From 266a81492d48cb4f7c2ada9d490e1919fdc506aa Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Thu, 4 Dec 2014 17:00:06 +0800 Subject: [PATCH] [SPARK-4575] [mllib] [docs] spark.ml pipelines doc + bug fixes Documentation: * Added ml-guide.md, linked from mllib-guide.md * Updated mllib-guide.md with small section pointing to ml-guide.md Examples: * CrossValidatorExample * SimpleParamsExample * (I copied these + the SimpleTextClassificationPipeline example into the ml-guide.md) Bug fixes: * PipelineModel: did not use ParamMaps correctly * UnaryTransformer: issues with TypeTag serialization (Thanks to mengxr for that fix!) CC: mengxr shivaram etrain Documentation for Pipelines: I know the docs are not complete, but the goal is to have enough to let interested people get started using spark.ml and to add more docs once the package is more established/complete. Author: Joseph K. Bradley Author: jkbradley Author: Xiangrui Meng Closes #3588 from jkbradley/ml-package-docs and squashes the following commits: d393b5c [Joseph K. Bradley] fixed bug in Pipeline (typo from last commit). updated examples for CV and Params for spark.ml c38469c [Joseph K. Bradley] Updated ml-guide with CV examples 99f88c2 [Joseph K. Bradley] Fixed bug in PipelineModel.transform* with usage of params. Updated CrossValidatorExample to use more training examples so it is less likely to get a 0-size fold. ea34dc6 [jkbradley] Merge pull request #4 from mengxr/ml-package-docs 3b83ec0 [Xiangrui Meng] replace TypeTag with explicit datatype 41ad9b1 [Joseph K. Bradley] Added examples for spark.ml: SimpleParamsExample + Java version, CrossValidatorExample + Java version. CrossValidatorExample not working yet. Added programming guide for spark.ml, but need to add CrossValidatorExample to it once CrossValidatorExample works. (cherry picked from commit 469a6e5f3bdd5593b3254bc916be8236e7c6cb74) Signed-off-by: Xiangrui Meng --- docs/img/ml-Pipeline.png | Bin 0 -> 74030 bytes docs/img/ml-PipelineModel.png | Bin 0 -> 76019 bytes docs/img/ml-Pipelines.pptx | Bin 0 -> 56777 bytes docs/ml-guide.md | 702 ++++++++++++++++++ docs/mllib-guide.md | 13 +- .../ml/JavaCrossValidatorExample.java | 127 ++++ .../examples/ml/JavaSimpleParamsExample.java | 111 +++ .../JavaSimpleTextClassificationPipeline.java | 6 +- .../examples/ml/CrossValidatorExample.scala | 110 +++ .../examples/ml/SimpleParamsExample.scala | 101 +++ .../ml/SimpleTextClassificationPipeline.scala | 7 +- .../scala/org/apache/spark/ml/Pipeline.scala | 10 +- .../org/apache/spark/ml/Transformer.scala | 18 +- .../apache/spark/ml/feature/HashingTF.scala | 5 +- .../apache/spark/ml/feature/Tokenizer.scala | 4 +- .../org/apache/spark/ml/param/params.scala | 11 +- .../org/apache/spark/mllib/linalg/BLAS.scala | 4 +- 17 files changed, 1205 insertions(+), 24 deletions(-) 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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#param)**: 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`. + +

+ Spark ML Pipeline Example +

+ +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. + +

+ Spark ML PipelineModel Example +

+ +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). 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.ParamGridGuilder) 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. + diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index dcb6819f46cba..efd7dda310712 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -1,6 +1,6 @@ --- layout: global -title: Machine Learning Library (MLlib) +title: Machine Learning Library (MLlib) Programming Guide --- MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities, @@ -35,6 +35,17 @@ MLlib is under active development. The APIs marked `Experimental`/`DeveloperApi` may change in future releases, and the migration guide below will explain all changes between releases. +# spark.ml: The New ML Package + +Spark 1.2 includes a new machine learning package called `spark.ml`, currently an alpha component but potentially a successor to `spark.mllib`. 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. + +See the **[spark.ml programming guide](ml-guide.html)** for more information on this package. + +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`. + +Developers should contribute new algorithms to `spark.mllib` and can optionally contribute to `spark.ml`. +See the `spark.ml` programming guide linked above for more details. + # Dependencies MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java new file mode 100644 index 0000000000000..3b156fa0482fc --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java @@ -0,0 +1,127 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml; + +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; + +/** + * A simple example demonstrating model selection using CrossValidator. + * This example also demonstrates how Pipelines are Estimators. + * + * This example uses the Java bean classes {@link org.apache.spark.examples.ml.LabeledDocument} and + * {@link org.apache.spark.examples.ml.Document} defined in the Scala example + * {@link org.apache.spark.examples.ml.SimpleTextClassificationPipeline}. + * + * Run with + * 
+ * bin/run-example ml.JavaCrossValidatorExample
+ *
+ */ +public class JavaCrossValidatorExample { + + public static void main(String[] args) { + 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); + + // 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)); + } + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java new file mode 100644 index 0000000000000..cf58f4dfaa15b --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -0,0 +1,111 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml; + +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; + +/** + * A simple example demonstrating ways to specify parameters for Estimators and Transformers. + * Run with + * {{{ + * bin/run-example ml.JavaSimpleParamsExample + * }}} + */ +public class JavaSimpleParamsExample { + + public static void main(String[] args) { + 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 Java Beans + // into SchemaRDDs, where it uses the bean 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().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.scoreCol().w("probability")); // 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 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)); + } + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java index 22ba68d8c354c..54f18014e4b2f 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java @@ -80,14 +80,14 @@ public static void main(String[] args) { 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); + 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); + System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2) + + ", prediction=" + r.get(3)); } } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala new file mode 100644 index 0000000000000..ce6bc066bd70d --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +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} + +/** + * A simple example demonstrating model selection using CrossValidator. + * This example also demonstrates how Pipelines are Estimators. + * + * This example uses the [[LabeledDocument]] and [[Document]] case classes from + * [[SimpleTextClassificationPipeline]]. + * + * Run with + * {{{ + * bin/run-example ml.CrossValidatorExample + * }}} + */ +object CrossValidatorExample { + + def main(args: Array[String]) { + 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) + + // 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) + } + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala new file mode 100644 index 0000000000000..44d5b084c269a --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.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} + +/** + * A simple example demonstrating ways to specify parameters for Estimators and Transformers. + * Run with + * {{{ + * bin/run-example ml.SimpleParamsExample + * }}} + */ +object SimpleParamsExample { + + def main(args: Array[String]) { + 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 Java Beans + // into SchemaRDDs, where it uses the bean 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 -> 0.55) // Specify multiple Params. + + // One can also combine ParamMaps. + val paramMap2 = ParamMap(lr.scoreCol -> "probability") // 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, 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) + } + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala index ee7897d9062d9..92895a05e479a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala @@ -20,10 +20,11 @@ package org.apache.spark.examples.ml import scala.beans.BeanInfo 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.feature.{HashingTF, Tokenizer} -import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.{Row, SQLContext} @BeanInfo case class LabeledDocument(id: Long, text: String, label: Double) @@ -81,6 +82,8 @@ object SimpleTextClassificationPipeline { model.transform(test) .select('id, 'text, 'score, 'prediction) .collect() - .foreach(println) + .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) => + println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction) + } } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index e545df1e37b9c..081a574beea5d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -162,11 +162,15 @@ class PipelineModel private[ml] ( } override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = { - transformSchema(dataset.schema, paramMap, logging = true) - stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, paramMap)) + // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap + val map = (fittingParamMap ++ this.paramMap) ++ paramMap + transformSchema(dataset.schema, map, logging = true) + stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map)) } private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, paramMap)) + // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap + val map = (fittingParamMap ++ this.paramMap) ++ paramMap + stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map)) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index 490e6609ad311..23fbd228d01cb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -18,16 +18,14 @@ package org.apache.spark.ml import scala.annotation.varargs -import scala.reflect.runtime.universe.TypeTag import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ import org.apache.spark.sql.SchemaRDD import org.apache.spark.sql.api.java.JavaSchemaRDD -import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.analysis.Star -import org.apache.spark.sql.catalyst.dsl._ +import org.apache.spark.sql.catalyst.expressions.ScalaUdf import org.apache.spark.sql.catalyst.types._ /** @@ -86,7 +84,7 @@ abstract class Transformer extends PipelineStage with Params { * Abstract class for transformers that take one input column, apply transformation, and output the * result as a new column. */ -private[ml] abstract class UnaryTransformer[IN, OUT: TypeTag, T <: UnaryTransformer[IN, OUT, T]] +private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]] extends Transformer with HasInputCol with HasOutputCol with Logging { def setInputCol(value: String): T = set(inputCol, value).asInstanceOf[T] @@ -99,6 +97,11 @@ private[ml] abstract class UnaryTransformer[IN, OUT: TypeTag, T <: UnaryTransfor */ protected def createTransformFunc(paramMap: ParamMap): IN => OUT + /** + * Returns the data type of the output column. + */ + protected def outputDataType: DataType + /** * Validates the input type. Throw an exception if it is invalid. */ @@ -111,9 +114,8 @@ private[ml] abstract class UnaryTransformer[IN, OUT: TypeTag, T <: UnaryTransfor if (schema.fieldNames.contains(map(outputCol))) { throw new IllegalArgumentException(s"Output column ${map(outputCol)} already exists.") } - val output = ScalaReflection.schemaFor[OUT] val outputFields = schema.fields :+ - StructField(map(outputCol), output.dataType, output.nullable) + StructField(map(outputCol), outputDataType, !outputDataType.isPrimitive) StructType(outputFields) } @@ -121,7 +123,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT: TypeTag, T <: UnaryTransfor transformSchema(dataset.schema, paramMap, logging = true) import dataset.sqlContext._ val map = this.paramMap ++ paramMap - val udf = this.createTransformFunc(map) - dataset.select(Star(None), udf.call(map(inputCol).attr) as map(outputCol)) + val udf = ScalaUdf(this.createTransformFunc(map), outputDataType, Seq(map(inputCol).attr)) + dataset.select(Star(None), udf as map(outputCol)) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index b98b1755a3584..e0bfb1e484a2e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -21,7 +21,8 @@ import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer import org.apache.spark.ml.param.{IntParam, ParamMap} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.sql.catalyst.types.DataType /** * :: AlphaComponent :: @@ -39,4 +40,6 @@ class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { val hashingTF = new feature.HashingTF(paramMap(numFeatures)) hashingTF.transform } + + override protected def outputDataType: DataType = new VectorUDT() } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 0a6599b64c011..9352f40f372d3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer import org.apache.spark.ml.param.ParamMap -import org.apache.spark.sql.{DataType, StringType} +import org.apache.spark.sql.{DataType, StringType, ArrayType} /** * :: AlphaComponent :: @@ -36,4 +36,6 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { protected override def validateInputType(inputType: DataType): Unit = { require(inputType == StringType, s"Input type must be string type but got $inputType.") } + + override protected def outputDataType: DataType = new ArrayType(StringType, false) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 8fd46aef4b99d..4b4340af543b0 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -17,13 +17,12 @@ package org.apache.spark.ml.param -import java.lang.reflect.Modifier - -import org.apache.spark.annotation.AlphaComponent - import scala.annotation.varargs import scala.collection.mutable +import java.lang.reflect.Modifier + +import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.Identifiable /** @@ -221,7 +220,9 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten /** * Puts a list of param pairs (overwrites if the input params exists). + * Not usable from Java */ + @varargs def put(paramPairs: ParamPair[_]*): this.type = { paramPairs.foreach { p => put(p.param.asInstanceOf[Param[Any]], p.value) @@ -282,6 +283,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten * where the latter overwrites this if there exists conflicts. */ def ++(other: ParamMap): ParamMap = { + // TODO: Provide a better method name for Java users. new ParamMap(this.map ++ other.map) } @@ -290,6 +292,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten * Adds all parameters from the input param map into this param map. */ def ++=(other: ParamMap): this.type = { + // TODO: Provide a better method name for Java users. this.map ++= other.map this } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala index 8c4c9c6cf6ae2..9fed513becddc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala @@ -96,7 +96,9 @@ private[spark] object BLAS extends Serializable with Logging { * dot(x, y) */ def dot(x: Vector, y: Vector): Double = { - require(x.size == y.size) + require(x.size == y.size, + "BLAS.dot(x: Vector, y:Vector) was given Vectors with non-matching sizes:" + + " x.size = " + x.size + ", y.size = " + y.size) (x, y) match { case (dx: DenseVector, dy: DenseVector) => dot(dx, dy)