67c8c96784
- Introduce an abstract XGBoost Estimator - Update to the latest XGBoost parameters - Add all XGBoost parameters supported in XGBoost4j-spark. - Add setter and getter for these parameters. - Remove the deprecated parameters - Address the missing value handling - Remove any ETL operations in XGBoost - Rework the GPU plugin - Expand sanity tests for CPU and GPU consistency
163 lines
4.3 KiB
ReStructuredText
163 lines
4.3 KiB
ReStructuredText
########################################################
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Migration Guide: How to migrate to XGBoost-Spark jvm 3.x
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########################################################
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XGBoost-Spark jvm packages underwent significant modifications in version 3.0,
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which may cause compatibility issues with existing user code.
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This guide will walk you through the process of updating your code to ensure
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it's compatible with XGBoost-Spark 3.0 and later versions.
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**********************
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XGBoost Spark Packages
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**********************
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XGBoost-Spark 3.0 introduced a single uber package named xgboost-spark_2.12-3.0.0.jar, which bundles
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both xgboost4j and xgboost4j-spark. This means you can now simply use `xgboost-spark`` for your application.
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* For CPU
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.. code-block:: xml
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost-spark_${scala.binary.version}</artifactId>
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<version>3.0.0</version>
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</dependency>
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* For GPU
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.. code-block:: xml
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost-spark-gpu_${scala.binary.version}</artifactId>
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<version>3.0.0</version>
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</dependency>
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When submitting the XGBoost application to the Spark cluster, you only need to specify the single `xgboost-spark` package.
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* For CPU
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.. code-block:: bash
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spark-submit \
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--jars xgboost-spark_2.12-3.0.0.jar \
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... \
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* For GPU
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.. code-block:: bash
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spark-submit \
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--jars xgboost-spark_2.12-3.0.0.jar \
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... \
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**************
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XGBoost Ranking
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**************
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Learning to rank using XGBoostRegressor has been replaced by a dedicated `XGBoostRanker`, which is specifically designed
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to support ranking algorithms.
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.. code-block:: scala
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// before 3.0
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val regressor = new XGBoostRegressor().setObjective("rank:ndcg")
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// after 3.0
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val ranker = new XGBoostRanker()
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******************************
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XGBoost Constructor Parameters
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******************************
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XGBoost Spark now categorizes parameters into two groups: XGBoost-Spark parameters and XGBoost parameters.
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When constructing an XGBoost estimator, only XGBoost-specific parameters are permitted. XGBoost-Spark specific
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parameters must be configured using the estimator's setter methods. It's worth noting that
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`XGBoost Parameters <https://xgboost.readthedocs.io/en/stable/parameter.html>`_
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can be set both during construction and through the estimator's setter methods.
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.. code-block:: scala
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// before 3.0
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val xgboost_paras = Map(
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"eta" -> "1",
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"max_depth" -> "6",
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"objective" -> "binary:logistic",
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"num_round" -> 5,
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"num_workers" -> 1,
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"features" -> "feature_column",
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"label" -> "label_column",
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)
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val classifier = new XGBoostClassifier(xgboost_paras)
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// after 3.0
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val xgboost_paras = Map(
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"eta" -> "1",
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"max_depth" -> "6",
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"objective" -> "binary:logistic",
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)
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val classifier = new XGBoostClassifier(xgboost_paras)
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.setNumRound(5)
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.setNumWorkers(1)
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.setFeaturesCol("feature_column")
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.setLabelCol("label_column")
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// Or you can use setter to set all parameters
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val classifier = new XGBoostClassifier()
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.setNumRound(5)
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.setNumWorkers(1)
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.setFeaturesCol("feature_column")
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.setLabelCol("label_column")
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.setEta(1)
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.setMaxDepth(6)
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.setObjective("binary:logistic")
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******************
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Removed Parameters
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******************
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Starting from 3.0, below parameters are removed.
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- cacheTrainingSet
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If you wish to cache the training dataset, you have the option to implement caching
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in your code prior to fitting the data to an estimator.
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.. code-block:: scala
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val df = input.cache()
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val model = new XGBoostClassifier().fit(df)
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- trainTestRatio
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The following method can be employed to do the evaluation.
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.. code-block:: scala
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val Array(train, eval) = trainDf.randomSplit(Array(0.7, 0.3))
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val classifier = new XGBoostClassifer().setEvalDataset(eval)
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val model = classifier.fit(train)
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- tracker_conf
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The following method can be used to configure RabitTracker.
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.. code-block:: scala
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val classifier = new XGBoostClassifer()
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.setRabitTrackerTimeout(100)
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.setRabitTrackerHostIp("192.168.0.2")
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.setRabitTrackerPort(19203)
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- rabitRingReduceThreshold
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- rabitTimeout
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- rabitConnectRetry
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- singlePrecisionHistogram
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- lambdaBias
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- objectiveType
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