(grad = grad, hess = hess))
 }
-param <- list(max_depth=2, eta=1, nthread = 2, silent=1, 
-              objective=logregobjattr, eval_metric=evalerror)
+param <- list(max_depth = 2, eta = 1, nthread  =  2, verbosity = 0,
+              objective = logregobjattr, eval_metric = evalerror)
 print ('start training with user customized objective, with additional attributes in DMatrix')
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train

R-package/demo/early_stopping.R

@@ -1,20 +1,20 @@
 require(xgboost)
 # load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 # note: for customized objective function, we leave objective as default
 # note: what we are getting is margin value in prediction
 # you must know what you are doing
-param <- list(max_depth=2, eta=1, nthread = 2, silent=1)
+param <- list(max_depth = 2, eta = 1, nthread = 2, verbosity = 0)
 watchlist <- list(eval = dtest)
 num_round <- 20
 # user define objective function, given prediction, return gradient and second order gradient
 # this is loglikelihood loss
 logregobj <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
-  preds <- 1/(1 + exp(-preds))
+  preds <- 1 / (1 + exp(-preds))
   grad <- preds - labels
   hess <- preds * (1 - preds)
   return(list(grad = grad, hess = hess))
@@ -27,14 +27,14 @@ logregobj <- function(preds, dtrain) {
 # Take this in mind when you use the customization, and maybe you need write customized evaluation function
 evalerror <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
-  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
   return(list(metric = "error", value = err))
 }
 print ('start training with early Stopping setting')
 
-bst <- xgb.train(param, dtrain, num_round, watchlist, 
+bst <- xgb.train(param, dtrain, num_round, watchlist,
                  objective = logregobj, eval_metric = evalerror, maximize = FALSE,
                  early_stopping_round = 3)
-bst <- xgb.cv(param, dtrain, num_round, nfold = 5, 
+bst <- xgb.cv(param, dtrain, num_round, nfold = 5,
               objective = logregobj, eval_metric = evalerror,
               maximize = FALSE, early_stopping_rounds = 3)

R-package/demo/generalized_linear_model.R

@@ -1,7 +1,7 @@
 require(xgboost)
 # load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 ##
@@ -11,14 +11,14 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 ##
 
 # change booster to gblinear, so that we are fitting a linear model
-# alpha is the L1 regularizer 
+# alpha is the L1 regularizer
 # lambda is the L2 regularizer
 # you can also set lambda_bias which is L2 regularizer on the bias term
 param <- list(objective = "binary:logistic", booster = "gblinear",
               nthread = 2, alpha = 0.0001, lambda = 1)
 
 # normally, you do not need to set eta (step_size)
-# XGBoost uses a parallel coordinate descent algorithm (shotgun), 
+# XGBoost uses a parallel coordinate descent algorithm (shotgun),
 # there could be affection on convergence with parallelization on certain cases
 # setting eta to be smaller value, e.g 0.5 can make the optimization more stable
 
@@ -30,5 +30,4 @@ num_round <- 2
 bst <- xgb.train(param, dtrain, num_round, watchlist)
 ypred <- predict(bst, dtest)
 labels <- getinfo(dtest, 'label')
-cat('error of preds=', mean(as.numeric(ypred>0.5)!=labels),'\n')
-
+cat('error of preds=', mean(as.numeric(ypred > 0.5) != labels), '\n')

R-package/demo/gpu_accelerated.R

@@ -1,9 +1,9 @@
 # An example of using GPU-accelerated tree building algorithms
-# 
-# NOTE: it can only run if you have a CUDA-enable GPU and the package was 
+#
+# NOTE: it can only run if you have a CUDA-enable GPU and the package was
 #       specially compiled with GPU support.
 #
-# For the current functionality, see 
+# For the current functionality, see
 # https://xgboost.readthedocs.io/en/latest/gpu/index.html
 #
 
@@ -21,8 +21,8 @@ m <- X[, sel] %*% betas - 1 + rnorm(N)
 y <- rbinom(N, 1, plogis(m))
 
 tr <- sample.int(N, N * 0.75)
-dtrain <- xgb.DMatrix(X[tr,], label = y[tr])
-dtest <- xgb.DMatrix(X[-tr,], label = y[-tr])
+dtrain <- xgb.DMatrix(X[tr, ], label = y[tr])
+dtest <- xgb.DMatrix(X[-tr, ], label = y[-tr])
 wl <- list(train = dtrain, test = dtest)
 
 # An example of running 'gpu_hist' algorithm
@@ -30,7 +30,7 @@ wl <- list(train = dtrain, test = dtest)
 # - similar to the 'hist'
 # - the fastest option for moderately large datasets
 # - current limitations: max_depth < 16, does not implement guided loss
-# You can use tree_method = 'gpu_exact' for another GPU accelerated algorithm,
+# You can use tree_method = 'gpu_hist' for another GPU accelerated algorithm,
 # which is slower, more memory-hungry, but does not use binning.
 param <- list(objective = 'reg:logistic', eval_metric = 'auc', subsample = 0.5, nthread = 4,
               max_bin = 64, tree_method = 'gpu_hist')

R-package/demo/interaction_constraints.R

@@ -4,34 +4,39 @@ library(data.table)
 set.seed(1024)
 
 # Function to obtain a list of interactions fitted in trees, requires input of maximum depth
-treeInteractions <- function(input_tree, input_max_depth){
-  trees <- copy(input_tree)  # copy tree input to prevent overwriting
+treeInteractions <- function(input_tree, input_max_depth) {
+  ID_merge <- i.id <- i.feature <- NULL  # Suppress warning "no visible binding for global variable"
+
+  trees <- data.table::copy(input_tree)  # copy tree input to prevent overwriting
   if (input_max_depth < 2) return(list())  # no interactions if max depth < 2
   if (nrow(input_tree) == 1) return(list())
 
   # Attach parent nodes
-  for (i in 2:input_max_depth){
-    if (i == 2) trees[, ID_merge:=ID] else trees[, ID_merge:=get(paste0('parent_',i-2))]
-    parents_left <- trees[!is.na(Split), list(i.id=ID, i.feature=Feature, ID_merge=Yes)]
-    parents_right <- trees[!is.na(Split), list(i.id=ID, i.feature=Feature, ID_merge=No)]
+  for (i in 2:input_max_depth) {
+    if (i == 2) trees[, ID_merge := ID] else trees[, ID_merge := get(paste0('parent_', i - 2))]
+    parents_left <- trees[!is.na(Split), list(i.id = ID, i.feature = Feature, ID_merge = Yes)]
+    parents_right <- trees[!is.na(Split), list(i.id = ID, i.feature = Feature, ID_merge = No)]
 
-    setorderv(trees, 'ID_merge')
-    setorderv(parents_left, 'ID_merge')
-    setorderv(parents_right, 'ID_merge')
+    data.table::setorderv(trees, 'ID_merge')
+    data.table::setorderv(parents_left, 'ID_merge')
+    data.table::setorderv(parents_right, 'ID_merge')
 
-    trees <- merge(trees, parents_left, by='ID_merge', all.x=T)
-    trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
-    trees[, c('i.id','i.feature'):=NULL]
+    trees <- merge(trees, parents_left, by = 'ID_merge', all.x = TRUE)
+    trees[!is.na(i.id), c(paste0('parent_', i - 1), paste0('parent_feat_', i - 1))
+          := list(i.id, i.feature)]
+    trees[, c('i.id', 'i.feature') := NULL]
 
-    trees <- merge(trees, parents_right, by='ID_merge', all.x=T)
-    trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
-    trees[, c('i.id','i.feature'):=NULL]
+    trees <- merge(trees, parents_right, by = 'ID_merge', all.x = TRUE)
+    trees[!is.na(i.id), c(paste0('parent_', i - 1), paste0('parent_feat_', i - 1))
+          := list(i.id, i.feature)]
+    trees[, c('i.id', 'i.feature') := NULL]
   }
 
   # Extract nodes with interactions
-  interaction_trees <- trees[!is.na(Split) & !is.na(parent_1), 
-                             c('Feature',paste0('parent_feat_',1:(input_max_depth-1))), with=F]
-  interaction_trees_split <- split(interaction_trees, 1:nrow(interaction_trees))
+  interaction_trees <- trees[!is.na(Split) & !is.na(parent_1),
+                             c('Feature', paste0('parent_feat_', 1:(input_max_depth - 1))),
+                             with = FALSE]
+  interaction_trees_split <- split(interaction_trees, seq_len(nrow(interaction_trees)))
   interaction_list <- lapply(interaction_trees_split, as.character)
 
   # Remove NAs (no parent interaction)
@@ -47,59 +52,62 @@ treeInteractions <- function(input_tree, input_max_depth){
 
 # Generate sample data
 x <- list()
-for (i in 1:10){
-  x[[i]] = i*rnorm(1000, 10)
+for (i in 1:10) {
+  x[[i]] <- i * rnorm(1000, 10)
 }
 x <- as.data.table(x)
 
-y = -1*x[, rowSums(.SD)] + x[['V1']]*x[['V2']] + x[['V3']]*x[['V4']]*x[['V5']] + rnorm(1000, 0.001) + 3*sin(x[['V7']])
+y <- -1 * x[, rowSums(.SD)] + x[['V1']] * x[['V2']] + x[['V3']] * x[['V4']] * x[['V5']]
+     + rnorm(1000, 0.001) + 3 * sin(x[['V7']])
 
-train = as.matrix(x)
+train <- as.matrix(x)
 
 # Interaction constraint list (column names form)
-interaction_list <- list(c('V1','V2'),c('V3','V4','V5'))
+interaction_list <- list(c('V1', 'V2'), c('V3', 'V4', 'V5'))
 
 # Convert interaction constraint list into feature index form
 cols2ids <- function(object, col_names) {
   LUT <- seq_along(col_names) - 1
   names(LUT) <- col_names
-  rapply(object, function(x) LUT[x], classes="character", how="replace")
+  rapply(object, function(x) LUT[x], classes = "character", how = "replace")
 }
-interaction_list_fid = cols2ids(interaction_list, colnames(train))
+interaction_list_fid <- cols2ids(interaction_list, colnames(train))
 
 # Fit model with interaction constraints
-bst = xgboost(data = train, label = y, max_depth = 4,
-              eta = 0.1, nthread = 2, nrounds = 1000,
-              interaction_constraints = interaction_list_fid)
+bst <- xgboost(data = train, label = y, max_depth = 4,
+               eta = 0.1, nthread = 2, nrounds = 1000,
+               interaction_constraints = interaction_list_fid)
 
 bst_tree <- xgb.model.dt.tree(colnames(train), bst)
-bst_interactions <- treeInteractions(bst_tree, 4)  # interactions constrained to combinations of V1*V2 and V3*V4*V5
+bst_interactions <- treeInteractions(bst_tree, 4)
+  # interactions constrained to combinations of V1*V2 and V3*V4*V5
 
 # Fit model without interaction constraints
-bst2 = xgboost(data = train, label = y, max_depth = 4,
-               eta = 0.1, nthread = 2, nrounds = 1000)
+bst2 <- xgboost(data = train, label = y, max_depth = 4,
+                eta = 0.1, nthread = 2, nrounds = 1000)
 
 bst2_tree <- xgb.model.dt.tree(colnames(train), bst2)
 bst2_interactions <- treeInteractions(bst2_tree, 4)  # much more interactions
 
 # Fit model with both interaction and monotonicity constraints
-bst3 = xgboost(data = train, label = y, max_depth = 4,
-               eta = 0.1, nthread = 2, nrounds = 1000,
-               interaction_constraints = interaction_list_fid,
-               monotone_constraints = c(-1,0,0,0,0,0,0,0,0,0))
+bst3 <- xgboost(data = train, label = y, max_depth = 4,
+                eta = 0.1, nthread = 2, nrounds = 1000,
+                interaction_constraints = interaction_list_fid,
+                monotone_constraints = c(-1, 0, 0, 0, 0, 0, 0, 0, 0, 0))
 
 bst3_tree <- xgb.model.dt.tree(colnames(train), bst3)
-bst3_interactions <- treeInteractions(bst3_tree, 4)  # interactions still constrained to combinations of V1*V2 and V3*V4*V5
+bst3_interactions <- treeInteractions(bst3_tree, 4)
+  # interactions still constrained to combinations of V1*V2 and V3*V4*V5
 
 # Show monotonic constraints still apply by checking scores after incrementing V1
 x1 <- sort(unique(x[['V1']]))
-for (i in 1:length(x1)){
-  testdata <- copy(x[, -c('V1')])
+for (i in seq_along(x1)){
+  testdata <- copy(x[, - ('V1')])
   testdata[['V1']] <- x1[i]
-  testdata <- testdata[, paste0('V',1:10), with=F]
+  testdata <- testdata[, paste0('V', 1:10), with = FALSE]
   pred <- predict(bst3, as.matrix(testdata))
-  
+
   # Should not print out anything due to monotonic constraints
   if (i > 1) if (any(pred > prev_pred)) print(i)
-  prev_pred <- pred 
+  prev_pred <- pred
 }

R-package/demo/poisson_regression.R

@@ -1,7 +1,6 @@
 data(mtcars)
 head(mtcars)
-bst = xgboost(data=as.matrix(mtcars[,-11]),label=mtcars[,11],
-              objective='count:poisson',nrounds=5)
-pred = predict(bst,as.matrix(mtcars[,-11]))
-sqrt(mean((pred-mtcars[,11])^2))
-
+bst <- xgboost(data = as.matrix(mtcars[, -11]), label = mtcars[, 11],
+               objective = 'count:poisson', nrounds = 5)
+pred <- predict(bst, as.matrix(mtcars[, -11]))
+sqrt(mean((pred - mtcars[, 11]) ^ 2))

R-package/demo/predict_first_ntree.R

@@ -1,23 +1,23 @@
 require(xgboost)
 # load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
-param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
+param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic')
 watchlist <- list(eval = dtest, train = dtrain)
-nrounds = 2
+nrounds <- 2
 
 # training the model for two rounds
-bst = xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
+bst <- xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
 cat('start testing prediction from first n trees\n')
-labels <- getinfo(dtest,'label')
+labels <- getinfo(dtest, 'label')
 
 ### predict using first 1 tree
-ypred1 = predict(bst, dtest, ntreelimit=1)
+ypred1 <- predict(bst, dtest, ntreelimit = 1)
 # by default, we predict using all the trees
-ypred2 = predict(bst, dtest)
+ypred2 <- predict(bst, dtest)
 
-cat('error of ypred1=', mean(as.numeric(ypred1>0.5)!=labels),'\n')
-cat('error of ypred2=', mean(as.numeric(ypred2>0.5)!=labels),'\n')
+cat('error of ypred1=', mean(as.numeric(ypred1 > 0.5) != labels), '\n')
+cat('error of ypred2=', mean(as.numeric(ypred2 > 0.5) != labels), '\n')

R-package/demo/predict_leaf_indices.R

@@ -5,39 +5,40 @@ require(Matrix)
 set.seed(1982)
 
 # load in the agaricus dataset
-data(agaricus.train, package='xgboost')
-data(agaricus.test, package='xgboost')
+data(agaricus.train, package = 'xgboost')
+data(agaricus.test, package = 'xgboost')
 dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
 
-param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-nrounds = 4
+param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic')
+nrounds <- 4
 
 # training the model for two rounds
-bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
+bst <- xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
 
 # Model accuracy without new features
-accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
+accuracy.before <- (sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label)
+                    / length(agaricus.test$label))
 
 # by default, we predict using all the trees
-
-pred_with_leaf = predict(bst, dtest, predleaf = TRUE)
+pred_with_leaf <- predict(bst, dtest, predleaf = TRUE)
 head(pred_with_leaf)
 
 create.new.tree.features <- function(model, original.features){
   pred_with_leaf <- predict(model, original.features, predleaf = TRUE)
   cols <- list()
-  for(i in 1:model$niter){
+  for (i in 1:model$niter) {
     # max is not the real max but it s not important for the purpose of adding features
-    leaf.id <- sort(unique(pred_with_leaf[,i]))
-    cols[[i]] <- factor(x = pred_with_leaf[,i], level = leaf.id)
+    leaf.id <- sort(unique(pred_with_leaf[, i]))
+    cols[[i]] <- factor(x = pred_with_leaf[, i], level = leaf.id)
   }
-  cbind(original.features, sparse.model.matrix( ~ . -1, as.data.frame(cols)))
+  cbind(original.features, sparse.model.matrix(~ . - 1, as.data.frame(cols)))
 }
 
 # Convert previous features to one hot encoding
 new.features.train <- create.new.tree.features(bst, agaricus.train$data)
 new.features.test <- create.new.tree.features(bst, agaricus.test$data)
+colnames(new.features.test) <- colnames(new.features.train)
 
 # learning with new features
 new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
@@ -46,7 +47,9 @@ watchlist <- list(train = new.dtrain)
 bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
 
 # Model accuracy with new features
-accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
+accuracy.after <- (sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label)
+                   / length(agaricus.test$label))
 
 # Here the accuracy was already good and is now perfect.
-cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!\n"))
+cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
+          accuracy.after, "!\n"))

R-package/demo/runall.R

@@ -1,14 +1,14 @@
 # running all scripts in demo folder
-demo(basic_walkthrough)
-demo(custom_objective)
-demo(boost_from_prediction)
-demo(predict_first_ntree)
-demo(generalized_linear_model)
-demo(cross_validation)
-demo(create_sparse_matrix)
-demo(predict_leaf_indices)
-demo(early_stopping)
-demo(poisson_regression)
-demo(caret_wrapper)
-demo(tweedie_regression)
-#demo(gpu_accelerated) # can only run when built with GPU support
+demo(basic_walkthrough, package = 'xgboost')
+demo(custom_objective, package = 'xgboost')
+demo(boost_from_prediction, package = 'xgboost')
+demo(predict_first_ntree, package = 'xgboost')
+demo(generalized_linear_model, package = 'xgboost')
+demo(cross_validation, package = 'xgboost')
+demo(create_sparse_matrix, package = 'xgboost')
+demo(predict_leaf_indices, package = 'xgboost')
+demo(early_stopping, package = 'xgboost')
+demo(poisson_regression, package = 'xgboost')
+demo(caret_wrapper, package = 'xgboost')
+demo(tweedie_regression, package = 'xgboost')
+#demo(gpu_accelerated, package = 'xgboost')  # can only run when built with GPU support

R-package/demo/tweedie_regression.R

@@ -8,12 +8,12 @@ data(AutoClaim)
 dt <- data.table(AutoClaim)
 
 # exclude these columns from the model matrix
-exclude <-  c('POLICYNO', 'PLCYDATE', 'CLM_FREQ5', 'CLM_AMT5', 'CLM_FLAG', 'IN_YY')
+exclude <- c('POLICYNO', 'PLCYDATE', 'CLM_FREQ5', 'CLM_AMT5', 'CLM_FLAG', 'IN_YY')
 
 # retains the missing values
 # NOTE: this dataset is comes ready out of the box
 options(na.action = 'na.pass')
-x <- sparse.model.matrix(~ . - 1, data = dt[, -exclude, with = F])
+x <- sparse.model.matrix(~ . - 1, data = dt[, -exclude, with = FALSE])
 options(na.action = 'na.omit')
 
 # response
@@ -21,29 +21,29 @@ y <- dt[, CLM_AMT5]
 
 d_train <- xgb.DMatrix(data = x, label = y, missing = NA)
 
-# the tweedie_variance_power parameter determines the shape of 
+# the tweedie_variance_power parameter determines the shape of
 # distribution
 # - closer to 1 is more poisson like and the mass
-#   is more concentrated near zero 
-# - closer to 2 is more gamma like and the mass spreads to the 
+#   is more concentrated near zero
+# - closer to 2 is more gamma like and the mass spreads to the
 #   the right with less concentration near zero
 
 params <- list(
   objective = 'reg:tweedie',
-  eval_metric = 'rmse', 
+  eval_metric = 'rmse',
   tweedie_variance_power = 1.4,
   max_depth = 6,
   eta = 1)
 
 bst <- xgb.train(
-  data = d_train, 
-  params = params, 
+  data = d_train,
+  params = params,
   maximize = FALSE,
-  watchlist = list(train = d_train), 
+  watchlist = list(train = d_train),
   nrounds = 20)
 
 var_imp <- xgb.importance(attr(x, 'Dimnames')[[2]], model = bst)
 
 preds <- predict(bst, d_train)
 
-rmse <- sqrt(sum(mean((y - preds)^2)))
+rmse <- sqrt(sum(mean((y - preds) ^ 2)))

R-package/inst/make-r-def.R

@@ -0,0 +1,96 @@
+# [description]
+#     Create a definition file (.def) from a .dll file, using objdump. This
+#     is used by FindLibR.cmake when building the R package with MSVC.
+#
+# [usage]
+#
+#     Rscript make-r-def.R something.dll something.def
+#
+# [references]
+#    * https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html
+
+args <- commandArgs(trailingOnly = TRUE)
+
+IN_DLL_FILE <- args[[1L]]
+OUT_DEF_FILE <- args[[2L]]
+DLL_BASE_NAME <- basename(IN_DLL_FILE)
+
+message(sprintf("Creating '%s' from '%s'", OUT_DEF_FILE, IN_DLL_FILE))
+
+# system() will not raise an R exception if the process called
+# fails. Wrapping it here to get that behavior.
+#
+# system() introduces a lot of overhead, at least on Windows,
+# so trying processx if it is available
+.pipe_shell_command_to_stdout <- function(command, args, out_file) {
+    has_processx <- suppressMessages({
+        suppressWarnings({
+            require("processx")  # nolint
+        })
+    })
+    if (has_processx) {
+        p <- processx::process$new(
+            command = command
+            , args = args
+            , stdout = out_file
+            , windows_verbatim_args = FALSE
+        )
+        invisible(p$wait())
+    } else {
+        message(paste0(
+            "Using system2() to run shell commands. Installing "
+            , "'processx' with install.packages('processx') might "
+            , "make this faster."
+        ))
+        exit_code <- system2(
+            command = command
+            , args = shQuote(args)
+            , stdout = out_file
+        )
+        if (exit_code != 0L) {
+            stop(paste0("Command failed with exit code: ", exit_code))
+        }
+    }
+    return(invisible(NULL))
+}
+
+# use objdump to dump all the symbols
+OBJDUMP_FILE <- "objdump-out.txt"
+.pipe_shell_command_to_stdout(
+    command = "objdump"
+    , args = c("-p", IN_DLL_FILE)
+    , out_file = OBJDUMP_FILE
+)
+
+objdump_results <- readLines(OBJDUMP_FILE)
+result <- file.remove(OBJDUMP_FILE)
+
+# Only one table in the objdump results matters for our purposes,
+# see https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html
+start_index <- which(
+    grepl(
+        pattern = "[Ordinal/Name Pointer] Table"
+        , x = objdump_results
+        , fixed = TRUE
+    )
+)
+empty_lines <- which(objdump_results == "")
+end_of_table <- empty_lines[empty_lines > start_index][1L]
+
+# Read the contents of the table
+exported_symbols <- objdump_results[(start_index + 1L):end_of_table]
+exported_symbols <- gsub("\t", "", exported_symbols)
+exported_symbols <- gsub(".*\\] ", "", exported_symbols)
+exported_symbols <- gsub(" ", "", exported_symbols)
+
+# Write R.def file
+writeLines(
+    text = c(
+        paste0("LIBRARY \"", DLL_BASE_NAME, "\"")
+        , "EXPORTS"
+        , exported_symbols
+    )
+    , con = OUT_DEF_FILE
+    , sep = "\n"
+)
+message(sprintf("Successfully created '%s'", OUT_DEF_FILE))

R-package/man/a-compatibility-note-for-saveRDS-save.Rd

@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{a-compatibility-note-for-saveRDS-save}
+\alias{a-compatibility-note-for-saveRDS-save}
+\title{Do not use \code{\link[base]{saveRDS}} or \code{\link[base]{save}} for long-term archival of
+models. Instead, use \code{\link{xgb.save}} or \code{\link{xgb.save.raw}}.}
+\description{
+It is a common practice to use the built-in \code{\link[base]{saveRDS}} function (or
+\code{\link[base]{save}}) to persist R objects to the disk. While it is possible to persist
+\code{xgb.Booster} objects using \code{\link[base]{saveRDS}}, it is not advisable to do so if
+the model is to be accessed in the future. If you train a model with the current version of
+XGBoost and persist it with \code{\link[base]{saveRDS}}, the model is not guaranteed to be
+accessible in later releases of XGBoost. To ensure that your model can be accessed in future
+releases of XGBoost, use \code{\link{xgb.save}} or \code{\link{xgb.save.raw}} instead.
+}
+\details{
+Use \code{\link{xgb.save}} to save the XGBoost model as a stand-alone file. You may opt into
+the JSON format by specifying the JSON extension. To read the model back, use
+\code{\link{xgb.load}}.
+
+Use \code{\link{xgb.save.raw}} to save the XGBoost model as a sequence (vector) of raw bytes
+in a future-proof manner. Future releases of XGBoost will be able to read the raw bytes and
+re-construct the corresponding model. To read the model back, use \code{\link{xgb.load.raw}}.
+The \code{\link{xgb.save.raw}} function is useful if you'd like to persist the XGBoost model
+as part of another R object.
+
+Note: Do not use \code{\link{xgb.serialize}} to store models long-term. It persists not only the
+model but also internal configurations and parameters, and its format is not stable across
+multiple XGBoost versions. Use \code{\link{xgb.serialize}} only for checkpointing.
+
+For more details and explanation about model persistence and archival, consult the page
+\url{https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html}.
+}
+\examples{
+data(agaricus.train, package='xgboost')
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
+               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+
+# Save as a stand-alone file; load it with xgb.load()
+xgb.save(bst, 'xgb.model')
+bst2 <- xgb.load('xgb.model')
+
+# Save as a stand-alone file (JSON); load it with xgb.load()
+xgb.save(bst, 'xgb.model.json')
+bst2 <- xgb.load('xgb.model.json')
+if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
+
+# Save as a raw byte vector; load it with xgb.load.raw()
+xgb_bytes <- xgb.save.raw(bst)
+bst2 <- xgb.load.raw(xgb_bytes)
+
+# Persist XGBoost model as part of another R object
+obj <- list(xgb_model_bytes = xgb.save.raw(bst), description = "My first XGBoost model")
+# Persist the R object. Here, saveRDS() is okay, since it doesn't persist
+# xgb.Booster directly. What's being persisted is the future-proof byte representation
+# as given by xgb.save.raw().
+saveRDS(obj, 'my_object.rds')
+# Read back the R object
+obj2 <- readRDS('my_object.rds')
+# Re-construct xgb.Booster object from the bytes
+bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
+if (file.exists('my_object.rds')) file.remove('my_object.rds')
+
+}

R-package/man/agaricus.test.Rd

@@ -4,8 +4,10 @@
 \name{agaricus.test}
 \alias{agaricus.test}
 \title{Test part from Mushroom Data Set}
-\format{A list containing a label vector, and a dgCMatrix object with 1611 
-rows and 126 variables}
+\format{
+A list containing a label vector, and a dgCMatrix object with 1611
+rows and 126 variables
+}
 \usage{
 data(agaricus.test)
 }
@@ -24,8 +26,8 @@ This data set includes the following fields:
 \references{
 https://archive.ics.uci.edu/ml/datasets/Mushroom
 
-Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository 
-[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, 
+Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
+[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
 School of Information and Computer Science.
 }
 \keyword{datasets}

R-package/man/agaricus.train.Rd

@@ -4,8 +4,10 @@
 \name{agaricus.train}
 \alias{agaricus.train}
 \title{Training part from Mushroom Data Set}
-\format{A list containing a label vector, and a dgCMatrix object with 6513 
-rows and 127 variables}
+\format{
+A list containing a label vector, and a dgCMatrix object with 6513
+rows and 127 variables
+}
 \usage{
 data(agaricus.train)
 }
@@ -24,8 +26,8 @@ This data set includes the following fields:
 \references{
 https://archive.ics.uci.edu/ml/datasets/Mushroom
 
-Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository 
-[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, 
+Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
+[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
 School of Information and Computer Science.
 }
 \keyword{datasets}

R-package/man/callbacks.Rd

@@ -5,24 +5,24 @@
 \title{Callback closures for booster training.}
 \description{
 These are used to perform various service tasks either during boosting iterations or at the end.
-This approach helps to modularize many of such tasks without bloating the main training methods, 
+This approach helps to modularize many of such tasks without bloating the main training methods,
 and it offers .
 }
 \details{
 By default, a callback function is run after each boosting iteration.
 An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
 
-When a callback function has \code{finalize} parameter, its finalizer part will also be run after 
+When a callback function has \code{finalize} parameter, its finalizer part will also be run after
 the boosting is completed.
 
-WARNING: side-effects!!! Be aware that these callback functions access and modify things in 
+WARNING: side-effects!!! Be aware that these callback functions access and modify things in
 the environment from which they are called from, which is a fairly uncommon thing to do in R.
 
-To write a custom callback closure, make sure you first understand the main concepts about R envoronments.
-Check either R documentation on \code{\link[base]{environment}} or the 
-\href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R" 
+To write a custom callback closure, make sure you first understand the main concepts about R environments.
+Check either R documentation on \code{\link[base]{environment}} or the
+\href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
 book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
-choose ones that do something similar to what you want to achieve. Also, you would need to get familiar 
+choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
 with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
 }
 \seealso{

R-package/man/cb.cv.predict.Rd

@@ -11,11 +11,11 @@ cb.cv.predict(save_models = FALSE)
 }
 \value{
 Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
-depending on the number of prediction outputs per data row. The order of predictions corresponds 
-to the order of rows in the original dataset. Note that when a custom \code{folds} list is 
-provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a 
-non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be 
-meaningful when user-profided folds have overlapping indices as in, e.g., random sampling splits.
+depending on the number of prediction outputs per data row. The order of predictions corresponds
+to the order of rows in the original dataset. Note that when a custom \code{folds} list is
+provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a
+non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
+meaningful when user-provided folds have overlapping indices as in, e.g., random sampling splits.
 When some of the indices in the training dataset are not included into user-provided \code{folds},
 their prediction value would be \code{NA}.
 }

R-package/man/cb.early.stop.Rd

@@ -4,19 +4,23 @@
 \alias{cb.early.stop}
 \title{Callback closure to activate the early stopping.}
 \usage{
-cb.early.stop(stopping_rounds, maximize = FALSE, metric_name = NULL,
-  verbose = TRUE)
+cb.early.stop(
+  stopping_rounds,
+  maximize = FALSE,
+  metric_name = NULL,
+  verbose = TRUE
+)
 }
 \arguments{
-\item{stopping_rounds}{The number of rounds with no improvement in 
+\item{stopping_rounds}{The number of rounds with no improvement in
 the evaluation metric in order to stop the training.}
 
 \item{maximize}{whether to maximize the evaluation metric}
 
 \item{metric_name}{the name of an evaluation column to use as a criteria for early
 stopping. If not set, the last column would be used.
-Let's say the test data in \code{watchlist} was labelled as \code{dtest}, 
-and one wants to use the AUC in test data for early stopping regardless of where 
+Let's say the test data in \code{watchlist} was labelled as \code{dtest},
+and one wants to use the AUC in test data for early stopping regardless of where
 it is in the \code{watchlist}, then one of the following would need to be set:
 \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
 All dash '-' characters in metric names are considered equivalent to '_'.}
@@ -27,7 +31,7 @@ All dash '-' characters in metric names are considered equivalent to '_'.}
 Callback closure to activate the early stopping.
 }
 \details{
-This callback function determines the condition for early stopping 
+This callback function determines the condition for early stopping
 by setting the \code{stop_condition = TRUE} flag in its calling frame.
 
 The following additional fields are assigned to the model's R object:

R-package/man/cb.evaluation.log.Rd

@@ -13,12 +13,12 @@ Callback closure for logging the evaluation history
 This callback function appends the current iteration evaluation results \code{bst_evaluation}
 available in the calling parent frame to the \code{evaluation_log} list in a calling frame.
 
-The finalizer callback (called with \code{finalize = TURE} in the end) converts 
+The finalizer callback (called with \code{finalize = TURE} in the end) converts
 the \code{evaluation_log} list into a final data.table.
 
-The iteration evaluation result \code{bst_evaluation} must be a named numeric vector. 
+The iteration evaluation result \code{bst_evaluation} must be a named numeric vector.
 
-Note: in the column names of the final data.table, the dash '-' character is replaced with 
+Note: in the column names of the final data.table, the dash '-' character is replaced with
 the underscore '_' in order to make the column names more like regular R identifiers.
 
 Callback function expects the following values to be set in its calling frame:

R-package/man/cb.reset.parameters.Rd

@@ -2,27 +2,27 @@
 % Please edit documentation in R/callbacks.R
 \name{cb.reset.parameters}
 \alias{cb.reset.parameters}
-\title{Callback closure for restetting the booster's parameters at each iteration.}
+\title{Callback closure for resetting the booster's parameters at each iteration.}
 \usage{
 cb.reset.parameters(new_params)
 }
 \arguments{
 \item{new_params}{a list where each element corresponds to a parameter that needs to be reset.
-Each element's value must be either a vector of values of length \code{nrounds} 
-to be set at each iteration, 
-or a function of two parameters \code{learning_rates(iteration, nrounds)} 
-which returns a new parameter value by using the current iteration number 
+Each element's value must be either a vector of values of length \code{nrounds}
+to be set at each iteration,
+or a function of two parameters \code{learning_rates(iteration, nrounds)}
+which returns a new parameter value by using the current iteration number
 and the total number of boosting rounds.}
 }
 \description{
-Callback closure for restetting the booster's parameters at each iteration.
+Callback closure for resetting the booster's parameters at each iteration.
 }
 \details{
 This is a "pre-iteration" callback function used to reset booster's parameters
 at the beginning of each iteration.
 
-Note that when training is resumed from some previous model, and a function is used to 
-reset a parameter value, the \code{nrounds} argument in this function would be the 
+Note that when training is resumed from some previous model, and a function is used to
+reset a parameter value, the \code{nrounds} argument in this function would be the
 the number of boosting rounds in the current training.
 
 Callback function expects the following values to be set in its calling frame:

R-package/man/cb.save.model.Rd

@@ -7,13 +7,13 @@
 cb.save.model(save_period = 0, save_name = "xgboost.model")
 }
 \arguments{
-\item{save_period}{save the model to disk after every 
+\item{save_period}{save the model to disk after every
 \code{save_period} iterations; 0 means save the model at the end.}
 
 \item{save_name}{the name or path for the saved model file.
-It can contain a \code{\link[base]{sprintf}} formatting specifier 
+It can contain a \code{\link[base]{sprintf}} formatting specifier
 to include the integer iteration number in the file name.
-E.g., with \code{save_name} = 'xgboost_%04d.model', 
+E.g., with \code{save_name} = 'xgboost_%04d.model',
 the file saved at iteration 50 would be named "xgboost_0050.model".}
 }
 \description{

R-package/man/dim.xgb.DMatrix.Rd

@@ -13,7 +13,7 @@
 Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
 }
 \details{
-Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also 
+Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
 be directly used with an \code{xgb.DMatrix} object.
 }
 \examples{

R-package/man/dimnames.xgb.DMatrix.Rd

@@ -16,8 +16,8 @@
 and the second one is column names}
 }
 \description{
-Only column names are supported for \code{xgb.DMatrix}, thus setting of 
-row names would have no effect and returnten row names would be NULL.
+Only column names are supported for \code{xgb.DMatrix}, thus setting of
+row names would have no effect and returned row names would be NULL.
 }
 \details{
 Generic \code{dimnames} methods are used by \code{colnames}.

R-package/man/getinfo.Rd

@@ -27,7 +27,7 @@ The \code{name} field can be one of the following:
     \item \code{weight}: to do a weight rescale ;
     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
     \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
-    
+
 }
 
 \code{group} can be setup by \code{setinfo} but can't be retrieved by \code{getinfo}.

R-package/man/normalize.Rd

@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.ggplot.R
+\name{normalize}
+\alias{normalize}
+\title{Scale feature value to have mean 0, standard deviation 1}
+\usage{
+normalize(x)
+}
+\arguments{
+\item{x}{Numeric vector}
+}
+\value{
+Numeric vector with mean 0 and sd 1.
+}
+\description{
+This is used to compare multiple features on the same plot.
+Internal utility function
+}

R-package/man/predict.xgb.Booster.Rd

@@ -5,10 +5,20 @@
 \alias{predict.xgb.Booster.handle}
 \title{Predict method for eXtreme Gradient Boosting model}
 \usage{
-\method{predict}{xgb.Booster}(object, newdata, missing = NA,
-  outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE,
-  predcontrib = FALSE, approxcontrib = FALSE,
-  predinteraction = FALSE, reshape = FALSE, ...)
+\method{predict}{xgb.Booster}(
+  object,
+  newdata,
+  missing = NA,
+  outputmargin = FALSE,
+  ntreelimit = NULL,
+  predleaf = FALSE,
+  predcontrib = FALSE,
+  approxcontrib = FALSE,
+  predinteraction = FALSE,
+  reshape = FALSE,
+  training = FALSE,
+  ...
+)
 
 \method{predict}{xgb.Booster.handle}(object, ...)
 }
@@ -39,6 +49,9 @@ It will use all the trees by default (\code{NULL} value).}
 prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
 or predinteraction flags is TRUE.}
 
+\item{training}{whether is the prediction result used for training.  For dart booster,
+training predicting will perform dropout.}
+
 \item{...}{Parameters passed to \code{predict.xgb.Booster}}
 }
 \value{
@@ -91,7 +104,7 @@ in \url{http://blog.datadive.net/interpreting-random-forests/}.
 
 With \code{predinteraction = TRUE}, SHAP values of contributions of interaction of each pair of features
 are computed. Note that this operation might be rather expensive in terms of compute and memory.
-Since it quadratically depends on the number of features, it is recommended to perfom selection
+Since it quadratically depends on the number of features, it is recommended to perform selection
 of the most important features first. See below about the format of the returned results.
 }
 \examples{

R-package/man/prepare.ggplot.shap.data.Rd

@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.ggplot.R
+\name{prepare.ggplot.shap.data}
+\alias{prepare.ggplot.shap.data}
+\title{Combine and melt feature values and SHAP contributions for sample
+observations.}
+\usage{
+prepare.ggplot.shap.data(data_list, normalize = FALSE)
+}
+\arguments{
+\item{data_list}{List containing 'data' and 'shap_contrib' returned by
+\code{xgb.shap.data()}.}
+
+\item{normalize}{Whether to standardize feature values to have mean 0 and
+standard deviation 1 (useful for comparing multiple features on the same
+plot). Default \code{FALSE}.}
+}
+\value{
+A data.table containing the observation ID, the feature name, the
+  feature value (normalized if specified), and the SHAP contribution value.
+}
+\description{
+Conforms to data format required for ggplot functions.
+}
+\details{
+Internal utility function.
+}

R-package/man/print.xgb.DMatrix.Rd

@@ -14,7 +14,7 @@
 \item{...}{not currently used}
 }
 \description{
-Print information about xgb.DMatrix. 
+Print information about xgb.DMatrix.
 Currently it displays dimensions and presence of info-fields and colnames.
 }
 \examples{

R-package/man/print.xgb.cv.Rd

@@ -17,7 +17,7 @@
 Prints formatted results of \code{xgb.cv}.
 }
 \details{
-When not verbose, it would only print the evaluation results, 
+When not verbose, it would only print the evaluation results,
 including the best iteration (when available).
 }
 \examples{

R-package/man/slice.xgb.DMatrix.Rd

@@ -5,7 +5,7 @@
 \alias{slice.xgb.DMatrix}
 \alias{[.xgb.DMatrix}
 \title{Get a new DMatrix containing the specified rows of
-orginal xgb.DMatrix object}
+original xgb.DMatrix object}
 \usage{
 slice(object, ...)
 
@@ -24,7 +24,7 @@ slice(object, ...)
 }
 \description{
 Get a new DMatrix containing the specified rows of
-orginal xgb.DMatrix object
+original xgb.DMatrix object
 }
 \examples{
 data(agaricus.train, package='xgboost')

R-package/man/xgb.Booster.complete.Rd

@@ -28,7 +28,7 @@ E.g., when an \code{xgb.Booster} model is saved as an R object and then is loade
 its handle (pointer) to an internal xgboost model would be invalid. The majority of xgboost methods
 should still work for such a model object since those methods would be using
 \code{xgb.Booster.complete} internally. However, one might find it to be more efficient to call the
-\code{xgb.Booster.complete} function explicitely once after loading a model as an R-object.
+\code{xgb.Booster.complete} function explicitly once after loading a model as an R-object.
 That would prevent further repeated implicit reconstruction of an internal booster model.
 }
 \examples{
@@ -38,7 +38,10 @@ bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_dep
                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 saveRDS(bst, "xgb.model.rds")
 
+# Warning: The resulting RDS file is only compatible with the current XGBoost version.
+# Refer to the section titled "a-compatibility-note-for-saveRDS-save".
 bst1 <- readRDS("xgb.model.rds")
+if (file.exists("xgb.model.rds")) file.remove("xgb.model.rds")
 # the handle is invalid:
 print(bst1$handle)
 

R-package/man/xgb.DMatrix.Rd

@@ -7,7 +7,7 @@
 xgb.DMatrix(data, info = list(), missing = NA, silent = FALSE, ...)
 }
 \arguments{
-\item{data}{a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character 
+\item{data}{a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character
 string representing a filename.}
 
 \item{info}{a named list of additional information to store in the \code{xgb.DMatrix} object.
@@ -31,4 +31,5 @@ train <- agaricus.train
 dtrain <- xgb.DMatrix(train$data, label=train$label)
 xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
 dtrain <- xgb.DMatrix('xgb.DMatrix.data')
+if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
 }

R-package/man/xgb.DMatrix.save.Rd

@@ -20,4 +20,5 @@ train <- agaricus.train
 dtrain <- xgb.DMatrix(train$data, label=train$label)
 xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
 dtrain <- xgb.DMatrix('xgb.DMatrix.data')
+if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
 }

R-package/man/xgb.attr.Rd

@@ -55,7 +55,7 @@ than for \code{xgb.Booster}, since only just a handle (pointer) would need to be
 That would only matter if attributes need to be set many times.
 Note, however, that when feeding a handle of an \code{xgb.Booster} object to the attribute setters,
 the raw model cache of an \code{xgb.Booster} object would not be automatically updated,
-and it would be user's responsibility to call \code{xgb.save.raw} to update it.
+and it would be user's responsibility to call \code{xgb.serialize} to update it.
 
 The \code{xgb.attributes<-} setter either updates the existing or adds one or several attributes,
 but it doesn't delete the other existing attributes.
@@ -73,6 +73,7 @@ xgb.attributes(bst) <- list(a = 123, b = "abc")
 
 xgb.save(bst, 'xgb.model')
 bst1 <- xgb.load('xgb.model')
+if (file.exists('xgb.model')) file.remove('xgb.model')
 print(xgb.attr(bst1, "my_attribute"))
 print(xgb.attributes(bst1))
 

R-package/man/xgb.config.Rd

@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.Booster.R
+\name{xgb.config}
+\alias{xgb.config}
+\alias{xgb.config<-}
+\title{Accessors for model parameters as JSON string.}
+\usage{
+xgb.config(object)
+
+xgb.config(object) <- value
+}
+\arguments{
+\item{object}{Object of class \code{xgb.Booster}}
+
+\item{value}{A JSON string.}
+}
+\description{
+Accessors for model parameters as JSON string.
+}
+\examples{
+data(agaricus.train, package='xgboost')
+train <- agaricus.train
+
+bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
+               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+config <- xgb.config(bst)
+
+}

R-package/man/xgb.create.features.Rd

@@ -24,9 +24,9 @@ This is the function inspired from the paragraph 3.1 of the paper:
 
 \strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
 
-\emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, 
+\emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers,
 Joaquin Quinonero Candela)}
- 
+
 International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
 
 \url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
@@ -37,10 +37,10 @@ Extract explaining the method:
 convenient way to implement non-linear and tuple transformations
 of the kind we just described. We treat each individual
 tree as a categorical feature that takes as value the
-index of the leaf an instance ends up falling in. We use 
-1-of-K coding of this type of features. 
+index of the leaf an instance ends up falling in. We use
+1-of-K coding of this type of features.
 
-For example, consider the boosted tree model in Figure 1 with 2 subtrees, 
+For example, consider the boosted tree model in Figure 1 with 2 subtrees,
 where the first subtree has 3 leafs and the second 2 leafs. If an
 instance ends up in leaf 2 in the first subtree and leaf 1 in
 second subtree, the overall input to the linear classifier will
@@ -87,6 +87,6 @@ accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /
 
 # Here the accuracy was already good and is now perfect.
 cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
-          accuracy.after, "!\\n"))
+          accuracy.after, "!\n"))
 
 }

R-package/man/xgb.cv.Rd

@@ -4,20 +4,39 @@
 \alias{xgb.cv}
 \title{Cross Validation}
 \usage{
-xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
-  missing = NA, prediction = FALSE, showsd = TRUE,
-  metrics = list(), obj = NULL, feval = NULL, stratified = TRUE,
-  folds = NULL, verbose = TRUE, print_every_n = 1L,
-  early_stopping_rounds = NULL, maximize = NULL, callbacks = list(),
-  ...)
+xgb.cv(
+  params = list(),
+  data,
+  nrounds,
+  nfold,
+  label = NULL,
+  missing = NA,
+  prediction = FALSE,
+  showsd = TRUE,
+  metrics = list(),
+  obj = NULL,
+  feval = NULL,
+  stratified = TRUE,
+  folds = NULL,
+  train_folds = NULL,
+  verbose = TRUE,
+  print_every_n = 1L,
+  early_stopping_rounds = NULL,
+  maximize = NULL,
+  callbacks = list(),
+  ...
+)
 }
 \arguments{
-\item{params}{the list of parameters. Commonly used ones are:
+\item{params}{the list of parameters. The complete list of parameters is
+  available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
+  is a shorter summary:
 \itemize{
   \item \code{objective} objective function, common ones are
   \itemize{
-    \item \code{reg:linear} linear regression
-    \item \code{binary:logistic} logistic regression for classification
+    \item \code{reg:squarederror} Regression with squared loss.
+    \item \code{binary:logistic} logistic regression for classification.
+    \item See \code{\link[=xgb.train]{xgb.train}()} for complete list of objectives.
   }
   \item \code{eta} step size of each boosting step
   \item \code{max_depth} maximum depth of the tree
@@ -35,11 +54,11 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
 
 \item{label}{vector of response values. Should be provided only when data is an R-matrix.}
 
-\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means 
-that NA values should be considered as 'missing' by the algorithm. 
+\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means
+that NA values should be considered as 'missing' by the algorithm.
 Sometimes, 0 or other extreme value might be used to represent missing values.}
 
-\item{prediction}{A logical value indicating whether to return the test fold predictions 
+\item{prediction}{A logical value indicating whether to return the test fold predictions
 from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.}
 
 \item{showsd}{\code{boolean}, whether to show standard deviation of cross validation}
@@ -51,33 +70,38 @@ from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callb
   \item \code{error} binary classification error rate
   \item \code{rmse} Rooted mean square error
   \item \code{logloss} negative log-likelihood function
+  \item \code{mae} Mean absolute error
+  \item \code{mape} Mean absolute percentage error
   \item \code{auc} Area under curve
   \item \code{aucpr} Area under PR curve
   \item \code{merror} Exact matching error, used to evaluate multi-class classification
 }}
 
-\item{obj}{customized objective function. Returns gradient and second order 
+\item{obj}{customized objective function. Returns gradient and second order
 gradient with given prediction and dtrain.}
 
-\item{feval}{custimized evaluation function. Returns 
-\code{list(metric='metric-name', value='metric-value')} with given 
+\item{feval}{customized evaluation function. Returns
+\code{list(metric='metric-name', value='metric-value')} with given
 prediction and dtrain.}
 
-\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified 
+\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified
 by the values of outcome labels.}
 
 \item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds
-(each element must be a vector of test fold's indices). When folds are supplied, 
+(each element must be a vector of test fold's indices). When folds are supplied,
 the \code{nfold} and \code{stratified} parameters are ignored.}
 
+\item{train_folds}{\code{list} list specifying which indicies to use for training. If \code{NULL}
+(the default) all indices not specified in \code{folds} will be used for training.}
+
 \item{verbose}{\code{boolean}, print the statistics during the process}
 
 \item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
-Default is 1 which means all messages are printed. This parameter is passed to the 
+Default is 1 which means all messages are printed. This parameter is passed to the
 \code{\link{cb.print.evaluation}} callback.}
 
-\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered. 
-If set to an integer \code{k}, training with a validation set will stop if the performance 
+\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
+If set to an integer \code{k}, training with a validation set will stop if the performance
 doesn't improve for \code{k} rounds.
 Setting this parameter engages the \code{\link{cb.early.stop}} callback.}
 
@@ -87,8 +111,8 @@ When it is \code{TRUE}, it means the larger the evaluation score the better.
 This parameter is passed to the \code{\link{cb.early.stop}} callback.}
 
 \item{callbacks}{a list of callback functions to perform various task during boosting.
-See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
-parameters' values. User can provide either existing or their own callback methods in order 
+See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
+parameters' values. User can provide either existing or their own callback methods in order
 to customize the training process.}
 
 \item{...}{other parameters to pass to \code{params}.}
@@ -97,26 +121,26 @@ to customize the training process.}
 An object of class \code{xgb.cv.synchronous} with the following elements:
 \itemize{
   \item \code{call} a function call.
-  \item \code{params} parameters that were passed to the xgboost library. Note that it does not 
+  \item \code{params} parameters that were passed to the xgboost library. Note that it does not
         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-  \item \code{callbacks} callback functions that were either automatically assigned or 
+  \item \code{callbacks} callback functions that were either automatically assigned or
         explicitly passed.
-  \item \code{evaluation_log} evaluation history storead as a \code{data.table} with the
-        first column corresponding to iteration number and the rest corresponding to the 
+  \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
+        first column corresponding to iteration number and the rest corresponding to the
         CV-based evaluation means and standard deviations for the training and test CV-sets.
         It is created by the \code{\link{cb.evaluation.log}} callback.
   \item \code{niter} number of boosting iterations.
   \item \code{nfeatures} number of features in training data.
-  \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds} 
+  \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
         parameter or randomly generated.
   \item \code{best_iteration} iteration number with the best evaluation metric value
         (only available with early stopping).
-  \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
+  \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
         which could further be used in \code{predict} method
         (only available with early stopping).
-  \item \code{pred} CV prediction values available when \code{prediction} is set. 
+  \item \code{pred} CV prediction values available when \code{prediction} is set.
         It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-  \item \code{models} a liost of the CV folds' models. It is only available with the explicit 
+  \item \code{models} a list of the CV folds' models. It is only available with the explicit
         setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
 }
 }
@@ -124,15 +148,15 @@ An object of class \code{xgb.cv.synchronous} with the following elements:
 The cross validation function of xgboost
 }
 \details{
-The original sample is randomly partitioned into \code{nfold} equal size subsamples. 
+The original sample is randomly partitioned into \code{nfold} equal size subsamples.
 
-Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data. 
+Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
 
 The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
 
 All observations are used for both training and validation.
 
-Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
+Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
 }
 \examples{
 data(agaricus.train, package='xgboost')

R-package/man/xgb.dump.Rd

@@ -4,20 +4,26 @@
 \alias{xgb.dump}
 \title{Dump an xgboost model in text format.}
 \usage{
-xgb.dump(model, fname = NULL, fmap = "", with_stats = FALSE,
-  dump_format = c("text", "json"), ...)
+xgb.dump(
+  model,
+  fname = NULL,
+  fmap = "",
+  with_stats = FALSE,
+  dump_format = c("text", "json"),
+  ...
+)
 }
 \arguments{
 \item{model}{the model object.}
 
-\item{fname}{the name of the text file where to save the model text dump. 
+\item{fname}{the name of the text file where to save the model text dump.
 If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.}
 
 \item{fmap}{feature map file representing feature types.
-Detailed description could be found at 
+Detailed description could be found at
 \url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
 See demo/ for walkthrough example in R, and
-\url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt} 
+\url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt}
 for example Format.}
 
 \item{with_stats}{whether to dump some additional statistics about the splits.
@@ -41,7 +47,7 @@ data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
 train <- agaricus.train
 test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
+bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 # save the model in file 'xgb.model.dump'
 dump_path = file.path(tempdir(), 'model.dump')

R-package/man/xgb.gblinear.history.Rd

@@ -12,7 +12,7 @@ using the \code{cb.gblinear.history()} callback.}
 
 \item{class_index}{zero-based class index to extract the coefficients for only that
 specific class in a multinomial multiclass model. When it is NULL, all the
-coeffients are returned. Has no effect in non-multiclass models.}
+coefficients are returned. Has no effect in non-multiclass models.}
 }
 \value{
 For an \code{xgb.train} result, a matrix (either dense or sparse) with the columns

R-package/man/xgb.importance.Rd

@@ -4,8 +4,14 @@
 \alias{xgb.importance}
 \title{Importance of features in a model.}
 \usage{
-xgb.importance(feature_names = NULL, model = NULL, trees = NULL,
-  data = NULL, label = NULL, target = NULL)
+xgb.importance(
+  feature_names = NULL,
+  model = NULL,
+  trees = NULL,
+  data = NULL,
+  label = NULL,
+  target = NULL
+)
 }
 \arguments{
 \item{feature_names}{character vector of feature names. If the model already
@@ -16,7 +22,7 @@ Non-null \code{feature_names} could be provided to override those in the model.}
 
 \item{trees}{(only for the gbtree booster) an integer vector of tree indices that should be included
 into the importance calculation. If set to \code{NULL}, all trees of the model are parsed.
-It could be useful, e.g., in multiclass classification to get feature importances 
+It could be useful, e.g., in multiclass classification to get feature importances
 for each class separately. IMPORTANT: the tree index in xgboost models
 is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).}
 
@@ -31,7 +37,7 @@ For a tree model, a \code{data.table} with the following columns:
 \itemize{
   \item \code{Features} names of the features used in the model;
   \item \code{Gain} represents fractional contribution of each feature to the model based on
-       the total gain of this feature's splits. Higher percentage means a more important 
+       the total gain of this feature's splits. Higher percentage means a more important
        predictive feature.
   \item \code{Cover} metric of the number of observation related to this feature;
   \item \code{Frequency} percentage representing the relative number of times
@@ -45,7 +51,7 @@ A linear model's importance \code{data.table} has the following columns:
   \item \code{Class} (only for multiclass models) class label.
 }
 
-If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names}, 
+If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names},
 index of the features will be used instead. Because the index is extracted from the model dump
 (based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
 }
@@ -55,21 +61,21 @@ Creates a \code{data.table} of feature importances in a model.
 \details{
 This function works for both linear and tree models.
 
-For linear models, the importance is the absolute magnitude of linear coefficients. 
-For that reason, in order to obtain a meaningful ranking by importance for a linear model, 
-the features need to be on the same scale (which you also would want to do when using either 
+For linear models, the importance is the absolute magnitude of linear coefficients.
+For that reason, in order to obtain a meaningful ranking by importance for a linear model,
+the features need to be on the same scale (which you also would want to do when using either
 L1 or L2 regularization).
 }
 \examples{
 
 # binomial classification using gbtree:
 data(agaricus.train, package='xgboost')
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 xgb.importance(model = bst)
 
 # binomial classification using gblinear:
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear", 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear",
                eta = 0.3, nthread = 1, nrounds = 20, objective = "binary:logistic")
 xgb.importance(model = bst)
 

R-package/man/xgb.load.Rd

@@ -17,8 +17,8 @@ Load xgboost model from the binary model file.
 }
 \details{
 The input file is expected to contain a model saved in an xgboost-internal binary format
-using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some 
-appropriate methods from other xgboost interfaces. E.g., a model trained in Python and 
+using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some
+appropriate methods from other xgboost interfaces. E.g., a model trained in Python and
 saved from there in xgboost format, could be loaded from R.
 
 Note: a model saved as an R-object, has to be loaded using corresponding R-methods,
@@ -29,10 +29,11 @@ data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
 train <- agaricus.train
 test <- agaricus.test
-bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
+bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 xgb.save(bst, 'xgb.model')
 bst <- xgb.load('xgb.model')
+if (file.exists('xgb.model')) file.remove('xgb.model')
 pred <- predict(bst, test$data)
 }
 \seealso{

R-package/man/xgb.load.raw.Rd

@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.load.raw.R
+\name{xgb.load.raw}
+\alias{xgb.load.raw}
+\title{Load serialised xgboost model from R's raw vector}
+\usage{
+xgb.load.raw(buffer)
+}
+\arguments{
+\item{buffer}{the buffer returned by xgb.save.raw}
+}
+\description{
+User can generate raw memory buffer by calling xgb.save.raw
+}

R-package/man/xgb.model.dt.tree.Rd

@@ -4,8 +4,14 @@
 \alias{xgb.model.dt.tree}
 \title{Parse a boosted tree model text dump}
 \usage{
-xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
-  trees = NULL, use_int_id = FALSE, ...)
+xgb.model.dt.tree(
+  feature_names = NULL,
+  model = NULL,
+  text = NULL,
+  trees = NULL,
+  use_int_id = FALSE,
+  ...
+)
 }
 \arguments{
 \item{feature_names}{character vector of feature names. If the model already
@@ -14,7 +20,7 @@ Non-null \code{feature_names} could be provided to override those in the model.}
 
 \item{model}{object of class \code{xgb.Booster}}
 
-\item{text}{\code{character} vector previously generated by the \code{xgb.dump} 
+\item{text}{\code{character} vector previously generated by the \code{xgb.dump}
 function  (where parameter \code{with_stats = TRUE} should have been set).
 \code{text} takes precedence over \code{model}.}
 
@@ -47,10 +53,10 @@ The columns of the \code{data.table} are:
  \item \code{Quality}: either the split gain (change in loss) or the leaf value
  \item \code{Cover}: metric related to the number of observation either seen by a split
                      or collected by a leaf during training.
-} 
+}
 
 When \code{use_int_id=FALSE}, columns "Yes", "No", and "Missing" point to model-wide node identifiers
-in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from 
+in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from
 the corresponding trees in the "Node" column.
 }
 \description{
@@ -61,17 +67,17 @@ Parse a boosted tree model text dump into a \code{data.table} structure.
 
 data(agaricus.train, package='xgboost')
 
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2, 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 
 (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
 
-# This bst model already has feature_names stored with it, so those would be used when 
+# This bst model already has feature_names stored with it, so those would be used when
 # feature_names is not set:
 (dt <- xgb.model.dt.tree(model = bst))
 
 # How to match feature names of splits that are following a current 'Yes' branch:
 
 merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
- 
+
 }

R-package/man/xgb.plot.deepness.Rd

@@ -5,11 +5,17 @@
 \alias{xgb.plot.deepness}
 \title{Plot model trees deepness}
 \usage{
-xgb.ggplot.deepness(model = NULL, which = c("2x1", "max.depth",
-  "med.depth", "med.weight"))
+xgb.ggplot.deepness(
+  model = NULL,
+  which = c("2x1", "max.depth", "med.depth", "med.weight")
+)
 
-xgb.plot.deepness(model = NULL, which = c("2x1", "max.depth",
-  "med.depth", "med.weight"), plot = TRUE, ...)
+xgb.plot.deepness(
+  model = NULL,
+  which = c("2x1", "max.depth", "med.depth", "med.weight"),
+  plot = TRUE,
+  ...
+)
 }
 \arguments{
 \item{model}{either an \code{xgb.Booster} model generated by the \code{xgb.train} function
@@ -17,7 +23,7 @@ or a data.table result of the \code{xgb.model.dt.tree} function.}
 
 \item{which}{which distribution to plot (see details).}
 
-\item{plot}{(base R barplot) whether a barplot should be produced. 
+\item{plot}{(base R barplot) whether a barplot should be produced.
 If FALSE, only a data.table is returned.}
 
 \item{...}{other parameters passed to \code{barplot} or \code{plot}.}
@@ -39,10 +45,10 @@ When \code{which="2x1"}, two distributions with respect to the leaf depth
 are plotted on top of each other:
 \itemize{
  \item the distribution of the number of leafs in a tree model at a certain depth;
- \item the distribution of average weighted number of observations ("cover") 
+ \item the distribution of average weighted number of observations ("cover")
        ending up in leafs at certain depth.
 }
-Those could be helpful in determining sensible ranges of the \code{max_depth} 
+Those could be helpful in determining sensible ranges of the \code{max_depth}
 and \code{min_child_weight} parameters.
 
 When \code{which="max.depth"} or \code{which="med.depth"}, plots of either maximum or median depth
@@ -50,7 +56,7 @@ per tree with respect to tree number are created. And \code{which="med.weight"}
 a tree's median absolute leaf weight changes through the iterations.
 
 This function was inspired by the blog post
-\url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}.
+\url{https://github.com/aysent/random-forest-leaf-visualization}.
 }
 \examples{
 

R-package/man/xgb.plot.importance.Rd

@@ -5,25 +5,38 @@
 \alias{xgb.plot.importance}
 \title{Plot feature importance as a bar graph}
 \usage{
-xgb.ggplot.importance(importance_matrix = NULL, top_n = NULL,
-  measure = NULL, rel_to_first = FALSE, n_clusters = c(1:10), ...)
+xgb.ggplot.importance(
+  importance_matrix = NULL,
+  top_n = NULL,
+  measure = NULL,
+  rel_to_first = FALSE,
+  n_clusters = c(1:10),
+  ...
+)
 
-xgb.plot.importance(importance_matrix = NULL, top_n = NULL,
-  measure = NULL, rel_to_first = FALSE, left_margin = 10,
-  cex = NULL, plot = TRUE, ...)
+xgb.plot.importance(
+  importance_matrix = NULL,
+  top_n = NULL,
+  measure = NULL,
+  rel_to_first = FALSE,
+  left_margin = 10,
+  cex = NULL,
+  plot = TRUE,
+  ...
+)
 }
 \arguments{
 \item{importance_matrix}{a \code{data.table} returned by \code{\link{xgb.importance}}.}
 
 \item{top_n}{maximal number of top features to include into the plot.}
 
-\item{measure}{the name of importance measure to plot. 
+\item{measure}{the name of importance measure to plot.
 When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.}
 
 \item{rel_to_first}{whether importance values should be represented as relative to the highest ranked feature.
 See Details.}
 
-\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range 
+\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range
 of the possible number of clusters of bars.}
 
 \item{...}{other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).}
@@ -33,7 +46,7 @@ When it is NULL, the existing \code{par('mar')} is used.}
 
 \item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
 
-\item{plot}{(base R barplot) whether a barplot should be produced. 
+\item{plot}{(base R barplot) whether a barplot should be produced.
 If FALSE, only a data.table is returned.}
 }
 \value{
@@ -53,14 +66,14 @@ Features are shown ranked in a decreasing importance order.
 It works for importances from both \code{gblinear} and \code{gbtree} models.
 
 When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
-For gbtree model, that would mean being normalized to the total of 1 
+For gbtree model, that would mean being normalized to the total of 1
 ("what is feature's importance contribution relative to the whole model?").
 For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
-Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of 
+Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
 "what is feature's importance contribution relative to the most important feature?"
 
-The ggplot-backend method also performs 1-D custering of the importance values, 
-with bar colors coresponding to different clusters that have somewhat similar importance values.
+The ggplot-backend method also performs 1-D clustering of the importance values,
+with bar colors corresponding to different clusters that have somewhat similar importance values.
 }
 \examples{
 data(agaricus.train)

R-package/man/xgb.plot.multi.trees.Rd

@@ -4,8 +4,15 @@
 \alias{xgb.plot.multi.trees}
 \title{Project all trees on one tree and plot it}
 \usage{
-xgb.plot.multi.trees(model, feature_names = NULL, features_keep = 5,
-  plot_width = NULL, plot_height = NULL, render = TRUE, ...)
+xgb.plot.multi.trees(
+  model,
+  feature_names = NULL,
+  features_keep = 5,
+  plot_width = NULL,
+  plot_height = NULL,
+  render = TRUE,
+  ...
+)
 }
 \arguments{
 \item{model}{produced by the \code{xgb.train} function.}

R-package/man/xgb.plot.shap.Rd

@@ -4,18 +4,38 @@
 \alias{xgb.plot.shap}
 \title{SHAP contribution dependency plots}
 \usage{
-xgb.plot.shap(data, shap_contrib = NULL, features = NULL, top_n = 1,
-  model = NULL, trees = NULL, target_class = NULL,
-  approxcontrib = FALSE, subsample = NULL, n_col = 1, col = rgb(0,
-  0, 1, 0.2), pch = ".", discrete_n_uniq = 5, discrete_jitter = 0.01,
-  ylab = "SHAP", plot_NA = TRUE, col_NA = rgb(0.7, 0, 1, 0.6),
-  pch_NA = ".", pos_NA = 1.07, plot_loess = TRUE, col_loess = 2,
-  span_loess = 0.5, which = c("1d", "2d"), plot = TRUE, ...)
+xgb.plot.shap(
+  data,
+  shap_contrib = NULL,
+  features = NULL,
+  top_n = 1,
+  model = NULL,
+  trees = NULL,
+  target_class = NULL,
+  approxcontrib = FALSE,
+  subsample = NULL,
+  n_col = 1,
+  col = rgb(0, 0, 1, 0.2),
+  pch = ".",
+  discrete_n_uniq = 5,
+  discrete_jitter = 0.01,
+  ylab = "SHAP",
+  plot_NA = TRUE,
+  col_NA = rgb(0.7, 0, 1, 0.6),
+  pch_NA = ".",
+  pos_NA = 1.07,
+  plot_loess = TRUE,
+  col_loess = 2,
+  span_loess = 0.5,
+  which = c("1d", "2d"),
+  plot = TRUE,
+  ...
+)
 }
 \arguments{
 \item{data}{data as a \code{matrix} or \code{dgCMatrix}.}
 
-\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above 
+\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above
 \code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.}
 
 \item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
@@ -63,7 +83,7 @@ more than 5 distinct values.}
 
 \item{col_loess}{a color to use for the loess curves.}
 
-\item{span_loess}{the \code{span} paramerer in \code{\link[stats]{loess}}'s call.}
+\item{span_loess}{the \code{span} parameter in \code{\link[stats]{loess}}'s call.}
 
 \item{which}{whether to do univariate or bivariate plotting. NOTE: only 1D is implemented so far.}
 
@@ -104,13 +124,14 @@ a meaningful thing to do.
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
 
-bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50, 
+bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
                eta = 0.1, max_depth = 3, subsample = .5,
                method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
 
 xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
 contr <- predict(bst, agaricus.test$data, predcontrib = TRUE)
 xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
+xgb.ggplot.shap.summary(agaricus.test$data, contr, model = bst, top_n = 12)  # Summary plot
 
 # multiclass example - plots for each class separately:
 nclass <- 3
@@ -129,6 +150,7 @@ xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4,
               n_col = 2, col = col, pch = 16, pch_NA = 17)
 xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4,
               n_col = 2, col = col, pch = 16, pch_NA = 17)
+xgb.ggplot.shap.summary(x, model = mbst, target_class = 0, top_n = 4)  # Summary plot
 
 }
 \references{

R-package/man/xgb.plot.shap.summary.Rd

@@ -0,0 +1,78 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.shap.R
+\name{xgb.ggplot.shap.summary}
+\alias{xgb.ggplot.shap.summary}
+\alias{xgb.plot.shap.summary}
+\title{SHAP contribution dependency summary plot}
+\usage{
+xgb.ggplot.shap.summary(
+  data,
+  shap_contrib = NULL,
+  features = NULL,
+  top_n = 10,
+  model = NULL,
+  trees = NULL,
+  target_class = NULL,
+  approxcontrib = FALSE,
+  subsample = NULL
+)
+
+xgb.plot.shap.summary(
+  data,
+  shap_contrib = NULL,
+  features = NULL,
+  top_n = 10,
+  model = NULL,
+  trees = NULL,
+  target_class = NULL,
+  approxcontrib = FALSE,
+  subsample = NULL
+)
+}
+\arguments{
+\item{data}{data as a \code{matrix} or \code{dgCMatrix}.}
+
+\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above
+\code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.}
+
+\item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
+feature importance is calculated, and \code{top_n} high ranked features are taken.}
+
+\item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are taken.}
+
+\item{model}{an \code{xgb.Booster} model. It has to be provided when either \code{shap_contrib}
+or \code{features} is missing.}
+
+\item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
+
+\item{target_class}{is only relevant for multiclass models. When it is set to a 0-based class index,
+only SHAP contributions for that specific class are used.
+If it is not set, SHAP importances are averaged over all classes.}
+
+\item{approxcontrib}{passed to \code{\link{predict.xgb.Booster}} when \code{shap_contrib = NULL}.}
+
+\item{subsample}{a random fraction of data points to use for plotting. When it is NULL,
+it is set so that up to 100K data points are used.}
+}
+\value{
+A \code{ggplot2} object.
+}
+\description{
+Compare SHAP contributions of different features.
+}
+\details{
+A point plot (each point representing one sample from \code{data}) is
+produced for each feature, with the points plotted on the SHAP value axis.
+Each point (observation) is coloured based on its feature value. The plot
+hence allows us to see which features have a negative / positive contribution
+on the model prediction, and whether the contribution is different for larger
+or smaller values of the feature. We effectively try to replicate the
+\code{summary_plot} function from https://github.com/slundberg/shap.
+}
+\examples{
+# See \code{\link{xgb.plot.shap}}.
+}
+\seealso{
+\code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
+  \url{https://github.com/slundberg/shap}
+}

R-package/man/xgb.plot.tree.Rd

@@ -4,9 +4,16 @@
 \alias{xgb.plot.tree}
 \title{Plot a boosted tree model}
 \usage{
-xgb.plot.tree(feature_names = NULL, model = NULL, trees = NULL,
-  plot_width = NULL, plot_height = NULL, render = TRUE,
-  show_node_id = FALSE, ...)
+xgb.plot.tree(
+  feature_names = NULL,
+  model = NULL,
+  trees = NULL,
+  plot_width = NULL,
+  plot_height = NULL,
+  render = TRUE,
+  show_node_id = FALSE,
+  ...
+)
 }
 \arguments{
 \item{feature_names}{names of each feature as a \code{character} vector.}
@@ -53,7 +60,7 @@ The content of each node is organised that way:
  \item \code{Gain} (for split nodes): the information gain metric of a split
        (corresponds to the importance of the node in the model).
  \item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
-} 
+}
 The tree root nodes also indicate the Tree index (0-based).
 
 The "Yes" branches are marked by the "< split_value" label.
@@ -73,7 +80,7 @@ xgb.plot.tree(model = bst)
 xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
 
 \dontrun{
-# Below is an example of how to save this plot to a file. 
+# Below is an example of how to save this plot to a file.
 # Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
 library(DiagrammeR)
 gr <- xgb.plot.tree(model=bst, trees=0:1, render=FALSE)