[SPARK-7752] [MLLIB] Use lowercase letters for NaiveBayes.modelType
to be consistent with other string names in MLlib. This PR also updates the implementation to use vals instead of hardcoded strings. jkbradley leahmcguire Author: Xiangrui Meng <meng@databricks.com> Closes #6277 from mengxr/SPARK-7752 and squashes the following commits: f38b662 [Xiangrui Meng] add another case _ back in test ae5c66a [Xiangrui Meng] model type -> modelType 711d1c6 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-7752 40ae53e [Xiangrui Meng] fix Java test suite 264a814 [Xiangrui Meng] add case _ back 3c456a8 [Xiangrui Meng] update NB user guide 17bba53 [Xiangrui Meng] update naive Bayes to use lowercase model type strings
This commit is contained in:
Xiangrui Meng
Joseph K. Bradley
parent
a25c1ab8f0
commit
13348e21b6
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@ -21,7 +21,7 @@ Within that context, each observation is a document and each
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feature represents a term whose value is the frequency of the term (in multinomial naive Bayes) or
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a zero or one indicating whether the term was found in the document (in Bernoulli naive Bayes).
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Feature values must be nonnegative. The model type is selected with an optional parameter
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"Multinomial" or "Bernoulli" with "Multinomial" as the default.
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"multinomial" or "bernoulli" with "multinomial" as the default.
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[Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by
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setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature
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vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of
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@ -35,7 +35,7 @@ sparsity. Since the training data is only used once, it is not necessary to cach
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[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements
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multinomial naive Bayes. It takes an RDD of
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[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional
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smoothing parameter `lambda` as input, an optional model type parameter (default is Multinomial), and outputs a
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smoothing parameter `lambda` as input, an optional model type parameter (default is "multinomial"), and outputs a
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[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which
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can be used for evaluation and prediction.
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@ -54,7 +54,7 @@ val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
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val training = splits(0)
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val test = splits(1)
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val model = NaiveBayes.train(training, lambda = 1.0, model = "Multinomial")
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val model = NaiveBayes.train(training, lambda = 1.0, model = "multinomial")
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val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
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val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
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@ -75,6 +75,8 @@ optionally smoothing parameter `lambda` as input, and output a
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can be used for evaluation and prediction.
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{% highlight java %}
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import scala.Tuple2;
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import org.apache.spark.api.java.JavaPairRDD;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.function.Function;
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@ -82,7 +84,6 @@ import org.apache.spark.api.java.function.PairFunction;
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import org.apache.spark.mllib.classification.NaiveBayes;
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import org.apache.spark.mllib.classification.NaiveBayesModel;
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import org.apache.spark.mllib.regression.LabeledPoint;
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import scala.Tuple2;
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JavaRDD<LabeledPoint> training = ... // training set
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JavaRDD<LabeledPoint> test = ... // test set
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@ -25,13 +25,12 @@ import org.json4s.JsonDSL._
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import org.json4s.jackson.JsonMethods._
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import org.apache.spark.{Logging, SparkContext, SparkException}
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import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, SparseVector, Vector, Vectors}
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import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, SparseVector, Vector}
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import org.apache.spark.mllib.regression.LabeledPoint
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import org.apache.spark.mllib.util.{Loader, Saveable}
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import org.apache.spark.rdd.RDD
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import org.apache.spark.sql.{DataFrame, SQLContext}
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/**
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* Model for Naive Bayes Classifiers.
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*
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@ -39,7 +38,7 @@ import org.apache.spark.sql.{DataFrame, SQLContext}
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* @param pi log of class priors, whose dimension is C, number of labels
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* @param theta log of class conditional probabilities, whose dimension is C-by-D,
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* where D is number of features
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* @param modelType The type of NB model to fit can be "Multinomial" or "Bernoulli"
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* @param modelType The type of NB model to fit can be "multinomial" or "bernoulli"
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*/
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class NaiveBayesModel private[mllib] (
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val labels: Array[Double],
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@ -48,11 +47,13 @@ class NaiveBayesModel private[mllib] (
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val modelType: String)
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extends ClassificationModel with Serializable with Saveable {
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import NaiveBayes.{Bernoulli, Multinomial, supportedModelTypes}
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private val piVector = new DenseVector(pi)
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private val thetaMatrix = new DenseMatrix(labels.size, theta(0).size, theta.flatten, true)
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private val thetaMatrix = new DenseMatrix(labels.length, theta(0).length, theta.flatten, true)
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private[mllib] def this(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) =
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this(labels, pi, theta, "Multinomial")
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this(labels, pi, theta, NaiveBayes.Multinomial)
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/** A Java-friendly constructor that takes three Iterable parameters. */
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private[mllib] def this(
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@ -61,12 +62,15 @@ class NaiveBayesModel private[mllib] (
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theta: JIterable[JIterable[Double]]) =
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this(labels.asScala.toArray, pi.asScala.toArray, theta.asScala.toArray.map(_.asScala.toArray))
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require(supportedModelTypes.contains(modelType),
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s"Invalid modelType $modelType. Supported modelTypes are $supportedModelTypes.")
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// Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0.
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// This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra
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// application of this condition (in predict function).
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private val (thetaMinusNegTheta, negThetaSum) = modelType match {
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case "Multinomial" => (None, None)
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case "Bernoulli" =>
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case Multinomial => (None, None)
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case Bernoulli =>
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val negTheta = thetaMatrix.map(value => math.log(1.0 - math.exp(value)))
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val ones = new DenseVector(Array.fill(thetaMatrix.numCols){1.0})
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val thetaMinusNegTheta = thetaMatrix.map { value =>
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@ -75,7 +79,7 @@ class NaiveBayesModel private[mllib] (
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(Option(thetaMinusNegTheta), Option(negTheta.multiply(ones)))
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case _ =>
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// This should never happen.
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throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType")
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throw new UnknownError(s"Invalid modelType: $modelType.")
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}
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override def predict(testData: RDD[Vector]): RDD[Double] = {
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@ -88,15 +92,15 @@ class NaiveBayesModel private[mllib] (
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override def predict(testData: Vector): Double = {
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modelType match {
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case "Multinomial" =>
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case Multinomial =>
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val prob = thetaMatrix.multiply(testData)
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BLAS.axpy(1.0, piVector, prob)
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labels(prob.argmax)
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case "Bernoulli" =>
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case Bernoulli =>
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testData.foreachActive { (index, value) =>
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if (value != 0.0 && value != 1.0) {
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throw new SparkException(
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s"Bernoulli Naive Bayes requires 0 or 1 feature values but found $testData.")
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s"Bernoulli naive Bayes requires 0 or 1 feature values but found $testData.")
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}
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}
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val prob = thetaMinusNegTheta.get.multiply(testData)
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@ -105,7 +109,7 @@ class NaiveBayesModel private[mllib] (
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labels(prob.argmax)
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case _ =>
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// This should never happen.
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throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType")
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throw new UnknownError(s"Invalid modelType: $modelType.")
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}
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}
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@ -230,16 +234,16 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
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s"($loadedClassName, $version). Supported:\n" +
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s" ($classNameV1_0, 1.0)")
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}
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assert(model.pi.size == numClasses,
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assert(model.pi.length == numClasses,
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s"NaiveBayesModel.load expected $numClasses classes," +
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s" but class priors vector pi had ${model.pi.size} elements")
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assert(model.theta.size == numClasses,
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s" but class priors vector pi had ${model.pi.length} elements")
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assert(model.theta.length == numClasses,
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s"NaiveBayesModel.load expected $numClasses classes," +
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s" but class conditionals array theta had ${model.theta.size} elements")
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assert(model.theta.forall(_.size == numFeatures),
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s" but class conditionals array theta had ${model.theta.length} elements")
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assert(model.theta.forall(_.length == numFeatures),
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s"NaiveBayesModel.load expected $numFeatures features," +
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s" but class conditionals array theta had elements of size:" +
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s" ${model.theta.map(_.size).mkString(",")}")
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s" ${model.theta.map(_.length).mkString(",")}")
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model
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}
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}
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@ -257,9 +261,11 @@ class NaiveBayes private (
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private var lambda: Double,
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private var modelType: String) extends Serializable with Logging {
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def this(lambda: Double) = this(lambda, "Multinomial")
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import NaiveBayes.{Bernoulli, Multinomial}
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def this() = this(1.0, "Multinomial")
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def this(lambda: Double) = this(lambda, NaiveBayes.Multinomial)
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def this() = this(1.0, NaiveBayes.Multinomial)
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/** Set the smoothing parameter. Default: 1.0. */
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def setLambda(lambda: Double): NaiveBayes = {
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@ -272,12 +278,11 @@ class NaiveBayes private (
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/**
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* Set the model type using a string (case-sensitive).
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* Supported options: "Multinomial" and "Bernoulli".
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* (default: Multinomial)
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* Supported options: "multinomial" (default) and "bernoulli".
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*/
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def setModelType(modelType: String): NaiveBayes = {
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require(NaiveBayes.supportedModelTypes.contains(modelType),
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s"NaiveBayes was created with an unknown ModelType: $modelType")
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s"NaiveBayes was created with an unknown modelType: $modelType.")
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this.modelType = modelType
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this
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}
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@ -308,7 +313,7 @@ class NaiveBayes private (
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}
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if (!values.forall(v => v == 0.0 || v == 1.0)) {
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throw new SparkException(
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s"Bernoulli Naive Bayes requires 0 or 1 feature values but found $v.")
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s"Bernoulli naive Bayes requires 0 or 1 feature values but found $v.")
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}
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}
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@ -317,7 +322,7 @@ class NaiveBayes private (
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// TODO: similar to reduceByKeyLocally to save one stage.
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val aggregated = data.map(p => (p.label, p.features)).combineByKey[(Long, DenseVector)](
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createCombiner = (v: Vector) => {
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if (modelType == "Bernoulli") {
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if (modelType == Bernoulli) {
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requireZeroOneBernoulliValues(v)
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} else {
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requireNonnegativeValues(v)
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@ -352,11 +357,11 @@ class NaiveBayes private (
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labels(i) = label
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pi(i) = math.log(n + lambda) - piLogDenom
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val thetaLogDenom = modelType match {
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case "Multinomial" => math.log(sumTermFreqs.values.sum + numFeatures * lambda)
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case "Bernoulli" => math.log(n + 2.0 * lambda)
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case Multinomial => math.log(sumTermFreqs.values.sum + numFeatures * lambda)
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case Bernoulli => math.log(n + 2.0 * lambda)
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case _ =>
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// This should never happen.
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throw new UnknownError(s"NaiveBayes was created with an unknown ModelType: $modelType")
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throw new UnknownError(s"Invalid modelType: $modelType.")
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}
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var j = 0
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while (j < numFeatures) {
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@ -375,8 +380,14 @@ class NaiveBayes private (
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*/
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object NaiveBayes {
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/** String name for multinomial model type. */
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private[classification] val Multinomial: String = "multinomial"
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/** String name for Bernoulli model type. */
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private[classification] val Bernoulli: String = "bernoulli"
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/* Set of modelTypes that NaiveBayes supports */
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private[mllib] val supportedModelTypes = Set("Multinomial", "Bernoulli")
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private[classification] val supportedModelTypes = Set(Multinomial, Bernoulli)
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/**
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* Trains a Naive Bayes model given an RDD of `(label, features)` pairs.
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@ -406,7 +417,7 @@ object NaiveBayes {
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* @param lambda The smoothing parameter
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*/
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def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
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new NaiveBayes(lambda, "Multinomial").run(input)
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new NaiveBayes(lambda, Multinomial).run(input)
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}
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/**
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@ -429,7 +440,7 @@ object NaiveBayes {
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*/
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def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
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require(supportedModelTypes.contains(modelType),
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s"NaiveBayes was created with an unknown ModelType: $modelType")
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s"NaiveBayes was created with an unknown modelType: $modelType.")
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new NaiveBayes(lambda, modelType).run(input)
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}
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@ -108,7 +108,7 @@ public class JavaNaiveBayesSuite implements Serializable {
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@Test
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public void testModelTypeSetters() {
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NaiveBayes nb = new NaiveBayes()
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.setModelType("Bernoulli")
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.setModelType("Multinomial");
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.setModelType("bernoulli")
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.setModelType("multinomial");
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}
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}
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@ -19,9 +19,8 @@ package org.apache.spark.mllib.classification
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import scala.util.Random
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import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum, Axis}
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import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
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import breeze.stats.distributions.{Multinomial => BrzMultinomial}
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import org.scalatest.FunSuite
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import org.apache.spark.SparkException
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@ -30,9 +29,10 @@ import org.apache.spark.mllib.regression.LabeledPoint
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import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext}
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import org.apache.spark.util.Utils
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object NaiveBayesSuite {
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import NaiveBayes.{Multinomial, Bernoulli}
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private def calcLabel(p: Double, pi: Array[Double]): Int = {
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var sum = 0.0
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for (j <- 0 until pi.length) {
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@ -48,7 +48,7 @@ object NaiveBayesSuite {
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theta: Array[Array[Double]], // CXD
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nPoints: Int,
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seed: Int,
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modelType: String = "Multinomial",
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modelType: String = Multinomial,
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sample: Int = 10): Seq[LabeledPoint] = {
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val D = theta(0).length
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val rnd = new Random(seed)
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@ -58,10 +58,10 @@ object NaiveBayesSuite {
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for (i <- 0 until nPoints) yield {
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val y = calcLabel(rnd.nextDouble(), _pi)
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val xi = modelType match {
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case "Bernoulli" => Array.tabulate[Double] (D) { j =>
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case Bernoulli => Array.tabulate[Double] (D) { j =>
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if (rnd.nextDouble () < _theta(y)(j) ) 1 else 0
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}
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case "Multinomial" =>
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case Multinomial =>
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val mult = BrzMultinomial(BDV(_theta(y)))
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val emptyMap = (0 until D).map(x => (x, 0.0)).toMap
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val counts = emptyMap ++ mult.sample(sample).groupBy(x => x).map {
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@ -70,7 +70,7 @@ object NaiveBayesSuite {
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counts.toArray.sortBy(_._1).map(_._2)
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case _ =>
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// This should never happen.
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throw new UnknownError(s"NaiveBayesSuite found unknown ModelType: $modelType")
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throw new UnknownError(s"Invalid modelType: $modelType.")
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}
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LabeledPoint(y, Vectors.dense(xi))
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@ -79,17 +79,17 @@ object NaiveBayesSuite {
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/** Bernoulli NaiveBayes with binary labels, 3 features */
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private val binaryBernoulliModel = new NaiveBayesModel(labels = Array(0.0, 1.0),
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pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)),
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"Bernoulli")
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pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), Bernoulli)
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/** Multinomial NaiveBayes with binary labels, 3 features */
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private val binaryMultinomialModel = new NaiveBayesModel(labels = Array(0.0, 1.0),
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pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)),
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"Multinomial")
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pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), Multinomial)
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}
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class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
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import NaiveBayes.{Multinomial, Bernoulli}
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def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
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val numOfPredictions = predictions.zip(input).count {
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case (prediction, expected) =>
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@ -117,6 +117,11 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
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}
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}
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test("model types") {
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assert(Multinomial === "multinomial")
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assert(Bernoulli === "bernoulli")
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}
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test("get, set params") {
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val nb = new NaiveBayes()
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nb.setLambda(2.0)
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@ -134,16 +139,15 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
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Array(0.10, 0.10, 0.70, 0.10) // label 2
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).map(_.map(math.log))
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val testData = NaiveBayesSuite.generateNaiveBayesInput(
|
||||
pi, theta, nPoints, 42, "Multinomial")
|
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val testData = NaiveBayesSuite.generateNaiveBayesInput(pi, theta, nPoints, 42, Multinomial)
|
||||
val testRDD = sc.parallelize(testData, 2)
|
||||
testRDD.cache()
|
||||
|
||||
val model = NaiveBayes.train(testRDD, 1.0, "Multinomial")
|
||||
val model = NaiveBayes.train(testRDD, 1.0, Multinomial)
|
||||
validateModelFit(pi, theta, model)
|
||||
|
||||
val validationData = NaiveBayesSuite.generateNaiveBayesInput(
|
||||
pi, theta, nPoints, 17, "Multinomial")
|
||||
pi, theta, nPoints, 17, Multinomial)
|
||||
val validationRDD = sc.parallelize(validationData, 2)
|
||||
|
||||
// Test prediction on RDD.
|
||||
|
|
@ -163,15 +167,15 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
|
|||
).map(_.map(math.log))
|
||||
|
||||
val testData = NaiveBayesSuite.generateNaiveBayesInput(
|
||||
pi, theta, nPoints, 45, "Bernoulli")
|
||||
pi, theta, nPoints, 45, Bernoulli)
|
||||
val testRDD = sc.parallelize(testData, 2)
|
||||
testRDD.cache()
|
||||
|
||||
val model = NaiveBayes.train(testRDD, 1.0, "Bernoulli")
|
||||
val model = NaiveBayes.train(testRDD, 1.0, Bernoulli)
|
||||
validateModelFit(pi, theta, model)
|
||||
|
||||
val validationData = NaiveBayesSuite.generateNaiveBayesInput(
|
||||
pi, theta, nPoints, 20, "Bernoulli")
|
||||
pi, theta, nPoints, 20, Bernoulli)
|
||||
val validationRDD = sc.parallelize(validationData, 2)
|
||||
|
||||
// Test prediction on RDD.
|
||||
|
|
@ -216,7 +220,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
|
|||
LabeledPoint(1.0, Vectors.dense(0.0)))
|
||||
|
||||
intercept[SparkException] {
|
||||
NaiveBayes.train(sc.makeRDD(badTrain, 2), 1.0, "Bernoulli")
|
||||
NaiveBayes.train(sc.makeRDD(badTrain, 2), 1.0, Bernoulli)
|
||||
}
|
||||
|
||||
val okTrain = Seq(
|
||||
|
|
@ -235,7 +239,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
|
|||
Vectors.dense(1.0),
|
||||
Vectors.dense(0.0))
|
||||
|
||||
val model = NaiveBayes.train(sc.makeRDD(okTrain, 2), 1.0, "Bernoulli")
|
||||
val model = NaiveBayes.train(sc.makeRDD(okTrain, 2), 1.0, Bernoulli)
|
||||
intercept[SparkException] {
|
||||
model.predict(sc.makeRDD(badPredict, 2)).collect()
|
||||
}
|
||||
|
|
@ -275,7 +279,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext {
|
|||
assert(model.labels === sameModel.labels)
|
||||
assert(model.pi === sameModel.pi)
|
||||
assert(model.theta === sameModel.theta)
|
||||
assert(model.modelType === "Multinomial")
|
||||
assert(model.modelType === Multinomial)
|
||||
} finally {
|
||||
Utils.deleteRecursively(tempDir)
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue