[jvm-packages] update version number for 1.2 branch (#6427)

* [jvm-packages]update version number of 1.2 branch

* update ver

.github/workflows/main.yml

@@ -81,7 +81,7 @@ jobs:
       run: |
         cd R-package
         R.exe CMD INSTALL .
-        Rscript.exe tests/run_lint.R
+        Rscript.exe tests/helper_scripts/run_lint.R
 
 
   test-with-R:

CMakeLists.txt

@@ -1,9 +1,10 @@
 cmake_minimum_required(VERSION 3.13)
-project(xgboost LANGUAGES CXX C VERSION 1.2.0)
+project(xgboost LANGUAGES CXX C VERSION 1.2.1)
 include(cmake/Utils.cmake)
 list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
 cmake_policy(SET CMP0022 NEW)
 cmake_policy(SET CMP0079 NEW)
+set(CMAKE_POLICY_DEFAULT_CMP0063 NEW)
 cmake_policy(SET CMP0063 NEW)
 
 if ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
@@ -173,9 +174,6 @@ foreach(lib rabit rabit_base rabit_empty rabit_mock rabit_mock_static)
   # from dmlc is correctly applied to rabit.
   if (TARGET ${lib})
     target_link_libraries(${lib} dmlc ${CMAKE_THREAD_LIBS_INIT})
-    if (HIDE_CXX_SYMBOLS)  # Hide all C++ symbols from Rabit
-      set_target_properties(${lib} PROPERTIES CXX_VISIBILITY_PRESET hidden)
-    endif (HIDE_CXX_SYMBOLS)
     if (ENABLE_ALL_WARNINGS)
       target_compile_options(${lib} PRIVATE -Wall -Wextra)
     endif (ENABLE_ALL_WARNINGS)
@@ -204,8 +202,9 @@ endif (USE_NVTX)
 
 #-- Hide all C++ symbols
 if (HIDE_CXX_SYMBOLS)
-  set_target_properties(objxgboost PROPERTIES CXX_VISIBILITY_PRESET hidden)
-  set_target_properties(xgboost PROPERTIES CXX_VISIBILITY_PRESET hidden)
+  foreach(target objxgboost xgboost dmlc rabit rabit_mock_static)
+    set_target_properties(${target} PROPERTIES CXX_VISIBILITY_PRESET hidden)
+  endforeach()
 endif (HIDE_CXX_SYMBOLS)
 
 target_include_directories(xgboost

Jenkinsfile

@@ -144,7 +144,7 @@ def ClangTidy() {
     echo "Running clang-tidy job..."
     def container_type = "clang_tidy"
     def docker_binary = "docker"
-    def dockerArgs = "--build-arg CUDA_VERSION=10.1"
+    def dockerArgs = "--build-arg CUDA_VERSION_ARG=10.1"
     sh """
     ${dockerRun} ${container_type} ${docker_binary} ${dockerArgs} python3 tests/ci_build/tidy.py
     """
@@ -261,7 +261,7 @@ def BuildCUDA(args) {
     echo "Build with CUDA ${args.cuda_version}"
     def container_type = GetCUDABuildContainerType(args.cuda_version)
     def docker_binary = "docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.cuda_version}"
     def arch_flag = ""
     if (env.BRANCH_NAME != 'master' && !(env.BRANCH_NAME.startsWith('release'))) {
       arch_flag = "-DGPU_COMPUTE_VER=75"
@@ -290,7 +290,7 @@ def BuildJVMPackagesWithCUDA(args) {
     echo "Build XGBoost4J-Spark with Spark ${args.spark_version}, CUDA ${args.cuda_version}"
     def container_type = "jvm_gpu_build"
     def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.cuda_version}"
     def arch_flag = ""
     if (env.BRANCH_NAME != 'master' && !(env.BRANCH_NAME.startsWith('release'))) {
       arch_flag = "-DGPU_COMPUTE_VER=75"
@@ -365,7 +365,7 @@ def TestPythonGPU(args) {
     echo "Test Python GPU: CUDA ${args.host_cuda_version}"
     def container_type = "gpu"
     def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.host_cuda_version}"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
     if (args.multi_gpu) {
       echo "Using multiple GPUs"
       // Allocate extra space in /dev/shm to enable NCCL
@@ -406,7 +406,7 @@ def TestCppGPU(args) {
     echo "Test C++, CUDA ${args.host_cuda_version}"
     def container_type = "gpu"
     def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.host_cuda_version}"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
     sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost"
     deleteDir()
   }
@@ -424,7 +424,7 @@ def CrossTestJVMwithJDKGPU(args) {
     }
     def container_type = "gpu_jvm"
     def docker_binary = "nvidia-docker"
-    def docker_args = "--build-arg CUDA_VERSION=${args.host_cuda_version}"
+    def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
     sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_jvm_gpu_cross.sh"
     deleteDir()
   }
@@ -476,7 +476,7 @@ def DeployJVMPackages(args) {
       ${dockerRun} jvm docker tests/ci_build/deploy_jvm_packages.sh ${args.spark_version} 0
       """
       sh """
-      ${dockerRun} jvm_gpu_build docker --build-arg CUDA_VERSION=10.0 tests/ci_build/deploy_jvm_packages.sh ${args.spark_version} 1
+      ${dockerRun} jvm_gpu_build docker --build-arg CUDA_VERSION_ARG=10.0 tests/ci_build/deploy_jvm_packages.sh ${args.spark_version} 1
       """
     }
     deleteDir()

Makefile

@@ -133,15 +133,16 @@ Rpack: clean_all
 	sed -i -e 's/@BACKTRACE_LIB@//g' xgboost/src/Makevars.win
 	sed -i -e 's/@OPENMP_LIB@//g' xgboost/src/Makevars.win
 	rm -f xgboost/src/Makevars.win-e   # OSX sed create this extra file; remove it
-	bash R-package/remove_warning_suppression_pragma.sh
+	bash xgboost/remove_warning_suppression_pragma.sh
 	rm xgboost/remove_warning_suppression_pragma.sh
+	rm -rfv xgboost/tests/helper_scripts/
 
 Rbuild: Rpack
 	R CMD build --no-build-vignettes xgboost
 	rm -rf xgboost
 
 Rcheck: Rbuild
-	R CMD check xgboost*.tar.gz
+	R CMD check --as-cran xgboost*.tar.gz
 
 -include build/*.d
 -include build/*/*.d

R-package/DESCRIPTION

@@ -2,7 +2,7 @@ Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
 Version: 1.2.0.1
-Date: 2020-02-21
+Date: 2020-08-28
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),

R-package/R/utils.R

@@ -349,6 +349,7 @@ NULL
 #' # Save as a stand-alone file (JSON); load it with xgb.load()
 #' xgb.save(bst, 'xgb.model.json')
 #' bst2 <- xgb.load('xgb.model.json')
+#' if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
 #'
 #' # Save as a raw byte vector; load it with xgb.load.raw()
 #' xgb_bytes <- xgb.save.raw(bst)
@@ -364,6 +365,7 @@ NULL
 #' obj2 <- readRDS('my_object.rds')
 #' # Re-construct xgb.Booster object from the bytes
 #' bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
+#' if (file.exists('my_object.rds')) file.remove('my_object.rds')
 #'
 #' @name a-compatibility-note-for-saveRDS-save
 NULL

R-package/R/xgb.cv.R

@@ -79,7 +79,7 @@
 #'
 #' All observations are used for both training and validation.
 #'
-#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
+#' Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
 #'
 #' @return
 #' An object of class \code{xgb.cv.synchronous} with the following elements:

R-package/R/xgb.train.R

@@ -130,16 +130,16 @@
 #' Note that when using a customized metric, only this single metric can be used.
 #' The following is the list of built-in metrics for which Xgboost provides optimized implementation:
 #'   \itemize{
-#'      \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
-#'      \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
-#'      \item \code{mlogloss} multiclass logloss. \url{http://wiki.fast.ai/index.php/Log_Loss}
+#'      \item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
+#'      \item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
+#'      \item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
 #'      \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
 #'            By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
 #'            Different threshold (e.g., 0.) could be specified as "error@0."
 #'      \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
-#'      \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
+#'      \item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
 #'      \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
-#'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
+#'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
 #'   }
 #'
 #' The following callbacks are automatically created when certain parameters are set:

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

@@ -43,6 +43,7 @@ bst2 <- xgb.load('xgb.model')
 # Save as a stand-alone file (JSON); load it with xgb.load()
 xgb.save(bst, 'xgb.model.json')
 bst2 <- xgb.load('xgb.model.json')
+if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
 
 # Save as a raw byte vector; load it with xgb.load.raw()
 xgb_bytes <- xgb.save.raw(bst)
@@ -58,5 +59,6 @@ saveRDS(obj, 'my_object.rds')
 obj2 <- readRDS('my_object.rds')
 # Re-construct xgb.Booster object from the bytes
 bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
+if (file.exists('my_object.rds')) file.remove('my_object.rds')
 
 }

R-package/man/xgb.cv.Rd

@@ -154,7 +154,7 @@ The cross-validation process is then repeated \code{nrounds} times, with each of
 
 All observations are used for both training and validation.
 
-Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
+Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
 }
 \examples{
 data(agaricus.train, package='xgboost')

R-package/man/xgb.train.Rd

@@ -215,16 +215,16 @@ User may set one or several \code{eval_metric} parameters.
 Note that when using a customized metric, only this single metric can be used.
 The following is the list of built-in metrics for which Xgboost provides optimized implementation:
   \itemize{
-     \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
-     \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
-     \item \code{mlogloss} multiclass logloss. \url{http://wiki.fast.ai/index.php/Log_Loss}
+     \item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
+     \item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
+     \item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
      \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
            By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
            Different threshold (e.g., 0.) could be specified as "error@0."
      \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
-     \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
+     \item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
      \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
-     \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
+     \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
   }
 
 The following callbacks are automatically created when certain parameters are set:

R-package/tests/generate_models_params.R

@@ -1,10 +0,0 @@
-model_generator_metadata <- function() {
-  return (list(
-    kRounds = 2,
-    kRows = 1000,
-    kCols = 4,
-    kForests = 2,
-    kMaxDepth = 2,
-    kClasses = 3
-  ))
-}

R-package/tests/helper_scripts/generate_models.R

@@ -5,7 +5,14 @@ library(Matrix)
 source('./generate_models_params.R')
 
 set.seed(0)
-metadata <- model_generator_metadata()
+metadata <- list(
+  kRounds = 2,
+  kRows = 1000,
+  kCols = 4,
+  kForests = 2,
+  kMaxDepth = 2,
+  kClasses = 3
+)
 X <- Matrix(data = rnorm(metadata$kRows * metadata$kCols), nrow = metadata$kRows,
             ncol = metadata$kCols, sparse = TRUE)
 w <- runif(metadata$kRows)

R-package/tests/testthat/test_model_compatibility.R

@@ -1,10 +1,16 @@
 require(xgboost)
 require(jsonlite)
-source('../generate_models_params.R')
 
 context("Models from previous versions of XGBoost can be loaded")
 
-metadata <- model_generator_metadata()
+metadata <- list(
+  kRounds = 2,
+  kRows = 1000,
+  kCols = 4,
+  kForests = 2,
+  kMaxDepth = 2,
+  kClasses = 3
+)
 
 run_model_param_check <- function (config) {
   testthat::expect_equal(config$learner$learner_model_param$num_feature, '4')

R-package/vignettes/discoverYourData.Rmd

@@ -57,7 +57,7 @@ To answer the question above we will convert *categorical* variables to `numeric
 
 In this Vignette we will see how to transform a *dense* `data.frame` (*dense* = few zeroes in the matrix) with *categorical* variables to a very *sparse* matrix (*sparse* = lots of zero in the matrix) of `numeric` features.
 
-The method we are going to see is usually called [one-hot encoding](http://en.wikipedia.org/wiki/One-hot).
+The method we are going to see is usually called [one-hot encoding](https://en.wikipedia.org/wiki/One-hot).
 
 The first step is to load `Arthritis` dataset in memory and wrap it with `data.table` package.
 
@@ -66,7 +66,7 @@ data(Arthritis)
 df <- data.table(Arthritis, keep.rownames = FALSE)
 ```
 
-> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
+> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](https://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
 
 The first thing we want to do is to have a look to the first few lines of the `data.table`:
 
@@ -137,8 +137,8 @@ levels(df[,Treatment])
 #### Encoding categorical features
 
 Next step, we will transform the categorical data to dummy variables.
-Several encoding methods exist, e.g., [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) is a common approach.
-We will use the [dummy contrast coding](http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm#dummy) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
+Several encoding methods exist, e.g., [one-hot encoding](https://en.wikipedia.org/wiki/One-hot) is a common approach.
+We will use the [dummy contrast coding](https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
 
 The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.
 
@@ -176,7 +176,7 @@ bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 4,
 
 You can see some `train-error: 0.XXXXX` lines followed by a number. It decreases. Each line shows how well the model explains your data. Lower is better.
 
-A model which fits too well may [overfit](http://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
+A model which fits too well may [overfit](https://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
 
 > Here you can see the numbers decrease until line 7 and then increase.
 >
@@ -304,7 +304,7 @@ Linear model may not be that smart in this scenario.
 Special Note: What about Random Forests™?
 -----------------------------------------
 
-As you may know, [Random Forests™](http://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](http://en.wikipedia.org/wiki/Ensemble_learning) family.
+As you may know, [Random Forests™](https://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](https://en.wikipedia.org/wiki/Ensemble_learning) family.
 
 Both trains several decision trees for one dataset. The *main* difference is that in Random Forests™, trees are independent and in boosting, the tree `N+1` focus its learning on the loss (<=> what has not been well modeled by the tree `N`).
 

R-package/vignettes/xgboost.bib

@@ -24,7 +24,7 @@
     author = "K. Bache and M. Lichman",
     year = "2013",
     title = "{UCI} Machine Learning Repository",
-    url = "http://archive.ics.uci.edu/ml",
+    url = "http://archive.ics.uci.edu/ml/",
     institution = "University of California, Irvine, School of Information and Computer Sciences" 
 }
 

R-package/vignettes/xgboostPresentation.Rmd

@@ -68,7 +68,7 @@ The version 0.4-2 is on CRAN, and you can install it by:
 install.packages("xgboost")
 ```
 
-Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost)
+Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost/)
 
 ## Learning
 

include/xgboost/version_config.h

@@ -6,6 +6,6 @@
 
 #define XGBOOST_VER_MAJOR 1
 #define XGBOOST_VER_MINOR 2
-#define XGBOOST_VER_PATCH 0
+#define XGBOOST_VER_PATCH 1
 
 #endif  // XGBOOST_VERSION_CONFIG_H_

jvm-packages/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>ml.dmlc</groupId>
     <artifactId>xgboost-jvm_2.12</artifactId>
-    <version>1.2.0</version>
+    <version>1.2.1</version>
     <packaging>pom</packaging>
     <name>XGBoost JVM Package</name>
     <description>JVM Package for XGBoost</description>

jvm-packages/xgboost4j-example/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.2.0</version>
+        <version>1.2.1</version>
     </parent>
     <artifactId>xgboost4j-example_2.12</artifactId>
-    <version>1.2.0</version>
+    <version>1.2.1</version>
     <packaging>jar</packaging>
     <build>
         <plugins>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
-            <version>1.2.0</version>
+            <version>1.2.1</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>
@@ -37,7 +37,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
-            <version>1.2.0</version>
+            <version>1.2.1</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

jvm-packages/xgboost4j-flink/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.2.0</version>
+        <version>1.2.1</version>
     </parent>
     <artifactId>xgboost4j-flink_2.12</artifactId>
-    <version>1.2.0</version>
+    <version>1.2.1</version>
     <build>
         <plugins>
             <plugin>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-            <version>1.2.0</version>
+            <version>1.2.1</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

jvm-packages/xgboost4j-spark/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.2.0</version>
+        <version>1.2.1</version>
     </parent>
     <artifactId>xgboost4j-spark_2.12</artifactId>
     <build>
@@ -24,7 +24,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-            <version>1.2.0</version>
+            <version>1.2.1</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>

jvm-packages/xgboost4j/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.2.0</version>
+        <version>1.2.1</version>
     </parent>
     <artifactId>xgboost4j_2.12</artifactId>
-    <version>1.2.0</version>
+    <version>1.2.1</version>
     <packaging>jar</packaging>
 
     <dependencies>

python-package/xgboost/VERSION

@@ -1 +1 @@
-1.2.0
+1.2.1

python-package/xgboost/core.py

@@ -40,7 +40,7 @@ class EarlyStopException(Exception):
     """
 
     def __init__(self, best_iteration):
-        super(EarlyStopException, self).__init__()
+        super().__init__()
         self.best_iteration = best_iteration
 
 

python-package/xgboost/sklearn.py

@@ -1017,7 +1017,7 @@ class XGBRFClassifier(XGBClassifier):
                          **kwargs)
 
     def get_xgb_params(self):
-        params = super(XGBRFClassifier, self).get_xgb_params()
+        params = super().get_xgb_params()
         params['num_parallel_tree'] = self.n_estimators
         return params
 
@@ -1049,7 +1049,7 @@ class XGBRFRegressor(XGBRegressor):
                          reg_lambda=reg_lambda, **kwargs)
 
     def get_xgb_params(self):
-        params = super(XGBRFRegressor, self).get_xgb_params()
+        params = super().get_xgb_params()
         params['num_parallel_tree'] = self.n_estimators
         return params
 

tests/ci_build/Dockerfile.clang_tidy

@@ -1,5 +1,6 @@
-ARG CUDA_VERSION
-FROM nvidia/cuda:$CUDA_VERSION-devel-ubuntu18.04
+ARG CUDA_VERSION_ARG
+FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-ubuntu18.04
+ARG CUDA_VERSION_ARG
 
 # Environment
 ENV DEBIAN_FRONTEND noninteractive

tests/ci_build/Dockerfile.gpu

@@ -1,5 +1,6 @@
-ARG CUDA_VERSION
-FROM nvidia/cuda:$CUDA_VERSION-runtime-ubuntu16.04
+ARG CUDA_VERSION_ARG
+FROM nvidia/cuda:$CUDA_VERSION_ARG-runtime-ubuntu16.04
+ARG CUDA_VERSION_ARG
 
 # Environment
 ENV DEBIAN_FRONTEND noninteractive
@@ -18,7 +19,7 @@ ENV PATH=/opt/python/bin:$PATH
 # Create new Conda environment with cuDF, Dask, and cuPy
 RUN \
     conda create -n gpu_test -c rapidsai-nightly -c rapidsai -c nvidia -c conda-forge -c defaults \
-        python=3.7 cudf=0.15* cudatoolkit=$CUDA_VERSION dask dask-cuda dask-cudf cupy \
+        python=3.7 cudf=0.15* cudatoolkit=$CUDA_VERSION_ARG dask dask-cuda dask-cudf cupy \
         numpy pytest scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz hypothesis
 
 ENV GOSU_VERSION 1.10

tests/ci_build/Dockerfile.gpu_build

@@ -1,6 +1,6 @@
-ARG CUDA_VERSION
-FROM nvidia/cuda:$CUDA_VERSION-devel-ubuntu16.04
-ARG CUDA_VERSION
+ARG CUDA_VERSION_ARG
+FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-ubuntu16.04
+ARG CUDA_VERSION_ARG
 
 # Environment
 ENV DEBIAN_FRONTEND noninteractive
@@ -19,7 +19,7 @@ RUN \
 
 # NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
 RUN \
-    export CUDA_SHORT=`echo $CUDA_VERSION | egrep -o '[0-9]+\.[0-9]'` && \
+    export CUDA_SHORT=`echo $CUDA_VERSION_ARG | egrep -o '[0-9]+\.[0-9]'` && \
     export NCCL_VERSION=2.7.5-1 && \
     apt-get update && \
     apt-get install -y --allow-downgrades --allow-change-held-packages libnccl2=${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-dev=${NCCL_VERSION}+cuda${CUDA_SHORT}

tests/ci_build/Dockerfile.gpu_build_centos6

@@ -1,6 +1,6 @@
-ARG CUDA_VERSION
-FROM nvidia/cuda:$CUDA_VERSION-devel-centos6
-ARG CUDA_VERSION
+ARG CUDA_VERSION_ARG
+FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-centos6
+ARG CUDA_VERSION_ARG
 
 # Environment
 ENV DEBIAN_FRONTEND noninteractive
@@ -33,7 +33,7 @@ RUN \
 
 # NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
 RUN \
-    export CUDA_SHORT=`echo $CUDA_VERSION | egrep -o '[0-9]+\.[0-9]'` && \
+    export CUDA_SHORT=`echo $CUDA_VERSION_ARG | egrep -o '[0-9]+\.[0-9]'` && \
     export NCCL_VERSION=2.4.8-1 && \
     wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
     rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \

tests/ci_build/Dockerfile.gpu_jvm

@@ -1,5 +1,6 @@
-ARG CUDA_VERSION
-FROM nvidia/cuda:$CUDA_VERSION-runtime-ubuntu16.04
+ARG CUDA_VERSION_ARG
+FROM nvidia/cuda:$CUDA_VERSION_ARG-runtime-ubuntu16.04
+ARG CUDA_VERSION_ARG
 ARG JDK_VERSION=8
 ARG SPARK_VERSION=3.0.0
 

tests/ci_build/Dockerfile.jvm_gpu_build

@@ -1,6 +1,6 @@
-ARG CUDA_VERSION
-FROM nvidia/cuda:$CUDA_VERSION-devel-centos6
-ARG CUDA_VERSION
+ARG CUDA_VERSION_ARG
+FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-centos6
+ARG CUDA_VERSION_ARG
 
 # Environment
 ENV DEBIAN_FRONTEND noninteractive
@@ -30,7 +30,7 @@ RUN \
 
 # NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
 RUN \
-    export CUDA_SHORT=`echo $CUDA_VERSION | egrep -o '[0-9]+\.[0-9]'` && \
+    export CUDA_SHORT=`echo $CUDA_VERSION_ARG | egrep -o '[0-9]+\.[0-9]'` && \
     export NCCL_VERSION=2.4.8-1 && \
     wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
     rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \