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

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Jiaming Yuan 2022-09-28 14:33:37 +08:00 committed by GitHub
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19 changed files with 38 additions and 39 deletions
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2
CMakeLists.txt
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@ -1,5 +1,5 @@
cmake_minimum_required(VERSION 3.14 FATAL_ERROR) 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) include(cmake/Utils.cmake)
list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules") list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
cmake_policy(SET CMP0022 NEW) cmake_policy(SET CMP0022 NEW)

4
R-package/DESCRIPTION
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@ -1,8 +1,8 @@
Package: xgboost Package: xgboost
Type: Package Type: Package
Title: Extreme Gradient Boosting Title: Extreme Gradient Boosting
Version: 2.0.0.1 Version: 1.7.0.1
Date: 2022-03-29 Date: 2022-09-28
Authors@R: c( Authors@R: c(
person("Tianqi", "Chen", role = c("aut"), person("Tianqi", "Chen", role = c("aut"),
email = "tianqi.tchen@gmail.com"), email = "tianqi.tchen@gmail.com"),

6
doc/jvm/xgboost4j_spark_tutorial.rst
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@ -347,7 +347,7 @@ and then loading the model in another session:
.. note:: .. 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 .. 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 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. 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 .. 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 = xgb.Booster({'nthread': 4})
bst.load_model("/tmp/xgbClassificationModel/data/XGBoostClassificationModel") 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 .. code-block:: scala

7
doc/parameter.rst
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@ -237,7 +237,7 @@ These parameters are only used for training with categorical data. See
.. versionadded:: 1.6 .. 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 - 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 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`` * ``max_cat_threshold``
.. versionadded:: 2.0 .. versionadded:: 1.7.0
.. note:: This parameter is experimental. ``exact`` and ``gpu_hist`` tree methods are .. note:: This parameter is experimental. ``exact`` tree method is not yet supported.
not supported yet.
- Maximum number of categories considered for each split. Used only by partition-based - Maximum number of categories considered for each split. Used only by partition-based
splits for preventing over-fitting. splits for preventing over-fitting.

2
doc/tutorials/dask.rst
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@ -508,7 +508,7 @@ dask config is used:
IPv6 Support 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 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 cluster support for IPv6 is partial (dual stack instead of IPv6 only), we require

4
doc/tutorials/spark_estimator.rst
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@ -1,8 +1,8 @@
############################### ###############################
Using XGBoost PySpark Estimator Using XGBoost PySpark Estimator
############################### ###############################
Starting from version 2.0, xgboost supports pyspark estimator APIs. Starting from version 1.7.0, xgboost supports pyspark estimator APIs. The feature is
The feature is still experimental and not yet ready for production use. still experimental and not yet ready for production use.
***************** *****************
SparkXGBRegressor SparkXGBRegressor

2
include/xgboost/c_api.h
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@ -453,7 +453,7 @@ XGB_DLL int XGQuantileDMatrixCreateFromCallback(DataIterHandle iter, DMatrixHand
/*! /*!
* \brief Create a Device Quantile DMatrix with data iterator. * \brief Create a Device Quantile DMatrix with data iterator.
* \deprecated since 2.0 * \deprecated since 1.7.0
* \see XGQuantileDMatrixCreateFromCallback() * \see XGQuantileDMatrixCreateFromCallback()
*/ */
XGB_DLL int XGDeviceQuantileDMatrixCreateFromCallback(DataIterHandle iter, DMatrixHandle proxy, XGB_DLL int XGDeviceQuantileDMatrixCreateFromCallback(DataIterHandle iter, DMatrixHandle proxy,

4
include/xgboost/version_config.h
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@ -4,8 +4,8 @@
#ifndef XGBOOST_VERSION_CONFIG_H_ #ifndef XGBOOST_VERSION_CONFIG_H_
#define XGBOOST_VERSION_CONFIG_H_ #define XGBOOST_VERSION_CONFIG_H_
#define XGBOOST_VER_MAJOR 2 #define XGBOOST_VER_MAJOR 1
#define XGBOOST_VER_MINOR 0 #define XGBOOST_VER_MINOR 7
#define XGBOOST_VER_PATCH 0 #define XGBOOST_VER_PATCH 0
#endif // XGBOOST_VERSION_CONFIG_H_ #endif // XGBOOST_VERSION_CONFIG_H_

2
jvm-packages/pom.xml
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@ -6,7 +6,7 @@
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId> <artifactId>xgboost-jvm_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
<packaging>pom</packaging> <packaging>pom</packaging>
<name>XGBoost JVM Package</name> <name>XGBoost JVM Package</name>
<description>JVM Package for XGBoost</description> <description>JVM Package for XGBoost</description>

8
jvm-packages/xgboost4j-example/pom.xml
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@ -6,10 +6,10 @@
<parent> <parent>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId> <artifactId>xgboost-jvm_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</parent> </parent>
<artifactId>xgboost4j-example_2.12</artifactId> <artifactId>xgboost4j-example_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
<packaging>jar</packaging> <packaging>jar</packaging>
<build> <build>
<plugins> <plugins>
@ -26,7 +26,7 @@
<dependency> <dependency>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost4j-spark_${scala.binary.version}</artifactId> <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.apache.spark</groupId> <groupId>org.apache.spark</groupId>
@ -37,7 +37,7 @@
<dependency> <dependency>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost4j-flink_${scala.binary.version}</artifactId> <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.apache.commons</groupId> <groupId>org.apache.commons</groupId>

6
jvm-packages/xgboost4j-flink/pom.xml
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@ -6,10 +6,10 @@
<parent> <parent>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId> <artifactId>xgboost-jvm_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</parent> </parent>
<artifactId>xgboost4j-flink_2.12</artifactId> <artifactId>xgboost4j-flink_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
<build> <build>
<plugins> <plugins>
<plugin> <plugin>
@ -26,7 +26,7 @@
<dependency> <dependency>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost4j_${scala.binary.version}</artifactId> <artifactId>xgboost4j_${scala.binary.version}</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.apache.commons</groupId> <groupId>org.apache.commons</groupId>

4
jvm-packages/xgboost4j-gpu/pom.xml
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@ -6,10 +6,10 @@
<parent> <parent>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId> <artifactId>xgboost-jvm_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</parent> </parent>
<artifactId>xgboost4j-gpu_2.12</artifactId> <artifactId>xgboost4j-gpu_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
<packaging>jar</packaging> <packaging>jar</packaging>
<dependencies> <dependencies>

4
jvm-packages/xgboost4j-spark-gpu/pom.xml
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@ -6,7 +6,7 @@
<parent> <parent>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId> <artifactId>xgboost-jvm_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</parent> </parent>
<artifactId>xgboost4j-spark-gpu_2.12</artifactId> <artifactId>xgboost4j-spark-gpu_2.12</artifactId>
<build> <build>
@ -24,7 +24,7 @@
<dependency> <dependency>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId> <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.apache.spark</groupId> <groupId>org.apache.spark</groupId>

4
jvm-packages/xgboost4j-spark/pom.xml
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@ -6,7 +6,7 @@
<parent> <parent>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId> <artifactId>xgboost-jvm_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</parent> </parent>
<artifactId>xgboost4j-spark_2.12</artifactId> <artifactId>xgboost4j-spark_2.12</artifactId>
<build> <build>
@ -24,7 +24,7 @@
<dependency> <dependency>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost4j_${scala.binary.version}</artifactId> <artifactId>xgboost4j_${scala.binary.version}</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.apache.spark</groupId> <groupId>org.apache.spark</groupId>

4
jvm-packages/xgboost4j/pom.xml
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@ -6,10 +6,10 @@
<parent> <parent>
<groupId>ml.dmlc</groupId> <groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId> <artifactId>xgboost-jvm_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
</parent> </parent>
<artifactId>xgboost4j_2.12</artifactId> <artifactId>xgboost4j_2.12</artifactId>
<version>2.0.0-SNAPSHOT</version> <version>1.7.0-SNAPSHOT</version>
<packaging>jar</packaging> <packaging>jar</packaging>
<dependencies> <dependencies>

2
python-package/xgboost/VERSION
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@ -1 +1 @@
2.0.0-dev 1.7.0-dev

4
python-package/xgboost/core.py
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@ -1249,7 +1249,7 @@ class QuantileDMatrix(DMatrix):
reference (the training dataset) ``QuantileDMatrix`` using ``ref`` as some reference (the training dataset) ``QuantileDMatrix`` using ``ref`` as some
information may be lost in quantisation. information may be lost in quantisation.
.. versionadded:: 2.0.0 .. versionadded:: 1.7.0
Parameters Parameters
---------- ----------
@ -1373,7 +1373,7 @@ class QuantileDMatrix(DMatrix):
class DeviceQuantileDMatrix(QuantileDMatrix): class DeviceQuantileDMatrix(QuantileDMatrix):
""" Use `QuantileDMatrix` instead. """ Use `QuantileDMatrix` instead.
.. deprecated:: 2.0.0 .. deprecated:: 1.7.0
.. versionadded:: 1.1.0 .. versionadded:: 1.1.0

2
python-package/xgboost/dask.py
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@ -695,7 +695,7 @@ class DaskQuantileDMatrix(DaskDMatrix):
class DaskDeviceQuantileDMatrix(DaskQuantileDMatrix): class DaskDeviceQuantileDMatrix(DaskQuantileDMatrix):
"""Use `DaskQuantileDMatrix` instead. """Use `DaskQuantileDMatrix` instead.
.. deprecated:: 2.0.0 .. deprecated:: 1.7.0
.. versionadded:: 1.2.0 .. versionadded:: 1.2.0

6
python-package/xgboost/sklearn.py
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@ -235,7 +235,7 @@ __model_doc = f"""
feature_types : FeatureTypes feature_types : FeatureTypes
.. versionadded:: 2.0.0 .. versionadded:: 1.7.0
Used for specifying feature types without constructing a dataframe. See Used for specifying feature types without constructing a dataframe. See
:py:class:`DMatrix` for details. :py:class:`DMatrix` for details.
@ -1402,9 +1402,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
# must match the parameters for `get_params` # must match the parameters for `get_params`
self.use_label_encoder = use_label_encoder self.use_label_encoder = use_label_encoder
if use_label_encoder is True: if use_label_encoder is True:
raise ValueError("Label encoder was removed in 1.6.") raise ValueError("Label encoder was removed in 1.6.0.")
if use_label_encoder is not None: if use_label_encoder is not None:
warnings.warn("`use_label_encoder` is deprecated in 2.0.0.") warnings.warn("`use_label_encoder` is deprecated in 1.7.0.")
super().__init__(objective=objective, **kwargs) super().__init__(objective=objective, **kwargs)
@_deprecate_positional_args @_deprecate_positional_args