Spark ML

Home » Spark ML

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 the spark.mllib features and expect more features coming. Developers should contribute new algorithms to spark.mllib and can optionally contribute to spark.ml.

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 SchemaRDD 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 SchemaRDD into another SchemaRDD. E.g., an ML model is a Transformerwhich transforms an RDD with features into an RDD with predictions.
  • Estimator: An Estimator is an algorithm which can be fit on a SchemaRDD to produce a Transformer. E.g., a learning algorithm is an Estimatorwhich 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 SchemaRDDfrom Spark SQL in order to support a variety of data types under a unified Dataset concept.

SchemaRDD 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, SchemaRDD can use ML Vector types.

SchemaRDD 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 SchemaRDD are named. The code examples below use names such as “text,” “features,” and “label.”

ML Algorithms

Transformers

Transformer is an abstraction which includes feature transformers and learned models. Technically, a Transformer implements a method transform() which converts one SchemaRDD 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 SchemaRDD 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

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 SchemaRDDs. 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 Pipelinefirst 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.

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 SchemaRDD.

Parameters

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

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:

  1. 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.
  2. 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 EstimatorTransformer, 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._

// Prepare training data.
// We use LabeledPoint, which is a case class.  Spark SQL can convert RDDs of case classes
// into SchemaRDDs, where it uses the case class metadata to infer the schema.
val training = sparkContext.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)
// 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 -> 0.5) // Specify multiple Params.

// One can also combine ParamMaps.
val paramMap2 = ParamMap(lr.scoreCol -> "probability") // Changes 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, paramMapCombined)
println("Model 2 was fit using parameters: " + model2.fittingParamMap)

// Prepare test documents.
val test = sparkContext.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 documents using the Transformer.transform() method.
// LogisticRegression.transform will only use the 'features' column.
// Note that model2.transform() outputs a 'probability' column instead of the usual 'score'
// column since we renamed the lr.scoreCol parameter previously.
model2.transform(test)
  .select('features, 'label, 'probability, 'prediction)
  .collect()
  .foreach { case Row(features: Vector, label: Double, prob: Double, prediction: Double) =>
    println("(" + features + ", " + label + ") -> prob=" + prob + ", prediction=" + prediction)
  }

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.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 SchemaRDD from sqlContext.
val conf = new SparkConf().setAppName("SimpleTextClassificationPipeline")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext._

// Prepare training documents, which are labeled.
val training = sparkContext.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)

// Prepare test documents, which are unlabeled.
val test = sparkContext.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)
  .select('id, 'text, 'score, 'prediction)
  .collect()
  .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) =>
    println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction)
  }

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 tuningPipelines 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=3k=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. CrossValidatoriterates through the set of ParamMaps. For each ParamMap, it trains the given Estimator and evaluates it using the given Evaluator. The ParamMapwhich produces the best evaluation metric (averaged over the kk folds) is selected as the best model. CrossValidator finally fits the Estimatorusing 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 (3×2)×2=12(3×2)×2=12 different models being trained. In realistic settings, it can be common to try many more parameters and use more folds (k=3k=3 and k=10k=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.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.sql.{Row, SQLContext}

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

// Prepare training documents, which are labeled.
val training = sparkContext.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)
// Get the best LogisticRegression model (with the best set of parameters from paramGrid).
val lrModel = cvModel.bestModel

// Prepare test documents, which are unlabeled.
val test = sparkContext.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)
  .select('id, 'text, 'score, 'prediction)
  .collect()
  .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) =>
  println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction)
}

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.

Source: Spark ML Programming Guide – Spark 1.2.2 Documentation

ThirdEye Data

Data Answers

ThirdEye answers your data questions and offers actionable insights, real-world experiences and strategic recommendations.

By | 2018-08-01T07:25:18+00:00 February 6th, 2018|Spark ML, Technologies|