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/*
* 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.mllib.classification
import java.lang.{Iterable => JIterable}
import scala.collection.JavaConverters._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
import org.apache.spark.{SparkContext, SparkException}
import org.apache.spark.annotation.Since
import org.apache.spark.internal.Logging
import org.apache.spark.ml.classification.{NaiveBayes => NewNaiveBayes}
import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, Vector}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.{Loader, Saveable}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession
/**
* Model for Naive Bayes Classifiers.
*
* @param labels list of labels
* @param pi log of class priors, whose dimension is C, number of labels
* @param theta log of class conditional probabilities, whose dimension is C-by-D,
* where D is number of features
* @param modelType The type of NB model to fit can be "multinomial" or "bernoulli"
*/
@Since("0.9.0")
class NaiveBayesModel private[spark] (
@Since("1.0.0") val labels: Array[Double],
@Since("0.9.0") val pi: Array[Double],
@Since("0.9.0") val theta: Array[Array[Double]],
@Since("1.4.0") val modelType: String)
extends ClassificationModel with Serializable with Saveable {
import NaiveBayes.{Bernoulli, Multinomial, supportedModelTypes}
private val piVector = new DenseVector(pi)
private val thetaMatrix = new DenseMatrix(labels.length, theta(0).length, theta.flatten, true)
private[mllib] def this(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) =
this(labels, pi, theta, NaiveBayes.Multinomial)
/** A Java-friendly constructor that takes three Iterable parameters. */
private[mllib] def this(
labels: JIterable[Double],
pi: JIterable[Double],
theta: JIterable[JIterable[Double]]) =
this(labels.asScala.toArray, pi.asScala.toArray, theta.asScala.toArray.map(_.asScala.toArray))
require(supportedModelTypes.contains(modelType),
s"Invalid modelType $modelType. Supported modelTypes are $supportedModelTypes.")
// Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0.
// This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra
// application of this condition (in predict function).
private val (thetaMinusNegTheta, negThetaSum) = modelType match {
case Multinomial => (None, None)
case Bernoulli =>
val negTheta = thetaMatrix.map(value => math.log1p(-math.exp(value)))
val ones = new DenseVector(Array.fill(thetaMatrix.numCols)(1.0))
val thetaMinusNegTheta = thetaMatrix.map { value =>
value - math.log1p(-math.exp(value))
}
(Option(thetaMinusNegTheta), Option(negTheta.multiply(ones)))
case _ =>
// This should never happen.
throw new IllegalArgumentException(s"Invalid modelType: $modelType.")
}
@Since("1.0.0")
override def predict(testData: RDD[Vector]): RDD[Double] = {
val bcModel = testData.context.broadcast(this)
testData.mapPartitions { iter =>
val model = bcModel.value
iter.map(model.predict)
}
}
@Since("1.0.0")
override def predict(testData: Vector): Double = {
modelType match {
case Multinomial =>
labels(multinomialCalculation(testData).argmax)
case Bernoulli =>
labels(bernoulliCalculation(testData).argmax)
}
}
/**
* Predict values for the given data set using the model trained.
*
* @param testData RDD representing data points to be predicted
* @return an RDD[Vector] where each entry contains the predicted posterior class probabilities,
* in the same order as class labels
*/
@Since("1.5.0")
def predictProbabilities(testData: RDD[Vector]): RDD[Vector] = {
val bcModel = testData.context.broadcast(this)
testData.mapPartitions { iter =>
val model = bcModel.value
iter.map(model.predictProbabilities)
}
}
/**
* Predict posterior class probabilities for a single data point using the model trained.
*
* @param testData array representing a single data point
* @return predicted posterior class probabilities from the trained model,
* in the same order as class labels
*/
@Since("1.5.0")
def predictProbabilities(testData: Vector): Vector = {
modelType match {
case Multinomial =>
posteriorProbabilities(multinomialCalculation(testData))
case Bernoulli =>
posteriorProbabilities(bernoulliCalculation(testData))
}
}
private def multinomialCalculation(testData: Vector) = {
val prob = thetaMatrix.multiply(testData)
BLAS.axpy(1.0, piVector, prob)
prob
}
private def bernoulliCalculation(testData: Vector) = {
testData.foreachNonZero((_, value) =>
if (value != 1.0) {
throw new SparkException(
s"Bernoulli naive Bayes requires 0 or 1 feature values but found $testData.")
}
)
val prob = thetaMinusNegTheta.get.multiply(testData)
BLAS.axpy(1.0, piVector, prob)
BLAS.axpy(1.0, negThetaSum.get, prob)
prob
}
private def posteriorProbabilities(logProb: DenseVector) = {
val logProbArray = logProb.toArray
val maxLog = logProbArray.max
val scaledProbs = logProbArray.map(lp => math.exp(lp - maxLog))
val probSum = scaledProbs.sum
new DenseVector(scaledProbs.map(_ / probSum))
}
@Since("1.3.0")
override def save(sc: SparkContext, path: String): Unit = {
val data = NaiveBayesModel.SaveLoadV2_0.Data(labels, pi, theta, modelType)
NaiveBayesModel.SaveLoadV2_0.save(sc, path, data)
}
}
@Since("1.3.0")
object NaiveBayesModel extends Loader[NaiveBayesModel] {
import org.apache.spark.mllib.util.Loader._
private[mllib] object SaveLoadV2_0 {
def thisFormatVersion: String = "2.0"
/** Hard-code class name string in case it changes in the future */
def thisClassName: String = "org.apache.spark.mllib.classification.NaiveBayesModel"
/** Model data for model import/export */
case class Data(
labels: Array[Double],
pi: Array[Double],
theta: Array[Array[Double]],
modelType: String)
def save(sc: SparkContext, path: String, data: Data): Unit = {
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
// Create JSON metadata.
val metadata = compact(render(
("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~
("numFeatures" -> data.theta(0).length) ~ ("numClasses" -> data.pi.length)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path))
// Create Parquet data.
spark.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath(path))
}
@Since("1.3.0")
def load(sc: SparkContext, path: String): NaiveBayesModel = {
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
// Load Parquet data.
val dataRDD = spark.read.parquet(dataPath(path))
// Check schema explicitly since erasure makes it hard to use match-case for checking.
checkSchema[Data](dataRDD.schema)
val dataArray = dataRDD.select("labels", "pi", "theta", "modelType").take(1)
assert(dataArray.length == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}")
val data = dataArray(0)
val labels = data.getAs[Seq[Double]](0).toArray
val pi = data.getAs[Seq[Double]](1).toArray
val theta = data.getSeq[scala.collection.Seq[Double]](2).map(_.toArray).toArray
val modelType = data.getString(3)
new NaiveBayesModel(labels, pi, theta, modelType)
}
}
private[mllib] object SaveLoadV1_0 {
def thisFormatVersion: String = "1.0"
/** Hard-code class name string in case it changes in the future */
def thisClassName: String = "org.apache.spark.mllib.classification.NaiveBayesModel"
/** Model data for model import/export */
case class Data(
labels: Array[Double],
pi: Array[Double],
theta: Array[Array[Double]])
def save(sc: SparkContext, path: String, data: Data): Unit = {
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
// Create JSON metadata.
val metadata = compact(render(
("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~
("numFeatures" -> data.theta(0).length) ~ ("numClasses" -> data.pi.length)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path))
// Create Parquet data.
spark.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath(path))
}
def load(sc: SparkContext, path: String): NaiveBayesModel = {
val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
// Load Parquet data.
val dataRDD = spark.read.parquet(dataPath(path))
// Check schema explicitly since erasure makes it hard to use match-case for checking.
checkSchema[Data](dataRDD.schema)
val dataArray = dataRDD.select("labels", "pi", "theta").take(1)
assert(dataArray.length == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}")
val data = dataArray(0)
val labels = data.getAs[Seq[Double]](0).toArray
val pi = data.getAs[Seq[Double]](1).toArray
val theta = data.getSeq[scala.collection.Seq[Double]](2).map(_.toArray).toArray
new NaiveBayesModel(labels, pi, theta)
}
}
override def load(sc: SparkContext, path: String): NaiveBayesModel = {
val (loadedClassName, version, metadata) = loadMetadata(sc, path)
val classNameV1_0 = SaveLoadV1_0.thisClassName
val classNameV2_0 = SaveLoadV2_0.thisClassName
val (model, numFeatures, numClasses) = (loadedClassName, version) match {
case (className, "1.0") if className == classNameV1_0 =>
val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata)
val model = SaveLoadV1_0.load(sc, path)
(model, numFeatures, numClasses)
case (className, "2.0") if className == classNameV2_0 =>
val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata)
val model = SaveLoadV2_0.load(sc, path)
(model, numFeatures, numClasses)
case _ => throw new Exception(
s"NaiveBayesModel.load did not recognize model with (className, format version):" +
s"($loadedClassName, $version). Supported:\n" +
s" ($classNameV1_0, 1.0)")
}
assert(model.pi.length == numClasses,
s"NaiveBayesModel.load expected $numClasses classes," +
s" but class priors vector pi had ${model.pi.length} elements")
assert(model.theta.length == numClasses,
s"NaiveBayesModel.load expected $numClasses classes," +
s" but class conditionals array theta had ${model.theta.length} elements")
assert(model.theta.forall(_.length == numFeatures),
s"NaiveBayesModel.load expected $numFeatures features," +
s" but class conditionals array theta had elements of size:" +
s" ${model.theta.map(_.length).mkString(",")}")
model
}
}
/**
* Trains a Naive Bayes model given an RDD of `(label, features)` pairs.
*
* This is the Multinomial NB (see here) which can
* handle all kinds of discrete data. For example, by converting documents into TF-IDF
* vectors, it can be used for document classification. By making every vector a 0-1 vector,
* it can also be used as Bernoulli NB (see here).
* The input feature values must be nonnegative.
*/
@Since("0.9.0")
class NaiveBayes private (
private var lambda: Double,
private var modelType: String) extends Serializable with Logging {
@Since("1.4.0")
def this(lambda: Double) = this(lambda, NaiveBayes.Multinomial)
@Since("0.9.0")
def this() = this(1.0, NaiveBayes.Multinomial)
/** Set the smoothing parameter. Default: 1.0. */
@Since("0.9.0")
def setLambda(lambda: Double): NaiveBayes = {
require(lambda >= 0,
s"Smoothing parameter must be nonnegative but got $lambda")
this.lambda = lambda
this
}
/** Get the smoothing parameter. */
@Since("1.4.0")
def getLambda: Double = lambda
/**
* Set the model type using a string (case-sensitive).
* Supported options: "multinomial" (default) and "bernoulli".
*/
@Since("1.4.0")
def setModelType(modelType: String): NaiveBayes = {
require(NaiveBayes.supportedModelTypes.contains(modelType),
s"NaiveBayes was created with an unknown modelType: $modelType.")
this.modelType = modelType
this
}
/** Get the model type. */
@Since("1.4.0")
def getModelType: String = this.modelType
/**
* Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
*
* @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
*/
@Since("0.9.0")
def run(data: RDD[LabeledPoint]): NaiveBayesModel = {
val spark = SparkSession
.builder()
.sparkContext(data.context)
.getOrCreate()
import spark.implicits._
val nb = new NewNaiveBayes()
.setModelType(modelType)
.setSmoothing(lambda)
val dataset = data.map { case LabeledPoint(label, features) => (label, features.asML) }
.toDF("label", "features")
// mllib NaiveBayes allows input labels like {-1, +1}, so set `positiveLabel` as false.
val newModel = nb.trainWithLabelCheck(dataset, positiveLabel = false)
val pi = newModel.pi.toArray
val theta = Array.ofDim[Double](newModel.numClasses, newModel.numFeatures)
newModel.theta.foreachActive {
case (i, j, v) =>
theta(i)(j) = v
}
assert(newModel.oldLabels != null,
"The underlying ML NaiveBayes training does not produce labels.")
new NaiveBayesModel(newModel.oldLabels, pi, theta, modelType)
}
}
/**
* Top-level methods for calling naive Bayes.
*/
@Since("0.9.0")
object NaiveBayes {
/** String name for multinomial model type. */
private[classification] val Multinomial: String = "multinomial"
/** String name for Bernoulli model type. */
private[classification] val Bernoulli: String = "bernoulli"
/* Set of modelTypes that NaiveBayes supports */
private[classification] val supportedModelTypes = Set(Multinomial, Bernoulli)
/**
* Trains a Naive Bayes model given an RDD of `(label, features)` pairs.
*
* This is the default Multinomial NB (see here)
* which can handle all kinds of discrete data. For example, by converting documents into
* TF-IDF vectors, it can be used for document classification.
*
* This version of the method uses a default smoothing parameter of 1.0.
*
* @param input RDD of `(label, array of features)` pairs. Every vector should be a frequency
* vector or a count vector.
*/
@Since("0.9.0")
def train(input: RDD[LabeledPoint]): NaiveBayesModel = {
new NaiveBayes().run(input)
}
/**
* Trains a Naive Bayes model given an RDD of `(label, features)` pairs.
*
* This is the default Multinomial NB (see here)
* which can handle all kinds of discrete data. For example, by converting documents
* into TF-IDF vectors, it can be used for document classification.
*
* @param input RDD of `(label, array of features)` pairs. Every vector should be a frequency
* vector or a count vector.
* @param lambda The smoothing parameter
*/
@Since("0.9.0")
def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
new NaiveBayes(lambda, Multinomial).run(input)
}
/**
* Trains a Naive Bayes model given an RDD of `(label, features)` pairs.
*
* The model type can be set to either Multinomial NB (see
* here) or Bernoulli NB (see here).
* The Multinomial NB can handle discrete count data and can be called by setting the model
* type to "multinomial".
* For example, it can be used with word counts or TF_IDF vectors of documents.
* The Bernoulli model fits presence or absence (0-1) counts. By making every vector a
* 0-1 vector and setting the model type to "bernoulli", the fits and predicts as
* Bernoulli NB.
*
* @param input RDD of `(label, array of features)` pairs. Every vector should be a frequency
* vector or a count vector.
* @param lambda The smoothing parameter
*
* @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be
* multinomial or bernoulli
*/
@Since("1.4.0")
def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
require(supportedModelTypes.contains(modelType),
s"NaiveBayes was created with an unknown modelType: $modelType.")
new NaiveBayes(lambda, modelType).run(input)
}
}
