Separate out restricted and unrestricted tasks (#3736)

* Fix #3730: scikit-learn 0.20 compatibility fix

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

* Display test names for Python tests for clarity

.clang-tidy

@@ -5,6 +5,7 @@ CheckOptions:
   - { key: readability-identifier-naming.TypeAliasCase,          value: CamelCase  }
   - { key: readability-identifier-naming.TypedefCase,          value: CamelCase  }
   - { key: readability-identifier-naming.TypeTemplateParameterCase,          value: CamelCase  }
+  - { key: readability-identifier-naming.LocalVariableCase,          value: lower_case  }
   - { key: readability-identifier-naming.MemberCase,          value: lower_case  }
   - { key: readability-identifier-naming.PrivateMemberSuffix,           value: '_'  }
   - { key: readability-identifier-naming.ProtectedMemberSuffix,           value: '_'  }

.editorconfig

@@ -1,11 +0,0 @@
-root = true
-
-[*]
-charset=utf-8
-indent_style = space
-indent_size = 2
-insert_final_newline = true
-
-[*.py]
-indent_style = space
-indent_size = 4

.github/ISSUE_TEMPLATE.md

@@ -1,7 +0,0 @@
-Thanks for participating in the XGBoost community! We use https://discuss.xgboost.ai for any general usage questions and discussions. The issue tracker is used for actionable items such as feature proposals discussion, roadmaps, and bug tracking.  You are always welcomed to post on the forum first :)
-
-Issues that are inactive for a period of time may get closed. We adopt this policy so that we won't lose track of actionable issues that may fall at the bottom of the pile. Feel free to reopen a new one if you feel there is an additional problem that needs attention when an old one gets closed.
-
-For bug reports, to help the developer act on the issues, please include a description of your environment, preferably a minimum script to reproduce the problem.
-
-For feature proposals, list clear, small actionable items so we can track the progress of the change.

.github/lock.yml

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-# Label to add before locking, such as `outdated`.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

.gitmodules

@@ -4,6 +4,9 @@
 [submodule "rabit"]
 	path = rabit
 	url = https://github.com/dmlc/rabit
+[submodule "nccl"]
+	path = nccl
+	url = https://github.com/dmlc/nccl
 [submodule "cub"]
 	path = cub
 	url = https://github.com/NVlabs/cub

.travis.yml

@@ -26,10 +26,6 @@ env:
     - TASK=cmake_test
     # c++ test
     - TASK=cpp_test
-    # distributed test
-    - TASK=distributed_test
-    # address sanitizer test
-    - TASK=sanitizer_test
 
 matrix:
   exclude:
@@ -43,10 +39,6 @@ matrix:
       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:
@@ -66,8 +58,6 @@ addons:
       - graphviz
       - gcc-4.8
       - g++-4.8
-      - gcc-7
-      - g++-7
 
 before_install:
   - source dmlc-core/scripts/travis/travis_setup_env.sh

CMakeLists.txt

@@ -8,25 +8,19 @@ set_default_configuration_release()
 msvc_use_static_runtime()
 
 # Options
-option(USE_CUDA  "Build with GPU acceleration")
+option(USE_CUDA  "Build with GPU acceleration") 
+option(USE_AVX  "Build with AVX instructions. May not produce identical results due to approximate math." OFF) 
+option(USE_NCCL "Build using NCCL for multi-GPU. Also requires USE_CUDA") 
 option(JVM_BINDINGS "Build JVM bindings" OFF)
 option(GOOGLE_TEST "Build google tests" OFF)
 option(R_LIB "Build shared library for R package" OFF)
 set(GPU_COMPUTE_VER "" CACHE STRING
   "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
-option(PLUGIN_LZ4 "Build lz4 plugin" OFF)
-option(PLUGIN_DENSE_PARSER "Build dense parser plugin" OFF)
 
 # Deprecation warning
-if(USE_AVX)
-  message(WARNING "The option 'USE_AVX' is deprecated as experimental AVX features have been removed from xgboost.")
+if(PLUGIN_UPDATER_GPU)
+  set(USE_CUDA ON)
+  message(WARNING "The option 'PLUGIN_UPDATER_GPU' is deprecated. Set 'USE_CUDA' instead.")
 endif()
 
 # Compiler flags
@@ -45,22 +39,17 @@ else()
   # Performance
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -funroll-loops")
 endif()
-if(WIN32 AND MINGW)
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -static-libstdc++")
+
+# AVX
+if(USE_AVX)
+  if(MSVC)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX")
+  else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx")
+  endif()
+  add_definitions(-DXGBOOST_USE_AVX)
 endif()
 
-# Sanitizer
-if(USE_SANITIZER)
-  include(cmake/Sanitizer.cmake)
-  enable_sanitizers("${ENABLED_SANITIZERS}")
-endif(USE_SANITIZER)
-
-# dmlc-core
-add_subdirectory(dmlc-core)
-set(LINK_LIBRARIES dmlc rabit)
-
-# enable custom logging
-add_definitions(-DDMLC_LOG_CUSTOMIZE=1)
 
 # compiled code customizations for R package
 if(R_LIB)
@@ -75,14 +64,13 @@ if(R_LIB)
   )
 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
+file(GLOB_RECURSE SOURCES 
     src/*.cc
     src/*.h
     include/*.h
@@ -91,22 +79,11 @@ file(GLOB_RECURSE SOURCES
 # 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
 # TODO: Create rabit cmakelists.txt
 set(RABIT_SOURCES
@@ -126,17 +103,22 @@ else()
   add_library(rabit STATIC ${RABIT_SOURCES})
 endif()
 
+
+# dmlc-core
+add_subdirectory(dmlc-core)
+set(LINK_LIBRARIES dmlc rabit)
+
+
 if(USE_CUDA)
   find_package(CUDA 8.0 REQUIRED)
   cmake_minimum_required(VERSION 3.5)
 
   add_definitions(-DXGBOOST_USE_CUDA)
-
+  
   include_directories(cub)
 
   if(USE_NCCL)
-    find_package(Nccl REQUIRED)
-    include_directories(${NCCL_INCLUDE_DIR})
+    include_directories(nccl/src)
     add_definitions(-DXGBOOST_USE_NCCL)
   endif()
 
@@ -149,13 +131,16 @@ if(USE_CUDA)
     set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS};-Xcompiler -fPIC; -Xcompiler -Werror; -std=c++11")
   endif()
 
-  cuda_add_library(gpuxgboost ${CUDA_SOURCES} STATIC)
-
   if(USE_NCCL)
-    link_directories(${NCCL_LIBRARY})
-    target_link_libraries(gpuxgboost ${NCCL_LIB_NAME})
+    add_subdirectory(nccl)
   endif()
-  list(APPEND LINK_LIBRARIES gpuxgboost)
+
+  cuda_add_library(gpuxgboost ${CUDA_SOURCES} STATIC)
+  
+  if(USE_NCCL)
+    target_link_libraries(gpuxgboost nccl)
+  endif()
+  list(APPEND LINK_LIBRARIES gpuxgboost) 
 endif()
 
 
@@ -189,9 +174,6 @@ if(R_LIB)
   target_link_libraries(xgboost ${LINK_LIBRARIES})
   # R uses no lib prefix in shared library names of its packages
   set_target_properties(xgboost PROPERTIES PREFIX "")
-  if(APPLE)
-    set_target_properties(xgboost PROPERTIES SUFFIX ".so")
-  endif()
 
   setup_rpackage_install_target(xgboost ${CMAKE_CURRENT_BINARY_DIR})
   # use a dummy location for any other remaining installs
@@ -239,11 +221,12 @@ endif()
 
 # Test
 if(GOOGLE_TEST)
-  enable_testing()
   find_package(GTest REQUIRED)
+  enable_testing()
 
+  file(GLOB_RECURSE TEST_SOURCES "tests/cpp/*.cc")
   auto_source_group("${TEST_SOURCES}")
-  include_directories(${GTEST_INCLUDE_DIRS})
+  include_directories(${GTEST_INCLUDE_DIR})
 
   if(USE_CUDA)
     file(GLOB_RECURSE CUDA_TEST_SOURCES "tests/cpp/*.cu")

CONTRIBUTORS.md

@@ -6,30 +6,21 @@ Committers
 ----------
 Committers are people who have made substantial contribution to the project and granted write access to the project.
 * [Tianqi Chen](https://github.com/tqchen), University of Washington
-  - Tianqi is a Ph.D. student working on large-scale machine learning. He is the creator of the project.
+  - Tianqi is a PhD working on large-scale machine learning, he is the creator of the project.
 * [Tong He](https://github.com/hetong007), Amazon AI
-  - Tong is an applied scientist in Amazon AI. He is the maintainer of XGBoost R package.
+  - Tong is an applied scientist in Amazon AI, he is the maintainer of xgboost R package.
 * [Vadim Khotilovich](https://github.com/khotilov)
   - Vadim contributes many improvements in R and core packages.
 * [Bing Xu](https://github.com/antinucleon)
-  - Bing is the original creator of XGBoost Python package and currently the maintainer of [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl).
+  - Bing is the original creator of xgboost python package and currently the maintainer of [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl).
 * [Michael Benesty](https://github.com/pommedeterresautee)
-  - Michael is a lawyer and data scientist in France. He is the creator of XGBoost interactive analysis module in R.
-* [Yuan Tang](https://github.com/terrytangyuan), Ant Financial
-  - Yuan is a software engineer in Ant Financial. He contributed mostly in R and Python packages.
-* [Nan Zhu](https://github.com/CodingCat), Uber
-  - Nan is a software engineer in Uber. He contributed mostly in JVM packages.
-* [Sergei Lebedev](https://github.com/superbobry), Criteo
-  - Sergei is a software engineer in Criteo. He contributed mostly in JVM packages.
-* [Hongliang Liu](https://github.com/phunterlau)
-* [Scott Lundberg](http://scottlundberg.com/), University of Washington
-  - Scott is a Ph.D. student at University of Washington. He is the creator of SHAP, a unified approach to explain the output of machine learning models such as decision tree ensembles. He also helps maintain the XGBoost Julia package.
-* [Rory Mitchell](https://github.com/RAMitchell), University of Waikato
-  - Rory is a Ph.D. student at University of Waikato. He is the original creator of the GPU training algorithms. He improved the CMake build system and continuous integration. 
-* [Hyunsu Cho](http://hyunsu-cho.io/), Amazon AI
-  - Hyunsu is an applied scientist in Amazon AI. He is the maintainer of the XGBoost Python package. He also manages the Jenkins continuous integration system (https://xgboost-ci.net/). He is the initial author of the CPU 'hist' updater. 
-* [Jiaming](https://github.com/trivialfis)
-  - Jiaming contributed to the GPU algorithms. He has also introduced new abstractions to improve the quality of the C++ codebase.
+  - Micheal is a lawyer, data scientist in France, he is the creator of xgboost interactive analysis module in R.
+* [Yuan Tang](https://github.com/terrytangyuan)
+  - Yuan is a data scientist in Chicago, US. He contributed mostly in R and Python packages.
+* [Nan Zhu](https://github.com/CodingCat)
+  - Nan is a software engineer in Microsoft. He contributed mostly in JVM packages.
+* [Sergei Lebedev](https://github.com/superbobry)
+  - Serget is a software engineer in Criteo. He contributed mostly in JVM packages.
 
 Become a Committer
 ------------------
@@ -45,25 +36,28 @@ List of Contributors
 * [Full List of Contributors](https://github.com/dmlc/xgboost/graphs/contributors)
   - To contributors: please add your name to the list when you submit a patch to the project:)
 * [Kailong Chen](https://github.com/kalenhaha)
-  - Kailong is an early contributor of XGBoost, he is creator of ranking objectives in XGBoost.
+  - Kailong is an early contributor of xgboost, he is creator of ranking objectives in xgboost.
 * [Skipper Seabold](https://github.com/jseabold)
-  - Skipper is the major contributor to the scikit-learn module of XGBoost.
+  - Skipper is the major contributor to the scikit-learn module of xgboost.
 * [Zygmunt Zając](https://github.com/zygmuntz)
   - Zygmunt is the master behind the early stopping feature frequently used by kagglers.
 * [Ajinkya Kale](https://github.com/ajkl)
 * [Boliang Chen](https://github.com/cblsjtu)
 * [Yangqing Men](https://github.com/yanqingmen)
-  - Yangqing is the creator of XGBoost java package.
+  - Yangqing is the creator of xgboost java package.
 * [Engpeng Yao](https://github.com/yepyao)
 * [Giulio](https://github.com/giuliohome)
-  - Giulio is the creator of Windows project of XGBoost
+  - Giulio is the creator of windows project of xgboost
 * [Jamie Hall](https://github.com/nerdcha)
-  - Jamie is the initial creator of XGBoost scikit-learn module.
+  - Jamie is the initial creator of xgboost sklearn module.
 * [Yen-Ying Lee](https://github.com/white1033)
 * [Masaaki Horikoshi](https://github.com/sinhrks)
-  - Masaaki is the initial creator of XGBoost Python plotting module.
+  - Masaaki is the initial creator of xgboost python plotting module.
+* [Hongliang Liu](https://github.com/phunterlau)
+* [Hyunsu Cho](http://hyunsu-cho.io/)
+  - Hyunsu is the maintainer of the XGBoost Python package. He is in charge of submitting the Python package to Python Package Index (PyPI). He is also the initial author of the CPU 'hist' updater.
 * [daiyl0320](https://github.com/daiyl0320)
-  - daiyl0320 contributed patch to XGBoost distributed version more robust, and scales stably on TB scale datasets.
+  - daiyl0320 contributed patch to xgboost distributed version more robust, and scales stably on TB scale datasets.
 * [Huayi Zhang](https://github.com/irachex)
 * [Johan Manders](https://github.com/johanmanders)
 * [yoori](https://github.com/yoori)
@@ -74,15 +68,8 @@ List of Contributors
 * [Alex Bain](https://github.com/convexquad)
 * [Baltazar Bieniek](https://github.com/bbieniek)
 * [Adam Pocock](https://github.com/Craigacp)
+* [Rory Mitchell](https://github.com/RAMitchell)
+  - Rory is the author of the GPU plugin and also contributed the cmake build system and windows continuous integration
 * [Gideon Whitehead](https://github.com/gaw89)
 * [Yi-Lin Juang](https://github.com/frankyjuang)
 * [Andrew Hannigan](https://github.com/andrewhannigan)
-* [Andy Adinets](https://github.com/canonizer)
-* [Henry Gouk](https://github.com/henrygouk)
-* [Pierre de Sahb](https://github.com/pdesahb)
-* [liuliang01](https://github.com/liuliang01)
-  - liuliang01 added support for the qid column for LibSVM input format. This makes ranking task easier in distributed setting.
-* [Andrew Thia](https://github.com/BlueTea88)
-  - Andrew Thia implemented feature interaction constraints
-* [Wei Tian](https://github.com/weitian)
-* [Chen Qin] (https://github.com/chenqin)

ISSUE_TEMPLATE.md

@@ -0,0 +1,44 @@
+For bugs or installation issues, please provide the following information.
+The more information you provide, the more easily we will be able to offer
+help and advice.
+
+## Environment info
+Operating System:
+
+Compiler:
+
+Package used (python/R/jvm/C++):
+
+`xgboost` version used:
+
+If installing from source, please provide 
+
+1. The commit hash (`git rev-parse HEAD`)
+2. Logs will be helpful (If logs are large, please upload as attachment).
+
+If you are using jvm package, please 
+
+1. add [jvm-packages] in the title to make it quickly be identified
+2. the gcc version and distribution
+
+If you are using python package, please provide
+
+1. The python version and distribution
+2. The command to install `xgboost` if you are not installing from source
+
+If you are using R package, please provide
+
+1. The R `sessionInfo()`
+2. The command to install `xgboost` if you are not installing from source
+
+## Steps to reproduce
+
+1.
+2.
+3.
+
+## What have you tried?
+
+1.
+2.
+3.

Jenkinsfile

@@ -14,8 +14,7 @@ def dockerRun = 'tests/ci_build/ci_build.sh'
 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": "9.1" ],
     [ "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" ],
 ]
@@ -68,41 +67,22 @@ def buildPlatformCmake(buildName, conf, nodeReq, dockerTarget) {
     // Destination dir for artifacts
     def distDir = "dist/${buildName}"
     def dockerArgs = ""
-    if (conf["withGpu"]) {
+    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"
-                """
-            }
-        }
+    node(nodeReq) {
+        unstash name: 'srcs'
+        echo """
+        |===== XGBoost CMake build =====
+        |  dockerTarget: ${dockerTarget}
+        |  cmakeOpts   : ${opts}
+        |=========================
+        """.stripMargin('|')
+        // Invoke command inside docker
+        sh """
+        ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/build_via_cmake.sh ${opts}
+        ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/test_${dockerTarget}.sh
+        """
     }
 }

Jenkinsfile-restricted

@@ -13,10 +13,6 @@ 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" ],
@@ -46,28 +42,27 @@ pipeline {
                 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') {
+            agent {
+                label 'linux && cpu && restricted'
+            }
             steps {
+                unstash name: 'srcs'
                 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"
-                                }
-                            }
+                    def commit_id = "${GIT_COMMIT}"
+                    def branch_name = "${GIT_LOCAL_BRANCH}"
+                    echo 'Building doc...'
+                    dir ('jvm-packages') {
+                        sh "bash ./build_doc.sh ${commit_id}"
+                        archiveArtifacts artifacts: "${commit_id}.tar.bz2", allowEmptyArchive: true
+                        echo 'Deploying doc...'
+                        withAWS(credentials:'xgboost-doc-bucket') {
+                            s3Upload file: "${commit_id}.tar.bz2", bucket: 'xgboost-docs', acl: 'PublicRead', path: "${branch_name}.tar.bz2"
                         }
                     }
                 }
@@ -99,25 +94,28 @@ def buildPlatformCmake(buildName, conf, nodeReq, dockerTarget) {
         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
-        }
+    node(nodeReq) {
+        unstash name: 'srcs'
+        echo """
+        |===== XGBoost CMake build =====
+        |  dockerTarget: ${dockerTarget}
+        |  cmakeOpts   : ${opts}
+        |=========================
+        """.stripMargin('|')
+        // Invoke command inside docker
+        sh """
+        ${dockerRun} ${dockerTarget} ${dockerArgs} tests/ci_build/build_via_cmake.sh ${opts}
+        ${dockerRun} ${dockerTarget} ${dockerArgs} bash -c "cd python-package; rm -f dist/*; python setup.py bdist_wheel --universal"
+        rm -rf "${distDir}"; mkdir -p "${distDir}/py"
+        cp xgboost "${distDir}"
+        cp -r lib "${distDir}"
+        cp -r python-package/dist "${distDir}/py"
+        # Test the wheel for compatibility on a barebones CPU container
+        ${dockerRun} release ${dockerArgs} bash -c " \
+            auditwheel show xgboost-*-py2-none-any.whl
+            pip install --user python-package/dist/xgboost-*-none-any.whl && \
+            python -m nose tests/python"
+        """
+        archiveArtifacts artifacts: "${distDir}/**/*.*", allowEmptyArchive: true
     }
 }

LICENSE

@@ -1,201 +1,13 @@
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Makefile

@@ -68,7 +68,7 @@ endif
 endif
 
 export LDFLAGS= -pthread -lm $(ADD_LDFLAGS) $(DMLC_LDFLAGS) $(PLUGIN_LDFLAGS)
-export CFLAGS= -DDMLC_LOG_CUSTOMIZE=1 -std=c++11 -Wall -Wno-unknown-pragmas -Iinclude $(ADD_CFLAGS) $(PLUGIN_CFLAGS)
+export CFLAGS=  -std=c++11 -Wall -Wno-unknown-pragmas -Iinclude $(ADD_CFLAGS) $(PLUGIN_CFLAGS)
 CFLAGS += -I$(DMLC_CORE)/include -I$(RABIT)/include -I$(GTEST_PATH)/include
 #java include path
 export JAVAINCFLAGS = -I${JAVA_HOME}/include -I./java

NEWS.md

@@ -3,219 +3,6 @@ XGBoost Change Log
 
 This file records the changes in xgboost library in reverse chronological order.
 
-## v0.81 (2018.11.04)
-### New feature: feature interaction constraints
-* Users are now able to control which features (independent variables) are allowed to interact by specifying feature interaction constraints (#3466).
-* [Tutorial](https://xgboost.readthedocs.io/en/release_0.81/tutorials/feature_interaction_constraint.html) is available, as well as [R](https://github.com/dmlc/xgboost/blob/9254c58e4dfff6a59dc0829a2ceb02e45ed17cd0/R-package/demo/interaction_constraints.R) and [Python](https://github.com/dmlc/xgboost/blob/9254c58e4dfff6a59dc0829a2ceb02e45ed17cd0/tests/python/test_interaction_constraints.py) examples.
-
-### New feature: learning to rank using scikit-learn interface
-* Learning to rank task is now available for the scikit-learn interface of the Python package (#3560, #3848). It is now possible to integrate the XGBoost ranking model into the scikit-learn learning pipeline.
-* Examples of using `XGBRanker` class is found at [demo/rank/rank_sklearn.py](https://github.com/dmlc/xgboost/blob/24a268a2e3cb17302db3d72da8f04016b7d352d9/demo/rank/rank_sklearn.py).
-
-### New feature: R interface for SHAP interactions
-* SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. Previously, this feature was only available from the Python package; now it is available from the R package as well (#3636).
-
-### New feature: GPU predictor now use multiple GPUs to predict
-* GPU predictor is now able to utilize multiple GPUs at once to accelerate prediction (#3738)
-
-### New feature: Scale distributed XGBoost to large-scale clusters
-* Fix OS file descriptor limit assertion error on large cluster (#3835, dmlc/rabit#73) by replacing `select()` based AllReduce/Broadcast with `poll()` based implementation.
-* Mitigate tracker "thundering herd" issue on large cluster. Add exponential backoff retry when workers connect to tracker.
-* With this change, we were able to scale to 1.5k executors on a 12 billion row dataset after some tweaks here and there.
-
-### Major bug fix: learning to rank with XGBoost4J-Spark
-* Previously, `repartitionForData` would shuffle data and lose ordering necessary for ranking task.
-* To fix this issue, data points within each RDD partition is explicitly group by their group (query session) IDs (#3654). Also handle empty RDD partition carefully (#3750).
-
-### Major bug fix: early stopping fixed in XGBoost4J-Spark
-* Earlier implementation of early stopping had incorrect semantics and didn't let users to specify direction for optimizing (maximize / minimize)
-* A parameter `maximize_evaluation_metrics` is defined so as to tell whether a metric should be maximized or minimized as part of early stopping criteria (#3808). Also early stopping now has correct semantics.
-
-### API changes
-* Column sampling by level (`colsample_bylevel`) is now functional for `hist` algorithm (#3635, #3862)
-* Add `disable_default_eval_metric` parameter to disable default metric (#3606)
-* Experimental AVX support for gradient computation is removed (#3752)
-* XGBoost4J-Spark
-  - Add `rank:ndcg` and `rank:map` to supported objectives (#3697)
-* Python package
-  - Add `callbacks` argument to `fit()` function of sciki-learn API (#3682)
-  - Add `XGBRanker` to scikit-learn interface (#3560, #3848)
-  - Add `validate_features` argument to `predict()` function of scikit-learn API (#3653)
-  - Allow scikit-learn grid search over parameters specified as keyword arguments (#3791)
-  - Add `coef_` and `intercept_` as properties of scikit-learn wrapper (#3855). Some scikit-learn functions expect these properties.
-
-### Performance improvements
-* Address very high GPU memory usage for large data (#3635)
-* Fix performance regression within `EvaluateSplits()` of `gpu_hist` algorithm. (#3680)
-
-### Bug-fixes
-* Fix a problem in GPU quantile sketch with tiny instance weights. (#3628)
-* Fix copy constructor for `HostDeviceVectorImpl` to prevent dangling pointers (#3657)
-* Fix a bug in partitioned file loading (#3673)
-* Fixed an uninitialized pointer in `gpu_hist` (#3703)
-* Reshared data among GPUs when number of GPUs is changed (#3721)
-* Add back `max_delta_step` to split evaluation (#3668)
-* Do not round up integer thresholds for integer features in JSON dump (#3717)
-* Use `dmlc::TemporaryDirectory` to handle temporaries in cross-platform way (#3783)
-* Fix accuracy problem with `gpu_hist` when `min_child_weight` and `lambda` are set to 0 (#3793)
-* Make sure that `tree_method` parameter is recognized and not silently ignored (#3849)
-* XGBoost4J-Spark
-  - Make sure `thresholds` are considered when executing `predict()` method (#3577)
-  - Avoid losing precision when computing probabilities by converting to `Double` early (#3576)
-  - `getTreeLimit()` should return `Int` (#3602)
-  - Fix checkpoint serialization on HDFS (#3614)
-  - Throw `ControlThrowable` instead of `InterruptedException` so that it is properly re-thrown (#3632)
-  - Remove extraneous output to stdout (#3665)
-  - Allow specification of task type for custom objectives and evaluations (#3646)
-  - Fix distributed updater check (#3739)
-  - Fix issue when spark job execution thread cannot return before we execute `first()` (#3758)
-* Python package
-  - Fix accessing `DMatrix.handle` before it is set (#3599)
-  - `XGBClassifier.predict()` should return margin scores when `output_margin` is set to true (#3651)
-  - Early stopping callback should maximize metric of form `NDCG@n-` (#3685)
-  - Preserve feature names when slicing `DMatrix` (#3766)
-* R package
-  - Replace `nround` with `nrounds` to match actual parameter (#3592)
-  - Amend `xgb.createFolds` to handle classes of a single element (#3630)
-  - Fix buggy random generator and make `colsample_bytree` functional (#3781)
-
-### Maintenance: testing, continuous integration, build system
-* Add sanitizers tests to Travis CI (#3557)
-* Add NumPy, Matplotlib, Graphviz as requirements for doc build (#3669)
-* Comply with CRAN submission policy (#3660, #3728)
-* Remove copy-paste error in JVM test suite (#3692)
-* Disable flaky tests in `R-package/tests/testthat/test_update.R` (#3723)
-* Make Python tests compatible with scikit-learn 0.20 release (#3731)
-* Separate out restricted and unrestricted tasks, so that pull requests don't build downloadable artifacts (#3736)
-* Add multi-GPU unit test environment (#3741)
-* Allow plug-ins to be built by CMake (#3752)
-* Test wheel compatibility on CPU containers for pull requests (#3762)
-* Fix broken doc build due to Matplotlib 3.0 release (#3764)
-* Produce `xgboost.so` for XGBoost-R on Mac OSX, so that `make install` works (#3767)
-* Retry Jenkins CI tests up to 3 times to improve reliability (#3769, #3769, #3775, #3776, #3777)
-* Add basic unit tests for `gpu_hist` algorithm (#3785)
-* Fix Python environment for distributed unit tests (#3806)
-* Test wheels on CUDA 10.0 container for compatibility (#3838)
-* Fix JVM doc build (#3853)
-
-### Maintenance: Refactor C++ code for legibility and maintainability
-* Merge generic device helper functions into `GPUSet` class (#3626)
-* Re-factor column sampling logic into `ColumnSampler` class (#3635, #3637)
-* Replace `std::vector` with `HostDeviceVector` in `MetaInfo` and `SparsePage` (#3446)
-* Simplify `DMatrix` class (#3395)
-* De-duplicate CPU/GPU code using `Transform` class (#3643, #3751)
-* Remove obsoleted `QuantileHistMaker` class (#3761)
-* Remove obsoleted `NoConstraint` class (#3792)
-
-### Other Features
-* C++20-compliant Span class for safe pointer indexing (#3548, #3588)
-* Add helper functions to manipulate multiple GPU devices (#3693)
-* XGBoost4J-Spark
-  - Allow specifying host ip from the `xgboost-tracker.properties file` (#3833). This comes in handy when `hosts` files doesn't correctly define localhost.
-
-### Usability Improvements
-* Add reference to GitHub repository in `pom.xml` of JVM packages (#3589)
-* Add R demo of multi-class classification (#3695)
-* Document JSON dump functionality (#3600, #3603)
-* Document CUDA requirement and lack of external memory for GPU algorithms (#3624)
-* Document LambdaMART objectives, both pairwise and listwise (#3672)
-* Document `aucpr` evaluation metric (#3687)
-* Document gblinear parameters: `feature_selector` and `top_k` (#3780)
-* Add instructions for using MinGW-built XGBoost with Python. (#3774)
-* Removed nonexistent parameter `use_buffer` from documentation (#3610)
-* Update Python API doc to include all classes and members (#3619, #3682)
-* Fix typos and broken links in documentation (#3618, #3640, #3676, #3713, #3759, #3784, #3843, #3852)
-* Binary classification demo should produce LIBSVM with 0-based indexing (#3652)
-* Process data once for Python and CLI examples of learning to rank (#3666)
-* Include full text of Apache 2.0 license in the repository (#3698)
-* Save predictor parameters in model file (#3856)
-* JVM packages
-  - Let users specify feature names when calling `getModelDump` and `getFeatureScore` (#3733)
-  - Warn the user about the lack of over-the-wire encryption (#3667)
-  - Fix errors in examples (#3719)
-  - Document choice of trackers (#3831)
-  - Document that vanilla Apache Spark is required (#3854)
-* Python package
-  - Document that custom objective can't contain colon (:) (#3601)
-  - Show a better error message for failed library loading (#3690)
-  - Document that feature importance is unavailable for non-tree learners (#3765)
-  - Document behavior of `get_fscore()` for zero-importance features (#3763)
-  - Recommend pickling as the way to save `XGBClassifier` / `XGBRegressor` / `XGBRanker` (#3829)
-* R package
-  - Enlarge variable importance plot to make it more visible (#3820)
-
-### BREAKING CHANGES
-* External memory page files have changed, breaking backwards compatibility for temporary storage used during external memory training. This only affects external memory users upgrading their xgboost version - we recommend clearing all `*.page` files before resuming training. Model serialization is unaffected.
-
-### Known issues
-* Quantile sketcher fails to produce any quantile for some edge cases (#2943)
-* The `hist` algorithm leaks memory when used with learning rate decay callback (#3579)
-* Using custom evaluation funciton together with early stopping causes assertion failure in XGBoost4J-Spark (#3595)
-* Early stopping doesn't work with `gblinear` learner (#3789)
-* Label and weight vectors are not reshared upon the change in number of GPUs (#3794). To get around this issue, delete the `DMatrix` object and re-load.
-* The `DMatrix` Python objects are initialized with incorrect values when given array slices (#3841)
-* The `gpu_id` parameter is broken and not yet properly supported (#3850)
-
-### Acknowledgement
-**Contributors** (in no particular order): Hyunsu Cho (@hcho3), Jiaming Yuan (@trivialfis), Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), Andy Adinets (@canonizer), Vadim Khotilovich (@khotilov), Sergei Lebedev (@superbobry)
-
-**First-time Contributors** (in no particular order): Matthew Tovbin (@tovbinm), Jakob Richter (@jakob-r), Grace Lam (@grace-lam), Grant W Schneider (@grantschneider), Andrew Thia (@BlueTea88), Sergei Chipiga (@schipiga), Joseph Bradley (@jkbradley), Chen Qin (@chenqin), Jerry Lin (@linjer), Dmitriy Rybalko (@rdtft), Michael Mui (@mmui), Takahiro Kojima (@515hikaru), Bruce Zhao (@BruceZhaoR), Wei Tian (@weitian), Saumya Bhatnagar (@Sam1301), Juzer Shakir (@JuzerShakir), Zhao Hang (@cleghom), Jonathan Friedman (@jontonsoup), Bruno Tremblay (@meztez), @Shiki-H, @mrgutkun, @gorogm, @htgeis, @jakehoare, @zengxy, @KOLANICH
-
-**First-time Reviewers** (in no particular order): Nikita Titov (@StrikerRUS), Xiangrui Meng (@mengxr), Nirmal Borah (@Nirmal-Neel)
-
-
-## v0.80 (2018.08.13)
-* **JVM packages received a major upgrade**: To consolidate the APIs and improve the user experience, we refactored the design of XGBoost4J-Spark in a significant manner. (#3387)
-  - Consolidated APIs: It is now much easier to integrate XGBoost models into a Spark ML pipeline. Users can control behaviors like output leaf prediction results by setting corresponding column names. Training is now more consistent with other Estimators in Spark MLLIB: there is now one single method `fit()` to train decision trees.
-  - Better user experience: we refactored the parameters relevant modules in XGBoost4J-Spark to provide both camel-case (Spark ML style) and underscore (XGBoost style) parameters
-  - A brand-new tutorial is [available](https://xgboost.readthedocs.io/en/release_0.80/jvm/xgboost4j_spark_tutorial.html) for XGBoost4J-Spark.
-  - Latest API documentation is now hosted at https://xgboost.readthedocs.io/.
-* XGBoost documentation now keeps track of multiple versions:
-  - Latest master: https://xgboost.readthedocs.io/en/latest
-  - 0.80 stable: https://xgboost.readthedocs.io/en/release_0.80
-  - 0.72 stable: https://xgboost.readthedocs.io/en/release_0.72
-* Ranking task now uses instance weights (#3379)
-* 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.
-  - Hinge loss for binary classification (`binary:hinge`) (#3477)
-  - Ability to specify delimiter and instance weight column for CSV files (#3546)
-  - Ability to use 1-based indexing instead of 0-based (#3546)
-* GPU support
-  - Quantile sketch, binning, and index compression are now performed on GPU, eliminating PCIe transfer for 'gpu_hist' algorithm (#3319, #3393)
-  - Upgrade to NCCL2 for multi-GPU training (#3404).
-  - Use shared memory atomics for faster training (#3384).
-  - Dynamically allocate GPU memory, to prevent large allocations for deep trees (#3519)
-  - Fix memory copy bug for large files (#3472)
-* Python package
-  - Importing data from Python datatable (#3272)
-  - Pre-built binary wheels available for 64-bit Linux and Windows (#3424, #3443)
-  - Add new importance measures 'total_gain', 'total_cover' (#3498)
-  - Sklearn API now supports saving and loading models (#3192)
-  - Arbitrary cross validation fold indices (#3353)
-  - `predict()` function in Sklearn API uses `best_ntree_limit` if available, to make early stopping easier to use (#3445)
-  - Informational messages are now directed to Python's `print()` rather than standard output (#3438). This way, messages appear inside Jupyter notebooks.
-* R package
-  - Oracle Solaris support, per CRAN policy (#3372)
-* JVM packages
-  - Single-instance prediction (#3464)
-  - Pre-built JARs are now available from Maven Central (#3401)
-  - Add NULL pointer check (#3021)
-  - Consider `spark.task.cpus` when controlling parallelism (#3530)
-  - Handle missing values in prediction (#3529)
-  - Eliminate outputs of `System.out` (#3572)
-* Refactored C++ DMatrix class for simplicity and de-duplication (#3301)
-* Refactored C++ histogram facilities (#3564)
-* Refactored constraints / regularization mechanism for split finding (#3335, #3429). Users may specify an elastic net (L2 + L1 regularization) on leaf weights as well as monotonic constraints on test nodes. The refactor will be useful for a future addition of feature interaction constraints.
-* Statically link `libstdc++` for MinGW32 (#3430)
-* Enable loading from `group`, `base_margin` and `weight` (see [here](http://xgboost.readthedocs.io/en/latest/tutorials/input_format.html#auxiliary-files-for-additional-information)) for Python, R, and JVM packages (#3431)
-* Fix model saving for `count:possion` so that `max_delta_step` doesn't get truncated (#3515)
-* Fix loading of sparse CSC matrix (#3553)
-* Fix incorrect handling of `base_score` parameter for Tweedie regression (#3295)
-
-## v0.72.1 (2018.07.08)
-This version is only applicable for the Python package. The content is identical to that of v0.72.
-
 ## v0.72 (2018.06.01)
 * Starting with this release, we plan to make a new release every two months. See #3252 for more details.
 * Fix a pathological behavior (near-zero second-order gradients) in multiclass objective (#3304)

R-package/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
-Version: 0.81.0.1
-Date: 2018-08-13
+Version: 0.71.1
+Date: 2018-05-11
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),
@@ -51,7 +51,6 @@ Suggests:
     Ckmeans.1d.dp (>= 3.3.1),
     vcd (>= 1.3),
     testthat,
-    lintr,
     igraph (>= 1.0.1)
 Depends:
     R (>= 3.3.0)
@@ -61,5 +60,5 @@ Imports:
     data.table (>= 1.9.6),
     magrittr (>= 1.5),
     stringi (>= 0.5.2)
-RoxygenNote: 6.1.0
+RoxygenNote: 6.0.1
 SystemRequirements: GNU make, C++11

R-package/R/callbacks.R

@@ -168,7 +168,7 @@ cb.evaluation.log <- function() {
 #' 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 
+#' reset a parameter value, the \code{nround} 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/R/utils.R

@@ -74,19 +74,6 @@ check.booster.params <- function(params, ...) {
         params[['monotone_constraints']] = vec2str
   }
   
-  # interaction constraints parser (convert from list of column indices to string)
-  if (!is.null(params[['interaction_constraints']]) && 
-      typeof(params[['interaction_constraints']]) != "character"){
-    # check input class
-    if (class(params[['interaction_constraints']]) != 'list') stop('interaction_constraints should be class list')
-    if (!all(unique(sapply(params[['interaction_constraints']], class)) %in% c('numeric','integer'))) {
-      stop('interaction_constraints should be a list of numeric/integer vectors')
-    }
-
-    # recast parameter as string
-    interaction_constraints <- sapply(params[['interaction_constraints']], function(x) paste0('[', paste(x, collapse=','), ']'))
-    params[['interaction_constraints']] <- paste0('[', paste(interaction_constraints, collapse=','), ']')
-  }
   return(params)
 }
 
@@ -275,8 +262,7 @@ xgb.createFolds <- function(y, k = 10)
       ## add enough random integers to get  length(seqVector) == numInClass[i]
       if (numInClass[i] %% k > 0) seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
       ## shuffle the integers for fold assignment and assign to this classes's data
-      ## seqVector[sample.int(length(seqVector))] is used to handle length(seqVector) == 1
-      foldVector[y == dimnames(numInClass)$y[i]] <- seqVector[sample.int(length(seqVector))]
+      foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)
     }
   } else {
     foldVector <- seq(along = y)

R-package/R/xgb.Booster.R

@@ -129,13 +129,11 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'        logistic regression would result in predictions for log-odds instead of probabilities.
 #' @param ntreelimit limit the number of model's trees or boosting iterations used in prediction (see Details).
 #'        It will use all the trees by default (\code{NULL} value).
-#' @param predleaf whether predict leaf index.
-#' @param predcontrib whether to return feature contributions to individual predictions (see Details).
+#' @param predleaf whether predict leaf index instead.
+#' @param predcontrib whether to return feature contributions to individual predictions instead (see Details).
 #' @param approxcontrib whether to use a fast approximation for feature contributions (see Details).
-#' @param predinteraction whether to return contributions of feature interactions to individual predictions (see Details).
 #' @param reshape whether to reshape the vector of predictions to a matrix form when there are several
-#'        prediction outputs per case. This option has no effect when either of predleaf, predcontrib,
-#'        or predinteraction flags is TRUE.
+#'        prediction outputs per case. This option has no effect when \code{predleaf = TRUE}.
 #' @param ... Parameters passed to \code{predict.xgb.Booster}
 #'
 #' @details
@@ -160,11 +158,6 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' Setting \code{approxcontrib = TRUE} approximates these values following the idea explained
 #' in \url{http://blog.datadive.net/interpreting-random-forests/}.
 #'
-#' With \code{predinteraction = TRUE}, SHAP values of contributions of interaction of each pair of features
-#' are computed. Note that this operation might be rather expensive in terms of compute and memory.
-#' Since it quadratically depends on the number of features, it is recommended to perfom selection
-#' of the most important features first. See below about the format of the returned results.
-#'
 #' @return
 #' For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
 #' For multiclass classification, either a \code{num_class * nrows(newdata)} vector or
@@ -180,14 +173,6 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' such a matrix. The contribution values are on the scale of untransformed margin
 #' (e.g., for binary classification would mean that the contributions are log-odds deviations from bias).
 #'
-#' When \code{predinteraction = TRUE} and it is not a multiclass setting, the output is a 3d array with
-#' dimensions \code{c(nrow, num_features + 1, num_features + 1)}. The off-diagonal (in the last two dimensions)
-#' elements represent different features interaction contributions. The array is symmetric WRT the last
-#' two dimensions. The "+ 1" columns corresponds to bias. Summing this array along the last dimension should
-#' produce practically the same result as predict with \code{predcontrib = TRUE}.
-#' For a multiclass case, a list of \code{num_class} elements is returned, where each element is
-#' such an array.
-#'
 #' @seealso
 #' \code{\link{xgb.train}}.
 #' 
@@ -284,8 +269,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' @rdname predict.xgb.Booster
 #' @export
 predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
-                                predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
-                                reshape = FALSE, ...) {
+                                predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, reshape = FALSE, ...) {
 
   object <- xgb.Booster.complete(object, saveraw = FALSE)
   if (!inherits(newdata, "xgb.DMatrix"))
@@ -301,8 +285,7 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   if (ntreelimit < 0)
     stop("ntreelimit cannot be negative")
 
-  option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) +
-    8L * as.logical(approxcontrib) + 16L * as.logical(predinteraction)
+  option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) + 8L * as.logical(approxcontrib)
 
   ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1], as.integer(ntreelimit))
 
@@ -322,28 +305,17 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   } else if (predcontrib) {
     n_col1 <- ncol(newdata) + 1
     n_group <- npred_per_case / n_col1
-    cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
+    dnames <- if (!is.null(colnames(newdata))) list(NULL, c(colnames(newdata), "BIAS")) else NULL
     ret <- if (n_ret == n_row) {
-      matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
+      matrix(ret, ncol = 1, dimnames = dnames)
     } else if (n_group == 1) {
-      matrix(ret, nrow = n_row, byrow = TRUE, dimnames = list(NULL, cnames))
+      matrix(ret, nrow = n_row, byrow = TRUE, dimnames = dnames)
     } else {
-      arr <- array(ret, c(n_col1, n_group, n_row),
-                   dimnames = list(cnames, NULL, NULL)) %>% aperm(c(2,3,1)) # [group, row, col]
-      lapply(seq_len(n_group), function(g) arr[g,,])
-    }
-  } else if (predinteraction) {
-    n_col1 <- ncol(newdata) + 1
-    n_group <- npred_per_case / n_col1^2
-    cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
-    ret <- if (n_ret == n_row) {
-      matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
-    } else if (n_group == 1) {
-      array(ret, c(n_col1, n_col1, n_row), dimnames = list(cnames, cnames, NULL)) %>% aperm(c(3,1,2))
-    } else {
-      arr <- array(ret, c(n_col1, n_col1, n_group, n_row),
-                   dimnames = list(cnames, cnames, NULL, NULL)) %>% aperm(c(3,4,1,2)) # [group, row, col1, col2]
-      lapply(seq_len(n_group), function(g) arr[g,,,])
+      grp_mask <- rep(seq_len(n_col1), n_row) +
+        rep((seq_len(n_row) - 1) * n_col1 * n_group, each = n_col1)
+      lapply(seq_len(n_group), function(g) {
+        matrix(ret[grp_mask + n_col1 * (g - 1)], nrow = n_row, byrow = TRUE, dimnames = dnames)
+      })
     }
   } else if (reshape && npred_per_case > 1) {
     ret <- matrix(ret, nrow = n_row, byrow = TRUE)

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

@@ -52,9 +52,9 @@
 #' dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
 #'
 #' param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-#' nrounds = 4
+#' nround = 4
 #'
-#' bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
+#' bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
 #' 
 #' # Model accuracy without new features
 #' accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
@@ -68,7 +68,7 @@
 #' new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
 #' new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
 #' watchlist <- list(train = new.dtrain)
-#' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
+#' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nround, nthread = 2)
 #' 
 #' # Model accuracy with new features
 #' accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /

R-package/R/xgb.ggplot.R

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

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

@@ -212,7 +212,6 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
   }
   if (plot && which == "2d") {
     # TODO
-    warning("Bivariate plotting is currently not available.")
   }
   invisible(list(data = data, shap_contrib = shap_contrib))
 }

R-package/R/xgb.train.R

@@ -22,11 +22,10 @@
 #'   \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{nround}. 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

R-package/demo/00Index

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

R-package/demo/cross_validation.R

@@ -5,20 +5,20 @@ data(agaricus.test, package='xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
-nrounds <- 2
+nround <- 2
 param <- list(max_depth=2, eta=1, silent=1, nthread=2, objective='binary:logistic')
 
 cat('running cross validation\n')
 # do cross validation, this will print result out as
 # [iteration]  metric_name:mean_value+std_value
 # std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold=5, metrics={'error'})
+xgb.cv(param, dtrain, nround, nfold=5, metrics={'error'})
 
 cat('running cross validation, disable standard deviation display\n')
 # do cross validation, this will print result out as
 # [iteration]  metric_name:mean_value+std_value
 # std_value is standard deviation of the metric
-xgb.cv(param, dtrain, nrounds, nfold=5,
+xgb.cv(param, dtrain, nround, nfold=5,
        metrics='error', showsd = FALSE)
 
 ###
@@ -43,9 +43,9 @@ evalerror <- function(preds, dtrain) {
 param <- list(max_depth=2, eta=1, silent=1,
               objective = logregobj, eval_metric = evalerror)
 # train with customized objective
-xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5)
+xgb.cv(params = param, data = dtrain, nrounds = nround, nfold = 5)
 
 # do cross validation with prediction values for each fold
-res <- xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5, prediction = TRUE)
+res <- xgb.cv(params = param, data = dtrain, nrounds = nround, nfold = 5, prediction = TRUE)
 res$evaluation_log
 length(res$pred)

R-package/demo/interaction_constraints.R

@@ -1,105 +0,0 @@
-library(xgboost)
-library(data.table)
-
-set.seed(1024)
-
-# Function to obtain a list of interactions fitted in trees, requires input of maximum depth
-treeInteractions <- function(input_tree, input_max_depth){
-  trees <- copy(input_tree)  # copy tree input to prevent overwriting
-  if (input_max_depth < 2) return(list())  # no interactions if max depth < 2
-  if (nrow(input_tree) == 1) return(list())
-
-  # Attach parent nodes
-  for (i in 2:input_max_depth){
-    if (i == 2) trees[, ID_merge:=ID] else trees[, ID_merge:=get(paste0('parent_',i-2))]
-    parents_left <- trees[!is.na(Split), list(i.id=ID, i.feature=Feature, ID_merge=Yes)]
-    parents_right <- trees[!is.na(Split), list(i.id=ID, i.feature=Feature, ID_merge=No)]
-
-    setorderv(trees, 'ID_merge')
-    setorderv(parents_left, 'ID_merge')
-    setorderv(parents_right, 'ID_merge')
-
-    trees <- merge(trees, parents_left, by='ID_merge', all.x=T)
-    trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
-    trees[, c('i.id','i.feature'):=NULL]
-
-    trees <- merge(trees, parents_right, by='ID_merge', all.x=T)
-    trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
-    trees[, c('i.id','i.feature'):=NULL]
-  }
-
-  # Extract nodes with interactions
-  interaction_trees <- trees[!is.na(Split) & !is.na(parent_1), 
-                             c('Feature',paste0('parent_feat_',1:(input_max_depth-1))), with=F]
-  interaction_trees_split <- split(interaction_trees, 1:nrow(interaction_trees))
-  interaction_list <- lapply(interaction_trees_split, as.character)
-
-  # Remove NAs (no parent interaction)
-  interaction_list <- lapply(interaction_list, function(x) x[!is.na(x)])
-
-  # Remove non-interactions (same variable)
-  interaction_list <- lapply(interaction_list, unique)  # remove same variables
-  interaction_length <- sapply(interaction_list, length)
-  interaction_list <- interaction_list[interaction_length > 1]
-  interaction_list <- unique(lapply(interaction_list, sort))
-  return(interaction_list)
-}
-
-# Generate sample data
-x <- list()
-for (i in 1:10){
-  x[[i]] = i*rnorm(1000, 10)
-}
-x <- as.data.table(x)
-
-y = -1*x[, rowSums(.SD)] + x[['V1']]*x[['V2']] + x[['V3']]*x[['V4']]*x[['V5']] + rnorm(1000, 0.001) + 3*sin(x[['V7']])
-
-train = as.matrix(x)
-
-# Interaction constraint list (column names form)
-interaction_list <- list(c('V1','V2'),c('V3','V4','V5'))
-
-# Convert interaction constraint list into feature index form
-cols2ids <- function(object, col_names) {
-  LUT <- seq_along(col_names) - 1
-  names(LUT) <- col_names
-  rapply(object, function(x) LUT[x], classes="character", how="replace")
-}
-interaction_list_fid = cols2ids(interaction_list, colnames(train))
-
-# Fit model with interaction constraints
-bst = xgboost(data = train, label = y, max_depth = 4,
-              eta = 0.1, nthread = 2, nrounds = 1000,
-              interaction_constraints = interaction_list_fid)
-
-bst_tree <- xgb.model.dt.tree(colnames(train), bst)
-bst_interactions <- treeInteractions(bst_tree, 4)  # interactions constrained to combinations of V1*V2 and V3*V4*V5
-
-# Fit model without interaction constraints
-bst2 = xgboost(data = train, label = y, max_depth = 4,
-               eta = 0.1, nthread = 2, nrounds = 1000)
-
-bst2_tree <- xgb.model.dt.tree(colnames(train), bst2)
-bst2_interactions <- treeInteractions(bst2_tree, 4)  # much more interactions
-
-# Fit model with both interaction and monotonicity constraints
-bst3 = xgboost(data = train, label = y, max_depth = 4,
-               eta = 0.1, nthread = 2, nrounds = 1000,
-               interaction_constraints = interaction_list_fid,
-               monotone_constraints = c(-1,0,0,0,0,0,0,0,0,0))
-
-bst3_tree <- xgb.model.dt.tree(colnames(train), bst3)
-bst3_interactions <- treeInteractions(bst3_tree, 4)  # interactions still constrained to combinations of V1*V2 and V3*V4*V5
-
-# Show monotonic constraints still apply by checking scores after incrementing V1
-x1 <- sort(unique(x[['V1']]))
-for (i in 1:length(x1)){
-  testdata <- copy(x[, -c('V1')])
-  testdata[['V1']] <- x1[i]
-  testdata <- testdata[, paste0('V',1:10), with=F]
-  pred <- predict(bst3, as.matrix(testdata))
-  
-  # Should not print out anything due to monotonic constraints
-  if (i > 1) if (any(pred > prev_pred)) print(i)
-  prev_pred <- pred 
-}

R-package/demo/predict_first_ntree.R

@@ -7,10 +7,10 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
 
 param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
 watchlist <- list(eval = dtest, train = dtrain)
-nrounds = 2
+nround = 2
 
 # training the model for two rounds
-bst = xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
+bst = xgb.train(param, dtrain, nround, nthread = 2, watchlist)
 cat('start testing prediction from first n trees\n')
 labels <- getinfo(dtest,'label')
 

R-package/demo/predict_leaf_indices.R

@@ -11,10 +11,10 @@ dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
 
 param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-nrounds = 4
+nround = 4
 
 # training the model for two rounds
-bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
+bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
 
 # Model accuracy without new features
 accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
@@ -43,7 +43,7 @@ new.features.test <- create.new.tree.features(bst, agaricus.test$data)
 new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
 new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
 watchlist <- list(train = new.dtrain)
-bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
+bst <- xgb.train(params = param, data = new.dtrain, nrounds = nround, nthread = 2)
 
 # Model accuracy with new features
 accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)

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

@@ -22,7 +22,7 @@ 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 
+reset a parameter value, the \code{nround} 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/predict.xgb.Booster.Rd

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

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

@@ -63,9 +63,9 @@ dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
 
 param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
-nrounds = 4
+nround = 4
 
-bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
+bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
 
 # Model accuracy without new features
 accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
@@ -79,7 +79,7 @@ new.features.test <- xgb.create.features(model = bst, agaricus.test$data)
 new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
 new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
 watchlist <- list(train = new.dtrain)
-bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
+bst <- xgb.train(params = param, data = new.dtrain, nrounds = nround, nthread = 2)
 
 # Model accuracy with new features
 accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /

R-package/man/xgb.cv.Rd

@@ -4,12 +4,11 @@
 \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:

R-package/man/xgb.dump.Rd

@@ -44,8 +44,8 @@ test <- agaricus.test
 bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 # save the model in file 'xgb.model.dump'
-dump_path = file.path(tempdir(), 'model.dump')
-xgb.dump(bst, dump_path, with_stats = TRUE)
+dump.path = file.path(tempdir(), 'model.dump')
+xgb.dump(bst, dump.path, with_stats = TRUE)
 
 # print the model without saving it to a file
 print(xgb.dump(bst, with_stats = TRUE))

R-package/man/xgb.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

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

@@ -9,8 +9,8 @@ xgb.ggplot.importance(importance_matrix = NULL, top_n = NULL,
   measure = NULL, rel_to_first = FALSE, n_clusters = c(1:10), ...)
 
 xgb.plot.importance(importance_matrix = NULL, top_n = NULL,
-  measure = NULL, rel_to_first = FALSE, left_margin = 10,
-  cex = NULL, plot = TRUE, ...)
+  measure = NULL, rel_to_first = FALSE, left_margin = 10, cex = NULL,
+  plot = TRUE, ...)
 }
 \arguments{
 \item{importance_matrix}{a \code{data.table} returned by \code{\link{xgb.importance}}.}

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, ...)

R-package/man/xgb.train.Rd

@@ -5,17 +5,15 @@
 \alias{xgboost}
 \title{eXtreme Gradient Boosting Training}
 \usage{
-xgb.train(params = list(), data, nrounds, watchlist = list(),
-  obj = NULL, feval = NULL, verbose = 1, print_every_n = 1L,
+xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
+  feval = NULL, verbose = 1, print_every_n = 1L,
   early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
-  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(),
-  ...)
+  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ...)
 
 xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
   params = list(), nrounds, verbose = 1, print_every_n = 1L,
   early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
-  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(),
-  ...)
+  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ...)
 }
 \arguments{
 \item{params}{the list of parameters. 
@@ -37,7 +35,7 @@ xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
   \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{nround}. 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.

R-package/tests/testthat/test_basic.R

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

R-package/tests/testthat/test_dmatrix.R

@@ -77,18 +77,6 @@ test_that("xgb.DMatrix: slice, dim", {
   expect_equal(getinfo(dsub1, 'label'), getinfo(dsub2, 'label'))
 })
 
-test_that("xgb.DMatrix: slice, trailing empty rows", {
-  data(agaricus.train, package='xgboost')
-  train_data <- agaricus.train$data
-  train_label <- agaricus.train$label
-  dtrain <- xgb.DMatrix(data=train_data, label=train_label)
-  slice(dtrain, 6513L)
-  train_data[6513, ] <- 0
-  dtrain <- xgb.DMatrix(data=train_data, label=train_label)
-  slice(dtrain, 6513L)
-  expect_equal(nrow(dtrain), 6513)
-})
-
 test_that("xgb.DMatrix: colnames", {
   dtest <- xgb.DMatrix(test_data, label=test_label)
   expect_equal(colnames(dtest), colnames(test_data))

R-package/tests/testthat/test_gc_safety.R

@@ -9,7 +9,7 @@ test_that("train and prediction when gctorture is on", {
   test <- agaricus.test
   gctorture(TRUE)
   bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
-  eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+  eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
   pred <- predict(bst, test$data)
   gctorture(FALSE)
 })

R-package/tests/testthat/test_helpers.R

@@ -7,9 +7,6 @@ require(vcd, quietly = TRUE)
 
 float_tolerance = 5e-6
 
-# disable some tests for Win32
-win32_flag = .Platform$OS.type == "windows" && .Machine$sizeof.pointer != 8
-
 set.seed(1982)
 data(Arthritis)
 df <- data.table(Arthritis, keep.rownames = F)
@@ -44,8 +41,7 @@ mbst.GLM <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
 
 
 test_that("xgb.dump works", {
-  if (!win32_flag)
-    expect_length(xgb.dump(bst.Tree), 200)
+  expect_length(xgb.dump(bst.Tree), 200)
   dump_file = file.path(tempdir(), 'xgb.model.dump')
   expect_true(xgb.dump(bst.Tree, dump_file, with_stats = T))
   expect_true(file.exists(dump_file))
@@ -54,8 +50,7 @@ test_that("xgb.dump works", {
   # JSON format
   dmp <- xgb.dump(bst.Tree, dump_format = "json")
   expect_length(dmp, 1)
-  if (!win32_flag)
-    expect_length(grep('nodeid', strsplit(dmp, '\n')[[1]]), 188)
+  expect_length(grep('nodeid', strsplit(dmp, '\n')[[1]]), 188)
 })
 
 test_that("xgb.dump works for gblinear", {
@@ -215,8 +210,7 @@ test_that("xgb.model.dt.tree works with and without feature names", {
   names.dt.trees <- c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
   dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
   expect_equal(names.dt.trees, names(dt.tree))
-  if (!win32_flag)
-    expect_equal(dim(dt.tree), c(188, 10))
+  expect_equal(dim(dt.tree), c(188, 10))
   expect_output(str(dt.tree), 'Feature.*\\"Age\\"')
 
   dt.tree.0 <- xgb.model.dt.tree(model = bst.Tree)
@@ -242,8 +236,7 @@ test_that("xgb.model.dt.tree throws error for gblinear", {
 
 test_that("xgb.importance works with and without feature names", {
   importance.Tree <- xgb.importance(feature_names = feature.names, model = bst.Tree)
-  if (!win32_flag)
-    expect_equal(dim(importance.Tree), c(7, 4))
+  expect_equal(dim(importance.Tree), c(7, 4))
   expect_equal(colnames(importance.Tree), c("Feature", "Gain", "Cover", "Frequency"))
   expect_output(str(importance.Tree), 'Feature.*\\"Age\\"')
 

R-package/tests/testthat/test_interaction_constraints.R

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

R-package/tests/testthat/test_interactions.R

@@ -1,108 +0,0 @@
-context('Test prediction of feature interactions')
-
-require(xgboost)
-require(magrittr)
-
-set.seed(123)
-
-test_that("predict feature interactions works", {
-  # simulate some binary data and a linear outcome with an interaction term
-  N <- 1000
-  P <- 5
-  X <- matrix(rbinom(N * P, 1, 0.5), ncol=P, dimnames = list(NULL, letters[1:P]))
-  # center the data (as contributions are computed WRT feature means)
-  X <- scale(X, scale=FALSE)
-
-  # outcome without any interactions, without any noise:
-  f <- function(x) 2 * x[, 1] - 3 * x[, 2]
-  # outcome with interactions, without noise:
-  f_int <- function(x) f(x) + 2 * x[, 2] * x[, 3]
-  # outcome with interactions, with noise:
-  #f_int_noise <- function(x) f_int(x) + rnorm(N, 0, 0.3)
-
-  y <- f_int(X)
-
-  dm <- xgb.DMatrix(X, label = y)
-  param <- list(eta=0.1, max_depth=4, base_score=mean(y), lambda=0, nthread=2)
-  b <- xgb.train(param, dm, 100)
-  
-  pred = predict(b, dm, outputmargin=TRUE)
-
-  # SHAP contributions:
-  cont <- predict(b, dm, predcontrib=TRUE)
-  expect_equal(dim(cont), c(N, P+1))
-  # make sure for each row they add up to marginal predictions
-  max(abs(rowSums(cont) - pred)) %>% expect_lt(0.001)
-  # Hand-construct the 'ground truth' feature contributions:
-  gt_cont <- cbind(
-      2. * X[, 1],
-     -3. * X[, 2] + 1. * X[, 2] * X[, 3], # attribute a HALF of the interaction term to feature #2
-      1. * X[, 2] * X[, 3]               # and another HALF of the interaction term to feature #3
-     )
-  gt_cont <- cbind(gt_cont, matrix(0, nrow=N, ncol=P + 1 - 3))
-  # These should be relatively close:
-  expect_lt(max(abs(cont - gt_cont)), 0.05)
-
-
-  # SHAP interaction contributions:
-  intr <- predict(b, dm, predinteraction=TRUE)
-  expect_equal(dim(intr), c(N, P+1, P+1))
-  # check assigned colnames
-  cn <- c(letters[1:P], "BIAS")
-  expect_equal(dimnames(intr), list(NULL, cn, cn))
-
-  # check the symmetry
-  max(abs(aperm(intr, c(1,3,2)) - intr)) %>% expect_lt(0.00001)
-
-  # sums WRT columns must be close to feature contributions
-  max(abs(apply(intr, c(1,2), sum) - cont)) %>% expect_lt(0.00001)
-
-  # diagonal terms for features 3,4,5 must be close to zero
-  Reduce(max, sapply(3:P, function(i) max(abs(intr[, i, i])))) %>% expect_lt(0.05)
-
-  # BIAS must have no interactions
-  max(abs(intr[, 1:P, P+1])) %>% expect_lt(0.00001)
-
-  # interactions other than 2 x 3 must be close to zero
-  intr23 <- intr
-  intr23[,2,3] <- 0
-  Reduce(max, sapply(1:P, function(i) max(abs(intr23[, i, (i+1):(P+1)])))) %>% expect_lt(0.05)
-
-  # Construct the 'ground truth' contributions of interactions directly from the linear terms:
-  gt_intr <- array(0, c(N, P+1, P+1))
-  gt_intr[,2,3] <- 1. * X[, 2] * X[, 3] # attribute a HALF of the interaction term to each symmetric element
-  gt_intr[,3,2] <- gt_intr[, 2, 3]
-  # merge-in the diagonal based on 'ground truth' feature contributions
-  intr_diag = gt_cont - apply(gt_intr, c(1,2), sum)
-  for(j in seq_len(P)) {
-    gt_intr[,j,j] = intr_diag[,j]
-  }
-  # These should be relatively close:
-  expect_lt(max(abs(intr - gt_intr)), 0.1)
-})
-
-
-test_that("multiclass feature interactions work", {
-  dm <- xgb.DMatrix(as.matrix(iris[,-5]), label=as.numeric(iris$Species)-1)
-  param <- list(eta=0.1, max_depth=4, objective='multi:softprob', num_class=3)
-  b <- xgb.train(param, dm, 40)
-  pred = predict(b, dm, outputmargin=TRUE) %>% array(c(3, 150)) %>% t
-
-  # SHAP contributions:
-  cont <- predict(b, dm, predcontrib=TRUE)
-  expect_length(cont, 3)
-  # rewrap them as a 3d array
-  cont <- unlist(cont) %>% array(c(150, 5, 3))
-  # make sure for each row they add up to marginal predictions
-  max(abs(apply(cont, c(1,3), sum) - pred)) %>% expect_lt(0.001)
-
-  # SHAP interaction contributions:
-  intr <- predict(b, dm, predinteraction=TRUE)
-  expect_length(intr, 3)
-  # rewrap them as a 4d array
-  intr <- unlist(intr) %>% array(c(150, 5, 5, 3)) %>% aperm(c(4, 1, 2, 3)) # [grp, row, col, col]
-  # check the symmetry
-  max(abs(aperm(intr, c(1,2,4,3)) - intr)) %>% expect_lt(0.00001)
-  # sums WRT columns must be close to feature contributions
-  max(abs(apply(intr, c(1,2,3), sum) - aperm(cont, c(3,1,2)))) %>% expect_lt(0.00001)
-})

R-package/tests/testthat/test_update.R

@@ -7,10 +7,6 @@ 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)
 
-# Disable flaky tests for 32-bit Windows.
-# See https://github.com/dmlc/xgboost/issues/3720
-win32_flag = .Platform$OS.type == "windows" && .Machine$sizeof.pointer != 8
-
 test_that("updating the model works", {
   watchlist = list(train = dtrain, test = dtest)
 
@@ -33,9 +29,7 @@ test_that("updating the model works", {
   tr1r <- xgb.model.dt.tree(model = bst1r)
   # all should be the same when no subsampling
   expect_equal(bst1$evaluation_log, bst1r$evaluation_log)
-  if (!win32_flag) {
-    expect_equal(tr1, tr1r, tolerance = 0.00001, check.attributes = FALSE)
-  }
+  expect_equal(tr1, tr1r, tolerance = 0.00001, check.attributes = FALSE)
 
   # the same boosting with subsampling with an extra 'refresh' updater:
   p2r <- modifyList(p2, list(updater = 'grow_colmaker,prune,refresh', refresh_leaf = FALSE))
@@ -44,9 +38,7 @@ test_that("updating the model works", {
   tr2r <- xgb.model.dt.tree(model = bst2r)
   # should be the same evaluation but different gains and larger cover
   expect_equal(bst2$evaluation_log, bst2r$evaluation_log)
-  if (!win32_flag) {
-    expect_equal(tr2[Feature == 'Leaf']$Quality, tr2r[Feature == 'Leaf']$Quality)
-  }
+  expect_equal(tr2[Feature == 'Leaf']$Quality, tr2r[Feature == 'Leaf']$Quality)
   expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2r[Feature != 'Leaf']$Quality)), 100)
   expect_gt(sum(tr2r$Cover) / sum(tr2$Cover), 1.5)
 
@@ -69,9 +61,7 @@ test_that("updating the model works", {
   expect_gt(sum(tr2u$Cover) / sum(tr2$Cover), 1.5)
   # the results should be the same as for the model with an extra 'refresh' updater
   expect_equal(bst2r$evaluation_log, bst2u$evaluation_log)
-  if (!win32_flag) {
-    expect_equal(tr2r, tr2u, tolerance = 0.00001, check.attributes = FALSE)
-  }
+  expect_equal(tr2r, tr2u, tolerance = 0.00001, check.attributes = FALSE)
   
   # process type 'update' for no-subsampling model, refreshing only the tree stats from TEST data:
   p1ut <- modifyList(p1, list(process_type = 'update', updater = 'refresh', refresh_leaf = FALSE))

README.md

@@ -6,28 +6,46 @@
 [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)
 [![CRAN Status Badge](http://www.r-pkg.org/badges/version/xgboost)](http://cran.r-project.org/web/packages/xgboost)
 [![PyPI version](https://badge.fury.io/py/xgboost.svg)](https://pypi.python.org/pypi/xgboost/)
+[![Gitter chat for developers at https://gitter.im/dmlc/xgboost](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
-[Community](https://xgboost.ai/community) |
 [Documentation](https://xgboost.readthedocs.org) |
 [Resources](demo/README.md) |
-[Contributors](CONTRIBUTORS.md) |
-[Release Notes](NEWS.md)
+[Installation](https://xgboost.readthedocs.org/en/latest/build.html) |
+[Release Notes](NEWS.md) |
+[RoadMap](https://github.com/dmlc/xgboost/issues/873)
 
 XGBoost is an optimized distributed gradient boosting library designed to be highly ***efficient***, ***flexible*** and ***portable***.
 It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework.
 XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
 The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
 
+What's New
+----------
+* [XGBoost GPU support with fast histogram algorithm](https://github.com/dmlc/xgboost/tree/master/plugin/updater_gpu)
+* [XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow](http://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgboost-in-spark-flink-and-dataflow.html), see [JVM-Package](https://github.com/dmlc/xgboost/tree/master/jvm-packages)
+* [Story and Lessons Behind the Evolution of XGBoost](http://homes.cs.washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html)
+* [Tutorial: Distributed XGBoost on AWS with YARN](https://xgboost.readthedocs.io/en/latest/tutorials/aws_yarn.html)
+* [XGBoost brick](NEWS.md) Release
+
+Ask a Question
+--------------
+* For reporting bugs please use the [xgboost/issues](https://github.com/dmlc/xgboost/issues) page.
+* For generic questions or to share your experience using XGBoost please use the [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/)
+
+Help to Make XGBoost Better
+---------------------------
+XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
+- Check out [call for contributions](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Acall-for-contribution+is%3Aopen) and [Roadmap](https://github.com/dmlc/xgboost/issues/873) to see what can be improved, or open an issue if you want something.
+- Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users.
+- Add your stories and experience to [Awesome XGBoost](demo/README.md).
+- Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) and after your patch has been merged.
+  - Please also update [NEWS.md](NEWS.md) on changes and improvements in API and docs.
+
 License
 -------
 © Contributors, 2016. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
 
-Contribute to XGBoost
----------------------
-XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
-Checkout the [Community Page](https://xgboost.ai/community)
-
 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.
+- 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, see also the [Project Page at UW](http://dmlc.cs.washington.edu/xgboost.html).

amalgamation/xgboost-all0.cc

@@ -20,7 +20,6 @@
 #include "../src/objective/regression_obj.cc"
 #include "../src/objective/multiclass_obj.cc"
 #include "../src/objective/rank_obj.cc"
-#include "../src/objective/hinge.cc"
 
 // gbms
 #include "../src/gbm/gbm.cc"
@@ -44,7 +43,6 @@
 #endif
 
 // tress
-#include "../src/tree/split_evaluator.cc"
 #include "../src/tree/tree_model.cc"
 #include "../src/tree/tree_updater.cc"
 #include "../src/tree/updater_colmaker.cc"

appveyor.yml

@@ -52,10 +52,8 @@ install:
           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
-          $DEPS = "c('data.table','magrittr','stringi','ggplot2','DiagrammeR','Ckmeans.1d.dp','vcd','testthat','lintr','knitr','rmarkdown')"
+          $DEPS = "c('data.table','magrittr','stringi','ggplot2','DiagrammeR','Ckmeans.1d.dp','vcd','testthat','igraph','knitr','rmarkdown')"
           cmd.exe /c "R.exe -q -e ""install.packages($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:

cmake/Sanitizer.cmake

@@ -1,58 +0,0 @@
-# Set appropriate compiler and linker flags for sanitizers.
-#
-# Usage of this module:
-#  enable_sanitizers("address;leak")
-
-# Add flags
-macro(enable_sanitizer santizer)
-  if(${santizer} MATCHES "address")
-    find_package(ASan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=address")
-    link_libraries(${ASan_LIBRARY})
-
-  elseif(${santizer} MATCHES "thread")
-    find_package(TSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=thread")
-    link_libraries(${TSan_LIBRARY})
-
-  elseif(${santizer} MATCHES "leak")
-    find_package(LSan REQUIRED)
-    set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=leak")
-    link_libraries(${LSan_LIBRARY})
-
-  else()
-    message(FATAL_ERROR "Santizer ${santizer} not supported.")
-  endif()
-endmacro()
-
-macro(enable_sanitizers SANITIZERS)
-  # Check sanitizers compatibility.
-  # Idealy, we should use if(san IN_LIST SANITIZERS) ... endif()
-  # But I haven't figure out how to make it work.
-  foreach ( _san ${SANITIZERS} )
-    string(TOLOWER ${_san} _san)
-    if (_san MATCHES "thread")
-      if (${_use_other_sanitizers})
-        message(FATAL_ERROR
-          "thread sanitizer is not compatible with ${_san} sanitizer.")
-      endif()
-      set(_use_thread_sanitizer 1)
-    else ()
-      if (${_use_thread_sanitizer})
-        message(FATAL_ERROR
-          "${_san} sanitizer is not compatible with thread sanitizer.")
-      endif()
-      set(_use_other_sanitizers 1)
-    endif()
-  endforeach()
-
-  message("Sanitizers: ${SANITIZERS}")
-
-  foreach( _san ${SANITIZERS} )
-    string(TOLOWER ${_san} _san)
-    enable_sanitizer(${_san})
-  endforeach()
-  message("Sanitizers compile flags: ${SAN_COMPILE_FLAGS}")
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SAN_COMPILE_FLAGS}")
-  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SAN_COMPILE_FLAGS}")
-endmacro()

cmake/modules/FindASan.cmake

@@ -1,13 +0,0 @@
-set(ASan_LIB_NAME ASan)
-
-find_library(ASan_LIBRARY
-  NAMES libasan.so libasan.so.4 libasan.so.3 libasan.so.2 libasan.so.1 libasan.so.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(ASan DEFAULT_MSG
-  ASan_LIBRARY)
-
-mark_as_advanced(
-  ASan_LIBRARY
-  ASan_LIB_NAME)

cmake/modules/FindGTest.cmake

@@ -0,0 +1,79 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Tries to find GTest headers and libraries.
+#
+# Usage of this module as follows:
+#
+#  find_package(GTest)
+#
+# Variables used by this module, they can change the default behaviour and need
+# to be set before calling find_package:
+#
+#  GTest_HOME - When set, this path is inspected instead of standard library
+#                locations as the root of the GTest installation.
+#                The environment variable GTEST_HOME overrides this veriable.
+#
+# This module defines
+#  GTEST_INCLUDE_DIR, directory containing headers
+#  GTEST_LIBS, directory containing gtest libraries
+#  GTEST_STATIC_LIB, path to libgtest.a
+#  GTEST_SHARED_LIB, path to libgtest's shared library
+#  GTEST_FOUND, whether gtest has been found
+
+find_path(GTEST_INCLUDE_DIR NAMES gtest/gtest.h gtest.h PATHS ${CMAKE_SOURCE_DIR}/gtest/include NO_DEFAULT_PATH)
+find_library(GTEST_LIBRARIES NAMES gtest PATHS ${CMAKE_SOURCE_DIR}/gtest/lib NO_DEFAULT_PATH)
+
+if (GTEST_INCLUDE_DIR )
+  message(STATUS "Found the GTest includes: ${GTEST_INCLUDE_DIR}")
+endif ()
+
+
+if (GTEST_INCLUDE_DIR AND GTEST_LIBRARIES)
+  set(GTEST_FOUND TRUE)
+  get_filename_component( GTEST_LIBS ${GTEST_LIBRARIES} PATH )
+  set(GTEST_LIB_NAME gtest)
+  set(GTEST_STATIC_LIB ${GTEST_LIBS}/${CMAKE_STATIC_LIBRARY_PREFIX}${GTEST_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX})
+  set(GTEST_MAIN_STATIC_LIB ${GTEST_LIBS}/${CMAKE_STATIC_LIBRARY_PREFIX}${GTEST_LIB_NAME}_main${CMAKE_STATIC_LIBRARY_SUFFIX})
+  set(GTEST_SHARED_LIB ${GTEST_LIBS}/${CMAKE_SHARED_LIBRARY_PREFIX}${GTEST_LIB_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX})
+else ()
+  set(GTEST_FOUND FALSE)
+endif ()
+
+if (GTEST_FOUND)
+  if (NOT GTest_FIND_QUIETLY)
+    message(STATUS "Found the GTest library: ${GTEST_LIBRARIES}")
+  endif ()
+else ()
+  if (NOT GTest_FIND_QUIETLY)
+    set(GTEST_ERR_MSG "Could not find the GTest library. Looked in ")
+    if ( _gtest_roots )
+      set(GTEST_ERR_MSG "${GTEST_ERR_MSG} in ${_gtest_roots}.")
+    else ()
+      set(GTEST_ERR_MSG "${GTEST_ERR_MSG} system search paths.")
+    endif ()
+    if (GTest_FIND_REQUIRED)
+      message(FATAL_ERROR "${GTEST_ERR_MSG}")
+    else (GTest_FIND_REQUIRED)
+      message(STATUS "${GTEST_ERR_MSG}")
+    endif (GTest_FIND_REQUIRED)
+  endif ()
+endif ()
+
+mark_as_advanced(
+  GTEST_INCLUDE_DIR
+  GTEST_LIBS
+  GTEST_LIBRARIES
+  GTEST_STATIC_LIB
+  GTEST_SHARED_LIB
+)

cmake/modules/FindLSan.cmake

@@ -1,13 +0,0 @@
-set(LSan_LIB_NAME lsan)
-
-find_library(LSan_LIBRARY
-  NAMES liblsan.so liblsan.so.0 liblsan.so.0.0.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(LSan DEFAULT_MSG
-  LSan_LIBRARY)
-
-mark_as_advanced(
-  LSan_LIBRARY
-  LSan_LIB_NAME)

cmake/modules/FindNccl.cmake

@@ -1,58 +0,0 @@
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Tries to find NCCL headers and libraries.
-#
-# Usage of this module as follows:
-#
-#  find_package(NCCL)
-#
-# Variables used by this module, they can change the default behaviour and need
-# to be set before calling find_package:
-#
-#  NCCL_ROOT - When set, this path is inspected instead of standard library
-#              locations as the root of the NCCL installation.
-#              The environment variable NCCL_ROOT overrides this veriable.
-#
-# This module defines
-#  Nccl_FOUND, whether nccl has been found
-#  NCCL_INCLUDE_DIR, directory containing header
-#  NCCL_LIBRARY, directory containing nccl library
-#  NCCL_LIB_NAME, nccl library name
-#
-# This module assumes that the user has already called find_package(CUDA)
-
-
-set(NCCL_LIB_NAME nccl_static)
-
-find_path(NCCL_INCLUDE_DIR
-  NAMES nccl.h
-  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 ${CUDA_INCLUDE_DIRS}/../lib /usr/lib)
-
-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
-                                  NCCL_INCLUDE_DIR NCCL_LIBRARY)
-
-mark_as_advanced(
-  NCCL_INCLUDE_DIR
-  NCCL_LIBRARY
-  NCCL_LIB_NAME
-)

cmake/modules/FindTSan.cmake

@@ -1,13 +0,0 @@
-set(TSan_LIB_NAME tsan)
-
-find_library(TSan_LIBRARY
-  NAMES libtsan.so libtsan.so.0 libtsan.so.0.0.0
-  PATHS ${SANITIZER_PATH} /usr/lib64 /usr/lib /usr/local/lib64 /usr/local/lib ${CMAKE_PREFIX_PATH}/lib)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(TSan DEFAULT_MSG
-  TSan_LIBRARY)
-
-mark_as_advanced(
-  TSan_LIBRARY
-  TSan_LIB_NAME)

demo/binary_classification/mapfeat.py

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

demo/guide-python/custom_objective.py

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

demo/guide-python/runall.sh

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

demo/kaggle-higgs/higgs-train.R

@@ -24,9 +24,9 @@ param <- list("objective" = "binary:logitraw",
               "silent" = 1,
               "nthread" = 16)
 watchlist <- list("train" = xgmat)
-nrounds = 120
+nround = 120
 print ("loading data end, start to boost trees")
-bst = xgb.train(param, xgmat, nrounds, watchlist );
+bst = xgb.train(param, xgmat, nround, watchlist );
 # save out model
 xgb.save(bst, "higgs.model")
 print ('finish training')

demo/kaggle-higgs/speedtest.R

@@ -39,9 +39,9 @@ for (i in 1:length(threads)){
                   "silent" = 1,
                   "nthread" = thread)
     watchlist <- list("train" = xgmat)
-    nrounds = 120
+    nround = 120
     print ("loading data end, start to boost trees")
-    bst = xgb.train(param, xgmat, nrounds, watchlist );
+    bst = xgb.train(param, xgmat, nround, watchlist );
     # save out model
     xgb.save(bst, "higgs.model")
     print ('finish training')

demo/kaggle-otto/otto_train_pred.R

@@ -23,13 +23,13 @@ param <- list("objective" = "multi:softprob",
               "nthread" = 8)
 
 # Run Cross Validation
-cv.nrounds = 50
+cv.nround = 50
 bst.cv = xgb.cv(param=param, data = x[trind,], label = y, 
-                nfold = 3, nrounds=cv.nrounds)
+                nfold = 3, nrounds=cv.nround)
 
 # Train the model
-nrounds = 50
-bst = xgboost(param=param, data = x[trind,], label = y, nrounds=nrounds)
+nround = 50
+bst = xgboost(param=param, data = x[trind,], label = y, nrounds=nround)
 
 # Make prediction
 pred = predict(bst,x[teind,])

demo/kaggle-otto/understandingXGBoostModel.Rmd

@@ -121,19 +121,19 @@ param <- list("objective" = "multi:softprob",
               "eval_metric" = "mlogloss",
               "num_class" = numberOfClasses)
 
-cv.nrounds <- 5
+cv.nround <- 5
 cv.nfold <- 3
 
 bst.cv = xgb.cv(param=param, data = trainMatrix, label = y,
-                nfold = cv.nfold, nrounds = cv.nrounds)
+                nfold = cv.nfold, nrounds = cv.nround)
 ```
 > As we can see the error rate is low on the test dataset (for a 5mn trained model).
 
 Finally, we are ready to train the real model!!!
 
 ```{r modelTraining}
-nrounds = 50
-bst = xgboost(param=param, data = trainMatrix, label = y, nrounds=nrounds)
+nround = 50
+bst = xgboost(param=param, data = trainMatrix, label = y, nrounds=nround)
 ```
 
 Model understanding
@@ -142,7 +142,7 @@ Model understanding
 Feature importance
 ------------------
 
-So far, we have built a model made of **`r nrounds`** trees.
+So far, we have built a model made of **`r nround`** trees.
 
 To build a tree, the dataset is divided recursively several times. At the end of the process, you get groups of observations (here, these observations are properties regarding **Otto** products).
 

demo/multiclass_classification/README.md

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

demo/multiclass_classification/train.R

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

demo/rank/README.md

@@ -1,6 +1,6 @@
 Learning to rank
 ====
-XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the [group information file](../../doc/tutorials/input_format.rst#group-input-format) to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank, this function is not yet completed. Currently, we provide pairwise rank.
+XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the [group information file](../../doc/input_format.md#group-input-format) to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank, this function is not yet completed. Currently, we provide pairwise rank.
 
 ### Parameters
 The configuration setting is similar to the regression and binary classification setting, except user need to specify the objectives:
@@ -14,28 +14,8 @@ For more usage details please refer to the [binary classification demo](../binar
 
 Instructions
 ====
-The dataset for ranking demo is from LETOR04 MQ2008 fold1.
-Before running the examples, you need to get the data by running:
+The dataset for ranking demo is from LETOR04 MQ2008 fold1,
+You can use the following command to run the example
 
-```
-./wgetdata.sh
-```
-
-### Command Line
-Run the example:
-```
-./runexp.sh
-```
-
-### Python
-There are two ways of doing ranking in python.  
-
-Run the example using `xgboost.train`:
-```
-python rank.py
-```
-
-Run the example using `XGBRanker`:
-```
-python rank_sklearn.py
-```
+Get the data: ./wgetdata.sh
+Run the example: ./runexp.sh

demo/rank/rank.py

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

demo/rank/rank_sklearn.py

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

demo/rank/runexp.sh

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

demo/rank/wgetdata.sh

@@ -1,10 +1,4 @@
 #!/bin/bash
-wget https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.rar
+wget http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2008.rar
 unrar x MQ2008.rar
 mv -f MQ2008/Fold1/*.txt .
-
-python trans_data.py train.txt mq2008.train mq2008.train.group
-
-python trans_data.py test.txt mq2008.test mq2008.test.group
-
-python trans_data.py vali.txt mq2008.vali mq2008.vali.group

doc/R-package/discoverYourData.md

@@ -222,7 +222,7 @@ The code below is very usual. For more information, you can look at the document
 
 ```r
 bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 4,
-               eta = 1, nthread = 2, nrounds = 10,objective = "binary:logistic")
+               eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
 ```
 
 ```
@@ -244,7 +244,7 @@ A model which fits too well may [overfit](http://en.wikipedia.org/wiki/Overfitti
 
 > Here you can see the numbers decrease until line 7 and then increase.
 >
-> It probably means we are overfitting. To fix that I should reduce the number of rounds to `nrounds = 4`. I will let things like that because I don't really care for the purpose of this example :-)
+> It probably means we are overfitting. To fix that I should reduce the number of rounds to `nround = 4`. I will let things like that because I don't really care for the purpose of this example :-)
 
 Feature importance
 ------------------
@@ -448,7 +448,7 @@ train <- agaricus.train
 test <- agaricus.test
 
 #Random Forest™ - 1000 trees
-bst <- xgboost(data = train$data, label = train$label, max.depth = 4, num_parallel_tree = 1000, subsample = 0.5, colsample_bytree =0.5, nrounds = 1, objective = "binary:logistic")
+bst <- xgboost(data = train$data, label = train$label, max.depth = 4, num_parallel_tree = 1000, subsample = 0.5, colsample_bytree =0.5, nround = 1, objective = "binary:logistic")
 ```
 
 ```
@@ -457,7 +457,7 @@ bst <- xgboost(data = train$data, label = train$label, max.depth = 4, num_parall
 
 ```r
 #Boosting - 3 rounds
-bst <- xgboost(data = train$data, label = train$label, max.depth = 4, nrounds = 3, objective = "binary:logistic")
+bst <- xgboost(data = train$data, label = train$label, max.depth = 4, nround = 3, objective = "binary:logistic")
 ```
 
 ```

doc/R-package/xgboostPresentation.md

@@ -178,11 +178,11 @@ We will train decision tree model using the following parameters:
 * `objective = "binary:logistic"`: we will train a binary classification model ;
 * `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.
+* `nround = 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.
 
 
 ```r
-bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+bstSparse <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
 ```
 
 ```
@@ -200,7 +200,7 @@ Alternatively, you can put your dataset in a *dense* matrix, i.e. a basic **R**
 
 
 ```r
-bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
 ```
 
 ```
@@ -215,7 +215,7 @@ bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth
 
 ```r
 dtrain <- xgb.DMatrix(data = train$data, label = train$label)
-bstDMatrix <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+bstDMatrix <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
 ```
 
 ```
@@ -232,13 +232,13 @@ One of the simplest way to see the training progress is to set the `verbose` opt
 
 ```r
 # verbose = 0, no message
-bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
+bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic", verbose = 0)
 ```
 
 
 ```r
 # verbose = 1, print evaluation metric
-bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 1)
+bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic", verbose = 1)
 ```
 
 ```
@@ -249,7 +249,7 @@ bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2,
 
 ```r
 # verbose = 2, also print information about tree
-bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 2)
+bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic", verbose = 2)
 ```
 
 ```
@@ -372,7 +372,7 @@ For the purpose of this example, we use `watchlist` parameter. It is a list of `
 ```r
 watchlist <- list(train=dtrain, test=dtest)
 
-bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic")
+bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nround=2, watchlist=watchlist, objective = "binary:logistic")
 ```
 
 ```
@@ -380,7 +380,7 @@ bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nrounds=2, watchl
 ## [1]	train-error:0.022263	test-error:0.021726
 ```
 
-**XGBoost** has computed at each round the same average error metric than seen above (we set `nrounds` to 2, that is why we have two lines). Obviously, the `train-error` number is related to the training dataset (the one the algorithm learns from) and the `test-error` number to the test dataset.
+**XGBoost** has computed at each round the same average error metric than seen above (we set `nround` to 2, that is why we have two lines). Obviously, the `train-error` number is related to the training dataset (the one the algorithm learns from) and the `test-error` number to the test dataset.
 
 Both training and test error related metrics are very similar, and in some way, it makes sense: what we have learned from the training dataset matches the observations from the test dataset.
 
@@ -390,7 +390,7 @@ For a better understanding of the learning progression, you may want to have som
 
 
 ```r
-bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
+bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nthread = 2, nround=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
 ```
 
 ```
@@ -407,7 +407,7 @@ Until now, all the learnings we have performed were based on boosting trees. **X
 
 
 ```r
-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")
+bst <- xgb.train(data=dtrain, booster = "gblinear", max.depth=2, nthread = 2, nround=2, watchlist=watchlist, eval.metric = "error", eval.metric = "logloss", objective = "binary:logistic")
 ```
 
 ```
@@ -445,7 +445,7 @@ dtrain2 <- xgb.DMatrix("dtrain.buffer")
 ```
 
 ```r
-bst <- xgb.train(data=dtrain2, max.depth=2, eta=1, nthread = 2, nrounds=2, watchlist=watchlist, objective = "binary:logistic")
+bst <- xgb.train(data=dtrain2, max.depth=2, eta=1, nthread = 2, nround=2, watchlist=watchlist, objective = "binary:logistic")
 ```
 
 ```

doc/build.md

@@ -0,0 +1,377 @@
+Installation Guide
+==================
+
+This page gives instructions on how to build and install the xgboost package from
+scratch on various systems. It consists of two steps:
+
+1. First build the shared library from the C++ codes (`libxgboost.so` for Linux/OSX and `xgboost.dll` for Windows).
+   - Exception: for R-package installation please directly refer to the R package section.
+2. Then install the language packages (e.g. Python Package).
+
+***Important*** the newest version of xgboost uses submodule to maintain packages. So when you clone the repo, remember to use the recursive option as follows.
+```bash
+git clone --recursive https://github.com/dmlc/xgboost
+```
+For windows users who use github tools, you can open the git shell, and type the following command.
+```bash
+git submodule init
+git submodule update
+```
+
+Please refer to [Trouble Shooting Section](#trouble-shooting) first if you had any problem
+during installation. If the instructions do not work for you, please feel free
+to ask questions at [xgboost/issues](https://github.com/dmlc/xgboost/issues), or
+even better to send pull request if you can fix the problem.
+
+## Contents
+- [Build the Shared Library](#build-the-shared-library)
+  - [Building on Ubuntu/Debian](#building-on-ubuntu-debian)
+  - [Building on macOS](#building-on-macos)
+  - [Building on Windows](#building-on-windows)
+  - [Building with GPU support](#building-with-gpu-support)
+  - [Windows Binaries](#windows-binaries)
+  - [Customized Building](#customized-building)
+- [Python Package Installation](#python-package-installation)
+- [R Package Installation](#r-package-installation)
+- [Trouble Shooting](#trouble-shooting)
+
+## Build the Shared Library
+
+Our goal is to build the shared library:
+- On Linux/OSX the target library is `libxgboost.so`
+- On Windows the target library is `xgboost.dll`
+
+The minimal building requirement is
+
+- A recent c++ compiler supporting C++ 11 (g++-4.8 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
+
+```bash
+git clone --recursive https://github.com/dmlc/xgboost
+cd xgboost; make -j4
+```
+
+### Building on macOS
+
+**Install with pip - simple method**
+
+First, make sure you obtained *gcc-5* (newer version does not work with this method yet). Note: installation of `gcc` can take a while (~ 30 minutes)
+
+```bash
+brew install gcc5
+```
+
+You might need to run the following command with `sudo` if you run into some permission errors:
+
+```bash
+pip install xgboost
+```
+
+**Build from the source code - advanced method**
+
+First, obtain gcc-7.x.x with brew (https://brew.sh/) if you want multi-threaded version, otherwise, Clang is ok if OpenMP / multi-threaded is not required. Note: installation of `gcc` can take a while (~ 30 minutes)
+
+```bash
+brew install gcc
+```
+
+Now, clone the repository
+
+```bash
+git clone --recursive https://github.com/dmlc/xgboost
+cd xgboost; cp make/config.mk ./config.mk
+```
+
+Open config.mk and uncomment these two lines
+
+```config.mk
+export CC = gcc
+export CXX = g++
+```
+
+and replace these two lines into(5 or 6 or 7; depending on your gcc-version)
+
+```config.mk
+export CC = gcc-7
+export CXX = g++-7
+```
+
+To find your gcc version
+
+```bash
+gcc-version
+```
+
+and build using the following commands
+
+```bash
+make -j4
+```
+head over to `Python Package Installation` for the next steps
+
+### Building on Windows
+You need to first clone the xgboost repo with recursive option clone the submodules.
+If you are using github tools, you can open the git-shell, and type the following command.
+We recommend using [Git for Windows](https://git-for-windows.github.io/)
+because it brings a standard bash shell. This will highly ease the installation process.
+
+```bash
+git submodule init
+git submodule update
+```
+
+XGBoost support both build by MSVC or MinGW. Here is how you can build xgboost library using MinGW.
+
+After installing [Git for Windows](https://git-for-windows.github.io/), you should have a shortcut `Git Bash`.
+All the following steps are in the `Git Bash`.
+
+In MinGW, `make` command comes with the name `mingw32-make`. You can add the following line into the `.bashrc` file.
+```bash
+alias make='mingw32-make'
+```
+(On 64-bit Windows, you should get [mingw64](https://sourceforge.net/projects/mingw-w64/) instead.) Make sure
+that the path to MinGW is in the system PATH.
+
+To build with MinGW, type:
+
+```bash
+cp make/mingw64.mk config.mk; make -j4
+```
+
+To build with Visual Studio 2013 use cmake. Make sure you have a recent version of cmake added to your path and then from the xgboost directory:
+
+```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.
+
+Other versions of Visual Studio may work but are untested.
+
+### 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](#installing-r-package-with-gpu-support) for special instructions for R.
+
+An up-to-date version of the CUDA toolkit is required.
+
+From the command line on Linux starting from the xgboost directory:
+
+```bash
+$ mkdir build
+$ cd build
+$ cmake .. -DUSE_CUDA=ON
+$ make -j
+```
+**Windows requirements** for GPU build: only Visual C++ 2015 or 2013 with CUDA v8.0 were fully tested. Either install Visual C++ 2015 Build Tools separately, or as a part of Visual Studio 2015. If you already have Visual Studio 2017, the Visual C++ 2015 Toolchain componenet has to be installed using the VS 2017 Installer. Likely, you would need to use the VS2015 x64 Native Tools command prompt to run the cmake commands given below. In some situations, however, things run just fine from MSYS2 bash command line.
+
+On Windows, using cmake, see what options for Generators you have for cmake, and choose one with [arch] replaced by Win64:
+```bash
+cmake -help
+```
+Then run cmake as:
+```bash
+$ mkdir build
+$ cd build
+$ cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON
+```
+To speed up compilation, 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:
+```
+cmake --build . --target xgboost --config Release
+```
+If build seems to use only a single process, you might try to append an option like ` -- /m:6` to the above command.
+
+### Windows Binaries
+
+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. And you are good to follow the below instructions.
+
+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/)
+
+### Customized Building
+
+The configuration of xgboost can be modified by ```config.mk```
+- modify configuration on various distributed filesystem such as HDFS/Amazon S3/...
+- First copy [make/config.mk](../make/config.mk) to the project root, on which
+  any local modification will be ignored by git, then modify the according flags.
+
+
+
+## Python Package Installation
+
+The python package is located at [python-package](../python-package).
+There are several ways to install the package:
+
+1. Install system-widely, which requires root permission
+
+   ```bash
+   cd python-package; sudo python setup.py install
+   ```
+
+   You will however need Python `distutils` module for this to
+   work. It is often part of the core python package or it can be installed using your
+   package manager, e.g. in Debian use
+
+   ```bash
+   sudo apt-get install python-setuptools
+   ```
+
+   *NOTE: 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`.
+    It is ***recommended for developers*** who may change the codes. The changes will be immediately reflected once you pulled the code and rebuild the project (no need to call ```setup``` again)
+
+    ```bash
+    export PYTHONPATH=~/xgboost/python-package
+    ```
+
+3. Install only for the current user.
+
+    ```bash
+    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:
+
+    ```python
+    import os
+    os.environ['PATH'] = os.environ['PATH'] + ';C:\\Program Files\\mingw-w64\\x86_64-5.3.0-posix-seh-rt_v4-rev0\\mingw64\\bin'
+    ```
+
+## R Package Installation
+
+### Installing pre-packaged version
+
+You can install xgboost from CRAN just like any other R package:
+
+```r
+install.packages("xgboost")
+```
+
+Or you can install it from our weekly updated drat repo:
+
+```r
+install.packages("drat", repos="https://cran.rstudio.com")
+drat:::addRepo("dmlc")
+install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
+```
+
+For OSX users, single threaded version will be installed. To install multi-threaded version,
+first follow [Building on OSX](#building-on-osx) to get the OpenMP enabled compiler, then:
+
+- Set the `Makevars` file in highest piority for R.
+
+  The point is, there are three `Makevars` : `~/.R/Makevars`, `xgboost/R-package/src/Makevars`, and `/usr/local/Cellar/r/3.2.0/R.framework/Resources/etc/Makeconf` (the last one obtained by running `file.path(R.home("etc"), "Makeconf")` in R), and `SHLIB_OPENMP_CXXFLAGS` is not set by default!! After trying, it seems that the first one has highest piority (surprise!).
+
+  Then inside R, run
+
+  ```R
+  install.packages("drat", repos="https://cran.rstudio.com")
+  drat:::addRepo("dmlc")
+  install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
+  ```
+
+### Installing the development version
+
+Make sure you have installed git and a recent C++ compiler supporting C++11 (e.g., g++-4.8 or higher).
+On Windows, Rtools must be installed, and its bin directory has to be added to PATH during the installation.
+And see the previous subsection for an OSX tip.
+
+Due to the use of git-submodules, `devtools::install_github` can no longer be used to install the latest version of R package.
+Thus, one has to run git to check out the code first:
+
+```bash
+git clone --recursive https://github.com/dmlc/xgboost
+cd xgboost
+git submodule init
+git submodule update
+cd R-package
+R CMD INSTALL .
+```
+
+If the last line fails because of "R: command not found", it means that R was not set up to run from command line.
+In this case, just start R as you would normally do and run the following:
+
+```r
+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 the next section, but without GPU support (omit the `-DUSE_CUDA=ON` cmake parameter).
+
+If all fails, try [building the shared library](#build-the-shared-library) to see whether a problem is specific to R package or not.
+
+### Installing R package with GPU support
+
+The procedure and requirements are similar as in [Building with GPU support](#building-with-gpu-support), so make sure to read it first.
+
+On Linux, starting from the xgboost directory:
+
+```bash
+mkdir build
+cd build
+cmake .. -DUSE_CUDA=ON -DR_LIB=ON
+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`.
+
+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).
+```bash
+mkdir build
+cd build
+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`.
+
+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"`.
+
+If on Windows you get a "permission denied" error when trying to write to ...Program Files/R/... during the package installation, create a `.Rprofile` file in your personal home directory (if you don't already have one in there), and add a line to it which specifies the location of your R packages user library, like the following:
+```r
+.libPaths( unique(c("C:/Users/USERNAME/Documents/R/win-library/3.4", .libPaths())))
+```
+You might find the exact location by running `.libPaths()` in R GUI or RStudio.
+
+## Trouble Shooting
+
+1. **Compile failed after `git pull`**
+
+   Please first update the submodules, clean all and recompile:
+
+   ```bash
+   git submodule update && make clean_all && make -j4
+   ```
+
+2. **Compile failed after `config.mk` is modified**
+
+   Need to clean all first:
+
+    ```bash
+    make clean_all && make -j4
+    ```
+
+
+3. **Makefile: dmlc-core/make/dmlc.mk: No such file or directory**
+
+   We need to recursively clone the submodule, you can do:
+
+    ```bash
+    git submodule init
+    git submodule update
+    ```
+    Alternatively, do another clone
+    ```bash
+    git clone https://github.com/dmlc/xgboost --recursive
+    ```

doc/build.rst

@@ -4,17 +4,15 @@ Installation Guide
 
 .. note:: Pre-built binary wheel for Python
 
-  If you are planning to use Python, consider installing XGBoost from a pre-built binary wheel, available from Python Package Index (PyPI). You may download and install it by running
+  If you are planning to use Python on a Linux system, consider installing XGBoost from a pre-built binary wheel. The wheel is available from Python Package Index (PyPI). You may download and install it by running
 
   .. code-block:: bash
 
-    # Ensure that you are downloading one of the following:
-    #   * xgboost-{version}-py2.py3-none-manylinux1_x86_64.whl
-    #   * xgboost-{version}-py2.py3-none-win_amd64.whl
+    # Ensure that you are downloading xgboost-{version}-py2.py3-none-manylinux1_x86_64.whl
     pip3 install xgboost
 
-  * 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.
+  * This package will support GPU algorithms (`gpu_exact`, `gpu_hist`) on machines with NVIDIA GPUs.
+  * Currently, PyPI has a binary wheel only for 64-bit Linux.
 
 ****************************
 Building XGBoost from source
@@ -90,11 +88,11 @@ Building on OSX
 Install with pip: simple method
 --------------------------------
 
-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.
+First, make sure you obtained ``gcc-5`` (newer version does not work with this method yet). Note: installation of ``gcc`` can take a while (~ 30 minutes).
 
 .. code-block:: bash
 
-  brew install gcc@7
+  brew install gcc@5
 
 Then install XGBoost with ``pip``:
 
@@ -102,30 +100,42 @@ Then install XGBoost with ``pip``:
 
   pip3 install xgboost
 
-You might need to run the command with ``--user`` flag if you run into permission errors.
+You might need to run the command with ``sudo`` if you run into permission errors.
 
 Build from the source code - advanced method
 --------------------------------------------
 
-Obtain ``gcc-7`` from Homebrew:
+First, obtain ``gcc-7`` with homebrew (https://brew.sh/) if you want multi-threaded version. Clang is okay if multithreading is not required. Note: installation of ``gcc`` can take a while (~ 30 minutes).
 
 .. code-block:: bash
 
   brew install gcc@7
 
-Now clone the repository:
+Now, clone the repository:
 
 .. code-block:: bash
 
   git clone --recursive https://github.com/dmlc/xgboost
+  cd xgboost; cp make/config.mk ./config.mk
 
-Create the ``build/`` directory and invoke CMake. Make sure to add ``CC=gcc-7 CXX=g++-7`` so that Homebrew GCC is selected. After invoking CMake, you can build XGBoost with ``make``:
+Open ``config.mk`` and uncomment these two lines:
+
+.. code-block:: bash
+
+  export CC = gcc
+  export CXX = g++
+
+and replace these two lines as follows: (specify the GCC version)
+
+.. code-block:: bash
+
+  export CC = gcc-7
+  export CXX = g++-7
+
+Now, you may build XGBoost using the following command:
 
 .. code-block:: bash
 
-  mkdir build
-  cd build
-  CC=gcc-7 CXX=g++-7 cmake ..
   make -j4
 
 You may now continue to `Python Package Installation`_.
@@ -161,8 +171,6 @@ To build with MinGW, type:
 
   cp make/mingw64.mk config.mk; make -j4
 
-See :ref:`mingw_python` for buildilng XGBoost for Python.
-
 Compile XGBoost with Microsoft Visual Studio
 --------------------------------------------
 To build with Visual Studio, we will need CMake. Make sure to install a recent version of CMake. Then run the following from the root of the XGBoost directory:
@@ -179,33 +187,24 @@ After the build process successfully ends, you will find a ``xgboost.dll`` libra
 
 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.
 
-From the command line on Linux starting from the XGBoost directory:
+From the command line on Linux starting from the xgboost directory:
 
 .. code-block:: bash
 
   mkdir build
   cd build
   cmake .. -DUSE_CUDA=ON
-  make -j4
+  make -j
 
-.. note:: Enabling multi-GPU training
+.. note:: Windows requirements for GPU build
 
-  By default, multi-GPU training is disabled and only a single GPU will be used. To enable multi-GPU training, set the option ``USE_NCCL=ON``. Multi-GPU training depends on NCCL2, available at `this link <https://developer.nvidia.com/nccl>`_. Since NCCL2 is only available for Linux machines, **multi-GPU training is available only for Linux**.
-
-  .. code-block:: bash
-
-    mkdir build
-    cd build
-    cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2
-    make -j4
+  Only Visual C++ 2015 or 2013 with CUDA v8.0 were fully tested. Either install Visual C++ 2015 Build Tools separately, or as a part of Visual Studio 2015. If you already have Visual Studio 2017, the Visual C++ 2015 Toolchain componenet has to be installed using the VS 2017 Installer. Likely, you would need to use the VS2015 x64 Native Tools command prompt to run the cmake commands given below. In some situations, however, things run just fine from MSYS2 bash command line.
 
 On Windows, see what options for generators you have for CMake, and choose one with ``[arch]`` replaced with Win64:
 
@@ -248,12 +247,10 @@ The configuration file ``config.mk`` modifies several compilation flags:
 
 To customize, first copy ``make/config.mk`` to the project root and then modify the copy.
 
-Alternatively, use CMake.
-
 Python Package Installation
 ===========================
 
-The Python package is located at ``python-package/``.
+The python package is located at ``python-package/``.
 There are several ways to install the package:
 
 1. Install system-wide, which requires root permission:
@@ -263,7 +260,7 @@ There are several ways to install the package:
   cd python-package; sudo python setup.py install
 
 You will however need Python ``distutils`` module for this to
-work. It is often part of the core Python package or it can be installed using your
+work. It is often part of the core python package or it can be installed using your
 package manager, e.g. in Debian use
 
 .. code-block:: bash
@@ -274,7 +271,7 @@ 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
+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)
@@ -296,25 +293,6 @@ package manager, e.g. in Debian use
     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
---------------------------------------------------------------
-
-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 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 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.
-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
 ======================
 
@@ -327,13 +305,35 @@ You can install xgboost from CRAN just like any other R package:
 
   install.packages("xgboost")
 
-For OSX users, single-threaded version will be installed. So only one thread will be used for training. To enable use of multiple threads (and utilize capacity of multi-core CPUs), see the section :ref:`osx_multithread` to install XGBoost from source.
+Or you can install it from our weekly updated drat repo:
+
+.. code-block:: R
+
+  install.packages("drat", repos="https://cran.rstudio.com")
+  drat:::addRepo("dmlc")
+  install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
+
+For OSX users, single threaded version will be installed. To install multi-threaded version,
+first follow `Building on OSX`_ to get the OpenMP enabled compiler. Then
+
+- Set the ``Makevars`` file in highest piority for R.
+
+  The point is, there are three ``Makevars`` : ``~/.R/Makevars``, ``xgboost/R-package/src/Makevars``, and ``/usr/local/Cellar/r/3.2.0/R.framework/Resources/etc/Makeconf`` (the last one obtained by running ``file.path(R.home("etc"), "Makeconf")`` in R), and ``SHLIB_OPENMP_CXXFLAGS`` is not set by default!! After trying, it seems that the first one has highest piority (surprise!).
+
+  Then inside R, run
+
+.. code-block:: R
+
+  install.packages("drat", repos="https://cran.rstudio.com")
+  drat:::addRepo("dmlc")
+  install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
 
 Installing the development version
 ----------------------------------
 
 Make sure you have installed git and a recent C++ compiler supporting C++11 (e.g., g++-4.8 or higher).
-On Windows, Rtools must be installed, and its bin directory has to be added to ``PATH`` during the installation.
+On Windows, Rtools must be installed, and its bin directory has to be added to PATH during the installation.
+And see the previous subsection for an OSX tip.
 
 Due to the use of git-submodules, ``devtools::install_github`` can no longer be used to install the latest version of R package.
 Thus, one has to run git to check out the code first:
@@ -359,33 +359,6 @@ The package could also be built and installed with cmake (and Visual C++ 2015 on
 
 If all fails, try `Building the shared library`_ to see whether a problem is specific to R package or not.
 
-.. _osx_multithread:
-
-Installing R package on Mac OSX with multi-threading
-----------------------------------------------------
-
-First, obtain ``gcc-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@7
-
-Now, clone the repository:
-
-.. code-block:: bash
-
-  git clone --recursive https://github.com/dmlc/xgboost
-
-Create the ``build/`` directory and invoke CMake with option ``R_LIB=ON``. Make sure to add ``CC=gcc-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
-
-  mkdir build
-  cd build
-  CC=gcc-7 CXX=g++-7 cmake .. -DR_LIB=ON
-  make -j4
-  make install
-
 Installing R package with GPU support
 -------------------------------------
 
@@ -403,7 +376,7 @@ On Linux, starting from the XGBoost directory type:
 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``.
 
-On Windows, CMake with Visual C++ Build Tools (or Visual Studio) has to be used to build an R package with GPU support. Rtools must also be installed (perhaps, some other MinGW distributions with ``gendef.exe`` and ``dlltool.exe`` would work, but that was not tested).
+On Windows, cmake with Visual C++ Build Tools (or Visual Studio) has to be used to build an R package with GPU support. Rtools must also be installed (perhaps, some other MinGW distributions with ``gendef.exe`` and ``dlltool.exe`` would work, but that was not tested).
 
 .. code-block:: bash
 

doc/conf.py

@@ -41,7 +41,7 @@ sys.path.insert(0, curr_path)
 
 # -- mock out modules
 import mock
-MOCK_MODULES = ['scipy', 'scipy.sparse', 'sklearn', 'pandas']
+MOCK_MODULES = ['numpy', 'scipy', 'scipy.sparse', 'sklearn', 'matplotlib', 'pandas', 'graphviz']
 for mod_name in MOCK_MODULES:
   sys.modules[mod_name] = mock.Mock()
 
@@ -62,7 +62,6 @@ release = xgboost.__version__
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
 extensions = [
-    'matplotlib.sphinxext.plot_directive',
     'sphinx.ext.autodoc',
     'sphinx.ext.napoleon',
     'sphinx.ext.mathjax',
@@ -70,11 +69,6 @@ extensions = [
     'breathe'
 ]
 
-graphviz_output_format = 'png'
-plot_formats = [('svg', 300), ('png', 100), ('hires.png', 300)]
-plot_html_show_source_link = False
-plot_html_show_formats = False
-
 # Breathe extension variables
 breathe_projects = {"xgboost": "doxyxml/"}
 breathe_default_project = "xgboost"
@@ -156,7 +150,7 @@ extensions.append("guzzle_sphinx_theme")
 # Guzzle theme options (see theme.conf for more information)
 html_theme_options = {
     # Set the name of the project to appear in the sidebar
-    "project_nav_name": "XGBoost"
+    "project_nav_name": "XGBoost (0.72)"
 }
 
 html_sidebars = {

doc/contribute.rst

@@ -18,7 +18,6 @@ Everyone is more than welcome to contribute. It is a way to make the project bet
 
 * `Documents`_
 * `Testcases`_
-* `Sanitizers`_
 * `Examples`_
 * `Core Library`_
 * `Python Package`_
@@ -122,54 +121,6 @@ Testcases
 * All the testcases are in `tests <https://github.com/dmlc/xgboost/tree/master/tests>`_.
 * We use python nose for python test cases.
 
-**********
-Sanitizers
-**********
-
-By default, sanitizers are bundled in GCC and Clang/LLVM. One can enable
-sanitizers with GCC >= 4.8 or LLVM >= 3.1, But some distributions might package
-sanitizers separately.  Here is a list of supported sanitizers with
-corresponding library names:
-
-- Address sanitizer: libasan
-- Leak sanitizer:    liblsan
-- Thread sanitizer:  libtsan
-
-Memory sanitizer is exclusive to LLVM, hence not supported in XGBoost.
-
-How to build XGBoost with sanitizers
-====================================
-One can build XGBoost with sanitizer support by specifying -DUSE_SANITIZER=ON.
-By default, address sanitizer and leak sanitizer are used when you turn the
-USE_SANITIZER flag on.  You can always change the default by providing a
-semicolon separated list of sanitizers to ENABLED_SANITIZERS.  Note that thread
-sanitizer is not compatible with the other two sanitizers.
-
-  .. code-block:: bash
-
-    cmake -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak" /path/to/xgboost
-
-By default, CMake will search regular system paths for sanitizers, you can also
-supply a specified SANITIZER_PATH.
-
-  .. code-block:: bash
-
-    cmake -DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak" \
-    -DSANITIZER_PATH=/path/to/sanitizers /path/to/xgboost
-
-How to use sanitizers with CUDA support
-=======================================
-Runing XGBoost on CUDA with address sanitizer (asan) will raise memory error.
-To use asan with CUDA correctly, you need to configure asan via ASAN_OPTIONS
-environment variable:
-
-  .. code-block:: bash
-
-    ASAN_OPTIONS=protect_shadow_gap=0 ../testxgboost
-
-For details, please consult `official documentation <https://github.com/google/sanitizers/wiki>`_ for sanitizers.
-
-
 ********
 Examples
 ********

doc/get_started.rst

@@ -42,7 +42,7 @@ R
   train <- agaricus.train
   test <- agaricus.test
   # fit model
-  bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nrounds = 2,
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
                  nthread = 2, objective = "binary:logistic")
   # predict
   pred <- predict(bst, test$data)

doc/get_started/index.md

@@ -0,0 +1,79 @@
+# Get Started with XGBoost
+
+This is a quick start tutorial showing snippets for you to quickly try out xgboost
+on the demo dataset on a binary classification task.
+
+## Links to Helpful Other Resources
+- See [Installation Guide](../build.md) on how to install xgboost.
+- See [How to pages](../how_to/index.md) on various tips on using xgboost.
+- See [Tutorials](../tutorials/index.md) on tutorials on specific tasks.
+- See [Learning to use XGBoost by Examples](../../demo) for more code examples.
+
+## Python
+```python
+import xgboost as xgb
+# read in data
+dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
+dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
+# specify parameters via map
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+num_round = 2
+bst = xgb.train(param, dtrain, num_round)
+# make prediction
+preds = bst.predict(dtest)
+```
+
+## R
+
+```r
+# load data
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+train <- agaricus.train
+test <- agaricus.test
+# fit model
+bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
+               nthread = 2, objective = "binary:logistic")
+# predict
+pred <- predict(bst, test$data)
+```
+
+## Julia
+```julia
+using XGBoost
+# read data
+train_X, train_Y = readlibsvm("demo/data/agaricus.txt.train", (6513, 126))
+test_X, test_Y = readlibsvm("demo/data/agaricus.txt.test", (1611, 126))
+# fit model
+num_round = 2
+bst = xgboost(train_X, num_round, label=train_Y, eta=1, max_depth=2)
+# predict
+pred = predict(bst, test_X)
+```
+
+## Scala
+```scala
+import ml.dmlc.xgboost4j.scala.DMatrix
+import ml.dmlc.xgboost4j.scala.XGBoost
+
+object XGBoostScalaExample {
+  def main(args: Array[String]) {
+    // read trainining data, available at xgboost/demo/data
+    val trainData =
+      new DMatrix("/path/to/agaricus.txt.train")
+    // define parameters
+    val paramMap = List(
+      "eta" -> 0.1,
+      "max_depth" -> 2,
+      "objective" -> "binary:logistic").toMap
+    // number of iterations
+    val round = 2
+    // train the model
+    val model = XGBoost.train(trainData, paramMap, round)
+    // run prediction
+    val predTrain = model.predict(trainData)
+    // save model to the file.
+    model.saveModel("/local/path/to/model")
+  }
+}
+```

doc/gpu/index.rst

@@ -14,7 +14,7 @@ To install GPU support, checkout the :doc:`/build`.
 *********************************************
 CUDA Accelerated Tree Construction Algorithms
 *********************************************
-Tree construction (training) and prediction can be accelerated with CUDA-capable GPUs.
+This plugin adds GPU accelerated tree construction and prediction algorithms to XGBoost.
 
 Usage
 =====
@@ -65,11 +65,7 @@ The device ordinal can be selected using the ``gpu_id`` parameter, which default
 
 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
-
-  Default installation may not enable multi-GPU training. To use multiple GPUs, make sure to read :ref:`build_gpu_support`.
-
-The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/build` for details.
+This plugin currently works with the CLI, python and R - see :doc:`/build` for details.
 
 .. code-block:: python
   :caption: Python example

doc/how_to/index.md

@@ -0,0 +1,17 @@
+# XGBoost How To
+
+This page contains guidelines to use and develop XGBoost.
+
+## Installation
+- [How to Install XGBoost](../build.md)
+
+## Use XGBoost in Specific Ways
+- [Parameter tuning guide](param_tuning.md)
+- [Use out of core computation for large dataset](external_memory.md)
+- [Use XGBoost GPU algorithms](../gpu/index.md)
+
+## Develop and Hack XGBoost
+- [Contribute to XGBoost](contribute.md)
+
+## Frequently Ask Questions
+- [FAQ](../faq.md)

doc/input_format.md

@@ -0,0 +1,56 @@
+Text Input Format of DMatrix
+============================
+
+## Basic Input Format
+As we have mentioned, XGBoost takes LibSVM format. For training or predicting, XGBoost takes an instance file with the format as below:
+
+train.txt
+```
+1 101:1.2 102:0.03
+0 1:2.1 10001:300 10002:400
+0 0:1.3 1:0.3
+1 0:0.01 1:0.3
+0 0:0.2 1:0.3
+```
+Each line represent a single instance, and in the first line '1' is the instance label,'101' and '102' are feature indices, '1.2' and '0.03' are feature values. In the binary classification case, '1' is used to indicate positive samples, and '0' is used to indicate negative samples. We also support probability values in [0,1] as label, to indicate the probability of the instance being positive.
+
+Additional Information
+----------------------
+Note: these additional information are only applicable to single machine version of the package.
+
+### Group Input Format
+As XGBoost supports accomplishing [ranking task](../demo/rank), we support the group input format. In ranking task, instances are categorized into different groups in real world scenarios, for example, in the learning to rank web pages scenario, the web page instances are grouped by their queries. Except the instance file mentioned in the group input format, XGBoost need an file indicating the group information. For example, if the instance file is the "train.txt" shown above,
+and the group file is as below:
+
+train.txt.group
+```
+2
+3
+```
+This means that, the data set contains 5 instances, and the first two instances are in a group and the other three are in another group. The numbers in the group file are actually indicating the number of instances in each group in the instance file in order.
+While configuration, you do not have to indicate the path of the group file. If the instance file name is "xxx", XGBoost will check whether there is a file named "xxx.group" in the same directory and decides whether to read the data as group input format.
+
+### Instance Weight File
+XGBoost supports providing each instance an weight to differentiate the importance of instances. For example, if we provide an instance weight file for the "train.txt" file in the example as below:
+
+train.txt.weight
+```
+1
+0.5
+0.5
+1
+0.5
+```
+It means that XGBoost will emphasize more on the first and fourth instance， that is to say positive instances while training.
+The configuration is similar to configuring the group information. If the instance file name is "xxx", XGBoost will check whether there is a file named "xxx.weight" in the same directory and if there is, will use the weights while training models. Weights will be included into an "xxx.buffer" file that is created by XGBoost automatically. If you want to update the weights, you need to delete the "xxx.buffer" file prior to launching XGBoost.
+
+### Initial Margin file
+XGBoost supports providing each instance an initial margin prediction. For example, if we have a initial prediction using logistic regression for "train.txt" file, we can create the following file:
+
+train.txt.base_margin
+```
+-0.4
+1.0
+3.4
+```
+XGBoost will take these values as initial margin prediction and boost from that. An important note about base_margin is that it should be margin prediction before transformation, so if you are doing logistic loss, you will need to put in value before logistic transformation. If you are using XGBoost predictor, use pred_margin=1 to output margin values.

doc/jvm/index.rst

@@ -58,11 +58,10 @@ For sbt, please add the repository and dependency in build.sbt as following:
 
 If you want to use XGBoost4J-Spark, replace ``xgboost4j`` with ``xgboost4j-spark``.
 
-.. note:: XGBoost4J-Spark requires Apache Spark 2.3+
+.. note:: Spark 2.0 Required
 
-  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.
+  After integrating with Dataframe/Dataset APIs of Spark 2.0, XGBoost4J-Spark only supports compile with Spark 2.x. You can build XGBoost4J-Spark as a component of XGBoost4J by running ``mvn package``, and you can specify the version of spark with ``mvn -Dspark.version=2.0.0 package``.   (To continue working with Spark 1.x, the users are supposed to update pom.xml by modifying the properties like ``spark.version``, ``scala.version``, and ``scala.binary.version``. Users also need to change the implementation by replacing ``SparkSession`` with ``SQLContext`` and the type of API parameters from ``Dataset[_]`` to ``Dataframe``)
 
-  Also, make sure to install Spark directly from `Apache website <https://spark.apache.org/>`_. **Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark.** Consult appropriate third parties to obtain their distribution of XGBoost.
 
 Installation from maven repo
 ============================
@@ -149,7 +148,6 @@ Contents
   :maxdepth: 2
 
   java_intro
-  XGBoost4J-Spark Tutorial <xgboost4j_spark_tutorial>
   Code Examples <https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example>
   XGBoost4J Java API <javadocs/index>
   XGBoost4J Scala API <scaladocs/xgboost4j/index>

doc/jvm/xgboost4j_spark_tutorial.rst

@@ -1,530 +0,0 @@
-#######################################
-XGBoost4J-Spark Tutorial (version 0.8+)
-#######################################
-
-**XGBoost4J-Spark** is a project aiming to seamlessly integrate XGBoost and Apache Spark by fitting XGBoost to Apache Spark's MLLIB framework. With the integration, user can not only uses the high-performant algorithm implementation of XGBoost, but also leverages the powerful  data processing engine of Spark for:
-
-* Feature Engineering: feature extraction, transformation, dimensionality reduction, and selection, etc.
-* Pipelines: constructing, evaluating, and tuning ML Pipelines
-* Persistence: persist and load machine learning models and even whole Pipelines
-
-This tutorial is to cover the end-to-end process to build a machine learning pipeline with XGBoost4J-Spark. We will discuss
-
-* Using Spark to preprocess data to fit to XGBoost/XGBoost4J-Spark's data interface
-* Training a XGBoost model with XGBoost4J-Spark
-* Serving XGBoost model (prediction) with Spark
-* Building a Machine Learning Pipeline with XGBoost4J-Spark
-* Running XGBoost4J-Spark in Production
-
-.. contents::
-  :backlinks: none
-  :local:
-
-********************************************
-Build an ML Application with XGBoost4J-Spark
-********************************************
-
-Refer to XGBoost4J-Spark Dependency
-===================================
-
-Before we go into the tour of how to use XGBoost4J-Spark, we would bring a brief introduction about how to build a machine learning application with XGBoost4J-Spark. The first thing you need to do is to refer to the dependency in Maven Central.
-
-You can add the following dependency in your ``pom.xml``.
-
-.. code-block:: xml
-
-  <dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j-spark</artifactId>
-    <version>latest_version_num</version>
-  </dependency>
-
-For the latest release version number, please check `here <https://github.com/dmlc/xgboost/releases>`_.
-
-We also publish some functionalities which would be included in the coming release in the form of snapshot version. To access these functionalities, you can add dependency to the snapshot artifacts. We publish snapshot version in github-based repo, so you can add the following repo in ``pom.xml``:
-
-.. code-block:: xml
-
-  <repository>
-    <id>XGBoost4J-Spark Snapshot Repo</id>
-    <name>XGBoost4J-Spark Snapshot Repo</name>
-    <url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</url>
-  </repository>
-
-and then refer to the snapshot dependency by adding:
-
-.. code-block:: xml
-
-  <dependency>
-      <groupId>ml.dmlc</groupId>
-      <artifactId>xgboost4j</artifactId>
-      <version>next_version_num-SNAPSHOT</version>
-  </dependency>
-
-.. note:: XGBoost4J-Spark requires Apache Spark 2.3+
-
-  XGBoost4J-Spark now requires **Apache Spark 2.3+**. Latest versions of XGBoost4J-Spark uses facilities of `org.apache.spark.ml.param.shared` extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.
-
-  Also, make sure to install Spark directly from `Apache website <https://spark.apache.org/>`_. **Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark.** Consult appropriate third parties to obtain their distribution of XGBoost.
-
-Installation from maven repo
-
-.. note:: Use of Python in XGBoost4J-Spark
-
-  By default, we use the tracker in `dmlc-core <https://github.com/dmlc/dmlc-core/tree/master/tracker>`_ to drive the training with XGBoost4J-Spark. It requires Python 2.7+. We also have an experimental Scala version of tracker which can be enabled by passing the parameter ``tracker_conf`` as ``scala``.
-
-Data Preparation
-================
-
-As aforementioned, XGBoost4J-Spark seamlessly integrates Spark and XGBoost. The integration enables
-users to apply various types of transformation over the training/test datasets with the convenient
-and powerful data processing framework, Spark.
-
-In this section, we use `Iris <https://archive.ics.uci.edu/ml/datasets/iris>`_ dataset as an example to
-showcase how we use Spark to transform raw dataset and make it fit to the data interface of XGBoost.
-
-Iris dataset is shipped in CSV format. Each instance contains 4 features, "sepal length", "sepal width",
-"petal length" and "petal width". In addition, it contains the "class" columnm, which is essentially the label with three possible values: "Iris Setosa", "Iris Versicolour" and "Iris Virginica".
-
-Read Dataset with Spark's Built-In Reader
------------------------------------------
-
-The first thing in data transformation is to load the dataset as Spark's structured data abstraction, DataFrame.
-
-.. code-block:: scala
-
-  import org.apache.spark.sql.SparkSession
-  import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
-
-  val spark = SparkSession.builder().getOrCreate()
-  val schema = new StructType(Array(
-    StructField("sepal length", DoubleType, true),
-    StructField("sepal width", DoubleType, true),
-    StructField("petal length", DoubleType, true),
-    StructField("petal width", DoubleType, true),
-    StructField("class", StringType, true)))
-  val rawInput = spark.read.schema(schema).csv("input_path")
-
-At the first line, we create a instance of `SparkSession <http://spark.apache.org/docs/latest/sql-programming-guide.html#starting-point-sparksession>`_ which is the entry of any Spark program working with DataFrame. The ``schema`` variable defines the schema of DataFrame wrapping Iris data. With this explicitly set schema, we can define the columns' name as well as their types; otherwise the column name would be the default ones derived by Spark, such as ``_col0``, etc. Finally, we can use Spark's built-in csv reader to load Iris csv file as a DataFrame named ``rawInput``.
-
-Spark also contains many built-in readers for other format. The latest version of Spark supports CSV, JSON, Parquet, and LIBSVM.
-
-Transform Raw Iris Dataset
---------------------------
-
-To make Iris dataset be recognizable to XGBoost, we need to
-
-1. Transform String-typed label, i.e. "class", to Double-typed label.
-2. Assemble the feature columns as a vector to fit to the data interface of Spark ML framework.
-
-To convert String-typed label to Double, we can use Spark's built-in feature transformer `StringIndexer <https://spark.apache.org/docs/2.3.1/api/scala/index.html#org.apache.spark.ml.feature.StringIndexer>`_.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.feature.StringIndexer
-  val stringIndexer = new StringIndexer().
-    setInputCol("class").
-    setOutputCol("classIndex").
-    fit(rawInput)
-  val labelTransformed = stringIndexer.transform(rawInput).drop("class")
-
-With a newly created StringIndexer instance:
-
-1. we set input column, i.e. the column containing String-typed label
-2. we set output column, i.e. the column to contain the Double-typed label.
-3. Then we ``fit`` StringIndex with our input DataFrame ``rawInput``, so that Spark internals can get information like total number of distinct values, etc.
-
-Now we have a StringIndexer which is ready to be applied to our input DataFrame. To execute the transformation logic of StringIndexer, we ``transform`` the input DataFrame ``rawInput`` and to keep a concise DataFrame,
-we drop the column "class" and only keeps the feature columns and the transformed Double-typed label column (in the last line of the above code snippet).
-
-The ``fit`` and ``transform`` are two key operations in MLLIB. Basically, ``fit`` produces a "transformer", e.g. StringIndexer, and each transformer applies ``transform`` method on DataFrame to add new column(s) containing transformed features/labels or prediction results, etc. To understand more about ``fit`` and ``transform``, You can find more details in `here <http://spark.apache.org/docs/latest/ml-pipeline.html#pipeline-components>`_.
-
-Similarly, we can use another transformer, `VectorAssembler <https://spark.apache.org/docs/2.3.1/api/scala/index.html#org.apache.spark.ml.feature.VectorAssembler>`_, to assemble feature columns "sepal length", "sepal width", "petal length" and "petal width" as a vector.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.feature.VectorAssembler
-  val vectorAssembler = new VectorAssembler().
-    setInputCols(Array("sepal length", "sepal width", "petal length", "petal width")).
-    setOutputCol("features")
-  val xgbInput = vectorAssembler.transform(labelTransformed).select("features", "classIndex")
-
-Now, we have a DataFrame containing only two columns, "features" which contains vector-represented
-"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.
-
-Training
-========
-
-XGBoost supports both regression and classification. While we use Iris dataset in this tutorial to show how we use XGBoost/XGBoost4J-Spark to resolve a multi-classes classification problem, the usage in Regression is very similar to classification.
-
-To train a XGBoost model for classification, we need to claim a XGBoostClassifier first:
-
-.. code-block:: scala
-
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
-  val xgbParam = Map("eta" -> 0.1f,
-        "max_depth" -> 2,
-        "objective" -> "multi:softprob",
-        "num_class" -> 3,
-        "num_round" -> 100,
-        "num_workers" -> 2)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-        setFeaturesCol("features").
-        setLabelCol("classIndex")
-
-The available parameters for training a XGBoost model can be found in :doc:`here </parameter>`. In XGBoost4J-Spark, we support not only the default set of parameters but also the camel-case variant of these parameters to keep consistent with Spark's MLLIB parameters.
-
-Specifically, each parameter in :doc:`this page </parameter>` has its
-equivalent form in XGBoost4J-Spark with camel case. For example, to set ``max_depth`` for each tree, you can pass parameter just like what we did in the above code snippet (as ``max_depth`` wrapped in a Map), or you can do it through setters in XGBoostClassifer:
-
-.. code-block:: scala
-
-  val xgbClassifier = new XGBoostClassifier().
-    setFeaturesCol("features").
-    setLabelCol("classIndex")
-  xgbClassifier.setMaxDepth(2)
-
-After we set XGBoostClassifier parameters and feature/label column, we can build a transformer, XGBoostClassificationModel by fitting XGBoostClassifier with the input DataFrame. This ``fit`` operation is essentially the training process and the generated model can then be used in prediction.
-
-.. code-block:: scala
-
-  val xgbClassificationModel = xgbClassifier.fit(xgbInput)
-
-Early Stopping
-----------------
-
-Early stopping is a feature to prevent the unnecessary training iterations. By specifying ``num_early_stopping_rounds`` or directly call ``setNumEarlyStoppingRounds`` over a XGBoostClassifier or XGBoostRegressor, we can define number of rounds for the evaluation metric going to the unexpected direction to tolerate before stopping the training.
-
-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.
-
-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
-==========
-
-XGBoost4j-Spark supports two ways for model serving: batch prediction and single instance prediction.
-
-Batch Prediction
-----------------
-
-When we get a model, either XGBoostClassificationModel or XGBoostRegressionModel, it takes a DataFrame, read the column containing feature vectors, predict for each feature vector, and output a new DataFrame with the following columns by default:
-
-* XGBoostClassificationModel will output margins (``rawPredictionCol``), probabilities(``probabilityCol``) and the eventual prediction labels (``predictionCol``) for each possible label.
-* XGBoostRegressionModel will output prediction label(``predictionCol``).
-
-Batch prediction expects the user to pass the testset in the form of a DataFrame. XGBoost4J-Spark starts a XGBoost worker for each partition of DataFrame for parallel prediction and generates prediction results for the whole DataFrame in a batch.
-
-.. code-block:: scala
-
-  val xgbClassificationModel = xgbClassifier.fit(xgbInput)
-  val results = xgbClassificationModel.transform(testSet)
-
-With the above code snippet, we get a result DataFrame, result containing margin, probability for each class and the prediction for each instance
-
-.. code-block:: none
-
-  +-----------------+----------+--------------------+--------------------+----------+
-  |         features|classIndex|       rawPrediction|         probability|prediction|
-  +-----------------+----------+--------------------+--------------------+----------+
-  |[5.1,3.5,1.4,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[4.9,3.0,1.4,0.2]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[4.7,3.2,1.3,0.2]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
-  |[4.6,3.1,1.5,0.2]|       0.0|[3.45569849014282...|[0.99636095762252...|       0.0|
-  |[5.0,3.6,1.4,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[5.4,3.9,1.7,0.4]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
-  |[4.6,3.4,1.4,0.3]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
-  |[5.0,3.4,1.5,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[4.4,2.9,1.4,0.2]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[4.9,3.1,1.5,0.1]|       0.0|[3.45569849014282...|[0.99636095762252...|       0.0|
-  |[5.4,3.7,1.5,0.2]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
-  |[4.8,3.4,1.6,0.2]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
-  |[4.8,3.0,1.4,0.1]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[4.3,3.0,1.1,0.1]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
-  |[5.8,4.0,1.2,0.2]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
-  |[5.7,4.4,1.5,0.4]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
-  |[5.4,3.9,1.3,0.4]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
-  |[5.1,3.5,1.4,0.3]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  |[5.7,3.8,1.7,0.3]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
-  |[5.1,3.8,1.5,0.3]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
-  +-----------------+----------+--------------------+--------------------+----------+
-
-Single instance prediction
---------------------------
-
-XGBoostClassificationModel or XGBoostRegressionModel support make prediction on single instance as well.
-It accepts a single Vector as feature, and output the prediction label.
-
-However, the overhead of single-instance prediction is high due to the internal overhead of XGBoost, use it carefully!
-
-.. code-block:: scala
-
-  val features = xgbInput.head().getAs[Vector]("features")
-  val result = xgbClassificationModel.predict(features)
-
-Model Persistence
-=================
-
-Model and pipeline persistence
-------------------------------
-
-A data scientist produces an ML model and hands it over to an engineering team for deployment in a production environment. Reversely, a trained model may be used by data scientists, for example as a baseline, across the process of data exploration. So it's important to support model persistence to make the models available across usage scenarios and programming languages.
-
-XGBoost4j-Spark supports saving and loading XGBoostClassifier/XGBoostClassificationModel and XGBoostRegressor/XGBoostRegressionModel. It also supports saving and loading a ML pipeline which includes these estimators and models.
-
-We can save the XGBoostClassificationModel to file system:
-
-.. code-block:: scala
-
-  val xgbClassificationModelPath = "/tmp/xgbClassificationModel"
-  xgbClassificationModel.write.overwrite().save(xgbClassificationModelPath)
-
-and then loading the model in another session:
-
-.. code-block:: scala
-
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel
-
-  val xgbClassificationModel2 = XGBoostClassificationModel.load(xgbClassificationModelPath)
-  xgbClassificationModel2.transform(xgbInput)
-
-With regards to ML pipeline save and load, please refer the next section.
-
-Interact with Other Bindings of XGBoost
----------------------------------------
-After we train a model with XGBoost4j-Spark on massive dataset, sometimes we want to do model serving in single machine or integrate it with other single node libraries for further processing. XGBoost4j-Spark supports export model to local by:
-
-.. code-block:: scala
-
-  val nativeModelPath = "/tmp/nativeModel"
-  xgbClassificationModel.nativeBooster.saveModel(nativeModelPath)
-
-Then we can load this model with single node Python XGBoost:
-
-.. code-block:: python
-
-  import xgboost as xgb
-  bst = xgb.Booster({'nthread': 4})
-  bst.load_model(nativeModelPath)
-
-.. note:: Using HDFS and S3 for exporting the models with nativeBooster.saveModel()
-
-  When interacting with other language bindings, XGBoost also supports saving-models-to and loading-models-from file systems other than the local one. You can use HDFS and S3 by prefixing the path with ``hdfs://`` and ``s3://`` respectively. However, for this capability, you must do **one** of the following:
-
-  1. Build XGBoost4J-Spark with the steps described in `here <https://xgboost.readthedocs.io/en/latest/jvm/index.html#installation-from-source>`_, but turning `USE_HDFS <https://github.com/dmlc/xgboost/blob/e939192978a0c152ad7b49b744630e99d54cffa8/jvm-packages/create_jni.py#L18>`_ (or USE_S3, etc. in the same place) switch on. With this approach, you can reuse the above code example by replacing "nativeModelPath" with a HDFS path.
-
-     - However, if you build with USE_HDFS, etc. you have to ensure that the involved shared object file, e.g. libhdfs.so, is put in the LIBRARY_PATH of your cluster. To avoid the complicated cluster environment configuration, choose the other option.
-
-  2. Use bindings of HDFS, S3, etc. to pass model files around. Here are the steps (taking HDFS as an example):
-
-     - Create a new file with
-
-       .. code-block:: scala
-
-         val outputStream = fs.create("hdfs_path")
-
-       where "fs" is an instance of `org.apache.hadoop.fs.FileSystem <https://hadoop.apache.org/docs/stable/api/org/apache/hadoop/fs/FileSystem.html>`_ class in Hadoop.
-
-     - Pass the returned OutputStream in the first step to nativeBooster.saveModel():
-
-       .. code-block:: scala
-
-         xgbClassificationModel.nativeBooster.saveModel(outputStream)
-
-     - Download file in other languages from HDFS and load with the pre-built (without the requirement of libhdfs.so) version of XGBoost. (The function "download_from_hdfs" is a helper function to be implemented by the user)
-
-       .. code-block:: python
-
-         import xgboost as xgb
-         bst = xgb.Booster({'nthread': 4})
-         local_path = download_from_hdfs("hdfs_path")
-         bst.load_model(local_path)
-
-.. note:: Consistency issue between XGBoost4J-Spark and other bindings
-
-  There is a consistency issue between XGBoost4J-Spark and other language bindings of XGBoost.
-
-  When users use Spark to load training/test data in LIBSVM format with the following code snippet:
-
-  .. code-block:: scala
-
-    spark.read.format("libsvm").load("trainingset_libsvm")
-
-  Spark assumes that the dataset is using 1-based indexing (feature indices staring with 1). However, when you do prediction with other bindings of XGBoost (e.g. Python API of XGBoost), XGBoost assumes that the dataset is using 0-based indexing (feature indices starting with 0) by default. It creates a pitfall for the users who train model with Spark but predict with the dataset in the same format in other bindings of XGBoost. The solution is to transform the dataset to 0-based indexing before you predict with, for example, Python API, or you append ``?indexing_mode=1`` to your file path when loading with DMatirx. For example in Python:
-
-  .. code-block:: python
-
-    xgb.DMatrix('test.libsvm?indexing_mode=1')
-
-*******************************************
-Building a ML Pipeline with XGBoost4J-Spark
-*******************************************
-
-Basic ML Pipeline
-=================
-
-Spark ML pipeline can combine multiple algorithms or functions into a single pipeline.
-It covers from feature extraction, transformation, selection to model training and prediction.
-XGBoost4j-Spark makes it feasible to embed XGBoost into such a pipeline seamlessly.
-The following example shows how to build such a pipeline consisting of Spark MLlib feature transformer
-and XGBoostClassifier estimator.
-
-We still use `Iris <https://archive.ics.uci.edu/ml/datasets/iris>`_ dataset and the ``rawInput`` DataFrame.
-First we need to split the dataset into training and test dataset.
-
-.. code-block:: scala
-
-  val Array(training, test) = rawInput.randomSplit(Array(0.8, 0.2), 123)
-
-The we build the ML pipeline which includes 4 stages:
-
-* Assemble all features into a single vector column.
-* From string label to indexed double label.
-* Use XGBoostClassifier to train classification model.
-* Convert indexed double label back to original string label.
-
-We have shown the first three steps in the earlier sections, and the last step is finished with a new transformer `IndexToString <https://spark.apache.org/docs/2.3.1/api/scala/index.html#org.apache.spark.ml.feature.IndexToString>`_:
-
-.. code-block:: scala
-
-	val labelConverter = new IndexToString()
-        .setInputCol("prediction")
-        .setOutputCol("realLabel")
-        .setLabels(stringIndexer.labels)
-
-We need to organize these steps as a Pipeline in Spark ML framework and evaluate the whole pipeline to get a PipelineModel:
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.feature._
-  import org.apache.spark.ml.Pipeline
-
-  val pipeline = new Pipeline()
-      .setStages(Array(assembler, stringIndexer, booster, labelConverter))
-  val model = pipeline.fit(training)
-
-After we get the PipelineModel, we can make prediction on the test dataset and evaluate the model accuracy.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
-  val prediction = model.transform(test)
-  val evaluator = new MulticlassClassificationEvaluator()
-  val accuracy = evaluator.evaluate(prediction)
-
-Pipeline with Hyper-parameter Tunning
-=====================================
-The most critical operation to maximize the power of XGBoost is to select the optimal parameters for the model. Tuning parameters manually is a tedious and labor-consuming process. With the latest version of XGBoost4J-Spark, we can utilize the Spark model selecting tool to automate this process.
-
-The following example shows the code snippet utilizing CrossValidation and MulticlassClassificationEvaluator
-to search the optimal combination of two XGBoost parameters, ``max_depth`` and ``eta``. (See :doc:`/parameter`.)
-The model producing the maximum accuracy defined by MulticlassClassificationEvaluator is selected and used to generate the prediction for the test set.
-
-.. code-block:: scala
-
-  import org.apache.spark.ml.tuning._
-  import org.apache.spark.ml.PipelineModel
-  import ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel
-
-  val paramGrid = new ParamGridBuilder()
-      .addGrid(booster.maxDepth, Array(3, 8))
-      .addGrid(booster.eta, Array(0.2, 0.6))
-      .build()
-  val cv = new CrossValidator()
-      .setEstimator(pipeline)
-      .setEvaluator(evaluator)
-      .setEstimatorParamMaps(paramGrid)
-      .setNumFolds(3)
-
-  val cvModel = cv.fit(training)
-
-  val bestModel = cvModel.bestModel.asInstanceOf[PipelineModel].stages(2)
-      .asInstanceOf[XGBoostClassificationModel]
-  bestModel.extractParamMap()
-
-*********************************
-Run XGBoost4J-Spark in Production
-*********************************
-
-XGBoost4J-Spark is one of the most important steps to bring XGBoost to production environment easier. In this section, we introduce three key features to run XGBoost4J-Spark in production.
-
-Parallel/Distributed Training
-=============================
-The massive size of training dataset is one of the most significant characteristics in production environment. To ensure that training in XGBoost scales with the data size, XGBoost4J-Spark bridges the distributed/parallel processing framework of Spark and the parallel/distributed training mechanism of XGBoost.
-
-In XGBoost4J-Spark, each XGBoost worker is wrapped by a Spark task and the training dataset in Spark's memory space is fed to XGBoost workers in a transparent approach to the user.
-
-In the code snippet where we build XGBoostClassifier, we set parameter ``num_workers`` (or ``numWorkers``).
-This parameter controls how many parallel workers we want to have when training a XGBoostClassificationModel.
-
-.. note:: Regarding OpenMP optimization
-
-  By default, we allocate a core per each XGBoost worker. Therefore, the OpenMP optimization within each XGBoost worker does not take effect and the parallelization of training is achieved
-  by running multiple workers (i.e. Spark tasks) at the same time.
-
-  If you do want OpenMP optimization, you have to
-
-  1. set ``nthread`` to a value larger than 1 when creating XGBoostClassifier/XGBoostRegressor
-  2. set ``spark.task.cpus`` in Spark to the same value as ``nthread``
-
-Gang Scheduling
-===============
-XGBoost uses `AllReduce <http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/>`_.
-algorithm to synchronize the stats, e.g. histogram values, of each worker during training. Therefore XGBoost4J-Spark requires that all of ``nthread * numWorkers`` cores should be available before the training runs.
-
-In the production environment where many users share the same cluster, it's hard to guarantee that your XGBoost4J-Spark application can get all requested resources for every run. By default, the communication layer in XGBoost will block the whole application when it requires more resources to be available. This process usually brings unnecessary resource waste as it keeps the ready resources and try to claim more. Additionally, this usually happens silently and does not bring the attention of users.
-
-XGBoost4J-Spark allows the user to setup a timeout threshold for claiming resources from the cluster. If the application cannot get enough resources within this time period, the application would fail instead of wasting resources for hanging long. To enable this feature, you can set with XGBoostClassifier/XGBoostRegressor:
-
-.. code-block:: scala
-
-  xgbClassifier.setTimeoutRequestWorkers(60000L)
-
-or pass in ``timeout_request_workers`` in ``xgbParamMap`` when building XGBoostClassifier:
-
-.. code-block:: scala
-
-  val xgbParam = Map("eta" -> 0.1f,
-     "max_depth" -> 2,
-     "objective" -> "multi:softprob",
-     "num_class" -> 3,
-     "num_round" -> 100,
-     "num_workers" -> 2,
-     "timeout_request_workers" -> 60000L)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-      setFeaturesCol("features").
-      setLabelCol("classIndex")
-
-If XGBoost4J-Spark cannot get enough resources for running two XGBoost workers, the application would fail. Users can have external mechanism to monitor the status of application and get notified for such case.
-
-Checkpoint During Training
-==========================
-
-Transient failures are also commonly seen in production environment. To simplify the design of XGBoost,
-we stop training if any of the distributed workers fail. However, if the training fails after having been through a long time, it would be a great waste of resources.
-
-We support creating checkpoint during training to facilitate more efficient recovery from failture. To enable this feature, you can set how many iterations we build each checkpoint with ``setCheckpointInterval`` and the location of checkpoints with ``setCheckpointPath``:
-
-.. code-block:: scala
-
-  xgbClassifier.setCheckpointInterval(2)
-  xgbClassifier.setCheckpointPath("/checkpoint_path")
-
-An equivalent way is to pass in parameters in XGBoostClassifier's constructor:
-
-.. code-block:: scala
-
-  val xgbParam = Map("eta" -> 0.1f,
-     "max_depth" -> 2,
-     "objective" -> "multi:softprob",
-     "num_class" -> 3,
-     "num_round" -> 100,
-     "num_workers" -> 2,
-     "checkpoint_path" -> "/checkpoints_path",
-     "checkpoint_interval" -> 2)
-  val xgbClassifier = new XGBoostClassifier(xgbParam).
-      setFeaturesCol("features").
-      setLabelCol("classIndex")
-
-If the training failed during these 100 rounds, the next run of training would start by reading the latest checkpoint file in ``/checkpoints_path`` and start from the iteration when the checkpoint was built until to next failure or the specified 100 rounds.

doc/parameter.rst

@@ -12,10 +12,6 @@ Before running XGBoost, we must set three types of parameters: general parameter
 
   In R-package, you can use ``.`` (dot) to replace underscore in the parameters, for example, you can use ``max.depth`` to indicate ``max_depth``. The underscore parameters are also valid in R.
 
-.. contents::
-  :backlinks: none
-  :local:
-
 ******************
 General Parameters
 ******************
@@ -31,10 +27,6 @@ General Parameters
 
   - Number of parallel threads used to run XGBoost
 
-* ``disable_default_eval_metric`` [default=0]
-
-  - Flag to disable default metric. Set to >0 to disable.
-
 * ``num_pbuffer`` [set automatically by XGBoost, no need to be set by user]
 
   - Size of prediction buffer, normally set to number of training instances. The buffers are used to save the prediction results of last boosting step.
@@ -82,7 +74,7 @@ Parameters for Tree Booster
 
 * ``colsample_bylevel`` [default=1]
 
-  - Subsample ratio of columns for each split, in each level. Subsampling will occur each time a new split is made.
+  - Subsample ratio of columns for each split, in each level. Subsampling will occur each time a new split is made. This paramter has no effect when ``tree_method`` is set to ``hist``.
   - range: (0,1]
 
 * ``lambda`` [default=1, alias: ``reg_lambda``]
@@ -156,7 +148,7 @@ Parameters for Tree Booster
 
   - Controls a way new nodes are added to the tree.
   - Currently supported only if ``tree_method`` is set to ``hist``.
-  - Choices: ``depthwise``, ``lossguide``
+  - Choices: ``depthwise``, ```lossguide``
 
     - ``depthwise``: split at nodes closest to the root.
     - ``lossguide``: split at nodes with highest loss change.
@@ -180,18 +172,6 @@ Parameters for Tree Booster
 
 Additional parameters for Dart Booster (``booster=dart``)
 =========================================================
-
-.. note:: Using ``predict()`` with DART booster
-
-  If the booster object is DART type, ``predict()`` will perform dropouts, i.e. only
-  some of the trees will be evaluated. This will produce incorrect results if ``data`` is
-  not the training data. To obtain correct results on test sets, set ``ntree_limit`` to
-  a nonzero value, e.g.
-
-  .. code-block:: python
-
-    preds = bst.predict(dtest, ntree_limit=num_round)
-
 * ``sample_type`` [default= ``uniform``]
 
   - Type of sampling algorithm.
@@ -232,7 +212,7 @@ Additional parameters for Dart Booster (``booster=dart``)
   - range: [0.0, 1.0]
 
 Parameters for Linear Booster (``booster=gblinear``)
-====================================================
+==================================================
 * ``lambda`` [default=0, alias: ``reg_lambda``]
 
   - L2 regularization term on weights. Increasing this value will make model more conservative. Normalised to number of training examples.
@@ -248,20 +228,6 @@ Parameters for Linear Booster (``booster=gblinear``)
     - ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run. 
     - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution. 
 
-* ``feature_selector`` [default= ``cyclic``]
-
-  - Feature selection and ordering method
-
-    * ``cyclic``: Deterministic selection by cycling through features one at a time.
-    * ``shuffle``: Similar to ``cyclic`` but with random feature shuffling prior to each update.
-    * ``random``: A random (with replacement) coordinate selector.
-    * ``greedy``: Select coordinate with the greatest gradient magnitude.  It has ``O(num_feature^2)`` complexity. It is fully deterministic. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter. Doing so would reduce the complexity to ``O(num_feature*top_k)``.
-    * ``thrifty``: Thrifty, approximately-greedy feature selector. Prior to cyclic updates, reorders features in descending magnitude of their univariate weight changes. This operation is multithreaded and is a linear complexity approximation of the quadratic greedy selection. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter.
-
-* ``top_k`` [default=0]
-
-  - The number of top features to select in ``greedy`` and ``thrifty`` feature selector. The value of 0 means using all the features.
-
 Parameters for Tweedie Regression (``objective=reg:tweedie``)
 =============================================================
 * ``tweedie_variance_power`` [default=1.5]
@@ -282,7 +248,6 @@ Specify the learning task and the corresponding learning objective. The objectiv
   - ``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
@@ -294,9 +259,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
     Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function ``h(t) = h0(t) * HR``).
   - ``multi:softmax``: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)
   - ``multi:softprob``: same as softmax, but output a vector of ``ndata * nclass``, which can be further reshaped to ``ndata * nclass`` matrix. The result contains predicted probability of each data point belonging to each class.
-  - ``rank:pairwise``: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
-  - ``rank:ndcg``: Use LambdaMART to perform list-wise ranking where `Normalized Discounted Cumulative Gain (NDCG) <http://en.wikipedia.org/wiki/NDCG>`_ is maximized
-  - ``rank:map``: Use LambdaMART to perform list-wise ranking where `Mean Average Precision (MAP) <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_ is maximized
+  - ``rank:pairwise``: set XGBoost to do ranking task by minimizing the pairwise loss
   - ``reg:gamma``: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Applications>`_.
   - ``reg:tweedie``: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be `Tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Applications>`_.
 
@@ -319,9 +282,8 @@ Specify the learning task and the corresponding learning objective. The objectiv
     - ``merror``: Multiclass classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``.
     - ``mlogloss``: `Multiclass logloss <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html>`_.
     - ``auc``: `Area under the curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_
-    - ``aucpr``: `Area under the PR curve <https://en.wikipedia.org/wiki/Precision_and_recall>`_
     - ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
-    - ``map``: `Mean Average Precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
+    - ``map``: `Mean average precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
     - ``ndcg@n``, ``map@n``: 'n' can be assigned as an integer to cut off the top positions in the lists for evaluation.
     - ``ndcg-``, ``map-``, ``ndcg@n-``, ``map@n-``: In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding "-" in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
     - ``poisson-nloglik``: negative log-likelihood for Poisson regression

doc/python/python_api.rst

@@ -39,10 +39,6 @@ Scikit-Learn API
     :members:
     :inherited-members:
     :show-inheritance:
-.. autoclass:: xgboost.XGBRanker
-    :members:
-    :inherited-members:
-    :show-inheritance:
 
 Plotting API
 ------------
@@ -53,15 +49,3 @@ Plotting API
 .. autofunction:: xgboost.plot_tree
 
 .. autofunction:: xgboost.to_graphviz
-
-.. _callback_api:
-
-Callback API
-------------
-.. autofunction:: xgboost.callback.print_evaluation
-
-.. autofunction:: xgboost.callback.record_evaluation
-
-.. autofunction:: xgboost.callback.reset_learning_rate
-
-.. autofunction:: xgboost.callback.early_stop

doc/python/python_intro.rst

@@ -25,8 +25,7 @@ The XGBoost python module is able to load data from:
 - LibSVM text format file
 - Comma-separated values (CSV) file
 - NumPy 2D array
-- SciPy 2D sparse array
-- Pandas data frame, and
+- SciPy 2D sparse array, and
 - XGBoost binary buffer file.
 
 (See :doc:`/tutorials/input_format` for detailed description of text input format.)
@@ -67,14 +66,6 @@ The data is stored in a :py:class:`DMatrix <xgboost.DMatrix>` object.
     csr = scipy.sparse.csr_matrix((dat, (row, col)))
     dtrain = xgb.DMatrix(csr)
 
-* To load a Pandas data frame into :py:class:`DMatrix <xgboost.DMatrix>`:
-
-  .. code-block:: python
-
-    data = pandas.DataFrame(np.arange(12).reshape((4,3)), columns=['a', 'b', 'c'])
-    label = pandas.DataFrame(np.random.randint(2, size=4))
-    dtrain = xgb.DMatrix(data, label=label)
-
 * Saving :py:class:`DMatrix <xgboost.DMatrix>` into a XGBoost binary file will make loading faster:
 
   .. code-block:: python

doc/requirements.txt

@@ -3,6 +3,3 @@ mock
 guzzle_sphinx_theme
 breathe
 sh>=1.12.14
-matplotlib>=2.1
-graphviz
-numpy

doc/tutorials/aws_yarn.rst

@@ -5,9 +5,9 @@ This is a step-by-step tutorial on how to setup and run distributed `XGBoost <ht
 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
+.. note:: XGBoost on Spark
 
-  If you are preprocessing training data with Spark, consider using :doc:`XGBoost4J-Spark </jvm/xgboost4j_spark_tutorial>`.
+  If you are preprocessing training data with Spark, you may want to look at `XGBoost4J-Spark <https://xgboost.ai/2016/10/26/a-full-integration-of-xgboost-and-spark.html>`_, which supports distributed training on Resilient Distributed Dataset (RDD).
 
 ************
 Prerequisite

doc/tutorials/dart.rst

@@ -111,9 +111,3 @@ Sample Script
   # make prediction
   # ntree_limit must not be 0
   preds = bst.predict(dtest, ntree_limit=num_round)
-
-.. note:: Specify ``ntree_limit`` when predicting with test sets
-
-  By default, ``bst.predict()`` will perform dropouts on trees. To obtain
-  correct results on test sets, disable dropouts by specifying
-  a nonzero value for ``ntree_limit``.

doc/tutorials/feature_interaction_constraint.rst

@@ -1,177 +0,0 @@
-###############################
-Feature Interaction Constraints
-###############################
-
-The decision tree is a powerful tool to discover interaction among independent
-variables (features). Variables that appear together in a traversal path
-are interacting with one another, since the condition of a child node is
-predicated on the condition of the parent node. For example, the highlighted
-red path in the diagram below contains three variables: :math:`x_1`, :math:`x_7`,
-and :math:`x_{10}`, so the highlighted prediction (at the highlighted leaf node)
-is the product of interaction between :math:`x_1`, :math:`x_7`, and
-:math:`x_{10}`.
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration1 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">10</FONT></SUB> &lt; -1.5 ?>, shape=box, color=red, fontcolor=red];
-      1 [label=<x<SUB><FONT POINT-SIZE="11">2</FONT></SUB> &lt; 2 ?>, shape=box];
-      2 [label=<x<SUB><FONT POINT-SIZE="11">7</FONT></SUB> &lt; 0.3 ?>, shape=box, color=red, fontcolor=red];
-      3 [label="...", shape=none];
-      4 [label="...", shape=none];
-      5 [label=<x<SUB><FONT POINT-SIZE="11">1</FONT></SUB> &lt; 0.5 ?>, shape=box, color=red, fontcolor=red];
-      6 [label="...", shape=none];
-      7 [label="...", shape=none];
-      8 [label="Predict +1.3", color=red, fontcolor=red];
-      0 -> 1 [labeldistance=2.0, labelangle=45, headlabel="Yes/Missing           "];
-      0 -> 2 [labeldistance=2.0, labelangle=-45,
-              headlabel="No", color=red, fontcolor=red];
-      1 -> 3 [labeldistance=2.0, labelangle=45, headlabel="Yes"];
-      1 -> 4 [labeldistance=2.0, labelangle=-45, headlabel="             No/Missing"];
-      2 -> 5 [labeldistance=2.0, labelangle=-45, headlabel="Yes",
-              color=red, fontcolor=red];
-      2 -> 6 [labeldistance=2.0, labelangle=-45, headlabel="           No/Missing"];
-      5 -> 7;
-      5 -> 8 [color=red];
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration1', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration1', view=False)
-
-.. raw:: html
-
-  <p>
-  <img src="../_static/feature_interaction_illustration1.svg"
-    onerror="this.src='../_static/feature_interaction_illustration1.png'; this.onerror=null;">
-  </p>
-
-When the tree depth is larger than one, many variables interact on
-the sole basis of minimizing training loss, and the resulting decision tree may
-capture a spurious relationship (noise) rather than a legitimate relationship
-that generalizes across different datasets. **Feature interaction constraints**
-allow users to decide which variables are allowed to interact and which are not.
-
-Potential benefits include:
-
-* Better predictive performance from focusing on interactions that work --
-  whether through domain specific knowledge or algorithms that rank interactions
-* Less noise in predictions; better generalization
-* More control to the user on what the model can fit. For example, the user may
-  want to exclude some interactions even if they perform well due to regulatory
-  constraints
-
-****************
-A Simple Example
-****************
-
-Feature interaction constraints are expressed in terms of groups of variables
-that are allowed to interact. For example, the constraint
-``[0, 1]`` indicates that variables :math:`x_0` and :math:`x_1` are allowed to
-interact with each other but with no other variable. Similarly, ``[2, 3, 4]``
-indicates that :math:`x_2`, :math:`x_3`, and :math:`x_4` are allowed to
-interact with one another but with no other variable. A set of feature
-interaction constraints is expressed as a nested list, e.g.
-``[[0, 1], [2, 3, 4]]``, where each inner list is a group of indices of features
-that are allowed to interact with each other.
-
-In the following diagram, the left decision tree is in violation of the first
-constraint (``[0, 1]``), whereas the right decision tree complies with both the
-first and second constraints (``[0, 1]``, ``[2, 3, 4]``).
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration2 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">0</FONT></SUB> &lt; 5.0 ?>, shape=box];
-      1 [label=<x<SUB><FONT POINT-SIZE="11">2</FONT></SUB> &lt; -3.0 ?>, shape=box];
-      2 [label="+0.6"];
-      3 [label="-0.4"];
-      4 [label="+1.2"];
-      0 -> 1 [labeldistance=2.0, labelangle=45, headlabel="Yes/Missing           "];
-      0 -> 2 [labeldistance=2.0, labelangle=-45, headlabel="No"];
-      1 -> 3 [labeldistance=2.0, labelangle=45, headlabel="Yes"];
-      1 -> 4 [labeldistance=2.0, labelangle=-45, headlabel="           No/Missing"];
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration2', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration2', view=False)
-
-.. plot::
-  :nofigs:
-
-  from graphviz import Source
-  source = r"""
-    digraph feature_interaction_illustration3 {
-      graph [fontname = "helvetica"];
-      node [fontname = "helvetica"];
-      edge [fontname = "helvetica"];
-      0 [label=<x<SUB><FONT POINT-SIZE="11">3</FONT></SUB> &lt; 2.5 ?>, shape=box];
-      1 [label="+1.6"];
-      2 [label=<x<SUB><FONT POINT-SIZE="11">2</FONT></SUB> &lt; -1.2 ?>, shape=box];
-      3 [label="+0.1"];
-      4 [label="-0.3"];
-      0 -> 1 [labeldistance=2.0, labelangle=45, headlabel="Yes"];
-      0 -> 2 [labeldistance=2.0, labelangle=-45, headlabel="           No/Missing"];
-      2 -> 3 [labeldistance=2.0, labelangle=45, headlabel="Yes/Missing           "];
-      2 -> 4 [labeldistance=2.0, labelangle=-45, headlabel="No"];
-    }
-  """
-  Source(source, format='png').render('../_static/feature_interaction_illustration3', view=False)
-  Source(source, format='svg').render('../_static/feature_interaction_illustration3', view=False)
-
-.. raw:: html
-
-  <p>
-  <img src="../_static/feature_interaction_illustration2.svg"
-       onerror="this.src='../_static/feature_interaction_illustration2.png'; this.onerror=null;">
-  <img src="../_static/feature_interaction_illustration3.svg"
-       onerror="this.src='../_static/feature_interaction_illustration3.png'; this.onerror=null;">
-  </p>
-
-****************************************************
-Enforcing Feature Interaction Constraints in XGBoost
-****************************************************
-
-It is very simple to enforce monotonicity constraints in XGBoost.  Here we will
-give an example using Python, but the same general idea generalizes to other
-platforms.
-
-Suppose the following code fits your model without monotonicity constraints:
-
-.. code-block:: python
-
-  model_no_constraints = xgb.train(params, dtrain,
-                                   num_boost_round = 1000, evals = evallist,
-                                   early_stopping_rounds = 10)
-
-Then fitting with monotonicity constraints only requires adding a single
-parameter:
-
-.. code-block:: python
-
-  params_constrained = params.copy()
-  # Use nested list to define feature interaction constraints
-  params_constrained['interaction_constraints'] = '[[0, 2], [1, 3, 4], [5, 6]]'
-  # Features 0 and 2 are allowed to interact with each other but with no other feature
-  # Features 1, 3, 4 are allowed to interact with one another but with no other feature
-  # Features 5 and 6 are allowed to interact with each other but with no other feature
-
-  model_with_constraints = xgb.train(params_constrained, dtrain,
-                                     num_boost_round = 1000, evals = evallist,
-                                     early_stopping_rounds = 10)
-
-**Choice of tree construction algorithm**. To use feature interaction
-constraints, be sure to set the ``tree_method`` parameter to either ``exact``
-or ``hist``. Currently, GPU algorithms (``gpu_hist``, ``gpu_exact``) do not
-support feature interaction constraints.

doc/tutorials/index.rst

@@ -10,11 +10,9 @@ See `Awesome XGBoost <https://github.com/dmlc/xgboost/tree/master/demo>`_ for mo
   :caption: Contents:
 
   model
-  Distributed XGBoost with AWS YARN <aws_yarn>
-  Distributed XGBoost with XGBoost4J-Spark <https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html>
+  aws_yarn
   dart
   monotonic
-  feature_interaction_constraint
   input_format
   param_tuning
   external_memory

doc/tutorials/monotonic.rst

@@ -82,7 +82,7 @@ Some other examples:
 - ``(1,0)``: An increasing constraint on the first predictor and no constraint on the second.
 - ``(0,-1)``: No constraint on the first predictor and a decreasing constraint on the second.
 
-**Choice of tree construction algorithm**. To use monotonic constraints, be
+**Choise of tree construction algorithm**. To use monotonic constraints, be
 sure to set the ``tree_method`` parameter to one of ``exact``, ``hist``, and
 ``gpu_hist``.
 

include/xgboost/base.h

@@ -69,7 +69,7 @@
 /*!
  * \brief Tag function as usable by device
  */
-#if defined (__CUDA__) || defined(__NVCC__)
+#ifdef __NVCC__
 #define XGBOOST_DEVICE __host__ __device__
 #else
 #define XGBOOST_DEVICE

include/xgboost/c_api.h

@@ -96,15 +96,6 @@ XGB_EXTERN_C typedef int XGBCallbackDataIterNext(  // NOLINT(*)
  */
 XGB_DLL const char *XGBGetLastError(void);
 
-/*!
- * \brief register callback function for LOG(INFO) messages -- helpful messages
- *        that are not errors.
- * Note: this function can be called by multiple threads. The callback function
- *       will run on the thread that registered it
- * \return 0 for success, -1 for failure
- */
-XGB_DLL int XGBRegisterLogCallback(void (*callback)(const char*));
-
 /*!
  * \brief load a data matrix
  * \param fname the name of the file
@@ -228,22 +219,6 @@ XGB_DLL int XGDMatrixCreateFromMat_omp(const float *data,  // NOLINT
                                        bst_ulong nrow, bst_ulong ncol,
                                        float missing, DMatrixHandle *out,
                                        int nthread);
-/*!
- * \brief create matrix content from python data table
- * \param data pointer to pointer to column data
- * \param feature_stypes pointer to strings
- * \param nrow number of rows
- * \param ncol number columns
- * \param out created dmatrix
- * \param nthread number of threads (up to maximum cores available, if <=0 use all cores)
- * \return 0 when success, -1 when failure happens
- */
-XGB_DLL int XGDMatrixCreateFromDT(void** data,
-                                  const char ** feature_stypes,
-                                  bst_ulong nrow,
-                                  bst_ulong ncol,
-                                  DMatrixHandle* out,
-                                  int nthread);
 /*!
  * \brief create a new dmatrix from sliced content of existing matrix
  * \param handle instance of data matrix to be sliced
@@ -286,7 +261,7 @@ XGB_DLL int XGDMatrixSetFloatInfo(DMatrixHandle handle,
  * \brief set uint32 vector to a content in info
  * \param handle a instance of data matrix
  * \param field field name
- * \param array pointer to unsigned int vector
+ * \param array pointer to float vector
  * \param len length of array
  * \return 0 when success, -1 when failure happens
  */

include/xgboost/data.h

@@ -9,17 +9,11 @@
 
 #include <dmlc/base.h>
 #include <dmlc/data.h>
-#include <cstring>
-#include <memory>
-#include <numeric>
-#include <algorithm>
 #include <string>
+#include <memory>
 #include <vector>
+#include <numeric>
 #include "./base.h"
-#include "../../src/common/span.h"
-#include "../../src/common/group_data.h"
-
-#include "../../src/common/host_device_vector.h"
 
 namespace xgboost {
 // forward declare learner.
@@ -45,7 +39,7 @@ class MetaInfo {
   /*! \brief number of nonzero entries in the data */
   uint64_t num_nonzero_{0};
   /*! \brief label of each instance */
-  HostDeviceVector<bst_float> labels_;
+  std::vector<bst_float> labels_;
   /*!
    * \brief specified root index of each instance,
    *  can be used for multi task setting
@@ -57,19 +51,15 @@ class MetaInfo {
    */
   std::vector<bst_uint> group_ptr_;
   /*! \brief weights of each instance, optional */
-  HostDeviceVector<bst_float> weights_;
-  /*! \brief session-id of each instance, optional */
-  std::vector<uint64_t> qids_;
+  std::vector<bst_float> weights_;
   /*!
    * \brief initialized margins,
    * if specified, xgboost will start from this init margin
    * can be used to specify initial prediction to boost from.
    */
-  HostDeviceVector<bst_float> base_margin_;
+  std::vector<bst_float> base_margin_;
   /*! \brief version flag, used to check version of this info */
-  static const int kVersion = 2;
-  /*! \brief version that introduced qid field */
-  static const int kVersionQidAdded = 2;
+  static const int kVersion = 1;
   /*! \brief default constructor */
   MetaInfo()  = default;
   /*!
@@ -78,7 +68,7 @@ class MetaInfo {
    * \return The weight.
    */
   inline bst_float GetWeight(size_t i) const {
-    return weights_.Size() != 0 ?  weights_.HostVector()[i] : 1.0f;
+    return weights_.size() != 0 ?  weights_[i] : 1.0f;
   }
   /*!
    * \brief Get the root index of i-th instance.
@@ -90,12 +80,12 @@ class MetaInfo {
   }
   /*! \brief get sorted indexes (argsort) of labels by absolute value (used by cox loss) */
   inline const std::vector<size_t>& LabelAbsSort() const {
-    if (label_order_cache_.size() == labels_.Size()) {
+    if (label_order_cache_.size() == labels_.size()) {
       return label_order_cache_;
     }
-    label_order_cache_.resize(labels_.Size());
+    label_order_cache_.resize(labels_.size());
     std::iota(label_order_cache_.begin(), label_order_cache_.end(), 0);
-    const auto& l = labels_.HostVector();
+    const auto l = labels_;
     XGBOOST_PARALLEL_SORT(label_order_cache_.begin(), label_order_cache_.end(),
               [&l](size_t i1, size_t i2) {return std::abs(l[i1]) < std::abs(l[i2]);});
 
@@ -127,230 +117,74 @@ class MetaInfo {
   mutable std::vector<size_t> label_order_cache_;
 };
 
-/*! \brief Element from a sparse vector */
-struct Entry {
-  /*! \brief feature index */
-  bst_uint index;
-  /*! \brief feature value */
-  bst_float fvalue;
-  /*! \brief default constructor */
-  Entry() = default;
-  /*!
-   * \brief constructor with index and value
-   * \param index The feature or row index.
-   * \param fvalue The feature value.
-   */
-  Entry(bst_uint index, bst_float fvalue) : index(index), fvalue(fvalue) {}
-  /*! \brief reversely compare feature values */
-  inline static bool CmpValue(const Entry& a, const Entry& b) {
-    return a.fvalue < b.fvalue;
-  }
-  inline bool operator==(const Entry& other) const {
-    return (this->index == other.index && this->fvalue == other.fvalue);
+/*! \brief read-only sparse instance batch in CSR format */
+struct SparseBatch {
+  /*! \brief an entry of sparse vector */
+  struct Entry {
+    /*! \brief feature index */
+    bst_uint index;
+    /*! \brief feature value */
+    bst_float fvalue;
+    /*! \brief default constructor */
+    Entry() = default;
+    /*!
+     * \brief constructor with index and value
+     * \param index The feature or row index.
+     * \param fvalue THe feature value.
+     */
+    Entry(bst_uint index, bst_float fvalue) : index(index), fvalue(fvalue) {}
+    /*! \brief reversely compare feature values */
+    inline static bool CmpValue(const Entry& a, const Entry& b) {
+      return a.fvalue < b.fvalue;
+    }
+  };
+
+  /*! \brief an instance of sparse vector in the batch */
+  struct Inst {
+    /*! \brief pointer to the elements*/
+    const Entry *data{nullptr};
+    /*! \brief length of the instance */
+    bst_uint length{0};
+    /*! \brief constructor */
+    Inst()  = default;
+    Inst(const Entry *data, bst_uint length) : data(data), length(length) {}
+    /*! \brief get i-th pair in the sparse vector*/
+    inline const Entry& operator[](size_t i) const {
+      return data[i];
+    }
+  };
+
+  /*! \brief batch size */
+  size_t size;
+};
+
+/*! \brief read-only row batch, used to access row continuously */
+struct RowBatch : public SparseBatch {
+  /*! \brief the offset of rowid of this batch */
+  size_t base_rowid;
+  /*! \brief array[size+1], row pointer of each of the elements */
+  const size_t *ind_ptr;
+  /*! \brief array[ind_ptr.back()], content of the sparse element */
+  const Entry *data_ptr;
+  /*! \brief get i-th row from the batch */
+  inline Inst operator[](size_t i) const {
+    return {data_ptr + ind_ptr[i], static_cast<bst_uint>(ind_ptr[i + 1] - ind_ptr[i])};
   }
 };
 
 /*!
- * \brief In-memory storage unit of sparse batch, stored in CSR format.
+ * \brief read-only column batch, used to access columns,
+ * the columns are not required to be continuous
  */
-class SparsePage {
- public:
-  // Offset for each row.
-  HostDeviceVector<size_t> offset;
-  /*! \brief the data of the segments */
-  HostDeviceVector<Entry> data;
-
-  size_t base_rowid;
-
-  /*! \brief an instance of sparse vector in the batch */
-  using Inst = common::Span<Entry const>;
-
-  /*! \brief get i-th row from the batch */
+struct ColBatch : public SparseBatch {
+  /*! \brief column index of each columns in the data */
+  const bst_uint *col_index;
+  /*! \brief pointer to the column data */
+  const Inst *col_data;
+  /*! \brief get i-th column from the batch */
   inline Inst operator[](size_t i) const {
-    const auto& data_vec = data.HostVector();
-    const auto& offset_vec = offset.HostVector();
-    return {data_vec.data() + offset_vec[i],
-            static_cast<Inst::index_type>(offset_vec[i + 1] - offset_vec[i])};
+    return col_data[i];
   }
-
-  /*! \brief constructor */
-  SparsePage() {
-    this->Clear();
-  }
-  /*! \return number of instance in the page */
-  inline size_t Size() const {
-    return offset.Size() - 1;
-  }
-  /*! \return estimation of memory cost of this page */
-  inline size_t MemCostBytes() const {
-    return offset.Size() * sizeof(size_t) + data.Size() * sizeof(Entry);
-  }
-  /*! \brief clear the page */
-  inline void Clear() {
-    base_rowid = 0;
-    auto& offset_vec = offset.HostVector();
-    offset_vec.clear();
-    offset_vec.push_back(0);
-    data.HostVector().clear();
-  }
-
-  SparsePage GetTranspose(int num_columns) const {
-    SparsePage transpose;
-    common::ParallelGroupBuilder<Entry> builder(&transpose.offset.HostVector(),
-                                                &transpose.data.HostVector());
-    const int nthread = omp_get_max_threads();
-    builder.InitBudget(num_columns, nthread);
-    long batch_size = static_cast<long>(this->Size());  // NOLINT(*)
-#pragma omp parallel for schedule(static)
-    for (long i = 0; i < batch_size; ++i) {  // NOLINT(*)
-      int tid = omp_get_thread_num();
-      auto inst = (*this)[i];
-      for (bst_uint j = 0; j < inst.size(); ++j) {
-        builder.AddBudget(inst[j].index, tid);
-      }
-    }
-    builder.InitStorage();
-#pragma omp parallel for schedule(static)
-    for (long i = 0; i < batch_size; ++i) {  // NOLINT(*)
-      int tid = omp_get_thread_num();
-      auto inst = (*this)[i];
-      for (bst_uint j = 0; j < inst.size(); ++j) {
-        builder.Push(
-            inst[j].index,
-            Entry(static_cast<bst_uint>(this->base_rowid + i), inst[j].fvalue),
-            tid);
-      }
-    }
-    return transpose;
-  }
-
-  void SortRows() {
-    auto ncol = static_cast<bst_omp_uint>(this->Size());
-#pragma omp parallel for schedule(dynamic, 1)
-    for (bst_omp_uint i = 0; i < ncol; ++i) {
-      if (this->offset.HostVector()[i] < this->offset.HostVector()[i + 1]) {
-        std::sort(
-            this->data.HostVector().begin() + this->offset.HostVector()[i],
-            this->data.HostVector().begin() + this->offset.HostVector()[i + 1],
-            Entry::CmpValue);
-      }
-    }
-  }
-
-  /*!
-   * \brief Push row block into the page.
-   * \param batch the row batch.
-   */
-  inline void Push(const dmlc::RowBlock<uint32_t>& batch) {
-    auto& data_vec = data.HostVector();
-    auto& offset_vec = offset.HostVector();
-    data_vec.reserve(data.Size() + batch.offset[batch.size] - batch.offset[0]);
-    offset_vec.reserve(offset.Size() + batch.size);
-    CHECK(batch.index != nullptr);
-    for (size_t i = 0; i < batch.size; ++i) {
-      offset_vec.push_back(offset_vec.back() + batch.offset[i + 1] - batch.offset[i]);
-    }
-    for (size_t i = batch.offset[0]; i < batch.offset[batch.size]; ++i) {
-      uint32_t index = batch.index[i];
-      bst_float fvalue = batch.value == nullptr ? 1.0f : batch.value[i];
-      data_vec.emplace_back(index, fvalue);
-    }
-    CHECK_EQ(offset_vec.back(), data.Size());
-  }
-  /*!
-   * \brief Push a sparse page
-   * \param batch the row page
-   */
-  inline void Push(const SparsePage &batch) {
-    auto& data_vec = data.HostVector();
-    auto& offset_vec = offset.HostVector();
-    const auto& batch_offset_vec = batch.offset.HostVector();
-    const auto& batch_data_vec = batch.data.HostVector();
-    size_t top = offset_vec.back();
-    data_vec.resize(top + batch.data.Size());
-    std::memcpy(dmlc::BeginPtr(data_vec) + top,
-                dmlc::BeginPtr(batch_data_vec),
-                sizeof(Entry) * batch.data.Size());
-    size_t begin = offset.Size();
-    offset_vec.resize(begin + batch.Size());
-    for (size_t i = 0; i < batch.Size(); ++i) {
-      offset_vec[i + begin] = top + batch_offset_vec[i + 1];
-    }
-  }
-  /*!
-   * \brief Push one instance into page
-   *  \param inst an instance row
-   */
-  inline void Push(const Inst &inst) {
-    auto& data_vec = data.HostVector();
-    auto& offset_vec = offset.HostVector();
-    offset_vec.push_back(offset_vec.back() + inst.size());
-
-    size_t begin = data_vec.size();
-    data_vec.resize(begin + inst.size());
-    if (inst.size() != 0) {
-      std::memcpy(dmlc::BeginPtr(data_vec) + begin, inst.data(),
-                  sizeof(Entry) * inst.size());
-    }
-  }
-
-  size_t Size() { return offset.Size() - 1; }
-};
-
-class BatchIteratorImpl {
- public:
-  virtual ~BatchIteratorImpl() {}
-  virtual BatchIteratorImpl* Clone() = 0;
-  virtual const SparsePage& operator*() const = 0;
-  virtual void operator++() = 0;
-  virtual bool AtEnd() const = 0;
-};
-
-class BatchIterator {
- public:
-  using iterator_category = std::forward_iterator_tag;
-  explicit BatchIterator(BatchIteratorImpl* impl) { impl_.reset(impl); }
-
-  BatchIterator(const BatchIterator& other) {
-    if (other.impl_) {
-      impl_.reset(other.impl_->Clone());
-    } else {
-      impl_.reset();
-    }
-  }
-
-  void operator++() {
-    CHECK(impl_ != nullptr);
-    ++(*impl_);
-  }
-
-  const SparsePage& operator*() const {
-    CHECK(impl_ != nullptr);
-    return *(*impl_);
-  }
-
-  bool operator!=(const BatchIterator& rhs) const {
-    CHECK(impl_ != nullptr);
-    return !impl_->AtEnd();
-  }
-
-  bool AtEnd() const {
-    CHECK(impl_ != nullptr);
-    return impl_->AtEnd();
-  }
-
- private:
-  std::unique_ptr<BatchIteratorImpl> impl_;
-};
-
-class BatchSet {
- public:
-  explicit BatchSet(BatchIterator begin_iter) : begin_iter_(begin_iter) {}
-  BatchIterator begin() { return begin_iter_; }
-  BatchIterator end() { return BatchIterator(nullptr); }
-
- private:
-  BatchIterator begin_iter_;
 };
 
 /*!
@@ -360,7 +194,7 @@ class BatchSet {
  *
  *  On distributed setting, usually an customized dmlc::Parser is needed instead.
  */
-class DataSource : public dmlc::DataIter<SparsePage> {
+class DataSource : public dmlc::DataIter<RowBatch> {
  public:
   /*!
    * \brief Meta information about the dataset
@@ -422,17 +256,43 @@ class DMatrix {
   virtual MetaInfo& Info() = 0;
   /*! \brief meta information of the dataset */
   virtual const MetaInfo& Info() const = 0;
-  /**
-   * \brief Gets row batches. Use range based for loop over BatchSet to access individual batches.
+  /*!
+   * \brief get the row iterator, reset to beginning position
+   * \note Only either RowIterator or  column Iterator can be active.
    */
-  virtual BatchSet GetRowBatches() = 0;
-  virtual BatchSet GetSortedColumnBatches() = 0;
-  virtual BatchSet GetColumnBatches() = 0;
+  virtual dmlc::DataIter<RowBatch>* RowIterator() = 0;
+  /*!\brief get column iterator, reset to the beginning position */
+  virtual dmlc::DataIter<ColBatch>* ColIterator() = 0;
+  /*!
+   * \brief get the column iterator associated with subset of column features.
+   * \param fset is the list of column index set that must be contained in the returning Column iterator
+   * \return the column iterator, initialized so that it reads the elements in fset
+   */
+  virtual dmlc::DataIter<ColBatch>* ColIterator(const std::vector<bst_uint>& fset) = 0;
+  /*!
+   * \brief check if column access is supported, if not, initialize column access.
+   * \param enabled whether certain feature should be included in column access.
+   * \param subsample subsample ratio when generating column access.
+   * \param max_row_perbatch auxiliary information, maximum row used in each column batch.
+   *         this is a hint information that can be ignored by the implementation.
+   * \param sorted If column features should be in sorted order           
+   * \return Number of column blocks in the column access.
+   */
+
+  virtual void InitColAccess(const std::vector<bool>& enabled,
+                             float subsample,
+                             size_t max_row_perbatch, bool sorted) = 0;
   // the following are column meta data, should be able to answer them fast.
+  /*! \return whether column access is enabled */
+  virtual bool HaveColAccess(bool sorted) const = 0;
   /*! \return Whether the data columns single column block. */
   virtual bool SingleColBlock() const = 0;
+  /*! \brief get number of non-missing entries in column */
+  virtual size_t GetColSize(size_t cidx) const = 0;
   /*! \brief get column density */
-  virtual float GetColDensity(size_t cidx) = 0;
+  virtual float GetColDensity(size_t cidx) const = 0;
+  /*! \return reference of buffered rowset, in column access */
+  virtual const RowSet& BufferedRowset() const = 0;
   /*! \brief virtual destructor */
   virtual ~DMatrix() = default;
   /*!
@@ -479,6 +339,12 @@ class DMatrix {
    */
   static DMatrix* Create(dmlc::Parser<uint32_t>* parser,
                          const std::string& cache_prefix = "");
+
+ private:
+  // allow learner class to access this field.
+  friend class LearnerImpl;
+  /*! \brief public field to back ref cached matrix. */
+  LearnerImpl* cache_learner_ptr_{nullptr};
 };
 
 // implementation of inline functions
@@ -522,7 +388,7 @@ inline bool RowSet::Load(dmlc::Stream* fi) {
 }  // namespace xgboost
 
 namespace dmlc {
-DMLC_DECLARE_TRAITS(is_pod, xgboost::Entry, true);
+DMLC_DECLARE_TRAITS(is_pod, xgboost::SparseBatch::Entry, true);
 DMLC_DECLARE_TRAITS(has_saveload, xgboost::RowSet, true);
 }
 #endif  // XGBOOST_DATA_H_

include/xgboost/gbm.h

@@ -94,7 +94,7 @@ class GradientBooster {
    * \param root_index the root index
    * \sa Predict
    */
-  virtual void PredictInstance(const SparsePage::Inst& inst,
+  virtual void PredictInstance(const SparseBatch::Inst& inst,
                        std::vector<bst_float>* out_preds,
                        unsigned ntree_limit = 0,
                        unsigned root_index = 0) = 0;

include/xgboost/learner.h

@@ -10,7 +10,6 @@
 
 #include <rabit/rabit.h>
 #include <utility>
-#include <map>
 #include <string>
 #include <vector>
 #include "./base.h"
@@ -168,7 +167,7 @@ class Learner : public rabit::Serializable {
    * \param out_preds output vector to hold the predictions
    * \param ntree_limit limit the number of trees used in prediction
    */
-  inline void Predict(const SparsePage::Inst &inst,
+  inline void Predict(const SparseBatch::Inst &inst,
                       bool output_margin,
                       HostDeviceVector<bst_float> *out_preds,
                       unsigned ntree_limit = 0) const;
@@ -179,12 +178,6 @@ class Learner : public rabit::Serializable {
    */
   static Learner* Create(const std::vector<std::shared_ptr<DMatrix> >& cache_data);
 
-  /*!
-   * \brief Get configuration arguments currently stored by the learner
-   * \return Key-value pairs representing configuration arguments
-   */
-  virtual const std::map<std::string, std::string>& GetConfigurationArguments() const = 0;
-
  protected:
   /*! \brief internal base score of the model */
   bst_float base_score_;
@@ -197,7 +190,7 @@ class Learner : public rabit::Serializable {
 };
 
 // implementation of inline functions.
-inline void Learner::Predict(const SparsePage::Inst& inst,
+inline void Learner::Predict(const SparseBatch::Inst& inst,
                              bool output_margin,
                              HostDeviceVector<bst_float>* out_preds,
                              unsigned ntree_limit) const {

include/xgboost/logging.h

@@ -9,7 +9,6 @@
 #define XGBOOST_LOGGING_H_
 
 #include <dmlc/logging.h>
-#include <dmlc/thread_local.h>
 #include <sstream>
 #include "./base.h"
 
@@ -38,36 +37,6 @@ class TrackerLogger : public BaseLogger {
   ~TrackerLogger();
 };
 
-// custom logging callback; disabled for R wrapper
-#if !defined(XGBOOST_STRICT_R_MODE) || XGBOOST_STRICT_R_MODE == 0
-class LogCallbackRegistry {
- public:
-  using Callback = void (*)(const char*);
-  LogCallbackRegistry()
-    : log_callback_([] (const char* msg) { std::cerr << msg << std::endl; }) {}
-  inline void Register(Callback log_callback) {
-    this->log_callback_ = log_callback;
-  }
-  inline Callback Get() const {
-    return log_callback_;
-  }
- private:
-  Callback log_callback_;
-};
-#else
-class LogCallbackRegistry {
- public:
-  using Callback = void (*)(const char*);
-  LogCallbackRegistry() {}
-  inline void Register(Callback log_callback) {}
-  inline Callback Get() const {
-    return nullptr;
-  }
-};
-#endif
-
-using LogCallbackRegistryStore = dmlc::ThreadLocalStore<LogCallbackRegistry>;
-
 // redefines the logging macro if not existed
 #ifndef LOG
 #define LOG(severity) LOG_##severity.stream()

include/xgboost/objective.h

@@ -44,7 +44,7 @@ class ObjFunction {
    * \param iteration current iteration number.
    * \param out_gpair output of get gradient, saves gradient and second order gradient in
    */
-  virtual void GetGradient(const HostDeviceVector<bst_float>& preds,
+  virtual void GetGradient(HostDeviceVector<bst_float>* preds,
                            const MetaInfo& info,
                            int iteration,
                            HostDeviceVector<GradientPair>* out_gpair) = 0;

include/xgboost/predictor.h

@@ -88,7 +88,7 @@ class Predictor {
       int num_new_trees) = 0;
 
   /**
-   * \fn  virtual void Predictor::PredictInstance( const SparsePage::Inst&
+   * \fn  virtual void Predictor::PredictInstance( const SparseBatch::Inst&
    * inst, std::vector<bst_float>* out_preds, const gbm::GBTreeModel& model,
    * unsigned ntree_limit = 0, unsigned root_index = 0) = 0;
    *
@@ -104,7 +104,7 @@ class Predictor {
    * \param           root_index  (Optional) Zero-based index of the root.
    */
 
-  virtual void PredictInstance(const SparsePage::Inst& inst,
+  virtual void PredictInstance(const SparseBatch::Inst& inst,
                                std::vector<bst_float>* out_preds,
                                const gbm::GBTreeModel& model,
                                unsigned ntree_limit = 0,

include/xgboost/tree_model.h

@@ -447,12 +447,12 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat> {
      * \brief fill the vector with sparse vector
      * \param inst The sparse instance to fill.
      */
-    inline void Fill(const SparsePage::Inst& inst);
+    inline void Fill(const RowBatch::Inst& inst);
     /*!
      * \brief drop the trace after fill, must be called after fill.
      * \param inst The sparse instance to drop.
      */
-    inline void Drop(const SparsePage::Inst& inst);
+    inline void Drop(const RowBatch::Inst& inst);
     /*!
      * \brief returns the size of the feature vector
      * \return the size of the feature vector
@@ -573,15 +573,15 @@ inline void RegTree::FVec::Init(size_t size) {
   std::fill(data_.begin(), data_.end(), e);
 }
 
-inline void RegTree::FVec::Fill(const SparsePage::Inst& inst) {
-  for (bst_uint i = 0; i < inst.size(); ++i) {
+inline void RegTree::FVec::Fill(const RowBatch::Inst& inst) {
+  for (bst_uint i = 0; i < inst.length; ++i) {
     if (inst[i].index >= data_.size()) continue;
     data_[inst[i].index].fvalue = inst[i].fvalue;
   }
 }
 
-inline void RegTree::FVec::Drop(const SparsePage::Inst& inst) {
-  for (bst_uint i = 0; i < inst.size(); ++i) {
+inline void RegTree::FVec::Drop(const RowBatch::Inst& inst) {
+  for (bst_uint i = 0; i < inst.length; ++i) {
     if (inst[i].index >= data_.size()) continue;
     data_[inst[i].index].flag = -1;
   }

jvm-packages/README.md

@@ -20,27 +20,6 @@ You can find more about XGBoost on [Documentation](https://xgboost.readthedocs.o
 
 XGBoost4J, XGBoost4J-Spark, etc. in maven repository is compiled with g++-4.8.5  
 
-### Access release version
-
-<b>maven</b> 
-
-```
-<dependency>
-    <groupId>ml.dmlc</groupId>
-    <artifactId>xgboost4j</artifactId>
-    <version>latest_version_num</version>
-</dependency>
-``` 
- 
-<b>sbt</b> 
-```sbt
- "ml.dmlc" % "xgboost4j" % "latest_version_num"
-``` 
-
-For the latest release version number, please check [here](https://github.com/dmlc/xgboost/releases).
-
-if you want to use `xgboost4j-spark`, you just need to replace xgboost4j with `xgboost4j-spark`
-
 ### Access SNAPSHOT version
 
 You need to add github as repo:
@@ -78,7 +57,7 @@ the add dependency as following:
  "ml.dmlc" % "xgboost4j" % "latest_version_num"
 ``` 
 
-For the latest release version number, please check [here](https://github.com/CodingCat/xgboost/tree/maven-repo/ml/dmlc/xgboost4j).
+For the latest release version number, please check [here](https://github.com/dmlc/xgboost/releases).
 
 if you want to use `xgboost4j-spark`, you just need to replace xgboost4j with `xgboost4j-spark`
 

jvm-packages/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>ml.dmlc</groupId>
     <artifactId>xgboost-jvm</artifactId>
-    <version>0.81</version>
+    <version>0.72</version>
     <packaging>pom</packaging>
     <name>XGBoost JVM Package</name>
     <description>JVM Package for XGBoost</description>
@@ -33,9 +33,9 @@
         <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
         <maven.compiler.source>1.7</maven.compiler.source>
         <maven.compiler.target>1.7</maven.compiler.target>
-        <flink.version>1.5.0</flink.version>
-        <spark.version>2.3.1</spark.version>
-        <scala.version>2.11.12</scala.version>
+        <flink.version>0.10.2</flink.version>
+        <spark.version>2.3.0</spark.version>
+        <scala.version>2.11.8</scala.version>
         <scala.binary.version>2.11</scala.binary.version>
     </properties>
     <repositories>
@@ -123,6 +123,24 @@
                             <autoReleaseAfterClose>false</autoReleaseAfterClose>
                         </configuration>
                     </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-javadoc-plugin</artifactId>
+                        <version>2.9.1</version>
+                        <configuration>
+                            <excludePackageNames>
+                                ml.dmlc.xgboost4j.java.example
+                            </excludePackageNames>
+                        </configuration>
+                        <executions>
+                            <execution>
+                                <id>attach-javadocs</id>
+                                <goals>
+                                    <goal>jar</goal>
+                                </goals>
+                            </execution>
+                        </executions>
+                    </plugin>
                 </plugins>
             </build>
         </profile>
@@ -213,6 +231,19 @@
     </distributionManagement>
     <build>
         <plugins>
+            <plugin>
+                <groupId>org.scala-tools</groupId>
+                <artifactId>maven-scala-plugin</artifactId>
+                <version>2.15.2</version>
+                <executions>
+                    <execution>
+                        <goals>
+                            <goal>compile</goal>
+                            <goal>testCompile</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
             <plugin>
                 <groupId>org.scalastyle</groupId>
                 <artifactId>scalastyle-maven-plugin</artifactId>
@@ -319,7 +350,6 @@
                 <version>2.19.1</version>
                 <configuration>
                     <skipTests>false</skipTests>
-                    <useSystemClassLoader>false</useSystemClassLoader>
                 </configuration>
             </plugin>
             <plugin>