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While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs. Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown. Author: Sean Owen <sowen@cloudera.com> Closes #653 from srowen/SPARK-1727 and squashes the following commits: 6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count 8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output) 99966a9 [Sean Owen] Update issue tracker URL in docs 23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak) 8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs
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| layout | title |
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| global | <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. For more details, please visit the Wikipedia page Naive Bayes classifier.
In MLlib, we implemented multinomial naive Bayes, which is typically used for document
classification. Within that context, each observation is a document, each feature represents a term,
whose value is the frequency of the term. For its formulation, please visit the Wikipedia page
Multinomial Naive Bayes
or the section
Naive Bayes text classification
from the book Introduction to Information
Retrieval. Additive smoothing can be used by
setting the parameter \lambda (default to $1.0$). For document classification, the input feature
vectors are usually sparse. Please supply sparse vectors 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, and output a
NaiveBayesModel, which
can be used for evaluation and prediction.
{% highlight scala %} import org.apache.spark.mllib.classification.NaiveBayes import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint
val data = sc.textFile("mllib/data/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) val prediction = model.predict(test.map(_.features))
val predictionAndLabel = prediction.zip(test.map(_.label)) val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() {% 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 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.mllib.classification.NaiveBayes; import org.apache.spark.mllib.classification.NaiveBayesModel; import org.apache.spark.mllib.regression.LabeledPoint; import scala.Tuple2;
JavaRDD training = ... // training set JavaRDD test = ... // test set
final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
JavaRDD prediction = test.map(new Function<LabeledPoint, Double>() { @Override public Double call(LabeledPoint p) { return model.predict(p.features()); } }); JavaPairRDD<Double, Double> predictionAndLabel = prediction.zip(test.map(new Function<LabeledPoint, Double>() { @Override public Double call(LabeledPoint p) { return p.label(); } })); double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { @Override public Boolean call(Tuple2<Double, Double> pl) { return pl._1() == pl._2(); } }).count() / test.count(); {% 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.
{% highlight python %} from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.classification import NaiveBayes
an RDD of LabeledPoint
data = sc.parallelize([ LabeledPoint(0.0, [0.0, 0.0]) ... # more labeled points ])
Train a naive Bayes model.
model = NaiveBayes.train(data, 1.0)
Make prediction.
prediction = model.predict([0.0, 0.0]) {% endhighlight %}