XGBoost4J-Spark Tutorial (version 0.8+)

XGBoost4J-Spark is a project aiming to seamlessly integrate XGBoost and Apache Spark by fitting XGBoost to Apache Spark’s MLLIB framework. With the integration, user can not only uses the high-performant algorithm implementation of XGBoost, but also leverages the powerful data processing engine of Spark for:

  • Feature Engineering: feature extraction, transformation, dimensionality reduction, and selection, etc.
  • Pipelines: constructing, evaluating, and tuning ML Pipelines
  • Persistence: persist and load machine learning models and even whole Pipelines

This tutorial is to cover the end-to-end process to build a machine learning pipeline with XGBoost4J-Spark. We will discuss

  • Using Spark to preprocess data to fit to XGBoost/XGBoost4J-Spark’s data interface
  • Training a XGBoost model with XGBoost4J-Spark
  • Serving XGBoost model (prediction) with Spark
  • Building a Machine Learning Pipeline with XGBoost4J-Spark
  • Running XGBoost4J-Spark in Production

Build an ML Application with XGBoost4J-Spark

Refer to XGBoost4J-Spark Dependency

Before we go into the tour of how to use XGBoost4J-Spark, we would bring a brief introduction about how to build a machine learning application with XGBoost4J-Spark. The first thing you need to do is to refer to the dependency in Maven Central.

You can add the following dependency in your pom.xml.

<dependency>
  <groupId>ml.dmlc</groupId>
  <artifactId>xgboost4j-spark</artifactId>
  <version>latest_version_num</version>
</dependency>

For the latest release version number, please check here.

We also publish some functionalities which would be included in the coming release in the form of snapshot version. To access these functionalities, you can add dependency to the snapshot artifacts. We publish snapshot version in github-based repo, so you can add the following repo in pom.xml:

<repository>
  <id>XGBoost4J-Spark Snapshot Repo</id>
  <name>XGBoost4J-Spark Snapshot Repo</name>
  <url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</url>
</repository>

and then refer to the snapshot dependency by adding:

<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j-spark</artifactId>
    <version>next_version_num-SNAPSHOT</version>
</dependency>

Note

XGBoost4J-Spark requires Apache Spark 2.4+

XGBoost4J-Spark now requires Apache Spark 2.4+. Latest versions of XGBoost4J-Spark uses facilities of org.apache.spark.ml.param.shared extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.

Also, make sure to install Spark directly from Apache website. Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark. Consult appropriate third parties to obtain their distribution of XGBoost.

Installation from maven repo

Note

Use of Python in XGBoost4J-Spark

By default, we use the tracker in dmlc-core to drive the training with XGBoost4J-Spark. It requires Python 2.7+. We also have an experimental Scala version of tracker which can be enabled by passing the parameter tracker_conf as scala.

Data Preparation

As aforementioned, XGBoost4J-Spark seamlessly integrates Spark and XGBoost. The integration enables users to apply various types of transformation over the training/test datasets with the convenient and powerful data processing framework, Spark.

In this section, we use Iris dataset as an example to showcase how we use Spark to transform raw dataset and make it fit to the data interface of XGBoost.

Iris dataset is shipped in CSV format. Each instance contains 4 features, “sepal length”, “sepal width”, “petal length” and “petal width”. In addition, it contains the “class” columnm, which is essentially the label with three possible values: “Iris Setosa”, “Iris Versicolour” and “Iris Virginica”.

Read Dataset with Spark’s Built-In Reader

The first thing in data transformation is to load the dataset as Spark’s structured data abstraction, DataFrame.

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}

val spark = SparkSession.builder().getOrCreate()
val schema = new StructType(Array(
  StructField("sepal length", DoubleType, true),
  StructField("sepal width", DoubleType, true),
  StructField("petal length", DoubleType, true),
  StructField("petal width", DoubleType, true),
  StructField("class", StringType, true)))
val rawInput = spark.read.schema(schema).csv("input_path")

At the first line, we create a instance of SparkSession which is the entry of any Spark program working with DataFrame. The schema variable defines the schema of DataFrame wrapping Iris data. With this explicitly set schema, we can define the columns’ name as well as their types; otherwise the column name would be the default ones derived by Spark, such as _col0, etc. Finally, we can use Spark’s built-in csv reader to load Iris csv file as a DataFrame named rawInput.

Spark also contains many built-in readers for other format. The latest version of Spark supports CSV, JSON, Parquet, and LIBSVM.

Transform Raw Iris Dataset

To make Iris dataset be recognizable to XGBoost, we need to

  1. Transform String-typed label, i.e. “class”, to Double-typed label.
  2. Assemble the feature columns as a vector to fit to the data interface of Spark ML framework.

To convert String-typed label to Double, we can use Spark’s built-in feature transformer StringIndexer.

import org.apache.spark.ml.feature.StringIndexer
val stringIndexer = new StringIndexer().
  setInputCol("class").
  setOutputCol("classIndex").
  fit(rawInput)
val labelTransformed = stringIndexer.transform(rawInput).drop("class")

With a newly created StringIndexer instance:

  1. we set input column, i.e. the column containing String-typed label
  2. we set output column, i.e. the column to contain the Double-typed label.
  3. Then we fit StringIndex with our input DataFrame rawInput, so that Spark internals can get information like total number of distinct values, etc.

Now we have a StringIndexer which is ready to be applied to our input DataFrame. To execute the transformation logic of StringIndexer, we transform the input DataFrame rawInput and to keep a concise DataFrame, we drop the column “class” and only keeps the feature columns and the transformed Double-typed label column (in the last line of the above code snippet).

The fit and transform are two key operations in MLLIB. Basically, fit produces a “transformer”, e.g. StringIndexer, and each transformer applies transform method on DataFrame to add new column(s) containing transformed features/labels or prediction results, etc. To understand more about fit and transform, You can find more details in here.

Similarly, we can use another transformer, VectorAssembler, to assemble feature columns “sepal length”, “sepal width”, “petal length” and “petal width” as a vector.

import org.apache.spark.ml.feature.VectorAssembler
val vectorAssembler = new VectorAssembler().
  setInputCols(Array("sepal length", "sepal width", "petal length", "petal width")).
  setOutputCol("features")
val xgbInput = vectorAssembler.transform(labelTransformed).select("features", "classIndex")

Now, we have a DataFrame containing only two columns, “features” which contains vector-represented “sepal length”, “sepal width”, “petal length” and “petal width” and “classIndex” which has Double-typed labels. A DataFrame like this (containing vector-represented features and numeric labels) can be fed to XGBoost4J-Spark’s training engine directly.

Training

XGBoost supports both regression and classification. While we use Iris dataset in this tutorial to show how we use XGBoost/XGBoost4J-Spark to resolve a multi-classes classification problem, the usage in Regression is very similar to classification.

To train a XGBoost model for classification, we need to claim a XGBoostClassifier first:

import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
val xgbParam = Map("eta" -> 0.1f,
      "max_depth" -> 2,
      "objective" -> "multi:softprob",
      "num_class" -> 3,
      "num_round" -> 100,
      "num_workers" -> 2)
val xgbClassifier = new XGBoostClassifier(xgbParam).
      setFeaturesCol("features").
      setLabelCol("classIndex")

The available parameters for training a XGBoost model can be found in here. In XGBoost4J-Spark, we support not only the default set of parameters but also the camel-case variant of these parameters to keep consistent with Spark’s MLLIB parameters.

Specifically, each parameter in this page has its equivalent form in XGBoost4J-Spark with camel case. For example, to set max_depth for each tree, you can pass parameter just like what we did in the above code snippet (as max_depth wrapped in a Map), or you can do it through setters in XGBoostClassifer:

val xgbClassifier = new XGBoostClassifier().
  setFeaturesCol("features").
  setLabelCol("classIndex")
xgbClassifier.setMaxDepth(2)

After we set XGBoostClassifier parameters and feature/label column, we can build a transformer, XGBoostClassificationModel by fitting XGBoostClassifier with the input DataFrame. This fit operation is essentially the training process and the generated model can then be used in prediction.

val xgbClassificationModel = xgbClassifier.fit(xgbInput)

Early Stopping

Early stopping is a feature to prevent the unnecessary training iterations. By specifying num_early_stopping_rounds or directly call setNumEarlyStoppingRounds over a XGBoostClassifier or XGBoostRegressor, we can define number of rounds if the evaluation metric going away from the best iteration and early stop training iterations.

In additional to num_early_stopping_rounds, you also need to define maximize_evaluation_metrics or call setMaximizeEvaluationMetrics to specify whether you want to maximize or minimize the metrics in training.

For example, we need to maximize the evaluation metrics (set maximize_evaluation_metrics with true), and set num_early_stopping_rounds with 5. The evaluation metric of 10th iteration is the maximum one until now. In the following iterations, if there is no evaluation metric greater than the 10th iteration’s (best one), the traning would be early stopped at 15th iteration.

Training with Evaluation Sets

You can also monitor the performance of the model during training with multiple evaluation datasets. By specifying eval_sets or call setEvalSets over a XGBoostClassifier or XGBoostRegressor, you can pass in multiple evaluation datasets typed as a Map from String to DataFrame.

Prediction

XGBoost4j-Spark supports two ways for model serving: batch prediction and single instance prediction.

Batch Prediction

When we get a model, either XGBoostClassificationModel or XGBoostRegressionModel, it takes a DataFrame, read the column containing feature vectors, predict for each feature vector, and output a new DataFrame with the following columns by default:

  • XGBoostClassificationModel will output margins (rawPredictionCol), probabilities(probabilityCol) and the eventual prediction labels (predictionCol) for each possible label.
  • XGBoostRegressionModel will output prediction label(predictionCol).

Batch prediction expects the user to pass the testset in the form of a DataFrame. XGBoost4J-Spark starts a XGBoost worker for each partition of DataFrame for parallel prediction and generates prediction results for the whole DataFrame in a batch.

val xgbClassificationModel = xgbClassifier.fit(xgbInput)
val results = xgbClassificationModel.transform(testSet)

With the above code snippet, we get a result DataFrame, result containing margin, probability for each class and the prediction for each instance

+-----------------+----------+--------------------+--------------------+----------+
|         features|classIndex|       rawPrediction|         probability|prediction|
+-----------------+----------+--------------------+--------------------+----------+
|[5.1,3.5,1.4,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
|[4.9,3.0,1.4,0.2]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
|[4.7,3.2,1.3,0.2]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
|[4.6,3.1,1.5,0.2]|       0.0|[3.45569849014282...|[0.99636095762252...|       0.0|
|[5.0,3.6,1.4,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
|[5.4,3.9,1.7,0.4]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
|[4.6,3.4,1.4,0.3]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
|[5.0,3.4,1.5,0.2]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
|[4.4,2.9,1.4,0.2]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
|[4.9,3.1,1.5,0.1]|       0.0|[3.45569849014282...|[0.99636095762252...|       0.0|
|[5.4,3.7,1.5,0.2]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
|[4.8,3.4,1.6,0.2]|       0.0|[3.45569849014282...|[0.99643349647521...|       0.0|
|[4.8,3.0,1.4,0.1]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
|[4.3,3.0,1.1,0.1]|       0.0|[3.45569849014282...|[0.99618089199066...|       0.0|
|[5.8,4.0,1.2,0.2]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
|[5.7,4.4,1.5,0.4]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
|[5.4,3.9,1.3,0.4]|       0.0|[3.45569849014282...|[0.99428516626358...|       0.0|
|[5.1,3.5,1.4,0.3]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
|[5.7,3.8,1.7,0.3]|       0.0|[3.45569849014282...|[0.97809928655624...|       0.0|
|[5.1,3.8,1.5,0.3]|       0.0|[3.45569849014282...|[0.99579632282257...|       0.0|
+-----------------+----------+--------------------+--------------------+----------+

Single instance prediction

XGBoostClassificationModel or XGBoostRegressionModel support make prediction on single instance as well. It accepts a single Vector as feature, and output the prediction label.

However, the overhead of single-instance prediction is high due to the internal overhead of XGBoost, use it carefully!

val features = xgbInput.head().getAs[Vector]("features")
val result = xgbClassificationModel.predict(features)

Model Persistence

Model and pipeline persistence

A data scientist produces an ML model and hands it over to an engineering team for deployment in a production environment. Reversely, a trained model may be used by data scientists, for example as a baseline, across the process of data exploration. So it’s important to support model persistence to make the models available across usage scenarios and programming languages.

XGBoost4j-Spark supports saving and loading XGBoostClassifier/XGBoostClassificationModel and XGBoostRegressor/XGBoostRegressionModel. It also supports saving and loading a ML pipeline which includes these estimators and models.

We can save the XGBoostClassificationModel to file system:

val xgbClassificationModelPath = "/tmp/xgbClassificationModel"
xgbClassificationModel.write.overwrite().save(xgbClassificationModelPath)

and then loading the model in another session:

import ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel

val xgbClassificationModel2 = XGBoostClassificationModel.load(xgbClassificationModelPath)
xgbClassificationModel2.transform(xgbInput)

With regards to ML pipeline save and load, please refer the next section.

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. XGBoost4j-Spark supports export model to local by:

val nativeModelPath = "/tmp/nativeModel"
xgbClassificationModel.nativeBooster.saveModel(nativeModelPath)

Then we can load this model with single node Python XGBoost:

import xgboost as xgb
bst = xgb.Booster({'nthread': 4})
bst.load_model(nativeModelPath)

Note

Using HDFS and S3 for exporting the models with nativeBooster.saveModel()

When interacting with other language bindings, XGBoost also supports saving-models-to and loading-models-from file systems other than the local one. You can use HDFS and S3 by prefixing the path with hdfs:// and s3:// respectively. However, for this capability, you must do one of the following:

  1. Build XGBoost4J-Spark with the steps described in here, but turning USE_HDFS (or USE_S3, etc. in the same place) switch on. With this approach, you can reuse the above code example by replacing “nativeModelPath” with a HDFS path.

    • However, if you build with USE_HDFS, etc. you have to ensure that the involved shared object file, e.g. libhdfs.so, is put in the LIBRARY_PATH of your cluster. To avoid the complicated cluster environment configuration, choose the other option.
  2. Use bindings of HDFS, S3, etc. to pass model files around. Here are the steps (taking HDFS as an example):

    • Create a new file with

      val outputStream = fs.create("hdfs_path")
      

      where “fs” is an instance of org.apache.hadoop.fs.FileSystem class in Hadoop.

    • Pass the returned OutputStream in the first step to nativeBooster.saveModel():

      xgbClassificationModel.nativeBooster.saveModel(outputStream)
      
    • Download file in other languages from HDFS and load with the pre-built (without the requirement of libhdfs.so) version of XGBoost. (The function “download_from_hdfs” is a helper function to be implemented by the user)

      import xgboost as xgb
      bst = xgb.Booster({'nthread': 4})
      local_path = download_from_hdfs("hdfs_path")
      bst.load_model(local_path)
      

Note

Consistency issue between XGBoost4J-Spark and other bindings

There is a consistency issue between XGBoost4J-Spark and other language bindings of XGBoost.

When users use Spark to load training/test data in LIBSVM format with the following code snippet:

spark.read.format("libsvm").load("trainingset_libsvm")

Spark assumes that the dataset is using 1-based indexing (feature indices staring with 1). However, when you do prediction with other bindings of XGBoost (e.g. Python API of XGBoost), XGBoost assumes that the dataset is using 0-based indexing (feature indices starting with 0) by default. It creates a pitfall for the users who train model with Spark but predict with the dataset in the same format in other bindings of XGBoost. The solution is to transform the dataset to 0-based indexing before you predict with, for example, Python API, or you append ?indexing_mode=1 to your file path when loading with DMatirx. For example in Python:

xgb.DMatrix('test.libsvm?indexing_mode=1')

Building a ML Pipeline with XGBoost4J-Spark

Basic ML Pipeline

Spark ML pipeline can combine multiple algorithms or functions into a single pipeline. It covers from feature extraction, transformation, selection to model training and prediction. XGBoost4j-Spark makes it feasible to embed XGBoost into such a pipeline seamlessly. The following example shows how to build such a pipeline consisting of Spark MLlib feature transformer and XGBoostClassifier estimator.

We still use Iris dataset and the rawInput DataFrame. First we need to split the dataset into training and test dataset.

val Array(training, test) = rawInput.randomSplit(Array(0.8, 0.2), 123)

The we build the ML pipeline which includes 4 stages:

  • Assemble all features into a single vector column.
  • From string label to indexed double label.
  • Use XGBoostClassifier to train classification model.
  • Convert indexed double label back to original string label.

We have shown the first three steps in the earlier sections, and the last step is finished with a new transformer IndexToString:

val labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("realLabel")
.setLabels(stringIndexer.labels)

We need to organize these steps as a Pipeline in Spark ML framework and evaluate the whole pipeline to get a PipelineModel:

import org.apache.spark.ml.feature._
import org.apache.spark.ml.Pipeline

val pipeline = new Pipeline()
    .setStages(Array(assembler, stringIndexer, booster, labelConverter))
val model = pipeline.fit(training)

After we get the PipelineModel, we can make prediction on the test dataset and evaluate the model accuracy.

import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

val prediction = model.transform(test)
val evaluator = new MulticlassClassificationEvaluator()
val accuracy = evaluator.evaluate(prediction)

Pipeline with Hyper-parameter Tunning

The most critical operation to maximize the power of XGBoost is to select the optimal parameters for the model. Tuning parameters manually is a tedious and labor-consuming process. With the latest version of XGBoost4J-Spark, we can utilize the Spark model selecting tool to automate this process.

The following example shows the code snippet utilizing CrossValidation and MulticlassClassificationEvaluator to search the optimal combination of two XGBoost parameters, max_depth and eta. (See XGBoost Parameters.) The model producing the maximum accuracy defined by MulticlassClassificationEvaluator is selected and used to generate the prediction for the test set.

import org.apache.spark.ml.tuning._
import org.apache.spark.ml.PipelineModel
import ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel

val paramGrid = new ParamGridBuilder()
    .addGrid(booster.maxDepth, Array(3, 8))
    .addGrid(booster.eta, Array(0.2, 0.6))
    .build()
val cv = new CrossValidator()
    .setEstimator(pipeline)
    .setEvaluator(evaluator)
    .setEstimatorParamMaps(paramGrid)
    .setNumFolds(3)

val cvModel = cv.fit(training)

val bestModel = cvModel.bestModel.asInstanceOf[PipelineModel].stages(2)
    .asInstanceOf[XGBoostClassificationModel]
bestModel.extractParamMap()

Run XGBoost4J-Spark in Production

XGBoost4J-Spark is one of the most important steps to bring XGBoost to production environment easier. In this section, we introduce three key features to run XGBoost4J-Spark in production.

Parallel/Distributed Training

The massive size of training dataset is one of the most significant characteristics in production environment. To ensure that training in XGBoost scales with the data size, XGBoost4J-Spark bridges the distributed/parallel processing framework of Spark and the parallel/distributed training mechanism of XGBoost.

In XGBoost4J-Spark, each XGBoost worker is wrapped by a Spark task and the training dataset in Spark’s memory space is fed to XGBoost workers in a transparent approach to the user.

In the code snippet where we build XGBoostClassifier, we set parameter num_workers (or numWorkers). This parameter controls how many parallel workers we want to have when training a XGBoostClassificationModel.

Note

Regarding OpenMP optimization

By default, we allocate a core per each XGBoost worker. Therefore, the OpenMP optimization within each XGBoost worker does not take effect and the parallelization of training is achieved by running multiple workers (i.e. Spark tasks) at the same time.

If you do want OpenMP optimization, you have to

  1. set nthread to a value larger than 1 when creating XGBoostClassifier/XGBoostRegressor
  2. set spark.task.cpus in Spark to the same value as nthread

Gang Scheduling

XGBoost uses AllReduce. algorithm to synchronize the stats, e.g. histogram values, of each worker during training. Therefore XGBoost4J-Spark requires that all of nthread * numWorkers cores should be available before the training runs.

In the production environment where many users share the same cluster, it’s hard to guarantee that your XGBoost4J-Spark application can get all requested resources for every run. By default, the communication layer in XGBoost will block the whole application when it requires more resources to be available. This process usually brings unnecessary resource waste as it keeps the ready resources and try to claim more. Additionally, this usually happens silently and does not bring the attention of users.

XGBoost4J-Spark allows the user to setup a timeout threshold for claiming resources from the cluster. If the application cannot get enough resources within this time period, the application would fail instead of wasting resources for hanging long. To enable this feature, you can set with XGBoostClassifier/XGBoostRegressor:

xgbClassifier.setTimeoutRequestWorkers(60000L)

or pass in timeout_request_workers in xgbParamMap when building XGBoostClassifier:

val xgbParam = Map("eta" -> 0.1f,
   "max_depth" -> 2,
   "objective" -> "multi:softprob",
   "num_class" -> 3,
   "num_round" -> 100,
   "num_workers" -> 2,
   "timeout_request_workers" -> 60000L)
val xgbClassifier = new XGBoostClassifier(xgbParam).
    setFeaturesCol("features").
    setLabelCol("classIndex")

If XGBoost4J-Spark cannot get enough resources for running two XGBoost workers, the application would fail. Users can have external mechanism to monitor the status of application and get notified for such case.

Checkpoint During Training

Transient failures are also commonly seen in production environment. To simplify the design of XGBoost, we stop training if any of the distributed workers fail. However, if the training fails after having been through a long time, it would be a great waste of resources.

We support creating checkpoint during training to facilitate more efficient recovery from failture. To enable this feature, you can set how many iterations we build each checkpoint with setCheckpointInterval and the location of checkpoints with setCheckpointPath:

xgbClassifier.setCheckpointInterval(2)
xgbClassifier.setCheckpointPath("/checkpoint_path")

An equivalent way is to pass in parameters in XGBoostClassifier’s constructor:

val xgbParam = Map("eta" -> 0.1f,
   "max_depth" -> 2,
   "objective" -> "multi:softprob",
   "num_class" -> 3,
   "num_round" -> 100,
   "num_workers" -> 2,
   "checkpoint_path" -> "/checkpoints_path",
   "checkpoint_interval" -> 2)
val xgbClassifier = new XGBoostClassifier(xgbParam).
    setFeaturesCol("features").
    setLabelCol("classIndex")

If the training failed during these 100 rounds, the next run of training would start by reading the latest checkpoint file in /checkpoints_path and start from the iteration when the checkpoint was built until to next failure or the specified 100 rounds.