Mark next release as 1.7 instead of 2.0 (dmlc#8281)

Author: trivialfis

CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
-project(xgboost LANGUAGES CXX C VERSION 2.0.0)
+project(xgboost LANGUAGES CXX C VERSION 1.7.0)
 include(cmake/Utils.cmake)
 list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
 cmake_policy(SET CMP0022 NEW)

R-package/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
-Version: 2.0.0.1
-Date: 2022-03-29
+Version: 1.7.0.1
+Date: 2022-09-28
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),
          email = "tianqi.tchen@gmail.com"),

doc/jvm/xgboost4j_spark_tutorial.rst

@@ -347,7 +347,7 @@ and then loading the model in another session:
 
 .. note::
 
-  Besides dumping the model to raw format, users are able to dump the model to be json or ubj format from ``version 2.0.0+``.
+  Besides dumping the model to raw format, users are able to dump the model to be json or ubj format from ``version 1.7.0+``.
 
   .. code-block:: scala
 
@@ -362,7 +362,7 @@ 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.
 
-After saving the model, we can load this model with single node Python XGBoost directly from ``version 2.0.0+``.
+After saving the model, we can load this model with single node Python XGBoost directly from ``version 1.7.0+``.
 
 .. code-block:: scala
 
@@ -375,7 +375,7 @@ After saving the model, we can load this model with single node Python XGBoost d
   bst = xgb.Booster({'nthread': 4})
   bst.load_model("/tmp/xgbClassificationModel/data/XGBoostClassificationModel")
 
-Before ``version 2.0.0``, XGBoost4j-Spark needs to export model to local manually by:
+Before ``version 1.7.0``, XGBoost4j-Spark needs to export model to local manually by:
 
 .. code-block:: scala
 

doc/parameter.rst

@@ -237,7 +237,7 @@ These parameters are only used for training with categorical data. See
 
   .. versionadded:: 1.6
 
-  .. note:: This parameter is experimental. ``exact`` tree method is not supported yet.
+  .. note:: This parameter is experimental. ``exact`` tree method is not yet supported.
 
   - A threshold for deciding whether XGBoost should use one-hot encoding based split for
     categorical data.  When number of categories is lesser than the threshold then one-hot
@@ -247,10 +247,9 @@ These parameters are only used for training with categorical data. See
 
 * ``max_cat_threshold``
 
-  .. versionadded:: 2.0
+  .. versionadded:: 1.7.0
 
-  .. note:: This parameter is experimental. ``exact`` and ``gpu_hist`` tree methods are
-            not supported yet.
+  .. note:: This parameter is experimental. ``exact`` tree method is not yet supported.
 
   - Maximum number of categories considered for each split. Used only by partition-based
     splits for preventing over-fitting.

doc/tutorials/dask.rst

@@ -508,7 +508,7 @@ dask config is used:
 IPv6 Support
 ************
 
-.. versionadded:: 2.0.0
+.. versionadded:: 1.7.0
 
 XGBoost has initial IPv6 support for the dask interface on Linux. Due to most of the
 cluster support for IPv6 is partial (dual stack instead of IPv6 only), we require

doc/tutorials/spark_estimator.rst

@@ -1,8 +1,8 @@
 ###############################
 Using XGBoost PySpark Estimator
 ###############################
-Starting from version 2.0, xgboost supports pyspark estimator APIs.
-The feature is still experimental and not yet ready for production use.
+Starting from version 1.7.0, xgboost supports pyspark estimator APIs.  The feature is
+still experimental and not yet ready for production use.
 
 *****************
 SparkXGBRegressor

include/xgboost/c_api.h

@@ -453,7 +453,7 @@ XGB_DLL int XGQuantileDMatrixCreateFromCallback(DataIterHandle iter, DMatrixHand
 
 /*!
  * \brief Create a Device Quantile DMatrix with data iterator.
- * \deprecated since 2.0
+ * \deprecated since 1.7.0
  * \see XGQuantileDMatrixCreateFromCallback()
  */
 XGB_DLL int XGDeviceQuantileDMatrixCreateFromCallback(DataIterHandle iter, DMatrixHandle proxy,

include/xgboost/version_config.h

@@ -4,8 +4,8 @@
 #ifndef XGBOOST_VERSION_CONFIG_H_
 #define XGBOOST_VERSION_CONFIG_H_
 
-#define XGBOOST_VER_MAJOR 2
-#define XGBOOST_VER_MINOR 0
+#define XGBOOST_VER_MAJOR 1
+#define XGBOOST_VER_MINOR 7
 #define XGBOOST_VER_PATCH 0
 
 #endif  // XGBOOST_VERSION_CONFIG_H_

jvm-packages/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>ml.dmlc</groupId>
     <artifactId>xgboost-jvm_2.12</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>1.7.0-SNAPSHOT</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>2.0.0-SNAPSHOT</version>
+        <version>1.7.0-SNAPSHOT</version>
     </parent>
     <artifactId>xgboost4j-example_2.12</artifactId>
-    <version>2.0.0-SNAPSHOT</version>
+    <version>1.7.0-SNAPSHOT</version>
     <packaging>jar</packaging>
     <build>
         <plugins>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
-            <version>2.0.0-SNAPSHOT</version>
+            <version>1.7.0-SNAPSHOT</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>
@@ -37,7 +37,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
-            <version>2.0.0-SNAPSHOT</version>
+            <version>1.7.0-SNAPSHOT</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>