Document GPU objectives in NEWS. (#3866)

.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  }

.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 +3,71 @@ 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
+    # address sanitizer test
+    - TASK=sanitizer_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
+    - os: osx
+      env: TASK=sanitizer_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
+      - gcc-7
+      - g++-7
 
 before_install:
   - source dmlc-core/scripts/travis/travis_setup_env.sh

CMakeLists.txt

@@ -1,229 +1,264 @@
-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(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)
+option(R_LIB "Build shared library for R package" 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
+  "Space separated list of compute versions to be built against, e.g. '35 61'")
 option(USE_SANITIZER "Use santizer flags" OFF)
 option(SANITIZER_PATH "Path to sanitizes.")
 set(ENABLED_SANITIZERS "address" "leak" CACHE STRING
   "Semicolon separated list of sanitizer names. E.g 'address;leak'. Supported sanitizers are
 address, leak and thread.")
-## Plugins
+
+# Plugins
 option(PLUGIN_LZ4 "Build lz4 plugin" OFF)
 option(PLUGIN_DENSE_PARSER "Build dense parser plugin" OFF)
 
-## Deprecation warning
-if (USE_AVX)
+# 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)
+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)
-
-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)
+endif(USE_SANITIZER)
 
 # 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()
+
+# Gather source files
+include_directories (
+    ${PROJECT_SOURCE_DIR}/include
+    ${PROJECT_SOURCE_DIR}/dmlc-core/include
+    ${PROJECT_SOURCE_DIR}/rabit/include
+)
+
+file(GLOB_RECURSE SOURCES
+    src/*.cc
+    src/*.h
+    include/*.h
+)
+
+# Only add main function for executable target
+list(REMOVE_ITEM SOURCES ${PROJECT_SOURCE_DIR}/src/cli_main.cc)
+
+file(GLOB_RECURSE TEST_SOURCES "tests/cpp/*.cc")
+
+file(GLOB_RECURSE CUDA_SOURCES
+    src/*.cu
+    src/*.cuh
+)
+
+# Add plugins to source files
+if(PLUGIN_LZ4)
+  list(APPEND SOURCES plugin/lz4/sparse_page_lz4_format.cc)
+  link_libraries(lz4)
+endif()
+if(PLUGIN_DENSE_PARSER)
+  list(APPEND SOURCES plugin/dense_parser/dense_libsvm.cc)
+endif()
 
 # 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)
+  if(APPLE)
     set_target_properties(xgboost PROPERTIES SUFFIX ".so")
-  endif (APPLE)
+  endif()
+
   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 ${PROJECT_BINARY_DIR})
-  set_tests_properties(TestXGBoostCLI
-    PROPERTIES
-    PASS_REGULAR_EXPRESSION ".*test-rmse:0.087.*")
-endif (GOOGLE_TEST)
+  auto_source_group("${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

@@ -85,7 +85,4 @@ List of Contributors
 * [Andrew Thia](https://github.com/BlueTea88)
   - Andrew Thia implemented feature interaction constraints
 * [Wei Tian](https://github.com/weitian)
-* [Chen Qin](https://github.com/chenqin)
-* [Sam Wilkinson](https://samwilkinson.io)
-* [Matthew Jones](https://github.com/mt-jones)
-* [Jiaxiang Li](https://github.com/JiaxiangBU)
+* [Chen Qin] (https://github.com/chenqin)

Jenkinsfile

@@ -3,343 +3,106 @@
 // 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", "multiGpu": true],
+    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "9.2" ],
+    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
+    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": false, "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
+]
 
 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() }
-          ])
+}
+
+/**
+ * Build platform and test it via cmake.
+ */
+def buildPlatformCmake(buildName, conf, nodeReq, dockerTarget) {
+    def opts = utils.cmakeOptions(conf)
+    // Destination dir for artifacts
+    def distDir = "dist/${buildName}"
+    def dockerArgs = ""
+    if (conf["withGpu"]) {
+        dockerArgs = "--build-arg CUDA_VERSION=" + conf["cudaVersion"]
+    }
+    def test_suite = conf["withGpu"] ? (conf["multiGpu"] ? "mgpu" : "gpu") : "cpu"
+    // Build node - this is returned result
+    retry(3) {
+        node(nodeReq) {
+            unstash name: 'srcs'
+            echo """
+            |===== XGBoost CMake build =====
+            |  dockerTarget: ${dockerTarget}
+            |  cmakeOpts   : ${opts}
+            |=========================
+            """.stripMargin('|')
+            // Invoke command inside docker
+            sh """
+            ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/build_via_cmake.sh ${opts}
+            ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/test_${test_suite}.sh
+            """
+            if (!conf["multiGpu"]) {
+                sh """
+                ${dockerRun} ${dockerTarget} ${dockerArgs} bash -c "cd python-package; rm -f dist/*; python setup.py bdist_wheel --universal"
+                rm -rf "${distDir}"; mkdir -p "${distDir}/py"
+                cp xgboost "${distDir}"
+                cp -r python-package/dist "${distDir}/py"
+                # Test the wheel for compatibility on a barebones CPU container
+                ${dockerRun} release ${dockerArgs} bash -c " \
+                    pip install --user python-package/dist/xgboost-*-none-any.whl && \
+                    python -m nose -v tests/python"
+                # Test the wheel for compatibility on CUDA 10.0 container
+                ${dockerRun} gpu --build-arg CUDA_VERSION=10.0 bash -c " \
+                    pip install --user python-package/dist/xgboost-*-none-any.whl && \
+                    python -m nose -v --eval-attr='(not slow) and (not mgpu)' tests/python-gpu"
+                """
+            }
         }
-        milestone ordinal: 3
-      }
     }
-    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
-      }
-    }
-  }
-}
-
-// 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 +1,123 @@
+#!/usr/bin/groovy
+// -*- mode: groovy -*-
+// Jenkins pipeline
+// See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
+
+import groovy.transform.Field
+
+/* Restricted tasks: tasks generating artifacts, such as binary wheels and
+                     documentation */
+
+// Command to run command inside a docker container
+def dockerRun = 'tests/ci_build/ci_build.sh'
+// Utility functions
+@Field
+def utils
+@Field
+def commit_id
+@Field
+def branch_name
+
+def buildMatrix = [
+    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "9.2" ],
+    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": true,  "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
+    [ "enabled": true,  "os" : "linux", "withGpu": true, "withNccl": false, "withOmp": true, "pythonVersion": "2.7", "cudaVersion": "8.0" ],
+]
+
+pipeline {
+    // Each stage specify its own agent
+    agent none
+
+    // Setup common job properties
+    options {
+        ansiColor('xterm')
+        timestamps()
+        timeout(time: 120, unit: 'MINUTES')
+        buildDiscarder(logRotator(numToKeepStr: '10'))
+    }
+
+    // Build stages
+    stages {
+        stage('Jenkins: Get sources') {
+            agent {
+                label 'restricted'
+            }
+            steps {
+                script {
+                    utils = load('tests/ci_build/jenkins_tools.Groovy')
+                    utils.checkoutSrcs()
+                    commit_id = "${GIT_COMMIT}"
+                    branch_name = "${GIT_LOCAL_BRANCH}"
+                }
+                stash name: 'srcs', excludes: '.git/'
+                milestone label: 'Sources ready', ordinal: 1
+            }
+        }
+        stage('Jenkins: Build doc') {
+            steps {
+                script {
+                    retry(3) {
+                        node('linux && cpu && restricted') {
+                            unstash name: 'srcs'
+                            echo 'Building doc...'
+                            dir ('jvm-packages') {
+                                sh "bash ./build_doc.sh ${commit_id}"
+                                archiveArtifacts artifacts: "${commit_id}.tar.bz2", allowEmptyArchive: true
+                                echo 'Deploying doc...'
+                                withAWS(credentials:'xgboost-doc-bucket') {
+                                    s3Upload file: "${commit_id}.tar.bz2", bucket: 'xgboost-docs', acl: 'PublicRead', path: "${branch_name}.tar.bz2"
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        stage('Jenkins: Build artifacts') {
+            steps {
+                script {
+                    parallel (buildMatrix.findAll{it['enabled']}.collectEntries{ c ->
+                        def buildName = utils.getBuildName(c)
+                        utils.buildFactory(buildName, c, true, this.&buildPlatformCmake)
+                    })
+                }
+            }
+        }
+    }
+}
+
+/**
+ * Build platform and test it via cmake.
+ */
+def buildPlatformCmake(buildName, conf, nodeReq, dockerTarget) {
+    def opts = utils.cmakeOptions(conf)
+    // Destination dir for artifacts
+    def distDir = "dist/${buildName}"
+    def dockerArgs = ""
+    if(conf["withGpu"]){
+        dockerArgs = "--build-arg CUDA_VERSION=" + conf["cudaVersion"]
+    }
+    // Build node - this is returned result
+    retry(3) {
+        node(nodeReq) {
+            unstash name: 'srcs'
+            echo """
+            |===== XGBoost CMake build =====
+            |  dockerTarget: ${dockerTarget}
+            |  cmakeOpts   : ${opts}
+            |=========================
+            """.stripMargin('|')
+            // Invoke command inside docker
+            sh """
+            ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/build_via_cmake.sh ${opts}
+            ${dockerRun} ${dockerTarget} ${dockerArgs} bash -c "cd python-package; rm -f dist/*; python setup.py bdist_wheel --universal"
+            rm -rf "${distDir}"; mkdir -p "${distDir}/py"
+            cp xgboost "${distDir}"
+            cp -r lib "${distDir}"
+            cp -r python-package/dist "${distDir}/py"
+            """
+            archiveArtifacts artifacts: "${distDir}/**/*.*", allowEmptyArchive: true
+        }
+    }
+}

Jenkinsfile-win64

@@ -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()
-  }
-}

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

@@ -3,301 +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).
@@ -474,7 +179,7 @@ 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: 0.90.0.1
-Date: 2019-05-18
+Version: 0.81.0.1
+Date: 2018-08-13
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),
@@ -52,9 +52,7 @@ Suggests:
     vcd (>= 1.3),
     testthat,
     lintr,
-    igraph (>= 1.0.1),
-    jsonlite,
-    float
+    igraph (>= 1.0.1)
 Depends:
     R (>= 3.3.0)
 Imports:

R-package/R/callbacks.R

@@ -1,26 +1,26 @@
 #' Callback closures for booster training.
 #'
 #' These are used to perform various service tasks either during boosting iterations or at the end.
-#' This approach helps to modularize many of such tasks without bloating the main training methods,
+#' This approach helps to modularize many of such tasks without bloating the main training methods, 
 #' and it offers .
-#'
+#' 
 #' @details
 #' By default, a callback function is run after each boosting iteration.
 #' An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
-#'
-#' When a callback function has \code{finalize} parameter, its finalizer part will also be run after
+#' 
+#' When a callback function has \code{finalize} parameter, its finalizer part will also be run after 
 #' the boosting is completed.
-#'
-#' WARNING: side-effects!!! Be aware that these callback functions access and modify things in
+#' 
+#' WARNING: side-effects!!! Be aware that these callback functions access and modify things in 
 #' the environment from which they are called from, which is a fairly uncommon thing to do in R.
-#'
-#' To write a custom callback closure, make sure you first understand the main concepts about R environments.
-#' Check either R documentation on \code{\link[base]{environment}} or the
-#' \href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
+#' 
+#' 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 -
-#' choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
+#' choose ones that do something similar to what you want to achieve. Also, you would need to get familiar 
 #' with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
-#'
+#' 
 #' @seealso
 #' \code{\link{cb.print.evaluation}},
 #' \code{\link{cb.evaluation.log}},
@@ -30,42 +30,42 @@
 #' \code{\link{cb.cv.predict}},
 #' \code{\link{xgb.train}},
 #' \code{\link{xgb.cv}}
-#'
+#' 
 #' @name callbacks
 NULL
 
 #
 # Callbacks -------------------------------------------------------------------
-#
+# 
 
 #' Callback closure for printing the result of evaluation
-#'
+#' 
 #' @param period  results would be printed every number of periods
 #' @param showsd  whether standard deviations should be printed (when available)
-#'
+#' 
 #' @details
 #' The callback function prints the result of evaluation at every \code{period} iterations.
 #' The initial and the last iteration's evaluations are always printed.
-#'
+#' 
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{bst_evaluation} (also \code{bst_evaluation_err} when available),
 #' \code{iteration},
 #' \code{begin_iteration},
 #' \code{end_iteration}.
-#'
+#' 
 #' @seealso
 #' \code{\link{callbacks}}
-#'
+#' 
 #' @export
 cb.print.evaluation <- function(period = 1, showsd = TRUE) {
-
+  
   callback <- function(env = parent.frame()) {
     if (length(env$bst_evaluation) == 0 ||
         period == 0 ||
         NVL(env$rank, 0) != 0 )
       return()
-
-    i <- env$iteration
+    
+    i <- env$iteration 
     if ((i-1) %% period == 0 ||
         i == env$begin_iteration ||
         i == env$end_iteration) {
@@ -81,48 +81,48 @@ cb.print.evaluation <- function(period = 1, showsd = TRUE) {
 
 
 #' Callback closure for logging the evaluation history
-#'
+#' 
 #' @details
 #' This callback function appends the current iteration evaluation results \code{bst_evaluation}
 #' available in the calling parent frame to the \code{evaluation_log} list in a calling frame.
-#'
-#' The finalizer callback (called with \code{finalize = TURE} in the end) converts
+#' 
+#' The finalizer callback (called with \code{finalize = TURE} in the end) converts 
 #' the \code{evaluation_log} list into a final data.table.
-#'
-#' The iteration evaluation result \code{bst_evaluation} must be a named numeric vector.
-#'
-#' Note: in the column names of the final data.table, the dash '-' character is replaced with
+#' 
+#' The iteration evaluation result \code{bst_evaluation} must be a named numeric vector. 
+#' 
+#' Note: in the column names of the final data.table, the dash '-' character is replaced with 
 #' the underscore '_' in order to make the column names more like regular R identifiers.
-#'
+#' 
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{evaluation_log},
 #' \code{bst_evaluation},
 #' \code{iteration}.
-#'
+#' 
 #' @seealso
 #' \code{\link{callbacks}}
-#'
+#' 
 #' @export
 cb.evaluation.log <- function() {
 
   mnames <- NULL
-
+  
   init <- function(env) {
     if (!is.list(env$evaluation_log))
       stop("'evaluation_log' has to be a list")
     mnames <<- names(env$bst_evaluation)
     if (is.null(mnames) || any(mnames == ""))
       stop("bst_evaluation must have non-empty names")
-
+    
     mnames <<- gsub('-', '_', names(env$bst_evaluation))
     if(!is.null(env$bst_evaluation_err))
       mnames <<- c(paste0(mnames, '_mean'), paste0(mnames, '_std'))
   }
-
+  
   finalizer <- function(env) {
     env$evaluation_log <- as.data.table(t(simplify2array(env$evaluation_log)))
     setnames(env$evaluation_log, c('iter', mnames))
-
+    
     if(!is.null(env$bst_evaluation_err)) {
       # rearrange col order from _mean,_mean,...,_std,_std,...
       # to be _mean,_std,_mean,_std,...
@@ -135,18 +135,18 @@ cb.evaluation.log <- function() {
       env$evaluation_log <- env$evaluation_log[, c('iter', cnames), with = FALSE]
     }
   }
-
+  
   callback <- function(env = parent.frame(), finalize = FALSE) {
     if (is.null(mnames))
       init(env)
 
     if (finalize)
       return(finalizer(env))
-
+    
     ev <- env$bst_evaluation
     if(!is.null(env$bst_evaluation_err))
       ev <- c(ev, env$bst_evaluation_err)
-    env$evaluation_log <- c(env$evaluation_log,
+    env$evaluation_log <- c(env$evaluation_log, 
                             list(c(iter = env$iteration, ev)))
   }
   attr(callback, 'call') <- match.call()
@@ -154,21 +154,21 @@ cb.evaluation.log <- function() {
   callback
 }
 
-#' Callback closure for 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}
-#'        to be set at each iteration,
-#'        or a function of two parameters \code{learning_rates(iteration, nrounds)}
-#'        which returns a new parameter value by using the current iteration number
+#'        Each element's value must be either a vector of values of length \code{nrounds} 
+#'        to be set at each iteration, 
+#'        or a function of two parameters \code{learning_rates(iteration, nrounds)} 
+#'        which returns a new parameter value by using the current iteration number 
 #'        and the total number of boosting rounds.
-#'
-#' @details
+#' 
+#' @details 
 #' This is a "pre-iteration" callback function used to reset booster's parameters
 #' at the beginning of each iteration.
-#'
-#' Note that when training is resumed from some previous model, and a function is used to
-#' reset a parameter value, the \code{nrounds} argument in this function would be the
+#' 
+#' Note that when training is resumed from some previous model, and a function is used to 
+#' reset a parameter value, the \code{nrounds} argument in this function would be the 
 #' the number of boosting rounds in the current training.
 #'
 #' Callback function expects the following values to be set in its calling frame:
@@ -176,32 +176,32 @@ cb.evaluation.log <- function() {
 #' \code{iteration},
 #' \code{begin_iteration},
 #' \code{end_iteration}.
-#'
+#' 
 #' @seealso
 #' \code{\link{callbacks}}
-#'
+#' 
 #' @export
 cb.reset.parameters <- function(new_params) {
 
-  if (typeof(new_params) != "list")
+  if (typeof(new_params) != "list") 
     stop("'new_params' must be a list")
   pnames <- gsub("\\.", "_", names(new_params))
   nrounds <- NULL
-
+  
   # run some checks in the begining
   init <- function(env) {
     nrounds <<- env$end_iteration - env$begin_iteration + 1
-
+    
     if (is.null(env$bst) && is.null(env$bst_folds))
       stop("Parent frame has neither 'bst' nor 'bst_folds'")
-
+    
     # Some parameters are not allowed to be changed,
     # since changing them would simply wreck some chaos
-    not_allowed <- pnames %in%
+    not_allowed <- pnames %in% 
       c('num_class', 'num_output_group', 'size_leaf_vector', 'updater_seq')
     if (any(not_allowed))
       stop('Parameters ', paste(pnames[not_allowed]), " cannot be changed during boosting.")
-
+    
     for (n in pnames) {
       p <- new_params[[n]]
       if (is.function(p)) {
@@ -215,18 +215,18 @@ cb.reset.parameters <- function(new_params) {
       }
     }
   }
-
+  
   callback <- function(env = parent.frame()) {
     if (is.null(nrounds))
       init(env)
-
+    
     i <- env$iteration
     pars <- lapply(new_params, function(p) {
       if (is.function(p))
         return(p(i, nrounds))
       p[i]
     })
-
+    
     if (!is.null(env$bst)) {
       xgb.parameters(env$bst$handle) <- pars
     } else {
@@ -242,23 +242,23 @@ cb.reset.parameters <- function(new_params) {
 
 
 #' Callback closure to activate the early stopping.
-#'
-#' @param stopping_rounds The number of rounds with no improvement in
+#' 
+#' @param stopping_rounds The number of rounds with no improvement in 
 #'        the evaluation metric in order to stop the training.
 #' @param maximize whether to maximize the evaluation metric
 #' @param metric_name the name of an evaluation column to use as a criteria for early
 #'        stopping. If not set, the last column would be used.
-#'        Let's say the test data in \code{watchlist} was labelled as \code{dtest},
-#'        and one wants to use the AUC in test data for early stopping regardless of where
+#'        Let's say the test data in \code{watchlist} was labelled as \code{dtest}, 
+#'        and one wants to use the AUC in test data for early stopping regardless of where 
 #'        it is in the \code{watchlist}, then one of the following would need to be set:
 #'        \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
 #'        All dash '-' characters in metric names are considered equivalent to '_'.
 #' @param verbose whether to print the early stopping information.
-#'
+#' 
 #' @details
-#' This callback function determines the condition for early stopping
+#' This callback function determines the condition for early stopping 
 #' by setting the \code{stop_condition = TRUE} flag in its calling frame.
-#'
+#' 
 #' The following additional fields are assigned to the model's R object:
 #' \itemize{
 #' \item \code{best_score} the evaluation score at the best iteration
@@ -266,13 +266,13 @@ cb.reset.parameters <- function(new_params) {
 #' \item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
 #'      It differs from \code{best_iteration} in multiclass or random forest settings.
 #' }
-#'
+#' 
 #' The Same values are also stored as xgb-attributes:
 #' \itemize{
 #' \item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
 #' \item \code{best_msg} message string is also stored.
 #' }
-#'
+#' 
 #' At least one data element is required in the evaluation watchlist for early stopping to work.
 #'
 #' Callback function expects the following values to be set in its calling frame:
@@ -284,13 +284,13 @@ cb.reset.parameters <- function(new_params) {
 #' \code{begin_iteration},
 #' \code{end_iteration},
 #' \code{num_parallel_tree}.
-#'
+#' 
 #' @seealso
 #' \code{\link{callbacks}},
 #' \code{\link{xgb.attr}}
-#'
+#' 
 #' @export
-cb.early.stop <- function(stopping_rounds, maximize = FALSE,
+cb.early.stop <- function(stopping_rounds, maximize = FALSE, 
                           metric_name = NULL, verbose = TRUE) {
   # state variables
   best_iteration <- -1
@@ -298,11 +298,11 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
   best_score <- Inf
   best_msg <- NULL
   metric_idx <- 1
-
+  
   init <- function(env) {
     if (length(env$bst_evaluation) == 0)
       stop("For early stopping, watchlist must have at least one element")
-
+    
     eval_names <- gsub('-', '_', names(env$bst_evaluation))
     if (!is.null(metric_name)) {
       metric_idx <<- which(gsub('-', '_', metric_name) == eval_names)
@@ -314,25 +314,25 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
         length(env$bst_evaluation) > 1) {
       metric_idx <<- length(eval_names)
       if (verbose)
-        cat('Multiple eval metrics are present. Will use ',
+        cat('Multiple eval metrics are present. Will use ', 
             eval_names[metric_idx], ' for early stopping.\n', sep = '')
     }
-
+    
     metric_name <<- eval_names[metric_idx]
-
+    
     # maximize is usually NULL when not set in xgb.train and built-in metrics
     if (is.null(maximize))
       maximize <<- grepl('(_auc|_map|_ndcg)', metric_name)
 
     if (verbose && NVL(env$rank, 0) == 0)
-      cat("Will train until ", metric_name, " hasn't improved in ",
+      cat("Will train until ", metric_name, " hasn't improved in ", 
           stopping_rounds, " rounds.\n\n", sep = '')
 
     best_iteration <<- 1
     if (maximize) best_score <<- -Inf
-
+    
     env$stop_condition <- FALSE
-
+    
     if (!is.null(env$bst)) {
       if (!inherits(env$bst, 'xgb.Booster'))
         stop("'bst' in the parent frame must be an 'xgb.Booster'")
@@ -348,7 +348,7 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
       stop("Parent frame has neither 'bst' nor ('bst_folds' and 'basket')")
     }
   }
-
+  
   finalizer <- function(env) {
     if (!is.null(env$bst)) {
       attr_best_score = as.numeric(xgb.attr(env$bst$handle, 'best_score'))
@@ -367,16 +367,16 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
   callback <- function(env = parent.frame(), finalize = FALSE) {
     if (best_iteration < 0)
       init(env)
-
+    
     if (finalize)
       return(finalizer(env))
-
+    
     i <- env$iteration
     score = env$bst_evaluation[metric_idx]
-
+    
     if (( maximize && score > best_score) ||
         (!maximize && score < best_score)) {
-
+      
       best_msg <<- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
       best_score <<- score
       best_iteration <<- i
@@ -403,37 +403,37 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
 
 
 #' Callback closure for saving a model file.
-#'
-#' @param save_period save the model to disk after every
+#' 
+#' @param save_period save the model to disk after every 
 #'        \code{save_period} iterations; 0 means save the model at the end.
 #' @param save_name the name or path for the saved model file.
-#'        It can contain a \code{\link[base]{sprintf}} formatting specifier
+#'        It can contain a \code{\link[base]{sprintf}} formatting specifier 
 #'        to include the integer iteration number in the file name.
-#'        E.g., with \code{save_name} = 'xgboost_%04d.model',
+#'        E.g., with \code{save_name} = 'xgboost_%04d.model', 
 #'        the file saved at iteration 50 would be named "xgboost_0050.model".
-#'
-#' @details
+#' 
+#' @details 
 #' This callback function allows to save an xgb-model file, either periodically after each \code{save_period}'s or at the end.
-#'
+#' 
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{bst},
 #' \code{iteration},
 #' \code{begin_iteration},
 #' \code{end_iteration}.
-#'
+#' 
 #' @seealso
 #' \code{\link{callbacks}}
-#'
+#' 
 #' @export
 cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
-
+  
   if (save_period < 0)
     stop("'save_period' cannot be negative")
 
   callback <- function(env = parent.frame()) {
     if (is.null(env$bst))
       stop("'save_model' callback requires the 'bst' booster object in its calling frame")
-
+    
     if ((save_period > 0 && (env$iteration - env$begin_iteration) %% save_period == 0) ||
         (save_period == 0 && env$iteration == env$end_iteration))
       xgb.save(env$bst, sprintf(save_name, env$iteration))
@@ -445,16 +445,16 @@ cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
 
 
 #' Callback closure for returning cross-validation based predictions.
-#'
+#' 
 #' @param save_models a flag for whether to save the folds' models.
-#'
-#' @details
+#' 
+#' @details 
 #' This callback function saves predictions for all of the test folds,
 #' and also allows to save the folds' models.
-#'
+#' 
 #' It is a "finalizer" callback and it uses early stopping information whenever it is available,
 #' thus it must be run after the early stopping callback if the early stopping is used.
-#'
+#' 
 #' Callback function expects the following values to be set in its calling frame:
 #' \code{bst_folds},
 #' \code{basket},
@@ -463,36 +463,36 @@ cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
 #' \code{params},
 #' \code{num_parallel_tree},
 #' \code{num_class}.
-#'
-#' @return
+#' 
+#' @return 
 #' Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
-#' depending on the number of prediction outputs per data row. The order of predictions corresponds
-#' to the order of rows in the original dataset. Note that when a custom \code{folds} list is
-#' provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a
-#' non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
-#' meaningful when user-provided folds have overlapping indices as in, e.g., random sampling splits.
+#' depending on the number of prediction outputs per data row. The order of predictions corresponds 
+#' to the order of rows in the original dataset. Note that when a custom \code{folds} list is 
+#' provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a 
+#' non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be 
+#' meaningful when user-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}.
-#'
+#' 
 #' @seealso
 #' \code{\link{callbacks}}
-#'
+#' 
 #' @export
 cb.cv.predict <- function(save_models = FALSE) {
-
+  
   finalizer <- function(env) {
     if (is.null(env$basket) || is.null(env$bst_folds))
       stop("'cb.cv.predict' callback requires 'basket' and 'bst_folds' lists in its calling frame")
-
+    
     N <- nrow(env$data)
-    pred <-
+    pred <- 
       if (env$num_class > 1) {
         matrix(NA_real_, N, env$num_class)
       } else {
         rep(NA_real_, N)
       }
 
-    ntreelimit <- NVL(env$basket$best_ntreelimit,
+    ntreelimit <- NVL(env$basket$best_ntreelimit, 
                       env$end_iteration * env$num_parallel_tree)
     if (NVL(env$params[['booster']], '') == 'gblinear') {
       ntreelimit <- 0 # must be 0 for gblinear
@@ -569,7 +569,7 @@ cb.cv.predict <- function(save_models = FALSE) {
 #' # Extract the coefficients' path and plot them vs boosting iteration number:
 #' coef_path <- xgb.gblinear.history(bst)
 #' matplot(coef_path, type = 'l')
-#'
+#' 
 #' # With the deterministic coordinate descent updater, it is safer to use higher learning rates.
 #' # Will try the classical componentwise boosting which selects a single best feature per round:
 #' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8,
@@ -586,7 +586,7 @@ cb.cv.predict <- function(save_models = FALSE) {
 #' # coefficients in the CV fold #3
 #' xgb.gblinear.history(bst)[[3]] %>% matplot(type = 'l')
 #'
-#'
+#' 
 #' #### Multiclass classification:
 #' #
 #' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
@@ -681,9 +681,9 @@ cb.gblinear.history <- function(sparse=FALSE) {
 #'        using the \code{cb.gblinear.history()} callback.
 #' @param class_index zero-based class index to extract the coefficients for only that
 #'        specific class in a multinomial multiclass model. When it is NULL, all the
-#'        coefficients are returned. Has no effect in non-multiclass models.
+#'        coeffients are returned. Has no effect in non-multiclass models.
 #'
-#' @return
+#' @return 
 #' For an \code{xgb.train} result, a matrix (either dense or sparse) with the columns
 #' corresponding to iteration's coefficients (in the order as \code{xgb.dump()} would
 #' return) and the rows corresponding to boosting iterations.
@@ -731,7 +731,7 @@ xgb.gblinear.history <- function(model, class_index = NULL) {
   coef_path <- environment(model$callbacks$cb.gblinear.history)[["coefs"]]
   if (!is.null(class_index) && num_class > 1) {
     coef_path <- if (is.list(coef_path)) {
-      lapply(coef_path,
+      lapply(coef_path, 
              function(x) x[, seq(1 + class_index, by=num_class, length.out=num_feat)])
     } else {
       coef_path <- coef_path[, seq(1 + class_index, by=num_class, length.out=num_feat)]
@@ -743,7 +743,7 @@ xgb.gblinear.history <- function(model, class_index = NULL) {
 
 #
 # Internal utility functions for callbacks ------------------------------------
-#
+# 
 
 # Format the evaluation metric string
 format.eval.string <- function(iter, eval_res, eval_err = NULL) {
@@ -773,7 +773,7 @@ callback.calls <- function(cb_list) {
   unlist(lapply(cb_list, function(x) attr(x, 'call')))
 }
 
-# Add a callback cb to the list and make sure that
+# Add a callback cb to the list and make sure that 
 # cb.early.stop and cb.cv.predict are at the end of the list
 # with cb.cv.predict being the last (when present)
 add.cb <- function(cb_list, cb) {
@@ -782,11 +782,11 @@ add.cb <- function(cb_list, cb) {
   if ('cb.early.stop' %in% names(cb_list)) {
     cb_list <- c(cb_list, cb_list['cb.early.stop'])
     # this removes only the first one
-    cb_list['cb.early.stop'] <- NULL
+    cb_list['cb.early.stop'] <- NULL 
   }
   if ('cb.cv.predict' %in% names(cb_list)) {
     cb_list <- c(cb_list, cb_list['cb.cv.predict'])
-    cb_list['cb.cv.predict'] <- NULL
+    cb_list['cb.cv.predict'] <- NULL 
   }
   cb_list
 }
@@ -796,7 +796,7 @@ categorize.callbacks <- function(cb_list) {
   list(
     pre_iter = Filter(function(x) {
         pre <- attr(x, 'is_pre_iteration')
-        !is.null(pre) && pre
+        !is.null(pre) && pre 
       }, cb_list),
     post_iter = Filter(function(x) {
         pre <- attr(x, 'is_pre_iteration')

R-package/R/utils.R

@@ -28,12 +28,12 @@ NVL <- function(x, val) {
 # Merges booster params with whatever is provided in ...
 # plus runs some checks
 check.booster.params <- function(params, ...) {
-  if (typeof(params) != "list")
+  if (typeof(params) != "list") 
     stop("params must be a list")
-
+  
   # in R interface, allow for '.' instead of '_' in parameter names
   names(params) <- gsub("\\.", "_", names(params))
-
+  
   # merge parameters from the params and the dots-expansion
   dot_params <- list(...)
   names(dot_params) <- gsub("\\.", "_", names(dot_params))
@@ -41,15 +41,15 @@ check.booster.params <- function(params, ...) {
                        names(dot_params))) > 0)
     stop("Same parameters in 'params' and in the call are not allowed. Please check your 'params' list.")
   params <- c(params, dot_params)
-
+  
   # providing a parameter multiple times makes sense only for 'eval_metric'
   name_freqs <- table(names(params))
   multi_names <- setdiff(names(name_freqs[name_freqs > 1]), 'eval_metric')
   if (length(multi_names) > 0) {
     warning("The following parameters were provided multiple times:\n\t",
             paste(multi_names, collapse = ', '), "\n  Only the last value for each of them will be used.\n")
-    # While xgboost internals would choose the last value for a multiple-times parameter,
-    # enforce it here in R as well (b/c multi-parameters might be used further in R code,
+    # While xgboost internals would choose the last value for a multiple-times parameter, 
+    # enforce it here in R as well (b/c multi-parameters might be used further in R code, 
     # and R takes the 1st value when multiple elements with the same name are present in a list).
     for (n in multi_names) {
       del_idx <- which(n == names(params))
@@ -57,25 +57,25 @@ check.booster.params <- function(params, ...) {
       params[[del_idx]] <- NULL
     }
   }
-
+  
   # for multiclass, expect num_class to be set
   if (typeof(params[['objective']]) == "character" &&
       substr(NVL(params[['objective']], 'x'), 1, 6) == 'multi:' &&
       as.numeric(NVL(params[['num_class']], 0)) < 2) {
         stop("'num_class' > 1 parameter must be set for multiclass classification")
   }
-
+  
   # monotone_constraints parser
-
+  
   if (!is.null(params[['monotone_constraints']]) &&
       typeof(params[['monotone_constraints']]) != "character") {
         vec2str = paste(params[['monotone_constraints']], collapse = ',')
         vec2str = paste0('(', vec2str, ')')
         params[['monotone_constraints']] = vec2str
   }
-
+  
   # interaction constraints parser (convert from list of column indices to string)
-  if (!is.null(params[['interaction_constraints']]) &&
+  if (!is.null(params[['interaction_constraints']]) && 
       typeof(params[['interaction_constraints']]) != "character"){
     # check input class
     if (class(params[['interaction_constraints']]) != 'list') stop('interaction_constraints should be class list')
@@ -96,10 +96,10 @@ check.booster.params <- function(params, ...) {
 check.custom.obj <- function(env = parent.frame()) {
   if (!is.null(env$params[['objective']]) && !is.null(env$obj))
     stop("Setting objectives in 'params' and 'obj' at the same time is not allowed")
-
+  
   if (!is.null(env$obj) && typeof(env$obj) != 'closure')
     stop("'obj' must be a function")
-
+  
   # handle the case when custom objective function was provided through params
   if (!is.null(env$params[['objective']]) &&
       typeof(env$params$objective) == 'closure') {
@@ -113,21 +113,21 @@ check.custom.obj <- function(env = parent.frame()) {
 check.custom.eval <- function(env = parent.frame()) {
   if (!is.null(env$params[['eval_metric']]) && !is.null(env$feval))
     stop("Setting evaluation metrics in 'params' and 'feval' at the same time is not allowed")
-
+  
   if (!is.null(env$feval) && typeof(env$feval) != 'closure')
     stop("'feval' must be a function")
-
+  
   # handle a situation when custom eval function was provided through params
   if (!is.null(env$params[['eval_metric']]) &&
       typeof(env$params$eval_metric) == 'closure') {
     env$feval <- env$params$eval_metric
     env$params$eval_metric <- NULL
   }
-
+  
   # require maximize to be set when custom feval and early stopping are used together
   if (!is.null(env$feval) &&
       is.null(env$maximize) && (
-        !is.null(env$early_stopping_rounds) ||
+        !is.null(env$early_stopping_rounds) || 
         has.callbacks(env$callbacks, 'cb.early.stop')))
     stop("Please set 'maximize' to indicate whether the evaluation metric needs to be maximized or not")
 }
@@ -154,15 +154,15 @@ xgb.iter.update <- function(booster_handle, dtrain, iter, obj = NULL) {
 
 
 # Evaluate one iteration.
-# Returns a named vector of evaluation metrics
+# Returns a named vector of evaluation metrics 
 # with the names in a 'datasetname-metricname' format.
 xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
   if (!identical(class(booster_handle), "xgb.Booster.handle"))
     stop("class of booster_handle must be xgb.Booster.handle")
 
-  if (length(watchlist) == 0)
+  if (length(watchlist) == 0) 
     return(NULL)
-
+  
   evnames <- names(watchlist)
   if (is.null(feval)) {
     msg <- .Call(XGBoosterEvalOneIter_R, booster_handle, as.integer(iter), watchlist, as.list(evnames))
@@ -189,7 +189,7 @@ xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
 
 # Generates random (stratified if needed) CV folds
 generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
-
+  
   # cannot do it for rank
   if (exists('objective', where = params) &&
       is.character(params$objective) &&
@@ -209,14 +209,13 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
     if (exists('objective', where = params) &&
         is.character(params$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)
     }
@@ -294,22 +293,22 @@ xgb.createFolds <- function(y, k = 10)
 #
 
 #' Deprecation notices.
-#'
+#' 
 #' At this time, some of the parameter names were changed in order to make the code style more uniform.
 #' The deprecated parameters would be removed in the next release.
-#'
+#' 
 #' To see all the current deprecated and new parameters, check the \code{xgboost:::depr_par_lut} table.
-#'
-#' A deprecation warning is shown when any of the deprecated parameters is used in a call.
-#' An additional warning is shown when there was a partial match to a deprecated parameter
+#' 
+#' A deprecation warning is shown when any of the deprecated parameters is used in a call. 
+#' An additional warning is shown when there was a partial match to a deprecated parameter 
 #' (as R is able to partially match parameter names).
-#'
+#' 
 #' @name xgboost-deprecated
 NULL
 
 # Lookup table for the deprecated parameters bookkeeping
 depr_par_lut <- matrix(c(
-  'print.every.n', 'print_every_n',
+  'print.every.n', 'print_every_n', 
   'early.stop.round', 'early_stopping_rounds',
   'training.data', 'data',
   'with.stats', 'with_stats',

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
@@ -95,7 +95,6 @@ xgb.get.handle <- function(object) {
 #' saveRDS(bst, "xgb.model.rds")
 #'
 #' bst1 <- readRDS("xgb.model.rds")
-#' if (file.exists("xgb.model.rds")) file.remove("xgb.model.rds")
 #' # the handle is invalid:
 #' print(bst1$handle)
 #'
@@ -163,7 +162,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'
 #' With \code{predinteraction = TRUE}, SHAP values of contributions of interaction of each pair of features
 #' are computed. Note that this operation might be rather expensive in terms of compute and memory.
-#' Since it quadratically depends on the number of features, it is recommended to perform selection
+#' Since it quadratically depends on the number of features, it is recommended to perfom selection
 #' of the most important features first. See below about the format of the returned results.
 #'
 #' @return
@@ -191,7 +190,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'
 #' @seealso
 #' \code{\link{xgb.train}}.
-#'
+#' 
 #' @references
 #'
 #' Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
@@ -419,7 +418,6 @@ predict.xgb.Booster.handle <- function(object, ...) {
 #'
 #' xgb.save(bst, 'xgb.model')
 #' bst1 <- xgb.load('xgb.model')
-#' if (file.exists('xgb.model')) file.remove('xgb.model')
 #' print(xgb.attr(bst1, "my_attribute"))
 #' print(xgb.attributes(bst1))
 #'

R-package/R/xgb.DMatrix.R

@@ -1,25 +1,24 @@
 #' Construct xgb.DMatrix object
-#'
+#' 
 #' Construct xgb.DMatrix object from either a dense matrix, a sparse matrix, or a local file.
 #' Supported input file formats are either a libsvm text file or a binary file that was created previously by
 #' \code{\link{xgb.DMatrix.save}}).
-#'
-#' @param data a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character
+#' 
+#' @param data a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character 
 #'        string representing a filename.
 #' @param info a named list of additional information to store in the \code{xgb.DMatrix} object.
-#'        See \code{\link{setinfo}} for the specific allowed kinds of
+#'        See \code{\link{setinfo}} for the specific allowed kinds of 
 #' @param missing a float value to represents missing values in data (used only when input is a dense matrix).
 #'        It is useful when a 0 or some other extreme value represents missing values in data.
 #' @param silent whether to suppress printing an informational message after loading from a file.
 #' @param ... the \code{info} data could be passed directly as parameters, without creating an \code{info} list.
-#'
+#' 
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
 #' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
 #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
-#' if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
 #' @export
 xgb.DMatrix <- function(data, info = list(), missing = NA, silent = FALSE, ...) {
   cnames <- NULL
@@ -79,23 +78,23 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
 
 
 #' Dimensions of xgb.DMatrix
-#'
+#' 
 #' Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
 #' @param x Object of class \code{xgb.DMatrix}
-#'
+#' 
 #' @details
-#' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
+#' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also 
 #' be directly used with an \code{xgb.DMatrix} object.
 #'
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
+#' 
 #' stopifnot(nrow(dtrain) == nrow(train$data))
 #' stopifnot(ncol(dtrain) == ncol(train$data))
 #' stopifnot(all(dim(dtrain) == dim(train$data)))
-#'
+#' 
 #' @export
 dim.xgb.DMatrix <- function(x) {
   c(.Call(XGDMatrixNumRow_R, x), .Call(XGDMatrixNumCol_R, x))
@@ -103,14 +102,14 @@ dim.xgb.DMatrix <- function(x) {
 
 
 #' Handling of column names of \code{xgb.DMatrix}
-#'
-#' Only column names are supported for \code{xgb.DMatrix}, thus setting of
-#' row names would have no effect and returned row names would be NULL.
-#'
+#' 
+#' Only column names are supported for \code{xgb.DMatrix}, thus setting of 
+#' row names would have no effect and returnten row names would be NULL.
+#' 
 #' @param x object of class \code{xgb.DMatrix}
 #' @param value a list of two elements: the first one is ignored
-#'        and the second one is column names
-#'
+#'        and the second one is column names 
+#' 
 #' @details
 #' Generic \code{dimnames} methods are used by \code{colnames}.
 #' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
@@ -123,7 +122,7 @@ dim.xgb.DMatrix <- function(x) {
 #' colnames(dtrain)
 #' colnames(dtrain) <- make.names(1:ncol(train$data))
 #' print(dtrain, verbose=TRUE)
-#'
+#' 
 #' @rdname dimnames.xgb.DMatrix
 #' @export
 dimnames.xgb.DMatrix <- function(x) {
@@ -141,8 +140,8 @@ dimnames.xgb.DMatrix <- function(x) {
     attr(x, '.Dimnames') <- NULL
     return(x)
   }
-  if (ncol(x) != length(value[[2]]))
-    stop("can't assign ", length(value[[2]]), " colnames to a ",
+  if (ncol(x) != length(value[[2]])) 
+    stop("can't assign ", length(value[[2]]), " colnames to a ", 
          ncol(x), " column xgb.DMatrix")
   attr(x, '.Dimnames') <- value
   x
@@ -150,33 +149,33 @@ dimnames.xgb.DMatrix <- function(x) {
 
 
 #' Get information of an xgb.DMatrix object
-#'
+#' 
 #' Get information of an xgb.DMatrix object
 #' @param object Object of class \code{xgb.DMatrix}
 #' @param name the name of the information field to get (see details)
 #' @param ... other parameters
-#'
+#' 
 #' @details
 #' The \code{name} field can be one of the following:
-#'
+#' 
 #' \itemize{
 #'     \item \code{label}: label Xgboost learn from ;
 #'     \item \code{weight}: to do a weight rescale ;
 #'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
 #'     \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
-#'
+#'     
 #' }
-#'
+#' 
 #' \code{group} can be setup by \code{setinfo} but can't be retrieved by \code{getinfo}.
-#'
+#' 
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
+#' 
 #' labels <- getinfo(dtrain, 'label')
 #' setinfo(dtrain, 'label', 1-labels)
-#'
+#' 
 #' labels2 <- getinfo(dtrain, 'label')
 #' stopifnot(all(labels2 == 1-labels))
 #' @rdname getinfo
@@ -203,9 +202,9 @@ getinfo.xgb.DMatrix <- function(object, name, ...) {
 
 
 #' Set information of an xgb.DMatrix object
-#'
+#' 
 #' Set information of an xgb.DMatrix object
-#'
+#' 
 #' @param object Object of class "xgb.DMatrix"
 #' @param name the name of the field to get
 #' @param info the specific field of information to set
@@ -213,19 +212,19 @@ getinfo.xgb.DMatrix <- function(object, name, ...) {
 #'
 #' @details
 #' The \code{name} field can be one of the following:
-#'
+#' 
 #' \itemize{
 #'     \item \code{label}: label Xgboost learn from ;
 #'     \item \code{weight}: to do a weight rescale ;
 #'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
 #'     \item \code{group}: number of rows in each group (to use with \code{rank:pairwise} objective).
 #' }
-#'
+#' 
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
+#' 
 #' labels <- getinfo(dtrain, 'label')
 #' setinfo(dtrain, 'label', 1-labels)
 #' labels2 <- getinfo(dtrain, 'label')
@@ -267,27 +266,27 @@ 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
 #' @param colset currently not used (columns subsetting is not available)
 #' @param ... other parameters (currently not used)
-#'
+#' 
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
+#' 
 #' dsub <- slice(dtrain, 1:42)
 #' labels1 <- getinfo(dsub, 'label')
 #' dsub <- dtrain[1:42, ]
 #' labels2 <- getinfo(dsub, 'label')
 #' all.equal(labels1, labels2)
-#'
+#' 
 #' @rdname slice.xgb.DMatrix
 #' @export
 slice <- function(object, ...) UseMethod("slice")
@@ -302,17 +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"))
@@ -326,22 +320,22 @@ slice.xgb.DMatrix <- function(object, idxset, ...) {
 
 
 #' Print xgb.DMatrix
-#'
-#' Print information about xgb.DMatrix.
+#' 
+#' Print information about xgb.DMatrix. 
 #' Currently it displays dimensions and presence of info-fields and colnames.
-#'
+#' 
 #' @param x an xgb.DMatrix object
 #' @param verbose whether to print colnames (when present)
 #' @param ... not currently used
-#'
+#' 
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#'
+#' 
 #' dtrain
 #' print(dtrain, verbose=TRUE)
-#'
+#' 
 #' @method print xgb.DMatrix
 #' @export
 print.xgb.DMatrix <- function(x, verbose = FALSE, ...) {

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

@@ -11,7 +11,6 @@
 #' dtrain <- xgb.DMatrix(train$data, label=train$label)
 #' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
 #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
-#' if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')
 #' @export
 xgb.DMatrix.save <- function(dmatrix, fname) {
   if (typeof(fname) != "character")

R-package/R/xgb.cv.R

@@ -1,12 +1,12 @@
 #' Cross Validation
-#'
+#' 
 #' The cross validation function of xgboost
-#'
+#' 
 #' @param 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
@@ -18,12 +18,12 @@
 #'   See also demo/ for walkthrough example in R.
 #' @param data takes an \code{xgb.DMatrix}, \code{matrix}, or \code{dgCMatrix} as the input.
 #' @param nrounds the max number of iterations
-#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
+#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples. 
 #' @param label vector of response values. Should be provided only when data is an R-matrix.
-#' @param missing is only used when input is a dense matrix. By default is set to NA, which means
-#'        that NA values should be considered as 'missing' by the algorithm.
+#' @param missing is only used when input is a dense matrix. By default is set to NA, which means 
+#'        that NA values should be considered as 'missing' by the algorithm. 
 #'        Sometimes, 0 or other extreme value might be used to represent missing values.
-#' @param prediction A logical value indicating whether to return the test fold predictions
+#' @param prediction A logical value indicating whether to return the test fold predictions 
 #'        from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.
 #' @param showsd \code{boolean}, whether to show standard deviation of cross validation
 #' @param metrics, list of evaluation metrics to be used in cross validation,
@@ -37,22 +37,22 @@
 #'   \item \code{aucpr} Area under PR curve
 #'   \item \code{merror} Exact matching error, used to evaluate multi-class classification
 #' }
-#' @param obj customized objective function. Returns gradient and second order
+#' @param obj customized objective function. Returns gradient and second order 
 #'        gradient with given prediction and dtrain.
-#' @param feval customized evaluation function. Returns
-#'        \code{list(metric='metric-name', value='metric-value')} with given
+#' @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
+#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified 
 #'        by the values of outcome labels.
 #' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
-#'        (each element must be a vector of test fold's indices). When folds are supplied,
+#'        (each element must be a vector of test fold's indices). When folds are supplied, 
 #'        the \code{nfold} and \code{stratified} parameters are ignored.
 #' @param verbose \code{boolean}, print the statistics during the process
 #' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
-#'        Default is 1 which means all messages are printed. This parameter is passed to the
+#'        Default is 1 which means all messages are printed. This parameter is passed to the 
 #'        \code{\link{cb.print.evaluation}} callback.
-#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
-#'        If set to an integer \code{k}, training with a validation set will stop if the performance
+#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered. 
+#'        If set to an integer \code{k}, training with a validation set will stop if the performance 
 #'        doesn't improve for \code{k} rounds.
 #'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
 #' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
@@ -60,46 +60,46 @@
 #'        When it is \code{TRUE}, it means the larger the evaluation score the better.
 #'        This parameter is passed to the \code{\link{cb.early.stop}} callback.
 #' @param callbacks a list of callback functions to perform various task during boosting.
-#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-#'        parameters' values. User can provide either existing or their own callback methods in order
+#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
+#'        parameters' values. User can provide either existing or their own callback methods in order 
 #'        to customize the training process.
 #' @param ... other parameters to pass to \code{params}.
-#'
-#' @details
-#' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
-#'
-#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
-#'
+#' 
+#' @details 
+#' The original sample is randomly partitioned into \code{nfold} equal size subsamples. 
+#' 
+#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data. 
+#' 
 #' The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
-#'
+#' 
 #' All observations are used for both training and validation.
-#'
+#' 
 #' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
 #'
-#' @return
+#' @return 
 #' An object of class \code{xgb.cv.synchronous} with the following elements:
 #' \itemize{
 #'   \item \code{call} a function call.
-#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
+#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not 
 #'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-#'   \item \code{callbacks} callback functions that were either automatically assigned or
+#'   \item \code{callbacks} callback functions that were either automatically assigned or 
 #'         explicitly passed.
-#'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
-#'         first column corresponding to iteration number and the rest corresponding to the
+#'   \item \code{evaluation_log} evaluation history 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.
 #'   \item \code{niter} number of boosting iterations.
 #'   \item \code{nfeatures} number of features in training data.
-#'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
+#'   \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds} 
 #'         parameter or randomly generated.
 #'   \item \code{best_iteration} iteration number with the best evaluation metric value
 #'         (only available with early stopping).
-#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
+#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
 #'         which could further be used in \code{predict} method
 #'         (only available with early stopping).
-#'   \item \code{pred} CV prediction values available when \code{prediction} is set.
+#'   \item \code{pred} CV prediction values available when \code{prediction} is set. 
 #'         It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-#'   \item \code{models} a liost of the CV folds' models. It is only available with the explicit
+#'   \item \code{models} a liost of the CV folds' models. It is only available with the explicit 
 #'         setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
 #' }
 #'
@@ -110,32 +110,32 @@
 #'                   max_depth = 3, eta = 1, objective = "binary:logistic")
 #' print(cv)
 #' print(cv, verbose=TRUE)
-#'
+#' 
 #' @export
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
                    prediction = FALSE, showsd = TRUE, metrics=list(),
-                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
+                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, 
                    verbose = TRUE, print_every_n=1L,
                    early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
 
   check.deprecation(...)
-
+  
   params <- check.booster.params(params, ...)
   # TODO: should we deprecate the redundant 'metrics' parameter?
   for (m in metrics)
     params <- c(params, list("eval_metric" = m))
-
+  
   check.custom.obj()
   check.custom.eval()
 
   #if (is.null(params[['eval_metric']]) && is.null(feval))
   #  stop("Either 'eval_metric' or 'feval' must be provided for CV")
-
+  
   # Check the labels
   if ( (inherits(data, 'xgb.DMatrix') && is.null(getinfo(data, 'label'))) ||
        (!inherits(data, 'xgb.DMatrix') && is.null(label)))
     stop("Labels must be provided for CV either through xgb.DMatrix, or through 'label=' when 'data' is matrix")
-
+  
   # CV folds
   if(!is.null(folds)) {
     if(!is.list(folds) || length(folds) < 2)
@@ -146,7 +146,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
       stop("'nfold' must be > 1")
     folds <- generate.cv.folds(nfold, nrow(data), stratified, label, params)
   }
-
+  
   # Potential TODO: sequential CV
   #if (strategy == 'sequential')
   #  stop('Sequential CV strategy is not yet implemented')
@@ -166,7 +166,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   stop_condition <- FALSE
   if (!is.null(early_stopping_rounds) &&
       !has.callbacks(callbacks, 'cb.early.stop')) {
-    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
+    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds, 
                                                  maximize = maximize, verbose = verbose))
   }
   # CV-predictions callback
@@ -177,7 +177,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   # Sort the callbacks into categories
   cb <- categorize.callbacks(callbacks)
 
-
+  
   # create the booster-folds
   dall <- xgb.get.DMatrix(data, label, missing)
   bst_folds <- lapply(seq_along(folds), function(k) {
@@ -197,12 +197,12 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   # those are fixed for CV (no training continuation)
   begin_iteration <- 1
   end_iteration <- nrounds
-
+  
   # synchronous CV boosting: run CV folds' models within each iteration
   for (iteration in begin_iteration:end_iteration) {
-
+    
     for (f in cb$pre_iter) f()
-
+    
     msg <- lapply(bst_folds, function(fd) {
       xgb.iter.update(fd$bst, fd$dtrain, iteration - 1, obj)
       xgb.iter.eval(fd$bst, fd$watchlist, iteration - 1, feval)
@@ -210,9 +210,9 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     msg <- simplify2array(msg)
     bst_evaluation <- rowMeans(msg)
     bst_evaluation_err <- sqrt(rowMeans(msg^2) - bst_evaluation^2)
-
+    
     for (f in cb$post_iter) f()
-
+    
     if (stop_condition) break
   }
   for (f in cb$finalize) f(finalize = TRUE)
@@ -236,17 +236,17 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 
 
 #' Print xgb.cv result
-#'
+#' 
 #' Prints formatted results of \code{xgb.cv}.
-#'
+#' 
 #' @param x an \code{xgb.cv.synchronous} object
 #' @param verbose whether to print detailed data
 #' @param ... passed to \code{data.table.print}
-#'
+#' 
 #' @details
-#' When not verbose, it would only print the evaluation results,
+#' When not verbose, it would only print the evaluation results, 
 #' including the best iteration (when available).
-#'
+#' 
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
@@ -254,13 +254,13 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #' print(cv)
 #' print(cv, verbose=TRUE)
-#'
+#' 
 #' @rdname print.xgb.cv
 #' @method print xgb.cv.synchronous
 #' @export
 print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
   cat('##### xgb.cv ', length(x$folds), '-folds\n', sep = '')
-
+  
   if (verbose) {
     if (!is.null(x$call)) {
       cat('call:\n  ')
@@ -268,8 +268,8 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
     }
     if (!is.null(x$params)) {
       cat('params (as set within xgb.cv):\n')
-      cat( '  ',
-           paste(names(x$params),
+      cat( '  ', 
+           paste(names(x$params), 
                  paste0('"', unlist(x$params), '"'),
                  sep = ' = ', collapse = ', '), '\n', sep = '')
     }
@@ -280,9 +280,9 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
         print(x)
       })
     }
-
+    
     for (n in c('niter', 'best_iteration', 'best_ntreelimit')) {
-      if (is.null(x[[n]]))
+      if (is.null(x[[n]])) 
         next
       cat(n, ': ', x[[n]], '\n', sep = '')
     }
@@ -293,10 +293,10 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
     }
   }
 
-  if (verbose)
+  if (verbose) 
     cat('evaluation_log:\n')
   print(x$evaluation_log, row.names = FALSE, ...)
-
+  
   if (!is.null(x$best_iteration)) {
     cat('Best iteration:\n')
     print(x$evaluation_log[x$best_iteration], row.names = FALSE, ...)

R-package/R/xgb.load.R

@@ -28,7 +28,6 @@
 #'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
-#' if (file.exists('xgb.model')) file.remove('xgb.model')
 #' pred <- predict(bst, test$data)
 #' @export
 xgb.load <- function(modelfile) {

R-package/R/xgb.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

@@ -5,16 +5,16 @@
 #'
 #' @param importance_matrix a \code{data.table} returned by \code{\link{xgb.importance}}.
 #' @param top_n maximal number of top features to include into the plot.
-#' @param measure the name of importance measure to plot.
+#' @param measure the name of importance measure to plot. 
 #'        When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.
 #' @param rel_to_first whether importance values should be represented as relative to the highest ranked feature.
 #'        See Details.
 #' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.
 #'        When it is NULL, the existing \code{par('mar')} is used.
 #' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}.
-#' @param plot (base R barplot) whether a barplot should be produced.
+#' @param plot (base R barplot) whether a barplot should be produced. 
 #'        If FALSE, only a data.table is returned.
-#' @param n_clusters (ggplot only) a \code{numeric} vector containing the min and the max range
+#' @param n_clusters (ggplot only) a \code{numeric} vector containing the min and the max range 
 #'        of the possible number of clusters of bars.
 #' @param ... other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).
 #'
@@ -22,27 +22,27 @@
 #' The graph represents each feature as a horizontal bar of length proportional to the importance of a feature.
 #' Features are shown ranked in a decreasing importance order.
 #' It works for importances from both \code{gblinear} and \code{gbtree} models.
-#'
+#' 
 #' When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
-#' For gbtree model, that would mean being normalized to the total of 1
+#' For gbtree model, that would mean being normalized to the total of 1 
 #' ("what is feature's importance contribution relative to the whole model?").
 #' For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
-#' Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
+#' Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of 
 #' "what is feature's importance contribution relative to the most important feature?"
-#'
-#' The ggplot-backend method also performs 1-D 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})
 #' and silently returns a processed data.table with \code{n_top} features sorted by importance.
-#'
+#' 
 #' The \code{xgb.ggplot.importance} function returns a ggplot graph which could be customized afterwards.
 #' E.g., to change the title of the graph, add \code{+ ggtitle("A GRAPH NAME")} to the result.
 #'
 #' @seealso
 #' \code{\link[graphics]{barplot}}.
-#'
+#' 
 #' @examples
 #' data(agaricus.train)
 #'
@@ -50,15 +50,15 @@
 #'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #'
 #' importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
-#'
+#' 
 #' xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")
-#'
+#' 
 #' (gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
 #' gg + ggplot2::ylab("Frequency")
 #'
 #' @rdname xgb.plot.importance
 #' @export
-xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL,
+xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL, 
                                 rel_to_first = FALSE, left_margin = 10, cex = NULL, plot = TRUE, ...) {
   check.deprecation(...)
   if (!is.data.table(importance_matrix))  {
@@ -80,13 +80,13 @@ xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure
     if (!"Feature" %in% imp_names)
       stop("Importance matrix column names are not as expected!")
   }
-
+  
   # also aggregate, just in case when the values were not yet summed up by feature
   importance_matrix <- importance_matrix[, Importance := sum(get(measure)), by = Feature]
-
+  
   # make sure it's ordered
   importance_matrix <- importance_matrix[order(-abs(Importance))]
-
+  
   if (!is.null(top_n)) {
     top_n <- min(top_n, nrow(importance_matrix))
     importance_matrix <- head(importance_matrix, top_n)
@@ -97,14 +97,14 @@ xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure
   if (is.null(cex)) {
     cex <- 2.5/log2(1 + nrow(importance_matrix))
   }
-
+  
   if (plot) {
     op <- par(no.readonly = TRUE)
     mar <- op$mar
     if (!is.null(left_margin))
       mar[2] <- left_margin
     par(mar = mar)
-
+    
     # reverse the order of rows to have the highest ranked at the top
     importance_matrix[nrow(importance_matrix):1,
                       barplot(Importance, horiz = TRUE, border = NA, cex.names = cex,
@@ -115,7 +115,7 @@ xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure
                       barplot(Importance, horiz = TRUE, border = NA, add = TRUE)]
     par(op)
   }
-
+  
   invisible(importance_matrix)
 }
 

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

@@ -1,9 +1,9 @@
 #' SHAP contribution dependency plots
 #'
 #' Visualizing the SHAP feature contribution to prediction dependencies on feature value.
-#'
+#' 
 #' @param data data as a \code{matrix} or \code{dgCMatrix}.
-#' @param shap_contrib a matrix of SHAP contributions that was computed earlier for the above
+#' @param shap_contrib a matrix of SHAP contributions that was computed earlier for the above 
 #'          \code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.
 #' @param features a vector of either column indices or of feature names to plot. When it is NULL,
 #'          feature importance is calculated, and \code{top_n} high ranked features are taken.
@@ -31,32 +31,32 @@
 #' @param plot_loess whether to plot loess-smoothed curves. The smoothing is only done for features with
 #'          more than 5 distinct values.
 #' @param col_loess a color to use for the loess curves.
-#' @param span_loess the \code{span} 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}.
-#'
+#' 
 #' @details
-#'
+#' 
 #' These scatterplots represent how SHAP feature contributions depend of feature values.
 #' The similarity to partial dependency plots is that they also give an idea for how feature values
 #' affect predictions. However, in partial dependency plots, we usually see marginal dependencies
 #' of model prediction on feature value, while SHAP contribution dependency plots display the estimated
 #' contributions of a feature to model prediction for each individual case.
-#'
+#' 
 #' When \code{plot_loess = TRUE} is set, feature values are rounded to 3 significant digits and
 #' weighted LOESS is computed and plotted, where weights are the numbers of data points
 #' at each rounded value.
-#'
+#' 
 #' Note: SHAP contributions are shown on the scale of model margin. E.g., for a logistic binomial objective,
 #' the margin is prediction before a sigmoidal transform into probability-like values.
 #' Also, since SHAP stands for "SHapley Additive exPlanation" (model prediction = sum of SHAP
 #' contributions for all features + bias), depending on the objective used, transforming SHAP
 #' contributions for a feature from the marginal to the prediction space is not necessarily
 #' a meaningful thing to do.
-#'
+#' 
 #' @return
-#'
+#' 
 #' In addition to producing plots (when \code{plot=TRUE}), it silently returns a list of two matrices:
 #' \itemize{
 #'  \item \code{data} the values of selected features;
@@ -70,11 +70,11 @@
 #' Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", \url{https://arxiv.org/abs/1706.06060}
 #'
 #' @examples
-#'
+#' 
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #'
-#' bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
+#' bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50, 
 #'                eta = 0.1, max_depth = 3, subsample = .5,
 #'                method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
 #'
@@ -99,7 +99,7 @@
 #'               n_col = 2, col = col, pch = 16, pch_NA = 17)
 #' xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4,
 #'               n_col = 2, col = col, pch = 16, pch_NA = 17)
-#'
+#' 
 #' @rdname xgb.plot.shap
 #' @export
 xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1, model = NULL,
@@ -109,7 +109,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
                           plot_NA = TRUE, col_NA = rgb(0.7, 0, 1, 0.6), pch_NA = '.', pos_NA = 1.07,
                           plot_loess = TRUE, col_loess = 2, span_loess = 0.5,
                           which = c("1d", "2d"), plot = TRUE, ...) {
-
+  
   if (!is.matrix(data) && !inherits(data, "dgCMatrix"))
     stop("data: must be either matrix or dgCMatrix")
 
@@ -122,7 +122,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
   if (!is.null(shap_contrib) &&
       (!is.matrix(shap_contrib) || nrow(shap_contrib) != nrow(data) || ncol(shap_contrib) != ncol(data) + 1))
     stop("shap_contrib is not compatible with the provided data")
-
+  
   nsample <- if (is.null(subsample)) min(100000, nrow(data)) else as.integer(subsample * nrow(data))
   idx <- sample(1:nrow(data), nsample)
   data <- data[idx,]
@@ -144,13 +144,13 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
       stop("top_n: must be an integer within [1, 100]")
     features <- imp$Feature[1:min(top_n, NROW(imp))]
   }
-
+  
   if (is.character(features)) {
     if (is.null(colnames(data)))
       stop("Either provide `data` with column names or provide `features` as column indices")
     features <- match(features, colnames(data))
   }
-
+  
   if (n_col > length(features)) n_col <- length(features)
 
   if (is.list(shap_contrib)) { # multiclass: either choose a class or merge
@@ -165,7 +165,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
   if (is.null(cols)) cols <- paste0('X', 1:ncol(data))
   colnames(data) <- cols
   colnames(shap_contrib) <- cols
-
+  
   if (plot && which == "1d") {
     op <- par(mfrow = c(ceiling(length(features) / n_col), n_col),
               oma = c(0,0,0,0) + 0.2,

R-package/R/xgb.save.R

@@ -27,7 +27,6 @@
 #'                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 #' xgb.save(bst, 'xgb.model')
 #' bst <- xgb.load('xgb.model')
-#' if (file.exists('xgb.model')) file.remove('xgb.model')
 #' pred <- predict(bst, test$data)
 #' @export
 xgb.save <- function(model, fname) {

R-package/R/xgb.train.R

@@ -1,48 +1,48 @@
 #' eXtreme Gradient Boosting Training
-#'
+#' 
 #' \code{xgb.train} is an advanced interface for training an xgboost model.
 #' The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
 #'
-#' @param params the list of parameters.
+#' @param params the list of parameters. 
 #'        The complete list of parameters is available at \url{http://xgboost.readthedocs.io/en/latest/parameter.html}.
 #'        Below is a shorter summary:
-#'
+#' 
 #' 1. General Parameters
-#'
+#' 
 #' \itemize{
 #'   \item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
 #' }
-#'
+#'  
 #' 2. Booster Parameters
-#'
+#' 
 #' 2.1. Parameter for Tree Booster
-#'
+#' 
 #' \itemize{
 #'   \item \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1} when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model more robust to overfitting but slower to compute. Default: 0.3
-#'   \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.
+#'   \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. 
 #'   \item \code{max_depth} maximum depth of a tree. Default: 6
 #'   \item \code{min_child_weight} minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1
-#'   \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1
+#'   \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1 
 #'   \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
 #'   \item \code{num_parallel_tree} Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample  < 1}  and \code{round = 1}) accordingly. Default: 1
 #'   \item \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length equals to the number of features in the training data. \code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.
 #'   \item \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions. Each item of the list represents one permitted interaction where specified features are allowed to interact with each other. Feature index values should start from \code{0} (\code{0} references the first column).  Leave argument unspecified for no interaction constraints.
 #' }
-#'
+#' 
 #' 2.2. Parameter for Linear Booster
-#'
+#'  
 #' \itemize{
 #'   \item \code{lambda} L2 regularization term on weights. Default: 0
 #'   \item \code{lambda_bias} L2 regularization term on bias. Default: 0
 #'   \item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
 #' }
-#'
-#' 3. Task Parameters
-#'
+#' 
+#' 3. Task Parameters 
+#' 
 #' \itemize{
 #' \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
 #'   \itemize{
-#'     \item \code{reg: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.
@@ -54,32 +54,32 @@
 #'   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
 #'   \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
 #' }
-#'
+#' 
 #' @param data training dataset. \code{xgb.train} accepts only an \code{xgb.DMatrix} as the input.
 #'        \code{xgboost}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.
 #' @param nrounds max number of boosting iterations.
 #' @param watchlist named list of xgb.DMatrix datasets to use for evaluating model performance.
 #'        Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
-#'        of these datasets during each boosting iteration, and stored in the end as a field named
-#'        \code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
+#'        of these datasets during each boosting iteration, and stored in the end as a field named 
+#'        \code{evaluation_log} in the resulting object. When either \code{verbose>=1} or 
 #'        \code{\link{cb.print.evaluation}} callback is engaged, the performance results are continuously
-#'        printed out during the training.
+#'        printed out during the training. 
 #'        E.g., specifying \code{watchlist=list(validation1=mat1, validation2=mat2)} allows to track
 #'        the performance of each round's model on mat1 and mat2.
-#' @param obj customized objective function. Returns gradient and second order
+#' @param obj customized objective function. Returns gradient and second order 
 #'        gradient with given prediction and dtrain.
-#' @param feval customized evaluation function. Returns
-#'        \code{list(metric='metric-name', value='metric-value')} with given
+#' @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.
 #'        If 2, some additional information will be printed out.
-#'        Note that setting \code{verbose > 0} automatically engages the
+#'        Note that setting \code{verbose > 0} automatically engages the 
 #'        \code{cb.print.evaluation(period=1)} callback function.
 #' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
-#'        Default is 1 which means all messages are printed. This parameter is passed to the
+#'        Default is 1 which means all messages are printed. This parameter is passed to the 
 #'        \code{\link{cb.print.evaluation}} callback.
-#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
-#'        If set to an integer \code{k}, training with a validation set will stop if the performance
+#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered. 
+#'        If set to an integer \code{k}, training with a validation set will stop if the performance 
 #'        doesn't improve for \code{k} rounds.
 #'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
 #' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
@@ -90,35 +90,35 @@
 #'        0 means save at the end. The saving is handled by the \code{\link{cb.save.model}} callback.
 #' @param save_name the name or path for periodically saved model file.
 #' @param xgb_model a previously built model to continue the training from.
-#'        Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
+#'        Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a 
 #'        file with a previously saved model.
 #' @param callbacks a list of callback functions to perform various task during boosting.
-#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-#'        parameters' values. User can provide either existing or their own callback methods in order
+#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
+#'        parameters' values. User can provide either existing or their own callback methods in order 
 #'        to customize the training process.
 #' @param ... other parameters to pass to \code{params}.
-#' @param label vector of response values. Should not be provided when data is
+#' @param label vector of response values. Should not be provided when data is 
 #'        a local data file name or an \code{xgb.DMatrix}.
 #' @param missing by default is set to NA, which means that NA values should be considered as 'missing'
 #'        by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values.
 #'        This parameter is only used when input is a dense matrix.
 #' @param weight a vector indicating the weight for each row of the input.
-#'
-#' @details
-#' These are the training functions for \code{xgboost}.
-#'
-#' The \code{xgb.train} interface supports advanced features such as \code{watchlist},
-#' customized objective and evaluation metric functions, therefore it is more flexible
+#' 
+#' @details 
+#' These are the training functions for \code{xgboost}. 
+#' 
+#' The \code{xgb.train} interface supports advanced features such as \code{watchlist}, 
+#' customized objective and evaluation metric functions, therefore it is more flexible 
 #' than the \code{xgboost} interface.
 #'
-#' Parallelization is automatically enabled if \code{OpenMP} is present.
+#' Parallelization is automatically enabled if \code{OpenMP} is present. 
 #' Number of threads can also be manually specified via \code{nthread} parameter.
-#'
+#' 
 #' The evaluation metric is chosen automatically by Xgboost (according to the objective)
 #' when the \code{eval_metric} parameter is not provided.
-#' User may set one or several \code{eval_metric} parameters.
+#' User may set one or several \code{eval_metric} parameters. 
 #' Note that when using a customized metric, only this single metric can be used.
-#' The 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}
@@ -131,7 +131,7 @@
 #'      \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
 #'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
 #'   }
-#'
+#' 
 #' The following callbacks are automatically created when certain parameters are set:
 #' \itemize{
 #'   \item \code{cb.print.evaluation} is turned on when \code{verbose > 0};
@@ -140,38 +140,38 @@
 #'   \item \code{cb.early.stop}: when \code{early_stopping_rounds} is set.
 #'   \item \code{cb.save.model}: when \code{save_period > 0} is set.
 #' }
-#'
-#' @return
+#' 
+#' @return 
 #' An object of class \code{xgb.Booster} with the following elements:
 #' \itemize{
 #'   \item \code{handle} a handle (pointer) to the xgboost model in memory.
 #'   \item \code{raw} a cached memory dump of the xgboost model saved as R's \code{raw} type.
 #'   \item \code{niter} number of boosting iterations.
-#'   \item \code{evaluation_log} evaluation history 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
+#'   \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.
+#'   \item \code{callbacks} callback functions that were either automatically assigned or 
+#'         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,
+#'   \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
 #'         which could further be used in \code{predict} method
 #'         (only available with early stopping).
 #'   \item \code{best_score} the best evaluation metric value during early stopping.
 #'         (only available with early stopping).
 #'   \item \code{feature_names} names of the training dataset features
-#'         (only when 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.
 #' }
-#'
+#' 
 #' @seealso
 #' \code{\link{callbacks}},
 #' \code{\link{predict.xgb.Booster}},
 #' \code{\link{xgb.cv}}
-#'
+#' 
 #' @references
 #'
 #' Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System",
@@ -180,17 +180,17 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
-#'
+#' 
 #' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 #' dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 #' watchlist <- list(train = dtrain, eval = dtest)
-#'
+#' 
 #' ## A simple xgb.train example:
-#' param <- list(max_depth = 2, eta = 1, 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)
-#'
-#'
+#' 
+#' 
 #' ## An xgb.train example where custom objective and evaluation metric are used:
 #' logregobj <- function(preds, dtrain) {
 #'    labels <- getinfo(dtrain, "label")
@@ -204,58 +204,58 @@
 #'   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
 #'   return(list(metric = "error", value = err))
 #' }
-#'
+#' 
 #' # These functions could be used by passing them either:
 #' #  as 'objective' and 'eval_metric' parameters in the params list:
-#' param <- list(max_depth = 2, eta = 1, 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)
-#'
+#' 
 #' #  or as dedicated 'obj' and 'feval' parameters of xgb.train:
 #' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
 #'                  obj = logregobj, feval = evalerror)
-#'
-#'
+#' 
+#' 
 #' ## An xgb.train example of using variable learning rates at each iteration:
-#' param <- list(max_depth = 2, eta = 1, 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,
 #'                  callbacks = list(cb.reset.parameters(my_etas)))
-#'
+#' 
 #' ## Early stopping:
 #' bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
 #'                  early_stopping_rounds = 3)
-#'
+#' 
 #' ## An 'xgboost' interface example:
-#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
-#'                max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, 
+#'                max_depth = 2, eta = 1, nthread = 2, nrounds = 2, 
 #'                objective = "binary:logistic")
 #' pred <- predict(bst, agaricus.test$data)
-#'
+#' 
 #' @rdname xgb.train
 #' @export
 xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
                       obj = NULL, feval = NULL, verbose = 1, print_every_n = 1L,
                       early_stopping_rounds = NULL, maximize = NULL,
-                      save_period = NULL, save_name = "xgboost.model",
+                      save_period = NULL, save_name = "xgboost.model", 
                       xgb_model = NULL, callbacks = list(), ...) {
-
+  
   check.deprecation(...)
-
+  
   params <- check.booster.params(params, ...)
 
   check.custom.obj()
   check.custom.eval()
-
+  
   # data & watchlist checks
   dtrain <- data
-  if (!inherits(dtrain, "xgb.DMatrix"))
+  if (!inherits(dtrain, "xgb.DMatrix")) 
     stop("second argument dtrain must be xgb.DMatrix")
   if (length(watchlist) > 0) {
     if (typeof(watchlist) != "list" ||
@@ -288,7 +288,7 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
   stop_condition <- FALSE
   if (!is.null(early_stopping_rounds) &&
       !has.callbacks(callbacks, 'cb.early.stop')) {
-    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
+    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds, 
                                                  maximize = maximize, verbose = verbose))
   }
   # Sort the callbacks into categories
@@ -318,22 +318,22 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
 
   # TODO: distributed code
   rank <- 0
-
+  
   niter_skip <- ifelse(is_update, 0, niter_init)
   begin_iteration <- niter_skip + 1
   end_iteration <- niter_skip + nrounds
-
+  
   # the main loop for boosting iterations
   for (iteration in begin_iteration:end_iteration) {
-
+    
     for (f in cb$pre_iter) f()
-
+    
     xgb.iter.update(bst$handle, dtrain, iteration - 1, obj)
-
+    
     bst_evaluation <- numeric(0)
     if (length(watchlist) > 0)
       bst_evaluation <- xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval)
-
+    
     xgb.attr(bst$handle, 'niter') <- iteration - 1
 
     for (f in cb$post_iter) f()
@@ -341,9 +341,9 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
     if (stop_condition) break
   }
   for (f in cb$finalize) f(finalize = TRUE)
-
+  
   bst <- xgb.Booster.complete(bst, saveraw = TRUE)
-
+  
   # store the total number of boosting iterations
   bst$niter = end_iteration
 

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/cleanup

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

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/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
@@ -32,9 +32,9 @@ evalerror <- function(preds, dtrain) {
 }
 print ('start training with early Stopping setting')
 
-bst <- xgb.train(param, dtrain, num_round, watchlist,
+bst <- xgb.train(param, dtrain, num_round, watchlist, 
                  objective = logregobj, eval_metric = evalerror, maximize = FALSE,
                  early_stopping_round = 3)
-bst <- xgb.cv(param, dtrain, num_round, nfold = 5,
+bst <- xgb.cv(param, dtrain, num_round, nfold = 5, 
               objective = logregobj, eval_metric = evalerror,
               maximize = FALSE, early_stopping_rounds = 3)

R-package/demo/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

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

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

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

@@ -8,15 +8,15 @@ cb.early.stop(stopping_rounds, maximize = FALSE, metric_name = NULL,
   verbose = TRUE)
 }
 \arguments{
-\item{stopping_rounds}{The number of rounds with no improvement in
+\item{stopping_rounds}{The number of rounds with no improvement in 
 the evaluation metric in order to stop the training.}
 
 \item{maximize}{whether to maximize the evaluation metric}
 
 \item{metric_name}{the name of an evaluation column to use as a criteria for early
 stopping. If not set, the last column would be used.
-Let's say the test data in \code{watchlist} was labelled as \code{dtest},
-and one wants to use the AUC in test data for early stopping regardless of where
+Let's say the test data in \code{watchlist} was labelled as \code{dtest}, 
+and one wants to use the AUC in test data for early stopping regardless of where 
 it is in the \code{watchlist}, then one of the following would need to be set:
 \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
 All dash '-' characters in metric names are considered equivalent to '_'.}
@@ -27,7 +27,7 @@ All dash '-' characters in metric names are considered equivalent to '_'.}
 Callback closure to activate the early stopping.
 }
 \details{
-This callback function determines the condition for early stopping
+This callback function determines the condition for early stopping 
 by setting the \code{stop_condition = TRUE} flag in its calling frame.
 
 The following additional fields are assigned to the model's R object:

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

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

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

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

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

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

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

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

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

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

R-package/man/getinfo.Rd

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

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

@@ -7,8 +7,8 @@
 \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,
+  predinteraction = FALSE, reshape = FALSE, ...)
 
 \method{predict}{xgb.Booster.handle}(object, ...)
 }
@@ -91,7 +91,7 @@ in \url{http://blog.datadive.net/interpreting-random-forests/}.
 
 With \code{predinteraction = TRUE}, SHAP values of contributions of interaction of each pair of features
 are computed. Note that this operation might be rather expensive in terms of compute and memory.
-Since it quadratically depends on the number of features, it is recommended to perform selection
+Since it quadratically depends on the number of features, it is recommended to perfom selection
 of the most important features first. See below about the format of the returned results.
 }
 \examples{

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

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

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

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

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

@@ -5,7 +5,7 @@
 \alias{slice.xgb.DMatrix}
 \alias{[.xgb.DMatrix}
 \title{Get a new DMatrix containing the specified rows of
-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{
@@ -39,7 +39,6 @@ bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_dep
 saveRDS(bst, "xgb.model.rds")
 
 bst1 <- readRDS("xgb.model.rds")
-if (file.exists("xgb.model.rds")) file.remove("xgb.model.rds")
 # the handle is invalid:
 print(bst1$handle)
 

R-package/man/xgb.DMatrix.Rd

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

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

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

R-package/man/xgb.attr.Rd

@@ -73,7 +73,6 @@ xgb.attributes(bst) <- list(a = 123, b = "abc")
 
 xgb.save(bst, 'xgb.model')
 bst1 <- xgb.load('xgb.model')
-if (file.exists('xgb.model')) file.remove('xgb.model')
 print(xgb.attr(bst1, "my_attribute"))
 print(xgb.attributes(bst1))
 

R-package/man/xgb.cv.Rd

@@ -4,18 +4,19 @@
 \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
@@ -34,11 +35,11 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL, missing = NA,
 
 \item{label}{vector of response values. Should be provided only when data is an R-matrix.}
 
-\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means
-that NA values should be considered as 'missing' by the algorithm.
+\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means 
+that NA values should be considered as 'missing' by the algorithm. 
 Sometimes, 0 or other extreme value might be used to represent missing values.}
 
-\item{prediction}{A logical value indicating whether to return the test fold predictions
+\item{prediction}{A logical value indicating whether to return the test fold predictions 
 from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.}
 
 \item{showsd}{\code{boolean}, whether to show standard deviation of cross validation}
@@ -55,28 +56,28 @@ from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callb
   \item \code{merror} Exact matching error, used to evaluate multi-class classification
 }}
 
-\item{obj}{customized objective function. Returns gradient and second order
+\item{obj}{customized objective function. Returns gradient and second order 
 gradient with given prediction and dtrain.}
 
-\item{feval}{customized evaluation function. Returns
-\code{list(metric='metric-name', value='metric-value')} with given
+\item{feval}{custimized evaluation function. Returns 
+\code{list(metric='metric-name', value='metric-value')} with given 
 prediction and dtrain.}
 
-\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified
+\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified 
 by the values of outcome labels.}
 
 \item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds
-(each element must be a vector of test fold's indices). When folds are supplied,
+(each element must be a vector of test fold's indices). When folds are supplied, 
 the \code{nfold} and \code{stratified} parameters are ignored.}
 
 \item{verbose}{\code{boolean}, print the statistics during the process}
 
 \item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
-Default is 1 which means all messages are printed. This parameter is passed to the
+Default is 1 which means all messages are printed. This parameter is passed to the 
 \code{\link{cb.print.evaluation}} callback.}
 
-\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
-If set to an integer \code{k}, training with a validation set will stop if the performance
+\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered. 
+If set to an integer \code{k}, training with a validation set will stop if the performance 
 doesn't improve for \code{k} rounds.
 Setting this parameter engages the \code{\link{cb.early.stop}} callback.}
 
@@ -86,8 +87,8 @@ When it is \code{TRUE}, it means the larger the evaluation score the better.
 This parameter is passed to the \code{\link{cb.early.stop}} callback.}
 
 \item{callbacks}{a list of callback functions to perform various task during boosting.
-See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-parameters' values. User can provide either existing or their own callback methods in order
+See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
+parameters' values. User can provide either existing or their own callback methods in order 
 to customize the training process.}
 
 \item{...}{other parameters to pass to \code{params}.}
@@ -96,26 +97,26 @@ to customize the training process.}
 An object of class \code{xgb.cv.synchronous} with the following elements:
 \itemize{
   \item \code{call} a function call.
-  \item \code{params} parameters that were passed to the xgboost library. Note that it does not
+  \item \code{params} parameters that were passed to the xgboost library. Note that it does not 
         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
-  \item \code{callbacks} callback functions that were either automatically assigned or
+  \item \code{callbacks} callback functions that were either automatically assigned or 
         explicitly passed.
-  \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
-        first column corresponding to iteration number and the rest corresponding to the
+  \item \code{evaluation_log} evaluation history 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.
   \item \code{niter} number of boosting iterations.
   \item \code{nfeatures} number of features in training data.
-  \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
+  \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds} 
         parameter or randomly generated.
   \item \code{best_iteration} iteration number with the best evaluation metric value
         (only available with early stopping).
-  \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
+  \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
         which could further be used in \code{predict} method
         (only available with early stopping).
-  \item \code{pred} CV prediction values available when \code{prediction} is set.
+  \item \code{pred} CV prediction values available when \code{prediction} is set. 
         It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-  \item \code{models} a liost of the CV folds' models. It is only available with the explicit
+  \item \code{models} a liost of the CV folds' models. It is only available with the explicit 
         setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
 }
 }
@@ -123,9 +124,9 @@ An object of class \code{xgb.cv.synchronous} with the following elements:
 The cross validation function of xgboost
 }
 \details{
-The original sample is randomly partitioned into \code{nfold} equal size subsamples.
+The original sample is randomly partitioned into \code{nfold} equal size subsamples. 
 
-Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
+Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data. 
 
 The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
 

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.load.Rd

@@ -33,7 +33,6 @@ bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 xgb.save(bst, 'xgb.model')
 bst <- xgb.load('xgb.model')
-if (file.exists('xgb.model')) file.remove('xgb.model')
 pred <- predict(bst, test$data)
 }
 \seealso{

R-package/man/xgb.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

@@ -9,21 +9,21 @@ 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}}.}
 
 \item{top_n}{maximal number of top features to include into the plot.}
 
-\item{measure}{the name of importance measure to plot.
+\item{measure}{the name of importance measure to plot. 
 When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.}
 
 \item{rel_to_first}{whether importance values should be represented as relative to the highest ranked feature.
 See Details.}
 
-\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range
+\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range 
 of the possible number of clusters of bars.}
 
 \item{...}{other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).}
@@ -33,7 +33,7 @@ When it is NULL, the existing \code{par('mar')} is used.}
 
 \item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
 
-\item{plot}{(base R barplot) whether a barplot should be produced.
+\item{plot}{(base R barplot) whether a barplot should be produced. 
 If FALSE, only a data.table is returned.}
 }
 \value{
@@ -53,14 +53,14 @@ Features are shown ranked in a decreasing importance order.
 It works for importances from both \code{gblinear} and \code{gbtree} models.
 
 When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
-For gbtree model, that would mean being normalized to the total of 1
+For gbtree model, that would mean being normalized to the total of 1 
 ("what is feature's importance contribution relative to the whole model?").
 For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
-Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
+Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of 
 "what is feature's importance contribution relative to the most important feature?"
 
-The ggplot-backend method also performs 1-D 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, ...)
@@ -15,7 +15,7 @@ xgb.plot.shap(data, shap_contrib = NULL, features = NULL, top_n = 1,
 \arguments{
 \item{data}{data as a \code{matrix} or \code{dgCMatrix}.}
 
-\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above
+\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above 
 \code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.}
 
 \item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
@@ -63,7 +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.}
 
@@ -104,7 +104,7 @@ a meaningful thing to do.
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
 
-bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
+bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50, 
                eta = 0.1, max_depth = 3, subsample = .5,
                method = "hist", objective = "binary:logistic", nthread = 2, verbose = 0)
 

R-package/man/xgb.save.Rd

@@ -33,7 +33,6 @@ bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 xgb.save(bst, 'xgb.model')
 bst <- xgb.load('xgb.model')
-if (file.exists('xgb.model')) file.remove('xgb.model')
 pred <- predict(bst, test$data)
 }
 \seealso{

R-package/man/xgb.train.Rd

@@ -5,18 +5,20 @@
 \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.
+\item{params}{the list of parameters. 
        The complete list of parameters is available at \url{http://xgboost.readthedocs.io/en/latest/parameter.html}.
        Below is a shorter summary:
 
@@ -25,37 +27,36 @@ xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
 \itemize{
   \item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
 }
-
+ 
 2. Booster Parameters
 
 2.1. Parameter for Tree Booster
 
 \itemize{
   \item \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1} when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model more robust to overfitting but slower to compute. Default: 0.3
-  \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.
+  \item \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. 
   \item \code{max_depth} maximum depth of a tree. Default: 6
   \item \code{min_child_weight} minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1
-  \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1
+  \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1 
   \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
   \item \code{num_parallel_tree} Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample  < 1}  and \code{round = 1}) accordingly. Default: 1
   \item \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length equals to the number of features in the training data. \code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.
-  \item \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions. Each item of the list represents one permitted interaction where specified features are allowed to interact with each other. Feature index values should start from \code{0} (\code{0} references the first column).  Leave argument unspecified for no interaction constraints.
 }
 
 2.2. Parameter for Linear Booster
-
+ 
 \itemize{
   \item \code{lambda} L2 regularization term on weights. Default: 0
   \item \code{lambda_bias} L2 regularization term on bias. Default: 0
   \item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
 }
 
-3. Task Parameters
+3. Task Parameters 
 
 \itemize{
 \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
   \itemize{
-    \item \code{reg: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.
@@ -75,31 +76,31 @@ xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
 
 \item{watchlist}{named list of xgb.DMatrix datasets to use for evaluating model performance.
 Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
-of these datasets during each boosting iteration, and stored in the end as a field named
-\code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
+of these datasets during each boosting iteration, and stored in the end as a field named 
+\code{evaluation_log} in the resulting object. When either \code{verbose>=1} or 
 \code{\link{cb.print.evaluation}} callback is engaged, the performance results are continuously
-printed out during the training.
+printed out during the training. 
 E.g., specifying \code{watchlist=list(validation1=mat1, validation2=mat2)} allows to track
 the performance of each round's model on mat1 and mat2.}
 
-\item{obj}{customized objective function. Returns gradient and second order
+\item{obj}{customized objective function. Returns gradient and second order 
 gradient with given prediction and dtrain.}
 
-\item{feval}{customized evaluation function. Returns
-\code{list(metric='metric-name', value='metric-value')} with given
+\item{feval}{custimized evaluation function. Returns 
+\code{list(metric='metric-name', value='metric-value')} with given 
 prediction and dtrain.}
 
 \item{verbose}{If 0, xgboost will stay silent. If 1, it will print information about performance.
 If 2, some additional information will be printed out.
-Note that setting \code{verbose > 0} automatically engages the
+Note that setting \code{verbose > 0} automatically engages the 
 \code{cb.print.evaluation(period=1)} callback function.}
 
 \item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
-Default is 1 which means all messages are printed. This parameter is passed to the
+Default is 1 which means all messages are printed. This parameter is passed to the 
 \code{\link{cb.print.evaluation}} callback.}
 
-\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
-If set to an integer \code{k}, training with a validation set will stop if the performance
+\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered. 
+If set to an integer \code{k}, training with a validation set will stop if the performance 
 doesn't improve for \code{k} rounds.
 Setting this parameter engages the \code{\link{cb.early.stop}} callback.}
 
@@ -114,17 +115,17 @@ This parameter is passed to the \code{\link{cb.early.stop}} callback.}
 \item{save_name}{the name or path for periodically saved model file.}
 
 \item{xgb_model}{a previously built model to continue the training from.
-Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
+Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a 
 file with a previously saved model.}
 
 \item{callbacks}{a list of callback functions to perform various task during boosting.
-See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
-parameters' values. User can provide either existing or their own callback methods in order
+See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the 
+parameters' values. User can provide either existing or their own callback methods in order 
 to customize the training process.}
 
 \item{...}{other parameters to pass to \code{params}.}
 
-\item{label}{vector of response values. Should not be provided when data is
+\item{label}{vector of response values. Should not be provided when data is 
 a local data file name or an \code{xgb.DMatrix}.}
 
 \item{missing}{by default is set to NA, which means that NA values should be considered as 'missing'
@@ -139,23 +140,23 @@ 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
+  \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.
+  \item \code{callbacks} callback functions that were either automatically assigned or 
+        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,
+  \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration, 
         which could further be used in \code{predict} method
         (only available with early stopping).
   \item \code{best_score} the best evaluation metric value during early stopping.
         (only available with early stopping).
   \item \code{feature_names} names of the training dataset features
-        (only when 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.
 }
 }
@@ -164,20 +165,20 @@ An object of class \code{xgb.Booster} with the following elements:
 The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
 }
 \details{
-These are the training functions for \code{xgboost}.
+These are the training functions for \code{xgboost}. 
 
-The \code{xgb.train} interface supports advanced features such as \code{watchlist},
-customized objective and evaluation metric functions, therefore it is more flexible
+The \code{xgb.train} interface supports advanced features such as \code{watchlist}, 
+customized objective and evaluation metric functions, therefore it is more flexible 
 than the \code{xgboost} interface.
 
-Parallelization is automatically enabled if \code{OpenMP} is present.
+Parallelization is automatically enabled if \code{OpenMP} is present. 
 Number of threads can also be manually specified via \code{nthread} parameter.
 
 The evaluation metric is chosen automatically by Xgboost (according to the objective)
 when the \code{eval_metric} parameter is not provided.
-User may set one or several \code{eval_metric} parameters.
+User may set one or several \code{eval_metric} parameters. 
 Note that when using a customized metric, only this single metric can be used.
-The 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}
@@ -209,7 +210,7 @@ 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)
 
@@ -230,12 +231,12 @@ 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)
 
@@ -245,7 +246,7 @@ 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,
@@ -256,8 +257,8 @@ bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
                  early_stopping_rounds = 3)
 
 ## An 'xgboost' interface example:
-bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
-               max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, 
+               max_depth = 2, eta = 1, nthread = 2, nrounds = 2, 
                objective = "binary:logistic")
 pred <- predict(bst, agaricus.test$data)
 

R-package/man/xgboost-deprecated.Rd

@@ -10,7 +10,7 @@ The deprecated parameters would be removed in the next release.
 \details{
 To see all the current deprecated and new parameters, check the \code{xgboost:::depr_par_lut} table.
 
-A deprecation warning is shown when any of the deprecated parameters is used in a call.
-An additional warning is shown when there was a partial match to a deprecated parameter
+A deprecation warning is shown when any of the deprecated parameters is used in a call. 
+An additional warning is shown when there was a partial match to a deprecated parameter 
 (as R is able to partially match parameter names).
 }

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

@@ -1,5 +1,5 @@
 /* Copyright (c) 2015 by Contributors
- *
+ * 
  * This file was initially generated using the following R command:
  * tools::package_native_routine_registration_skeleton('.', con = 'src/init.c', character_only = F)
  * and edited to conform to xgboost C linter requirements. For details, see
@@ -10,7 +10,7 @@
 #include <stdlib.h>
 #include <R_ext/Rdynload.h>
 
-/* FIXME:
+/* FIXME: 
 Check these declarations against the C/Fortran source code.
 */
 
@@ -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)

R-package/tests/testthat/test_callbacks.R

@@ -236,7 +236,7 @@ test_that("early stopping using a specific metric works", {
   expect_equal(length(pred), 1611)
   logloss_pred <- sum(-ltest * log(pred) - (1 - ltest) * log(1 - pred)) / length(ltest)
   logloss_log <- bst$evaluation_log[bst$best_iteration, test_logloss]
-  expect_equal(logloss_log, logloss_pred, tolerance = 1e-5)
+  expect_equal(logloss_log, logloss_pred, tolerance = 5e-6)
 })
 
 test_that("early stopping xgb.cv works", {
@@ -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)
@@ -45,7 +45,7 @@ test_that("custom objective in CV works", {
 })
 
 test_that("custom objective using DMatrix attr works", {
-
+  
   attr(dtrain, 'label') <- getinfo(dtrain, 'label')
 
   logregobjattr <- function(preds, dtrain) {
@@ -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_dmatrix.R

@@ -10,12 +10,12 @@ test_label <- agaricus.test$label[1:100]
 test_that("xgb.DMatrix: basic construction", {
   # from sparse matrix
   dtest1 <- xgb.DMatrix(test_data, label=test_label)
-
-  # from dense matrix
+  
+  # from dense matrix 
   dtest2 <- xgb.DMatrix(as.matrix(test_data), label=test_label)
   expect_equal(getinfo(dtest1, 'label'), getinfo(dtest2, 'label'))
   expect_equal(dim(dtest1), dim(dtest2))
-
+  
   #from dense integer matrix
   int_data <- as.matrix(test_data)
   storage.mode(int_data) <- "integer"
@@ -33,7 +33,7 @@ test_that("xgb.DMatrix: saving, loading", {
   expect_output(dtest3 <- xgb.DMatrix(tmp_file, silent = TRUE), NA)
   unlink(tmp_file)
   expect_equal(getinfo(dtest1, 'label'), getinfo(dtest3, 'label'))
-
+  
   # from a libsvm text file
   tmp <- c("0 1:1 2:1","1 3:1","0 1:1")
   tmp_file <- 'tmp.libsvm'
@@ -49,7 +49,7 @@ test_that("xgb.DMatrix: getinfo & setinfo", {
   expect_true(setinfo(dtest, 'label', test_label))
   labels <- getinfo(dtest, 'label')
   expect_equal(test_label, getinfo(dtest, 'label'))
-
+  
   expect_true(length(getinfo(dtest, 'weight')) == 0)
   expect_true(length(getinfo(dtest, 'base_margin')) == 0)
 
@@ -57,10 +57,10 @@ test_that("xgb.DMatrix: getinfo & setinfo", {
   expect_true(setinfo(dtest, 'base_margin', test_label))
   expect_true(setinfo(dtest, 'group', c(50,50)))
   expect_error(setinfo(dtest, 'group', test_label))
-
+  
   # providing character values will give a warning
   expect_warning( setinfo(dtest, 'weight', rep('a', nrow(test_data))) )
-
+  
   # any other label should error
   expect_error(setinfo(dtest, 'asdf', test_label))
 })
@@ -71,7 +71,7 @@ test_that("xgb.DMatrix: slice, dim", {
   dsub1 <- slice(dtest, 1:42)
   expect_equal(nrow(dsub1), 42)
   expect_equal(ncol(dsub1), ncol(test_data))
-
+  
   dsub2 <- dtest[1:42,]
   expect_equal(dim(dtest), dim(test_data))
   expect_equal(getinfo(dsub1, 'label'), getinfo(dsub2, 'label'))

R-package/tests/testthat/test_helpers.R

@@ -163,7 +163,6 @@ test_that("xgb-attribute functionality", {
   # serializing:
   xgb.save(bst.Tree, 'xgb.model')
   bst <- xgb.load('xgb.model')
-  if (file.exists('xgb.model')) file.remove('xgb.model')
   expect_equal(xgb.attr(bst, "my_attr"), val)
   expect_equal(xgb.attributes(bst), list.ch)
   # deletion:
@@ -200,12 +199,10 @@ if (grepl('Windows', Sys.info()[['sysname']]) ||
 test_that("xgb.Booster serializing as R object works", {
   saveRDS(bst.Tree, 'xgb.model.rds')
   bst <- readRDS('xgb.model.rds')
-  if (file.exists('xgb.model.rds')) file.remove('xgb.model.rds')
   dtrain <- xgb.DMatrix(sparse_matrix, label = label)
   expect_equal(predict(bst.Tree, dtrain), predict(bst, dtrain), tolerance = float_tolerance)
   expect_equal(xgb.dump(bst.Tree), xgb.dump(bst))
   xgb.save(bst, 'xgb.model')
-  if (file.exists('xgb.model')) file.remove('xgb.model')
   nil_ptr <- new("externalptr")
   class(nil_ptr) <- "xgb.Booster.handle"
   expect_true(identical(bst$handle, nil_ptr))

R-package/tests/testthat/test_interactions.R

@@ -81,39 +81,6 @@ test_that("predict feature interactions works", {
   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)

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$dates), 
-  label = labels,
-  nthread = 2,
-  nrounds = 1,
-  objective = "binary:logistic",
-  missing = NA,
-  max_depth = 1
-)
-```
-
-## Comparing results
-We will now dump the model to JSON and attempt to illustrate a variety of issues that can arise, and how to properly deal with them.
-
-First let's dump the model to JSON:
-
-```{r}
-bst_json <- xgb.dump(bst, with_stats = FALSE, dump_format='json')
-bst_from_json <- fromJSON(bst_json, simplifyDataFrame = FALSE)
-node <- bst_from_json[[1]]
-cat(bst_json)
-```
-
-The tree JSON shown by the above code-chunk tells us that if the data is less than 20180132, the tree will output the value in the first leaf.  Otherwise it will output the value in the second leaf.  Let's try to reproduce this manually with the data we have and confirm that it matches the model predictions we've already calculated.
-
-```{r}
-bst_preds_logodds <- predict(bst,as.matrix(data$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:
@@ -102,11 +96,10 @@ 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&&
-        set _R_CHECK_FORCE_SUGGESTS_=FALSE&&
         R.exe CMD check xgboost*.tar.gz --no-manual --no-build-vignettes --as-cran --install-args=--build
       )
     # MSVC R package: run only the unit tests

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
@@ -32,42 +29,25 @@ 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)
 
 # Set output directory of target, ignoring debug or release
 function(set_output_directory target dir)
-	set_target_properties(${target} PROPERTIES
+	set_target_properties(${target} PROPERTIES 
 		RUNTIME_OUTPUT_DIRECTORY ${dir}
 		RUNTIME_OUTPUT_DIRECTORY_DEBUG ${dir}
 		RUNTIME_OUTPUT_DIRECTORY_RELEASE ${dir}
-		LIBRARY_OUTPUT_DIRECTORY ${dir}
-		LIBRARY_OUTPUT_DIRECTORY_DEBUG ${dir}
-		LIBRARY_OUTPUT_DIRECTORY_RELEASE ${dir}
+		LIBRARY_OUTPUT_DIRECTORY ${dir} 
+		LIBRARY_OUTPUT_DIRECTORY_DEBUG ${dir} 
+		LIBRARY_OUTPUT_DIRECTORY_RELEASE ${dir} 
 	)
 endfunction(set_output_directory)
 
 # Set a default build type to release if none was specified
 function(set_default_configuration_release)
     if(CMAKE_CONFIGURATION_TYPES STREQUAL "Debug;Release;MinSizeRel;RelWithDebInfo") # multiconfig generator?
-        set(CMAKE_CONFIGURATION_TYPES Release CACHE STRING "" FORCE)
+        set(CMAKE_CONFIGURATION_TYPES Release CACHE STRING "" FORCE) 
 	elseif(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
 	  message(STATUS "Setting build type to 'Release' as none was specified.")
 	  set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE )
@@ -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(NOT flags) 
+    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")
@@ -92,12 +67,12 @@ 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/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/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/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/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/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

@@ -1,17 +1,17 @@
 # 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
 eta = 1.0
 # minimum loss reduction required to make a further partition
-gamma = 1.0
+gamma = 1.0 
 # minimum sum of instance weight(hessian) needed in a child
-min_child_weight = 1
+min_child_weight = 1 
 # maximum depth of a tree
 max_depth = 5
 
@@ -20,10 +20,11 @@ base_score = 2001
 # the number of round to do boosting
 num_round = 100
 # 0 means do not save any model except the final round model
-save_period = 0
+save_period = 0 
 # The path of training data
 data = "yearpredMSD.libsvm.train"
 # The path of validation data, used to monitor training process, here [test] sets name of the validation set
 eval[test] = "yearpredMSD.libsvm.test"
-# The path of test data
+# 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.
 
 ****************************
@@ -28,7 +28,7 @@ This page gives instructions on how to build and install XGBoost from scratch on
 .. note:: Use of Git submodules
 
   XGBoost uses Git submodules to manage dependencies. So when you clone the repo, remember to specify ``--recursive`` option:
-
+  
   .. code-block:: bash
 
     git clone --recursive https://github.com/dmlc/xgboost
@@ -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, obtain ``gcc-7`` 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
+  brew install gcc@7
 
 Then install XGBoost with ``pip``:
 
@@ -109,11 +107,11 @@ You might need to run the command with ``--user`` flag if you run into permissio
 Build from the source code - advanced method
 --------------------------------------------
 
-Obtain ``gcc-8`` from Homebrew:
+Obtain ``gcc-7`` from Homebrew:
 
 .. code-block:: bash
 
-  brew install gcc@8
+  brew install gcc@7
 
 Now clone the repository:
 
@@ -121,13 +119,13 @@ Now clone the repository:
 
   git clone --recursive https://github.com/dmlc/xgboost
 
-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``:
+Create the ``build/`` directory and invoke CMake. Make sure to add ``CC=gcc-7 CXX=g++-7`` so that Homebrew GCC is selected. After invoking CMake, you can build XGBoost with ``make``:
 
 .. code-block:: bash
 
   mkdir build
   cd build
-  CC=gcc-8 CXX=g++-8 cmake ..
+  CC=gcc-7 CXX=g++-7 cmake ..
   make -j4
 
 You may now continue to `Python Package Installation`_.
@@ -144,20 +142,6 @@ 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``.
@@ -179,15 +163,29 @@ To build with MinGW, type:
 
 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:
 
 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:
 
@@ -209,7 +207,13 @@ From the command line on Linux starting from the XGBoost directory:
     cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2
     make -j4
 
-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 +221,13 @@ 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:
@@ -239,15 +241,15 @@ 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
 
 To customize, first copy ``make/config.mk`` to the project root and then modify the copy.
 
+Alternatively, use CMake.
+
 Python Package Installation
 ===========================
 
@@ -273,9 +275,9 @@ 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).
+   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
 
@@ -287,22 +289,30 @@ package manager, e.g. in Debian use
 
   cd python-package; python setup.py develop --user
 
+4. If you are installing the latest XGBoost version which requires compilation, add MinGW to the system PATH:
+
+.. code-block:: bash
+
+    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'
+
 .. _mingw_python:
 
-Building XGBoost library for Python for Windows with MinGW-w64 (Advanced)
--------------------------------------------------------------------------
+Building XGBoost library for Python for Windows with MinGW-w64
+--------------------------------------------------------------
 
-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:
+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, raising several potential concerns.
 
-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.
+1. VS is proprietary and commercial software. Microsoft provides a freeware "Community" edition, but its licensing terms are unsuitable for many organizations.
+2. Visual Studio contains telemetry, as documented in `Microsoft Visual Studio Licensing Terms <https://visualstudio.microsoft.com/license-terms/mt736442/>`_. It `has been inserting telemetry <https://old.reddit.com/r/cpp/comments/4ibauu/visual_studio_adding_telemetry_function_calls_to/>`_ into apps for some time. In order to download VS distribution from MS servers one has to run the application containing telemetry. These facts have raised privacy and security concerns among some users and system administrators. Running software with telemetry may be against the policy of your organization.
+3. g++ usually generates faster code on ``-O3``.
 
-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.
+So you may want to build XGBoost with g++ own your own risk. This opens a can of worms, 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.
+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``.
 
 R Package Installation
@@ -345,7 +355,7 @@ 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.
 
@@ -354,11 +364,11 @@ If all fails, try `Building the shared library`_ to see whether a problem is spe
 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.
+First, obtain ``gcc-7`` 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
+  brew install gcc@7
 
 Now, clone the repository:
 
@@ -366,7 +376,7 @@ Now, clone the repository:
 
   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``:
+Create the ``build/`` directory and invoke CMake with option ``R_LIB=ON``. Make sure to add ``CC=gcc-7 CXX=g++-7`` so that Homebrew GCC is selected. After invoking CMake, you can install the R package by running ``make`` and ``make install``:
 
 .. code-block:: bash
 
@@ -376,8 +386,6 @@ Create the ``build/`` directory and invoke CMake with option ``R_LIB=ON``. Make
   make -j4
   make install
 
-.. _r_gpu_support:
-
 Installing R package with GPU support
 -------------------------------------
 
@@ -393,7 +401,7 @@ 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).
 
@@ -404,8 +412,8 @@ 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 +458,4 @@ 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

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`_
@@ -166,35 +165,10 @@ 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

@@ -18,7 +18,7 @@ Tree construction (training) and prediction can be accelerated with CUDA-capable
 
 Usage
 =====
-Specify the ``tree_method`` parameter as one of the following algorithms.
+Specify the ``tree_method`` parameter as one of the following algorithms. 
 
 Algorithms
 ----------
@@ -31,43 +31,39 @@ Algorithms
 | gpu_hist     | Equivalent to the XGBoost fast histogram algorithm. Much faster and uses considerably less memory. NOTE: Will run very slowly on GPUs older than Pascal architecture. |
 +--------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 
-Supported parameters
+Supported parameters 
 --------------------
 
-.. |tick| unicode:: U+2714
-.. |cross| unicode:: U+2718
+.. |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/xgboost4j_spark_tutorial.rst

@@ -57,13 +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 Apache 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.
+  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.
 
@@ -153,49 +153,6 @@ 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
 ========
 
@@ -237,16 +194,11 @@ After we set XGBoostClassifier parameters and feature/label column, we can build
 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.
+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 for the evaluation metric going to the unexpected direction to tolerate before stopping the training.
 
-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.
+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 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.
+After specifying these two parameters, the training would stop when the metrics goes to the other direction against the one specified by ``maximize_evaluation_metrics`` for ``num_early_stopping_rounds`` iterations.
 
 Prediction
 ==========

doc/parameter.rst

@@ -23,16 +23,9 @@ 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]
 
@@ -64,8 +57,8 @@ 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 +75,15 @@ 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.
+  - range: (0,1]
 
 * ``lambda`` [default=1, alias: ``reg_lambda``]
 
@@ -110,7 +96,7 @@ 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 +138,7 @@ 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]
 
@@ -192,9 +178,6 @@ 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``)
 =========================================================
 
@@ -262,8 +245,8 @@ Parameters for Linear Booster (``booster=gblinear``)
 
   - Choice of algorithm to fit linear model
 
-    - ``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.
+    - ``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``]
 
@@ -293,13 +276,16 @@ 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)

doc/python/python_intro.rst

@@ -48,15 +48,9 @@ The data is stored in a :py:class:`DMatrix <xgboost.DMatrix>` object.
     dtrain = xgb.DMatrix('train.csv?format=csv&label_column=0')
     dtest = xgb.DMatrix('test.csv?format=csv&label_column=0')
 
-  .. note:: Categorical features not supported
-
-    Note that XGBoost does not support categorical features; if your data contains
-    categorical features, load it as a NumPy array first and then perform
-    `one-hot encoding <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html>`_.
-
-  .. note:: Use Pandas to load CSV files with headers
-
-    Currently, the DMLC data parser cannot parse CSV files with headers. Use Pandas (see below) to read CSV files with headers.
+  (Note that XGBoost does not support categorical features; if your data contains
+  categorical features, load it as a NumPy array first and then perform
+  `one-hot encoding <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html>`_.)
 
 * To load a NumPy array into :py:class:`DMatrix <xgboost.DMatrix>`:
 
@@ -101,10 +95,6 @@ The data is stored in a :py:class:`DMatrix <xgboost.DMatrix>` object.
     w = np.random.rand(5, 1)
     dtrain = xgb.DMatrix(data, label=label, missing=-999.0, weight=w)
 
-When performing ranking tasks, the number of weights should be equal
-to number of groups.
-
-
 Setting Parameters
 ------------------
 XGBoost can use either a list of pairs or a dictionary to set :doc:`parameters </parameter>`. For instance:
@@ -165,10 +155,6 @@ A saved model can be loaded as follows:
   bst = xgb.Booster({'nthread': 4})  # init model
   bst.load_model('model.bin')  # load data
 
-Methods including `update` and `boost` from `xgboost.Booster` are designed for
-internal usage only.  The wrapper function `xgboost.train` does some
-pre-configuration including setting up caches and some other parameters.
-
 Early Stopping
 --------------
 If you have a validation set, you can use early stopping to find the optimal number of boosting rounds.
@@ -223,3 +209,4 @@ When you use ``IPython``, you can use the :py:meth:`xgboost.to_graphviz` functio
 .. code-block:: python
 
   xgb.to_graphviz(bst, num_trees=2)
+

doc/tutorials/aws_yarn.rst

@@ -1,8 +1,216 @@
 ###############################
 Distributed XGBoost YARN on AWS
 ###############################
-[This page is under construction.]
+This is a step-by-step tutorial on how to setup and run distributed `XGBoost <https://github.com/dmlc/xgboost>`_
+on an AWS EC2 cluster. Distributed XGBoost runs on various platforms such as MPI, SGE and Hadoop YARN.
+In this tutorial, we use YARN as an example since this is a widely used solution for distributed computing.
 
 .. note:: XGBoost with Spark
 
   If you are preprocessing training data with Spark, consider using :doc:`XGBoost4J-Spark </jvm/xgboost4j_spark_tutorial>`.
+
+************
+Prerequisite
+************
+We need to get a `AWS key-pair <http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html>`_
+to access the AWS services. Let us assume that we are using a key ``mykey`` and  the corresponding permission file ``mypem.pem``.
+
+We also need `AWS credentials <https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html>`_,
+which includes an ``ACCESS_KEY_ID`` and a ``SECRET_ACCESS_KEY``.
+
+Finally, we will need a S3 bucket to host the data and the model, ``s3://mybucket/``
+
+***************************
+Setup a Hadoop YARN Cluster
+***************************
+This sections shows how to start a Hadoop YARN cluster from scratch.
+You can skip this step if you have already have one.
+We will be using `yarn-ec2 <https://github.com/tqchen/yarn-ec2>`_ to start the cluster.
+
+We can first clone the yarn-ec2 script by the following command.
+
+.. code-block:: bash
+
+  git clone https://github.com/tqchen/yarn-ec2
+
+To use the script, we must set the environment variables ``AWS_ACCESS_KEY_ID`` and
+``AWS_SECRET_ACCESS_KEY`` properly. This can be done by adding the following two lines in
+``~/.bashrc`` (replacing the strings with the correct ones)
+
+.. code-block:: bash
+
+  export AWS_ACCESS_KEY_ID=[your access ID]
+  export AWS_SECRET_ACCESS_KEY=[your secret access key]
+
+Now we can launch a master machine of the cluster from EC2:
+
+.. code-block:: bash
+
+  ./yarn-ec2 -k mykey -i mypem.pem launch xgboost
+
+Wait a few mininutes till the master machine gets up.
+
+After the master machine gets up, we can query the public DNS of the master machine using the following command.
+
+.. code-block:: bash
+
+  ./yarn-ec2 -k mykey -i mypem.pem get-master xgboost
+
+It will show the public DNS of the master machine like ``ec2-xx-xx-xx.us-west-2.compute.amazonaws.com``
+Now we can open the browser, and type (replace the DNS with the master DNS)
+
+.. code-block:: none
+
+  ec2-xx-xx-xx.us-west-2.compute.amazonaws.com:8088
+
+This will show the job tracker of the YARN cluster. Note that we may have to wait a few minutes before the master finishes bootstrapping and starts the
+job tracker.
+
+After the master machine gets up, we can freely add more slave machines to the cluster.
+The following command add m3.xlarge instances to the cluster.
+
+.. code-block:: bash
+
+  ./yarn-ec2 -k mykey -i mypem.pem -t m3.xlarge -s 2 addslave xgboost
+
+We can also choose to add two spot instances
+
+.. code-block:: bash
+
+  ./yarn-ec2 -k mykey -i mypem.pem -t m3.xlarge -s 2 addspot xgboost
+
+The slave machines will start up, bootstrap  and report to the master.
+You can check if the slave machines are connected by clicking on the Nodes link on the job tracker.
+Or simply type the following URL (replace DNS ith the master DNS)
+
+.. code-block:: none
+
+  ec2-xx-xx-xx.us-west-2.compute.amazonaws.com:8088/cluster/nodes
+
+One thing we should note is that not all the links in the job tracker work.
+This is due to that many of them use the private IP of AWS, which can only be accessed by EC2.
+We can use ssh proxy to access these packages.
+Now that we have set up a cluster with one master and two slaves, we are ready to run the experiment.
+
+*********************
+Build XGBoost with S3
+*********************
+We can log into the master machine by the following command.
+
+.. code-block:: bash
+
+  ./yarn-ec2 -k mykey -i mypem.pem login xgboost
+
+We will be using S3 to host the data and the result model, so the data won't get lost after the cluster shutdown.
+To do so, we will need to build XGBoost with S3 support. The only thing we need to do is to set ``USE_S3``
+variable to be true. This can be achieved by the following command.
+
+.. code-block:: bash
+
+  git clone --recursive https://github.com/dmlc/xgboost
+  cd xgboost
+  cp make/config.mk config.mk
+  echo "USE_S3=1" >> config.mk
+  make -j4
+
+Now we have built the XGBoost with S3 support. You can also enable HDFS support if you plan to store data on HDFS by turning on ``USE_HDFS`` option.
+XGBoost also relies on the environment variable to access S3, so you will need to add the following two lines to ``~/.bashrc`` (replacing the strings with the correct ones)
+on the master machine as well.
+
+.. code-block:: bash
+
+  export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
+  export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+  export BUCKET=mybucket
+
+*******************
+Host the Data on S3
+*******************
+In this example, we will copy the example dataset in XGBoost to the S3 bucket as input.
+In normal usecases, the dataset is usually created from existing distributed processing pipeline.
+We can use `s3cmd <http://s3tools.org/s3cmd>`_ to copy the data into mybucket (replace ``${BUCKET}`` with the real bucket name).
+
+.. code-block:: bash
+
+  cd xgboost
+  s3cmd put demo/data/agaricus.txt.train s3://${BUCKET}/xgb-demo/train/
+  s3cmd put demo/data/agaricus.txt.test s3://${BUCKET}/xgb-demo/test/
+
+***************
+Submit the Jobs
+***************
+Now everything is ready, we can submit the XGBoost distributed job to the YARN cluster.
+We will use the `dmlc-submit <https://github.com/dmlc/dmlc-core/tree/master/tracker>`_ script to submit the job.
+
+Now we can run the following script in the distributed training folder (replace ``${BUCKET}`` with the real bucket name)
+
+.. code-block:: bash
+
+  cd xgboost/demo/distributed-training
+  # Use dmlc-submit to submit the job.
+  ../../dmlc-core/tracker/dmlc-submit --cluster=yarn --num-workers=2 --worker-cores=2\
+      ../../xgboost mushroom.aws.conf nthread=2\
+      data=s3://${BUCKET}/xgb-demo/train\
+      eval[test]=s3://${BUCKET}/xgb-demo/test\
+      model_dir=s3://${BUCKET}/xgb-demo/model
+
+All the configurations such as ``data`` and ``model_dir`` can also be directly written into the configuration file.
+Note that we only specified the folder path to the file, instead of the file name.
+XGBoost will read in all the files in that folder as the training and evaluation data.
+
+In this command, we are using two workers, and each worker uses two running threads.
+XGBoost can benefit from using multiple cores in each worker.
+A common choice of working cores can range from 4 to 8.
+The trained model will be saved into the specified model folder. You can browse the model folder.
+
+.. code-block:: bash
+
+  s3cmd ls s3://${BUCKET}/xgb-demo/model/
+
+The following is an example output from distributed training.
+
+.. code-block:: none
+
+  16/02/26 05:41:59 INFO dmlc.Client: jobname=DMLC[nworker=2]:xgboost,username=ubuntu
+  16/02/26 05:41:59 INFO dmlc.Client: Submitting application application_1456461717456_0015
+  16/02/26 05:41:59 INFO impl.YarnClientImpl: Submitted application application_1456461717456_0015
+  2016-02-26 05:42:05,230 INFO @tracker All of 2 nodes getting started
+  2016-02-26 05:42:14,027 INFO [05:42:14] [0]  test-error:0.016139        train-error:0.014433
+  2016-02-26 05:42:14,186 INFO [05:42:14] [1]  test-error:0.000000        train-error:0.001228
+  2016-02-26 05:42:14,947 INFO @tracker All nodes finishes job
+  2016-02-26 05:42:14,948 INFO @tracker 9.71754479408 secs between node start and job finish
+  Application application_1456461717456_0015 finished with state FINISHED at 1456465335961
+
+*****************
+Analyze the Model
+*****************
+After the model is trained, we can analyse the learnt model and use it for future prediction tasks.
+XGBoost is a portable framework, meaning the models in all platforms are *exchangeable*.
+This means we can load the trained model in python/R/Julia and take benefit of data science pipelines
+in these languages to do model analysis and prediction.
+
+For example, you can use `this IPython notebook <https://github.com/dmlc/xgboost/tree/master/demo/distributed-training/plot_model.ipynb>`_
+to plot feature importance and visualize the learnt model.
+
+***************
+Troubleshooting
+***************
+
+If you encounter a problem, the best way might be to use the following command
+to get logs of stdout and stderr of the containers and check what causes the problem.
+
+.. code-block:: bash
+
+  yarn logs -applicationId yourAppId
+
+*****************
+Future Directions
+*****************
+You have learned to use distributed XGBoost on YARN in this tutorial.
+XGBoost is a portable and scalable framework for gradient boosting.
+You can check out more examples and resources in the `resources page <https://github.com/dmlc/xgboost/blob/master/demo/README.md>`_.
+
+The project goal is to make the best scalable machine learning solution available to all platforms.
+The API is designed to be able to portable, and the same code can also run on other platforms such as MPI and SGE.
+XGBoost is actively evolving and we are working on even more exciting features
+such as distributed XGBoost python/R package.