---
layout: global
title: Spark ML Programming Guide
---
Spark ML is Spark's new machine learning package. It is currently an alpha component but is potentially a successor to [MLlib](mllib-guide.html). The `spark.ml` package aims to replace the old APIs with a cleaner, more uniform set of APIs which will help users create full machine learning pipelines.
MLlib vs. Spark ML:
* Users can use algorithms from either of the two packages, but APIs may differ. Currently, `spark.ml` offers a subset of the algorithms from `spark.mllib`. Since Spark ML is an alpha component, its API may change in future releases.
* Developers should contribute new algorithms to `spark.mllib` and can optionally contribute to `spark.ml`. See below for more details.
* Spark ML only has Scala and Java APIs, whereas MLlib also has a Python API.
**Table of Contents**
* This will become a table of contents (this text will be scraped).
{:toc}
# 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](ml-guide.html#ml-dataset)**: Spark ML uses the [`SchemaRDD`](api/scala/index.html#org.apache.spark.sql.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`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `SchemaRDD` into another `SchemaRDD`.
E.g., an ML model is a `Transformer` which transforms an RDD with features into an RDD with predictions.
* **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `SchemaRDD` to produce a `Transformer`.
E.g., a learning algorithm is an `Estimator` which trains on a dataset and produces a model.
* **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow.
* **[`Param`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s 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 [`SchemaRDD`](api/scala/index.html#org.apache.spark.sql.SchemaRDD) from 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](sql-programming-guide.html#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`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) types.
A `SchemaRDD` can be created either implicitly or explicitly from a regular `RDD`. See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
Columns in a `SchemaRDD` are named. The code examples below use names such as "text," "features," and "label."
## ML Algorithms
### Transformers
A [`Transformer`](api/scala/index.html#org.apache.spark.ml.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`](api/scala/index.html#org.apache.spark.ml.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
`Transformer`s and `Estimator`s 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`](api/scala/index.html#org.apache.spark.ml.Pipeline),
which consists of a sequence of [`PipelineStage`s](api/scala/index.html#org.apache.spark.ml.PipelineStage) (`Transformer`s and `Estimator`s) 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`.
Above, the top row represents a `Pipeline` with three stages.
The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red).
The bottom row represents data flowing through the pipeline, where cylinders indicate `SchemaRDD`s.
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.
In the figure above, the `PipelineModel` has the same number of stages as the original `Pipeline`, but all `Estimator`s in the original `Pipeline` have become `Transformer`s.
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.
`Pipeline`s and `PipelineModel`s help to ensure that training and test data go through identical feature processing steps.
### Details
*DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array. The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage. It is possible to create non-linear `Pipeline`s 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 `Pipeline`s can operate on datasets with varied types, they cannot use compile-time type checking. `Pipeline`s and `PipelineModel`s 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 `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
A [`Param`](api/scala/index.html#org.apache.spark.ml.param.Param) is a named parameter with self-contained documentation.
A [`ParamMap`](api/scala/index.html#org.apache.spark.ml.param.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 `Estimator`s and `Transformer`s.
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](api/scala/index.html#org.apache.spark.ml.package). Spark ML algorithms are currently wrappers for MLlib algorithms, and the [MLlib programming guide](mllib-guide.html) has details on specific algorithms.
## Example: Estimator, Transformer, and Param
This example covers the concepts of `Estimator`, `Transformer`, and `Param`.
{% highlight scala %}
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)
}
{% endhighlight %}
{% highlight java %}
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.api.java.JavaSQLContext;
import org.apache.spark.sql.api.java.JavaSchemaRDD;
import org.apache.spark.sql.api.java.Row;
SparkConf conf = new SparkConf().setAppName("JavaSimpleParamsExample");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaSQLContext jsql = new JavaSQLContext(jsc);
// 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.
List 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)));
JavaSchemaRDD training = jsql.applySchema(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(), 20); // Specify 1 Param.
paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
paramMap.put(lr.regParam(), 0.1);
// One can also combine ParamMaps.
ParamMap paramMap2 = new ParamMap();
paramMap2.put(lr.scoreCol(), "probability"); // Changes 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 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)));
JavaSchemaRDD test = jsql.applySchema(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 'probability' column instead of the usual 'score'
// column since we renamed the lr.scoreCol parameter previously.
model2.transform(test).registerAsTable("results");
JavaSchemaRDD results =
jsql.sql("SELECT features, label, probability, prediction FROM results");
for (Row r: results.collect()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
{% endhighlight %}
## Example: Pipeline
This example follows the simple text document `Pipeline` illustrated in the figures above.
{% highlight scala %}
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)
}
{% endhighlight %}
{% highlight java %}
import java.io.Serializable;
import java.util.List;
import com.google.common.collect.Lists;
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.api.java.JavaSQLContext;
import org.apache.spark.sql.api.java.JavaSchemaRDD;
import org.apache.spark.sql.api.java.Row;
import org.apache.spark.SparkConf;
// 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);
JavaSQLContext jsql = new JavaSQLContext(jsc);
// Prepare training documents, which are labeled.
List 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));
JavaSchemaRDD training =
jsql.applySchema(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 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"));
JavaSchemaRDD test =
jsql.applySchema(jsc.parallelize(localTest), Document.class);
// Make predictions on test documents.
model.transform(test).registerAsTable("prediction");
JavaSchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
for (Row r: predictions.collect()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2)
+ ", prediction=" + r.get(3));
}
{% endhighlight %}
## 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*.
`Pipeline`s 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`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.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 `ParamMap`s. 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`](api/scala/index.html#org.apache.spark.ml.tuning.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.
{% highlight scala %}
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)
}
{% endhighlight %}
{% highlight java %}
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.Model;
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.api.java.JavaSQLContext;
import org.apache.spark.sql.api.java.JavaSchemaRDD;
import org.apache.spark.sql.api.java.Row;
SparkConf conf = new SparkConf().setAppName("JavaCrossValidatorExample");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaSQLContext jsql = new JavaSQLContext(jsc);
// Prepare training documents, which are labeled.
List 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));
JavaSchemaRDD training =
jsql.applySchema(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);
// Get the best LogisticRegression model (with the best set of parameters from paramGrid).
Model lrModel = cvModel.bestModel();
// Prepare test documents, which are unlabeled.
List 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"));
JavaSchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
// Make predictions on test documents. cvModel uses the best model found (lrModel).
cvModel.transform(test).registerAsTable("prediction");
JavaSchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
for (Row r: predictions.collect()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2)
+ ", prediction=" + r.get(3));
}
{% endhighlight %}
# Dependencies
Spark ML currently depends on MLlib and has the same dependencies.
Please see the [MLlib Dependencies guide](mllib-guide.html#Dependencies) for more info.
Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not bring additional dependencies.
# Developers
**Development plan**
If all goes well, `spark.ml` will become the primary ML package at the time of the Spark 1.3 release. Initially, simple wrappers will be used to port algorithms to `spark.ml`, but eventually, code will be moved to `spark.ml` and `spark.mllib` will be deprecated.
**Advice to developers**
During the next development cycle, new algorithms should be contributed to `spark.mllib`, but we welcome patches sent to either package. If an algorithm is best expressed using the new API (e.g., feature transformers), we may ask for developers to use the new `spark.ml` API.
Wrappers for old and new algorithms can be contributed to `spark.ml`.
Users will be able to use algorithms from either of the two packages. The main difficulty will be the differences in APIs between the two packages.