Spark ML Programming Guide

spark.ml is a new package introduced in Spark 1.2, which aims to provide a uniform set of high-level APIs that help users create and tune practical machine learning pipelines. It is currently an alpha component, and we would like to hear back from the community about how it fits real-world use cases and how it could be improved.

Note that we will keep supporting and adding features to spark.mllib along with the development of spark.ml. Users should be comfortable using spark.mllib features and expect more features coming. Developers should contribute new algorithms to spark.mllib and can optionally contribute to spark.ml.

Table of Contents

Main Concepts
Code Examples
Dependencies
Migration Guide
- From 1.2 to 1.3

Main Concepts

Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow. This section covers the key concepts introduced by the Spark ML API.

ML Dataset: Spark ML uses the DataFrame from Spark SQL as a dataset which can hold a variety of data types. E.g., a dataset could have different columns storing text, feature vectors, true labels, and predictions.
Transformer: A Transformer is an algorithm which can transform one DataFrame into another DataFrame. E.g., an ML model is a Transformer which transforms an RDD with features into an RDD with predictions.
Estimator: An Estimator is an algorithm which can be fit on a DataFrame to produce a Transformer. E.g., a learning algorithm is an Estimator which trains on a dataset and produces a model.
Pipeline: A Pipeline chains multiple Transformers and Estimators together to specify an ML workflow.
Param: All Transformers and Estimators now share a common API for specifying parameters.

ML Dataset

Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data. Spark ML adopts the DataFrame from Spark SQL in order to support a variety of data types under a unified Dataset concept.

DataFrame supports many basic and structured types; see the Spark SQL datatype reference for a list of supported types. In addition to the types listed in the Spark SQL guide, DataFrame can use ML Vector types.

A DataFrame can be created either implicitly or explicitly from a regular RDD. See the code examples below and the Spark SQL programming guide for examples.

Columns in a DataFrame are named. The code examples below use names such as “text,” “features,” and “label.”

ML Algorithms

Transformers

A Transformer is an abstraction which includes feature transformers and learned models. Technically, a Transformer implements a method transform() which converts one DataFrame into another, generally by appending one or more columns. For example:

A feature transformer might take a dataset, read a column (e.g., text), convert it into a new column (e.g., feature vectors), append the new column to the dataset, and output the updated dataset.
A learning model might take a dataset, read the column containing feature vectors, predict the label for each feature vector, append the labels as a new column, and output the updated dataset.

Estimators

An Estimator abstracts the concept of a learning algorithm or any algorithm which fits or trains on data. Technically, an Estimator implements a method fit() which accepts a DataFrame and produces a Transformer. For example, a learning algorithm such as LogisticRegression is an Estimator, and calling fit() trains a LogisticRegressionModel, which is a Transformer.

Properties of ML Algorithms

Transformers and Estimators are both stateless. In the future, stateful algorithms may be supported via alternative concepts.

Each instance of a Transformer or Estimator has a unique ID, which is useful in specifying parameters (discussed below).

Pipeline

In machine learning, it is common to run a sequence of algorithms to process and learn from data. E.g., a simple text document processing workflow might include several stages:

Split each document’s text into words.
Convert each document’s words into a numerical feature vector.
Learn a prediction model using the feature vectors and labels.

Spark ML represents such a workflow as a Pipeline, which consists of a sequence of PipelineStages (Transformers and Estimators) to be run in a specific order. We will use this simple workflow as a running example in this section.

How It Works

A Pipeline is specified as a sequence of stages, and each stage is either a Transformer or an Estimator. These stages are run in order, and the input dataset is modified as it passes through each stage. For Transformer stages, the transform() method is called on the dataset. For Estimator stages, the fit() method is called to produce a Transformer (which becomes part of the PipelineModel, or fitted Pipeline), and that Transformer’s transform() method is called on the dataset.

We illustrate this for the simple text document workflow. The figure below is for the training time usage of a Pipeline.

Spark ML Pipeline Example

Above, the top row represents a Pipeline with three stages. The first two (Tokenizer and HashingTF) are Transformers (blue), and the third (LogisticRegression) is an Estimator (red). The bottom row represents data flowing through the pipeline, where cylinders indicate DataFrames. The Pipeline.fit() method is called on the original dataset which has raw text documents and labels. The Tokenizer.transform() method splits the raw text documents into words, adding a new column with words into the dataset. The HashingTF.transform() method converts the words column into feature vectors, adding a new column with those vectors to the dataset. Now, since LogisticRegression is an Estimator, the Pipeline first calls LogisticRegression.fit() to produce a LogisticRegressionModel. If the Pipeline had more stages, it would call the LogisticRegressionModel’s transform() method on the dataset before passing the dataset to the next stage.

A Pipeline is an Estimator. Thus, after a Pipeline’s fit() method runs, it produces a PipelineModel which is a Transformer. This PipelineModel is used at test time; the figure below illustrates this usage.

Spark ML PipelineModel Example

In the figure above, the PipelineModel has the same number of stages as the original Pipeline, but all Estimators in the original Pipeline have become Transformers. When the PipelineModel’s transform() method is called on a test dataset, the data are passed through the Pipeline in order. Each stage’s transform() method updates the dataset and passes it to the next stage.

Pipelines and PipelineModels help to ensure that training and test data go through identical feature processing steps.

Details

DAG Pipelines: A Pipeline’s stages are specified as an ordered array. The examples given here are all for linear Pipelines, i.e., Pipelines in which each stage uses data produced by the previous stage. It is possible to create non-linear Pipelines as long as the data flow graph forms a Directed Acyclic Graph (DAG). This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters). If the Pipeline forms a DAG, then the stages must be specified in topological order.

Runtime checking: Since Pipelines can operate on datasets with varied types, they cannot use compile-time type checking. Pipelines and PipelineModels instead do runtime checking before actually running the Pipeline. This type checking is done using the dataset schema, a description of the data types of columns in the DataFrame.

Parameters

Spark ML Estimators and Transformers use a uniform API for specifying parameters.

A Param is a named parameter with self-contained documentation. A ParamMap is a set of (parameter, value) pairs.

There are two main ways to pass parameters to an algorithm:

Set parameters for an instance. E.g., if lr is an instance of LogisticRegression, one could call lr.setMaxIter(10) to make lr.fit() use at most 10 iterations. This API resembles the API used in MLlib.
Pass a ParamMap to fit() or transform(). Any parameters in the ParamMap will override parameters previously specified via setter methods.

Parameters belong to specific instances of Estimators and Transformers. For example, if we have two LogisticRegression instances lr1 and lr2, then we can build a ParamMap with both maxIter parameters specified: ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20). This is useful if there are two algorithms with the maxIter parameter in a Pipeline.

Code Examples

This section gives code examples illustrating the functionality discussed above. There is not yet documentation for specific algorithms in Spark ML. For more info, please refer to the API Documentation. Spark ML algorithms are currently wrappers for MLlib algorithms, and the MLlib programming guide has details on specific algorithms.

Example: Estimator, Transformer, and Param

This example covers the concepts of Estimator, Transformer, and Param.

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.sql.{Row, SQLContext}

val conf = new SparkConf().setAppName("SimpleParamsExample")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._

// Prepare training data.
// We use LabeledPoint, which is a case class.  Spark SQL can convert RDDs of case classes
// into DataFrames, where it uses the case class metadata to infer the schema.
val training = sc.parallelize(Seq(
  LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
  LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
  LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
  LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))

// Create a LogisticRegression instance.  This instance is an Estimator.
val lr = new LogisticRegression()
// Print out the parameters, documentation, and any default values.
println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")

// We may set parameters using setter methods.
lr.setMaxIter(10)
  .setRegParam(0.01)

// Learn a LogisticRegression model.  This uses the parameters stored in lr.
val model1 = lr.fit(training.toDF)
// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
// we can view the parameters it used during fit().
// This prints the parameter (name: value) pairs, where names are unique IDs for this
// LogisticRegression instance.
println("Model 1 was fit using parameters: " + model1.fittingParamMap)

// We may alternatively specify parameters using a ParamMap,
// which supports several methods for specifying parameters.
val paramMap = ParamMap(lr.maxIter -> 20)
paramMap.put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
paramMap.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.

// One can also combine ParamMaps.
val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
val paramMapCombined = paramMap ++ paramMap2

// Now learn a new model using the paramMapCombined parameters.
// paramMapCombined overrides all parameters set earlier via lr.set* methods.
val model2 = lr.fit(training.toDF, paramMapCombined)
println("Model 2 was fit using parameters: " + model2.fittingParamMap)

// Prepare test data.
val test = sc.parallelize(Seq(
  LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
  LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
  LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))

// Make predictions on test data using the Transformer.transform() method.
// LogisticRegression.transform will only use the 'features' column.
// Note that model2.transform() outputs a 'myProbability' column instead of the usual
// 'probability' column since we renamed the lr.probabilityCol parameter previously.
model2.transform(test.toDF)
  .select("features", "label", "myProbability", "prediction")
  .collect()
  .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>
    println("($features, $label) -> prob=$prob, prediction=$prediction")
  }

sc.stop()

import java.util.List;
import com.google.common.collect.Lists;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.ml.param.ParamMap;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.Row;

SparkConf conf = new SparkConf().setAppName("JavaSimpleParamsExample");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext jsql = new SQLContext(jsc);

// Prepare training data.
// We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
// into DataFrames, where it uses the bean metadata to infer the schema.
List<LabeledPoint> localTraining = Lists.newArrayList(
  new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
  new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
  new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
  new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class);

// Create a LogisticRegression instance.  This instance is an Estimator.
LogisticRegression lr = new LogisticRegression();
// Print out the parameters, documentation, and any default values.
System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");

// We may set parameters using setter methods.
lr.setMaxIter(10)
  .setRegParam(0.01);

// Learn a LogisticRegression model.  This uses the parameters stored in lr.
LogisticRegressionModel model1 = lr.fit(training);
// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
// we can view the parameters it used during fit().
// This prints the parameter (name: value) pairs, where names are unique IDs for this
// LogisticRegression instance.
System.out.println("Model 1 was fit using parameters: " + model1.fittingParamMap());

// We may alternatively specify parameters using a ParamMap.
ParamMap paramMap = new ParamMap();
paramMap.put(lr.maxIter().w(20)); // Specify 1 Param.
paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
paramMap.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.

// One can also combine ParamMaps.
ParamMap paramMap2 = new ParamMap();
paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name
ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);

// Now learn a new model using the paramMapCombined parameters.
// paramMapCombined overrides all parameters set earlier via lr.set* methods.
LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
System.out.println("Model 2 was fit using parameters: " + model2.fittingParamMap());

// Prepare test documents.
List<LabeledPoint> localTest = Lists.newArrayList(
    new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
    new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
    new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class);

// Make predictions on test documents using the Transformer.transform() method.
// LogisticRegression.transform will only use the 'features' column.
// Note that model2.transform() outputs a 'myProbability' column instead of the usual
// 'probability' column since we renamed the lr.probabilityCol parameter previously.
DataFrame results = model2.transform(test);
for (Row r: results.select("features", "label", "myProbability", "prediction").collect()) {
  System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
      + ", prediction=" + r.get(3));
}

jsc.stop();

Example: Pipeline

This example follows the simple text document Pipeline illustrated in the figures above.

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.sql.{Row, SQLContext}

// Labeled and unlabeled instance types.
// Spark SQL can infer schema from case classes.
case class LabeledDocument(id: Long, text: String, label: Double)
case class Document(id: Long, text: String)

// Set up contexts.  Import implicit conversions to DataFrame from sqlContext.
val conf = new SparkConf().setAppName("SimpleTextClassificationPipeline")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._

// Prepare training documents, which are labeled.
val training = sc.parallelize(Seq(
  LabeledDocument(0L, "a b c d e spark", 1.0),
  LabeledDocument(1L, "b d", 0.0),
  LabeledDocument(2L, "spark f g h", 1.0),
  LabeledDocument(3L, "hadoop mapreduce", 0.0)))

// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
  .setInputCol("text")
  .setOutputCol("words")
val hashingTF = new HashingTF()
  .setNumFeatures(1000)
  .setInputCol(tokenizer.getOutputCol)
  .setOutputCol("features")
val lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.01)
val pipeline = new Pipeline()
  .setStages(Array(tokenizer, hashingTF, lr))

// Fit the pipeline to training documents.
val model = pipeline.fit(training.toDF)

// Prepare test documents, which are unlabeled.
val test = sc.parallelize(Seq(
  Document(4L, "spark i j k"),
  Document(5L, "l m n"),
  Document(6L, "mapreduce spark"),
  Document(7L, "apache hadoop")))

// Make predictions on test documents.
model.transform(test.toDF)
  .select("id", "text", "probability", "prediction")
  .collect()
  .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
    println("($id, $text) --> prob=$prob, prediction=$prediction")
  }

sc.stop()

import java.util.List;
import com.google.common.collect.Lists;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.feature.HashingTF;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;

// Labeled and unlabeled instance types.
// Spark SQL can infer schema from Java Beans.
public class Document implements Serializable {
  private Long id;
  private String text;

  public Document(Long id, String text) {
    this.id = id;
    this.text = text;
  }

  public Long getId() { return this.id; }
  public void setId(Long id) { this.id = id; }

  public String getText() { return this.text; }
  public void setText(String text) { this.text = text; }
}

public class LabeledDocument extends Document implements Serializable {
  private Double label;

  public LabeledDocument(Long id, String text, Double label) {
    super(id, text);
    this.label = label;
  }

  public Double getLabel() { return this.label; }
  public void setLabel(Double label) { this.label = label; }
}

// Set up contexts.
SparkConf conf = new SparkConf().setAppName("JavaSimpleTextClassificationPipeline");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext jsql = new SQLContext(jsc);

// Prepare training documents, which are labeled.
List<LabeledDocument> localTraining = Lists.newArrayList(
  new LabeledDocument(0L, "a b c d e spark", 1.0),
  new LabeledDocument(1L, "b d", 0.0),
  new LabeledDocument(2L, "spark f g h", 1.0),
  new LabeledDocument(3L, "hadoop mapreduce", 0.0));
DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class);

// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
Tokenizer tokenizer = new Tokenizer()
  .setInputCol("text")
  .setOutputCol("words");
HashingTF hashingTF = new HashingTF()
  .setNumFeatures(1000)
  .setInputCol(tokenizer.getOutputCol())
  .setOutputCol("features");
LogisticRegression lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.01);
Pipeline pipeline = new Pipeline()
  .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});

// Fit the pipeline to training documents.
PipelineModel model = pipeline.fit(training);

// Prepare test documents, which are unlabeled.
List<Document> localTest = Lists.newArrayList(
  new Document(4L, "spark i j k"),
  new Document(5L, "l m n"),
  new Document(6L, "mapreduce spark"),
  new Document(7L, "apache hadoop"));
DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);

// Make predictions on test documents.
DataFrame predictions = model.transform(test);
for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) {
  System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
      + ", prediction=" + r.get(3));
}

jsc.stop();

from pyspark import SparkContext
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.sql import Row, SQLContext

sc = SparkContext(appName="SimpleTextClassificationPipeline")
sqlCtx = SQLContext(sc)

# Prepare training documents, which are labeled.
LabeledDocument = Row("id", "text", "label")
training = sc.parallelize([(0L, "a b c d e spark", 1.0),
                           (1L, "b d", 0.0),
                           (2L, "spark f g h", 1.0),
                           (3L, "hadoop mapreduce", 0.0)]) \
    .map(lambda x: LabeledDocument(*x)).toDF()

# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.01)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])

# Fit the pipeline to training documents.
model = pipeline.fit(training)

# Prepare test documents, which are unlabeled.
Document = Row("id", "text")
test = sc.parallelize([(4L, "spark i j k"),
                       (5L, "l m n"),
                       (6L, "mapreduce spark"),
                       (7L, "apache hadoop")]) \
    .map(lambda x: Document(*x)).toDF()

# Make predictions on test documents and print columns of interest.
prediction = model.transform(test)
selected = prediction.select("id", "text", "prediction")
for row in selected.collect():
    print row

sc.stop()

Example: Model Selection via Cross-Validation

An important task in ML is model selection, or using data to find the best model or parameters for a given task. This is also called tuning. Pipelines facilitate model selection by making it easy to tune an entire Pipeline at once, rather than tuning each element in the Pipeline separately.

Currently, spark.ml supports model selection using the CrossValidator class, which takes an Estimator, a set of ParamMaps, and an Evaluator. CrossValidator begins by splitting the dataset into a set of folds which are used as separate training and test datasets; e.g., with $k=3$ folds, CrossValidator will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. CrossValidator iterates through the set of ParamMaps. For each ParamMap, it trains the given Estimator and evaluates it using the given Evaluator. The ParamMap which produces the best evaluation metric (averaged over the $k$ folds) is selected as the best model. CrossValidator finally fits the Estimator using the best ParamMap and the entire dataset.

The following example demonstrates using CrossValidator to select from a grid of parameters. To help construct the parameter grid, we use the ParamGridBuilder utility.

Note that cross-validation over a grid of parameters is expensive. E.g., in the example below, the parameter grid has 3 values for hashingTF.numFeatures and 2 values for lr.regParam, and CrossValidator uses 2 folds. This multiplies out to $(3 \times 2) \times 2 = 12$ different models being trained. In realistic settings, it can be common to try many more parameters and use more folds ( $k=3$ and $k=10$ are common). In other words, using CrossValidator can be very expensive. However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning.

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.sql.{Row, SQLContext}

val conf = new SparkConf().setAppName("CrossValidatorExample")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._

// Prepare training documents, which are labeled.
val training = sc.parallelize(Seq(
  LabeledDocument(0L, "a b c d e spark", 1.0),
  LabeledDocument(1L, "b d", 0.0),
  LabeledDocument(2L, "spark f g h", 1.0),
  LabeledDocument(3L, "hadoop mapreduce", 0.0),
  LabeledDocument(4L, "b spark who", 1.0),
  LabeledDocument(5L, "g d a y", 0.0),
  LabeledDocument(6L, "spark fly", 1.0),
  LabeledDocument(7L, "was mapreduce", 0.0),
  LabeledDocument(8L, "e spark program", 1.0),
  LabeledDocument(9L, "a e c l", 0.0),
  LabeledDocument(10L, "spark compile", 1.0),
  LabeledDocument(11L, "hadoop software", 0.0)))

// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
  .setInputCol("text")
  .setOutputCol("words")
val hashingTF = new HashingTF()
  .setInputCol(tokenizer.getOutputCol)
  .setOutputCol("features")
val lr = new LogisticRegression()
  .setMaxIter(10)
val pipeline = new Pipeline()
  .setStages(Array(tokenizer, hashingTF, lr))

// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
// This will allow us to jointly choose parameters for all Pipeline stages.
// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
val crossval = new CrossValidator()
  .setEstimator(pipeline)
  .setEvaluator(new BinaryClassificationEvaluator)
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
val paramGrid = new ParamGridBuilder()
  .addGrid(hashingTF.numFeatures, Array(10, 100, 1000))
  .addGrid(lr.regParam, Array(0.1, 0.01))
  .build()
crossval.setEstimatorParamMaps(paramGrid)
crossval.setNumFolds(2) // Use 3+ in practice

// Run cross-validation, and choose the best set of parameters.
val cvModel = crossval.fit(training.toDF)

// Prepare test documents, which are unlabeled.
val test = sc.parallelize(Seq(
  Document(4L, "spark i j k"),
  Document(5L, "l m n"),
  Document(6L, "mapreduce spark"),
  Document(7L, "apache hadoop")))

// Make predictions on test documents. cvModel uses the best model found (lrModel).
cvModel.transform(test.toDF)
  .select("id", "text", "probability", "prediction")
  .collect()
  .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
  println(s"($id, $text) --> prob=$prob, prediction=$prediction")
}

sc.stop()

import java.util.List;
import com.google.common.collect.Lists;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
import org.apache.spark.ml.feature.HashingTF;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.ml.param.ParamMap;
import org.apache.spark.ml.tuning.CrossValidator;
import org.apache.spark.ml.tuning.CrossValidatorModel;
import org.apache.spark.ml.tuning.ParamGridBuilder;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;

SparkConf conf = new SparkConf().setAppName("JavaCrossValidatorExample");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext jsql = new SQLContext(jsc);

// Prepare training documents, which are labeled.
List<LabeledDocument> localTraining = Lists.newArrayList(
  new LabeledDocument(0L, "a b c d e spark", 1.0),
  new LabeledDocument(1L, "b d", 0.0),
  new LabeledDocument(2L, "spark f g h", 1.0),
  new LabeledDocument(3L, "hadoop mapreduce", 0.0),
  new LabeledDocument(4L, "b spark who", 1.0),
  new LabeledDocument(5L, "g d a y", 0.0),
  new LabeledDocument(6L, "spark fly", 1.0),
  new LabeledDocument(7L, "was mapreduce", 0.0),
  new LabeledDocument(8L, "e spark program", 1.0),
  new LabeledDocument(9L, "a e c l", 0.0),
  new LabeledDocument(10L, "spark compile", 1.0),
  new LabeledDocument(11L, "hadoop software", 0.0));
DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class);

// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
Tokenizer tokenizer = new Tokenizer()
  .setInputCol("text")
  .setOutputCol("words");
HashingTF hashingTF = new HashingTF()
  .setNumFeatures(1000)
  .setInputCol(tokenizer.getOutputCol())
  .setOutputCol("features");
LogisticRegression lr = new LogisticRegression()
  .setMaxIter(10)
  .setRegParam(0.01);
Pipeline pipeline = new Pipeline()
  .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});

// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
// This will allow us to jointly choose parameters for all Pipeline stages.
// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
CrossValidator crossval = new CrossValidator()
    .setEstimator(pipeline)
    .setEvaluator(new BinaryClassificationEvaluator());
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
ParamMap[] paramGrid = new ParamGridBuilder()
    .addGrid(hashingTF.numFeatures(), new int[]{10, 100, 1000})
    .addGrid(lr.regParam(), new double[]{0.1, 0.01})
    .build();
crossval.setEstimatorParamMaps(paramGrid);
crossval.setNumFolds(2); // Use 3+ in practice

// Run cross-validation, and choose the best set of parameters.
CrossValidatorModel cvModel = crossval.fit(training);

// Prepare test documents, which are unlabeled.
List<Document> localTest = Lists.newArrayList(
  new Document(4L, "spark i j k"),
  new Document(5L, "l m n"),
  new Document(6L, "mapreduce spark"),
  new Document(7L, "apache hadoop"));
DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);

// Make predictions on test documents. cvModel uses the best model found (lrModel).
DataFrame predictions = cvModel.transform(test);
for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) {
  System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
      + ", prediction=" + r.get(3));
}

jsc.stop();

Dependencies

Spark ML currently depends on MLlib and has the same dependencies. Please see the MLlib Dependencies guide for more info.

Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not bring additional dependencies.

Migration Guide

From 1.2 to 1.3

The main API changes are from Spark SQL. We list the most important changes here:

The old SchemaRDD has been replaced with DataFrame with a somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.
In Spark 1.2, we used implicit conversions from RDDs of LabeledPoint into SchemaRDDs by calling import sqlContext._ where sqlContext was an instance of SQLContext. These implicits have been moved, so we now call import sqlContext.implicits._.
Java APIs for SQL have also changed accordingly. Please see the examples above and the Spark SQL Programming Guide for details.

Other changes were in LogisticRegression:

The scoreCol output column (with default value “score”) was renamed to be probabilityCol (with default value “probability”). The type was originally Double (for the probability of class 1.0), but it is now Vector (for the probability of each class, to support multiclass classification in the future).
In Spark 1.2, LogisticRegressionModel did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for spark.mllib.LogisticRegressionWithLBFGS. The option to use an intercept will be added in the future.