Separate out restricted and unrestricted tasks (#3736)

* Fix #3730: scikit-learn 0.20 compatibility fix

sklearn.cross_validation has been removed from scikit-learn 0.20,
so replace it with sklearn.model_selection

* Display test names for Python tests for clarity

.clang-tidy

@@ -1,4 +1,4 @@
-Checks: 'modernize-*,-modernize-make-*,-modernize-use-auto,-modernize-raw-string-literal,google-*,-google-default-arguments,-clang-diagnostic-#pragma-messages,readability-identifier-naming'
+Checks: 'modernize-*,-modernize-make-*,-modernize-raw-string-literal,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  }
@@ -6,8 +6,8 @@ CheckOptions:
   - { 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.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  }

.github/lock.yml

@@ -1,32 +0,0 @@
-# Configuration for lock-threads - https://github.com/dessant/lock-threads
-
-# Number of days of inactivity before a closed issue or pull request is locked
-daysUntilLock: 90
-
-# Issues and pull requests with these labels will not be locked. Set to `[]` to disable
-exemptLabels:
-  - feature-request
-
-# Label to add before locking, such as `outdated`. Set to `false` to disable
-lockLabel: false
-
-# Comment to post before locking. Set to `false` to disable
-lockComment: false
-
-# Assign `resolved` as the reason for locking. Set to `false` to disable
-setLockReason: true
-
-# Limit to only `issues` or `pulls`
-# only: issues
-
-# Optionally, specify configuration settings just for `issues` or `pulls`
-# issues:
-#   exemptLabels:
-#     - help-wanted
-#   lockLabel: outdated
-
-# pulls:
-#   daysUntilLock: 30
-
-# Repository to extend settings from
-# _extends: repo

.gitignore

@@ -91,8 +91,3 @@ lib/
 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

.travis.yml

@@ -3,30 +3,65 @@ sudo: required
 
 # Enabling test on Linux and OS X
 os:
+  - linux
   - osx
 
-osx_image: xcode9.3
+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
+    - TASK=cmake_test
+    # c++ test
+    - TASK=cpp_test
+    # distributed test
+    - TASK=distributed_test
+
+matrix:
+  exclude:
+    - os: osx
+      env: TASK=lint
+    - os: osx
+      env: TASK=cmake_test
+    - os: linux
+      env: TASK=r_test
+    - os: osx
+      env: TASK=python_lightweight_test
+    - os: osx
+      env: TASK=cpp_test
+    - os: osx
+      env: TASK=distributed_test
 
 # dependent apt packages
 addons:
-  homebrew:
+  apt:
+    sources:
+      - llvm-toolchain-trusty-5.0
+      - ubuntu-toolchain-r-test
+      - george-edison55-precise-backports
     packages:
-      - gcc@7
+      - clang
+      - clang-tidy-5.0
+      - cmake-data
+      - doxygen
+      - wget
+      - libcurl4-openssl-dev
+      - unzip
       - graphviz
-      - openssl
-      - libgit2
-      - r
-    update: true
+      - gcc-4.8
+      - g++-4.8
 
 before_install:
   - source dmlc-core/scripts/travis/travis_setup_env.sh

CMakeLists.txt

@@ -1,229 +1,258 @@
-cmake_minimum_required(VERSION 3.3)
-project(xgboost LANGUAGES CXX C VERSION 0.90)
+cmake_minimum_required (VERSION 3.2)
+project(xgboost)
 include(cmake/Utils.cmake)
 list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake/modules")
-cmake_policy(SET CMP0022 NEW)
-
-message(STATUS "CMake version ${CMAKE_VERSION}")
-if (MSVC)
-  cmake_minimum_required(VERSION 3.11)
-endif (MSVC)
+find_package(OpenMP)
 
 set_default_configuration_release()
+msvc_use_static_runtime()
 
-#-- Options
-option(BUILD_C_DOC "Build documentation for C APIs using Doxygen." OFF)
-option(USE_OPENMP "Build with OpenMP support." ON)
-## Bindings
+# Options
+option(USE_CUDA  "Build with GPU acceleration")
+option(USE_AVX  "Build with AVX instructions. May not produce identical results due to approximate math." OFF)
+option(USE_NCCL "Build using NCCL for multi-GPU. Also requires USE_CUDA")
 option(JVM_BINDINGS "Build JVM bindings" OFF)
-option(R_LIB "Build shared library for R package" OFF)
-## Dev
 option(GOOGLE_TEST "Build google tests" OFF)
-option(USE_DMLC_GTEST "Use google tests bundled with dmlc-core submodule (EXPERIMENTAL)" 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")
-## CUDA
-option(USE_CUDA  "Build with GPU acceleration" OFF)
-option(USE_NCCL  "Build with NCCL to enable multi-GPU support." OFF)
-option(BUILD_WITH_SHARED_NCCL "Build with shared NCCL library." OFF)
-set(GPU_COMPUTE_VER "" CACHE STRING
-  "Semicolon separated list of compute versions to be built against, e.g. '35;61'")
-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))
-## Sanitizers
+option(R_LIB "Build shared library for R package" OFF)
 option(USE_SANITIZER "Use santizer flags" OFF)
-option(SANITIZER_PATH "Path to sanitizes.")
+set(GPU_COMPUTE_VER "" CACHE STRING
+  "Space separated list of compute versions to be built against, e.g. '35 61'")
 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
-option(PLUGIN_LZ4 "Build lz4 plugin" OFF)
-option(PLUGIN_DENSE_PARSER "Build dense parser plugin" OFF)
 
-## Deprecation warning
-if (USE_AVX)
-  message(WARNING "The option 'USE_AVX' is deprecated as experimental AVX features have been removed from xgboost.")
-endif (USE_AVX)
+# Deprecation warning
+if(PLUGIN_UPDATER_GPU)
+  set(USE_CUDA ON)
+  message(WARNING "The option 'PLUGIN_UPDATER_GPU' is deprecated. Set 'USE_CUDA' instead.")
+endif()
+
+# 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)
-  # Older CMake versions have had troubles with Sanitizer
-  cmake_minimum_required(VERSION 3.12)
+if(USE_SANITIZER)
   include(cmake/Sanitizer.cmake)
   enable_sanitizers("${ENABLED_SANITIZERS}")
-endif (USE_SANITIZER)
+endif(USE_SANITIZER)
 
-if (USE_CUDA)
-  cmake_minimum_required(VERSION 3.12)
-  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: ${CMAKE_CUDA_HOST_COMPILER}")
-
-  enable_language(CUDA)
-  set(GEN_CODE "")
-  format_gencode_flags("${GPU_COMPUTE_VER}" GEN_CODE)
-  message(STATUS "CUDA GEN_CODE: ${GEN_CODE}")
-endif (USE_CUDA)
+# AVX
+if(USE_AVX)
+  if(MSVC)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX")
+  else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx")
+  endif()
+  add_definitions(-DXGBOOST_USE_AVX)
+endif()
 
 # dmlc-core
-msvc_use_static_runtime()
-add_subdirectory(${PROJECT_SOURCE_DIR}/dmlc-core)
-set_target_properties(dmlc PROPERTIES
-  CXX_STANDARD 11
-  CXX_STANDARD_REQUIRED ON
-  POSITION_INDEPENDENT_CODE ON)
-list(APPEND LINKED_LIBRARIES_PRIVATE dmlc)
+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()
+
+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 CUDA_SOURCES
+    src/*.cu
+    src/*.cuh
+)
 
 # rabit
-# full rabit doesn't build on windows, so we can't import it as subdirectory
-if(MINGW OR R_LIB)
-  set(RABIT_SOURCES
-    rabit/src/engine_empty.cc
-    rabit/src/c_api.cc)
-else ()
-  set(RABIT_SOURCES
+# 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)
-endif (MINGW OR R_LIB)
-add_library(rabit STATIC ${RABIT_SOURCES})
-target_include_directories(rabit PRIVATE
-  $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/dmlc-core/include>
-  $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/rabit/include/rabit>)
-set_target_properties(rabit
-  PROPERTIES
-  CXX_STANDARD 11
-  CXX_STANDARD_REQUIRED ON
-  POSITION_INDEPENDENT_CODE ON)
-list(APPEND LINKED_LIBRARIES_PRIVATE rabit)
+    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()
 
-# Exports some R specific definitions and objects
-if (R_LIB)
-  add_subdirectory(${PROJECT_SOURCE_DIR}/R-package)
-endif (R_LIB)
+if(USE_CUDA)
+  find_package(CUDA 8.0 REQUIRED)
+  cmake_minimum_required(VERSION 3.5)
 
-# core xgboost
-add_subdirectory(${PROJECT_SOURCE_DIR}/src)
-set(XGBOOST_OBJ_SOURCES "${XGBOOST_OBJ_SOURCES};$<TARGET_OBJECTS:objxgboost>")
+  add_definitions(-DXGBOOST_USE_CUDA)
 
-#-- Shared library
-add_library(xgboost SHARED ${XGBOOST_OBJ_SOURCES} ${PLUGINS_SOURCES})
-target_include_directories(xgboost
-  INTERFACE
-  $<INSTALL_INTERFACE:${CMAKE_INSTALL_PREFIX}/include>
-  $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/include>)
-target_link_libraries(xgboost PRIVATE ${LINKED_LIBRARIES_PRIVATE})
+  include_directories(cub)
 
-# This creates its own shared library `xgboost4j'.
-if (JVM_BINDINGS)
-  add_subdirectory(${PROJECT_SOURCE_DIR}/jvm-packages)
-endif (JVM_BINDINGS)
-#-- End shared library
+  if(USE_NCCL)
+    find_package(Nccl REQUIRED)
+    include_directories(${NCCL_INCLUDE_DIR})
+    add_definitions(-DXGBOOST_USE_NCCL)
+  endif()
 
-#-- CLI for xgboost
-add_executable(runxgboost ${PROJECT_SOURCE_DIR}/src/cli_main.cc ${XGBOOST_OBJ_SOURCES})
-# For cli_main.cc only
-if (USE_OPENMP)
-  find_package(OpenMP REQUIRED)
-  target_compile_options(runxgboost PRIVATE ${OpenMP_CXX_FLAGS})
-endif (USE_OPENMP)
-target_include_directories(runxgboost
-  PRIVATE
-  ${PROJECT_SOURCE_DIR}/include
-  ${PROJECT_SOURCE_DIR}/dmlc-core/include
-  ${PROJECT_SOURCE_DIR}/rabit/include)
-target_link_libraries(runxgboost PRIVATE ${LINKED_LIBRARIES_PRIVATE})
-set_target_properties(
-  runxgboost PROPERTIES
-  OUTPUT_NAME xgboost
-  CXX_STANDARD 11
-  CXX_STANDARD_REQUIRED ON)
-#-- End CLI for xgboost
+  set(GENCODE_FLAGS "")
+  format_gencode_flags("${GPU_COMPUTE_VER}" GENCODE_FLAGS)
+  message("cuda architecture flags: ${GENCODE_FLAGS}")
 
-set_output_directory(runxgboost ${PROJECT_SOURCE_DIR})
-set_output_directory(xgboost ${PROJECT_SOURCE_DIR}/lib)
-# Ensure these two targets do not build simultaneously, as they produce outputs with conflicting names
-add_dependencies(xgboost runxgboost)
+  set(CUDA_NVCC_FLAGS "${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()
 
-#-- Installing XGBoost
-if (R_LIB)
+  cuda_add_library(gpuxgboost ${CUDA_SOURCES} STATIC)
+
+  if(USE_NCCL)
+    link_directories(${NCCL_LIBRARY})
+    target_link_libraries(gpuxgboost ${NCCL_LIB_NAME})
+  endif()
+  list(APPEND LINK_LIBRARIES gpuxgboost)
+endif()
+
+
+# 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()
+
+add_library(objxgboost OBJECT ${SOURCES})
+
+
+# building shared library for R package
+if(R_LIB)
+  find_package(LibR REQUIRED)
+
+  list(APPEND LINK_LIBRARIES "${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
   set_target_properties(xgboost PROPERTIES PREFIX "")
-  if (APPLE)
-    set_target_properties(xgboost PROPERTIES SUFFIX ".so")
-  endif (APPLE)
+
   setup_rpackage_install_target(xgboost ${CMAKE_CURRENT_BINARY_DIR})
+  # use a dummy location for any other remaining installs
   set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_BINARY_DIR}/dummy_inst")
-endif (R_LIB)
-if (MINGW)
-  set_target_properties(xgboost PROPERTIES PREFIX "")
-endif (MINGW)
 
-if (BUILD_C_DOC)
-  include(cmake/Doc.cmake)
-  run_doxygen()
-endif (BUILD_C_DOC)
+# 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})
 
-include(GNUInstallDirs)
-# Exposing only C APIs.
-install(FILES
-  "${PROJECT_SOURCE_DIR}/include/xgboost/c_api.h"
-  DESTINATION
-  include/xgboost/)
+  # 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()
 
-install(TARGETS xgboost runxgboost
-  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)
+  #Ensure these two targets do not build simultaneously, as they produce outputs with conflicting names
+  add_dependencies(xgboost runxgboost)
+endif()
 
-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)
 
-#-- Test
-if (GOOGLE_TEST)
+# JVM
+if(JVM_BINDINGS)
+    find_package(JNI QUIET REQUIRED)
+
+    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}
+        ${JAVA_JVM_LIBRARY})
+endif()
+
+
+# Test
+if(GOOGLE_TEST)
   enable_testing()
-  # Unittests.
-  add_subdirectory(${PROJECT_SOURCE_DIR}/tests/cpp)
-  add_test(
-    NAME TestXGBoostLib
-    COMMAND testxgboost
-    WORKING_DIRECTORY ${PROJECT_BINARY_DIR})
+  find_package(GTest REQUIRED)
 
-  # CLI tests
-  configure_file(
-    ${PROJECT_SOURCE_DIR}/tests/cli/machine.conf.in
-    ${PROJECT_BINARY_DIR}/tests/cli/machine.conf
-    @ONLY)
-  add_test(
-    NAME TestXGBoostCLI
-    COMMAND runxgboost ${PROJECT_BINARY_DIR}/tests/cli/machine.conf
-    WORKING_DIRECTORY ${PROJECT_BINARY_DIR})
-  set_tests_properties(TestXGBoostCLI
-    PROPERTIES
-    PASS_REGULAR_EXPRESSION ".*test-rmse:0.087.*")
-endif (GOOGLE_TEST)
+  file(GLOB_RECURSE TEST_SOURCES "tests/cpp/*.cc")
+  auto_source_group("${TEST_SOURCES}")
+  include_directories(${GTEST_INCLUDE_DIRS})
 
-# 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()
+  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()
+
+  add_executable(testxgboost ${TEST_SOURCES} ${CUDA_TEST_OBJS} $<TARGET_OBJECTS:objxgboost>)
+  set_output_directory(testxgboost ${PROJECT_SOURCE_DIR})
+  target_link_libraries(testxgboost ${GTEST_LIBRARIES} ${LINK_LIBRARIES})
+
+  add_test(TestXGBoost testxgboost)
+endif()
+
+
+# Group sources
+auto_source_group("${SOURCES}")

CONTRIBUTORS.md

@@ -6,30 +6,21 @@ 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.
+  - Tianqi is a PhD 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.
+  - 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).
+  - 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.
+  - Micheal is a lawyer, data scientist in France, he is the creator of xgboost interactive analysis module in R.
+* [Yuan Tang](https://github.com/terrytangyuan)
+  - Yuan is a data scientist in Chicago, US. He contributed mostly in R and Python packages.
+* [Nan Zhu](https://github.com/CodingCat)
+  - Nan is a software engineer in Microsoft. He contributed mostly in JVM packages.
+* [Sergei Lebedev](https://github.com/superbobry)
+  - Serget is a software engineer in Criteo. He contributed mostly in JVM packages.
 
 Become a Committer
 ------------------
@@ -45,25 +36,28 @@ List of Contributors
 * [Full List of Contributors](https://github.com/dmlc/xgboost/graphs/contributors)
   - To contributors: please add your name to the list when you submit a patch to the project:)
 * [Kailong Chen](https://github.com/kalenhaha)
-  - Kailong is an early contributor of XGBoost, he is creator of ranking objectives in XGBoost.
+  - Kailong is an early contributor of xgboost, he is creator of ranking objectives in xgboost.
 * [Skipper Seabold](https://github.com/jseabold)
-  - Skipper is the major contributor to the scikit-learn module of XGBoost.
+  - Skipper is the major contributor to the scikit-learn module of xgboost.
 * [Zygmunt Zając](https://github.com/zygmuntz)
   - Zygmunt is the master behind the early stopping feature frequently used by kagglers.
 * [Ajinkya Kale](https://github.com/ajkl)
 * [Boliang Chen](https://github.com/cblsjtu)
 * [Yangqing Men](https://github.com/yanqingmen)
-  - Yangqing is the creator of XGBoost java package.
+  - Yangqing is the creator of xgboost java package.
 * [Engpeng Yao](https://github.com/yepyao)
 * [Giulio](https://github.com/giuliohome)
-  - Giulio is the creator of Windows project of XGBoost
+  - Giulio is the creator of windows project of xgboost
 * [Jamie Hall](https://github.com/nerdcha)
-  - Jamie is the initial creator of XGBoost scikit-learn module.
+  - Jamie is the initial creator of xgboost sklearn module.
 * [Yen-Ying Lee](https://github.com/white1033)
 * [Masaaki Horikoshi](https://github.com/sinhrks)
-  - Masaaki is the initial creator of XGBoost Python plotting module.
+  - Masaaki is the initial creator of xgboost python plotting module.
+* [Hongliang Liu](https://github.com/phunterlau)
+* [Hyunsu Cho](http://hyunsu-cho.io/)
+  - Hyunsu is the maintainer of the XGBoost Python package. He is in charge of submitting the Python package to Python Package Index (PyPI). He is also the initial author of the CPU 'hist' updater.
 * [daiyl0320](https://github.com/daiyl0320)
-  - daiyl0320 contributed patch to XGBoost distributed version more robust, and scales stably on TB scale datasets.
+  - daiyl0320 contributed patch to xgboost distributed version more robust, and scales stably on TB scale datasets.
 * [Huayi Zhang](https://github.com/irachex)
 * [Johan Manders](https://github.com/johanmanders)
 * [yoori](https://github.com/yoori)
@@ -74,6 +68,8 @@ List of Contributors
 * [Alex Bain](https://github.com/convexquad)
 * [Baltazar Bieniek](https://github.com/bbieniek)
 * [Adam Pocock](https://github.com/Craigacp)
+* [Rory Mitchell](https://github.com/RAMitchell)
+  - Rory is the author of the GPU plugin and also contributed the cmake build system and windows continuous integration
 * [Gideon Whitehead](https://github.com/gaw89)
 * [Yi-Lin Juang](https://github.com/frankyjuang)
 * [Andrew Hannigan](https://github.com/andrewhannigan)
@@ -82,10 +78,3 @@ List of Contributors
 * [Pierre de Sahb](https://github.com/pdesahb)
 * [liuliang01](https://github.com/liuliang01)
   - liuliang01 added support for the qid column for LibSVM input format. This makes ranking task easier in distributed setting.
-* [Andrew Thia](https://github.com/BlueTea88)
-  - Andrew Thia implemented feature interaction constraints
-* [Wei Tian](https://github.com/weitian)
-* [Chen Qin](https://github.com/chenqin)
-* [Sam Wilkinson](https://samwilkinson.io)
-* [Matthew Jones](https://github.com/mt-jones)
-* [Jiaxiang Li](https://github.com/JiaxiangBU)

Jenkinsfile

@@ -3,343 +3,86 @@
 // Jenkins pipeline
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
+import groovy.transform.Field
+
+/* Unrestricted tasks: tasks that do NOT generate artifacts */
+
 // Command to run command inside a docker container
-dockerRun = 'tests/ci_build/ci_build.sh'
+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" ],
+    [ "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
+    // Each stage specify its own agent
+    agent none
 
-  environment {
-    DOCKER_CACHE_REPO = '492475357299.dkr.ecr.us-west-2.amazonaws.com'
-  }
+    // Setup common job properties
+    options {
+        ansiColor('xterm')
+        timestamps()
+        timeout(time: 120, unit: 'MINUTES')
+        buildDiscarder(logRotator(numToKeepStr: '10'))
+    }
 
-  // Setup common job properties
-  options {
-    ansiColor('xterm')
-    timestamps()
-    timeout(time: 120, unit: 'MINUTES')
-    buildDiscarder(logRotator(numToKeepStr: '10'))
-    preserveStashes()
-  }
-
-  // Build stages
-  stages {
-    stage('Jenkins Linux: Get sources') {
-      agent { label 'linux && cpu' }
-      steps {
-        script {
-          checkoutSrcs()
+    // 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
+            }
         }
-        stash name: 'srcs'
-        milestone ordinal: 1
-      }
-    }
-    stage('Jenkins Linux: Formatting Check') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'clang-tidy': { ClangTidy() },
-            'lint': { Lint() },
-            'sphinx-doc': { SphinxDoc() },
-            'doxygen': { Doxygen() }
-          ])
+        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)
+                    })
+                }
+            }
         }
-        milestone ordinal: 2
-      }
     }
-    stage('Jenkins Linux: Build') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'build-cpu': { BuildCPU() },
-            'build-gpu-cuda8.0': { BuildCUDA(cuda_version: '8.0') },
-            'build-gpu-cuda9.0': { BuildCUDA(cuda_version: '9.0') },
-            'build-gpu-cuda10.0': { BuildCUDA(cuda_version: '10.0') },
-            'build-gpu-cuda10.1': { BuildCUDA(cuda_version: '10.1') },
-            'build-jvm-packages': { BuildJVMPackages(spark_version: '2.4.3') },
-            'build-jvm-doc': { BuildJVMDoc() }
-          ])
-        }
-        milestone ordinal: 3
-      }
+}
+
+/**
+ * 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"]
     }
-    stage('Jenkins Linux: Test') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'test-python-cpu': { TestPythonCPU() },
-            'test-python-gpu-cuda8.0': { TestPythonGPU(cuda_version: '8.0') },
-            'test-python-gpu-cuda9.0': { TestPythonGPU(cuda_version: '9.0') },
-            'test-python-gpu-cuda10.0': { TestPythonGPU(cuda_version: '10.0') },
-            'test-python-gpu-cuda10.1': { TestPythonGPU(cuda_version: '10.1') },
-            'test-python-mgpu-cuda10.1': { TestPythonGPU(cuda_version: '10.1', multi_gpu: true) },
-            'test-cpp-gpu': { TestCppGPU(cuda_version: '10.1') },
-            'test-cpp-mgpu': { TestCppGPU(cuda_version: '10.1', multi_gpu: true) },
-            'test-jvm-jdk8': { CrossTestJVMwithJDK(jdk_version: '8', spark_version: '2.4.3') },
-            'test-jvm-jdk11': { CrossTestJVMwithJDK(jdk_version: '11') },
-            'test-jvm-jdk12': { CrossTestJVMwithJDK(jdk_version: '12') },
-            'test-r-3.4.4': { TestR(use_r35: false) },
-            'test-r-3.5.3': { TestR(use_r35: true) }
-          ])
-        }
-        milestone ordinal: 4
-      }
+    // Build node - this is returned result
+    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} ${dockerArgs} tests/ci_build/test_${dockerTarget}.sh
+        """
     }
-  }
-}
-
-// 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 ClangTidy() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running clang-tidy job..."
-    def container_type = "clang_tidy"
-    def docker_binary = "docker"
-    def dockerArgs = "--build-arg CUDA_VERSION=9.2"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} ${dockerArgs} tests/ci_build/clang_tidy.sh
-    """
-    deleteDir()
-  }
-}
-
-def Lint() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running lint..."
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} make lint
-    """
-    deleteDir()
-  }
-}
-
-def SphinxDoc() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running sphinx-doc..."
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='-e SPHINX_GIT_BRANCH=${BRANCH_NAME}'"
-    sh """#!/bin/bash
-    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} make -C doc html
-    """
-    deleteDir()
-  }
-}
-
-def Doxygen() {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Running doxygen..."
-    def container_type = "cpu"
-    def docker_binary = "docker"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} tests/ci_build/doxygen.sh ${BRANCH_NAME}
-    """
-    archiveArtifacts artifacts: "build/${BRANCH_NAME}.tar.bz2", allowEmptyArchive: true
-    echo 'Uploading doc...'
-    s3Upload file: "build/${BRANCH_NAME}.tar.bz2", bucket: 'xgboost-docs', acl: 'PublicRead', path: "doxygen/${BRANCH_NAME}.tar.bz2"
-    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} tests/ci_build/build_via_cmake.sh
-    ${dockerRun} ${container_type} ${docker_binary} build/testxgboost
-    """
-    // Sanitizer test
-    def docker_extra_params = "CI_DOCKER_EXTRA_PARAMS_INIT='-e ASAN_SYMBOLIZER_PATH=/usr/bin/llvm-symbolizer -e ASAN_OPTIONS=symbolize=1 --cap-add SYS_PTRACE'"
-    def docker_args = "--build-arg CMAKE_VERSION=3.12"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_via_cmake.sh -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address" \
-      -DCMAKE_BUILD_TYPE=Debug -DSANITIZER_PATH=/usr/lib/x86_64-linux-gnu/
-    ${docker_extra_params} ${dockerRun} ${container_type} ${docker_binary} build/testxgboost
-    """
-    deleteDir()
-  }
-}
-
-def BuildCUDA(args) {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Build with CUDA ${args.cuda_version}"
-    def container_type = "gpu_build"
-    def docker_binary = "docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
-    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
-    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} bash -c "cd python-package && rm -rf dist/* && python setup.py bdist_wheel --universal"
-    """
-    // Stash wheel for CUDA 8.0 / 9.0 target
-    if (args.cuda_version == '8.0') {
-      echo 'Stashing Python wheel...'
-      stash name: 'xgboost_whl_cuda8', includes: 'python-package/dist/*.whl'
-    } else if (args.cuda_version == '9.0') {
-      echo 'Stashing Python wheel...'
-      stash name: 'xgboost_whl_cuda9', includes: 'python-package/dist/*.whl'
-      archiveArtifacts artifacts: "python-package/dist/*.whl", allowEmptyArchive: true
-      echo 'Stashing C++ test executable (testxgboost)...'
-      stash name: 'xgboost_cpp_tests', includes: 'build/testxgboost'
-    }
-    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 ${BRANCH_NAME}
-    """
-    archiveArtifacts artifacts: "jvm-packages/${BRANCH_NAME}.tar.bz2", allowEmptyArchive: true
-    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_cuda9'
-    unstash name: 'srcs'
-    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) {
-  nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
-  node(nodeReq) {
-    if (args.cuda_version == '8.0') {
-      unstash name: 'xgboost_whl_cuda8'
-    } else {
-      unstash name: 'xgboost_whl_cuda9'
-    }
-    unstash name: 'srcs'
-    echo "Test Python GPU: CUDA ${args.cuda_version}"
-    def container_type = "gpu"
-    def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
-    if (args.multi_gpu) {
-      echo "Using multiple GPUs"
-      sh """
-      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_python.sh mgpu
-      """
-    } else {
-      echo "Using a single GPU"
-      sh """
-      ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_python.sh gpu
-      """
-    }
-    deleteDir()
-  }
-}
-
-def TestCppGPU(args) {
-  nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
-  node(nodeReq) {
-    unstash name: 'xgboost_cpp_tests'
-    unstash name: 'srcs'
-    echo "Test C++, CUDA ${args.cuda_version}"
-    def container_type = "gpu"
-    def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
-    if (args.multi_gpu) {
-      echo "Using multiple GPUs"
-      sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost --gtest_filter=*.MGPU_*"
-    } else {
-      echo "Using a single GPU"
-      sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost --gtest_filter=-*.MGPU_*"
-    }
-    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} ${docker_args} tests/ci_build/test_jvm_cross.sh
-    """
-    deleteDir()
-  }
-}
-
-def TestR(args) {
-  node('linux && cpu') {
-    unstash name: 'srcs'
-    echo "Test R package"
-    def container_type = "rproject"
-    def docker_binary = "docker"
-    def use_r35_flag = (args.use_r35) ? "1" : "0"
-    def docker_args = "--build-arg USE_R35=${use_r35_flag}"
-    sh """
-    ${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_test_rpkg.sh
-    """
-    deleteDir()
-  }
 }

Jenkinsfile-restricted

@@ -0,0 +1,121 @@
+#!/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
+
+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()
+                }
+                stash name: 'srcs', excludes: '.git/'
+                milestone label: 'Sources ready', ordinal: 1
+            }
+        }
+        stage('Jenkins: Build doc') {
+            agent {
+                label 'linux && cpu && restricted'
+            }
+            steps {
+                unstash name: 'srcs'
+                script {
+                    def commit_id = "${GIT_COMMIT}"
+                    def branch_name = "${GIT_LOCAL_BRANCH}"
+                    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
+    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"
+        # Test the wheel for compatibility on a barebones CPU container
+        ${dockerRun} release ${dockerArgs} bash -c " \
+            auditwheel show xgboost-*-py2-none-any.whl
+            pip install --user python-package/dist/xgboost-*-none-any.whl && \
+            python -m nose tests/python"
+        """
+        archiveArtifacts artifacts: "${distDir}/**/*.*", allowEmptyArchive: true
+    }
+}

Jenkinsfile-win64

@@ -1,134 +0,0 @@
-#!/usr/bin/groovy
-// -*- mode: groovy -*-
-
-/* Jenkins pipeline for Windows AMD64 target */
-
-pipeline {
-  agent none
-  // Build stages
-  stages {
-    stage('Jenkins Win64: Get sources') {
-      agent { label 'win64 && build' }
-      steps {
-        script {
-          checkoutSrcs()
-        }
-        stash name: 'srcs'
-        milestone ordinal: 1
-      }
-    }
-    stage('Jenkins Win64: Build') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'build-win64-cuda9.0': { BuildWin64() }
-          ])
-        }
-        milestone ordinal: 2
-      }
-    }
-    stage('Jenkins Win64: Test') {
-      agent none
-      steps {
-        script {
-          parallel ([
-            'test-win64-cpu': { TestWin64CPU() },
-            'test-win64-gpu-cuda9.0': { TestWin64GPU(cuda_target: 'cuda9') },
-            'test-win64-gpu-cuda10.0': { TestWin64GPU(cuda_target: 'cuda10_0') },
-            'test-win64-gpu-cuda10.1': { TestWin64GPU(cuda_target: 'cuda10_1') }
-          ])
-        }
-        milestone ordinal: 3
-      }
-    }
-  }
-}
-
-// 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 && build') {
-    unstash name: 'srcs'
-    echo "Building XGBoost for Windows AMD64 target..."
-    bat "nvcc --version"
-    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
-    """
-    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
-    """
-    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'
-    archiveArtifacts artifacts: "python-package/dist/*.whl", allowEmptyArchive: true
-    echo 'Stashing C++ test executable (testxgboost)...'
-    stash name: 'xgboost_cpp_tests', includes: 'build/testxgboost.exe'
-    deleteDir()
-  }
-}
-
-def TestWin64CPU() {
-  node('win64 && cpu') {
-    unstash name: 'srcs'
-    unstash name: 'xgboost_whl'
-    echo "Test Win64 CPU"
-    echo "Installing Python wheel..."
-    bat "conda activate && (python -m pip uninstall -y xgboost || cd .)"
-    bat """
-    conda activate && for /R %%i in (python-package\\dist\\*.whl) DO python -m pip install "%%i"
-    """
-    echo "Running Python tests..."
-    bat "conda activate && python -m pytest -v -s --fulltrace tests\\python"
-    bat "conda activate && python -m pip uninstall -y xgboost"
-    deleteDir()
-  }
-}
-
-def TestWin64GPU(args) {
-  node("win64 && gpu && ${args.cuda_target}") {
-    unstash name: 'srcs'
-    unstash name: 'xgboost_whl'
-    unstash name: 'xgboost_cpp_tests'
-    echo "Test Win64 GPU (${args.cuda_target})"
-    bat "nvcc --version"
-    echo "Running C++ tests..."
-    bat "build\\testxgboost.exe"
-    echo "Installing Python wheel..."
-    bat "conda activate && (python -m pip uninstall -y xgboost || cd .)"
-    bat """
-    conda activate && for /R %%i in (python-package\\dist\\*.whl) DO python -m pip install "%%i"
-    """
-    echo "Running Python tests..."
-    bat """
-    conda activate && python -m pytest -v -s --fulltrace -m "(not slow) and (not mgpu)" tests\\python-gpu
-    """
-    bat "conda activate && python -m pip uninstall -y xgboost"
-    deleteDir()
-  }
-}

LICENSE

@@ -1,201 +1,13 @@
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+Copyright (c) 2016 by Contributors
 
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Makefile

@@ -173,14 +173,10 @@ xgboost: $(CLI_OBJ) $(ALL_DEP)
 	$(CXX) $(CFLAGS) -o $@  $(filter %.o %.a, $^)  $(LDFLAGS)
 
 rcpplint:
-	python3 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
+	python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
 
 lint: rcpplint
-	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 plugin python-package
+	python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} include src plugin python-package
 
 pylint:
 	flake8 --ignore E501 python-package
@@ -264,8 +260,7 @@ Rpack: clean_all
 	cp ./LICENSE xgboost
 	cat R-package/src/Makevars.in|sed '2s/.*/PKGROOT=./' | sed '3s/.*/ENABLE_STD_THREAD=0/' > xgboost/src/Makevars.in
 	cp xgboost/src/Makevars.in 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/@OPENMP_CXXFLAGS@/$$\(SHLIB_OPENMP_CFLAGS\)/g' xgboost/src/Makevars.win
 	bash R-package/remove_warning_suppression_pragma.sh
 	rm xgboost/remove_warning_suppression_pragma.sh
 

NEWS.md

@@ -3,467 +3,6 @@ XGBoost Change Log
 
 This file records the changes in xgboost library in reverse chronological order.
 
-## 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).
-* [Tutorial](https://xgboost.readthedocs.io/en/release_0.81/tutorials/feature_interaction_constraint.html) is available, as well as [R](https://github.com/dmlc/xgboost/blob/9254c58e4dfff6a59dc0829a2ceb02e45ed17cd0/R-package/demo/interaction_constraints.R) and [Python](https://github.com/dmlc/xgboost/blob/9254c58e4dfff6a59dc0829a2ceb02e45ed17cd0/tests/python/test_interaction_constraints.py) examples.
-
-### New feature: learning to rank using scikit-learn interface
-* Learning to rank task is now available for the scikit-learn interface of the Python package (#3560, #3848). It is now possible to integrate the XGBoost ranking model into the scikit-learn learning pipeline.
-* Examples of using `XGBRanker` class is found at [demo/rank/rank_sklearn.py](https://github.com/dmlc/xgboost/blob/24a268a2e3cb17302db3d72da8f04016b7d352d9/demo/rank/rank_sklearn.py).
-
-### New feature: R interface for SHAP interactions
-* SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. Previously, this feature was only available from the Python package; now it is available from the R package as well (#3636).
-
-### New feature: GPU predictor now use multiple GPUs to predict
-* GPU predictor is now able to utilize multiple GPUs at once to accelerate prediction (#3738)
-
-### New feature: Scale distributed XGBoost to large-scale clusters
-* Fix OS file descriptor limit assertion error on large cluster (#3835, dmlc/rabit#73) by replacing `select()` based AllReduce/Broadcast with `poll()` based implementation.
-* Mitigate tracker "thundering herd" issue on large cluster. Add exponential backoff retry when workers connect to tracker.
-* With this change, we were able to scale to 1.5k executors on a 12 billion row dataset after some tweaks here and there.
-
-### New feature: Additional objective functions for GPUs
-* New objective functions ported to GPU: `hinge`, `multi:softmax`, `multi:softprob`, `count:poisson`, `reg:gamma`, `"reg:tweedie`.
-* With supported objectives, XGBoost will select the correct devices based on your system and `n_gpus` parameter.
-
-### Major bug fix: learning to rank with XGBoost4J-Spark
-* Previously, `repartitionForData` would shuffle data and lose ordering necessary for ranking task.
-* To fix this issue, data points within each RDD partition is explicitly group by their group (query session) IDs (#3654). Also handle empty RDD partition carefully (#3750).
-
-### Major bug fix: early stopping fixed in XGBoost4J-Spark
-* Earlier implementation of early stopping had incorrect semantics and didn't let users to specify direction for optimizing (maximize / minimize)
-* A parameter `maximize_evaluation_metrics` is defined so as to tell whether a metric should be maximized or minimized as part of early stopping criteria (#3808). Also early stopping now has correct semantics.
-
-### API changes
-* Column sampling by level (`colsample_bylevel`) is now functional for `hist` algorithm (#3635, #3862)
-* GPU tag `gpu:` for regression objectives are now deprecated. XGBoost will select the correct devices automatically (#3643)
-* Add `disable_default_eval_metric` parameter to disable default metric (#3606)
-* Experimental AVX support for gradient computation is removed (#3752)
-* XGBoost4J-Spark
-  - Add `rank:ndcg` and `rank:map` to supported objectives (#3697)
-* Python package
-  - Add `callbacks` argument to `fit()` function of sciki-learn API (#3682)
-  - Add `XGBRanker` to scikit-learn interface (#3560, #3848)
-  - Add `validate_features` argument to `predict()` function of scikit-learn API (#3653)
-  - Allow scikit-learn grid search over parameters specified as keyword arguments (#3791)
-  - Add `coef_` and `intercept_` as properties of scikit-learn wrapper (#3855). Some scikit-learn functions expect these properties.
-
-### Performance improvements
-* Address very high GPU memory usage for large data (#3635)
-* Fix performance regression within `EvaluateSplits()` of `gpu_hist` algorithm. (#3680)
-
-### Bug-fixes
-* Fix a problem in GPU quantile sketch with tiny instance weights. (#3628)
-* Fix copy constructor for `HostDeviceVectorImpl` to prevent dangling pointers (#3657)
-* Fix a bug in partitioned file loading (#3673)
-* Fixed an uninitialized pointer in `gpu_hist` (#3703)
-* Reshared data among GPUs when number of GPUs is changed (#3721)
-* Add back `max_delta_step` to split evaluation (#3668)
-* Do not round up integer thresholds for integer features in JSON dump (#3717)
-* Use `dmlc::TemporaryDirectory` to handle temporaries in cross-platform way (#3783)
-* Fix accuracy problem with `gpu_hist` when `min_child_weight` and `lambda` are set to 0 (#3793)
-* Make sure that `tree_method` parameter is recognized and not silently ignored (#3849)
-* XGBoost4J-Spark
-  - Make sure `thresholds` are considered when executing `predict()` method (#3577)
-  - Avoid losing precision when computing probabilities by converting to `Double` early (#3576)
-  - `getTreeLimit()` should return `Int` (#3602)
-  - Fix checkpoint serialization on HDFS (#3614)
-  - Throw `ControlThrowable` instead of `InterruptedException` so that it is properly re-thrown (#3632)
-  - Remove extraneous output to stdout (#3665)
-  - Allow specification of task type for custom objectives and evaluations (#3646)
-  - Fix distributed updater check (#3739)
-  - Fix issue when spark job execution thread cannot return before we execute `first()` (#3758)
-* Python package
-  - Fix accessing `DMatrix.handle` before it is set (#3599)
-  - `XGBClassifier.predict()` should return margin scores when `output_margin` is set to true (#3651)
-  - Early stopping callback should maximize metric of form `NDCG@n-` (#3685)
-  - Preserve feature names when slicing `DMatrix` (#3766)
-* R package
-  - Replace `nround` with `nrounds` to match actual parameter (#3592)
-  - Amend `xgb.createFolds` to handle classes of a single element (#3630)
-  - Fix buggy random generator and make `colsample_bytree` functional (#3781)
-
-### Maintenance: testing, continuous integration, build system
-* Add sanitizers tests to Travis CI (#3557)
-* Add NumPy, Matplotlib, Graphviz as requirements for doc build (#3669)
-* Comply with CRAN submission policy (#3660, #3728)
-* Remove copy-paste error in JVM test suite (#3692)
-* Disable flaky tests in `R-package/tests/testthat/test_update.R` (#3723)
-* Make Python tests compatible with scikit-learn 0.20 release (#3731)
-* Separate out restricted and unrestricted tasks, so that pull requests don't build downloadable artifacts (#3736)
-* Add multi-GPU unit test environment (#3741)
-* Allow plug-ins to be built by CMake (#3752)
-* Test wheel compatibility on CPU containers for pull requests (#3762)
-* Fix broken doc build due to Matplotlib 3.0 release (#3764)
-* Produce `xgboost.so` for XGBoost-R on Mac OSX, so that `make install` works (#3767)
-* Retry Jenkins CI tests up to 3 times to improve reliability (#3769, #3769, #3775, #3776, #3777)
-* Add basic unit tests for `gpu_hist` algorithm (#3785)
-* Fix Python environment for distributed unit tests (#3806)
-* Test wheels on CUDA 10.0 container for compatibility (#3838)
-* Fix JVM doc build (#3853)
-
-### Maintenance: Refactor C++ code for legibility and maintainability
-* Merge generic device helper functions into `GPUSet` class (#3626)
-* Re-factor column sampling logic into `ColumnSampler` class (#3635, #3637)
-* Replace `std::vector` with `HostDeviceVector` in `MetaInfo` and `SparsePage` (#3446)
-* Simplify `DMatrix` class (#3395)
-* De-duplicate CPU/GPU code using `Transform` class (#3643, #3751)
-* Remove obsoleted `QuantileHistMaker` class (#3761)
-* Remove obsoleted `NoConstraint` class (#3792)
-
-### Other Features
-* C++20-compliant Span class for safe pointer indexing (#3548, #3588)
-* Add helper functions to manipulate multiple GPU devices (#3693)
-* XGBoost4J-Spark
-  - Allow specifying host ip from the `xgboost-tracker.properties file` (#3833). This comes in handy when `hosts` files doesn't correctly define localhost.
-
-### Usability Improvements
-* Add reference to GitHub repository in `pom.xml` of JVM packages (#3589)
-* Add R demo of multi-class classification (#3695)
-* Document JSON dump functionality (#3600, #3603)
-* Document CUDA requirement and lack of external memory for GPU algorithms (#3624)
-* Document LambdaMART objectives, both pairwise and listwise (#3672)
-* Document `aucpr` evaluation metric (#3687)
-* Document gblinear parameters: `feature_selector` and `top_k` (#3780)
-* Add instructions for using MinGW-built XGBoost with Python. (#3774)
-* Removed nonexistent parameter `use_buffer` from documentation (#3610)
-* Update Python API doc to include all classes and members (#3619, #3682)
-* Fix typos and broken links in documentation (#3618, #3640, #3676, #3713, #3759, #3784, #3843, #3852)
-* Binary classification demo should produce LIBSVM with 0-based indexing (#3652)
-* Process data once for Python and CLI examples of learning to rank (#3666)
-* Include full text of Apache 2.0 license in the repository (#3698)
-* Save predictor parameters in model file (#3856)
-* JVM packages
-  - Let users specify feature names when calling `getModelDump` and `getFeatureScore` (#3733)
-  - Warn the user about the lack of over-the-wire encryption (#3667)
-  - Fix errors in examples (#3719)
-  - Document choice of trackers (#3831)
-  - Document that vanilla Apache Spark is required (#3854)
-* Python package
-  - Document that custom objective can't contain colon (:) (#3601)
-  - Show a better error message for failed library loading (#3690)
-  - Document that feature importance is unavailable for non-tree learners (#3765)
-  - Document behavior of `get_fscore()` for zero-importance features (#3763)
-  - Recommend pickling as the way to save `XGBClassifier` / `XGBRegressor` / `XGBRanker` (#3829)
-* R package
-  - Enlarge variable importance plot to make it more visible (#3820)
-
-### BREAKING CHANGES
-* External memory page files have changed, breaking backwards compatibility for temporary storage used during external memory training. This only affects external memory users upgrading their xgboost version - we recommend clearing all `*.page` files before resuming training. Model serialization is unaffected.
-
-### Known issues
-* Quantile sketcher fails to produce any quantile for some edge cases (#2943)
-* The `hist` algorithm leaks memory when used with learning rate decay callback (#3579)
-* Using custom evaluation funciton together with early stopping causes assertion failure in XGBoost4J-Spark (#3595)
-* Early stopping doesn't work with `gblinear` learner (#3789)
-* Label and weight vectors are not reshared upon the change in number of GPUs (#3794). To get around this issue, delete the `DMatrix` object and re-load.
-* The `DMatrix` Python objects are initialized with incorrect values when given array slices (#3841)
-* The `gpu_id` parameter is broken and not yet properly supported (#3850)
-
-### Acknowledgement
-**Contributors** (in no particular order): Hyunsu Cho (@hcho3), Jiaming Yuan (@trivialfis), Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), Andy Adinets (@canonizer), Vadim Khotilovich (@khotilov), Sergei Lebedev (@superbobry)
-
-**First-time Contributors** (in no particular order): Matthew Tovbin (@tovbinm), Jakob Richter (@jakob-r), Grace Lam (@grace-lam), Grant W Schneider (@grantschneider), Andrew Thia (@BlueTea88), Sergei Chipiga (@schipiga), Joseph Bradley (@jkbradley), Chen Qin (@chenqin), Jerry Lin (@linjer), Dmitriy Rybalko (@rdtft), Michael Mui (@mmui), Takahiro Kojima (@515hikaru), Bruce Zhao (@BruceZhaoR), Wei Tian (@weitian), Saumya Bhatnagar (@Sam1301), Juzer Shakir (@JuzerShakir), Zhao Hang (@cleghom), Jonathan Friedman (@jontonsoup), Bruno Tremblay (@meztez), Boris Filippov (@frenzykryger), @Shiki-H, @mrgutkun, @gorogm, @htgeis, @jakehoare, @zengxy, @KOLANICH
-
-**First-time Reviewers** (in no particular order): Nikita Titov (@StrikerRUS), Xiangrui Meng (@mengxr), Nirmal Borah (@Nirmal-Neel)
-
-
 ## v0.80 (2018.08.13)
 * **JVM packages received a major upgrade**: To consolidate the APIs and improve the user experience, we refactored the design of XGBoost4J-Spark in a significant manner. (#3387)
   - Consolidated APIs: It is now much easier to integrate XGBoost models into a Spark ML pipeline. Users can control behaviors like output leaf prediction results by setting corresponding column names. Training is now more consistent with other Estimators in Spark MLLIB: there is now one single method `fit()` to train decision trees.
@@ -474,7 +13,7 @@ This release is packed with many new features and bug fixes.
   - 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
-* Support for per-group weights in ranking objective (#3379)
+* Ranking task now uses instance weights (#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

@@ -1,34 +0,0 @@
-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})
-
-set(R_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_)
-target_compile_definitions(xgboost-r
-  PRIVATE ${R_DEFINITIONS})
-target_include_directories(xgboost-r
-  PRIVATE
-  ${LIBR_INCLUDE_DIRS}
-  ${PROJECT_SOURCE_DIR}/include
-  ${PROJECT_SOURCE_DIR}/dmlc-core/include
-  ${PROJECT_SOURCE_DIR}/rabit/include)
-set_target_properties(
-  xgboost-r PROPERTIES
-  CXX_STANDARD 11
-  CXX_STANDARD_REQUIRED ON
-  POSITION_INDEPENDENT_CODE ON)
-
-set(XGBOOST_DEFINITIONS ${R_DEFINITIONS} PARENT_SCOPE)
-set(XGBOOST_OBJ_SOURCES $<TARGET_OBJECTS:xgboost-r> PARENT_SCOPE)
-set(LINKED_LIBRARIES_PRIVATE ${LINKED_LIBRARIES_PRIVATE} ${LIBR_CORE_LIBRARY} PARENT_SCOPE)

R-package/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
-Version: 0.82.0.1
-Date: 2019-03-11
+Version: 0.80.1
+Date: 2018-08-13
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),
@@ -61,5 +61,5 @@ Imports:
     data.table (>= 1.9.6),
     magrittr (>= 1.5),
     stringi (>= 0.5.2)
-RoxygenNote: 6.1.0
+RoxygenNote: 6.0.1
 SystemRequirements: GNU make, C++11

R-package/R/callbacks.R

@@ -14,7 +14,7 @@
 #' 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 environments.
+#' 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" 
 #' book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
@@ -154,7 +154,7 @@ cb.evaluation.log <- function() {
   callback
 }
 
-#' Callback closure for resetting the booster's parameters at each iteration.
+#' Callback closure for restetting 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} 
@@ -470,7 +470,7 @@ cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
 #' 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.
+#' meaningful when user-profided 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}.
 #' 
@@ -681,7 +681,7 @@ 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
-#'        coefficients are returned. Has no effect in non-multiclass models.
+#'        coeffients are returned. Has no effect in non-multiclass models.
 #'
 #' @return 
 #' For an \code{xgb.train} result, a matrix (either dense or sparse) with the columns

R-package/R/utils.R

@@ -74,19 +74,6 @@ check.booster.params <- function(params, ...) {
         params[['monotone_constraints']] = vec2str
   }
   
-  # interaction constraints parser (convert from list of column indices to string)
-  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'))) {
-      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=','), ']')
-  }
   return(params)
 }
 
@@ -209,14 +196,13 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
     if (exists('objective', where = params) &&
         is.character(params$objective)) {
       # If 'objective' provided in params, assume that y is a classification label
-      # unless objective is reg:squarederror
-      if (params$objective != 'reg:squarederror')
+      # unless objective is reg:linear
+      if (params$objective != 'reg:linear')
         y <- factor(y)
     } else {
-      # If no 'objective' given in params, it means that user either wants to
-      # use the default 'reg:squarederror' objective or has provided a custom
-      # obj function.  Here, assume classification setting when y has 5 or less
-      # unique values:
+      # If no 'objective' given in params, it means that user either wants to use
+      # the default 'reg:linear' objective or has provided a custom obj function.
+      # Here, assume classification setting when y has 5 or less unique values:
       if (length(unique(y)) <= 5)
         y <- factor(y)
     }
@@ -276,8 +262,7 @@ xgb.createFolds <- function(y, k = 10)
       ## add enough random integers to get  length(seqVector) == numInClass[i]
       if (numInClass[i] %% k > 0) seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
       ## shuffle the integers for fold assignment and assign to this classes's data
-      ## seqVector[sample.int(length(seqVector))] is used to handle length(seqVector) == 1
-      foldVector[y == dimnames(numInClass)$y[i]] <- seqVector[sample.int(length(seqVector))]
+      foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)
     }
   } else {
     foldVector <- seq(along = y)

R-package/R/xgb.Booster.R

@@ -81,7 +81,7 @@ 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 explicitly once after loading a model as an R-object.
+#' \code{xgb.Booster.complete} function explicitely once after loading a model as an R-object.
 #' That would prevent further repeated implicit reconstruction of an internal booster model.
 #'
 #' @return
@@ -129,13 +129,11 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'        logistic regression would result in predictions for log-odds instead of probabilities.
 #' @param ntreelimit limit the number of model's trees or boosting iterations used in prediction (see Details).
 #'        It will use all the trees by default (\code{NULL} value).
-#' @param predleaf whether predict leaf index.
-#' @param predcontrib whether to return feature contributions to individual predictions (see Details).
+#' @param predleaf whether predict leaf index instead.
+#' @param predcontrib whether to return feature contributions to individual predictions instead (see Details).
 #' @param approxcontrib whether to use a fast approximation for feature contributions (see Details).
-#' @param predinteraction whether to return contributions of feature interactions to individual predictions (see Details).
 #' @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.
+#'        prediction outputs per case. This option has no effect when \code{predleaf = TRUE}.
 #' @param ... Parameters passed to \code{predict.xgb.Booster}
 #'
 #' @details
@@ -160,11 +158,6 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' Setting \code{approxcontrib = TRUE} approximates these values following the idea explained
 #' 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 perform selection
-#' of the most important features first. See below about the format of the returned results.
-#'
 #' @return
 #' For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
 #' For multiclass classification, either a \code{num_class * nrows(newdata)} vector or
@@ -180,14 +173,6 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' such a matrix. The contribution values are on the scale of untransformed margin
 #' (e.g., for binary classification would mean that the contributions are log-odds deviations from bias).
 #'
-#' When \code{predinteraction = TRUE} and it is not a multiclass setting, the output is a 3d array with
-#' dimensions \code{c(nrow, num_features + 1, num_features + 1)}. The off-diagonal (in the last two dimensions)
-#' elements represent different features interaction contributions. The array is symmetric WRT the last
-#' two dimensions. The "+ 1" columns corresponds to bias. Summing this array along the last dimension should
-#' produce practically the same result as predict with \code{predcontrib = TRUE}.
-#' For a multiclass case, a list of \code{num_class} elements is returned, where each element is
-#' such an array.
-#'
 #' @seealso
 #' \code{\link{xgb.train}}.
 #' 
@@ -284,8 +269,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' @rdname predict.xgb.Booster
 #' @export
 predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
-                                predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
-                                reshape = FALSE, ...) {
+                                predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, reshape = FALSE, ...) {
 
   object <- xgb.Booster.complete(object, saveraw = FALSE)
   if (!inherits(newdata, "xgb.DMatrix"))
@@ -301,8 +285,7 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   if (ntreelimit < 0)
     stop("ntreelimit cannot be negative")
 
-  option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) +
-    8L * as.logical(approxcontrib) + 16L * as.logical(predinteraction)
+  option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) + 8L * as.logical(approxcontrib)
 
   ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1], as.integer(ntreelimit))
 
@@ -322,28 +305,17 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   } else if (predcontrib) {
     n_col1 <- ncol(newdata) + 1
     n_group <- npred_per_case / n_col1
-    cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
+    dnames <- if (!is.null(colnames(newdata))) list(NULL, c(colnames(newdata), "BIAS")) else NULL
     ret <- if (n_ret == n_row) {
-      matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
+      matrix(ret, ncol = 1, dimnames = dnames)
     } else if (n_group == 1) {
-      matrix(ret, nrow = n_row, byrow = TRUE, dimnames = list(NULL, cnames))
+      matrix(ret, nrow = n_row, byrow = TRUE, dimnames = dnames)
     } 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,,])
-    }
-  } else if (predinteraction) {
-    n_col1 <- ncol(newdata) + 1
-    n_group <- npred_per_case / n_col1^2
-    cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
-    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))
-    } 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,,,])
+      grp_mask <- rep(seq_len(n_col1), n_row) +
+        rep((seq_len(n_row) - 1) * n_col1 * n_group, each = n_col1)
+      lapply(seq_len(n_group), function(g) {
+        matrix(ret[grp_mask + n_col1 * (g - 1)], nrow = n_row, byrow = TRUE, dimnames = dnames)
+      })
     }
   } else if (reshape && npred_per_case > 1) {
     ret <- matrix(ret, nrow = n_row, byrow = TRUE)

R-package/R/xgb.DMatrix.R

@@ -104,7 +104,7 @@ 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 returned row names would be NULL.
+#' row names would have no effect and returnten 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
@@ -266,10 +266,10 @@ setinfo.xgb.DMatrix <- function(object, name, info, ...) {
 
 
 #' Get a new DMatrix containing the specified rows of
-#' original xgb.DMatrix object
+#' orginal xgb.DMatrix object
 #'
 #' Get a new DMatrix containing the specified rows of
-#' original xgb.DMatrix object
+#' orginal xgb.DMatrix object
 #' 
 #' @param object Object of class "xgb.DMatrix"
 #' @param idxset a integer vector of indices of rows needed
@@ -301,17 +301,12 @@ slice.xgb.DMatrix <- function(object, idxset, ...) {
 
   attr_list <- attributes(object)
   nr <- nrow(object)
-  len <- sapply(attr_list, NROW)
+  len <- sapply(attr_list, length)
   ind <- which(len == nr)
   if (length(ind) > 0) {
     nms <- names(attr_list)[ind]
     for (i in seq_along(ind)) {
-      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]
-      }
+      attr(ret, nms[i]) <- attr(object, nms[i])[idxset]
     }
   }
   return(structure(ret, class = "xgb.DMatrix"))

R-package/R/xgb.cv.R

@@ -6,7 +6,7 @@
 #' \itemize{
 #'   \item \code{objective} objective function, common ones are
 #'   \itemize{
-#'     \item \code{reg:squarederror} Regression with squared loss
+#'     \item \code{reg:linear} linear regression
 #'     \item \code{binary:logistic} logistic regression for classification
 #'   }
 #'   \item \code{eta} step size of each boosting step
@@ -39,7 +39,7 @@
 #' }
 #' @param obj customized objective function. Returns gradient and second order 
 #'        gradient with given prediction and dtrain.
-#' @param feval customized evaluation function. Returns
+#' @param feval custimized 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 
@@ -84,7 +84,7 @@
 #'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
 #'   \item \code{callbacks} callback functions that were either automatically assigned or 
 #'         explicitly passed.
-#'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
+#'   \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 
 #'         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.

R-package/R/xgb.ggplot.R

@@ -22,7 +22,7 @@ xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measur
 
   plot <-
     ggplot2::ggplot(importance_matrix, 
-                    ggplot2::aes(x = factor(Feature, levels = rev(Feature)), y = Importance, width = 0.5),
+                    ggplot2::aes(x = factor(Feature, levels = rev(Feature)), y = Importance, width = 0.05),
                     environment = environment()) + 
     ggplot2::geom_bar(ggplot2::aes(fill = Cluster), stat = "identity", position = "identity") + 
     ggplot2::coord_flip() + 

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

@@ -27,7 +27,7 @@
 #' a tree's median absolute leaf weight changes through the iterations.
 #'
 #' This function was inspired by the blog post
-#' \url{https://github.com/aysent/random-forest-leaf-visualization}.
+#' \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}.
 #' 
 #' @return
 #' 

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

@@ -30,8 +30,8 @@
 #' 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 clustering of the importance values,
-#' with bar colors corresponding to different clusters that have somewhat similar importance values.
+#' 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.
 #' 
 #' @return
 #' The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})

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

@@ -31,7 +31,7 @@
 #' @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} parameter in \code{\link[stats]{loess}}'s call.
+#' @param span_loess the \code{span} paramerer 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}.

R-package/R/xgb.train.R

@@ -26,7 +26,6 @@
 #'   \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
@@ -42,7 +41,7 @@
 #' \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:squarederror} Regression with squared loss (Default).
+#'     \item \code{reg:linear} linear regression (Default).
 #'     \item \code{reg:logistic} logistic regression.
 #'     \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.
@@ -68,7 +67,7 @@
 #'        the performance of each round's model on mat1 and mat2.
 #' @param obj customized objective function. Returns gradient and second order 
 #'        gradient with given prediction and dtrain.
-#' @param feval customized evaluation function. Returns
+#' @param feval custimized 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.
@@ -118,7 +117,7 @@
 #' when the \code{eval_metric} parameter is not provided.
 #' 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 following is the list of built-in metrics for which Xgboost provides optimized implementation:
+#' The folloiwing 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}
@@ -147,14 +146,14 @@
 #'   \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 stored as a \code{data.table} with the
+#'   \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 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 
 #'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
 #'   \item \code{callbacks} callback functions that were either automatically assigned or 
-#'         explicitly passed.
+#'         explicitely 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, 
@@ -163,7 +162,7 @@
 #'   \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 column names were defined in training data).
+#'         (only when comun names were defined in training data).
 #'   \item \code{nfeatures} number of features in training data.
 #' }
 #' 
@@ -186,7 +185,7 @@
 #' watchlist <- list(train = dtrain, eval = dtest)
 #' 
 #' ## A simple xgb.train example:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
+#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2, 
 #'               objective = "binary:logistic", eval_metric = "auc")
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
 #' 
@@ -207,12 +206,12 @@
 #' 
 #' # 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, verbose = 0, nthread = 2,
+#' param <- list(max_depth = 2, eta = 1, silent = 1, 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, verbose = 0, nthread = 2)
+#' param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 #'                  objective = logregobj, eval_metric = evalerror)
 #' 
@@ -222,7 +221,7 @@
 #' 
 #' 
 #' ## An xgb.train example of using variable learning rates at each iteration:
-#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
+#' param <- list(max_depth = 2, eta = 1, silent = 1, 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,

R-package/README.md

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

R-package/configure

@@ -1667,12 +1667,12 @@ OPENMP_CXXFLAGS=""
 
 if test `uname -s` = "Linux"
 then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)"
+  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CFLAGS)"
 fi
 
 if test `uname -s` = "Darwin"
 then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)"
+  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CFLAGS)"
   ac_pkg_openmp=no
   { $as_echo "$as_me:${as_lineno-$LINENO}: checking whether OpenMP will work in a package" >&5
 $as_echo_n "checking whether OpenMP will work in a package... " >&6; }

R-package/configure.ac

@@ -8,12 +8,12 @@ OPENMP_CXXFLAGS=""
 
 if test `uname -s` = "Linux"
 then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)"
+  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CFLAGS)"
 fi
 
 if test `uname -s` = "Darwin"
 then
-  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)"
+  OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CFLAGS)"
   ac_pkg_openmp=no
   AC_MSG_CHECKING([whether OpenMP will work in a package])
   AC_LANG_CONFTEST(

R-package/demo/00Index

@@ -11,5 +11,4 @@ early_stopping                  Early Stop in training
 poisson_regression              Poisson Regression on count data
 tweedie_regression              Tweddie Regression
 gpu_accelerated                 GPU-accelerated tree building algorithms
-interaction_constraints         Interaction constraints among features
 

R-package/demo/custom_objective.R

@@ -33,7 +33,7 @@ evalerror <- function(preds, dtrain) {
   return(list(metric = "error", value = err))
 }
 
-param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0, 
+param <- list(max_depth=2, eta=1, nthread = 2, silent=1, 
               objective=logregobj, eval_metric=evalerror)
 print ('start training with user customized objective')
 # training with customized objective, we can also do step by step training
@@ -57,7 +57,7 @@ logregobjattr <- function(preds, dtrain) {
   hess <- preds * (1 - preds)
   return(list(grad = grad, hess = hess))
 }
-param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0, 
+param <- list(max_depth=2, eta=1, nthread = 2, silent=1, 
               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

R-package/demo/early_stopping.R

@@ -7,7 +7,7 @@ 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, verbosity=0)
+param <- list(max_depth=2, eta=1, nthread = 2, silent=1)
 watchlist <- list(eval = dtest)
 num_round <- 20
 # user define objective function, given prediction, return gradient and second order gradient

R-package/demo/interaction_constraints.R

@@ -1,105 +0,0 @@
-library(xgboost)
-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
-  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)]
-
-    setorderv(trees, 'ID_merge')
-    setorderv(parents_left, 'ID_merge')
-    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_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]
-  }
-
-  # 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_list <- lapply(interaction_trees_split, as.character)
-
-  # Remove NAs (no parent interaction)
-  interaction_list <- lapply(interaction_list, function(x) x[!is.na(x)])
-
-  # Remove non-interactions (same variable)
-  interaction_list <- lapply(interaction_list, unique)  # remove same variables
-  interaction_length <- sapply(interaction_list, length)
-  interaction_list <- interaction_list[interaction_length > 1]
-  interaction_list <- unique(lapply(interaction_list, sort))
-  return(interaction_list)
-}
-
-# Generate sample data
-x <- list()
-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']])
-
-train = as.matrix(x)
-
-# Interaction constraint list (column names form)
-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")
-}
-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_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
-
-# Fit model without interaction constraints
-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_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
-
-# 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')])
-  testdata[['V1']] <- x1[i]
-  testdata <- testdata[, paste0('V',1:10), with=F]
-  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 
-}

R-package/demo/predict_leaf_indices.R

@@ -38,7 +38,6 @@ create.new.tree.features <- function(model, original.features){
 # 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)

R-package/man/callbacks.Rd

@@ -18,7 +18,7 @@ the boosting is completed.
 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 environments.
+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" 
 book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -

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

@@ -15,7 +15,7 @@ depending on the number of prediction outputs per data row. The order of predict
 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.
+meaningful when user-profided 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.reset.parameters.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/callbacks.R
 \name{cb.reset.parameters}
 \alias{cb.reset.parameters}
-\title{Callback closure for resetting the booster's parameters at each iteration.}
+\title{Callback closure for restetting the booster's parameters at each iteration.}
 \usage{
 cb.reset.parameters(new_params)
 }
@@ -15,7 +15,7 @@ which returns a new parameter value by using the current iteration number
 and the total number of boosting rounds.}
 }
 \description{
-Callback closure for resetting the booster's parameters at each iteration.
+Callback closure for restetting the booster's parameters at each iteration.
 }
 \details{
 This is a "pre-iteration" callback function used to reset booster's parameters

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

@@ -17,7 +17,7 @@ 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 returned row names would be NULL.
+row names would have no effect and returnten row names would be NULL.
 }
 \details{
 Generic \code{dimnames} methods are used by \code{colnames}.

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

@@ -7,8 +7,7 @@
 \usage{
 \method{predict}{xgb.Booster}(object, newdata, missing = NA,
   outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE,
-  predcontrib = FALSE, approxcontrib = FALSE,
-  predinteraction = FALSE, reshape = FALSE, ...)
+  predcontrib = FALSE, approxcontrib = FALSE, reshape = FALSE, ...)
 
 \method{predict}{xgb.Booster.handle}(object, ...)
 }
@@ -27,17 +26,14 @@ logistic regression would result in predictions for log-odds instead of probabil
 \item{ntreelimit}{limit the number of model's trees or boosting iterations used in prediction (see Details).
 It will use all the trees by default (\code{NULL} value).}
 
-\item{predleaf}{whether predict leaf index.}
+\item{predleaf}{whether predict leaf index instead.}
 
-\item{predcontrib}{whether to return feature contributions to individual predictions (see Details).}
+\item{predcontrib}{whether to return feature contributions to individual predictions instead (see Details).}
 
 \item{approxcontrib}{whether to use a fast approximation for feature contributions (see Details).}
 
-\item{predinteraction}{whether to return contributions of feature interactions to individual predictions (see Details).}
-
 \item{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.}
+prediction outputs per case. This option has no effect when \code{predleaf = TRUE}.}
 
 \item{...}{Parameters passed to \code{predict.xgb.Booster}}
 }
@@ -55,14 +51,6 @@ When \code{predcontrib = TRUE} and it is not a multiclass setting, the output is
 For a multiclass case, a list of \code{num_class} elements is returned, where each element is
 such a matrix. The contribution values are on the scale of untransformed margin
 (e.g., for binary classification would mean that the contributions are log-odds deviations from bias).
-
-When \code{predinteraction = TRUE} and it is not a multiclass setting, the output is a 3d array with
-dimensions \code{c(nrow, num_features + 1, num_features + 1)}. The off-diagonal (in the last two dimensions)
-elements represent different features interaction contributions. The array is symmetric WRT the last
-two dimensions. The "+ 1" columns corresponds to bias. Summing this array along the last dimension should
-produce practically the same result as predict with \code{predcontrib = TRUE}.
-For a multiclass case, a list of \code{num_class} elements is returned, where each element is
-such an array.
 }
 \description{
 Predicted values based on either xgboost model or model handle object.
@@ -88,11 +76,6 @@ values (Lundberg 2017) that sum to the difference between the expected output
 of the model and the current prediction (where the hessian weights are used to compute the expectations).
 Setting \code{approxcontrib = TRUE} approximates these values following the idea explained
 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 perform selection
-of the most important features first. See below about the format of the returned results.
 }
 \examples{
 ## binary classification:

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
-original xgb.DMatrix object}
+orginal xgb.DMatrix object}
 \usage{
 slice(object, ...)
 
@@ -24,7 +24,7 @@ slice(object, ...)
 }
 \description{
 Get a new DMatrix containing the specified rows of
-original xgb.DMatrix object
+orginal 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 explicitly once after loading a model as an R-object.
+\code{xgb.Booster.complete} function explicitely once after loading a model as an R-object.
 That would prevent further repeated implicit reconstruction of an internal booster model.
 }
 \examples{

R-package/man/xgb.cv.Rd

@@ -4,19 +4,18 @@
 \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, 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:
 \itemize{
   \item \code{objective} objective function, common ones are
   \itemize{
-    \item \code{reg:squarederror} Regression with squared loss
+    \item \code{reg:linear} linear regression
     \item \code{binary:logistic} logistic regression for classification
   }
   \item \code{eta} step size of each boosting step
@@ -59,7 +58,7 @@ from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callb
 \item{obj}{customized objective function. Returns gradient and second order 
 gradient with given prediction and dtrain.}
 
-\item{feval}{customized evaluation function. Returns
+\item{feval}{custimized evaluation function. Returns 
 \code{list(metric='metric-name', value='metric-value')} with given 
 prediction and dtrain.}
 
@@ -101,7 +100,7 @@ An object of class \code{xgb.cv.synchronous} with the following elements:
         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
   \item \code{callbacks} callback functions that were either automatically assigned or 
         explicitly passed.
-  \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
+  \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 
         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.

R-package/man/xgb.dump.Rd

@@ -44,8 +44,8 @@ test <- agaricus.test
 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)
+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))

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
-coefficients are returned. Has no effect in non-multiclass models.}
+coeffients 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.plot.deepness.Rd

@@ -5,11 +5,11 @@
 \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
@@ -50,7 +50,7 @@ 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{https://github.com/aysent/random-forest-leaf-visualization}.
+\url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}.
 }
 \examples{
 

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

@@ -9,8 +9,8 @@ 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, ...)
+  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}}.}
@@ -59,8 +59,8 @@ For linear models, \code{rel_to_first = FALSE} would show actual values of the c
 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 clustering of the importance values,
-with bar colors corresponding to different clusters that have somewhat similar importance values.
+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.
 }
 \examples{
 data(agaricus.train)

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

@@ -6,8 +6,8 @@
 \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,
+  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, ...)
@@ -63,7 +63,7 @@ more than 5 distinct values.}
 
 \item{col_loess}{a color to use for the loess curves.}
 
-\item{span_loess}{the \code{span} parameter in \code{\link[stats]{loess}}'s call.}
+\item{span_loess}{the \code{span} paramerer in \code{\link[stats]{loess}}'s call.}
 
 \item{which}{whether to do univariate or bivariate plotting. NOTE: only 1D is implemented so far.}
 

R-package/man/xgb.train.Rd

@@ -5,17 +5,15 @@
 \alias{xgboost}
 \title{eXtreme Gradient Boosting Training}
 \usage{
-xgb.train(params = list(), data, nrounds, watchlist = list(),
-  obj = NULL, feval = NULL, verbose = 1, print_every_n = 1L,
+xgb.train(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", xgb_model = NULL, callbacks = list(),
-  ...)
+  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ...)
 
 xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
   params = list(), nrounds, verbose = 1, print_every_n = 1L,
   early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
-  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(),
-  ...)
+  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ...)
 }
 \arguments{
 \item{params}{the list of parameters. 
@@ -41,7 +39,6 @@ xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
   \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
@@ -57,7 +54,7 @@ xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
 \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:squarederror} Regression with squared loss (Default).
+    \item \code{reg:linear} linear regression (Default).
     \item \code{reg:logistic} logistic regression.
     \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.
@@ -87,7 +84,7 @@ the performance of each round's model on mat1 and mat2.}
 \item{obj}{customized objective function. Returns gradient and second order 
 gradient with given prediction and dtrain.}
 
-\item{feval}{customized evaluation function. Returns
+\item{feval}{custimized evaluation function. Returns 
 \code{list(metric='metric-name', value='metric-value')} with given 
 prediction and dtrain.}
 
@@ -141,14 +138,14 @@ An object of class \code{xgb.Booster} with the following elements:
   \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 stored as a \code{data.table} with the
+  \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 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 
         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
   \item \code{callbacks} callback functions that were either automatically assigned or 
-        explicitly passed.
+        explicitely 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, 
@@ -157,7 +154,7 @@ An object of class \code{xgb.Booster} with the following elements:
   \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 column names were defined in training data).
+        (only when comun names were defined in training data).
   \item \code{nfeatures} number of features in training data.
 }
 }
@@ -179,7 +176,7 @@ The evaluation metric is chosen automatically by Xgboost (according to the objec
 when the \code{eval_metric} parameter is not provided.
 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 following is the list of built-in metrics for which Xgboost provides optimized implementation:
+The folloiwing 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}
@@ -211,7 +208,7 @@ 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, verbose = 0, nthread = 2,
+param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2, 
               objective = "binary:logistic", eval_metric = "auc")
 bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
 
@@ -232,12 +229,12 @@ evalerror <- function(preds, dtrain) {
 
 # 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, verbose = 0, nthread = 2,
+param <- list(max_depth = 2, eta = 1, silent = 1, 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, verbose = 0, nthread = 2)
+param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
 bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
                  objective = logregobj, eval_metric = evalerror)
 
@@ -247,7 +244,7 @@ bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 
 
 ## An xgb.train example of using variable learning rates at each iteration:
-param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
+param <- list(max_depth = 2, eta = 1, silent = 1, 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,

R-package/src/Makevars.in

@@ -17,8 +17,8 @@ endif
 $(foreach v, $(XGB_RFLAGS), $(warning $(v)))
 
 PKG_CPPFLAGS=  -I$(PKGROOT)/include -I$(PKGROOT)/dmlc-core/include -I$(PKGROOT)/rabit/include -I$(PKGROOT) $(XGB_RFLAGS)
-PKG_CXXFLAGS= @OPENMP_CXXFLAGS@ -pthread
-PKG_LIBS = @OPENMP_CXXFLAGS@ -pthread
+PKG_CXXFLAGS= @OPENMP_CXXFLAGS@ $(SHLIB_PTHREAD_FLAGS)
+PKG_LIBS = @OPENMP_CXXFLAGS@ $(SHLIB_PTHREAD_FLAGS)
 OBJECTS= ./xgboost_R.o ./xgboost_custom.o ./xgboost_assert.o ./init.o\
          $(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o\
          $(PKGROOT)/rabit/src/engine_empty.o $(PKGROOT)/rabit/src/c_api.o

R-package/src/Makevars.win

@@ -29,8 +29,8 @@ endif
 $(foreach v, $(XGB_RFLAGS), $(warning $(v)))
 
 PKG_CPPFLAGS=  -I$(PKGROOT)/include -I$(PKGROOT)/dmlc-core/include -I$(PKGROOT)/rabit/include -I$(PKGROOT) $(XGB_RFLAGS)
-PKG_CXXFLAGS= $(SHLIB_OPENMP_CXXFLAGS) $(SHLIB_PTHREAD_FLAGS)
-PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(SHLIB_PTHREAD_FLAGS)
+PKG_CXXFLAGS= $(SHLIB_OPENMP_CFLAGS) $(SHLIB_PTHREAD_FLAGS)
+PKG_LIBS = $(SHLIB_OPENMP_CFLAGS) $(SHLIB_PTHREAD_FLAGS)
 OBJECTS= ./xgboost_R.o ./xgboost_custom.o ./xgboost_assert.o ./init.o\
          $(PKGROOT)/amalgamation/xgboost-all0.o $(PKGROOT)/amalgamation/dmlc-minimum0.o\
          $(PKGROOT)/rabit/src/engine_empty.o $(PKGROOT)/rabit/src/c_api.o

R-package/src/init.c

@@ -70,7 +70,7 @@ static const R_CallMethodDef CallEntries[] = {
 
 #if defined(_WIN32)
 __declspec(dllexport)
-#endif  // defined(_WIN32)
+#endif
 void R_init_xgboost(DllInfo *dll) {
   R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
   R_useDynamicSymbols(dll, FALSE);

R-package/src/xgboost_R.h

@@ -1,6 +1,6 @@
 /*!
  * Copyright 2014 (c) by Contributors
- * \file xgboost_R.h
+ * \file xgboost_wrapper_R.h
  * \author Tianqi Chen
  * \brief R wrapper of xgboost
  */

R-package/src/xgboost_custom.cc

@@ -3,7 +3,7 @@
 // to change behavior of libxgboost
 
 #include <xgboost/logging.h>
-#include "../../src/common/random.h"
+#include "src/common/random.h"
 #include "./xgboost_R.h"
 
 // redirect the messages to R's console.
@@ -32,10 +32,7 @@ extern "C" {
 
 namespace xgboost {
 ConsoleLogger::~ConsoleLogger() {
-  if (cur_verbosity_ == LogVerbosity::kIgnore ||
-      cur_verbosity_ <= global_verbosity_) {
-    dmlc::CustomLogMessage::Log(log_stream_.str());
-  }
+  dmlc::CustomLogMessage::Log(log_stream_.str());
 }
 TrackerLogger::~TrackerLogger() {
   dmlc::CustomLogMessage::Log(log_stream_.str());
@@ -49,11 +46,10 @@ namespace common {
 bool CheckNAN(double v) {
   return ISNAN(v);
 }
-#if !defined(XGBOOST_USE_CUDA)
 double LogGamma(double v) {
   return lgammafn(v);
 }
-#endif  // !defined(XGBOOST_USE_CUDA)
+
 // customize random engine.
 void CustomGlobalRandomEngine::seed(CustomGlobalRandomEngine::result_type val) {
   // ignore the seed

R-package/tests/testthat/test_basic.R

@@ -182,7 +182,7 @@ test_that("xgb.cv works", {
   expect_is(cv, 'xgb.cv.synchronous')
   expect_false(is.null(cv$evaluation_log))
   expect_lt(cv$evaluation_log[, min(test_error_mean)], 0.03)
-  expect_lt(cv$evaluation_log[, min(test_error_std)], 0.008)
+  expect_lt(cv$evaluation_log[, min(test_error_std)], 0.004)
   expect_equal(cv$niter, 2)
   expect_false(is.null(cv$folds) && is.list(cv$folds))
   expect_length(cv$folds, 5)
@@ -223,42 +223,3 @@ test_that("train and predict with non-strict classes", {
   expect_error(pr <- predict(bst, train_dense), regexp = NA)
   expect_equal(pr0, pr)
 })
-
-test_that("max_delta_step works", {
-  dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-  watchlist <- list(train = dtrain)
-  param <- list(objective = "binary:logistic", eval_metric="logloss", max_depth = 2, nthread = 2, eta = 0.5)
-  nrounds = 5
-  # model with no restriction on max_delta_step
-  bst1 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1)
-  # model with restricted max_delta_step
-  bst2 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1, max_delta_step = 1)
-  # the no-restriction model is expected to have consistently lower loss during the initial interations
-  expect_true(all(bst1$evaluation_log$train_logloss < bst2$evaluation_log$train_logloss))
-  expect_lt(mean(bst1$evaluation_log$train_logloss)/mean(bst2$evaluation_log$train_logloss), 0.8)
-})
-
-test_that("colsample_bytree works", {
-  # Randomly generate data matrix by sampling from uniform distribution [-1, 1]
-  set.seed(1)
-  train_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
-  train_y <- as.numeric(rowSums(train_x) > 0)
-  test_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
-  test_y <- as.numeric(rowSums(test_x) > 0)
-  colnames(train_x) <- paste0("Feature_", sprintf("%03d", 1:100))
-  colnames(test_x) <- paste0("Feature_", sprintf("%03d", 1:100))
-   dtrain <- xgb.DMatrix(train_x, label = train_y)
-  dtest <- xgb.DMatrix(test_x, label = test_y)
-  watchlist <- list(train = dtrain, eval = dtest)
-   # Use colsample_bytree = 0.01, so that roughly one out of 100 features is
-  # chosen for each tree
-  param <- list(max_depth = 2, eta = 0, silent = 1, nthread = 2,
-                colsample_bytree = 0.01, objective = "binary:logistic",
-                eval_metric = "auc")
-   set.seed(2)
-  bst <- xgb.train(param, dtrain, nrounds = 100, watchlist, verbose = 0)
-  xgb.importance(model = bst)
-  # If colsample_bytree works properly, a variety of features should be used
-  # in the 100 trees
-  expect_gte(nrow(xgb.importance(model = bst)), 30)
-})

R-package/tests/testthat/test_callbacks.R

@@ -282,7 +282,7 @@ test_that("prediction in xgb.cv works for gblinear too", {
 })
 
 test_that("prediction in early-stopping xgb.cv works", {
-  set.seed(11)
+  set.seed(1)
   expect_output(
     cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.1, nrounds = 20,
                  early_stopping_rounds = 5, maximize = FALSE, prediction = TRUE)

R-package/tests/testthat/test_custom_objective.R

@@ -31,7 +31,7 @@ num_round <- 2
 test_that("custom objective works", {
   bst <- xgb.train(param, dtrain, num_round, watchlist)
   expect_equal(class(bst), "xgb.Booster")
-  expect_equal(length(bst$raw), 1100)
+  expect_equal(length(bst$raw), 1094)
   expect_false(is.null(bst$evaluation_log))
   expect_false(is.null(bst$evaluation_log$eval_error))
   expect_lt(bst$evaluation_log[num_round, eval_error], 0.03)
@@ -58,5 +58,5 @@ test_that("custom objective using DMatrix attr works", {
   param$objective = logregobjattr
   bst <- xgb.train(param, dtrain, num_round, watchlist)
   expect_equal(class(bst), "xgb.Booster")
-  expect_equal(length(bst$raw), 1100)
+  expect_equal(length(bst$raw), 1094)
 })

R-package/tests/testthat/test_interaction_constraints.R

@@ -1,38 +0,0 @@
-require(xgboost)
-
-context("interaction constraints")
-
-set.seed(1024)
-x1 <- rnorm(1000, 1)
-x2 <- rnorm(1000, 1)
-x3 <- sample(c(1,2,3), size=1000, replace=TRUE)
-y <- x1 + x2 + x3 + x1*x2*x3 + rnorm(1000, 0.001) + 3*sin(x1)
-train <- matrix(c(x1,x2,x3), ncol = 3)
-
-test_that("interaction constraints for regression", {
-  # Fit a model that only allows interaction between x1 and x2
-  bst <- xgboost(data = train, label = y, max_depth = 3,
-                 eta = 0.1, nthread = 2, nrounds = 100, verbose = 0,
-                 interaction_constraints = list(c(0,1)))
-  
-  # Set all observations to have the same x3 values then increment
-  #  by the same amount
-	preds <- lapply(c(1,2,3), function(x){
-		tmat <- matrix(c(x1,x2,rep(x,1000)), ncol=3)
-		return(predict(bst, tmat))
-	})
-
-  # Check incrementing x3 has the same effect on all observations
-  #   since x3 is constrained to be independent of x1 and x2
-  #   and all observations start off from the same x3 value
-	diff1 <- preds[[2]] - preds[[1]]
-	test1 <- all(abs(diff1 - diff1[1]) < 1e-4)
-	
-	diff2 <- preds[[3]] - preds[[2]]
-	test2 <- all(abs(diff2 - diff2[1]) < 1e-4)
-	
-  expect_true({
-    test1 & test2
-  }, "Interaction Contraint Satisfied")
-  
-})

R-package/tests/testthat/test_interactions.R

@@ -1,141 +0,0 @@
-context('Test prediction of feature interactions')
-
-require(xgboost)
-require(magrittr)
-
-set.seed(123)
-
-test_that("predict feature interactions works", {
-  # simulate some binary data and a linear outcome with an interaction term
-  N <- 1000
-  P <- 5
-  X <- matrix(rbinom(N * P, 1, 0.5), ncol=P, dimnames = list(NULL, letters[1:P]))
-  # center the data (as contributions are computed WRT feature means)
-  X <- scale(X, scale=FALSE)
-
-  # outcome without any interactions, without any noise:
-  f <- function(x) 2 * x[, 1] - 3 * x[, 2]
-  # outcome with interactions, without noise:
-  f_int <- function(x) f(x) + 2 * x[, 2] * x[, 3]
-  # outcome with interactions, with noise:
-  #f_int_noise <- function(x) f_int(x) + rnorm(N, 0, 0.3)
-
-  y <- f_int(X)
-
-  dm <- xgb.DMatrix(X, label = y)
-  param <- list(eta=0.1, max_depth=4, base_score=mean(y), lambda=0, nthread=2)
-  b <- xgb.train(param, dm, 100)
-  
-  pred = predict(b, dm, outputmargin=TRUE)
-
-  # SHAP contributions:
-  cont <- predict(b, dm, predcontrib=TRUE)
-  expect_equal(dim(cont), c(N, P+1))
-  # make sure for each row they add up to marginal predictions
-  max(abs(rowSums(cont) - pred)) %>% expect_lt(0.001)
-  # Hand-construct the 'ground truth' feature contributions:
-  gt_cont <- cbind(
-      2. * X[, 1],
-     -3. * X[, 2] + 1. * X[, 2] * X[, 3], # attribute a HALF of the interaction term to feature #2
-      1. * X[, 2] * X[, 3]               # and another HALF of the interaction term to feature #3
-     )
-  gt_cont <- cbind(gt_cont, matrix(0, nrow=N, ncol=P + 1 - 3))
-  # These should be relatively close:
-  expect_lt(max(abs(cont - gt_cont)), 0.05)
-
-
-  # SHAP interaction contributions:
-  intr <- predict(b, dm, predinteraction=TRUE)
-  expect_equal(dim(intr), c(N, P+1, P+1))
-  # check assigned colnames
-  cn <- c(letters[1:P], "BIAS")
-  expect_equal(dimnames(intr), list(NULL, cn, cn))
-
-  # check the symmetry
-  max(abs(aperm(intr, c(1,3,2)) - intr)) %>% expect_lt(0.00001)
-
-  # sums WRT columns must be close to feature contributions
-  max(abs(apply(intr, c(1,2), sum) - cont)) %>% expect_lt(0.00001)
-
-  # diagonal terms for features 3,4,5 must be close to zero
-  Reduce(max, sapply(3:P, function(i) max(abs(intr[, i, i])))) %>% expect_lt(0.05)
-
-  # BIAS must have no interactions
-  max(abs(intr[, 1:P, P+1])) %>% expect_lt(0.00001)
-
-  # interactions other than 2 x 3 must be close to zero
-  intr23 <- intr
-  intr23[,2,3] <- 0
-  Reduce(max, sapply(1:P, function(i) max(abs(intr23[, i, (i+1):(P+1)])))) %>% expect_lt(0.05)
-
-  # Construct the 'ground truth' contributions of interactions directly from the linear terms:
-  gt_intr <- array(0, c(N, P+1, P+1))
-  gt_intr[,2,3] <- 1. * X[, 2] * X[, 3] # attribute a HALF of the interaction term to each symmetric element
-  gt_intr[,3,2] <- gt_intr[, 2, 3]
-  # merge-in the diagonal based on 'ground truth' feature contributions
-  intr_diag = gt_cont - apply(gt_intr, c(1,2), sum)
-  for(j in seq_len(P)) {
-    gt_intr[,j,j] = intr_diag[,j]
-  }
-  # These should be relatively close:
-  expect_lt(max(abs(intr - gt_intr)), 0.1)
-})
-
-test_that("SHAP contribution values are not NAN", {
-  d <- data.frame(
-    x1 = c(-2.3, 1.4, 5.9, 2, 2.5, 0.3, -3.6, -0.2, 0.5, -2.8, -4.6, 3.3, -1.2,
-           -1.1, -2.3, 0.4, -1.5, -0.2, -1, 3.7),
-    x2 = c(291.179171, 269.198331, 289.942097, 283.191669, 269.673332,
-           294.158346, 287.255835, 291.530838, 285.899586, 269.290833,
-           268.649586, 291.530841, 280.074593, 269.484168, 293.94042,
-           294.327506, 296.20709, 295.441669, 283.16792, 270.227085),
-    y = c(9, 15, 5.7, 9.2, 22.4, 5, 9, 3.2, 7.2, 13.1, 7.8, 16.9, 6.5, 22.1,
-          5.3, 10.4, 11.1, 13.9, 11, 20.5),
-    fold = c(2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2))
-
-  ivs <- c("x1", "x2")
-
-  fit <- xgboost(
-    verbose = 0,
-    params = list(
-      objective = "reg:squarederror",
-      eval_metric = "rmse"),
-    data = as.matrix(subset(d, fold == 2)[, ivs]),
-    label = subset(d, fold == 2)$y,
-    nthread = 1,
-    nrounds = 3)
-
-  shaps <- as.data.frame(predict(fit,
-    newdata = as.matrix(subset(d, fold == 1)[, ivs]),
-    predcontrib = T))
-  result <- cbind(shaps, sum = rowSums(shaps), pred = predict(fit,
-      newdata = as.matrix(subset(d, fold == 1)[, ivs])))
-
-  expect_true(identical(TRUE, all.equal(result$sum, result$pred, tol = 1e-6)))
-})
-
-
-test_that("multiclass feature interactions work", {
-  dm <- xgb.DMatrix(as.matrix(iris[,-5]), label=as.numeric(iris$Species)-1)
-  param <- list(eta=0.1, max_depth=4, objective='multi:softprob', num_class=3)
-  b <- xgb.train(param, dm, 40)
-  pred = predict(b, dm, outputmargin=TRUE) %>% array(c(3, 150)) %>% t
-
-  # SHAP contributions:
-  cont <- predict(b, dm, predcontrib=TRUE)
-  expect_length(cont, 3)
-  # rewrap them as a 3d array
-  cont <- unlist(cont) %>% array(c(150, 5, 3))
-  # make sure for each row they add up to marginal predictions
-  max(abs(apply(cont, c(1,3), sum) - pred)) %>% expect_lt(0.001)
-
-  # SHAP interaction contributions:
-  intr <- predict(b, dm, predinteraction=TRUE)
-  expect_length(intr, 3)
-  # rewrap them as a 4d array
-  intr <- unlist(intr) %>% array(c(150, 5, 5, 3)) %>% aperm(c(4, 1, 2, 3)) # [grp, row, col, col]
-  # check the symmetry
-  max(abs(aperm(intr, c(1,2,4,3)) - intr)) %>% expect_lt(0.00001)
-  # sums WRT columns must be close to feature contributions
-  max(abs(apply(intr, c(1,2,3), sum) - aperm(cont, c(3,1,2)))) %>% expect_lt(0.00001)
-})

R-package/vignettes/discoverYourData.Rmd

@@ -138,7 +138,7 @@ levels(df[,Treatment])
 
 Next step, we will transform the categorical data to dummy variables.
 Several encoding methods exist, e.g., [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) is a common approach.
-We will use the [dummy contrast coding](http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm#dummy) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
+We will use the [dummy contrast coding](http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm#dummy) which is popular because it producess "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
 
 The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.
 
@@ -268,7 +268,7 @@ c2 <- chisq.test(df$Age, output_vector)
 print(c2)
 ```
 
-Pearson correlation between Age and illness disappearing is **`r round(c2$statistic, 2 )`**.
+Pearson correlation between Age and illness disapearing is **`r round(c2$statistic, 2 )`**.
 
 ```{r, warning=FALSE, message=FALSE}
 c2 <- chisq.test(df$AgeDiscret, output_vector)

R-package/vignettes/xgboostPresentation.Rmd

@@ -313,7 +313,7 @@ Until now, all the learnings we have performed were based on boosting trees. **X
 bst <- xgb.train(data=dtrain, booster = "gblinear", max_depth=2, nthread = 2, nrounds=2, watchlist=watchlist, eval_metric = "error", eval_metric = "logloss", objective = "binary:logistic")
 ```
 
-In this specific case, *linear boosting* gets slightly better performance metrics than decision trees based algorithm.
+In this specific case, *linear boosting* gets sligtly better performance metrics than decision trees based algorithm.
 
 In simple cases, it will happen because there is nothing better than a linear algorithm to catch a linear link. However, decision trees are much better to catch a non linear link between predictors and outcome. Because there is no silver bullet, we advise you to check both algorithms with your own datasets to have an idea of what to use.
 

R-package/vignettes/xgboostfromJSON.Rmd

@@ -1,189 +0,0 @@
----
-title: "XGBoost from JSON"
-output:
-  rmarkdown::html_vignette:
-    number_sections: yes
-    toc: yes
-author: Roland Stevenson
-vignette: >
-  %\VignetteIndexEntry{XGBoost from JSON}
-  %\VignetteEngine{knitr::rmarkdown}
-  \usepackage[utf8]{inputenc}
----
-
-XGBoost from JSON
-=================
-
-## Introduction
-
-The purpose of this Vignette is to show you how to correctly load and work with an **Xgboost** model that has been dumped to JSON.  **Xgboost** internally converts all data to [32-bit floats](https://en.wikipedia.org/wiki/Single-precision_floating-point_format), and the values dumped to JSON are decimal representations of these values.  When working with a model that has been parsed from a JSON file, care must be taken to correctly treat:
-
-- the input data, which should be converted to 32-bit floats
-- any 32-bit floats that were stored in JSON as decimal representations
-- any calculations must be done with 32-bit mathematical operators
-
-## Setup
-
-For the purpose of this tutorial we will load the xgboost, jsonlite, and float packages.  We'll also set `digits=22` in our options in case we want to inspect many digits of our results.
-
-```{r}
-require(xgboost)
-require(jsonlite)
-require(float)
-options(digits=22)
-```
-
-We will create a toy binary logistic model based on the example first provided [here](https://github.com/dmlc/xgboost/issues/3960), so that we can easily understand the structure of the dumped JSON model object.  This will allow us to understand where discrepancies can occur and how they should be handled.
-
-```{r}
-dates <- c(20180130, 20180130, 20180130,
-           20180130, 20180130, 20180130,
-           20180131, 20180131, 20180131,
-           20180131, 20180131, 20180131,
-           20180131, 20180131, 20180131,
-           20180134, 20180134, 20180134)
-
-labels <- c(1, 1, 1,
-            1, 1, 1,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0)
-
-data <- data.frame(dates = dates, labels=labels)
-
-bst <- xgboost(
-  data = as.matrix(data$dates), 
-  label = labels,
-  nthread = 2,
-  nrounds = 1,
-  objective = "binary:logistic",
-  missing = NA,
-  max_depth = 1
-)
-```
-
-## Comparing results
-We will now dump the model to JSON and attempt to illustrate a variety of issues that can arise, and how to properly deal with them.
-
-First let's dump the model to JSON:
-
-```{r}
-bst_json <- xgb.dump(bst, with_stats = FALSE, dump_format='json')
-bst_from_json <- jsonlite::fromJSON(bst_json, simplifyDataFrame = FALSE)
-node <- bst_from_json[[1]]
-cat(bst_json)
-```
-
-The tree JSON shown by the above code-chunk tells us that if the data is less than 20180132, the tree will output the value in the first leaf.  Otherwise it will output the value in the second leaf.  Let's try to reproduce this manually with the data we have and confirm that it matches the model predictions we've already calculated.
-
-```{r}
-bst_preds_logodds <- predict(bst,as.matrix(data$dates), outputmargin = TRUE)
-
-# calculate the logodds values using the JSON representation
-bst_from_json_logodds <- ifelse(data$dates<node$split_condition,
-                                node$children[[1]]$leaf,
-                                node$children[[2]]$leaf)
-
-bst_preds_logodds
-bst_from_json_logodds
-
-# test that values are equal
-bst_preds_logodds == bst_from_json_logodds
-
-```
-None are equal.  What happened?
-
-At this stage two things happened:
-
-- input data was not converted to 32-bit floats
-- the JSON variables were not converted to 32-bit floats
-
-### Lesson 1: All data is 32-bit floats
-
-> When working with imported JSON, all data must be converted to 32-bit floats
-
-To explain this, let's repeat the comparison and round to two decimals:
-
-```{r}
-round(bst_preds_logodds,2) == round(bst_from_json_logodds,2)
-```
-
-If we round to two decimals, we see that only the elements related to data values of `20180131` don't agree.  If we convert the data to floats, they agree:
-
-```{r}
-# now convert the dates to floats first
-bst_from_json_logodds <- ifelse(fl(data$dates)<node$split_condition,
-                                node$children[[1]]$leaf,
-                                node$children[[2]]$leaf)
-
-# test that values are equal
-round(bst_preds_logodds,2) == round(bst_from_json_logodds,2)
-```
-
-What's the lesson?  If we are going to work with an imported JSON model, any data must be converted to floats first.  In this case, since '20180131' cannot be represented as a 32-bit float, it is rounded up to 20180132, as shown here:
-
-```{r}
-fl(20180131)
-```
-
-
-### Lesson 2: JSON parameters are 32-bit floats
-
-> All JSON parameters stored as floats must be converted to floats.
-
-Let's now say we do care about numbers past the first two decimals.
-
-```{r}
-# test that values are equal
-bst_preds_logodds == bst_from_json_logodds
-```
-
-None are exactly equal.  What happened?  Although we've converted the data to 32-bit floats, we also need to convert the JSON parameters to 32-bit floats.  Let's do this:
-
-```{r}
-# now convert the dates to floats first
-bst_from_json_logodds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                                as.numeric(fl(node$children[[1]]$leaf)),
-                                as.numeric(fl(node$children[[2]]$leaf)))
-
-# test that values are equal
-bst_preds_logodds == bst_from_json_logodds
-```
-All equal.  What's the lesson?  If we are going to work with an imported JSON model, any JSON parameters that were stored as floats must also be converted to floats first.
-
-### Lesson 3: Use 32-bit math
-
-> Always use 32-bit numbers and operators
-
-We were able to get the log-odds to agree, so now let's manually calculate the sigmoid of the log-odds.  This should agree with the xgboost predictions.
-
-
-```{r}
-bst_preds <- predict(bst,as.matrix(data$dates))
-
-# calculate the predictions casting doubles to floats
-bst_from_json_preds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                              as.numeric(1/(1+exp(-1*fl(node$children[[1]]$leaf)))),
-                              as.numeric(1/(1+exp(-1*fl(node$children[[2]]$leaf))))
-)
-
-# test that values are equal
-bst_preds == bst_from_json_preds
-```
-
-None are exactly equal again.  What is going on here?  Well, since we are using the value `1` in the calcuations, we have introduced a double into the calculation.  Because of this, all float values are promoted to 64-bit doubles and the 64-bit version of the exponentiation operator `exp` is also used.  On the other hand, xgboost uses the 32-bit version of the exponentation operator in its [sigmoid function](https://github.com/dmlc/xgboost/blob/54980b8959680a0da06a3fc0ec776e47c8cbb0a1/src/common/math.h#L25-L27).
-
-How do we fix this?  We have to ensure we use the correct datatypes everywhere and the correct operators.  If we use only floats, the float library that we have loaded will ensure the 32-bit float exponention operator is applied. 
-```{r}
-# calculate the predictions casting doubles to floats
-bst_from_json_preds <- ifelse(fl(data$dates)<fl(node$split_condition),
-                              as.numeric(fl(1)/(fl(1)+exp(fl(-1)*fl(node$children[[1]]$leaf)))),
-                              as.numeric(fl(1)/(fl(1)+exp(fl(-1)*fl(node$children[[2]]$leaf))))
-)
-
-# test that values are equal
-bst_preds == bst_from_json_preds
-```
-
-All equal.  What's the lesson?  We have to ensure that all calculations are done with 32-bit floating point operators if we want to reproduce the results that we see with xgboost.

README.md

@@ -1,7 +1,6 @@
 <img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/xgboost.png width=135/>  eXtreme Gradient Boosting
 ===========
-[![Build Status](https://xgboost-ci.net/job/xgboost/job/master/badge/icon?style=plastic)](https://xgboost-ci.net/blue/organizations/jenkins/xgboost/activity)
-[![Build Status](https://img.shields.io/travis/dmlc/xgboost.svg?label=build&logo=travis&branch=master)](https://travis-ci.org/dmlc/xgboost)
+[![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost)
 [![Build Status](https://ci.appveyor.com/api/projects/status/5ypa8vaed6kpmli8?svg=true)](https://ci.appveyor.com/project/tqchen/xgboost)
 [![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org)
 [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)
@@ -32,35 +31,3 @@ Reference
 ---------
 - Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
 - XGBoost originates from research project at University of Washington.
-
-Sponsors
---------
-Become a sponsor and get a logo here. See details at [Sponsoring the XGBoost Project](https://xgboost.ai/sponsors). The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
-
-## Open Source Collective sponsors
-[![Backers on Open Collective](https://opencollective.com/xgboost/backers/badge.svg)](#backers) [![Sponsors on Open Collective](https://opencollective.com/xgboost/sponsors/badge.svg)](#sponsors) 
-
-### Sponsors
-[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]
-
-<!--<a href="https://opencollective.com/xgboost/sponsor/0/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/0/avatar.svg"></a>-->
-<a href="https://www.nvidia.com/en-us/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/nvidia.jpg" alt="NVIDIA" width="72" height="72"></a>
-<a href="https://opencollective.com/xgboost/sponsor/1/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/1/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/2/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/2/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/3/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/3/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/4/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/4/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/5/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/5/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/6/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/6/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/7/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/7/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/8/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/8/avatar.svg"></a>
-<a href="https://opencollective.com/xgboost/sponsor/9/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/9/avatar.svg"></a>
-
-### Backers
-[[Become a backer](https://opencollective.com/xgboost#backer)]
-
-<a href="https://opencollective.com/xgboost#backers" target="_blank"><img src="https://opencollective.com/xgboost/backers.svg?width=890"></a>
-
-## Other sponsors
-The sponsors in this list are donating cloud hours in lieu of cash donation.
-
-<a href="https://aws.amazon.com/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/aws.png" alt="Amazon Web Services" width="72" height="72"></a>

amalgamation/xgboost-all0.cc

@@ -48,7 +48,7 @@
 #include "../src/tree/tree_model.cc"
 #include "../src/tree/tree_updater.cc"
 #include "../src/tree/updater_colmaker.cc"
-#include "../src/tree/updater_quantile_hist.cc"
+#include "../src/tree/updater_fast_hist.cc"
 #include "../src/tree/updater_prune.cc"
 #include "../src/tree/updater_refresh.cc"
 #include "../src/tree/updater_sync.cc"

appveyor.yml

@@ -36,32 +36,26 @@ install:
     - set PATH=C:\msys64\mingw64\bin;C:\msys64\usr\bin;%PATH%
     - gcc -v
     - ls -l C:\
-    # Miniconda3
-    - call C:\Miniconda3-x64\Scripts\activate.bat
-    - conda info
+    # Miniconda2
+    - set PATH=;C:\Miniconda-x64;C:\Miniconda-x64\Scripts;%PATH%
     - where python
     - python --version
     # do python build for mingw and one of the msvc jobs
     - set DO_PYTHON=off
     - if /i "%target%" == "mingw" set DO_PYTHON=on
     - if /i "%target%_%ver%_%configuration%" == "msvc_2015_Release" set DO_PYTHON=on
-    - if /i "%DO_PYTHON%" == "on" (
-        conda config --set always_yes true &&
-        conda update -q conda &&
-        conda install -y numpy scipy pandas matplotlib pytest scikit-learn graphviz python-graphviz
-      )
-    - set PATH=C:\Miniconda3-x64\Library\bin\graphviz;%PATH%
+    - if /i "%DO_PYTHON%" == "on" conda install -y numpy scipy pandas matplotlib nose scikit-learn graphviz python-graphviz
     # R: based on https://github.com/krlmlr/r-appveyor
     - ps: |
         if($env:target -eq 'rmingw' -or $env:target -eq 'rmsvc') {
           #$ErrorActionPreference = "Stop"
-          Invoke-WebRequest https://raw.githubusercontent.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "$Env:TEMP\appveyor-tool.ps1"
+          Invoke-WebRequest http://raw.github.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "$Env:TEMP\appveyor-tool.ps1"
           Import-Module "$Env:TEMP\appveyor-tool.ps1"
           Bootstrap
-          $BINARY_DEPS = "c('XML','igraph')"
-          cmd.exe /c "R.exe -q -e ""install.packages($BINARY_DEPS, repos='$CRAN', type='win.binary')"" 2>&1"
           $DEPS = "c('data.table','magrittr','stringi','ggplot2','DiagrammeR','Ckmeans.1d.dp','vcd','testthat','lintr','knitr','rmarkdown')"
           cmd.exe /c "R.exe -q -e ""install.packages($DEPS, repos='$CRAN', type='both')"" 2>&1"
+          $BINARY_DEPS = "c('XML','igraph')"
+          cmd.exe /c "R.exe -q -e ""install.packages($BINARY_DEPS, repos='$CRAN', type='win.binary')"" 2>&1"
         }
 
 build_script:
@@ -102,7 +96,7 @@ build_script:
 
 test_script:
     - cd %APPVEYOR_BUILD_FOLDER%
-    - if /i "%DO_PYTHON%" == "on" python -m pytest tests/python
+    - if /i "%DO_PYTHON%" == "on" python -m nose tests/python
     # mingw R package: run the R check (which includes unit tests), and also keep the built binary package
     - if /i "%target%" == "rmingw" (
         set _R_CHECK_CRAN_INCOMING_=FALSE&&

build.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+# This is a simple script to make xgboost in MAC and Linux
+# Basically, it first try to make with OpenMP, if fails, disable OpenMP and make it again.
+# This will automatically make xgboost for MAC users who don't have OpenMP support.
+# In most cases, type make will give what you want.
+
+# See additional instruction in doc/build.md
+set -e
+
+if make; then
+    echo "Successfully build multi-thread xgboost"
+else
+
+    not_ready=0
+
+    if [[ ! -e ./rabit/Makefile ]]; then
+        echo ""
+        echo "Please init the rabit submodule:"
+        echo "git submodule update --init --recursive -- rabit"
+        not_ready=1
+    fi
+    
+    if [[ ! -e ./dmlc-core/Makefile ]]; then
+        echo ""
+        echo "Please init the dmlc-core submodule:"
+        echo "git submodule update --init --recursive -- dmlc-core"
+        not_ready=1
+    fi
+
+    if [[ "${not_ready}" == "1" ]]; then
+        echo ""
+        echo "Please fix the errors above and retry the build, or reclone the repository with:"
+        echo "git clone --recursive https://github.com/dmlc/xgboost.git"
+        echo ""
+        exit 1
+    fi
+
+
+    echo "-----------------------------"
+    echo "Building multi-thread xgboost failed"
+    echo "Start to build single-thread xgboost"
+    make clean_all
+    make config=make/minimum.mk
+    if [ $? -eq 0 ] ;then
+      echo "Successfully build single-thread xgboost"
+      echo "If you want multi-threaded version"
+      echo "See additional instructions in doc/build.md"
+    else
+      echo "Failed to build single-thread xgboost"
+    fi
+fi

cmake/Doc.cmake

@@ -1,16 +0,0 @@
-function (run_doxygen)
-  find_package(Doxygen REQUIRED)
-
-  if (NOT DOXYGEN_DOT_FOUND)
-    message(FATAL_ERROR "Command `dot` not found.  Please install graphviz.")
-  endif (NOT DOXYGEN_DOT_FOUND)
-
-  configure_file(
-    ${PROJECT_SOURCE_DIR}/doc/Doxyfile.in
-    ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile @ONLY)
-  add_custom_target( doc_doxygen ALL
-    COMMAND ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile
-    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
-    COMMENT "Generate C APIs documentation."
-    VERBATIM)
-endfunction (run_doxygen)

cmake/Utils.cmake

@@ -1,3 +1,4 @@
+
 # Automatically set source group based on folder
 function(auto_source_group SOURCES)
 
@@ -17,10 +18,6 @@ endfunction(auto_source_group)
 function(msvc_use_static_runtime)
   if(MSVC)
       set(variables
-          CMAKE_C_FLAGS_DEBUG
-          CMAKE_C_FLAGS_MINSIZEREL
-          CMAKE_C_FLAGS_RELEASE
-          CMAKE_C_FLAGS_RELWITHDEBINFO
           CMAKE_CXX_FLAGS_DEBUG
           CMAKE_CXX_FLAGS_MINSIZEREL
           CMAKE_CXX_FLAGS_RELEASE
@@ -32,23 +29,6 @@ function(msvc_use_static_runtime)
               set(${variable} "${${variable}}"  PARENT_SCOPE)
           endif()
       endforeach()
-      set(variables
-          CMAKE_CUDA_FLAGS
-          CMAKE_CUDA_FLAGS_DEBUG
-          CMAKE_CUDA_FLAGS_MINSIZEREL
-          CMAKE_CUDA_FLAGS_RELEASE
-          CMAKE_CUDA_FLAGS_RELWITHDEBINFO
-      )
-      foreach(variable ${variables})
-          if(${variable} MATCHES "-MD")
-              string(REGEX REPLACE "-MD" "-MT" ${variable} "${${variable}}")
-              set(${variable} "${${variable}}"  PARENT_SCOPE)
-          endif()
-          if(${variable} MATCHES "/MD")
-              string(REGEX REPLACE "/MD" "/MT" ${variable} "${${variable}}")
-              set(${variable} "${${variable}}"  PARENT_SCOPE)
-          endif()
-      endforeach()
   endif()
 endfunction(msvc_use_static_runtime)
 
@@ -77,14 +57,9 @@ endfunction(set_default_configuration_release)
 # Generate nvcc compiler flags given a list of architectures
 # Also generates PTX for the most recent architecture for forwards compatibility
 function(format_gencode_flags flags out)
-  if(CMAKE_CUDA_COMPILER_VERSION MATCHES "^([0-9]+\\.[0-9]+)")
-    set(CUDA_VERSION "${CMAKE_MATCH_1}")
-  endif()
   # Set up architecture flags
   if(NOT flags) 
-    if(CUDA_VERSION VERSION_GREATER_EQUAL "10.0")
-      set(flags "35;50;52;60;61;70;75")
-    elseif(CUDA_VERSION VERSION_GREATER_EQUAL "9.0")
+    if((CUDA_VERSION_MAJOR EQUAL 9) OR (CUDA_VERSION_MAJOR GREATER 9))
       set(flags "35;50;52;60;61;70")
     else()
       set(flags "35;50;52;60;61")
@@ -92,11 +67,11 @@ function(format_gencode_flags flags out)
   endif()
   # Generate SASS
   foreach(ver ${flags})
-    set(${out} "${${out}}--generate-code=arch=compute_${ver},code=sm_${ver};")
+    set(${out} "${${out}}-gencode arch=compute_${ver},code=sm_${ver};")
   endforeach()
   # Generate PTX for last architecture
   list(GET flags -1 ver)
-  set(${out} "${${out}}--generate-code=arch=compute_${ver},code=compute_${ver};")
+  set(${out} "${${out}}-gencode arch=compute_${ver},code=compute_${ver};")
   
   set(${out} "${${out}}" PARENT_SCOPE)
 endfunction(format_gencode_flags flags)
@@ -105,10 +80,6 @@ endfunction(format_gencode_flags flags)
 # if necessary, installs the main R package dependencies;
 # runs R CMD INSTALL.
 function(setup_rpackage_install_target rlib_target build_dir)
-  # backup cmake_install.cmake
-  install(CODE "file(COPY \"${build_dir}/R-package/cmake_install.cmake\"
-DESTINATION \"${build_dir}/bak\")")
-
   install(CODE "file(REMOVE_RECURSE \"${build_dir}/R-package\")")
   install(
     DIRECTORY "${PROJECT_SOURCE_DIR}/R-package"
@@ -127,8 +98,4 @@ DESTINATION \"${build_dir}/bak\")")
   install(CODE "execute_process(COMMAND \"${LIBR_EXECUTABLE}\" \"-q\" \"-e\" \"${XGB_DEPS_SCRIPT}\")")
   install(CODE "execute_process(COMMAND \"${LIBR_EXECUTABLE}\" CMD INSTALL\
     \"--no-multiarch\" \"--build\" \"${build_dir}/R-package\")")
-
-  # restore cmake_install.cmake
-  install(CODE "file(RENAME \"${build_dir}/bak/cmake_install.cmake\"
- \"${build_dir}/R-package/cmake_install.cmake\")")
 endfunction(setup_rpackage_install_target)

cmake/modules/FindASan.cmake

@@ -1,8 +1,8 @@
 set(ASan_LIB_NAME ASan)
 
 find_library(ASan_LIBRARY
-  NAMES libasan.so libasan.so.4 libasan.so.3 libasan.so.2 libasan.so.1 libasan.so.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
+  NAMES libasan.so libasan.so.4
+  PATHS /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib)
 
 include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(ASan DEFAULT_MSG

cmake/modules/FindLSan.cmake

@@ -2,7 +2,7 @@ set(LSan_LIB_NAME lsan)
 
 find_library(LSan_LIBRARY
   NAMES liblsan.so liblsan.so.0 liblsan.so.0.0.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
+  PATHS /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib)
 
 include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(LSan DEFAULT_MSG

cmake/modules/FindNccl.cmake

@@ -32,28 +32,20 @@
 #
 # This module assumes that the user has already called find_package(CUDA)
 
-if (NCCL_LIBRARY)
-  # Don't cache NCCL_LIBRARY to enable switching between static and shared.
-  unset(NCCL_LIBRARY CACHE)
-endif()
 
-if (BUILD_WITH_SHARED_NCCL)
-  # libnccl.so
-  set(NCCL_LIB_NAME nccl)
-else ()
-  # libnccl_static.a
-  set(NCCL_LIB_NAME nccl_static)
-endif (BUILD_WITH_SHARED_NCCL)
+set(NCCL_LIB_NAME nccl_static)
 
 find_path(NCCL_INCLUDE_DIR
   NAMES nccl.h
-  PATHS $ENV{NCCL_ROOT}/include ${NCCL_ROOT}/include)
+  PATHS $ENV{NCCL_ROOT}/include ${NCCL_ROOT}/include ${CUDA_INCLUDE_DIRS} /usr/include)
 
 find_library(NCCL_LIBRARY
   NAMES ${NCCL_LIB_NAME}
-  PATHS $ENV{NCCL_ROOT}/lib/ ${NCCL_ROOT}/lib)
+  PATHS $ENV{NCCL_ROOT}/lib ${NCCL_ROOT}/lib ${CUDA_INCLUDE_DIRS}/../lib /usr/lib)
 
-message(STATUS "Using nccl library: ${NCCL_LIBRARY}")
+if (NCCL_INCLUDE_DIR AND NCCL_LIBRARY)
+  get_filename_component(NCCL_LIBRARY ${NCCL_LIBRARY} PATH)
+endif ()
 
 include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(Nccl DEFAULT_MSG
@@ -62,4 +54,5 @@ find_package_handle_standard_args(Nccl DEFAULT_MSG
 mark_as_advanced(
   NCCL_INCLUDE_DIR
   NCCL_LIBRARY
+  NCCL_LIB_NAME
 )

cmake/modules/FindTSan.cmake

@@ -2,7 +2,7 @@ set(TSan_LIB_NAME tsan)
 
 find_library(TSan_LIBRARY
   NAMES libtsan.so libtsan.so.0 libtsan.so.0.0.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
+  PATHS /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib)
 
 include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(TSan DEFAULT_MSG

cmake/xgboost-config.cmake.in

@@ -1,5 +0,0 @@
-@PACKAGE_INIT@
-
-if(NOT TARGET xgboost::xgboost)
-  include(${CMAKE_CURRENT_LIST_DIR}/XGBoostTargets.cmake)
-endif()

demo/README.md

@@ -119,7 +119,6 @@ If you have particular usecase of xgboost that you would like to highlight.
 Send a PR to add a one sentence description:)
 
 - XGBoost is used in [Kaggle Script](https://www.kaggle.com/scripts) to solve data science challenges.
-- Distribute XGBoost as Rest API server from Jupyter notebook with [BentoML](https://github.com/bentoml/bentoml). [Link to notebook](https://github.com/bentoml/BentoML/blob/master/examples/xgboost-predict-titanic-survival/XGBoost-titanic-survival-prediction.ipynb)
 - [Seldon predictive service powered by XGBoost](http://docs.seldon.io/iris-demo.html)
 - XGBoost Distributed is used in [ODPS Cloud Service by Alibaba](https://yq.aliyun.com/articles/6355) (in Chinese)
 - XGBoost is incoporated as part of [Graphlab Create](https://dato.com/products/create/) for scalable machine learning.
@@ -136,7 +135,6 @@ Send a PR to add a one sentence description:)
 
 ## Awards
 - [John Chambers Award](http://stat-computing.org/awards/jmc/winners.html) - 2016 Winner: XGBoost R Package, by Tong He (Simon Fraser University) and Tianqi Chen (University of Washington)
-- [InfoWorld’s 2019 Technology of the Year Award](https://www.infoworld.com/article/3336072/application-development/infoworlds-2019-technology-of-the-year-award-winners.html)
 
 ## Windows Binaries
 Unofficial windows binaries and instructions on how to use them are hosted on [Guido Tapia's blog](http://www.picnet.com.au/blogs/guido/post/2016/09/22/xgboost-windows-x64-binaries-for-download/)

demo/binary_classification/README.md

@@ -62,7 +62,7 @@ test:data = "agaricus.txt.test"
 We use the tree booster and logistic regression objective in our setting. This indicates that we accomplish our task using classic gradient boosting regression tree(GBRT), which is a promising method for binary classification.
 
 The parameters shown in the example gives the most common ones that are needed to use xgboost.
-If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](../../doc/parameter.rst). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below:
+If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](../../doc/parameter.md). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below:
 
 ```
 ../../xgboost mushroom.conf max_depth=6

demo/binary_classification/mapfeat.py

@@ -18,7 +18,7 @@ def loadfmap( fname ):
             if it.strip() == '':
                 continue
             k , v = it.split('=')
-            fmap[ idx ][ v ] = len(nmap)
+            fmap[ idx ][ v ] = len(nmap) + 1
             nmap[ len(nmap) ] = ftype+'='+k
     return fmap, nmap
 

demo/c-api/Makefile

@@ -1,19 +0,0 @@
-SRC=c-api-demo.c
-TGT=c-api-demo
-
-cc=cc
-CFLAGS ?=-O3
-XGBOOST_ROOT ?=../..
-INCLUDE_DIR=-I$(XGBOOST_ROOT)/include -I$(XGBOOST_ROOT)/dmlc-core/include -I$(XGBOOST_ROOT)/rabit/include
-LIB_DIR=-L$(XGBOOST_ROOT)/lib
-
-build: $(TGT)
-
-$(TGT): $(SRC) Makefile
-	$(cc) $(CFLAGS) $(INCLUDE_DIR) $(LIB_DIR) -o $(TGT) $(SRC) -lxgboost
-
-run: $(TGT)
-	LD_LIBRARY_PATH=$(XGBOOST_ROOT)/lib ./$(TGT)
-
-clean:
-	rm -f $(TGT)

demo/c-api/README.md

@@ -1,30 +0,0 @@
-C-APIs
-===
-
-**XGBoost** implements a C API originally designed for various language
-bindings.  For detailed reference, please check xgboost/c_api.h.  Here is a
-demonstration of using the API.
-
-# CMake
-If you use **CMake** for your project, you can either install **XGBoost**
-somewhere in your system and tell CMake to find it by calling
-`find_package(xgboost)`, or put **XGBoost** inside your project's source tree
-and call **CMake** command: `add_subdirectory(xgboost)`.  To use
-`find_package()`, put the following in your **CMakeLists.txt**:
-
-``` CMake
-find_package(xgboost REQUIRED)
-add_executable(api-demo c-api-demo.c)
-target_link_libraries(api-demo xgboost::xgboost)
-```
-
-If you want to put XGBoost inside your project (like git submodule), use this
-instead:
-``` CMake
-add_subdirectory(xgboost)
-add_executable(api-demo c-api-demo.c)
-target_link_libraries(api-demo xgboost)
-```
-
-# make
-You can start by modifying the makefile in this directory to fit your need.

demo/c-api/c-api-demo.c

@@ -1,89 +0,0 @@
-/*!
- * Copyright 2019 XGBoost contributors
- *
- * \file c-api-demo.c
- * \brief A simple example of using xgboost C API.
- */
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <xgboost/c_api.h>
-
-#define safe_xgboost(call) {                                            \
-int err = (call);                                                       \
-if (err != 0) {                                                         \
-  fprintf(stderr, "%s:%d: error in %s: %s\n", __FILE__, __LINE__, #call, XGBGetLastError()); \
-  exit(1);                                                              \
-}                                                                       \
-}
-
-int main(int argc, char** argv) {
-  int silent = 0;
-  int use_gpu = 0;  // set to 1 to use the GPU for training
-  
-  // load the data
-  DMatrixHandle dtrain, dtest;
-  safe_xgboost(XGDMatrixCreateFromFile("../data/agaricus.txt.train", silent, &dtrain));
-  safe_xgboost(XGDMatrixCreateFromFile("../data/agaricus.txt.test", silent, &dtest));
-  
-  // create the booster
-  BoosterHandle booster;
-  DMatrixHandle eval_dmats[2] = {dtrain, dtest};
-  safe_xgboost(XGBoosterCreate(eval_dmats, 2, &booster));
-
-  // configure the training
-  // available parameters are described here:
-  //   https://xgboost.readthedocs.io/en/latest/parameter.html
-  safe_xgboost(XGBoosterSetParam(booster, "tree_method", use_gpu ? "gpu_hist" : "hist"));
-  if (use_gpu) {
-    // set the number of GPUs and the first GPU to use;
-    // this is not necessary, but provided here as an illustration
-    safe_xgboost(XGBoosterSetParam(booster, "n_gpus", "1"));
-    safe_xgboost(XGBoosterSetParam(booster, "gpu_id", "0"));
-  } else {
-    // avoid evaluating objective and metric on a GPU
-    safe_xgboost(XGBoosterSetParam(booster, "n_gpus", "0"));
-  }
-
-  safe_xgboost(XGBoosterSetParam(booster, "objective", "binary:logistic"));
-  safe_xgboost(XGBoosterSetParam(booster, "min_child_weight", "1"));
-  safe_xgboost(XGBoosterSetParam(booster, "gamma", "0.1"));
-  safe_xgboost(XGBoosterSetParam(booster, "max_depth", "3"));
-  safe_xgboost(XGBoosterSetParam(booster, "verbosity", silent ? "0" : "1"));
-  
-  // train and evaluate for 10 iterations
-  int n_trees = 10;
-  const char* eval_names[2] = {"train", "test"};
-  const char* eval_result = NULL;
-  for (int i = 0; i < n_trees; ++i) {
-    safe_xgboost(XGBoosterUpdateOneIter(booster, i, dtrain));
-    safe_xgboost(XGBoosterEvalOneIter(booster, i, eval_dmats, eval_names, 2, &eval_result));
-    printf("%s\n", eval_result);
-  }
-
-  // predict
-  bst_ulong out_len = 0;
-  const float* out_result = NULL;
-  int n_print = 10;
-
-  safe_xgboost(XGBoosterPredict(booster, dtest, 0, 0, &out_len, &out_result));
-  printf("y_pred: ");
-  for (int i = 0; i < n_print; ++i) {
-    printf("%1.4f ", out_result[i]);
-  }
-  printf("\n");
-
-  // print true labels
-  safe_xgboost(XGDMatrixGetFloatInfo(dtest, "label", &out_len, &out_result));
-  printf("y_test: ");
-  for (int i = 0; i < n_print; ++i) {
-    printf("%1.4f ", out_result[i]);
-  }
-  printf("\n");
-
-  // free everything
-  safe_xgboost(XGBoosterFree(booster));
-  safe_xgboost(XGDMatrixFree(dtrain));
-  safe_xgboost(XGDMatrixFree(dtest));
-  return 0;
-}

demo/guide-python/custom_objective.py

@@ -33,9 +33,9 @@ def logregobj(preds, dtrain):
 # Take this in mind when you use the customization, and maybe you need write customized evaluation function
 def evalerror(preds, dtrain):
     labels = dtrain.get_label()
-    # return a pair metric_name, result. The metric name must not contain a colon (:) or a space
+    # return a pair metric_name, result. The metric name must not contain a colon (:)
     # since preds are margin(before logistic transformation, cutoff at 0)
-    return 'my-error', float(sum(labels != (preds > 0.0))) / len(labels)
+    return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
 
 # training with customized objective, we can also do step by step training
 # simply look at xgboost.py's implementation of train

demo/guide-python/runall.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-export PYTHONPATH=$PYTHONPATH:../../python-package
+export PYTHONPATH=PYTHONPATH:../../python-package
 python basic_walkthrough.py
 python custom_objective.py
 python boost_from_prediction.py

demo/kaggle-otto/README.MD

@@ -1,4 +1,4 @@
-Benchmark for Otto Group Competition
+Benckmark for Otto Group Competition
 =========
 
 This is a folder containing the benchmark for the [Otto Group Competition on Kaggle](http://www.kaggle.com/c/otto-group-product-classification-challenge).
@@ -20,3 +20,5 @@ devtools::install_github('tqchen/xgboost',subdir='R-package')
 ```
 
 Windows users may need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
+
+

demo/multiclass_classification/README.md

@@ -1,10 +1,10 @@
 Demonstrating how to use XGBoost accomplish Multi-Class classification task on [UCI Dermatology dataset](https://archive.ics.uci.edu/ml/datasets/Dermatology)
 
-Make sure you make xgboost python module in ../../python
+Make sure you make make xgboost python module in ../../python
 
 1. Run runexp.sh
 ```bash
 ./runexp.sh
 ```
 
-**R version** please see the `train.R`.
+

demo/multiclass_classification/train.R

@@ -1,64 +0,0 @@
-library(data.table)
-library(xgboost)
-
-if (!file.exists("./dermatology.data")) {
-  download.file(
-    "https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data",
-    "dermatology.data",
-    method = "curl"
-  )
-}
-
-df <- fread("dermatology.data", sep = ",", header = FALSE)
-
-df[, `:=`(V34 = as.integer(ifelse(V34 == "?", 0L, V34)),
-          V35 = V35 - 1L)]
-
-idx <- sample(nrow(df), size = round(0.7 * nrow(df)), replace = FALSE)
-
-train <- df[idx,]
-test <- df[-idx,]
-
-train_x <- train[, 1:34]
-train_y <- train[, V35]
-
-test_x <- test[, 1:34]
-test_y <- test[, V35]
-
-xg_train <- xgb.DMatrix(data = as.matrix(train_x), label = train_y)
-xg_test = xgb.DMatrix(as.matrix(test_x), label = test_y)
-
-params <- list(
-  objective = 'multi:softmax',
-  num_class = 6,
-  max_depth = 6,
-  nthread = 4,
-  eta = 0.1
-)
-
-watchlist = list(train = xg_train, test = xg_test)
-
-bst <- xgb.train(
-  params = params,
-  data = xg_train,
-  watchlist = watchlist,
-  nrounds = 5
-)
-
-pred <- predict(bst, xg_test)
-error_rate <- sum(pred != test_y) / length(test_y)
-print(paste("Test error using softmax =", error_rate))
-
-# do the same thing again, but output probabilities
-params$objective <- 'multi:softprob'
-bst <- xgb.train(params, xg_train, nrounds = 5, watchlist)
-
-pred_prob <- predict(bst, xg_test)
-
-pred_mat <- matrix(pred_prob, ncol = 6, byrow = TRUE)
-# validation
-# rowSums(pred_mat)
-
-pred_label <- apply(pred_mat, 1, which.max) - 1L
-error_rate = sum(pred_label != test_y) / length(test_y)
-print(paste("Test error using softprob =", error_rate))

demo/rank/README.md

@@ -1,6 +1,6 @@
 Learning to rank
 ====
-XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the [group information file](../../doc/tutorials/input_format.rst#group-input-format) to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank, this function is not yet completed. Currently, we provide pairwise rank.
+XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the [group information file](../../doc/input_format.md#group-input-format) to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank, this function is not yet completed. Currently, we provide pairwise rank.
 
 ### Parameters
 The configuration setting is similar to the regression and binary classification setting, except user need to specify the objectives:
@@ -15,27 +15,14 @@ For more usage details please refer to the [binary classification demo](../binar
 Instructions
 ====
 The dataset for ranking demo is from LETOR04 MQ2008 fold1.
-Before running the examples, you need to get the data by running:
+You can use the following command to run the example:
 
+Get the data:
 ```
 ./wgetdata.sh
 ```
 
-### Command Line
 Run the example:
 ```
 ./runexp.sh
 ```
-
-### Python
-There are two ways of doing ranking in python.  
-
-Run the example using `xgboost.train`:
-```
-python rank.py
-```
-
-Run the example using `XGBRanker`:
-```
-python rank_sklearn.py
-```

demo/rank/rank.py

@@ -1,41 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-from xgboost import DMatrix
-from sklearn.datasets import load_svmlight_file
-
-
-#  This script demonstrate how to do ranking with xgboost.train
-x_train, y_train = load_svmlight_file("mq2008.train")
-x_valid, y_valid = load_svmlight_file("mq2008.vali")
-x_test, y_test = load_svmlight_file("mq2008.test")
-
-group_train = []
-with open("mq2008.train.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_train.append(int(line.split("\n")[0]))
-
-group_valid = []
-with open("mq2008.vali.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_valid.append(int(line.split("\n")[0]))
-
-group_test = []
-with open("mq2008.test.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_test.append(int(line.split("\n")[0]))
-
-train_dmatrix = DMatrix(x_train, y_train)
-valid_dmatrix = DMatrix(x_valid, y_valid)
-test_dmatrix = DMatrix(x_test)
-
-train_dmatrix.set_group(group_train)
-valid_dmatrix.set_group(group_valid)
-
-params = {'objective': 'rank:pairwise', 'eta': 0.1, 'gamma': 1.0,
-               'min_child_weight': 0.1, 'max_depth': 6}
-xgb_model = xgb.train(params, train_dmatrix, num_boost_round=4,
-                           evals=[(valid_dmatrix, 'validation')])
-pred = xgb_model.predict(test_dmatrix)

demo/rank/rank_sklearn.py

@@ -1,35 +0,0 @@
-#!/usr/bin/python
-import xgboost as xgb
-from sklearn.datasets import load_svmlight_file
-
-
-#  This script demonstrate how to do ranking with XGBRanker
-x_train, y_train = load_svmlight_file("mq2008.train")
-x_valid, y_valid = load_svmlight_file("mq2008.vali")
-x_test, y_test = load_svmlight_file("mq2008.test")
-
-group_train = []
-with open("mq2008.train.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_train.append(int(line.split("\n")[0]))
-
-group_valid = []
-with open("mq2008.vali.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_valid.append(int(line.split("\n")[0]))
-
-group_test = []
-with open("mq2008.test.group", "r") as f:
-    data = f.readlines()
-    for line in data:
-        group_test.append(int(line.split("\n")[0]))
-
-params = {'objective': 'rank:pairwise', 'learning_rate': 0.1,
-          'gamma': 1.0, 'min_child_weight': 0.1,
-          'max_depth': 6, 'n_estimators': 4}
-model = xgb.sklearn.XGBRanker(**params)
-model.fit(x_train, y_train, group_train,
-          eval_set=[(x_valid, y_valid)], eval_group=[group_valid])
-pred = model.predict(x_test)

demo/rank/runexp.sh

@@ -1,5 +1,11 @@
-#!/bin/bash
+python trans_data.py train.txt mq2008.train mq2008.train.group
+
+python trans_data.py test.txt mq2008.test mq2008.test.group
+
+python trans_data.py vali.txt mq2008.vali mq2008.vali.group
 
 ../../xgboost mq2008.conf
 
 ../../xgboost mq2008.conf task=pred model_in=0004.model
+
+

demo/rank/wgetdata.sh

@@ -2,9 +2,3 @@
 wget https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.rar
 unrar x MQ2008.rar
 mv -f MQ2008/Fold1/*.txt .
-
-python trans_data.py train.txt mq2008.train mq2008.train.group
-
-python trans_data.py test.txt mq2008.test mq2008.test.group
-
-python trans_data.py vali.txt mq2008.vali mq2008.vali.group

demo/regression/README.md

@@ -6,9 +6,9 @@ Using XGBoost for regression is very similar to using it for binary classificati
 The dataset we used is the [computer hardware dataset from UCI repository](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware). The demo for regression is almost the same as the [binary classification demo](../binary_classification), except a little difference in general parameter:
 ```
 # General parameter
-# this is the only difference with classification, use reg:squarederror to do linear regression
+# this is the only difference with classification, use reg:linear to do linear regression
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:squarederror
+objective = reg:linear
 ...
 
 ```

demo/regression/machine.conf

@@ -1,9 +1,9 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, can also change to gblinear
 booster = gbtree
-# this is the only difference with classification, use reg:squarederror to do linear classification
+# this is the only difference with classification, use reg:linear to do linear classification
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:squarederror
+objective = reg:linear
 
 # Tree Booster Parameters
 # step size shrinkage

demo/yearpredMSD/yearpredMSD.conf

@@ -1,9 +1,9 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, can also change to gblinear
 booster = gbtree
-# this is the only difference with classification, use reg:squarederror to do linear classification
+# this is the only difference with classification, use reg:linear to do linear classification
 # when labels are in [0,1] we can also use reg:logistic
-objective = reg:squarederror
+objective = reg:linear
 
 # Tree Booster Parameters
 # step size shrinkage
@@ -27,3 +27,4 @@ data = "yearpredMSD.libsvm.train"
 eval[test] = "yearpredMSD.libsvm.test"
 # The path of test data 
 #test:data = "yearpredMSD.libsvm.test"
+

dev/query_contributors.py

@@ -1,63 +0,0 @@
-"""Query list of all contributors and reviewers in a release"""
-
-from sh.contrib import git
-import sys
-import re
-import requests
-import json
-
-if len(sys.argv) != 5:
-    print(f'Usage: {sys.argv[0]} [starting commit/tag] [ending commit/tag] [GitHub username] [GitHub password]')
-    sys.exit(1)
-
-from_commit = sys.argv[1]
-to_commit = sys.argv[2]
-username = sys.argv[3]
-password = sys.argv[4]
-
-contributors = set()
-reviewers = set()
-
-for line in git.log(f'{from_commit}..{to_commit}', '--pretty=format:%s', '--reverse'):
-    m = re.search('\(#([0-9]+)\)', line.rstrip())
-    if m:
-        pr_id = m.group(1)
-        print(f'PR #{pr_id}')
-
-        r = requests.get(f'https://api.github.com/repos/dmlc/xgboost/pulls/{pr_id}/commits', auth=(username, password))
-        assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-        commit_list = json.loads(r.text)
-        try:
-            contributors.update([commit['author']['login'] for commit in commit_list])
-        except TypeError:
-            contributors.update(str(input(f'Error fetching contributors for PR #{pr_id}. Enter it manually, as a space-separated list:')).split(' '))
-
-        r = requests.get(f'https://api.github.com/repos/dmlc/xgboost/pulls/{pr_id}/reviews', auth=(username, password))
-        assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-        review_list = json.loads(r.text)
-        reviewers.update([x['user']['login'] for x in review_list])
-
-        r = requests.get(f'https://api.github.com/repos/dmlc/xgboost/issues/{pr_id}/comments', auth=(username, password))
-        assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-        comment_list = json.loads(r.text)
-        reviewers.update([x['user']['login'] for x in comment_list])
-
-print('Contributors:', end='')
-for x in sorted(contributors):
-    r = requests.get(f'https://api.github.com/users/{x}', auth=(username, password))
-    assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-    user_info = json.loads(r.text)
-    if user_info['name'] is None:
-        print(f"@{x}, ", end='')
-    else:
-        print(f"{user_info['name']} (@{x}), ", end='')
-
-print('Reviewers:', end='')
-for x in sorted(reviewers):
-    r = requests.get(f'https://api.github.com/users/{x}', auth=(username, password))
-    assert r.status_code == requests.codes.ok, f'Code: {r.status_code}, Text: {r.text}'
-    user_info = json.loads(r.text)
-    if user_info['name'] is None:
-        print(f"@{x}, ", end='')
-    else:
-        print(f"{user_info['name']} (@{x}), ", end='')

doc/Doxyfile

@@ -38,7 +38,7 @@ PROJECT_NAME           = "xgboost"
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = @XGBOOST_VERSION@
+PROJECT_NUMBER         =
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a
@@ -58,7 +58,7 @@ PROJECT_LOGO           =
 # entered, it will be relative to the location where doxygen was started. If
 # left blank the current directory will be used.
 
-OUTPUT_DIRECTORY       = @PROJECT_BINARY_DIR@/doc_doxygen
+OUTPUT_DIRECTORY       = doc/doxygen
 
 # If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
 # directories (in 2 levels) under the output directory of each output format and
@@ -753,7 +753,7 @@ WARN_LOGFILE           =
 # spaces.
 # Note: If this tag is empty the current directory is searched.
 
-INPUT                  = @PROJECT_SOURCE_DIR@/include @PROJECT_SOURCE_DIR@/src/common
+INPUT                  = include src/common
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses

doc/R-package/xgboostPresentation.md

@@ -176,7 +176,7 @@ In a *sparse* matrix, cells containing `0` are not stored in memory. Therefore,
 We will train decision tree model using the following parameters:
 
 * `objective = "binary:logistic"`: we will train a binary classification model ;
-* `max.depth = 2`: the trees won't be deep, because our case is very simple ;
+* `max.deph = 2`: the trees won't be deep, because our case is very simple ;
 * `nthread = 2`: the number of cpu threads we are going to use;
 * `nrounds = 2`: there will be two passes on the data, the second one will enhance the model by further reducing the difference between ground truth and prediction.
 
@@ -576,8 +576,8 @@ print(class(rawVec))
 bst3 <- xgb.load(rawVec)
 pred3 <- predict(bst3, test$data)
 
-# pred3 should be identical to pred
-print(paste("sum(abs(pred3-pred))=", sum(abs(pred3-pred))))
+# pred2 should be identical to pred
+print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
 ```
 
 ```

doc/build.rst

@@ -13,7 +13,7 @@ Installation Guide
     #   * xgboost-{version}-py2.py3-none-win_amd64.whl
     pip3 install xgboost
 
-  * The binary wheel will support GPU algorithms (`gpu_exact`, `gpu_hist`) on machines with NVIDIA GPUs. Please note that **training with multiple GPUs is only supported for Linux platform**. See :doc:`gpu/index`.
+  * The binary wheel will support GPU algorithms (`gpu_exact`, `gpu_hist`) on machines with NVIDIA GPUs. **However, it will not support multi-GPU training; only single GPU will be used.** To enable multi-GPU training, download and install the binary wheel from `this page <https://s3-us-west-2.amazonaws.com/xgboost-wheels/list.html>`_.
   * Currently, we provide binary wheels for 64-bit Linux and Windows.
 
 ****************************
@@ -70,21 +70,19 @@ Our goal is to build the shared library:
 The minimal building requirement is
 
 - A recent C++ compiler supporting C++11 (g++-4.8 or higher)
-- CMake 3.2 or higher
+
+We can edit ``make/config.mk`` to change the compile options, and then build by
+``make``. If everything goes well, we can go to the specific language installation section.
 
 Building on Ubuntu/Debian
 =========================
 
-On Ubuntu, one builds XGBoost by running CMake:
+On Ubuntu, one builds XGBoost by running
 
 .. code-block:: bash
 
   git clone --recursive https://github.com/dmlc/xgboost
-  cd xgboost
-  mkdir build
-  cd build
-  cmake ..
-  make -j4
+  cd xgboost; make -j4
 
 Building on OSX
 ===============
@@ -92,11 +90,11 @@ Building on OSX
 Install with pip: simple method
 --------------------------------
 
-First, obtain ``gcc-8`` with Homebrew (https://brew.sh/) to enable multi-threading (i.e. using multiple CPU threads for training). The default Apple Clang compiler does not support OpenMP, so using the default compiler would have disabled multi-threading.
+First, make sure you obtained ``gcc-5`` (newer version does not work with this method yet). Note: installation of ``gcc`` can take a while (~ 30 minutes).
 
 .. code-block:: bash
 
-  brew install gcc@8
+  brew install gcc@5
 
 Then install XGBoost with ``pip``:
 
@@ -104,30 +102,42 @@ Then install XGBoost with ``pip``:
 
   pip3 install xgboost
 
-You might need to run the command with ``--user`` flag if you run into permission errors.
+You might need to run the command with ``sudo`` if you run into permission errors.
 
 Build from the source code - advanced method
 --------------------------------------------
 
-Obtain ``gcc-8`` from Homebrew:
+First, obtain ``gcc-7`` with homebrew (https://brew.sh/) if you want multi-threaded version. Clang is okay if multithreading is not required. Note: installation of ``gcc`` can take a while (~ 30 minutes).
 
 .. code-block:: bash
 
-  brew install gcc@8
+  brew install gcc@7
 
-Now clone the repository:
+Now, clone the repository:
 
 .. code-block:: bash
 
   git clone --recursive https://github.com/dmlc/xgboost
+  cd xgboost; cp make/config.mk ./config.mk
 
-Create the ``build/`` directory and invoke CMake. Make sure to add ``CC=gcc-8 CXX=g++-8`` so that Homebrew GCC is selected. After invoking CMake, you can build XGBoost with ``make``:
+Open ``config.mk`` and uncomment these two lines:
+
+.. code-block:: bash
+
+  export CC = gcc
+  export CXX = g++
+
+and replace these two lines as follows: (specify the GCC version)
+
+.. code-block:: bash
+
+  export CC = gcc-7
+  export CXX = g++-7
+
+Now, you may build XGBoost using the following command:
 
 .. code-block:: bash
 
-  mkdir build
-  cd build
-  CC=gcc-8 CXX=g++-8 cmake ..
   make -j4
 
 You may now continue to `Python Package Installation`_.
@@ -144,20 +154,6 @@ We recommend you use `Git for Windows <https://git-for-windows.github.io/>`_, as
 
 XGBoost support compilation with Microsoft Visual Studio and MinGW.
 
-Compile XGBoost with Microsoft Visual Studio
---------------------------------------------
-To build with Visual Studio, we will need CMake. Make sure to install a recent version of CMake. Then run the following from the root of the XGBoost directory:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  cmake .. -G"Visual Studio 14 2015 Win64"
-
-This specifies an out of source build using the Visual Studio 64 bit generator. (Change the ``-G`` option appropriately if you have a different version of Visual Studio installed.) Open the ``.sln`` file in the build directory and build with Visual Studio.
-
-After the build process successfully ends, you will find a ``xgboost.dll`` library file inside ``./lib/`` folder.
-
 Compile XGBoost using MinGW
 ---------------------------
 After installing `Git for Windows <https://git-for-windows.github.io/>`_, you should have a shortcut named ``Git Bash``. You should run all subsequent steps in ``Git Bash``.
@@ -177,7 +173,21 @@ To build with MinGW, type:
 
   cp make/mingw64.mk config.mk; make -j4
 
-See :ref:`mingw_python` for buildilng XGBoost for Python.
+Compile XGBoost with Microsoft Visual Studio
+--------------------------------------------
+To build with Visual Studio, we will need CMake. Make sure to install a recent version of CMake. Then run the following from the root of the XGBoost directory:
+
+.. code-block:: bash
+
+  mkdir build
+  cd build
+  cmake .. -G"Visual Studio 12 2013 Win64"
+
+This specifies an out of source build using the MSVC 12 64 bit generator. Open the ``.sln`` file in the build directory and build with Visual Studio. To use the Python module you can copy ``xgboost.dll`` into ``python-package/xgboost``.
+
+After the build process successfully ends, you will find a ``xgboost.dll`` library file inside ``./lib/`` folder, copy this file to the the API package folder like ``python-package/xgboost`` if you are using Python API.
+
+Unofficial windows binaries and instructions on how to use them are hosted on `Guido Tapia's blog <http://www.picnet.com.au/blogs/guido/post/2016/09/22/xgboost-windows-x64-binaries-for-download/>`_.
 
 .. _build_gpu_support:
 
@@ -185,9 +195,7 @@ Building with GPU support
 =========================
 XGBoost can be built with GPU support for both Linux and Windows using CMake. GPU support works with the Python package as well as the CLI version. See `Installing R package with GPU support`_ for special instructions for R.
 
-An up-to-date version of the CUDA toolkit is required.  Please note that we
-skipped the support for compiling XGBoost with NVCC 10.1 due a small bug in its
-spliter, see `#4264 <https://github.com/dmlc/xgboost/issues/4264>`_.
+An up-to-date version of the CUDA toolkit is required.
 
 From the command line on Linux starting from the XGBoost directory:
 
@@ -196,7 +204,7 @@ From the command line on Linux starting from the XGBoost directory:
   mkdir build
   cd build
   cmake .. -DUSE_CUDA=ON
-  make -j4
+  make -j
 
 .. note:: Enabling multi-GPU training
 
@@ -206,10 +214,16 @@ From the command line on Linux starting from the XGBoost directory:
 
     mkdir build
     cd build
-    cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2
-    make -j4
+    cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON
+    make -j
 
-On Windows, run CMake as follows:
+On Windows, see what options for generators you have for CMake, and choose one with ``[arch]`` replaced with Win64:
+
+.. code-block:: bash
+
+  cmake -help
+
+Then run CMake as follows:
 
 .. code-block:: bash
 
@@ -217,15 +231,13 @@ On Windows, run CMake as follows:
   cd build
   cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON
 
-(Change the ``-G`` option appropriately if you have a different version of Visual Studio installed.)
-
 .. note:: Visual Studio 2017 Win64 Generator may not work
 
   Choosing the Visual Studio 2017 generator may cause compilation failure. When it happens, specify the 2015 compiler by adding the ``-T`` option:
 
   .. code-block:: bash
 
-    make .. -G"Visual Studio 15 2017 Win64" -T v140,cuda=8.0 -DUSE_CUDA=ON
+    make .. -G"Visual Studio 15 2017 Win64" -T v140,cuda=8.0 -DR_LIB=ON -DUSE_CUDA=ON
 
 To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., ``-DGPU_COMPUTE_VER=50``.
 The above cmake configuration run will create an ``xgboost.sln`` solution file in the build directory. Build this solution in release mode as a x64 build, either from Visual studio or from command line:
@@ -239,9 +251,7 @@ To speed up compilation, run multiple jobs in parallel by appending option ``--
 Customized Building
 ===================
 
-We recommend the use of CMake for most use cases. See the full range of building options in CMakeLists.txt.
-
-Alternatively, you may use Makefile. The Makefile uses a configuration file ``config.mk``, which lets you modify several compilation flags:
+The configuration file ``config.mk`` modifies several compilation flags:
 - Whether to enable support for various distributed filesystems such as HDFS and Amazon S3
 - Which compiler to use
 - And some more
@@ -251,7 +261,7 @@ To customize, first copy ``make/config.mk`` to the project root and then modify
 Python Package Installation
 ===========================
 
-The Python package is located at ``python-package/``.
+The python package is located at ``python-package/``.
 There are several ways to install the package:
 
 1. Install system-wide, which requires root permission:
@@ -261,7 +271,7 @@ There are several ways to install the package:
   cd python-package; sudo python setup.py install
 
 You will however need Python ``distutils`` module for this to
-work. It is often part of the core Python package or it can be installed using your
+work. It is often part of the core python package or it can be installed using your
 package manager, e.g. in Debian use
 
 .. code-block:: bash
@@ -272,10 +282,10 @@ package manager, e.g. in Debian use
 
   If you recompiled XGBoost, then you need to reinstall it again to make the new library take effect.
 
-2. Only set the environment variable ``PYTHONPATH`` to tell Python where to find
-   the library. For example, assume we cloned ``xgboost`` on the home directory
-   ``~``. then we can added the following line in ``~/.bashrc``.
-   This option is **recommended for developers** who change the code frequently. The changes will be immediately reflected once you pulled the code and rebuild the project (no need to call ``setup`` again).
+2. Only set the environment variable ``PYTHONPATH`` to tell python where to find
+   the library. For example, assume we cloned `xgboost` on the home directory
+   `~`. then we can added the following line in `~/.bashrc`.
+   This option is **recommended for developers** who change the code frequently. The changes will be immediately reflected once you pulled the code and rebuild the project (no need to call ``setup`` again)
 
 .. code-block:: bash
 
@@ -287,23 +297,12 @@ package manager, e.g. in Debian use
 
   cd python-package; python setup.py develop --user
 
-.. _mingw_python:
+4. If you are installing the latest XGBoost version which requires compilation, add MinGW to the system PATH:
 
-Building XGBoost library for Python for Windows with MinGW-w64 (Advanced)
--------------------------------------------------------------------------
+.. code-block:: bash
 
-Windows versions of Python are built with Microsoft Visual Studio. Usually Python binary modules are built with the same compiler the interpreter is built with. However, you may not be able to use Visual Studio, for following reasons:
-
-1. VS is proprietary and commercial software. Microsoft provides a freeware "Community" edition, but its licensing terms impose restrictions as to where and how it can be used.
-2. Visual Studio contains telemetry, as documented in `Microsoft Visual Studio Licensing Terms <https://visualstudio.microsoft.com/license-terms/mt736442/>`_. Running software with telemetry may be against the policy of your organization.
-
-So you may want to build XGBoost with GCC own your own risk. This presents some difficulties because MSVC uses Microsoft runtime and MinGW-w64 uses own runtime, and the runtimes have different incompatible memory allocators. But in fact this setup is usable if you know how to deal with it. Here is some experience.
-
-1. The Python interpreter will crash on exit if XGBoost was used. This is usually not a big issue.
-2. ``-O3`` is OK.
-3. ``-mtune=native`` is also OK.
-4. Don't use ``-march=native`` gcc flag. Using it causes the Python interpreter to crash if the DLL was actually used.
-5. You may need to provide the lib with the runtime libs. If ``mingw32/bin`` is not in ``PATH``, build a wheel (``python setup.py bdist_wheel``), open it with an archiver and put the needed dlls to the directory where ``xgboost.dll`` is situated. Then you can install the wheel with ``pip``.
+    import os
+    os.environ['PATH'] = os.environ['PATH'] + ';C:\\Program Files\\mingw-w64\\x86_64-5.3.0-posix-seh-rt_v4-rev0\\mingw64\\bin'
 
 R Package Installation
 ======================
@@ -317,13 +316,35 @@ You can install xgboost from CRAN just like any other R package:
 
   install.packages("xgboost")
 
-For OSX users, single-threaded version will be installed. So only one thread will be used for training. To enable use of multiple threads (and utilize capacity of multi-core CPUs), see the section :ref:`osx_multithread` to install XGBoost from source.
+Or you can install it from our weekly updated drat repo:
+
+.. code-block:: R
+
+  install.packages("drat", repos="https://cran.rstudio.com")
+  drat:::addRepo("dmlc")
+  install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
+
+For OSX users, single threaded version will be installed. To install multi-threaded version,
+first follow `Building on OSX`_ to get the OpenMP enabled compiler. Then
+
+- Set the ``Makevars`` file in highest piority for R.
+
+  The point is, there are three ``Makevars`` : ``~/.R/Makevars``, ``xgboost/R-package/src/Makevars``, and ``/usr/local/Cellar/r/3.2.0/R.framework/Resources/etc/Makeconf`` (the last one obtained by running ``file.path(R.home("etc"), "Makeconf")`` in R), and ``SHLIB_OPENMP_CXXFLAGS`` is not set by default!! After trying, it seems that the first one has highest piority (surprise!).
+
+  Then inside R, run
+
+.. code-block:: R
+
+  install.packages("drat", repos="https://cran.rstudio.com")
+  drat:::addRepo("dmlc")
+  install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
 
 Installing the development version
 ----------------------------------
 
 Make sure you have installed git and a recent C++ compiler supporting C++11 (e.g., g++-4.8 or higher).
-On Windows, Rtools must be installed, and its bin directory has to be added to ``PATH`` during the installation.
+On Windows, Rtools must be installed, and its bin directory has to be added to PATH during the installation.
+And see the previous subsection for an OSX tip.
 
 Due to the use of git-submodules, ``devtools::install_github`` can no longer be used to install the latest version of R package.
 Thus, one has to run git to check out the code first:
@@ -345,39 +366,10 @@ In this case, just start R as you would normally do and run the following:
   setwd('wherever/you/cloned/it/xgboost/R-package/')
   install.packages('.', repos = NULL, type="source")
 
-The package could also be built and installed with CMake (and Visual C++ 2015 on Windows) using instructions from :ref:`r_gpu_support`, but without GPU support (omit the ``-DUSE_CUDA=ON`` cmake parameter).
+The package could also be built and installed with cmake (and Visual C++ 2015 on Windows) using instructions from the next section, but without GPU support (omit the ``-DUSE_CUDA=ON`` cmake parameter).
 
 If all fails, try `Building the shared library`_ to see whether a problem is specific to R package or not.
 
-.. _osx_multithread:
-
-Installing R package on Mac OSX with multi-threading
-----------------------------------------------------
-
-First, obtain ``gcc-8`` with Homebrew (https://brew.sh/) to enable multi-threading (i.e. using multiple CPU threads for training). The default Apple Clang compiler does not support OpenMP, so using the default compiler would have disabled multi-threading.
-
-.. code-block:: bash
-
-  brew install gcc@8
-
-Now, clone the repository:
-
-.. code-block:: bash
-
-  git clone --recursive https://github.com/dmlc/xgboost
-
-Create the ``build/`` directory and invoke CMake with option ``R_LIB=ON``. Make sure to add ``CC=gcc-8 CXX=g++-8`` so that Homebrew GCC is selected. After invoking CMake, you can install the R package by running ``make`` and ``make install``:
-
-.. code-block:: bash
-
-  mkdir build
-  cd build
-  CC=gcc-7 CXX=g++-7 cmake .. -DR_LIB=ON
-  make -j4
-  make install
-
-.. _r_gpu_support:
-
 Installing R package with GPU support
 -------------------------------------
 
@@ -393,9 +385,9 @@ On Linux, starting from the XGBoost directory type:
   make install -j
 
 When default target is used, an R package shared library would be built in the ``build`` area.
-The ``install`` target, in addition, assembles the package files with this shared library under ``build/R-package`` and runs ``R CMD INSTALL``.
+The ``install`` target, in addition, assembles the package files with this shared library under ``build/R-package``, and runs ``R CMD INSTALL``.
 
-On Windows, CMake with Visual C++ Build Tools (or Visual Studio) has to be used to build an R package with GPU support. Rtools must also be installed (perhaps, some other MinGW distributions with ``gendef.exe`` and ``dlltool.exe`` would work, but that was not tested).
+On Windows, cmake with Visual C++ Build Tools (or Visual Studio) has to be used to build an R package with GPU support. Rtools must also be installed (perhaps, some other MinGW distributions with ``gendef.exe`` and ``dlltool.exe`` would work, but that was not tested).
 
 .. code-block:: bash
 
@@ -404,8 +396,8 @@ On Windows, CMake with Visual C++ Build Tools (or Visual Studio) has to be used
   cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON -DR_LIB=ON
   cmake --build . --target install --config Release
 
-When ``--target xgboost`` is used, an R package DLL would be built under ``build/Release``.
-The ``--target install``, in addition, assembles the package files with this dll under ``build/R-package`` and runs ``R CMD INSTALL``.
+When ``--target xgboost`` is used, an R package dll would be built under ``build/Release``.
+The ``--target install``, in addition, assembles the package files with this dll under ``build/R-package``, and runs ``R CMD INSTALL``.
 
 If cmake can't find your R during the configuration step, you might provide the location of its executable to cmake like this: ``-DLIBR_EXECUTABLE="C:/Program Files/R/R-3.4.1/bin/x64/R.exe"``.
 
@@ -450,3 +442,4 @@ Trouble Shooting
    .. code-block:: bash
 
      git clone https://github.com/dmlc/xgboost --recursive
+

doc/conf.py

@@ -22,22 +22,14 @@ import os, subprocess
 import shlex
 import guzzle_sphinx_theme
 
-git_branch = os.getenv('SPHINX_GIT_BRANCH', default=None)
-if git_branch is None:
-    # If SPHINX_GIT_BRANCH environment variable is not given, run git to determine branch name
-    git_branch = [re.sub(r'origin/', '', x.lstrip(' ')) for x in str(git.branch('-r', '--contains', 'HEAD')).rstrip('\n').split('\n')]
-    git_branch = [x for x in git_branch if 'HEAD' not in x]
+git_branch = [re.sub(r'origin/', '', x.lstrip(' ')) for x in str(git.branch('-r', '--contains', 'HEAD')).rstrip('\n').split('\n')]
+git_branch = [x for x in git_branch if 'HEAD' not in x]
 print('git_branch = {}'.format(git_branch[0]))
 try:
   filename, _ = urllib.request.urlretrieve('https://s3-us-west-2.amazonaws.com/xgboost-docs/{}.tar.bz2'.format(git_branch[0]))
   call('if [ -d tmp ]; then rm -rf tmp; fi; mkdir -p tmp/jvm; cd tmp/jvm; tar xvf {}'.format(filename), shell=True)
 except HTTPError:
   print('JVM doc not found. Skipping...')
-try:
-  filename, _ = urllib.request.urlretrieve('https://s3-us-west-2.amazonaws.com/xgboost-docs/doxygen/{}.tar.bz2'.format(git_branch[0]))
-  call('mkdir -p tmp/dev; cd tmp/dev; tar xvf {}; mv doc_doxygen/html/* .; rm -rf doc_doxygen'.format(filename), shell=True)
-except HTTPError:
-  print('C API doc not found. Skipping...')
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -49,7 +41,7 @@ sys.path.insert(0, curr_path)
 
 # -- mock out modules
 import mock
-MOCK_MODULES = ['scipy', 'scipy.sparse', 'sklearn', 'pandas']
+MOCK_MODULES = ['numpy', 'scipy', 'scipy.sparse', 'sklearn', 'matplotlib', 'pandas', 'graphviz']
 for mod_name in MOCK_MODULES:
   sys.modules[mod_name] = mock.Mock()
 
@@ -70,7 +62,6 @@ release = xgboost.__version__
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
 extensions = [
-    'matplotlib.sphinxext.plot_directive',
     'sphinx.ext.autodoc',
     'sphinx.ext.napoleon',
     'sphinx.ext.mathjax',
@@ -78,11 +69,6 @@ extensions = [
     'breathe'
 ]
 
-graphviz_output_format = 'png'
-plot_formats = [('svg', 300), ('png', 100), ('hires.png', 300)]
-plot_html_show_source_link = False
-plot_html_show_formats = False
-
 # Breathe extension variables
 breathe_projects = {"xgboost": "doxyxml/"}
 breathe_default_project = "xgboost"
@@ -164,7 +150,7 @@ extensions.append("guzzle_sphinx_theme")
 # Guzzle theme options (see theme.conf for more information)
 html_theme_options = {
     # Set the name of the project to appear in the sidebar
-    "project_nav_name": "XGBoost"
+    "project_nav_name": "XGBoost (0.80)"
 }
 
 html_sidebars = {

doc/contribute.rst

@@ -19,7 +19,6 @@ Everyone is more than welcome to contribute. It is a way to make the project bet
 * `Documents`_
 * `Testcases`_
 * `Sanitizers`_
-* `clang-tidy`_
 * `Examples`_
 * `Core Library`_
 * `Python Package`_
@@ -150,14 +149,6 @@ sanitizer is not compatible with the other two sanitizers.
 
     cmake -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak" /path/to/xgboost
 
-By default, CMake will search regular system paths for sanitizers, you can also
-supply a specified SANITIZER_PATH.
-
-  .. code-block:: bash
-
-    cmake -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak" \
-    -DSANITIZER_PATH=/path/to/sanitizers /path/to/xgboost
-
 How to use sanitizers with CUDA support
 =======================================
 Runing XGBoost on CUDA with address sanitizer (asan) will raise memory error.
@@ -166,35 +157,10 @@ environment variable:
 
   .. code-block:: bash
 
-    ASAN_OPTIONS=protect_shadow_gap=0 ${BUILD_DIR}/testxgboost
+    ASAN_OPTIONS=protect_shadow_gap=0 ../testxgboost
 
 For details, please consult `official documentation <https://github.com/google/sanitizers/wiki>`_ for sanitizers.
 
-**********
-clang-tidy
-**********
-To run clang-tidy on both C++ and CUDA source code,  run the following command
-from the top level source tree:
-
-  .. code-black:: bash
-    cd /path/to/xgboost/
-    python3 tests/ci_build/tidy.py --gtest-path=/path/to/google-test
-
-The script requires the full path of Google Test library via the ``--gtest-path`` argument.
-
-Also, the script accepts two optional integer arguments, namely ``--cpp`` and ``--cuda``.
-By default they are both set to 1.  If you want to exclude CUDA source from
-linting, use:
-
-  .. code-black:: bash
-    cd /path/to/xgboost/
-    python3 tests/ci_build/tidy.py --cuda=0
-
-Similarly, if you want to exclude C++ source from linting:
-
-  .. code-black:: bash
-    cd /path/to/xgboost/
-    python3 tests/ci_build/tidy.py --cpp=0
 
 ********
 Examples

doc/gpu/index.rst

@@ -37,37 +37,33 @@ Supported parameters
 .. |tick| unicode:: U+2714 
 .. |cross| unicode:: U+2718 
 
-+--------------------------------+---------------+--------------+
-| parameter                      | ``gpu_exact`` | ``gpu_hist`` |
-+================================+===============+==============+
-| ``subsample``                  | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``colsample_bytree``           | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``colsample_bylevel``          | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``max_bin``                    | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``gpu_id``                     | |tick|        | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``n_gpus``                     | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``predictor``                  | |tick|        | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``grow_policy``                | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``monotone_constraints``       | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
-| ``single_precision_histogram`` | |cross|       | |tick|       |
-+--------------------------------+---------------+--------------+
++--------------------------+---------------+--------------+
+| parameter                | ``gpu_exact`` | ``gpu_hist`` |
++==========================+===============+==============+
+| ``subsample``            | |cross|       | |tick|       |
++--------------------------+---------------+--------------+
+| ``colsample_bytree``     | |cross|       | |tick|       |
++--------------------------+---------------+--------------+
+| ``colsample_bylevel``    | |cross|       | |tick|       |
++--------------------------+---------------+--------------+
+| ``max_bin``              | |cross|       | |tick|       |
++--------------------------+---------------+--------------+
+| ``gpu_id``               | |tick|        | |tick|       |
++--------------------------+---------------+--------------+
+| ``n_gpus``               | |cross|       | |tick|       |
++--------------------------+---------------+--------------+
+| ``predictor``            | |tick|        | |tick|       |
++--------------------------+---------------+--------------+
+| ``grow_policy``          | |cross|       | |tick|       |
++--------------------------+---------------+--------------+
+| ``monotone_constraints`` | |cross|       | |tick|       |
++--------------------------+---------------+--------------+
 
 GPU accelerated prediction is enabled by default for the above mentioned ``tree_method`` parameters but can be switched to CPU prediction by setting ``predictor`` to ``cpu_predictor``. This could be useful if you want to conserve GPU memory. Likewise when using CPU algorithms, GPU accelerated prediction can be enabled by setting ``predictor`` to ``gpu_predictor``.
 
-The experimental parameter ``single_precision_histogram`` can be set to True to enable building histograms using single precision. This may improve speed, in particular on older architectures.
-
 The device ordinal can be selected using the ``gpu_id`` parameter, which defaults to 0.
 
-Multiple GPUs can be used with the ``gpu_hist`` tree method using the ``n_gpus`` parameter. which defaults to 1. If this is set to -1 all available GPUs will be used.  If ``gpu_id`` is specified as non-zero, the selected gpu devices will be from ``gpu_id`` to ``gpu_id+n_gpus``, please note that ``gpu_id+n_gpus`` must be less than or equal to the number of available GPUs on your system.  As with GPU vs. CPU, multi-GPU will not always be faster than a single GPU due to PCI bus bandwidth that can limit performance.
+Multiple GPUs can be used with the ``gpu_hist`` tree method using the ``n_gpus`` parameter. which defaults to 1. If this is set to -1 all available GPUs will be used.  If ``gpu_id`` is specified as non-zero, the gpu device order is ``mod(gpu_id + i) % n_visible_devices`` for ``i=0`` to ``n_gpus-1``.  As with GPU vs. CPU, multi-GPU will not always be faster than a single GPU due to PCI bus bandwidth that can limit performance.
 
 .. note:: Enabling multi-GPU training
 
@@ -82,95 +78,6 @@ The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/bu
   param['max_bin'] = 16
   param['tree_method'] = 'gpu_hist'
 
-Objective functions
-===================
-Most of the objective functions implemented in XGBoost can be run on GPU.  Following table shows current support status.
-
-.. |tick| unicode:: U+2714
-.. |cross| unicode:: U+2718
-
-+-----------------+-------------+
-| Objectives      | GPU support |
-+-----------------+-------------+
-| reg:squarederror| |tick|      |
-+-----------------+-------------+
-| reg:logistic    | |tick|      |
-+-----------------+-------------+
-| binary:logistic | |tick|      |
-+-----------------+-------------+
-| binary:logitraw | |tick|      |
-+-----------------+-------------+
-| binary:hinge    | |tick|      |
-+-----------------+-------------+
-| count:poisson   | |tick|      |
-+-----------------+-------------+
-| reg:gamma       | |tick|      |
-+-----------------+-------------+
-| reg:tweedie     | |tick|      |
-+-----------------+-------------+
-| multi:softmax   | |tick|      |
-+-----------------+-------------+
-| multi:softprob  | |tick|      |
-+-----------------+-------------+
-| survival:cox    | |cross|     |
-+-----------------+-------------+
-| rank:pairwise   | |cross|     |
-+-----------------+-------------+
-| rank:ndcg       | |cross|     |
-+-----------------+-------------+
-| rank:map        | |cross|     |
-+-----------------+-------------+
-
-For multi-gpu support, objective functions also honor the ``n_gpus`` parameter,
-which, by default is set to 1.  To disable running objectives on GPU, just set
-``n_gpus`` to 0.
-
-Metric functions
-===================
-Following table shows current support status for evaluation metrics on the GPU.
-
-.. |tick| unicode:: U+2714
-.. |cross| unicode:: U+2718
-
-+-----------------+-------------+
-| Metric          | GPU Support |
-+=================+=============+
-| rmse            | |tick|      |
-+-----------------+-------------+
-| mae             | |tick|      |
-+-----------------+-------------+
-| logloss         | |tick|      |
-+-----------------+-------------+
-| error           | |tick|      |
-+-----------------+-------------+
-| merror          | |cross|     |
-+-----------------+-------------+
-| mlogloss        | |cross|     |
-+-----------------+-------------+
-| auc             | |cross|     |
-+-----------------+-------------+
-| aucpr           | |cross|     |
-+-----------------+-------------+
-| ndcg            | |cross|     |
-+-----------------+-------------+
-| map             | |cross|     |
-+-----------------+-------------+
-| poisson-nloglik | |tick|      |
-+-----------------+-------------+
-| gamma-nloglik   | |tick|      |
-+-----------------+-------------+
-| cox-nloglik     | |cross|     |
-+-----------------+-------------+
-| gamma-deviance  | |tick|      |
-+-----------------+-------------+
-| tweedie-nloglik | |tick|      |
-+-----------------+-------------+
-
-As for objective functions, metrics honor the ``n_gpus`` parameter,
-which, by default is set to 1.  To disable running metrics on GPU, just set
-``n_gpus`` to 0.
-
-
 Benchmarks
 ==========
 You can run benchmarks on synthetic data for binary classification:
@@ -195,10 +102,6 @@ Training time time on 1,000,000 rows x 50 columns with 500 boosting iterations a
 
 See `GPU Accelerated XGBoost <https://xgboost.ai/2016/12/14/GPU-accelerated-xgboost.html>`_ and `Updates to the XGBoost GPU algorithms <https://xgboost.ai/2018/07/04/gpu-xgboost-update.html>`_ for additional performance benchmarks of the ``gpu_exact`` and ``gpu_hist`` tree methods.
 
-Developer notes
-==========
-The application may be profiled with annotations by specifying USE_NTVX to cmake and providing the path to the stand-alone nvtx header via NVTX_HEADER_DIR. Regions covered by the 'Monitor' class in cuda code will automatically appear in the nsight profiler.
-
 **********
 References
 **********
@@ -206,16 +109,13 @@ References
 
 `Nvidia Parallel Forall: Gradient Boosting, Decision Trees and XGBoost with CUDA <https://devblogs.nvidia.com/parallelforall/gradient-boosting-decision-trees-xgboost-cuda/>`_
 
-Contributors
+Authors
 =======
-Many thanks to the following contributors (alphabetical order):
-* Andrey Adinets
-* Jiaming Yuan
-* Jonathan C. McKinney
-* Matthew Jones
-* Philip Cho
 * Rory Mitchell
+* Jonathan C. McKinney
 * Shankara Rao Thejaswi Nanditale
 * Vinay Deshpande
+* ... and the rest of the H2O.ai and NVIDIA team.
 
 Please report bugs to the user forum https://discuss.xgboost.ai/.
+

doc/jvm/index.rst

@@ -58,11 +58,9 @@ For sbt, please add the repository and dependency in build.sbt as following:
 
 If you want to use XGBoost4J-Spark, replace ``xgboost4j`` with ``xgboost4j-spark``.
 
-.. note:: XGBoost4J-Spark requires Apache Spark 2.3+
+.. note:: XGBoost4J-Spark requires Spark 2.3+
 
-  XGBoost4J-Spark now requires **Apache Spark 2.3+**. Latest versions of XGBoost4J-Spark uses facilities of `org.apache.spark.ml.param.shared` extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.
-
-  Also, make sure to install Spark directly from `Apache website <https://spark.apache.org/>`_. **Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark.** Consult appropriate third parties to obtain their distribution of XGBoost.
+  XGBoost4J-Spark now requires Spark 2.3+. Latest versions of XGBoost4J-Spark uses facilities of `org.apache.spark.ml.param.shared` extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.
 
 Installation from maven repo
 ============================

doc/jvm/xgboost4j_spark_tutorial.rst

@@ -57,21 +57,13 @@ and then refer to the snapshot dependency by adding:
 
   <dependency>
       <groupId>ml.dmlc</groupId>
-      <artifactId>xgboost4j-spark</artifactId>
+      <artifactId>xgboost4j</artifactId>
       <version>next_version_num-SNAPSHOT</version>
   </dependency>
 
-.. note:: XGBoost4J-Spark requires Apache Spark 2.4+
+.. note:: XGBoost4J-Spark requires Spark 2.3+
 
-  XGBoost4J-Spark now requires **Apache Spark 2.4+**. Latest versions of XGBoost4J-Spark uses facilities of `org.apache.spark.ml.param.shared` extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.
-
-  Also, make sure to install Spark directly from `Apache website <https://spark.apache.org/>`_. **Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark.** Consult appropriate third parties to obtain their distribution of XGBoost.
-
-Installation from maven repo
-
-.. note:: Use of Python in XGBoost4J-Spark
-
-  By default, we use the tracker in `dmlc-core <https://github.com/dmlc/dmlc-core/tree/master/tracker>`_ to drive the training with XGBoost4J-Spark. It requires Python 2.7+. We also have an experimental Scala version of tracker which can be enabled by passing the parameter ``tracker_conf`` as ``scala``.
+  XGBoost4J-Spark now requires Spark 2.3+. Latest versions of XGBoost4J-Spark uses facilities of `org.apache.spark.ml.param.shared` extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.
 
 Data Preparation
 ================
@@ -153,49 +145,6 @@ Now, we have a DataFrame containing only two columns, "features" which contains
 "sepal length", "sepal width", "petal length" and "petal width" and "classIndex" which has Double-typed
 labels. A DataFrame like this (containing vector-represented features and numeric labels) can be fed to XGBoost4J-Spark's training engine directly.
 
-Dealing with missing values
-~~~~~~~~~~~~~~~~~~~~~~
-
-Strategies to handle missing values (and therefore overcome issues as above):
-
-In the case that a feature column contains missing values for any reason (could be related to business logic / wrong data ingestion process / etc.), the user should decide on a strategy of how to handle it. 
-The choice of approach depends on the value representing 'missing' which fall into four different categories:
-
-1. 0
-2. NaN
-3. Null
-4. Any other value which is not mentioned in (1) / (2) / (3)
-
-We introduce the following approaches dealing with missing value and their fitting scenarios:
-
-1. Skip VectorAssembler (using setHandleInvalid = "skip") directly. Used in (2), (3).
-2. Keep it (using setHandleInvalid = "keep"), and set the "missing" parameter in XGBClassifier/XGBRegressor as the value representing missing. Used in (2) and (4).
-3. Keep it (using setHandleInvalid = "keep") and transform to other irregular values. Used in (3).
-4. Nothing to be done, used in (1). 
-
-Then, XGBoost will automatically learn what's the ideal direction to go when a value is missing, based on that value and strategy.
-
-Example of setting a missing value (e.g. -999) to the "missing" parameter in XGBoostClassifier:
-
-.. code-block:: scala
-
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
-  val xgbParam = Map("eta" -> 0.1f,
-        "missing" -> -999,
-        "objective" -> "multi:softprob",
-        "num_class" -> 3,
-        "num_round" -> 100,
-        "num_workers" -> 2)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-        setFeaturesCol("features").
-        setLabelCol("classIndex")
-
-.. note:: Using 0 to represent meaningful value
-
-  Due to the fact that Spark's VectorAssembler transformer only accepts 0 as a missing values, this one creates a problem when the user has 0 as meaningful value plus there are enough 0's to use SparseVector (However, In case the dataset is represented by a DenseVector, the 0 is kept)
-
-  In this case, users are also supposed to transform 0 to some other values to avoid the issue.
-
 Training
 ========
 
@@ -234,20 +183,6 @@ After we set XGBoostClassifier parameters and feature/label column, we can build
 
   val xgbClassificationModel = xgbClassifier.fit(xgbInput)
 
-Early Stopping
-----------------
-
-Early stopping is a feature to prevent the unnecessary training iterations. By specifying ``num_early_stopping_rounds`` or directly call ``setNumEarlyStoppingRounds`` over a XGBoostClassifier or XGBoostRegressor, we can define number of rounds if the evaluation metric going away from the best iteration and early stop training iterations.
-
-When it comes to custom eval metrics, in additional to ``num_early_stopping_rounds``, you also need to define ``maximize_evaluation_metrics`` or call ``setMaximizeEvaluationMetrics`` to specify whether you want to maximize or minimize the metrics in training. For built-in eval metrics, XGBoost4J-Spark will automatically select the direction.
-
-For example, we need to maximize the evaluation metrics (set ``maximize_evaluation_metrics`` with true), and set ``num_early_stopping_rounds`` with 5. The evaluation metric of 10th iteration is the maximum one until now. In the following iterations, if there is no evaluation metric greater than the 10th iteration's (best one), the traning would be early stopped at 15th iteration.  
-
-Training with Evaluation Sets
-----------------
-
-You can also monitor 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.
-
 Prediction
 ==========
 

doc/parameter.rst

@@ -23,25 +23,14 @@ General Parameters
 
   - Which booster to use. Can be ``gbtree``, ``gblinear`` or ``dart``; ``gbtree`` and ``dart`` use tree based models while ``gblinear`` uses linear functions.
 
-* ``silent`` [default=0] [Deprecated]
+* ``silent`` [default=0]
 
-  - Deprecated.  Please use ``verbosity`` instead.
-
-* ``verbosity`` [default=1]
-
-  - Verbosity of printing messages.  Valid values are 0 (silent),
-    1 (warning), 2 (info), 3 (debug).  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.
+  - 0 means printing running messages, 1 means silent mode
 
 * ``nthread`` [default to maximum number of threads available if not set]
 
   - Number of parallel threads used to run XGBoost
 
-* ``disable_default_eval_metric`` [default=0]
-
-  - Flag to disable default metric. Set to >0 to disable.
-
 * ``num_pbuffer`` [set automatically by XGBoost, no need to be set by user]
 
   - Size of prediction buffer, normally set to number of training instances. The buffers are used to save the prediction results of last boosting step.
@@ -64,8 +53,8 @@ Parameters for Tree Booster
 
 * ``max_depth`` [default=6]
 
-  - Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in ``lossguided`` growing policy when tree_method is set as ``hist`` and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.
-  - range: [0,∞] (0 is only accepted in ``lossguided`` growing policy when tree_method is set as ``hist``)
+  - Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 indicates no limit. Note that limit is required when ``grow_policy`` is set of ``depthwise``.
+  - range: [0,∞]
 
 * ``min_child_weight`` [default=1]
 
@@ -82,22 +71,15 @@ Parameters for Tree Booster
   - Subsample ratio of the training instances. Setting it to 0.5 means that XGBoost would randomly sample half of the training data prior to growing trees. and this will prevent overfitting. Subsampling will occur once in every boosting iteration.
   - range: (0,1]
 
-* ``colsample_bytree``, ``colsample_bylevel``, ``colsample_bynode`` [default=1]
-  - This is a family of parameters for subsampling of columns.
-  - All ``colsample_by*`` parameters have a range of (0, 1], the default value of 1, and
-    specify the fraction of columns to be subsampled.
-  - ``colsample_bytree`` is the subsample ratio of columns when constructing each
-    tree. Subsampling occurs once for every tree constructed.
-  - ``colsample_bylevel`` is the subsample ratio of columns for each level. Subsampling
-    occurs once for every new depth level reached in a tree. Columns are subsampled from
-    the set of columns chosen for the current tree.
-  - ``colsample_bynode`` is the subsample ratio of columns for each node
-    (split). Subsampling occurs once every time a new split is evaluated. Columns are
-    subsampled from the set of columns chosen for the current level.
-  - ``colsample_by*`` parameters work cumulatively. For instance,
-    the combination ``{'colsample_bytree':0.5, 'colsample_bylevel':0.5,
-    'colsample_bynode':0.5}`` with 64 features will leave 8 features to choose from at
-    each split.
+* ``colsample_bytree`` [default=1]
+
+  - Subsample ratio of columns when constructing each tree. Subsampling will occur once in every boosting iteration.
+  - range: (0,1]
+
+* ``colsample_bylevel`` [default=1]
+
+  - Subsample ratio of columns for each split, in each level. Subsampling will occur each time a new split is made. This paramter has no effect when ``tree_method`` is set to ``hist``.
+  - range: (0,1]
 
 * ``lambda`` [default=1, alias: ``reg_lambda``]
 
@@ -110,7 +92,7 @@ Parameters for Tree Booster
 * ``tree_method`` string [default= ``auto``]
 
   - The tree construction algorithm used in XGBoost. See description in the `reference paper <http://arxiv.org/abs/1603.02754>`_.
-  - XGBoost supports ``hist`` and ``approx`` for distributed training and only support ``approx`` for external memory version.
+  - Distributed and external memory version only support ``tree_method=approx``.
   - Choices: ``auto``, ``exact``, ``approx``, ``hist``, ``gpu_exact``, ``gpu_hist``
 
     - ``auto``: Use heuristic to choose the fastest method.
@@ -152,7 +134,7 @@ Parameters for Tree Booster
     - ``refresh``: refreshes tree's statistics and/or leaf values based on the current data. Note that no random subsampling of data rows is performed.
     - ``prune``: prunes the splits where loss < min_split_loss (or gamma).
 
-  - In a distributed setting, the implicit updater sequence value would be adjusted to ``grow_histmaker,prune`` by default, and you can set ``tree_method`` as ``hist`` to use ``grow_histmaker``. 
+  - In a distributed setting, the implicit updater sequence value would be adjusted to ``grow_histmaker,prune``.
 
 * ``refresh_leaf`` [default=1]
 
@@ -170,7 +152,7 @@ Parameters for Tree Booster
 
   - Controls a way new nodes are added to the tree.
   - Currently supported only if ``tree_method`` is set to ``hist``.
-  - Choices: ``depthwise``, ``lossguide``
+  - Choices: ``depthwise``, ```lossguide``
 
     - ``depthwise``: split at nodes closest to the root.
     - ``lossguide``: split at nodes with highest loss change.
@@ -192,9 +174,6 @@ Parameters for Tree Booster
     - ``cpu_predictor``: Multicore CPU prediction algorithm.
     - ``gpu_predictor``: Prediction using GPU. Default when ``tree_method`` is ``gpu_exact`` or ``gpu_hist``.
 
-* ``num_parallel_tree``, [default=1]
-  - Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.
-
 Additional parameters for Dart Booster (``booster=dart``)
 =========================================================
 
@@ -265,20 +244,6 @@ Parameters for Linear Booster (``booster=gblinear``)
     - ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run. 
     - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution. 
 
-* ``feature_selector`` [default= ``cyclic``]
-
-  - Feature selection and ordering method
-
-    * ``cyclic``: Deterministic selection by cycling through features one at a time.
-    * ``shuffle``: Similar to ``cyclic`` but with random feature shuffling prior to each update.
-    * ``random``: A random (with replacement) coordinate selector.
-    * ``greedy``: Select coordinate with the greatest gradient magnitude.  It has ``O(num_feature^2)`` complexity. It is fully deterministic. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter. Doing so would reduce the complexity to ``O(num_feature*top_k)``.
-    * ``thrifty``: Thrifty, approximately-greedy feature selector. Prior to cyclic updates, reorders features in descending magnitude of their univariate weight changes. This operation is multithreaded and is a linear complexity approximation of the quadratic greedy selection. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter.
-
-* ``top_k`` [default=0]
-
-  - The number of top features to select in ``greedy`` and ``thrifty`` feature selector. The value of 0 means using all the features.
-
 Parameters for Tweedie Regression (``objective=reg:tweedie``)
 =============================================================
 * ``tweedie_variance_power`` [default=1.5]
@@ -293,13 +258,16 @@ Learning Task Parameters
 ************************
 Specify the learning task and the corresponding learning objective. The objective options are below:
 
-* ``objective`` [default=reg:squarederror]
+* ``objective`` [default=reg:linear]
 
-  - ``reg:squarederror``: regression with squared loss
+  - ``reg:linear``: linear regression
   - ``reg:logistic``: logistic regression
   - ``binary:logistic``: logistic regression for binary classification, output probability
   - ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
   - ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
+  - ``gpu:reg:linear``, ``gpu:reg:logistic``, ``gpu:binary:logistic``, ``gpu:binary:logitraw``: versions
+    of the corresponding objective functions evaluated on the GPU; note that like the GPU histogram algorithm,
+    they can only be used when the entire training session uses the same dataset
   - ``count:poisson`` --poisson regression for count data, output mean of poisson distribution
 
     - ``max_delta_step`` is set to 0.7 by default in poisson regression (used to safeguard optimization)
@@ -308,9 +276,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
     Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function ``h(t) = h0(t) * HR``).
   - ``multi:softmax``: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)
   - ``multi:softprob``: same as softmax, but output a vector of ``ndata * nclass``, which can be further reshaped to ``ndata * nclass`` matrix. The result contains predicted probability of each data point belonging to each class.
-  - ``rank:pairwise``: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
-  - ``rank:ndcg``: Use LambdaMART to perform list-wise ranking where `Normalized Discounted Cumulative Gain (NDCG) <http://en.wikipedia.org/wiki/NDCG>`_ is maximized
-  - ``rank:map``: Use LambdaMART to perform list-wise ranking where `Mean Average Precision (MAP) <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_ is maximized
+  - ``rank:pairwise``: set XGBoost to do ranking task by minimizing the pairwise loss
   - ``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 `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Applications>`_.
   - ``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 `Tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Applications>`_.
 
@@ -333,9 +299,8 @@ Specify the learning task and the corresponding learning objective. The objectiv
     - ``merror``: Multiclass classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``.
     - ``mlogloss``: `Multiclass logloss <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html>`_.
     - ``auc``: `Area under the curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_
-    - ``aucpr``: `Area under the PR curve <https://en.wikipedia.org/wiki/Precision_and_recall>`_
     - ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
-    - ``map``: `Mean Average Precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
+    - ``map``: `Mean average precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
     - ``ndcg@n``, ``map@n``: 'n' can be assigned as an integer to cut off the top positions in the lists for evaluation.
     - ``ndcg-``, ``map-``, ``ndcg@n-``, ``map@n-``: In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding "-" in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
     - ``poisson-nloglik``: negative log-likelihood for Poisson regression