Bump release version to 1.3.3. (#6624)

The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.

CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.13)
-project(xgboost LANGUAGES CXX C VERSION 1.3.1)
+project(xgboost LANGUAGES CXX C VERSION 1.3.3)
 include(cmake/Utils.cmake)
 list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
 cmake_policy(SET CMP0022 NEW)

R-package/DESCRIPTION

@@ -1,7 +1,7 @@
 Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
-Version: 1.3.1.1
+Version: 1.3.3.1
 Date: 2020-08-28
 Authors@R: c(
   person("Tianqi", "Chen", role = c("aut"),

include/xgboost/base.h

@@ -55,7 +55,7 @@
 #endif  // defined(__GNUC__) && ((__GNUC__ == 4 && __GNUC_MINOR__ >= 8) || __GNUC__ > 4)
 
 #if defined(__GNUC__) && ((__GNUC__ == 4 && __GNUC_MINOR__ >= 8) || __GNUC__ > 4) && \
-    !defined(__CUDACC__)
+    !defined(__CUDACC__) && !defined(__sun) && !defined(sun)
 #include <parallel/algorithm>
 #define XGBOOST_PARALLEL_SORT(X, Y, Z) __gnu_parallel::sort((X), (Y), (Z))
 #define XGBOOST_PARALLEL_STABLE_SORT(X, Y, Z) \

include/xgboost/version_config.h

@@ -6,6 +6,6 @@
 
 #define XGBOOST_VER_MAJOR 1
 #define XGBOOST_VER_MINOR 3
-#define XGBOOST_VER_PATCH 1
+#define XGBOOST_VER_PATCH 3
 
 #endif  // XGBOOST_VERSION_CONFIG_H_

jvm-packages/dev/change_version.sh

@@ -34,9 +34,9 @@ TO_VERSION=$2
 sed_i() {
   perl -p -000 -e "$1" "$2" > "$2.tmp" && mv "$2.tmp" "$2"
 }
-   
+
 export -f sed_i
- 
+
 BASEDIR=$(dirname $0)/..
 find "$BASEDIR" -name 'pom.xml' -not -path '*target*' -print \
   -exec bash -c \

jvm-packages/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>ml.dmlc</groupId>
     <artifactId>xgboost-jvm_2.12</artifactId>
-    <version>1.3.1</version>
+    <version>1.3.3</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>1.3.1</version>
+        <version>1.3.3</version>
     </parent>
     <artifactId>xgboost4j-example_2.12</artifactId>
-    <version>1.3.1</version>
+    <version>1.3.3</version>
     <packaging>jar</packaging>
     <build>
         <plugins>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
-            <version>1.3.1</version>
+            <version>1.3.3</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>
@@ -37,7 +37,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
-            <version>1.3.1</version>
+            <version>1.3.3</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>1.3.1</version>
+        <version>1.3.3</version>
     </parent>
     <artifactId>xgboost4j-flink_2.12</artifactId>
-    <version>1.3.1</version>
+    <version>1.3.3</version>
     <build>
         <plugins>
             <plugin>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j_${scala.binary.version}</artifactId>
-            <version>1.3.1</version>
+            <version>1.3.3</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

jvm-packages/xgboost4j-gpu/pom.xml

@@ -6,10 +6,10 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.3.1</version>
+        <version>1.3.3</version>
     </parent>
     <artifactId>xgboost4j-gpu_2.12</artifactId>
-    <version>1.3.1</version>
+    <version>1.3.3</version>
     <packaging>jar</packaging>
 
     <dependencies>

jvm-packages/xgboost4j-spark-gpu/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.3.1</version>
+        <version>1.3.3</version>
     </parent>
     <artifactId>xgboost4j-spark-gpu_2.12</artifactId>
     <build>
@@ -24,7 +24,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
-            <version>1.3.1</version>
+            <version>1.3.3</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>

jvm-packages/xgboost4j-spark/pom.xml

@@ -6,7 +6,7 @@
     <parent>
         <groupId>ml.dmlc</groupId>
         <artifactId>xgboost-jvm_2.12</artifactId>
-        <version>1.3.1</version>
+        <version>1.3.3</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>1.3.1</version>
+            <version>1.3.3</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>1.3.1</version>
+        <version>1.3.3</version>
     </parent>
     <artifactId>xgboost4j_2.12</artifactId>
-    <version>1.3.1</version>
+    <version>1.3.3</version>
     <packaging>jar</packaging>
 
     <dependencies>

python-package/xgboost/VERSION

@@ -1 +1 @@
-1.3.1
+1.3.3

python-package/xgboost/dask.py

@@ -1210,10 +1210,10 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
                                 early_stopping_rounds=early_stopping_rounds,
                                 verbose=verbose)
 
-    async def _predict_proba_async(self, data, output_margin=False,
+    async def _predict_proba_async(self, X, output_margin=False,
                                    base_margin=None):
         test_dmatrix = await DaskDMatrix(
-            client=self.client, data=data, base_margin=base_margin,
+            client=self.client, data=X, base_margin=base_margin,
             missing=self.missing
         )
         pred_probs = await predict(client=self.client,
@@ -1223,11 +1223,11 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
         return pred_probs
 
     # pylint: disable=arguments-differ,missing-docstring
-    def predict_proba(self, data, output_margin=False, base_margin=None):
+    def predict_proba(self, X, output_margin=False, base_margin=None):
         _assert_dask_support()
         return self.client.sync(
             self._predict_proba_async,
-            data,
+            X=X,
             output_margin=output_margin,
             base_margin=base_margin
         )

python-package/xgboost/sklearn.py

@@ -4,6 +4,7 @@
 import copy
 import warnings
 import json
+from typing import Optional
 import numpy as np
 from .core import Booster, DMatrix, XGBoostError, _deprecate_positional_args
 from .training import train
@@ -494,6 +495,13 @@ class XGBModel(XGBModelBase):
         # Delete the attribute after load
         self.get_booster().set_attr(scikit_learn=None)
 
+    def _set_evaluation_result(self, evals_result: Optional[dict]) -> None:
+        if evals_result:
+            for val in evals_result.items():
+                evals_result_key = list(val[1].keys())[0]
+                evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
+            self.evals_result_ = evals_result
+
     @_deprecate_positional_args
     def fit(self, X, y, *, sample_weight=None, base_margin=None,
             eval_set=None, eval_metric=None, early_stopping_rounds=None,
@@ -565,13 +573,6 @@ class XGBModel(XGBModelBase):
 
         """
         self.n_features_in_ = X.shape[1]
-
-        train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,
-                                base_margin=base_margin,
-                                missing=self.missing,
-                                nthread=self.n_jobs)
-        train_dmatrix.set_info(feature_weights=feature_weights)
-
         evals_result = {}
 
         train_dmatrix, evals = self._wrap_evaluation_matrices(
@@ -601,12 +602,7 @@ class XGBModel(XGBModelBase):
                               verbose_eval=verbose, xgb_model=xgb_model,
                               callbacks=callbacks)
 
-        if evals_result:
+        self._set_evaluation_result(evals_result)
-            for val in evals_result.items():
-                evals_result_key = list(val[1].keys())[0]
-                evals_result[val[0]][evals_result_key] = val[1][
-                    evals_result_key]
-            self.evals_result_ = evals_result
 
         if early_stopping_rounds is not None:
             self.best_score = self._Booster.best_score
@@ -919,12 +915,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
                               callbacks=callbacks)
 
         self.objective = xgb_options["objective"]
-        if evals_result:
+        self._set_evaluation_result(evals_result)
-            for val in evals_result.items():
-                evals_result_key = list(val[1].keys())[0]
-                evals_result[val[0]][
-                    evals_result_key] = val[1][evals_result_key]
-            self.evals_result_ = evals_result
 
         if early_stopping_rounds is not None:
             self.best_score = self._Booster.best_score
@@ -995,10 +986,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
             return self._le.inverse_transform(column_indexes)
         return column_indexes
 
-    def predict_proba(self, data, ntree_limit=None, validate_features=False,
+    def predict_proba(self, X, ntree_limit=None, validate_features=False,
                       base_margin=None):
-        """
+        """ Predict the probability of each `X` example being of a given class.
-        Predict the probability of each `data` example being of a given class.
 
         .. note:: This function is not thread safe
 
@@ -1008,21 +998,22 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
 
         Parameters
         ----------
-        data : array_like
+        X : array_like
             Feature matrix.
         ntree_limit : int
-            Limit number of trees in the prediction; defaults to best_ntree_limit if defined
+            Limit number of trees in the prediction; defaults to best_ntree_limit if
-            (i.e. it has been trained with early stopping), otherwise 0 (use all trees).
+            defined (i.e. it has been trained with early stopping), otherwise 0 (use all
+            trees).
         validate_features : bool
-            When this is True, validate that the Booster's and data's feature_names are identical.
+            When this is True, validate that the Booster's and data's feature_names are
-            Otherwise, it is assumed that the feature_names are the same.
+            identical.  Otherwise, it is assumed that the feature_names are the same.
 
         Returns
         -------
         prediction : numpy array
             a numpy array with the probability of each data example being of a given class.
         """
-        test_dmatrix = DMatrix(data, base_margin=base_margin,
+        test_dmatrix = DMatrix(X, base_margin=base_margin,
                                missing=self.missing, nthread=self.n_jobs)
         if ntree_limit is None:
             ntree_limit = getattr(self, "best_ntree_limit", 0)
@@ -1328,12 +1319,7 @@ class XGBRanker(XGBModel):
 
         self.objective = params["objective"]
 
-        if evals_result:
+        self._set_evaluation_result(evals_result)
-            for val in evals_result.items():
-                evals_result_key = list(val[1].keys())[0]
-                evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
-            self.evals_result = evals_result
-
         if early_stopping_rounds is not None:
             self.best_score = self._Booster.best_score
             self.best_iteration = self._Booster.best_iteration

python-package/xgboost/training.py

@@ -4,6 +4,7 @@
 """Training Library containing training routines."""
 import warnings
 import copy
+import json
 
 import numpy as np
 from .core import Booster, XGBoostError
@@ -123,7 +124,26 @@ def _train_internal(params, dtrain,
         bst.best_iteration = int(bst.attr('best_iteration'))
     else:
         bst.best_iteration = nboost - 1
+
+    config = json.loads(bst.save_config())
+    booster = config['learner']['gradient_booster']['name']
+    if booster == 'gblinear':
+        num_parallel_tree = 0
+    elif booster == 'dart':
+        num_parallel_tree = int(
+            config['learner']['gradient_booster']['gbtree']['gbtree_train_param'][
+                'num_parallel_tree'
+            ]
+        )
+    elif booster == 'gbtree':
+        num_parallel_tree = int(
+            config['learner']['gradient_booster']['gbtree_train_param'][
+                'num_parallel_tree']
+        )
+    else:
+        raise ValueError(f'Unknown booster: {booster}')
     bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
+
     # Copy to serialise and unserialise booster to reset state and free
     # training memory
     return bst.copy()
@@ -162,9 +182,10 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
         If there's more than one metric in the **eval_metric** parameter given in
         **params**, the last metric will be used for early stopping.
         If early stopping occurs, the model will have three additional fields:
-        ``bst.best_score``, ``bst.best_iteration`` and ``bst.best_ntree_limit``.
+        ``bst.best_score``, ``bst.best_iteration`` and ``bst.best_ntree_limit``.  Use
-        (Use ``bst.best_ntree_limit`` to get the correct value if
+        ``bst.best_ntree_limit`` to get the correct value if ``num_parallel_tree`` and/or
-        ``num_parallel_tree`` and/or ``num_class`` appears in the parameters)
+        ``num_class`` appears in the parameters.  ``best_ntree_limit`` is the result of
+        ``num_parallel_tree * best_iteration``.
     evals_result: dict
         This dictionary stores the evaluation results of all the items in watchlist.
 

rabit/include/rabit/internal/socket.h

@@ -25,6 +25,10 @@
 #include <sys/socket.h>
 #include <sys/ioctl.h>
 
+#if defined(__sun) || defined(sun)
+#include <sys/sockio.h>
+#endif  // defined(__sun) || defined(sun)
+
 #endif  // defined(_WIN32)
 
 #include <string>

src/gbm/gblinear_model.cc

@@ -10,10 +10,6 @@ namespace xgboost {
 namespace gbm {
 
 void GBLinearModel::SaveModel(Json* p_out) const {
-  using WeightType = std::remove_reference<decltype(std::declval<decltype(weight)>().back())>::type;
-  using JsonFloat = Number::Float;
-  static_assert(std::is_same<WeightType, JsonFloat>::value,
-                "Weight type should be of the same type with JSON float");
   auto& out = *p_out;
 
   size_t const n_weights = weight.size();

tests/ci_build/conda_env/win64_test.yml

@@ -9,7 +9,6 @@ dependencies:
 - scikit-learn
 - pandas
 - pytest
-- python-graphviz
 - boto3
 - hypothesis
 - jsonschema
@@ -17,3 +16,4 @@ dependencies:
 - pip:
   - cupy-cuda101
   - modin[all]
+  - graphviz

tests/python-gpu/test_gpu_with_dask.py

@@ -5,8 +5,10 @@ import numpy as np
 import asyncio
 import xgboost
 import subprocess
+import hypothesis
 from hypothesis import given, strategies, settings, note
 from hypothesis._settings import duration
+from hypothesis import HealthCheck
 from test_gpu_updaters import parameter_strategy
 
 if sys.platform.startswith("win"):
@@ -19,6 +21,11 @@ from test_with_dask import _get_client_workers  # noqa
 from test_with_dask import generate_array     # noqa
 import testing as tm                          # noqa
 
+if hasattr(HealthCheck, 'function_scoped_fixture'):
+    suppress = [HealthCheck.function_scoped_fixture]
+else:
+    suppress = hypothesis.utils.conventions.not_set
+
 
 try:
     import dask.dataframe as dd
@@ -161,19 +168,24 @@ class TestDistributedGPU:
             run_with_dask_dataframe(dxgb.DaskDMatrix, client)
             run_with_dask_dataframe(dxgb.DaskDeviceQuantileDMatrix, client)
 
-    @given(params=parameter_strategy, num_rounds=strategies.integers(1, 20),
+    @given(
-           dataset=tm.dataset_strategy)
+        params=parameter_strategy,
-    @settings(deadline=duration(seconds=120))
+        num_rounds=strategies.integers(1, 20),
+        dataset=tm.dataset_strategy,
+    )
+    @settings(deadline=duration(seconds=120), suppress_health_check=suppress)
     @pytest.mark.skipif(**tm.no_dask())
     @pytest.mark.skipif(**tm.no_dask_cuda())
-    @pytest.mark.parametrize('local_cuda_cluster', [{'n_workers': 2}], indirect=['local_cuda_cluster'])
+    @pytest.mark.parametrize(
+        "local_cuda_cluster", [{"n_workers": 2}], indirect=["local_cuda_cluster"]
+    )
     @pytest.mark.mgpu
     def test_gpu_hist(self, params, num_rounds, dataset, local_cuda_cluster):
         with Client(local_cuda_cluster) as client:
-            run_gpu_hist(params, num_rounds, dataset, dxgb.DaskDMatrix,
+            run_gpu_hist(params, num_rounds, dataset, dxgb.DaskDMatrix, client)
-                         client)
+            run_gpu_hist(
-            run_gpu_hist(params, num_rounds, dataset,
+                params, num_rounds, dataset, dxgb.DaskDeviceQuantileDMatrix, client
-                         dxgb.DaskDeviceQuantileDMatrix, client)
+            )
 
     @pytest.mark.skipif(**tm.no_cupy())
     @pytest.mark.skipif(**tm.no_dask())

tests/python/test_predict.py

@@ -33,9 +33,15 @@ def run_predict_leaf(predictor):
     y = rng.randint(low=0, high=classes, size=rows)
     m = xgb.DMatrix(X, y)
     booster = xgb.train(
-        {'num_parallel_tree': num_parallel_tree, 'num_class': classes,
+        {
-         'predictor': predictor, 'tree_method': 'hist'}, m,
+            "num_parallel_tree": num_parallel_tree,
-        num_boost_round=num_boost_round)
+            "num_class": classes,
+            "predictor": predictor,
+            "tree_method": "hist",
+        },
+        m,
+        num_boost_round=num_boost_round,
+    )
 
     empty = xgb.DMatrix(np.ones(shape=(0, cols)))
     empty_leaf = booster.predict(empty, pred_leaf=True)
@@ -52,12 +58,19 @@ def run_predict_leaf(predictor):
             end = classes * num_parallel_tree * (j + 1)
             layer = row[start: end]
             for c in range(classes):
-                tree_group = layer[c * num_parallel_tree:
+                tree_group = layer[c * num_parallel_tree: (c + 1) * num_parallel_tree]
-                                   (c+1) * num_parallel_tree]
                 assert tree_group.shape[0] == num_parallel_tree
                 # no subsampling so tree in same forest should output same
                 # leaf.
                 assert np.all(tree_group == tree_group[0])
+
+    ntree_limit = 2
+    sliced = booster.predict(
+        m, pred_leaf=True, ntree_limit=num_parallel_tree * ntree_limit
+    )
+    first = sliced[0, ...]
+
+    assert first.shape[0] == classes * num_parallel_tree * ntree_limit
     return leaf
 
 

tests/python/test_with_dask.py

@@ -8,7 +8,8 @@ import asyncio
 from sklearn.datasets import make_classification
 import os
 import subprocess
-from hypothesis import given, settings, note
+import hypothesis
+from hypothesis import given, settings, note, HealthCheck
 from test_updaters import hist_parameter_strategy, exact_parameter_strategy
 
 if sys.platform.startswith("win"):
@@ -17,6 +18,12 @@ if tm.no_dask()['condition']:
     pytest.skip(msg=tm.no_dask()['reason'], allow_module_level=True)
 
 
+if hasattr(HealthCheck, 'function_scoped_fixture'):
+    suppress = [HealthCheck.function_scoped_fixture]
+else:
+    suppress = hypothesis.utils.conventions.not_set
+
+
 try:
     from distributed import LocalCluster, Client, get_client
     from distributed.utils_test import client, loop, cluster_fixture
@@ -668,14 +675,14 @@ class TestWithDask:
 
     @given(params=hist_parameter_strategy,
            dataset=tm.dataset_strategy)
-    @settings(deadline=None)
+    @settings(deadline=None, suppress_health_check=suppress)
     def test_hist(self, params, dataset, client):
         num_rounds = 30
         self.run_updater_test(client, params, num_rounds, dataset, 'hist')
 
     @given(params=exact_parameter_strategy,
            dataset=tm.dataset_strategy)
-    @settings(deadline=None)
+    @settings(deadline=None, suppress_health_check=suppress)
     def test_approx(self, client, params, dataset):
         num_rounds = 30
         self.run_updater_test(client, params, num_rounds, dataset, 'approx')
@@ -795,7 +802,6 @@ class TestDaskCallbacks:
                 merged = xgb.dask._get_workers_from_data(train, evals=[(valid, 'Valid')])
                 assert len(merged) == 2
 
-
     def test_data_initialization(self):
         '''Assert each worker has the correct amount of data, and DMatrix initialization doesn't
         generate unnecessary copies of data.

tests/python/test_with_sklearn.py

@@ -78,6 +78,34 @@ def test_multiclass_classification():
         check_pred(preds4, labels, output_margin=False)
 
 
+def test_best_ntree_limit():
+    from sklearn.datasets import load_iris
+
+    X, y = load_iris(return_X_y=True)
+
+    def train(booster, forest):
+        rounds = 4
+        cls = xgb.XGBClassifier(
+            n_estimators=rounds, num_parallel_tree=forest, booster=booster
+        ).fit(
+            X, y, eval_set=[(X, y)], early_stopping_rounds=3
+        )
+
+        if forest:
+            assert cls.best_ntree_limit == rounds * forest
+        else:
+            assert cls.best_ntree_limit == 0
+
+        # best_ntree_limit is used by default, assert that under gblinear it's
+        # automatically ignored due to being 0.
+        cls.predict(X)
+
+    num_parallel_tree = 4
+    train('gbtree', num_parallel_tree)
+    train('dart', num_parallel_tree)
+    train('gblinear', None)
+
+
 def test_ranking():
     # generate random data
     x_train = np.random.rand(1000, 10)
@@ -94,6 +122,8 @@ def test_ranking():
     model = xgb.sklearn.XGBRanker(**params)
     model.fit(x_train, y_train, group=train_group,
               eval_set=[(x_valid, y_valid)], eval_group=[valid_group])
+    assert model.evals_result()
+
     pred = model.predict(x_test)
 
     train_data = xgb.DMatrix(x_train, y_train)