mllib-naive-bayes.md

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global	Naive Bayes - MLlib	<a href="mllib-guide.html">MLlib</a> - Naive Bayes

Naive Bayes is a simple multiclass classification algorithm with the assumption of independence between every pair of features. Naive Bayes can be trained very efficiently. Within a single pass to the training data, it computes the conditional probability distribution of each feature given label, and then it applies Bayes' theorem to compute the conditional probability distribution of label given an observation and use it for prediction.

MLlib supports multinomial naive Bayes and Bernoulli naive Bayes. These models are typically used for document classification. Within that context, each observation is a document and each feature represents a term whose value is the frequency of the term (in multinomial naive Bayes) or a zero or one indicating whether the term was found in the document (in Bernoulli naive Bayes). Feature values must be nonnegative. The model type is selected with an optional parameter "multinomial" or "bernoulli" with "multinomial" as the default. Additive smoothing can be used by setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of sparsity. Since the training data is only used once, it is not necessary to cache it.

Examples

NaiveBayes implements multinomial naive Bayes. It takes an RDD of LabeledPoint and an optional smoothing parameter lambda as input, an optional model type parameter (default is "multinomial"), and outputs a NaiveBayesModel, which can be used for evaluation and prediction.

{% highlight scala %} import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel} import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint

val data = sc.textFile("data/mllib/sample_naive_bayes_data.txt") val parsedData = data.map { line => val parts = line.split(',') LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble))) } // Split data into training (60%) and test (40%). val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) val training = splits(0) val test = splits(1)

val model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial")

val predictionAndLabel = test.map(p => (model.predict(p.features), p.label)) val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()

// Save and load model model.save(sc, "myModelPath") val sameModel = NaiveBayesModel.load(sc, "myModelPath") {% endhighlight %}

NaiveBayes implements multinomial naive Bayes. It takes a Scala RDD of LabeledPoint and an optionally smoothing parameter lambda as input, and output a NaiveBayesModel, which can be used for evaluation and prediction.

{% highlight java %} import scala.Tuple2;

import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.function.Function; import org.apache.spark.api.java.function.PairFunction; import org.apache.spark.mllib.classification.NaiveBayes; import org.apache.spark.mllib.classification.NaiveBayesModel; import org.apache.spark.mllib.regression.LabeledPoint;

JavaRDD training = ... // training set JavaRDD test = ... // test set

final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);

JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() { @Override public Tuple2<Double, Double> call(LabeledPoint p) { return new Tuple2<Double, Double>(model.predict(p.features()), p.label()); } }); double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { @Override public Boolean call(Tuple2<Double, Double> pl) { return pl._1().equals(pl._2()); } }).count() / (double) test.count();

// Save and load model model.save(sc.sc(), "myModelPath"); NaiveBayesModel sameModel = NaiveBayesModel.load(sc.sc(), "myModelPath"); {% endhighlight %}

NaiveBayes implements multinomial naive Bayes. It takes an RDD of LabeledPoint and an optionally smoothing parameter lambda as input, and output a NaiveBayesModel, which can be used for evaluation and prediction.

Note that the Python API does not yet support model save/load but will in the future.

{% highlight python %} from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel from pyspark.mllib.linalg import Vectors from pyspark.mllib.regression import LabeledPoint

def parseLine(line): parts = line.split(',') label = float(parts[0]) features = Vectors.dense([float(x) for x in parts[1].split(' ')]) return LabeledPoint(label, features)

data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)

Split data aproximately into training (60%) and test (40%)

training, test = data.randomSplit([0.6, 0.4], seed = 0)

Train a naive Bayes model.

model = NaiveBayes.train(training, 1.0)

Make prediction and test accuracy.

predictionAndLabel = test.map(lambda p : (model.predict(p.features), p.label)) accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()

Save and load model

model.save(sc, "myModelPath") sameModel = NaiveBayesModel.load(sc, "myModelPath") {% endhighlight %}